Data report for the safety assessment SR-Site

Technical Report

TR-10-52

Data report for the safetyassessment SR-Site
Svensk Kärnbränslehantering AB

December 2010

Svensk Kärnbränslehantering AB

Box 250, SE-101 24 Stockholm
Phone +46 8 459 84 00

TR-10-52

CM Gruppen AB, Bromma, 2011

Swedish Nuclear Fueland Waste Management Co

ISSN 1404-0344
Tänd ett lager:
SKB TR-10-52
P, R eller TR.

Data report for the safetyassessment SR-Site
Svensk Kärnbränslehantering AB

December 2010

Keywords: SKBdoc 1271174, KBS-3, Spent fuel repository, Safety assessment,
SR-Site.
A pdf version of this document can be downloaded from www.skb.se.

Preface
This document gives the data identified as essential for assessing the long-term safety of a KBS-3
repository. In addition, the process of qualifying the data for use in subsequent modelling is detailed.
The report forms a part of the documentation of the safety assessment SR-Site, which supports thelicence application for a final repository for spent nuclear fuel in Sweden.
The report has been produced by Martin Löfgren, Niressa AB, in collaboration with Fredrik Vahlund,
SKB, and Johan Andersson, JA Streamflow AB, with contributions from numerous experts withresponsibilities for specific scientific topics.
Stockholm, December 2010
Allan Hedin
Project leader SR-Site

TR-10-52

3

Contents
1
1.1
1.2

1.3
1.4

1.5

2
2.1
2.2
2.3

3
3.1

Introduction
Role of this Data report in the SR-Site assessment
1.1.1 SR-Site methodology in eleven steps
1.1.2 Key reports referenced
Objective and scope of this Data report
1.2.1 Compilation and qualification of essential input data
1.2.2 Identification of essential input data
1.2.3 Relation to specific sites
1.2.4 Intended audience of this report
Participating parties in this Data report
The engineered repository
1.4.1 The spent fuel and fuel assemblies
1.4.2 The copper canister and cast iron insert.
1.4.3 The buffer and backfill
1.4.4 The underground openings
1.4.5 The closure
The Forsmark site
1.5.1 The locations of the Forsmark site, drill sites, and boreholes
1.5.2 The locations of rock domains
1.5.3 The locations of deformation zones and fracture domains

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Methodology for identifying and qualifying data
Identifying data via assessment model flowcharts
Identifying data for radionuclide transport modelling2.2.1 Selected inventory
2.2.2 Input to radionuclide transport modelling
Qualification of input data – instruction to the supplier and customer
2.3.1 Modelling in SR-Site
2.3.2 Experience from SR-Can
2.3.3 Supplier input on use of data in SR-Site and SR-Can
2.3.4 Sources of information and documentation of data qualification
2.3.5 Conditions for which data are supplied
2.3.6 Conceptual uncertainty
2.3.7 Data uncertainty due to precision, bias, and representativity
2.3.8 Spatial and temporal variability of data
2.3.9 Correlations
2.3.10 Result of supplier’s data qualification
2.3.11 Judgements by the SR-Site team
2.3.12 Data recommended for use in SR-Site modelling

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Spent fuel data
Selected inventory
3.1.1 Modelling in SR-Site
3.1.2 Experience from SR-Can
3.1.3 Supplier input on use of data in SR-Site and SR-Can
3.1.4 Sources of information and documentation of data qualification
3.1.5 Conditions for which data are supplied
3.1.6 Conceptual uncertainty
3.1.7 Data uncertainty due to precision, bias, and representativity
3.1.8 Spatial and temporal variability of data
3.1.9 Correlations
3.1.10 Result of supplier’s data qualification
3.1.11 Judgements by the SR-Site team
3.1.12 Data recommended for use in SR-Site modelling

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3.2

3.3

3.4

4
4.1

4.2

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Instant release fraction and corrosion release fraction
3.2.1 Modelling in SR-Site
3.2.2 Experience from SR-Can
3.2.3 Supplier input on use of data in SR-Site and SR-Can
3.2.4 Sources of information and documentation of data qualification
3.2.5 Conditions for which data are supplied
3.2.6 Conceptual uncertainty
3.2.7 Data uncertainty due to precision, bias, and representativity
3.2.8 Spatial and temporal variability of data
3.2.9 Correlations
3.2.10 Result of supplier’s data qualification
3.2.11 Judgements by the SR-Site team
3.2.12 Data recommended for use in SR-Site modelling
Fuel conversion
3.3.1 Modelling in SR-Site
3.3.2 Experience from SR-Can
3.3.3 Supplier input on use of data in SR-Site and SR-Can
3.3.4 Sources of information and documentation of data qualification
3.3.5 Conditions for which data are supplied
3.3.6 Conceptual uncertainty
3.3.7 Data uncertainty due to precision, bias, and representativity
3.3.8 Spatial and temporal variability of data
3.3.9 Correlations
3.3.10 Result of supplier’s data qualification
3.3.11 Judgements by the SR-Site team
3.3.12 Data recommended for use in SR-Site modelling
Solubility data
3.4.1 Modelling in SR-Site
3.4.2 Experience from SR-Can
3.4.3 Supplier input on use of data in SR-Site and SR-Can
3.4.4 Sources of information and documentation of data qualification
3.4.5 Conditions for which data are supplied
3.4.6 Conceptual uncertainty
3.4.7 Data uncertainty due to precision, bias, and representativity
3.4.8 Spatial and temporal variability of data
3.4.9 Correlations
3.4.10 Result of supplier’s data qualification
3.4.11 Judgements by the SR-Site team
3.4.12 Data recommended for use in SR-Site modelling

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Canister data
Data of the intact canister
4.1.1 Modelling in SR-Site
4.1.2 Experience from SR-Can
4.1.3 Supplier input on use of data in SR-Site and SR-Can
4.1.4 Sources of information and documentation of data qualification
4.1.5 Conditions for which data are supplied
4.1.6 Conceptual uncertainty
4.1.7 Data uncertainty due to precision, bias, and representativity
4.1.8 Spatial and temporal variability of data
4.1.9 Correlations
4.1.10 Result of supplier’s data qualification
4.1.11 Judgements by the SR-Site team
4.1.12 Data recommended for use in SR-Site modelling
Evolving canister defect4.2.1 Modelling in SR-Site
4.2.2 Experience from SR-Can
4.2.3 Supplier input on use of data in SR-Site and SR-Can
4.2.4 Sources of information and documentation of data qualification

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4.2.6
4.2.7
4.2.8
4.2.9
4.2.104.2.114.2.12
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5.1

5.2

5.3

6
6.1

Conditions for which data are supplied
Conceptual uncertainty
Data uncertainty due to precision, bias, and representativity
Spatial and temporal variability of data
Correlations
Result of supplier’s data qualification
Judgements by the SR-Site team
Data recommended for use in SR-Site modelling

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Buffer and backfill data
Density and porosity of buffer and backfill
5.1.1 Modelling in SR-Site
5.1.2 Experience from SR-Can
5.1.3 Supplier input on use of data in SR-Site and SR-Can
5.1.4 Sources of information and documentation of data qualification
5.1.5 Conditions for which data are supplied
5.1.6 Conceptual uncertainty
5.1.7 Data uncertainty due to precision, bias, and representativity
5.1.8 Spatial and temporal variability of data
5.1.9 Correlations
5.1.10 Result of supplier’s data qualification
5.1.11 Judgements by the SR-Site team
5.1.12 Data recommended for use in SR-Site modelling
Hydraulic properties of buffer and backfill
5.2.1 Modelling in SR-Site
5.2.2 Experience from SR-Can
5.2.3 Supplier input on use of data in SR-Site and SR-Can
5.2.4 Sources of information and documentation of data qualification
5.2.5 Conditions for which data are supplied
5.2.6 Conceptual uncertainty
5.2.7 Data uncertainty due to precision, bias, and representativity
5.2.8 Spatial and temporal variability of data
5.2.9 Correlations
5.2.10 Result of supplier’s data qualification
5.2.11 Judgements by the SR-Site team
5.2.12 Data recommended for use in SR-Site modelling
Migration data of buffer and backfill
5.3.1 Modelling in SR-Site
5.3.2 Experience from SR-Can
5.3.3 Supplier input on use of data in SR-Site and SR-Can
5.3.4 Sources of information and documentation of data qualification
5.3.5 Conditions for which data are supplied
5.3.6 Conceptual uncertainty
5.3.7 Data uncertainty due to precision, bias, and representativity
5.3.8 Spatial and temporal variability of data
5.3.9 Correlations
5.3.10 Result of supplier’s data qualification
5.3.11 Judgements by the SR-Site team
5.3.12 Data recommended for use in SR-Site modelling

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Geosphere data
Groundwater chemical composition
6.1.1 Modelling in SR-Site
6.1.2 Experience from SR-Can
6.1.3 Supplier input on use of data in SR-Site and SR-Can
6.1.4 Sources of information and documentation of data qualification
6.1.5 Conditions for which data are supplied
6.1.6 Conceptual uncertainty
6.1.7 Data uncertainty due to precision, bias, and representativity

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6.2

6.3

6.4

6.5

8

6.1.8 Spatial and temporal variability of data
6.1.9 Correlations
6.1.10 Result of supplier’s data qualification
6.1.11 Judgements by the SR-Site team
6.1.12 Data recommended for use in SR-Site modelling
Bedrock thermal properties
6.2.1 Modelling in SR-Site
6.2.2 Experience from SR-Can
6.2.3 Supplier input on use of data in SR-Site and SR-Can
6.2.4 Sources of information and documentation of data qualification
6.2.5 Conditions for which data are supplied
6.2.6 Conceptual uncertainty
6.2.7 Data uncertainty due to precision, bias, and representativity
6.2.8 Spatial and temporal variability of data
6.2.9 Correlations
6.2.10 Result of supplier’s data qualification
6.2.11 Judgements by the SR-Site team
6.2.12 Data recommended for use in SR-Site modelling
Discrete-Fracture Network (DFN) models
6.3.1 Modelling in SR-Site
6.3.2 Experience from SR-Can
6.3.3 Supplier input on use of data in SR-Site and SR-Can
6.3.4 Sources of information and documentation of data qualification
6.3.5 Conditions for which data are supplied
6.3.6 Conceptual uncertainty
6.3.7 Data uncertainty due to precision, bias, and representativity
6.3.8 Spatial and temporal variability of data
6.3.9 Correlations
6.3.10 Result of supplier’s data qualification
6.3.11 Judgements by the SR-Site team
6.3.12 Models recommended for use in SR-Site modelling
Rock mechanics
6.4.1 Modelling in SR-Site
6.4.2 Experience from SR-Can
6.4.3 Supplier input on use of data in SR-Site and SR-Can
6.4.4 Sources of information and documentation of data qualification
6.4.5 Conditions for which data are supplied
6.4.6 Conceptual uncertainty
6.4.7 Data uncertainty due to precision, bias, and representativity
6.4.8 Spatial and temporal variability of data
6.4.9 Correlations
6.4.10 Result of supplier’s data qualification
6.4.11 Judgements by the SR-Site team
6.4.12 Data recommended for use in SR-Site modelling
Spalling and the excavation damaged zone
6.5.1 Modelling in SR-Site
6.5.2 Experience from SR-Can
6.5.3 Supplier input on use of data in SR-Site and SR-Can
6.5.4 Sources of information and documentation of data qualification
6.5.5 Conditions for which data are supplied
6.5.6 Conceptual uncertainty
6.5.7 Data uncertainty due to precision, bias, and representativity
6.5.8 Spatial and temporal variability of data
6.5.9 Correlations
6.5.10 Result of supplier’s data qualification
6.5.11 Judgements by the SR-Site team
6.5.12 Data recommended for use in SR-Site modelling

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6.7

6.8

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7.1

7.2

Quantities for groundwater flow modelling6.6.1 Modelling in SR-Site
6.6.2 Experience from SR-Can
6.6.3 Supplier input on use of data in SR-Site and SR-Can
6.6.4 Sources of information and documentation of data qualification
6.6.5 Conditions for which data are supplied
6.6.6 Conceptual uncertainty
6.6.7 Data uncertainty due to precision, bias, and representativity
6.6.8 Spatial and temporal variability of data
6.6.9 Correlations
6.6.10 Result of supplier’s data qualification
6.6.11 Judgements by the SR-Site team
6.6.12 Data recommended for use in SR-Site modelling
Flow related migration properties
6.7.1 Modelling in SR-Site
6.7.2 Experience from SR-Can
6.7.3 Supplier input on use of data in SR-Site and SR-Can
6.7.4 Sources of information and documentation of data qualification
6.7.5 Conditions for which data are supplied
6.7.6 Conceptual uncertainty
6.7.7 Data uncertainty due to precision, bias, and representativity
6.7.8 Spatial and temporal variability of data
6.7.9 Correlations
6.7.10 Result of supplier’s data qualification
6.7.11 Judgements by the SR-Site team
6.7.12 Data recommended for use in SR-Site modelling
Non-flow related migration properties
6.8.1 Modelling in SR-Site
6.8.2 Experience from SR-Can
6.8.3 Supplier input on use of data in SR-Site and SR-Can
6.8.4 Sources of information and documentation of data qualification
6.8.5 Conditions for which data are supplied
6.8.6 Conceptual uncertainty
6.8.7 Data uncertainty due to precision, bias, and representativity
6.8.8 Spatial and temporal variability of data
6.8.9 Correlations
6.8.10 Result of supplier’s data qualification
6.8.11 Judgements by the SR-Site team
6.8.12 Data recommended for use in SR-Site modelling

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Surface system data
Climate and climate related data
7.1.1 Modelling in SR-Site
7.1.2 Experience from SR-Can
7.1.3 Supplier input on use of data in SR-Site and SR-Can
7.1.4 Sources of information and documentation of data qualification
7.1.5 Conditions for which data are supplied
7.1.6 Conceptual uncertainty
7.1.7 Data uncertainty due to precision, bias, and representativity
7.1.8 Spatial and temporal variability of data
7.1.9 Correlations
7.1.10 Result of supplier’s data qualification
7.1.11 Judgements by the SR-Site team
7.1.12 Data recommended for use in SR-Site modelling
Landscape dose conversion factors
7.2.1 Modelling in SR-Site
7.2.2 Experience from SR-Can
7.2.3 Supplier input on use of data in SR-Site and SR-Can

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7.2.5
7.2.6
7.2.7
7.2.8
7.2.9
7.2.107.2.11
7.2.128

Sources of information and documentation of data qualification
Conditions for which data are supplied
Conceptual uncertainty
Data uncertainty due to precision, bias, and representativity
Spatial and temporal variability of data
Correlations
Result of supplier’s data qualification
Judgements by the SR-Site team
Data recommended for use in SR-Site modelling

Referenser

Appendix A Using results from in situ electrical resistivity loggings in
support of data needed in hydrogeological modelling

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1

Introduction

1.1

Role of this Data report in the SR-Site assessment

This report compiles, documents, and qualifies input data identified as essential for the long-term
safety assessment of a KBS-3 repository, and forms an important part of the reporting of the safetyassessment project SR-Site. The input data concern the repository system, broadly defined as thedeposited spent nuclear fuel, the engineered barriers surrounding it, the host rock, and the biospherein the proximity of the repository. The input data also concern external influences acting on the system,in terms of climate related data. Data are provided for a selection of relevant conditions and are
qualified through traceable standardised procedures.

1.1.1 SR-Site methodology in eleven steps
As described in the SR-Site Main report (Section 2.5), the safety assessment SR-Site consists ofeleven main steps, which are carried out partly concurrently and partly consecutively. We refrainfrom outlining the steps here, except for the step concerning the Data report, and instead refer to thedetailed description in the SR-Site Main report. Figure 1-1 is a graphical illustration of the steps,
with the present step highlighted.
The following can be read concerning step 6 “Compilation of input data” in Section 2.5 of the
SR-Site Main report:“In this step, data to be used in the quantification of repository evolution and in dose calculations
are selected using a structured procedure. The process of selection and the data values adopted arereported in a dedicated Data report. The process follows a template for discussion of input data
uncertainties. The template… and the selected data are provided in the Data report. The models forwhich data are required are given in the AMF described in step 4…
Compared to the SR-Can assessment the structured compilation of data has been extended to more
completely cover the calculations in the assessment… Also, the template has been updated in response
to findings in the review of the SR-Can assessment.”
The structured procedures and the template referred to are described in detail in Section 2.3 of thispresent report.

ReferencedesignSite description

Results of earlier
assessments

R&D results

FEP databases

1 Processing of features, event and processes (FEPs)
2a Description of site
initial state

2b Description of engineered
barrier system (EBS) initial state

3 Description of external
conditions
5 Definition of safety functions andfunction indicators

2c Description of
repository layouts

4 Compilation ofProcess reports
6 Compilation of
input data

7 Definition and analyses of reference evolution
8 Selection of scenarios

9 Analyses of selected scenarios

10 Additional analyses

11 Conclusions

Figure 1-1. The SR-Site methodology in eleven steps, with the Data report step highlighted. Reproduced
from Figure 9-1 of the SR-Site Main report.
TR-10-5211

1.1.2 Key reports referenced
Several of the steps carried out in the SR-Site safety assessment result in specific reports that areof central importance for the conclusions and analyses in the SR-Site Main report. These specificreports, referred to as main references, are 16 in number. Besides the main references, there are about
80 additional references, treating more narrow issues, supporting the main report and/or one or moreof the main references. The SR-Site report hierarchy is illustrated in Figure 1-2.
In addition to the dedicated SR-Site reports, references are made to a variety of documents, articles,
and publications, either from SKB or from other organisations, or as part of the open literature. SKB
reports that deserve mentioning are those from the SRCan safety assessment, and those from the site
investigations and site descriptions. A report produced outside the SR-Site framework, but that still isfundamental input to the safety assessment, is the SDM-Site version of the site-descriptive model ofthe Forsmark site, see the Site description Forsmark.
The main report and main references of the SR-Site project, as well as the site-descriptive model ofthe Forsmark site, are referenced by using abbreviations in this Data report. The abbreviations of
the discussed reports, as well as their full references, are given in Table 1-1.
Table 1-1. Abbreviations and full references of SKB reports, used in this Data report.
Abbreviation used
when referenced inthis Data report

Full reference, as given in the reference list

SR-Site Main report

SR-Site Main report, 2011. Long-term safety for the final repository for spent nuclear
fuel at Forsmark. Main report of the SR-Site project. SKB TR-11-01, Svensk
Kärnbränslehantering AB.

FEP report

FEP report, 2010. FEP report for the safety assessment SR-Site. SKB TR-10-45,
Svensk Kärnbränslehantering AB.

Spent fuel report

Spent fuel report, 2010. Spent nuclear fuel for disposal in the KBS-3 repository.
SKB TR-10-13, Svensk Kärnbränslehantering AB.

Canister production report

Canister production report, 2010. Design, production and initial state of the canister.
SKB TR-10-14, Svensk Kärnbränslehantering AB.

Buffer production report

Buffer production report, 2010. Design, production and initial state of the buffer.
SKB TR-10-15, Svensk Kärnbränslehantering AB.

Backfill production report

Backfill production report, 2010. Design, production and initial state of the backfill andplug in deposition tunnels. SKB TR-10-16, Svensk Kärnbränslehantering AB.

Closure production report

Closure production report, 2010. Design, production and initial state of the closure.
SKB TR-10-17, Svensk Kärnbränslehantering AB.

Underground openings
construction report

Underground openings construction report, 2010. Design, construction and initial stateof the underground openings. SKB TR-10-18, Svensk Kärnbränslehantering AB.

Fuel and canister
process report

Fuel and canister process report, 2010. Fuel and canister process report for the safetyassessment SR-Site. SKB TR-10-46, Svensk Kärnbränslehantering AB.

Buffer, backfill andclosure process report

Buffer, backfill and closure process report, 2010. Buffer, backfill and closure process reportfor the safety assessment SR-Site. SKB TR-10-47, Svensk Kärnbränsle­hantering AB.

Geosphere process report

Geosphere process report, 2010. Geosphere process report for the safety assessment
SR-Site. SKB TR-10-48, Svensk Kärnbränslehantering AB.

Climate report

Climate report, 2010. Climate and climate related issues for the safety assessment SR-Site.
SKB TR-10-49, Svensk Kärnbränslehantering AB.

Radionuclide transport
reportRadionuclide transport report, 2010. Radionuclide transport report for the safetyassessment SR-Site. SKB TR-10-50, Svensk Kärnbränslehantering AB.

Model summary report

Model summary report, 2010. Model summary report for the safety assessment
SR-Site. SKB TR-10-51, Svensk Kärnbränslehantering AB.

FHA report

FHA report, 2010. Handling of future human actions in the safety assessment SR-Site.
SKB TR-10-53, Svensk Kärnbränslehantering AB.

Biosphere synthesis report

Biosphere synthesis report, 2010. Biosphere analyses for the safety assessment SR-Site– synthesis and summary of results. SKB TR-10-09, Svensk Kärnbränsle­hantering AB.

Site description Forsmark

Site description Forsmark, 2008. Site description of Forsmark at the completion of the site
investigation phase, SDM-Site Forsmark. SKB TR-08-05, Svensk Kärnbränslehantering AB.

12

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SR-Site
Main report

Main references
FEP report

ClimatereportSix Production
reports
Biosphere
synthesis
reportFuel andcanisterprocess reportModelsummaryreportBuffer, backfill
and closure
process reportDatareportFHA
reportGeosphere
process
reportRadionuclidetransport
reportAdditional references

Figure 1-2. The hierarchy of the main and additional references to the SR-Site project. The main references
support the main report. The additional references may either support the main report directly or the mainreferences. The six production reports include the Spent fuel report and the Underground openings
construction report. Reproduced from Figure 2-3 of the SR-Site Main report.

1.2

Objective and scope of this Data report

1.2.1 Compilation and qualification of essential input data
In this Data report, essential input data are compiled and qualified. These data are intended for use
in subsequent SR-Site safety assessment modelling. The compiled data are divided on five different
chapters concerning the:
•
•
•
•
•

Spent fuel (Chapter 3).
Copper canister (Chapter 4).
Buffer and backfill (Chapter 5).
Geosphere (Chapter 6).
Surface system (Chapter 7).

The set of input parameters for the safety assessment is very large. Some “input data uncertainties”(including both data uncertainty and natural variability) will have a substantial influence on safety
related output uncertainty, which ultimately leads to uncertainty in assessed radiological risk. Other
data may range over orders of magnitude but still not influence the assessed radiological risk. An
example of the latter are transport properties in the geopshere of those radionuclides that will neverexit the engineered barrier.
It is therefore appropriate to identify input data to which safety related output is sensitive, and use these
insights in allocating resources to the determination and, where feasible, reduction of input data uncertainties. It is also important to have a high degree of confidence in the data that are used to conclude
that particular processes, nuclides, etc. will never contribute to radiological risk. The identification of
essential input data is discussed in the next section, and in Chapter 2.
The data presented in this Data report are either compiled from supporting reports and documents, aspart of previous tasks or as part of SR-Site, or produced and justified in this Data report. The majorityof data are compiled from background reports, such as the Site description Forsmark, the production
reports, SR-Can reports, etc. and in those cases the justification of data is mainly done in the supporting
documents. In such a case, the qualifying role of the Data report is to control that the suggested data aretraceable and applicable for SR-Site conditions, and to some extent to suggest their role in the safetyassessment. For example, a set of data suggested in the site descriptions may only be valid for a certain
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13

climate domain of the glacial cycle, or at some other specified condition. Furthermore, estimates of
uncertainties as well as of natural variability should be delivered by the Data report, so that the data canbe properly used in the subsequent safety assessment modelling. It is part of the qualifying role to makesure that reasonable, quantitative, and usable uncertainty estimates are delivered.
The way of qualifying data is through traceable standardised procedures where extra effort is put
into documenting the data qualification process, and to discuss uncertainty in data originating fromconceptual uncertainty, data uncertainty, and natural variability. These standardised procedures aredetailed in Section 2.3.
The compiled and qualified data are found in Section x.x.12 of each section in Chapter 3 to 7. In
this section, clear referencing to tables in the preceding sections may substitute duplication of thedata. Data that cannot be tabulated in this report, for example the co-ordinates of thousands of exit
locations for groundwater flow paths, are stored in referenced databases.

1.2.2 Identification of essential input data
The identification of essential input data has been performed in two ways. The primary approachis through analysing assessment model flow charts, AMFs, (see Section 7.5 of the SR-Site Main
report and Section 2.1 of this present report). AMFs are produced based on information from theother steps of the eleven step methodology (cf. Figure 1‑1), experience from previous safety assessments, as well as on other recent information. This approach has been generally used for identifying
the input data of this report and is described in Section 2.1. There are limitations in this approach
resulting in a situation where peripheral data that may be of importance for the safety assessment are
not included in the Data report, but are reported elsewhere. Such an example is the biosphere data
constituting the background for estimating Landscape dose conversion factors. In this specific case,a decision has been taken to limit the scope of the Data report to include the estimated Landscapedose conversion factors as the only biosphere related data.
A parallel approach has been used for identifying input data to radionuclide transport modelling.
In this approach, all input parameters of the computational codes COMP23 and FARF31 have beenclosely examined. Many of the associated data are qualified in this Data report, while some inputs
are taken from other sources, as outlined in Section 2.2 and as detailed in the Radionuclide transport
report. Carrying out this parallel approach in a systematic way is new for SR-Site, and was not donein SR-Can. This new approach is part of handling the comment made by the regulators in theirreview of SR-Can /Dverstorp and Strömberg 2008, Section 4.2/:“The data report contains far from all data that can be utilised in some way in SR-Can. A more
complete version is needed prior to SR-Site, where the extent and limitation of the presentation
is clearly justified.”
As result of these two parallel approaches, the inventory of data that are compiled and qualified inthis Data report has been extended, compared to in the SR-Can Data report /SKB 2006b/. On theother hand, based on experience from SR-Can, a few data sets have been excluded from this inventory.
In each subject area section of Chapters 3 to 7, a bullet list of the data that are supplied is found under
the heading “Defining the data requested from the supplier” in Section x.x.1.

14TR-10-52

1.2.3 Relation to specific sites
The data in Chapters 6 and 7, describing the geopshere and surface system, are site specific. SKB has
undertaken site characterisation at two different locations, Forsmark and Laxemar-Simpevarp, as candidate sites for a final repository for spent nuclear fuel. The SR-Site assessment builds on site-specific dataand site-descriptive models of the selected Forsmark site (Site description Forsmark), but site-specificdata and site-descriptive models of the Laxemar site /SKB 2009e/ are also used in evaluations to
support the site selection.
This Data report supplies recommended geosphere and surface system data for the Forsmark site only.
Corresponding data for the Laxemar-Simpevarp site are appended in the report “Comparative analysis
of safety related site characteristics” /SKB 2010b/. Data that are not site specific, concerning the deposited spent fuel and the engineered barriers (Chapters 3 to 5), are intended for use both in SR-Site andin support of the site selection.

1.2.4 Intended audience of this report
This report is written by, and for, experts in the concerned scientific fields. It should be possible forgeneralists in the area of long-term safety of geologic nuclear waste repositories to comprehend
the content of the report. However, it may be a difficult task for laymen to grasp the details of thisreport. This report is an important part of the documentation of the SR-Site project and an essentialreference within the project.

1.3

Participating parties in this Data report

The data recommended for use in SR-Site are generally based on a mixture of measurements, modelling,
and interpretation. Therefore, there is always a component of expert judgment involved in choosing
the recommended data (as stated in Section 1.2, trivial data are not handled in this report). The workof producing much of the data, as well as of compiling the data and writing this report, has been doneby experts working at, or on behalf of, SKB. Therefore, formally the expert should not be considered
as independent. The experts could have their expertise in a narrow subject area, such as thermal propertiesof the rock, or as generalists in the safety of spent nuclear fuel (which indeed could also be considered
as a narrow field of expertise).
In the review of SR-Can, the authorities found it difficult to separate expert judgment made by the
SR-Can team in the Data report, from that made by the experts authoring the supporting documents/Dverstorp and Strömberg 2008, Section 4.6/:“The authorities also consider that the description of how the different experts (outside SR-Can team)have contributed with their expert judgment is insufficient. The subject authors in the SR-Can team
have, as far as the authorities understand, summarised the reports of the experts in the data report.
However, it is not clear whether this is only a compilation of assessments in the underlying expert
reports or whether there has been any dialogue between the experts and the SR-Can team.”
For this reason, the structure of the Data report has been modified to, as far as possible, separate
the views of experts supplying the data (either directly or through supporting documents) from theviews of experts of the SR-Site team. This works well when the supplied data are independent fromassumptions made in the safety assessment (e.g. the rock porosity). However, when the supplied data
have been produced in close cooperation with SR-Site, or even within the project, such a separation
is difficult to obtain. Examples of the latter data are exit locations of flow paths during repository
evolution, or the groundwater composition during the glacial cycle.

TR-10-52

15

The vocabulary used for separating the teams or persons supplying the data, the groups or persons
within the SR-Site team responsible for the data, and the SR-Site team as a whole are:•••Supplier
Customer
The SR-Site teamThis may appear to be an odd choice of vocabulary, which is not used in previous safety assessments.
The terms supplier and customer are adopted from standard quality assurance systems (e.g. theISO 9001 standard), and is a response to the authorities overall review comment that the degree of
quality assurance in the SR-Site safety assessment needs to be increased, as compared to in SR-Can/Dverstorp and Strömberg 2008, e.g. summary/. These roles of the parties involved in producing theData report are further described in Section 2.3. The individuals involved in authoring each subject
area section, and their role in the supplier team or SR-Site team, are documented according to SKB’s
quality assurance system. Experts which have participated indirectly in producing the data, by wayof authoring supporting document, are identified by standard referencing.

1.4

The engineered repository

In the following a very short description of the engineered repository is given for orientation, withthe purpose of outlining the framework for data in individual subject area sections. Details on theengineered system are found in a multitude of other documents, for example the SR-Site Main report.
The engineered repository can be divided on the fuel, the copper canister, the buffer surrounding thecanister, the backfill of deposition tunnels and other underground openings, and the closure.

1.4.1 The spent fuel and fuel assemblies
The fuel and the fuel assemblies are described in the Spent fuel report. The spent fuel is predominantly
UOX fuel (Uranium OXide), but smaller amount of MOX fuel (Mixed OXide) and fuel residues fromresearch will also be deposited. According to the SKB spent fuel reference scenario, over 8,300 tonnes of
BWR fuel and about 2,800 tonnes of PWR fuel need to be deposited at year 2045. Here BWR and PWR
are abbreviations for Boiling light water reactors and Pressurised light water reactors, respectively. The
fuel is deposited together with fuel assemblies comprised of zirconium alloys, nickel alloys and stainlesssteel (cf. Table 2-4 of the Spent fuel report). Furthermore, some additional material such as control
rods, start-up neutron sources, boron glass rods, and plugs will be deposited.
1.4.2 The copper canister and cast iron insert.
The copper canister and the cast iron insert are described in the Canister production report. Morethan 6,000 canisters are needed to deposit the spent fuel. Figure 1-3 (upper left) shows the reference
canister, over 4.8 m in length, about one metre in diameter, and 5 cm in copper thickness.
The reference canister design comprises two different inserts, one for 12 BWR fuel assemblies and one
for 4 PWR fuel assemblies. Figure 1-3 (middle and upper right) shows the differences between the two.
The loaded canister weights between 24.6–26.8 tonnes, where the copper shell weights 7.5 tonnes, thecast iron insert with lid between 13.7–16.4 tonnes, and the fuel between 2.6–3.5 tonnes, depending on
the type (cf. Table 3-1 of the Canister production report).

1.4.3 The buffer and backfill
The buffer surrounding the canister is described in the Buffer production report while the backfillof the deposition tunnels is described in the Backfill production report. The reference material of
the buffer is bentonite clay, where examples of commercial bentonites given in the Buffer production
report are MX-80 and Ibeco RWC (Deponit CA-N). The buffer is manufactured as blocks and pellets
that are placed around the canister with the aim at achieving a specified density. The installed buffer
with the reference geometry, as given in the Buffer production report, is illustrated in Figure 1-4.

16

TR-10-52

1,050 mm

Ø 1,050

Ø 1,050

Ø 949

Ø 949
135

50
235

4,835 mm160

5 cm copper
BWR-type

PWR-type
Nodular cast ironFigure 1-3. Upper left: SKB’s reference canister with an outer corrosion barrier of copper and an insert ofnodular cast iron. Upper middle and right: Basic differences between the BWR and PWR cast iron inserts.
Lower: Exploded view of the reference canister and its components (from the left: copper base, copper tube,
insert, steel lid for insert and copper lid). Modified and reproduced from Figures 3-1 to 3-3 of the Canister
production report.

Nominal dimensions given as design premises:

Reference geometryof installed buffer:

Nominal thickness/height from canister surface 1.5 m
Pellets

Diameter of hole withinring shaped blocks

1,070 mm

Centre line of deposition holeand blocks

Total height 6.68 m

Width of pellet filled gap 50 mm
Accepted variation 25-100 mm

Nominal thickness from canister surface 35 cm
Nominal thickness/height from canister surface 0.5 m

Figure 1-4. Reference geometry of the installed buffer and the nominal dimensions given as design premises.
Reproduced from Figure 3-3 of the Buffer production report.

TR-10-52

17

The reference material of the backfill of the deposition tunnels is low grade bentonite clay, where
Milos Backfill (also called IBECO-RWC-BF) is given as an example in the Backfill production
report. The backfill is manufactured as blocks and pellets that are placed in the deposition tunnel
with the aim at achieving a specified density.

1.4.4 The underground openings
In the Underground openings construction report, the construction of deposition holes and tunnels,
the ramp and shafts, main and transport tunnels, and central area rock caverns is discussed. Figure 1-5
shows an illustration of the reference layout, where different underground openings are indicated.
1.4.5 The closure
In the Closure production report, the backfilling of main and transport tunnels, the central area,
the ramp and shafts, and boreholes is outlined. Discussed backfill materials include:•••

Low grade bentonite clay in main and transport tunnels, and ramp and shafts below the top sealing.
Crushed rock in the central area and top sealing of ramp and shafts.
Highly compacted bentonite clay in borehole seals.

Operation area
Rock heap

Ventilation

Ramp

Shaft

Ventilation

Transport tunnel

Repository area

Central area
Deposition tunnels
Main tunnel

Figure 1-5. Illustration of the reference layout for a final repository facility in Forsmark. Modified from
Figure 3-1 of the Underground openings construction report, with added indications for main, transport,
and deposition tunnels.

18

TR-10-52

1.5

The Forsmark site

The Forsmark site is extensively described in the Site description Forsmark and for an overview,
Chapter 11 of the Site description Forsmark is recommended. For a detailed description of the surface
system of Forsmark, /Lindborg 2008/ is recommended. This section does not aim to give a short summary of the Forsmark site, but merely to provide maps and graphic information so that the reader canorientate when site specific nomenclature is used in the subject area sections.

1.5.1 The locations of the Forsmark site, drill sites, and boreholes
The Forsmark site is located in northern Uppland within the municipality of Östhammar, about 120 km
north of Stockholm (see Figure 1-6). The candidate area for site investigation, approximately 6 kmlong and 2 km wide, is located along the shoreline of Öregrundsgrepen, a bay of the Baltic Sea.
The candidate area is encircled by the red line in Figure 1-6.

Forsmark nuclear
power plant

Candidate area

SFR

Forsmark nuclear
power plant

Regional model area

SFR

Candidate area

© Lantmäteriverket Gävle 2007
Medgivande I 2007/1092

Figure 1-6. Upper left: The location of the Forsmark site. Lower left: The regional model area and thecandidate area. Right: Photo of the Forsmark candidate area. Reproduced from Figures 1-3 and 1-6 of theSite description Forsmark.

TR-10-52

19

A key aspect of collecting data in the site investigation has been drilling boreholes and performing
downhole surveys and investigations. Two types of boreholes have been drilled into the bedrock,core-drilled boreholes extending down to a depth of 1,000 m and shorter percussion-drilled boreholes. Core-drilled boreholes are drilled from specific drill sites, and labelled so that the drill site(DS) can be easily identified. For example, borehole KFM01A and KFM12A are drilled from DS1
and DS12, respectively. Figure 1-7 shows the drill sites and boreholes of the Forsmark candidatearea. Core-drilled boreholes are marked by purple dots and percussion-drilled borehole by blue dots.
The yellow tube corresponding to each dot shows the direction and 2-D extension of the borehole.
The yellow dots in the right figure mark soil pipes. Especially core-drilled boreholes are referenced
in tables and figures providing data in Chapter 6.
As can be seen, the density of boreholes is increased in the north-western part of the candidate area
where the target area has been selected for the repository.

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2008-04-18, 16:00
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Figure 1-7. Drill sites and boreholes of Forsmark. Reproduced from Figures 2-1 and 2-2 of the Sitedescription Forsmark.

20

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! ! ! ! ! ! !
! ! ! ! !
träsket

KFM01C

!
!
!
!
!
!
!

!
.
KFM02A

DS5

!

!

!

!

!

!
!
!

!

!

!

!

!

.

!

200m

!

!

!

!

! ! ! ! !
! ! ! ! !

Puttan !
.!
?

DS5

?
!
?
!
?!
.!

!

! !
!! ! !

! ! ! ! !

DS6

DS1

!

!

!!

!

!

!

?
!!
.

!

!

50 100

KFM02B( SFM0008!
!
. KFM03A.
KFM03B !
? HFM06 HFM08(!
!
!
?
( SFM0007!
SFM0006

HFM04!
?!
( SFM0004!

!
!

6700000

! !

!!

!

?
!
.!
!
?

!!

SFM0003 HFM01
HFM02
(
!
!
?
HFM27
!
?!
!
?
?
( SFM0002
!
HFM03
!
.!
KFM01A.
KFM01B

DS4

!
.
.
!
? DS8
DS7 !

DS9

Pigträsket

KFM01D( SFM0001!

0Asphällsfjärden

DS3

DS2

DS1

!

!

!

!

!

!

!

!

!

!

viken

1634000

6702000silke

1632000!

6702000

1630000

TR-10-52

1.5.2 The locations of rock domains
In the site-descriptive model, the host rock surrounding the repository has been divided into numerous
volumes, which are often referenced in figures and tables providing data in this Data report. The rocklithology is described by rock domains, defined on the basis of composition, grain size, homogeneity,and style and degree of ductile deformation. Figure 1-8 shows the rock domains of the Forsmark
candidate area. The rock domains can be identified by the numbers. Two rock domains of special
importance for the target area are RFM029 and RFM045, marked by the numbers 29 and 45 in the
figure. According to repository layout D2 /SKB 2009a/, the repository should be located in thesetwo domains.

1640000

1630000

1632000

6708000

22

40

44

32

30

38
29R

26

36
8

17

18
26

29

12

17

26

1

2
6

a

3
25

35
13

11
4

1630000

44

26

23

14
31

24

669300037

1635000

1640000

51630000

±

0
1
2
4 km
Coast line
Candidate area
© Lantmäteriverket Gävle 2007
Consent I 2007/1092
Local model area
2007-02-26, 15:00
Local model area, stage 2.2
Regional model area
Rock domain boundary
Granite, fine- to medium-grained
Granite, granodiorite and tonalite, metamorphic, fine- to medium-grained
Granite, metamorphic, veined to migmatitic
Granitoid, metamorphic
Aplitic granite, medium-grained granite and felsic volcanic rock,metamorphic and, in part, albitised
Granite to granodiorite, metamorphic, medium-grained
Tonalite and granodiorite, metamorphic
Diorite, quartz diorite, gabbro and ultramafic rock, metamorphic
Felsic to intermediate volcanic rock, metamorphic and, in part, albitised
Sedimentary rock, metamorphic, veined to migmatitic

30
31

26

6698000

6698000

3

45

17

18
5

32

39

21

6700000

23

21

25

6698000

34
126693000

25
7

41

21

20
34

18
6703000

33

20 43

33

43

34

12

42

6698000

6703000

16

16
206702000

670800043

1634000

6702000

1635000

1632000

1634000

±

0
0,5
1
2 km
Coast line
© Lantmäteriverket Gävle 2007
Candidate area
Consent I 2007/1092
Local model area
2007-02-26, 15:00
Local model area, stage 2.2
Regional model area
Rock domain boundary
Aplitic granite, medium-grained granite and felsic volcanic rock,metamorphic and, in part, albitised
Granite to granodiorite, metamorphic, medium-grained
Tonalite and granodiorite, metamorphic
Diorite, quartz diorite, gabbro and ultramafic rock, metamorphic
Felsic to intermediate volcanic rock, metamorphic and, in part, albitisedb

Figure 1-8. Rock domains included in the two dimensional models at the ground surface. a) Model inside theregional model area. b) Model inside the local model area (darker colours). The different colours represent
the dominant rock type in each domain. Reproduced from Figure 5-24 in the Site description Forsmark.

TR-10-52

6700000

1630000

21

1.5.3 The locations of deformation zones and fracture domains
The rock domains are divided into the mutually exclusive volumes deformation zones and fracturedomains. Deformation zones of the north-western part of the target area are shown in Figure 1-9. The
figure illustrates the two dimensional horizontal surface at –500 m elevation in the local model volume.
Figure 1-10 shows the modelled deformation zones along a WNW-ESE cross-section through thecandidate volume, as well as boreholes.
Six different fracture domains, FFM01–FFM06, are defined within the candidate area, whereof FFM01–
FFM03 and FFM06 are found within the target area. Figure 1-11 shows a 3-D model of the fracturedomains of the north-western part of the target area. The figure also shows some of the deformation
zones of the area. Note that in the illustrations, north faces left.

Figure 1-9. Distribution of the two rock domains RFM029 and RFM045, and all deformation zones with
L > 1,000 m, on a surface at 500 m depth within the local model volume (model stage 2.2). Zones marked
in red are steeply dipping or vertical and have a trace length at the surface longer than 3,000 m. Zones
marked in blue-green are steeply dipping or vertical and are less than 3,000 m in length. Zones marked ingreen are gently dipping. Reproduced from Figure 5-13 in /Stephens et al. 2007/.

22

TR-10-52

KFM06B
KFM06C

HFM16

KFM08C

ENE

0810

KFM08A

B7

KFM08D- 500 m elevation
M
RF

F1

A6
A5

M0

17

A3
F1

A7

A4

B1
A3

KFM02A

3

0023

B5

B6

B2

5
006

RFM029R

KFM03A

B4

NE

ENE 0060A

RF

866

WNW

2

4

A1

A2

KFM02B

KFM06A03

3
M0
RF-2100 mA2
A8130 (SE)(1636032/
6696650)

HFM08
HFM07

WNW 0123

0m

KFM03B
HFM06

HFM04
HFM05

NNW 0101

HFM38
KFM08B
HFM22

ENE 0062A

310 (NW)(1630380/
6701267)E1

NW-SE profile along candidate volume

Figure 1-10. WNW-ESE cross-section through the candidate volume in the structural model showing rockdomains and deformation zones. Reproduced from Figure 11-13 of the Site description Forsmark.

Figure 1-11. Three dimensional views of fracture domains in the target area of Forsmark. Images
reproduced from Figures 5-4 and 5-7 of /Olofsson et al. 2007/.
TR-10-5223

2

Methodology for identifying and qualifying data

As mentioned in Section 1.2.2, the identification of essential data to be qualified in this Data reportis primarily performed by using the AMF approach. A parallel approach has been used in the case ofradionuclide transport modelling, where the input data needs of the computational codes COMP23
and FARF31 have been closely examined. These two approaches are outlined in Sections 2.1 and 2.2.
Once a set of data has been identified it is qualified according to a standardised procedure, as detailedin Section 2.3.

2.1

Identifying data via assessment model flowcharts

Assessment model flowcharts (AMFs) give an overview of models used in the evaluation of
repository evolution and safety, the dependencies/interactions between the models, and data usedin the modelling. For SR-Site, two assessment model flowcharts have been developed. One AMF
represents the excavation/operation and initial temperate period (cf. Figure 2-1) and one represents
permafrost and glacial conditions (cf. Figure 2-2). The role of AMFs in the safety assessment isfurther described in the SR-Site Main report (Section 7.5).
In the AMFs, each blue box represents one or more subject area sections of this Data report, and iseither labelled according to the section title or the concerned data. The section number is given in
the parentheses. The modelling activities and assessments that use the data as inputs are represented
by the yellow rounded rectangles and white rounded rhombuses, respectively. Modelling tasks arepresented in the Model summary report.
The couplings between subject area sections and modelling activities and assessments are summarisedin Table 2-1 to Table 2-5. An example of such a coupling is between the solubility data qualified in
Section 3.4 and the solubility modelling of radioelements inside the canister, using the data as inputs.
This coupling is shown in the lower corner of Figure 2-1 by the arrow from the blue box “Solubility
data (DR 3.4)” to the yellow modelling activity “solubilities (TR-10-501)”. The solubility data are
indirectly propagated to subsequent modelling activities, for example “Radionuclide transport, nearfield”. In Table 2-1 to Table 2-5, however, only the direct couplings of the AMFs are given.
There are also couplings between blue boxes in the AMFs. For example, “Inventory for RN transport(DR 3.1)” feeds into “IRF and CRF (DR 3.2)”. In such a case, data qualified in one section function
as direct inputs to another section. In Table 2-1 to Table 2-5, data that feeds more or less directly into
other sections are indicated at a greater level of detail than in the AMFs. In should be noted that neither
the AMFs nor the tables aim to present the complete picture of how data and models are coupled in
the safety assessment.

1

The number, in this case TR-10-50, refers to the SKB report where the modeling activity is reported.

TR-10-52

25

Strategy for thermal
dimensioning(R-09-04)

Decay heat

LegendBedrock thermal data
DR 6.2

26

Near-field stresses(TR-10-23)

Backfill and Buffer
production reports

Buffer and rocktemperature(TR-10-23)

THM Data report(TR-10-44)

Near-field rocktemperature
GW flow and salinity
during saturation(R-09-19)

THM saturation(TR-10-11)

Hydraulic properties(DR 6.6)
Flow related migration
parameters(DR 6.7)

Flow in fracture
FPC/EFPC

Canister production
reportInitial copper coverage(DR 4.1)

Migration data of buffer
and backfill(DR 5.3)

GW

Backfill andBuffer production
reports

Cation andsulphideconcentration

Fission Gas
ReleaseIRF and CRF(DR 3.2)

Fuel conversion(DR 3.3)

TR-10-52

GW

Solubility data
(DR 3.4)

Solubilities(TR-10-50)

Calculation

Assessment/
assumption

Non-flow related
migration properties(DR 6.8)
Shore-line
displacement(DR 7.1)

SDM-Site(TR-08-05)

Buffer chemistry
and migration in buffer(TR-10-59)

Hydraulicproperties(DR 6.6)

Long-term chemical alterations effects
Canisterproduction report

SDM-Site(TR-08-05)

Canister production
reportResistance toshear movement(DR 4.1)

Earthquakesummary report(TR-08-11)

Uneven bentonite
swelling pressure(DR 4.1)

FPI calculations(TR-10-21)

GEO-DFN(DR 6.3)

Inventory for RN
transport(DR 3.1)

Input to/output fromother AMF

SDM-Site(TR-08-05)

Hydraulic properties ofengineered barriers(DR 5.1, 5.2)

Sulphideconcentration(TR-10-39)

Spalling and
EDZ(DR 6.5)

Buffer and PW chemicalcomposition

Remaining oxygen

Spent fuel report Inventory

Input to/output fromother partsof the AMF

Earlychemicalalteration
effects

GW

Density
distribution

Flow related migration
parameters(DR 6.7)

Consumption of initiallyentrapped oxygen (buffer
and backf.) (R-06-106)

Selection ofradionuclides(TR-10-50)

Chemical alterations
during saturation(TR-10-59)

Loss of mass
(TR-10-11)

Earlychemicalalteration
effects

Fracturing(TR-10-23)

Grout degradation(TR-10-25)

Mechanicalerosion
Expansion of
buffer into tunnel(TR-10-11)

Rockmechanics(DR 6.4)

External
reportSpalling and EDZ(DR 6.5)

Piping/erosion(TR-10-11)

Swelling(TR-10-11)

Underground openings
construction report
including layout

Backfill production report
Buffer production report
Closure production report

Acceptablebuffer mass loss
for maintaining
diffusive
conditions

Saturation times
and implication forrepository function

Spalling and EDZ(DR 6.5)

Reactivation(TR-10-23)

Peak buffertemperature

SR-Site report
Earlymechanicalalterations/effects

Corrosion calculations(incl. buffer erosion)(TR-10-66)
Failure due to corrosion

Hydro temperatedomain(R-09-20, R-09-22)

Buffer initialstate atpermafrost/
glacialSDM-Site(TR-08-05)

Bentonite
colloids

Climatecase

GW
Site scalehydro model

Spalling and EDZ(DR 6.5)

Canister
failureUnderground openings,
canister, Backfill andBuffer production reports

GW
Geometries

GW

Ecosystem reports(TR-10-01,TR-10-02 and TR-10-03)

Colloid
concentration

Bentonite
colloids

Groundwatercomposition(DR 6.1)

Groundwater composition
over glacial cycle(TR-10-58)
National andinternationalguidelines

Failure due to mechanical load

Time for large
defect(DR 4.2)

Regional scale
Hydro model

Flow related migration
parameters(DR 6.7)

Repository scalehydro modelDensity and porosity of
buffer and backfill(DR 5.1)

Flow related
migration properties(DR 6.7)

Discharge
pointsElement reports(TR-10-07)

Landscapedevelopment(TR-10-05)
Biosphere landscapemodel(TR-10-06)
Activity
concentrations

Migration data of buffer
and backfill(DR 5.3)

Biosphere synthesis
report(TR-10-09)
Radiological effects
on non-human biota(TR-10-08)

LDF(DR 7.2)
Radionuclide
Transport, near-field(TR-10-50)

Near fieldreleaseRadionuclidetransport, far-field(TR-10-50)

Far-field release

Figure 2-1. The assessment model flowchart for the excavation/operation period and the initial temperate period after closure.

Doses(TR-11-01)

LegendIce sheetmodelling(TR-09-19)

TR-10-52

SDM-Site(TR-08-05)

Bedrock thermal data
DR 6.2

GIA modelling; (Global
Isostatic Adjustment)(TR-06-23)

Buffer initialstate atpermafrost/
glacialSpalling and EDZ(DR 6.5)
Hydraulic properties(DR 6.6)

Flow related migration
parameters(DR 6.7)
Canister production
reportBuffer initial stateat permafrost/
glacial

Acceptablebuffer mass loss
for maintaining
diffusive
conditions

Canister production
reportClimate reference
evolution(TR-09-19)

Permafrost modelling(TR-09-17)

Periglacial

GlacialClimatecase

Permafrost
depth(DR 7.1)

Ice thickness(DR 7.1)

GW

Buffer and backfill
freezing cases(TR-10-40)

Flow in fracture
FPC/EFPC

Near-field stresses(geosphere)(TR-10-23)

Buffer and PW chemicalcomposition

Concentrationof cations andcorrodantsLong-termchemical alterations
effects

Bentonite
colloids

Canisterproduction report

SDM-Site(TR-08-05)

Stress evolution
during glacial
cycle (TR-09-15)

Resistance toshear movement(DR 4.1)

Earthquakesummary report(TR-08-11)

Canisterproduction report

Selection ofradionuclides(TR-10-50)

Fission Gas
ReleaseInventory for RN
transport(DR 3.1)

GW

27

Solubility data
(DR 3.4)

Solubilities(TR-10-50)

Hydro glacial domain(R-09-21, R-09-22)

Spalling and EDZ(DR 6.5)

Uneven bentonite
swelling pressure(DR 4.1)

Regional scale
Hydro model

GW

Geometries

GW

Density and porosity of
buffer and backfill(DR 5.1)
Migration data of buffer
and backfill(DR 5.3)

Groundwater composition
over glacial cycle(TR-10-58)

SDM-Site(TR-08-05)

Groundwatercomposition(DR 6.1)

GW

Ecosystem reports(TR-10-01,TR-10-02 and TR-10-03)

Site scalehydro model

Climatecase

Hydraulic properties ofengineered barriers(DR 5.1, 5.2)

Bentonite
colloids

Flow related migration
parameters(DR 6.7)

Time for large
defect (DR 4.2)

Assessment/
assumption

Layout D2

Spalling and EDZ(DR 6.5)

Underground openings,
Canister, Backfill andBuffer production reports

Calculation

National andinternationalguidelines

Failure due to mechanical load
Canister
failureIRF and CRF(DR 3.2)

Fuel conversion(DR 3.3)

FPI calculations(TR-10-21)

GEO-DFN(DR 6.3)

Failure due to corrosion

Input to/output fromother AMF

Oxygen penetration
during glacial period(TR-10-57)

Effect of stressevolution on canister

Isostaticload

Input to/output fromother partsof the AMF

Hydraulic properties(DR 6.6)

Fracturing(TR-10-23)
Reactivation (postglacial faulting)(TR-10-23)

Corrosion calculations(incl. buffer erosion)(TR-10-66)

Isostatic failure(DR 4.1)

Modelling of stressevolution during
glaciation

Failure due isostatic load

Spent fuel report Inventory

Rock mechanics(DR 6.4)

THC behaviour(TR-10-59)

GW

External
reportIsostaticload

Migration data ofbuffer and backfill(DR 5.3)
Initial copper
coverage (DR 4.1)

Shore-line
displacement(DR 7.1)

SR-Site report

Colloid
concentration

Landscapedevelopment(TR-10-05)

Discharge
pointsRepository scalehydro model

Element reports(TR-10-07)

Activity
concentrations

Hydro, ice location II(R-09-20)
Non-flow related
migration properties(DR 6.8)

Radionuclidetransport,near-field(TR-10-50)

Figure 2-2. The assessment model flowchart for permafrost and glacial conditions.

Flow related
migration properties(DR 6.7)
Near-fieldreleaseRadionuclide
Transport, far-field(TR-10-50)

Biosphere landscapemodel (TR-10-06)

Biosphere synthesis
report(TR-10-09)
Radiological effects
on non-human biota(TR-10-08)

LDF(DR 7.2)

Far-field release

Doses(TR-11-01)

Data concerning the spent fuel are dealt with in Chapter 3, and are primarily used as inputs whenassessing the source term in near-field radionuclide transport modelling. The couplings between thedata in Chapter 3 and the AMFs are given in Table 2-1. It should be noted that a large quantity ofspent fuel data are also qualified in the Spent fuel report.
A majority of the data concerning the canister is qualified in the Canister production report,
wherein dimensions, geometries, material data, etc. are found. As a result there is a limited set ofcanister data that needs to be qualified in this Data report, as presented in Chapter 4. The couplings
between data in Chapter 4 and the AMFs are given in Table 2-2.
Data concerning the buffer and backfill are provided in Chapter 5 and are primarily inputs tonear-field radionuclide transport modelling, copper corrosion calculations, and hydrogeologicalmodelling. This is outlined in Table 2-3. Other buffer and backfill data, such as geometries andcompositions, are qualified in the Buffer production report and the Backfill production report.
Table 2-1. Couplings between subject area sections of Chapter 3 and items in the assessment
model flowcharts (AMF1 and AMF2) and other Data report sections (DR).
Section number/title

Data provided

Primarily supports

3.1 Selected inventory.

Radionuclide inventories of the
BWR and PWR type canisters andaverage canister, and half-lives andspecific activities.

AMF1:
AMF2:
DR:

Radionuclide transport, near-field.
Radionuclide transport, near-field.
3.2

3.2 Instant release
Instant release fraction, corrosion
AMF1:
fraction and corrosion release fraction, fission gas releases, AMF2:
release fraction.
and corrosion time for the BWR andPWR type canisters and average
canister.

Radionuclide transport, near-field.
Radionuclide transport, near-field.

3.3 Fuel conversion.

The fuel conversion rate for the UO2
fuel matrix.

AMF1:
AMF2:

Radionuclide transport, near-field.
Radionuclide transport, near-field.

3.4 Solubility data.

Solubility limiting phases withassociated reactions and thermodynamic data. For use inside thecanister.

AMF1:
AMF2:

Solubilities.
Solubilities.

AMF1 = Figure 2-1, AMF2 = Figure 2-2.

Table 2-2. Couplings between subject area sections of Chapter 4 and items in the assessment
model flowcharts (AMF1 and AMF2) and other Data report sections (DR).
Section number/title

Data provided

4.1 Data of theintact canister.

Initial minimum copper thickness,
AMF1:
canister void volumes, and canister’s
resistance to mechanical loads.

Corrosion calculations and buffer erosion.
FPI calculations.
Canister failure.
Radionuclide transport, near-field.

AMF2:

Corrosion calculations and buffer erosion.
FPI calculations.
Canister failure.
Radionuclide transport, near-field.

DR:

4.2

AMF1:

Radionuclide transport, near-field.

AMF2:

Radionuclide transport, near-field.

4.2 Evolving canister
defect.

Delay time, defect radius, and tlarge
for the different failure modes.

Primarily supports

AMF1 = Figure 2-1, AMF2 = Figure 2-2, void volume is input to Radionuclide transport, near-field, not shown in AMFs.

28

TR-10-52

Table 2-3. Couplings between subject area sections of Chapter 5 and items in the assessment
model flowcharts (AMF1 and AMF2) and other Data report sections (DR).
Section number/title

Data provided

Primarily supports

5.1 Density and porosityof buffer and backfill.

Dry density, saturated density, andphysical porosity of the buffer andbackfill.

AMF1:
AMF2:
DR:

Hydro temperate domain.
Hydro, ice location II.
3.2, 3.3

5.2 Hydraulic propertiesof buffer and backfill.

Hydraulic conductivity of the buffer
and backfill.

AMF1:
AMF2:
DR:

Hydro temperate domain.
Hydro, ice location II.
4.2, 6.6

5.3 Migration data ofbuffer and backfill.

Sorption partitioning coefficient,effective diffusivity, and diffusion
available porosity of the buffer
and backfill.

AMF1:

Radionuclide transport, near-field
Corrosion calculations and buffer erosion.
Radionuclide transport, near-field.
Corrosion calculations and buffer erosion.

AMF2:

AMF1 = Figure 2-1, AMF2 = Figure 2-2.

In Chapter 6, geosphere data are presented. This is the by far the lengthiest chapter of this report,
spanning over a variety of subject areas providing geochemical, thermo-hydro-mechanical, geological, hydrogeological, and solute transport data. The couplings between the geosphere data of
Chapter 6 and the AMFs are given in Table 2-4. What is not shown in the table is that geosphere data
in Appendix A supports hydrogeological modelling, as well as Section 6.6.
Chapter 7 concerns surface system data including climate related data and landscape dose conversion
factors. This chapter is new as compared to the SR-Can Data report /SKB 2006b/. Couplings between
surface system data and the AMFs are given in Table 2-5.

TR-10-52

29

Table 2-4. Couplings between subject area sections of Chapter 6 and items in the assessment
model flowcharts (AMF1 and AMF2) and other Data report sections (DR).
Section number/title

Data provided

Primarily supports

6.1

Groundwater chemical composition
data for main cations, anions, pH, andredox potential over the glacial cycle.

AMF1:

Corrosion calculations and buffer erosion.
Solubilities.
Buffer chemistry and migration.
Colloid concentration.

AMF2:

Corrosion calculations and buffer erosion.
Solubilities.
THC behaviour.
Colloid concentration.

DR:

3.2, 3.3, 5.3, 6.6, 6.8

AMF1:
AMF2:
DR:

Buffer and rock temperature.
Permafrost modelling.
6.4, 6.5, 7.1

AMF1:
AMF2:
DR:

FPI calculations.
FPI calculations.
6.6

Groundwaterchemical composition.

6.2

Bedrock thermal
properties.

Thermal properties of different rockvolumes, temperature margin, in situ
temperature, internal heat generation,and geothermal flow.

6.3

Discrete-Fracture
Parameters of the geological DFN
Network (DFN) models. model for different
fracture sets.

6.4

Rock mechanics.

Mechanical properties of the rock
AMF1:
mass and of fractures, as well as
in situ stresses during the glacial cycle
and the stress-transmissivity relation. AMF2:

DR:
6.5

Spalling andthe excavation
damaged zone.

Potential for spalling, and hydraulicAMF1:
and migration properties of the spalledzone and excavation damaged zone.
AMF2:
DR:

6.6

Quantities forgroundwaterflow modelling.

Parameters of the hydrogeological
DFN model, ECPM model, and CPM
model, including fracture aperture.

AMF1:
AMF2:
DR:

6.7

6.8

Flow related migration
properties.

Data from hydrogeological modelling
including Darcy fluxes, equivalent
flow rates, advective travel times, andflow related transport resistance. Also
dispersion and maximum penetration
depth for matrix diffusion.

Non-flow related migra- Sorption partitioning coefficient,tion properties.
effective diffusivity, diffusion
available porosity, and connectivityof the rock matrix.

AMF1:

AMF2:

Near-field stresses.
Reactivation.
Fracturing.
Near-field stresses.
Reactivation.
Fracturing.
Hydro glacial domain.
6.5, 6.6
Corrosion calculations and buffer erosion.
Hydro temperature domain.
Radionuclide transport, near-field.
Corrosion calculations and buffer erosion.
Hydro, ice location II.
Radionuclide transport, near-field.
6.6
Hydro temperate domain.
Corrosion calculations and buffer erosion.
Hydro glacial domain.
Corrosion calculations and buffer erosion.
6.1, 6.7
Radionuclide transport, near-field.
Radionuclide transport, far-field.
Corrosion calculations and buffer erosion.
Buffer chemistry and migration.
Radionuclide transport, near-field.
Radionuclide transport, far-field.
Corrosion calculations and buffer erosion.
Oxygen penetration.

DR:

6.1

AMF1:
AMF2:

Radionuclide transport, far-field.
Radionuclide transport, far-field.
Oxygen penetration.
6.1, 6.6, 6.7

DR:

AMF1 = Figure 2-1, AMF2 = Figure 2-2, requirements of oxygen penetration modelling not shown in AMFs.

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Table 2-5. Couplings between subject area sections of Chapter 7 and items in the assessment
model flowcharts (AMF1 and AMF2) and other Data report sections (DR).
Section number/title

Data provided

Primarily supports

7.1

Air and ground-surface temperatures,
ice thickness and ice surface gradients,
shore-level changes, and permafrost
and ground-freezing depths over theglacial cycle.

AMF1:

DR:

6.1, 6.4, 6.6, 7.2.

Landscape dose conversion factors, LDF,
for the different periods of the glacial cycle
and LDF pulse for the temperate period.

AMF1:
AMF2:

Dose assessment.
Dose assessment.

Climate and climate
related data.

7.2

Landscape doseconversion factors.

AMF2:

Hydro temperate domain
Biosphere landscape model.
Hydro glacial domain
Biosphere landscape model.

AMF1 = Figure 2-1, AMF2 = Figure 2-2.

2.2

Identifying data for radionuclide transport modelling

A special effort has been undertaken in the Radionuclide transport report to identify input dataneeded in radionuclide transport modelling. This effort concerns:
1. Identifying the radionuclides that may be of any significance for the safety assessment, and theneeds of data that are radionuclide or radioelement specific.
2. Identifying what input data should be taken from the Data report, and what input data should betaken from other sources.

2.2.1 Selected inventory
Some of the data provided in this Data report are radionuclide or radioelement specific. The spent
fuel is comprised of numerous of radionuclides whereof only a fraction is of importance for theassessment results. In the Radionuclide transport report (Appendix D), the radionuclides that may
be of any significance for the SR-Site safety assessment have been identified. These radionuclides
comprise the selected inventory, as listed in Table 2-6.
In Table 2-6, the radionuclides are sorted into the three categories “Important”, “Less important”,
and “Needs only inventory and half-lives”, as justified in the Radionuclide transport report(Appendix D). Based on this categorisation, there are different demands on the delivery from thisData report. For the important radionuclides, the entire set of nuclide or element specific data areprovided. This full set of data comprises:•

Inventory data and half-lives (Section 3.1).

•

Instant release fraction and corrosion release fraction (Section 3.2).

•

Solubility data (Section 3.4).

•

Diffusion available porosity, effective diffusivity, and sorption partitioning coefficient in thebuffer and backfill (Section 5.3).

•

Diffusion available porosity, effective diffusivity, and sorption partitioning coefficient in thefractured rock (Section 6.8).

•

Landscape dose conversion factors (Section 7.2).

For the less important radionuclides, all of the above data have been requested from the supplier,but in some instances the suppliers have not provided the data. For each such instance the SR-Siteteam have recommended data for use in radionuclide transport modelling, often by means of recommending pessimistic data. A note on each instance is given in the corresponding subject area section.
For the radionuclides for which only the inventory and half-life are required, such data are found in
Section 3.1.
A set of nuclide specific data that is not listed in the above bullet list is LDF pulse (Section 7.2), which
is only provided for radionuclides that may be released as a pulse, together with the prerequisite thatthey may be of any significance for the safety assessment.
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31

Table 2-6. Selected radionuclides for which data are needed in radionuclide transport modelling.
Based on Table D-6 of the Radionuclide transport report.
ImportantLess important1

Need only inventory and half-lives2

Fission and activation products

C-14

Ag-108m

Cl-36

Cd-113m

Cs-135

Eu-152

Cs-137

H-3

I-129

Nb-93m

Nb-94

Ni-63

Ni-59

Sm-151

Pd-107

Sn-121m

Se-79

Ho-166m

Sn-126

Mo-93

Sr-90
Tc-99
Zr-93
Decay chains
4nPu-240Cm-244

U-236
Th-2324n+1

Cm-245

Pu-241

Am-241

U-237
Pa-233

Np-237
U-233
Th-2294n+2

Cm-246Am-242m

Am-242

Pu-242

Pu-238

Cm-242

U-238

Np-238

U-234

Th-234

Th-230

Pa-234m

Ra-226
Pb-210
4n+3

Am-243

Cm-243

Np-239Pu-239
U-235
Pa-231
Ac-227
1
2

Radionuclides that might be of importance in the hypothetical cases of initial or early defects in the canister.
Radionuclides with such short half-life that their contribution in safety assessment modelling is through decay products.

32

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2.2.2 Input to radionuclide transport modelling
All input parameters to the radionuclide transport modelling codes COMP23 /Romero et al. 1999,
Cliffe and Kelly 2006, Vahlund and Hermansson 2006a/ and FARF31 /Norman and Kjellbert 1990,
Elert et al. 2004/ are listed in Table 3-2 and Table G-1 of the Radionuclide transport report. Outof all these parameters, those qualified in the Data report are listed in Table 2-7. In the leftmost
column the parameter name in the computational code is given. In the right part of the table, theconcerned section in the Data report is given together with the symbol used.
Table 2-7. Input parameters for the probabilistic calculations for the corrosion, shear load, isostatic load and pinhole scenarios. The first four columns are reproduced from the Radionuclidetransport report (Table 3-2) for selected rows concerning the Data report. NF = near field(COMP23), FF = far field (FARF31).
Parametername in code

Parameter

Unit

NF/FF

Data report section,
symbol, and comment

HALFLIFE
INVENTORY
FDMC
IRF
CRF
TCORR
TDELAYTLARGEADELAY, ALARGE
CSOL
KDB
DEB
EPSB
RHO for material 2
KDBF
DEBF
EPSBF
RHO for material 3
QEQ_1

Half-life.
Radionuclide inventory.
Fuel conversion time.
Instant release fraction of inventory.
Corrosion release fraction of inventory.
Corrosion time.
Delay time for onset of radionuclide transport.
Time for large canister defect.
Canister defect sizes.
Solubility limits.
Buffer sorption partitioning coefficients.
Buffer effective diffusivities.
Buffer porosities (diffusion available porosity).
Buffer density (density of the solid particles).
Backfill sorption partitioning coefficients.
Backfill effective diffusivities.
Backfill porosity (diffusion available porosity).
Backfill density (density of the solid particles).
Equivalent flow from deposition hole tofracture(s) intersecting deposition hole.
Equivalent flow to EDZ.
Equivalent flow to fractures intersecting
deposition tunnel.
Darcy flux at deposition hole.
Width of spalling zone.
Length of spalling zone.
Thickness of spalling zone.
Porosity of spalling zone.
Diffusion coefficient in spalling zone
(damaged zone).
Diffusivity in water.
Rock sorption partitioning coefficients.
Rock effective diffusivities.
Rock porosities (diffusion available porosity).
Rock density (solid density).
Rock transport resistance for paths
beginning at Q1, Q2 and Q3.
Rock advective travel time for paths
beginning at Q1, Q2 and Q3.
Rock Peclet number.
Max. penetration depth in rock matrix.
Biosphere LDF factors.
Void volume.

yrmol/can
yr
–
–
yr
yr
yr
m
mol/m3
m3/kg
m2/s
–
kg/m3
m3/kg
m2/s
–
kg/m3
m3/yr

NF/FF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF
NF

3.1
3.1
3.3
3.2
3.2
3.2
4.2
4.2
4.2
3.4
5.3
5.3
5.3
5.1
5.3
5.3
5.3
5.1
6.7

t½

m3/yr
m3/yr

NF
NF

6.7
6.7

Qeq2
Qeq3

m3/m2,yr
m
m
m
–
m2/s

NF
NF
NF
NF
NF
NF

6.7
6.5
6.5
6.5
6.5
6.5

q
Wzone
Lzone
dzone
ezone

m2/s
m3/kg
m2/s
–
kg/m3
yr/m

NF
FF
FF
FF
FF
FF

6.8
6.8
6.8
6.8
6.4
6.7

Dw
Kd
De
e
r
F

yr

FF

6.7

tw

–
m
Sv/y per Bq/y
m3

FF
FF
FF
NF

6.7
6.7
7.2
4.1

Pe
LD
LDF
V

QEQ_2
QEQ_3
U0_1
Wzone
Lzone
dzone
epszone
Dp
Dw
KDR
DER
EPSR
F_1, F_2, F_3
TW_1, TW_2, TW_3
PE
DPEN
LDF

a
b
IRF
CRF
tcorr
tdelaytlargerdefect
c
Kd
De
e
rs
Kd
De
e
rs
Qeq1

d

e

a. Inventory provided and used as mol/canister, although the unit in Table 3-2 is given as mol.
b. The Data report provides the Fuel conversion rate (yr-1).
c. The Data report provides input data to solubility limit calculations, but not solubility limits.
d. Dp is not provided in the Data report, but can be calculated from Dw/ty2, where ty is the zone tortuosity.
e. Void volume is not included in Table 3-2 but requested in Table G-1 of the Radionuclide transport report.

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2.3

Qualification of input data – instruction to the supplierand customer

The final objective of the Data report is at performing data qualification including estimates of bothconceptual and data uncertainty, as well as of natural variability, for various subject areas. In addition, thetraceability of the data is examined. The qualified data are intended for use as input data in the SR-Sitesafety assessment modelling.
The Data report does not concern all data used in the SR-Site safety assessment, but those which are
identified to be of particular significance for assessing repository safety (see Sections 2.1 and 2.2). Data
may concern both measured data from the laboratory and from the field, as well as output from detailed
modelling where measured data are interpreted, depending on the subject area. Even though the data
may represent both parameters and entities, in this instruction the word “data” is generally used.
It should be pointed out that in the process of qualifying data, the traceability that is the focus of many
quality assurance systems is only one aspect. An equally important aspect is the scrutinising of thescientific adequacy of the data.
Each data set provided in this report is categorised into one of many different subject areas. For eachsubject area, the data qualification process comprises a sequence of stages resulting in a text of astandard outline. The sequence of stages and the standard outline are shown in Figure 2-3.
Below the parties involved in the Data report and the sequence of stages shown in Figure 2-3 are
discussed. The standard outline is described in Sections 2.3.1 to 2.3.12. For each subject area, theData report team identifies the customer and supplier of data, and assigns a customer representative
and a supplier representative that co-author the subject area section2.
The customer is in broader terms the SR-Site team that is responsible for performing the SR-Sitesafety assessment. However, the entire team is generally not involved in each subject area but it israther embodied by a group of persons with special knowledge and responsibility. The customer
representative should represent the SR-Site team, and not rely solely upon own opinions.
The suppliers are the teams originating the sources of data, for example the site-descriptive model
reports, production reports, and other supporting documents. The supplier representative should
represent the team, and not rely solely upon own opinions.
The intended chronology of the writing of a subject area section is the following.
Stage A: The customer writes the first two sections defining what data are requested from the supplier,
how the data will be used in SR-Site modelling, and how similar data were used SR-Can modelling.
Stage B: The supplier writes the following eight sections that are the core of the data qualification.
This is done according to a standard outline where a number of issues such as traceability, data
uncertainty, and natural variability should be dealt with. These sections should result in sets ofqualified data that are the delivery to the customer.
Stage C: The customer, representing the entire SR-Site team, writes the last two sections making
judgements upon the delivery and recommending data for use in SR-Site modelling. The
text is produced in close cooperation with other persons within the SR-Site team with special
knowledge and responsibility. The text should reflect upon the formal decision taken in StageD (accordingly, it may need to be revised after Stage D).
The text of each stage should be made available in good time to the person or persons responsible for
writing the text of the subsequent stage. Upon completion of Stage C, a data qualification meeting isheld (Stage D) and the text is subjected to factual review (Stage E). If case the subject area text has been
well communicated during its preparation, and the customer and supplier share the views of the text, thedata qualification meeting (Stage D) may be held after the factual review (Stage E), to get an external
input on the data delivered.
Stage D: For each subject area, a data qualification meeting is held where the customer and supplierrepresentatives, and at least one member of the Data report team are invited. Appropriate
members of the SR-Site team and supplier team may also be are invited. At the meeting, thedata delivery to SR-Site is formally decided upon and the decision is recorded in minutes(documented according to SKB’s quality assurance system).
Stage E: The subject area section is subjected to a factual review according to standard procedures.
2

The terms customer and supplier come from standard quality assurance terminology.

34

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Stage A
The customer defines therequested delivery of thesubject area data.

1. Modelling in SR-Site

Stage BThe supplier delivers
qualified dataupon
going through a data
qualification process
on a standard outline

3. Supplier input on use ofdata in SR-Site and SR-Can

2. Experience from SR-Can

4. Sources of information anddocumentation of data
qualification
5. Conditions for which data
are supplied
6. Conceptual uncertainty
7. Spatial and temporalvariability of data
8. Spatial and temporalvariability
9. Correlations
10. Result of supplier’s data
qualification

Stage CThe SR-Site teamjudge the delivery
and recommend datafor use in SR-Site.

11. Judgements by the
SR-Site team

12. Data recommended foruse in SR-Site modellingStage D
A data qualification meeting is held
where the final data delivery to
SR-Site is formally decided upon.

Stage EIn the process of finalising the Data
report, it is reviewed according tostandard procedures.

Figure 2-3. Stages of writing and reviewing the Data report. The standard outline of a subject area isshown in the yellow boxes.

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35

Finally, within the SR-Site project but outside the scope of the Data report team, a follow-up is madewhere it is controlled that the correct data are used in SR-Site modelling. This could be seen as Stage F
in the data qualification process, but its falls upon the modellers using the data to carry this stage
through. It is therefore not shown in Figure 2-3.
In the following sections, the outline shown in the yellow boxes in Figure 2-3 is described in detail.
The instructions given are in essence the ones given in the SKB quality assurance document “Supplying
data for the SR-Site Data report”, which has been provided at an early stage to the authors of this report3.
However, to fit the format of this SKB report, editorial modifications have been made. To limit the numberof editorial modifications, the grammatical tense of the instruction has been kept.

2.3.1 Modelling in SR-Site
In this section, the customer should define what data are requested from the supplier, and give a brief
explanation of how the data of the subject area are intended to be used in SR-Site modelling activities.
Defining the data requested from the supplierHere, the customer should define the data (parameters) that should be part of the supplier’s delivery,in a bullet list. If applicable, the parameter symbol and unit should be provided in this list. If the suppliershould focus on providing data of certain ranges, or for certain conditions, this should be specified.
This text should not only facilitate the task of the supplier, but also assist the reader of the Data reportin understanding the scope of the subject area section.
SR-Site modelling activities in which data will be used

Here the customer should give a brief explanation of how the data are intended to be used in different SR-Sitemodelling activities. This explanation should cover both how the data are used in specific models, and in the
SR-Site model chain (unless evident from the assessment model flowcharts). Differences from the use of thistype of data in SR-Can should be highlighted. The justification for the use of these models in the assessment
is provided in other SR-Site documents, such as the SR-Site Main report and process reports.
As a result of the extensive work that will be conducted up to near completion of the SR-Site safetyassessment, details of the models and the model chain may be modified. As a result, this text may haveto be finalised in a late stage of the Data report project. Thus only a preliminary version is provided
early on to the supplier.

2.3.2 Experience from SR-Can
In this section the customer should give a brief summary on how the data of the subject area were usedin SR-Can. The experience from SR-Can should function as one of the bases for defining the input datarequired in SR-Site modelling. It should be noted that the teams undertaking the SR-Site and SR-Can
safety assessments largely are the same, so transferring experience from SR-Can to SR-Site should notpresent any substantial problem. The summary of how the data were used in SR-Can should conformto the following outline:•

Modelling in SR-Can.

•

Conditions for which data were used in SR-Can.

•

Sensitivity to assessment results in SR-Can.

•

Alternative modelling in SR-Can.

•

Correlations used in SR-Can modelling.

•

Identified limitations of the data used in SR-Can modelling.

More detailed guidance regarding what should be included in the summary in relation to each of thesebullets is given below.

During the work of this Data report the instruction has been updated, to better reflect the actual qualification
process. The different versions are documented according to SKB’s quality assurance system.
3

36

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Modelling in SR-Can

The use of the data in specific SR-Can models, as well as in the SR-Can model chain, should be
described. As such an account is generally included in the SR-Can Data report /SKB 2006b/, thesummary should be kept short and focus upon differences between the use of data in SR-Can and
SR-Site. Repetitions from the section “Modelling in SR-Site” should be avoided. If there is nodifference between the SR-Can and SR-Site modelling approaches, it is sufficient to state this.
Conditions for which data were used in SR-Can

In this subsection, the relevant conditions to which the subject area data were subjected to in SR-Can
modelling should be outlined. Relevant conditions are only those conditions that significantly
influence the data, in the context of demonstrating repository safety. Different subject area data are
affected by different conditions. For example, the sorption partition coefficient Kd may be strongly
influenced by groundwater salinity. Thus, in characterising the conditions under which Kd values
were used, it is likely to be appropriate to give the salinity range during repository evolution, forexample as assessed in the SR-Can hydrogeochemical modelling. Other types of conditions may
include gradients, boundary conditions, initial states, engineering circumstances, etc.
It is sufficient to state the relevant conditions used in SR-Can modelling (including those appliedin sensitivity analyses, various initial states, different scenarios, and evolution within scenarios) andto refer to SR-Can documents for background information. Justification as to why those conditions
were studied is not required. Where appropriate, the relevant conditions should be tabulated. It should
be noted that the stated conditions do not restrict qualification of data for use under other conditions,but merely underline the conditions considered appropriate within the modelling context of SR-Can.
If conditions of SR-Can were similar to those of SR-Site, it is sufficient to state this.
Sensitivity to assessment results in SR-Can

Where appropriate, an account should be given of results from sensitivity analyses performed as partof, or prior to, the SR-Can safety assessment. Such analyses were made in order to prioritise uncertainty
assessments for those data and conditions judged to be potentially important for performance, bothfor overall end-points such as risk and for conditions affecting the state of the system. If such sensitivity
analysis was performed, the following issues may be outlined:•

For what ranges of the data was the impact on the SR-Can safety assessment significant and are
there ranges where the impact was negligible? If sensitivity analyses show that only part of therange has an impact on repository safety, less effort may be given to quantifying parameter values
outside this range.

•

Was the impact monotonic, i.e. is there a unidirectional relationship between the data value andperformance, is there an “optimal” value, or is the impact dependent in a complicated manner
upon the values of other input data?

•

What degree of variation in the data is needed to have an impact on safety assessment results(this may be different for different data ranges)?

•

Were the results applicable to all conditions of interest – or only to some?

In discussing the above, the customer should consider if the cited sensitivity analyses were sufficiently
general to provide definitive answers.
Alternative modelling in SR-Can

Whenever it applies, the customer should summarise alternative modelling in SR-Can focusing on
the concerned data. The following issues should be reflected upon:•

What alternative models exist and what influence did they have on the safety assessment?

•

Were conceptual uncertainties, related to the models in which the data were used, identified
in SR-Can? In that case, what was the impact on assessment results?

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37

Correlations used in SR-Can modelling

A correct treatment of probabilistic input data requires that any correlations between those data are
identified and quantified. The correlations associated with the subject area data, as accounted for in
SR-Can, should be briefly described. This includes internal correlations within the subject area andcorrelations with data of other subject area sections. If the same correlations were used as will be
used in SR-Site, it is sufficient to state this.
Identified limitations of the data used in SR-Can modelling

If limitations or shortcomings of the data used in SR-Can have been identified, which may signi­
ficantly have affected the assessment, such should be accounted for. The limitations or shortcomings
can be due to, for example, lack of site-specific data or lack of data obtained at conditions representative for the repository. The limitations and shortcomings may have been identified by the regulatory
authorities, by SKB, or by other parties.

2.3.3 Supplier input on use of data in SR-Site and SR-Can
In this section the supplier has the opportunity to comment on the two above sections. The focus for the
supplier should be to help the SR-Site team in choosing appropriate data and modelling approaches,
and avoid repeating errors and propagating misconceptions from SR-Can or from earlier safety analyses.
Even if a single individual has the roles as both supplier and customer representative, he or she may
still make comment upon the use of data in SR-Site and SR-Can.
2.3.4 Sources of information and documentation of data qualification
This section is devoted to presenting the most important sources of data, as well as categorising different data sets on the basis of their traceability and transparency, and scientific adequacy. Sources of data
may include SKB reports, SKB databases, and public domain material. Documents of importance forthe data qualification may also consist of SKB internal documents. All underlying documents should
be properly cited throughout the Data report.
Sources of information

The supplier is asked to tabulate the most prominent references used as sources of data. In addition,
the references of important documents describing the process of acquiring, interpreting, and refining
data may be listed.
If the data qualification process is well documented in supporting documents, it is sufficient toreference these documents and to only briefly summarise the data qualification process. If not, theData report gives the supplier a chance to appropriately document the data qualification process ofthe subject area data.
Concerning sources of information, the supplier should:•

Fully cite all sources of information throughout the text. It is necessary to keep in mind that the
text may have readers with limited in-depth knowledge of the subject. Therefore, what normally
would seem as trivial may deserve references for further reading. It is strongly recommended tomake an extra effort to refer to the open literature where possible, and not only to SKB documents.

•

In case of referring to a document of many pages, for example a site-descriptive model report, givedetailed information on the section, figure, table, etc. where the relevant information can be found.

•

Properly cite databases, SKB internal documents, etc. even though they may not be available to
the general reader. In the case of referring to databases, the precise reference should be given to
the individual data set used. For example, it is not sufficient to refer to the SKB database Sicada if
not also giving detailed information, such as the activity or the number of a Sicada delivery note.
This is to ensure traceability within the SR-Site project.

•

Fully cite advanced modelling tools where the underlying code may have implications for data
qualification.

38

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Categorising data sets as qualified or supporting

The supplier should categorise data as either qualified data or supporting data. Qualified data has
been produced within, and/or in accordance with, the current framework of data qualification, whereas
supporting data has been produced outside, and/or in divergence with, this framework. Data takenfrom peer-reviewed literature take a special position in that they may be considered as qualified eventhough they are produced outside the SKB framework of data qualification. However, such data are notby necessity categorised as qualified, as they may be non-representative or lack in some other aspect.
Data recently produced by SKB, for example in the site investigations, should a priori be consideredas qualified. However, before the data are formally categorised as qualified, a number of considerations
need to be made as described below. Data produced outside the data qualification framework shoulda priori be considered as supporting data. This could for example be data produced by SKB prior to
the implementation of its quality assurance system, or data produced by other organisations. Before
formally categorise the data as supporting, a number of considerations need to be made as described
below.
Data taken from widespread textbooks, engineering handbooks, etc., which are considered to beestablished facts, need not to be scrutinised. Well-known data that should be excluded from the Data
report need not to be categorised as qualified or supporting data, although their exclusion may need
to be justified.
It is outside the scope of the Data report to deal with individual data. Instead the supplier should
characterise data sets as qualified or supporting. The supplier should decide to what extent various
data can be included in a single data set for the specific case. The following examples of naturalbarrier data sets could be used for inspiration:•

Data, or part of data, obtained by a specific method at a site, rock volume, borehole, etc.

•

Data, or part of data, obtained by various methods at certain conditions (e.g. saline water)
at a site, rock volume, borehole, etc.

•

Data, or part of data, taken from an external publication.

Qualified data
The following considerations should be made for data that a priori are identified as qualified, before
formally categorising them as qualified. Most of the data that is delivered to the Data report are refine­ments and interpretations of observed data. Such refinements and interpretations are performed
both for engineered and natural barrier data. For example, the multitude of data acquired within thesite investigation are normally refined within the site-descriptive modelling by use of more or lesscomplex models. The supplier should judge whether data acquisition and refinement, and associateddocumentation, are in accordance with the implemented data qualification framework. The following
considerations may form the basis for the judgement.
Considerations concerning data acquisition:•

Is the acquisition of observed data performed in conformance with a widely adopted quality
management system (e.g. the ISO 9000 series or equivalent)?

•

Is it possible to trace relevant quality assurance documents (for example method descriptions,field notes, etc.) for the measurements? It should be noted that even though the quality assurance
documents may not be available for the general reader, they are accessible for the SR-Site team.

•

Is it possible to extract relevant information on the data quality, variability, and representativity
from documents reporting the acquisition of data?

•

Are concerns associated with the observed data and nonconformities of the measurements
transparently described?

• Is the undertaken data acquisition programme sufficient to determine the full range of data uncertainty
and natural variability, and do the acquired data appropriately characterise the intended aspect of
the system (site, rock domain, copper canister, population, etc.)?

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39

Considerations concerning data refinement:•

Are concerns and nonconformities described in the supporting documents propagated to, andaddressed in, the data refinement?

•

In refining observed data by use of more or less complex modelling, is this done in accordancewith documented methods?

•

In case of more complex modelling, which may have implication for data qualification, is the detailsof the modelling described in either a task description or the document reporting the modelling
results? Furthermore, is the modelling tool developed in accordance with a widespread quality
assurance system and/or is its quality tested in other ways?

•

Has comparative/alternative modelling been performed to evaluate artefacts induced in themodelling, and to evaluate whether the modelled interpretation of the data is reasonable?

Going through these questions in detail for each data set may be a too extensive task. Accordingly,
the sorting of data to some degree is based on expert judgement. However, in making this judgment,
it may be helpful to revisit the above bullet lists.
If appropriate data qualification has been performed and documented in supporting documents, orcan be performed and documented as part of the delivery, the data should be formally categorisedas qualified data. If the documentation of the data qualification process is inadequate in supporting
documents, and appropriate data qualification cannot be performed as part of the delivery, the data
must be demoted to the category supporting data.
As mentioned before, data taken from peer-reviewed literature takes a special position in that they
may be considered as qualified even though they are produced outside the SKB framework of data
qualification. However, before formally categorising them, one needs to judge whether they are
representative for the intended KBS-3 repository system and the Forsmark site. A prerequisite formaking such a judgement is often that the documents are transparently written. In case the data are
non-representative for Swedish conditions, or their degree of representativity is difficult to evaluate,
the data may be categorised as supporting instead of as qualified.
Supporting data
The following considerations should be made for data that a priori are identified as supporting,
before formally categorising them as supporting. Such data are produced by SKB outside theframework of data qualification, or by other organisations. The supplier should:•

Consider how well the method used to acquire the data is described? The greater the transparency
with which the method is described in the supporting document, the greater the value should be
ascribed to the data.

•

Consider how well the method used to interpret and refining the data is described? The moretransparently the interpretation and refinement is described in the supporting document, thegreater the value should be ascribed to the data.

•

Consider if it is possible to identify and evaluate the data qualification process used in acquiringand refining the data? If it is shown that a sound data qualification process has been used, thedata should be ascribed greater value.

•

Judge, based on the above, whether the data can be used as part of the basis for recommending
data to SR-Site safety assessment modelling, as comparative data for other qualified data, or
should not be used at all. In some cases the transparency of a document is so poor that crucial
information concerning data qualification cannot be extracted. If this renders an assessment of
the data's scientific adequacy and their representativity for Swedish conditions impossible, thesupplier should recommend that the data are dismissed. This can be done even if the numericalvalues of the data are consistent with other, qualified data.

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In case data that a priori are assumed to be supporting are acquired, interpreted, and refined according to a similar data qualification framework as implemented by SKB, and the data are accurate andrepresentative, the supplier can promote the data to the category qualified data.
It should be noted that data taken from peer-review literature can be categorised as supporting data.
This can be done if, for example, data are only partially representative for the Swedish repository
concept and the Forsmark site.
Upon formally categorising the data sets as qualified or supporting, they should be tabulated as
exemplified in Table 2-8. As can be noted, justifications for the sorting are given in the same table
for the numbered items.
Table 2-8. Qualified and supporting data sets (for parameter Y).
Qualified data sets

Supporting data sets

1. /SKB, 20xx/, Section 4.5: All data on parameter Y
obtained for rock domain RFM029.
2. Data presented in the Underground construction
opening report in Figure x.
3. /Svensson, 20xx/, Table 2: Data between theborehole length 400–452 m in KFM01D, indicating
an average value of 2,650 m3/kg.
4. All parameter Y data stored in SKB Database X,
with the identity number xxx-yyy-zzz.

5. /Nilsson, 19xx/, Table 1. Data obtained in the pH range
6–9 in sedimentary rock.

1–2, 4: These data have been produced within the site investigation (item 1), within a production report (item 2),
or as part of the site-descriptive modelling (item 4). These data are produced within the SKB data qualification
framework and are judged as qualified.
3: /Svensson, 20xx/ is a peer-review article and the data are obtained at the Forsmark site and are judged as
representative. The data set is judged as qualified.
5: /Nilsson, 19xx/ is a peer-review article that is transparent and scientifically sound. However, the data are
predominantly representative for sedimentary rock. Accordingly they are judge as supporting.

Excluded data previously considered as important

Within the field of nuclear waste management, there are large quantities of data that are of little
significance for the SR-Site safety assessment, as they are less representative for the Forsmark site,
the KBS-3 repository concept, etc. than other available data. In general, excluding such data from
subsequent use in SR-Site does not require justification. The exception is if the data constitutes awell-known part of the basis of previous safety assessments (or equivalent tasks), and/or have asignificant impact on the perception of the appropriate choice of data values. If it could be seen
as a significant inconsistency or omission not to use the data, their exclusion should be explicitly
justified. Providing an appropriate justification is particularly important if the excluded data disagreewith the presently used data.

2.3.5 Conditions for which data are supplied
The data of the different subject areas are likely affected by different conditions. Conditions referto initial conditions, boundary conditions, barrier states, and other circumstances, which potentially
may affect the data to be estimated. In the process of qualifying data for subsequent use in safetyassessment, an important part is to account for the conditions for which data were acquired, andto compare these conditions with those of interest for the safety assessment.
In the section “Experience from SR-Can” it is stated for what conditions data were used in SR-Can.
These conditions should not limit the conditions for which data are examined, but merely point out
conditions that are likely to be of importance for a safety assessment. The supplier may have been
given instructions from the SR-Site team, or may have opinions about important conditions, whichlead to modifications of the SR-Can conditions.

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In this section, the conditions for which the data have been obtained should be discussed and, as
appropriate, justified as relevant to SR-Site. Such a condition is often a single value (e.g. temperature),a range (e.g. salinity range), or a gradient (e.g. hydraulic gradient). Other factors of relevance forrepository safety may be included as conditions, at the discretion of the supplier. Conditions that are
deemed to be of particular importance for repository safety should be highlighted. Other conditions
that do not significantly relate to repository safety, but may be of importance for data qualification,
are also important to note. Such information is valuable when, for example, crosschecking data setswith those of other studies or evaluations. The supplier may list ranges of applied conditions during
data acquisition, excluding conditions that are both general and self-evident (such as the gravitation).
In many cases, it is expected that the conditions for which data are supplied will differ from thoseassumed in the SR-Site safety assessment. For example, a set of supplied data may not represent thefull temperature range required, or may have been obtained at a different pressure than expected insitu. The differences identified by the supplier should be outlined in this section. Furthermore, for eachdeviating condition of importance for the assessment results, the implications should be discussed.

2.3.6 Conceptual uncertainty
This section concerns conceptual uncertainty of the subject area data. Two types of conceptual
uncertainty should be discussed. The first concerns how well the data, and the models wherein they
are used, represent the physical reality, and the second concerns conceptual uncertainties introduced in
the acquisition, interpretation, and refinement of the data. Generally data are included in models that
represent an idealised reality, which to some degree differs from the physical reality. Therefore, one can
expect that a degree of conceptual uncertainty is associated with all data compiled in this Data report.
To the extent possible, the supplier should describe such conceptual uncertainty. This should be done in
the context of the models in which the data are used, intended to describe certain postulated processes.
Also, it may be appropriate to discuss alternative conceptualisations in which the data may be usedin different ways. If comprehensive discussions on the subject have already been documented, such
documents may be referred to and a short summary of the conceptual uncertainty will suffice. Aspectsof the conceptual uncertainty that are obviously unrelated to repository safety may be disregarded.
Conceptual uncertainty may also be introduced in the acquisition, interpretation, and refinement of
the data. For example the data may have been obtained by inverse modelling of experimental results,
where conceptual uncertainty is introduced by the model. The data may also have been obtained byusing some correlation relationship, where there is conceptual uncertainty in the correlation. Manyother sources of conceptual uncertainty are conceivable and may be discussed at the discretion of the
supplier. In doing this, the supplier should carefully differentiate between uncertainties introduced
due to conceptual issues and data uncertainty introduced by measurement errors, etc. Data uncertainty
should be discussed in the following section.

2.3.7 Data uncertainty due to precision, bias, and representativity
In this section data uncertainty should, if possible, be discussed in terms of precision, bias, and representativity, in the context of their application in SR-Site. Such uncertainty is associated both withthe acquisition of data, for example in the site investigations, and subsequent refinement of data, forexample in the site-descriptive modelling. Data uncertainty includes neither conceptual uncertainty
nor natural variability.
If comprehensive discussions on these matters are documented elsewhere, such documents should
be referred to, and a short summary of the discussion will suffice. The supplier should begin withdiscussing the precision of the supplied data. To the extent possible, data spread due to the precision
should be separated from data spread due to natural variability. Precision issues are both associatedwith the method used in acquiring the raw data and subsequent interpretation of data. Concerning
acquiring raw data, limitations in precision are not only associated with the equipment and method
used when performing the measurements, but also with the sampling procedure, sample preparation,etc. Precision issues associated with interpretation of the data depend to a large degree on the procedureused, and should be discussed at the discretion of the supplier. As an example, it may not be straightforward to estimate the precision of data that are a function of other acquired data, with their intrinsic
limitations in precision.
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Thereafter, the supplier should discuss the bias of the supplied data. Similar considerations apply as
when discussing precision, both for bias associated with the acquisition of raw data and with theirsubsequent interpretation. Bias in observed data is often associated with the method used for acquiring
data and its calibration, and with effects of sample preparation. Bias is also associated with the sampling
procedure, sample size, and differences in conditions for example between those in the laboratory
and in situ. Bias issues associated with data interpretation depend to a large degree on how the inter­
pretation is made, and should be discussed at the discretion of the supplier.
Finally the supplier should discuss the representativity of the supplied data, both in terms of data acquisition, and data interpretation and refinement. Issues associated with the representativity of acquired
data often concern the sampling procedure, the sample size relative to natural variability and correlation
length, and differences in conditions between, for example, those in the laboratory and in situ.
An important issue is whether the data are generic or site and/or technique specific. In the case ofaccess to generic data only, the supplier should discuss whether, and to what degree, the lack of site
and/or technique specific data influences the data uncertainty. Representativity issues associatedwith data interpretation and refinement depend much on the specific interpretation and refinement
process, and should be discussed at the discretion of the supplier.
As well known, the precision, bias, and degree of representativity often depend on a mixture of
the above-suggested sources for data uncertainty, and may not be easily separated. However, thesupplier is asked to reflect carefully on these issues, as an assessment of data uncertainty is central
for the data qualification. In case data uncertainty cannot be discussed in terms of precision, bias,
and representativity, for example as the resolution in data does not allow for such separation, it will
suffice to make a general data uncertainty discussion.
Comprehensible illustrations of different data sets are of high value. The objective of the illustrations
is not necessarily to provide a detailed basis and description of the numerical values of the individual
data. Sometimes the objective may be to give the reader an understanding of how much, and in whatways, the data varies and the data sets differ from each other. An example of presenting different
data sets is given in Figure 2-4, where the reader can get an immediate perception about differences
between the data sets. Examples of other illustrations are given in Section 2.3.10.
4

Data Set

3

Data set 1
Data set 2
Data set 3

2

1

0
0

10

20

30

40

50

60

70

80

Data value

Figure 2-4. Example of presenting differences in data sets.

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2.3.8 Spatial and temporal variability of data
In this section the supplier should discuss the spatial and temporal variability of the subject area
parameters. The natural variability should as far as possible be separated from data uncertainty,
discussed in the above section.
The supplier should describe what is known about the spatial variation, sometimes referred to asheterogeneity, of the subject area data. This may result in different data sets for different volumes
or elements of the repository system, or for different time periods. If comprehensive discussions on
the natural variability are documented elsewhere, such documents should be referred to and a shortsummary of the natural variability will suffice.
In the process of describing the spatial variability, it may be helpful to reflect on the following lineof questions.
•

Is there spatial variability of the data, and if so is it of consequence for the safety assessment?

•

Is the spatial variability scale dependent? If so, can an appropriate approach of upscaling to safetyassessment scale be recommended?

•

What is known about correlation lengths from, for example, variograms?

•

Can the spatial variability be represented statistically as a mean of data qualification and, if so,
how is this done?

•

Is there any information about the uncertainty in the spatial variability?

In the process of describing the temporal variability, it may be helpful to reflect on the following lineof questions.
•

Is there temporal variability of the data, and if so is it of consequence for the safety assessment?

•

What processes affect the temporal variability of the data and how is the temporal variability
correlated with these processes?

•

Does the temporal variability follow any pattern, for example a cyclic pattern?

•

Could the temporal variability be represented statistically as a mean of data qualification andif so, how is this done?

•

Is there any information about the uncertainty in the temporal variability?

In addition, other relevant issues concerning the natural variability may be addresses at the discretionof the supplier. Comprehensible illustrations of different data sets from different volumes, elements,
or time periods are of high value.

2.3.9 Correlations
An appropriate treatment of probabilistic input data requires that any correlations and functional
dependencies between those data are identified and quantified. In the extensive work with the FEP
database and the Process reports, most correlations and functional dependencies between parameters
have been identified. Where appropriate, these correlations and functional dependencies should
also be implemented in the safety assessment models. It should be an aim to aid those performing
stochastic modelling, by giving well defined and usable information on how to handle correlations
between input data.
Correlations and functional dependencies may also have been used when acquiring, interpreting, andrefining data. For example, concerning sorption partition coefficients, data have not been acquired for
all relevant radioelements. For species for which there is a lack in observations, the supplied sorption
partition coefficient will have been estimated from data obtained for one of more analogue species.
This has implications for how to correlate input data in stochastic safety assessment modelling.

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In this section the supplier is requested to address the following questions:•

Are there correlations or functional dependencies between parameters within the subject area,
or with parameters of other subject areas? If so, account for these and if possible also outline theconsequences for subsequent modelling.

•

If correlations have been used in acquiring, interpreting, and refining data, how is this done?
Furthermore, is the outcome based solely upon correlations, or on both measurements andcorrelations?

•

If the data varies in space and time – is anything known about its autocorrelation structure?

•

Is there any other reason (apart from already cited correlations and functional dependencies)
to suspect correlations between parameters considered as input to SR-Site modelling?

2.3.10 Result of supplier’s data qualification
In this section the supplier is requested to present data that are considered to be appropriate as a basis forsuggesting input data for use in SR-Site. Comprehensive information relating to each parameter requested
in the bullet list under the heading “Defining the data requested from the supplier” (cf. Section 2.3.1)
should be given. Only one set of data should be delivered for each specified condition, volume, element,time period, alternative modelling approach, etc.
The general process of reducing and interpreting data, valuing different data sets, and finally selecting
the recommended data for delivery to the SR-Site team should be fully accounted for, if not alreadyaccounted for in the previous sections or in supporting documents. In the latter case, it is sufficient
to briefly summarise, or refer to, the process of selecting the delivered data.
In case the data presented in supporting documents need reinterpretation and further refinement,in the light of this instruction and/or other information, this should be fully documented. In case thesupporting documents give more than one data set for a specified condition, volume, element, time
period, etc., further data reduction is required. Such data reduction may include the merging of data
sets, and there may be a need to give different weight to different data sets. Much weight should begiven to peer-reviewed data judged as representative for the Swedish site and repository system.
Generally, more weight should be given to qualified data than to supporting data. The degree to which
the data are representative in the context of their application in SR-Site should also be a factor in
the weighting. Exactly how much weight should be given to individual data sets must be decided
upon by the supplier. The process of further reinterpretation, refinement, and data reduction should
be fully documented. If it increases the readability of the text to also utilise other sections for such
documentation, this is allowed. Also, if this requires much space, some information may be appended.
The data sets that the supplier recommends to the SR-Site team should be in the form of single pointvalues, probability distributions, mean or median values with standard deviations, percentiles, ranges,
or as otherwise appropriate.
If the data have significant variability and/or uncertainty, the spread in data could be described asa range. However, the meaning of the range has to be provided, e.g. does it represent all possible
values, all “realistically possible” values or just the more likely values? The supplier may provide
more than one range, representing different probabilities, as exemplified below:•

The range wherein the likelihood of finding the data is high.

•

The range for which the likelihood of finding data outside this range is very low.

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All data should be recommended in the context of input data to safety assessment modelling. Accordingly
the final uncertainty estimate should encompass conceptual uncertainty, data uncertainty, and naturalvariability (cf. Sections 2.3.6, 2.3.7, and 2.3.8). If the supplier has used some kind of mathematically
expression to account for the uncertainty and natural variability, this expression should be provided
and justified.
If the data are suggested to be described by a well-defined probability distribution, it should be justified
on statistical grounds that the data indeed are (sufficiently well) distributed accordingly. The usageof standard deviation is often perceived to imply that the data are normally distributed; even throughthe definition of standard deviation is unrelated to specific probability distributions. Therefore, whengiving the standard deviation, it should be remarked upon whether or not the normal distribution
appropriately describes the data. If there are obvious differences between how the data set at hand isactually distributed, and the probability distribution (or range) finally recommended, the reasons for,and implications of, this should be discussed. Outliers should not be dismissed without justification.
It should be noted that in many cases, at some stage probability distributions must be assigned
to numerical data being the input to probabilistic safety assessment modelling. If the supplier feels
inadequate to deliver a defined distribution, but for example delivers a best estimate, an upper, anda lower limit for data, it may fall on the SR-Site team to transform such information into probability
distributions. This is justified as the SR-Site team may have a better understanding of how the shapeof the assigned distributions (especially in their tails) affects the assessment results. The SR-Site team
may also, in some cases, have a better understanding of the underlying statistics of the suggested
distribution.
The above instructions are not applicable to all data, as all data are not necessarily in the form of
numerical values. Examples are exit locations for groundwater flowpaths, given as co-ordinates,
or information on solubility limiting phases, given as chemical species and reactions.
For a spatially varying function well described by a given stochastic process, e.g. through a variogramor as realised in a Discrete Fracture Network, a potential statement may be that all realisations of thisspatially varying function are equally probable.
Finally, it may be impossible to express the uncertainty by other means than a selection of alternative
data sets. There are a number of uncertainties that cannot be managed quantitatively in any otherrigorous manner, from the point of view of demonstrating compliance, than by pessimistic assumptions. This is allowed, as long as the supplier clearly documents this together with the justification
for adopting this approach.
Comprehensible illustrations and tables of the suggested data sets are of high value. Figure 2-5
shows some examples, taken from the SR-Can Data report /SKB 2006b/, displaying how data may
be represented. Figure 2-5a shows a histogram of formation factor data and also a fitted log-normal
probability distribution /SKB 2006b, Figure 6-20/. The distribution parameters are displayed in
the figure. Figure 2-5b shows an excerpt of a table displaying sorption partitioning coefficients/SKB 2006b, Table A-43/. Here a median value is given as the best estimate value. In addition two
ranges are given, one range wherein 50% of the data are estimated to be found, and one whereinroughly 99% of the data are estimated to be found. Figure 2-5c, which is an excerpt of /SKB 2006b,
Table A-6/ shows an example where no numerical data is given. Instead solubility limiting phases,
used in the analysis of the solubility limits, are shown.
For data which are impractical to tabulate in the Data report (for example the co-ordinates of thousandsof exit locations for groundwater flowpaths), it is sufficient to precisely refer to a database or equivalent.
However, if possible the data should be illustrated in figures or excerpts of tables.

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abcFigure 2-5. Examples of representations of recommended data. Reproduced from, or excerpts of, Figure 6-20,
Table A-5, and Table A-43 of the SR-Can Data report /SKB 2006b/.

2.3.11 Judgements by the SR-Site teamIn this section, the customer representative, on behalves of the SR-Site team, should document theexamination of the delivery provided by the supplier, and make judgment on the data qualification
process and on the qualified data. This text should be produced in close cooperation with persons of
the SR-Site team with special knowledge and responsibility. In case of unresolved issues, the final
phrasing should be decided upon by the SR-Site team. Comments should be made on all the sections
listed below:•

Sources of information and documentation of data qualification.

•

Conditions for which data are supplied.

•

Conceptual uncertainty.

•

Data uncertainty due to precision, bias, and representativity.

•

Spatial and temporal variability of data.

•

Correlations.

•

Result of supplier’s data qualification.

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If appropriate, a response to the section “Supplier input on use of data in SR-Site and SR-Can” mayalso be warranted.
Concerning the section “Sources of information and documentation of data qualification” the customer
should judge if appropriate documents are referenced, and if the categorisation of data sets into
qualified or supporting is adequately performed and justified.
Concerning the section “Conditions for which data are supplied” the customer should focus upon
whether the conditions given by the supplier are relevant for SR-Site modelling. If not, it should be
accounted for how this is handled in SR-Site (for example by extrapolating data, using generic data,or assuming pessimistic values) and what degree of uncertainty such a procedure induces.
Concerning the section “Conceptual uncertainty” the customer should judge whether the discussion
provided by the supplier is reasonable and sufficiently exhaustive. If the customer sees the need toinclude additional sources of conceptual uncertainty, such should be described and if possible quantified. Finally, where necessary the impact of the conceptual uncertainty on the assessment should be
discussed, as well as how conceptual uncertainty is handled in SR-Site modelling (for example byapplying pessimistic corrections factors to the data).
Concerning the section “Data uncertainty due to precision, bias, and representativity”, the customer
should make a judgment on the account provided by the supplier. Also, if the customer sees the needto include additional sources of data uncertainty, these should be described and if possible quantified. If necessary the impact of the data uncertainty on the assessment should be discussed, as well
as how data uncertainty is handled in SR-Site modelling (for example by applying data uncertainty
distributions or using corrections factors for the data).
Concerning the section “Spatial and temporal variability of data” the customer should focus upon
whether the spatial and temporal variability are adequately characterised and whether they are of
relevance for SR-Site modelling. Also, if the customer sees the need to include additional sourcesof spatial and temporal variability, such should be described and if possible quantified. In necessary,
the impact of the spatial and temporal variability on the assessment should be discussed, as well as
how this is handled in SR-Site modelling (for example by applying data distributions or different
data for different model times and volumes).
Concerning the section “Correlations” the customer should scrutinise the correlations and functional
relationships suggested by the supplier. Also, if correlations other than those suggested by the supplier
are identified in the SR-Site project (for example in Process reports) these should be briefly described
where necessary. If appropriate, a summary could be provided concerning which correlations are ofactual importance for safety assessment modelling and results.
Concerning the section “Result of supplier’s data qualification” the customer should make judgement on the choice of data by the supplier, based on scientific adequacy, usefulness for the safetyassessment, and the data qualification process. Comments could be made on the delivered estimatesof data uncertainty and natural variability, as well as on the data reinterpretation/refinement/reduction
process. Further­more, the delivered distributions, data ranges, etc. should be scrutinised from a statistical point of view. It should be judged whether the suggested way of representing data, for example bya log-normal distribution, is adequate for SR-Site modelling. If the SR-Site team chooses to promote
other data than those suggested by the supplier, the choice should be fully documented.
For all the sections listed above, supplier statements or supplied data believed to be extra uncertain,
dubious, or even erroneous should be highlighted by the customer. These matters should be raisedwith the supplier and, if possible, resolved and accounted for in this section.

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2.3.12 Data recommended for use in SR-Site modelling
The main delivery of the Data report to the SR-Site modelling is recommendations of data thatgenerally are numerically well defined. Such recommended data should be given in this section.
Based on all the available information, but also on the needs from SR-Site modelling, the customer
representative and SR-Site team should make a final choice of data in form of single point values,
ranges, or well-defined probability distributions, encompassing natural variability, data uncertainty,
and other uncertainty. These data should be clearly tabulated (or otherwise presented) in this section.
Alternatively, precise referencing to tables or equivalent in previous sections can be made. For data
which are impractical to tabulate in the Data report it is sufficient to precisely refer to a database orequivalent.
Also short guidelines for how to use the data in subsequent modelling should be given, as required.
Justifications and guidelines should be kept short so that this section mainly contains tabulated datathat are easily extractable for SR-Site safety assessment modelling.

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3

Spent fuel data

3.1

Selected inventory

The inventory of a canister is the quantity of radionuclides it holds, which in this section is given as
activity or amount of substance per canister. The data presented here represent the selected inventoryof an average canister, as well as the selected inventory of eight different type canisters. The inventory of an average canister is obtained by dividing the total inventory associated with the SKB spent
fuel reference scenario by the estimated total number of canisters. The type canisters are defined in
the Spent fuel report.
In this section, as in other parts of this Data report, the term selected inventory is used. At any given
time, the entire inventory of a canister is comprised of a great number of radionuclides. However,
only a handful of them are of significant consequence for the safety assessment, as they have longenough half-life, large enough radiotoxicity, exist in large enough amounts, and/or produce decay
products of the above standing attributes. One can through very basic calculations screen out thegreat majority of the radionuclides as obviously insignificant for the safety assessment. The resultof such a screening is presented in Section 2.2.1, in form of a list of selected radionuclides of potential
importance for the safety assessment. Thus the term selected inventory is short for the inventory of
selected radionuclides, out of the entire inventory comprising all radionuclides present at a given time.
As a result of nuclear decay, the inventory varies over time. If the inventory is known at one specified
time, one can through decay chain calculations determine the inventory at other times. The inventory
presented here is that for a selection of radionuclides expected in year 2045, at the time for shut downof the last nuclear power plant, in accordance with the SKB spent fuel reference scenario.
The entire inventory can be used for assessing the decay power and subcriticality of canisters. However,such an assessment is not part of SR-Site but is instead made in the Spent fuel report as part ofproviding a basis for selecting the type canisters.

3.1.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The supplier should deliver:•

The total inventory in activity per canister (Bq/canister) for an average canister, for the radionuclidesof the selected inventory.

•

The total inventory in activity per canister (Bq/canister) for each type canisters, for the radionuclidesof the selected inventory.

•

The inventory in activity per canister (Bq/canister) for each type canisters, as distributed on the
UO2 spent fuel inventory, construction material inventory, crud inventory, and control rod inventory.

•

The half-lives, t½ (yr), and specific activity (Bq/mol) for the radionuclides of the selected inventory.

If there are tabulated inventory data in the Spent fuel report that would take numerous pages toreproduce, they could instead be properly referred to. The inventory data should apply for the SKB
spent fuel reference scenario. In Section 2.2.1, the radionuclides of the selected inventory are listed.
For SR-Site radionuclide modelling, the inventory is needed in amount of substance per canister(mol/canister). Therefore, the SR-Site team should at the end of this section give:•

The supplied inventories in amounts per canister (mol/canister).

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SR-Site modelling activities in which data will be used

In the entire chain of SR-Site radionuclide transport modelling, the selected inventory is used forassessing the source term (see the Radionuclide transport report). In the near-field modelling it isused as one of the bases for calculating concentrations and quantities of radionuclides that can escapea failed canister. As the inventory is constantly evolving due to radioactive decay, inventories at latertimes must be assessed. This is done by standard methods using the Bateman equations.

3.1.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

No new modelling of the inventory was made in SR-Can. Instead the inventories produced within
the SR 97 project /Håkansson 2000/, calculated for the time of canister deposition, were used. The
modelling in SR 97 was mainly done using the ORNL SCALE 4.3 package, based on ORIGEN/ORNL 1996/.
In SR-Can, inventories at later times after deposition were estimated by using ORIGEN-S /ORNL 1996/
and used as the source term for radionuclide transport modelling.
Conditions for which data were used in SR-Can

SR-Can was based on the BWR (boiling light water reactor) and PWR (pressurised light water reactor)
fuel of the safety assessment SR 97. In SR 97, the produced inventories were based on two different
burnups for SVEA-97 BWR fuel elements (38 and 55 MWd thermal output/kg U) and two different
burnups for PWR fuel elements (42 and 60 MWd thermal output/kg U). Mixed oxide fuel (MOX)
and research reactor fuel were not considered. In SR-Can, the inventories presented in /Håkansson
2000/ were corrected in order to represent a canister deposition time at 40 years after discharging thefuel elements from the reactor. New information on the radionuclide half-lives, made available after
the publication of /Håkansson 2000/, resulted in a corresponding update in the radionuclide data
used in SR-Can.
Sensitivity to assessment results in SR-Can

In SR-Can, Ra-226 contributed to over 99% of the total annual effective dose in the important scenarios, at times where the dose came relatively close to the regulatory limit (see for example /SKB
2006a, Figure B-5/). Therefore, the inventories of Ra-226 and some of its decay parents in the 4n+2
decay chain were of great consequence for SR-Can assessment results. By taking their inventories/Håkansson 2000/ and their half-lives into account, a simple calculation reveals what decay parents,
existing at time of canister deposition, will produce Ra-226 that may subsequently be released into
the biosphere. These decay parents are Th-230, U-234, and U-238. However, as the fraction of U-238
in the UO2 spent fuel is close to unity throughout repository evolution (if not accounting for theoxygen in UO2), the assessment results were not sensitive to uncertainties in the U-238 inventory.
In some SR-Can scenarios, I-129 and Nb-94 dominated the annual effective dose at early times.
However, compared to the regulatory limit the doses were very small or even insignificant.
Alternative modelling in SR-Can

No alternative modelling with regard to the inventory was performed in SR-Can.
Correlations used in SR-Can modelling

Many of the radionuclides are correlated through decay chains. No other correlation with regard to
the inventory was used in SR-Can.

52

TR-10-52

Identified limitations of the data used in SR-Can modelling

The major limitation of the inventory data used in SR-Can was that data were taken from the SR 97
project. Therefore, they did not reflect the best estimate inventory, with regards to information on
burnup and other circumstances available at the time of SR-Can.

3.1.3 Supplier input on use of data in SR-Site and SR-Can
The data in the Spent fuel report have been produced in dialogue with the SR-Site team. There areremaining issues concerning the half-life of Ag-108m and Se-79 that the SR-Site team may need toinvestigate further. The supplier has no further input regarding the use of data in SR-Site.
BWR, PWR, and MOX fuel is estimated to be deposited in 6,103 canisters, as described in the Spentfuel report. The data supplied here are based on this number of canisters. In addition, the fuel residues
from research performed at Studsvik is estimated to generate about 25 special boxes that will be storedin PWR canisters (see Table 2.3 of the Spent fuel report). The contribution to the total inventory
from these canisters is deemed to be negligible, and the fuel residues from Studsvik are not further
mentioned in this Data report.

3.1.4

Sources of information and documentation of data qualification

Sources of information

The data presented in this section are qualified in the Spent fuel report. Therefore, that report isthe only source of information for the Data report (see Table 3-1). The scrutinising of lower levelreferences, i.e. documents referred to in the Spent fuel report, is part of the qualification processof the Spent fuel report and is not dealt with in this Data report.
Table 3-1. Main sources of information used in data qualification.
Spent fuel report, 2010. Spent nuclear fuel for disposal in the KBS-3 repository. SKB TR-10-13,
Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting data

Table 3-2 shows the data sets of the Spent fuel report used as bases for this section. Below the listed
items a justification for the sorting of the items is given.
Table 3-2. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Total inventory of the SKB spent fuel reference scenario: Table C-2, Appendix C,

None.

Spent fuel report.

2. Inventory of type canisters: Tables C-5 to C-13, Appendix C, Spent fuel report.
3. Number of type canisters: Tables C-5 in Appendix C, Spent fuel report. Total numberof canisters: 6,103, Tables C-5, Appendix C, Spent fuel report.
4. Radionuclide half-lives and specific activity: Table C-1, Appendix C, Spent fuel report.
1–4: Data delivered in the Spent fuel report are qualified in the Spent fuel report, in accordance with the SKB quality
assurance system. This qualification is found to be in compliance with the demands of the Data report.

Excluded data previously considered as important

No such data has been excluded. However, there is the matter of half-lives for Ag-108m and Se-79
where other half-lives than those presently used in inventory calculations have been suggested in thescientific literature.

TR-10-52

53

3.1.5 Conditions for which data are supplied
Conditions of importance for the radionuclide inventory are discussed in, for example, Section 6.2.1of the Spent fuel report. In summary, the burnup is the fuel parameter that has the largest impact on
the radionuclide inventory. The burnup reflects the amount of nuclear fission and neutron radiationthat has occurred in the assembly, and thus the content of fission and activation products and trans­
uranium elements. High burnup leads to higher content of fission products and, due to neutron capture,higher content of activation products and altered content of transuranium elements. The burnup andtotal radionuclide inventory in the final repository will ultimately depend on the total energy output
from the nuclear power plants.
Except for the burnup, the radionuclide inventory is to some extent also affected by the irradiationand power history of the assemblies. Also the fuel type, which is either UOX (uranium oxide fuel)or MOX (mixed oxide fuel), affects the inventory.
Part of the inventory will be in the construction materials of the fuel assemblies. When construction
material is exposed to neutron radiation in the reactor vessel, neutron capture will lead to formationof activations products. The amount and composition of the construction materials, and the time thatthe fuel assembly has been in the reactor, will determine the content of activation products in theconstruction material. Another source of radionuclides is crud (surface deposits).
The canister specific inventory depends on the above mentioned issues, on the stored spent fuel type
(PWR, BWR or MOX), the number of stored assemblies, and whether they include control rods andinserts such as start-up neutron sources, boron glass rods, and plugs.

3.1.6 Conceptual uncertainty
This section concerns already existing spent fuel, as well as a prognosis of spent fuel that will be
produced in the coming years. The amount of spent fuel to be stored in the repository is assumed
to be in accordance with the spent fuel reference scenario used by SKB (Section 2.2.1 of the Spentfuel report). There is conceptual uncertainty in the accuracy of the reference scenario that can only
be handled through sensitivity analysis. Other sources of uncertainty on the borderline of conceptual
uncertainty/data uncertainty are treated as data uncertainty.
3.1.7 Data uncertainty due to precision, bias, and representativity
At the time of closure of the final repository, when the encapsulation and deposition is finished, theburnup, irradiation history, power history, and age of the assemblies in each canister will be known.
From this information the radionuclide inventory of each individual canister can be calculated. How­ever, at this present stage it is not feasible to do this. To handle uncertainties in the inventory of theindividual canisters, results from two approaches are propagated to the safety assessment modelling.
In the first approach, the total inventory associated with the SKB spent fuel reference scenario is
divided by the estimated total number of canisters, to obtain the inventory of an average canister. The
second approach is to define a number of type canisters and to assess the inventory of these typecanisters. This approach facilitates comparisons between different type canisters and inventory ranges
can be evaluated. The major uncertainty concerns how representative the suggested data are for thecanisters to be deposited. Assessing the uncertainty in underlying data (from lower level references
or codes) is part of the data qualification made in the Spent fuel report, and is not discussed here.
This section concerns data applicable for the SKB spent fuel reference scenario. The main source of
deviations from this reference scenario would be changes in the total energy output from the nuclear
power plants, which is treated as conceptual uncertainty.
Inventory per average canister

In Table C-2 of the Spent fuel report, the total inventory based on the number of assemblies in the
SKB spent fuel reference scenario is given. This total inventory is divided by the estimated total
number of canisters, to get the inventory per average canister. The estimated total number of canisters is 6,103, which is the sum of the numbers of type canisters given in Table C-5 of Appendix Cof the Spent fuel report. No uncertainty range based on this approach is specified and therefore,
uncertainty estimates should be based on the type canister approach described below.
54

TR-10-52

Inventory per type canisters

Having the total number of assemblies to be disposed of (cf. Table C-2 of the Spent fuel report);
there is a large number of ways in which the assemblies can be combined in individual canisters.
As the inventories of the individual canisters cannot be predicted, this can be seen as an uncertainty.
To handle this uncertainty in a manageable fashion, a set of type canisters has been defined basedon the assumption that the criterion for maximum allowed total decay power (1,700 W) in a canister
will restrict the possible variation in the radionuclide inventory. Another criterion is that criticality
should be avoided. These type canisters should provide a basis for reasonable estimates of inventory
ranges and data uncertainty. The following type canisters are defined (cf. Tables 6-5 to 6-12 of theSpent fuel report).
•

BWR I represents the largest part (36%) of the deposited canisters, with the average burnup of
40.4 MWd/kgU. BWR I is representative for all canisters with 12 BWR assemblies in the burnup
interval 38 to 42 MWd/kgU. BWR I also includes a large number of canisters with the averageburnup of less than 38 MWd/kgU, resulting in an overestimated but still adequately defined inventory.

•

BWR II represents the high end radionuclide inventory in BWR canisters, with the averageburnup at 47.8 MWd/kgU. This type canister represents only 5% of the total number of canisters
and for this fraction, the inventory provided is considered as adequate.

•

BWR III represents the partly filled BWR canisters with the average burnup at 47.8 MWd/kgU.
This type canister makes up for 27% of total number of canisters and seen over the full set of
unfilled canisters, the inventory provided is considered as adequate.

•

BWR MOX represents BWR canisters containing one MOX assembly. The radionuclide inventory given is regarded to be representative for all BWR canisters containing a MOX assembly.
This type canister only makes up for 4% of total number of canisters.

•

PWR I represents the largest part of the PWR canisters, and 17% of the total number of canisters.
PWR I is representative for all canisters with 4 PWR assemblies in the burnup interval 42 to
47 MWd/kgU. PWR I also include a large number of canisters with the average burnup lessthan 42 MWd/kgU, resulting in an overestimated but still adequate inventory.

•PWR II represents the high end radionuclide inventory in PWR canisters, with the averageburnup at 57 MWd/kgU. This type canister only makes up for less than 1% of the total numberof canisters and the inventory provided is considered as adequate.

•

PWR III represents the partly filled PWR canisters with three assemblies and the average burnup
at 57 MWd/kgU. This type canister makes up for 9% of total number of canisters and the inventory
provided is considered as representative.

•PWR MOX represents PWR canisters containing one swap PWR MOX assembly. The radio­
nuclide inventory given is regarded to be representative for all PWR canisters containing a MOX
assembly. This type canister only makes up for less than 1% of total number of canisters.

Figure 3-1 shows the estimated numbers of different type canisters, as a pie chart.
PWR-MOX, 33
PWR III, 557
PWR II, 38
PWR I, 1024

BWR I, 2208

BWR-MOX,
267

BWR II, 321
BWR III, 1655

Figure 3-1. The number of type canisters, out of the total number of canisters (6,103). Data taken from
Tables 6-5 to 6-12 of the Spent fuel report.

TR-10-52

55

In Appendix C (Tables C-5 to C-13) of the Spent fuel report, the inventories of the different typecanisters are given. For many important radionuclides, the inventory per canister is similar for thedifferent type canisters. For other radionuclides, the activity is much higher in certain type canisters
than in others. In Figure 3-2, the examples of I-129 and Nb-94 are shown. As can be seen, Nb-94 is
predominantly associated with PWR type canisters, while the activity of I-129 is similar for all typecanisters. For Nb-94, an uneven distribution of BWR and PWR canisters in the repository, shouldsuch ever occur, may restrict the use of the inventory per average canister in safety assessment
modelling.
The spread in all inventory data for the type canisters is shown in Figure 3-3. To make the figure
more readable, the inventory per type canister is normalised to the inventory per average canister.
I-129

3.0·109

7·1011

2.5·109

6·1011
5·1011

2.0·109

4·1011

1.5·109

3·1011

1.0·1092·10118

1·10110

Average

PWR MOXPWR IIIPWR IIPWR IBWRMOX

BWR III

Average

PWR MOXPWR IIIPWR IIPWR IBWRMOX

BWR III

BWR IIBWR I

0BWR II

5.0·10Nb-94

8·1011BWR I

Activity (Bq/canister)

3.5·109

Figure 3-2. Total I-129 and Nb-94 inventories in terms of activity per canister for the different typecanisters. Data taken from Table C-5 of the Spent fuel report.

10

0.1

0.01

BRW I
BWR II
BWR III

0.001

BWR MOXPWR I
PWR II

0.0001PWR IIIPWR MOX

0.00001

Ac-227
Ag-108m
Am-241
Am-242
Am-242m
Am-243
C-14
Cd-113m
Cl-36
Cm-242
Cm-243
Cm-244
Cm-245
Cm-246
Cs-135
Cs-137
Eu-152
H-3
Ho-166m
I-129
Mo-93
Nb-93m
Nb-94
Ni-59
Ni-63
Np-237
Np-238
Np-239Pa-231
Pa-233
Pa-234m
Pb-210
Pd-107
Pu-238
Pu-239Pu-240Pu-241
Pu-242
Ra-226
Se-79
Sm-151
Sn-121m
Sn-126
Sr-90
Tc-99
Th-229Th-230Th-232
Th-234
U-233
U-234
U-235
U-236
U-237
U-238
Zr-93

Normalised inventory of type canister

1

Figure 3-3. Spread in data of inventory per type canister, as normalised with the inventory per average
canister. All data taken from Tables C-2 and C-5 of the Spent fuel report.

56

TR-10-52

As can be seen, for most of the radionuclides the spread of data is within a factor of few, while forsome radionuclides the spread is over orders of magnitude. Figure 3-4 shows the maximum spreadfor each radionuclide. Here the inventory of the type canister holding the most of the radionuclide
is divided with the inventory of the type canister holding the least. To make the figure more readable,
data are sorted based on this ratio.
In Tables C-6 to C-13 of the Spent fuel report, the inventory of a type canister is distributed on
different sources, which are the:•Total (sum of all sources).

•

UO2-matrix (including the gap and grain boundary inventory, see Section 3.2).

•

Construction material.

•

Crud.

•

Control rods (in case of PWR type canisters).

Here a note is needed on the term UO2-matrix, as used in the Spent fuel report where it includes thematrix as well as gap and grain boundary inventories. When discussing the instant release fraction in
Section 3.2, a terminology is used where the matrix inventory and gap and grain boundary inventory
are mutually exclusive. Therefore, in the remaining of this report, we will use the term UO2 spent
fuel inventory for the inventory including both the matrix and gap and grain boundary inventories.
For most radionuclides the UO2 spent fuel is the dominating source, while for a few nuclides, othersources dominate. Figure 3-5 shows the inventory for the BWR I and PWR I type canisters, as dis­tributed among these sources. The sorting of the radionuclides is based on descending total inventoryof the BWR I type canister. In this context, the total inventory means the total inventory of a typecanister, not of the SKB spent fuel reference scenario.

100000

10000

1000

100

10

1

Ni-59
U-235
Ni-63
U-237
Pu-241
Cm-244
Th-232
Am-243
Np-239U-233
Cl-36
Ac-227
U-234
Am-241
C-14
Pu-239Pu-240Pu-242
Np-237
Pa-233
Pa-231
Pa-234m
Th-234U-238
Ho-166m
Cs-135
Cm-243
Pd-107
Pu-238Sn-126
Zr-93
Se-79
I-129
Sm-151
Tc-99
U-236
Sr-90
Cs-137

H-3

Ag-108m
Cd-113m
Nb-94
Nb-93m
Pb-210Cm-246
Ra-226
Cm-245
Mo-93
Am-242m
Am-242
Np-238
Cm-242
Th-229Eu-152
Th-230Sn-121m

Max type canister inventory / min type canister inventory

As can be seen from Figure 3-5, only for a few nuclides the total inventory is significantly affected by
the inventory of the construction material, crud, or control rods. The radionuclides where the inventoryof the construction material, crud, and control rods comprises more than 1% of the total inventory are
for BWR I: Ag-108m, C-14, Cl-36, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Sn-121m, U-233, and Zr-93.
For PWR I, the same radionuclides apply with the addition of Cd-113m. For these species, uncertainty inthe inventory estimate in the construction material, crud, and control rods may add to the total uncertainty.
For the other species, the total uncertainty depends on the uncertainty of the UO2 spent fuel inventory.

Figure 3-4. Maximum spread in type canister inventory data. All data taken from Table C-5 of the Spentfuel report.
TR-10-52

57

1016
1015
10

14

1013
1012

11

109
10

8

107
106
105
104
10

3

102
10
1

-1

10
10-2

UO2 fuel BWR I
Constr. mtrl. BWR I
Crud BWR I
UO2 fuel PWR I

10-3Constr. mtrl. PWR I

10-4Crud PWR I

10

-6

10-5

Control rod PWR I

Cs-137
Sr-90
Pu-241
Am-241
Pu-238
Cm-244
Pu-240
Ni-63
Pu-239Sm-151
H-3
Nb-93mAm-243
Np-239Sn-121m
Tc-99
Cm-243Am-242m
Am-242
Cm-242
Ni-59
Pu-242
Zr-93
U-234
C-14
Eu-152
Sn-126
Cs-135
U-237
Cm-245
Np-237
Pa-233
U-236
Pa-234m
Th-234
U-238
Pd-107
Nb-94
Ho-166m
Cd-113m
Se-79
Cm-246
Np-238
I-129
U-235
Ag-108m
Cl-36
Mo-93
Th-230
U-233
Pa-231
Ac-227
Ra-226
Pb-210Th-229Th-232Inventory (Bq/canister)

10

1010

Radionuclide

Figure 3-5. Inventory per canister of the BWR I and PWR I type canisters, distributed among UO2 spent
fuel, construction material, crud, and control rods. All data from Tables C-6 and C-7 of the Spent fuel report.

3.1.8

Spatial and temporal variability of data

Spatial variability of data

As there is variability in the inventory of different canisters, there will be a spatial variability that
depends on where the canisters are deposited. If it is assumed that there is no trend in the canister
deposition locations, spatial variability may be discussed as data uncertainty. This is the approachtaken in this text.
Temporal variability of data

The temporal variability in data, due to decay, can be accounted for by decay chain modelling. The
data presented here are valid at year 2045.

3.1.9 Correlations
Radionuclides, as decay parents and daughters, are correlated through the decay chains.
3.1.10 Result of supplier’s data qualification
The inventory is given in two ways, either as the inventory per average canister or as the inventory
per type canister, representing each of the eight type canisters given in the Spent fuel report.
Inventory per average canister

The total inventory of the SKB spent fuel reference scenario is given in Table C-2 of the Spent fuelreport. In Table 3-3 of this present report, this inventory is reproduced, together with the activity
per average canister. It is assumed that the total number of canisters is 6,103. The data apply for theyear 2045, which is the year the last nuclear power plant is closed down according to the reference
scenario (see Section 6.2.2 of the Spent fuel report).

58

TR-10-52

Table 3-3. Total activity for the spent fuel reference scenario, and activity in Bq per average
canister. Total activity data reproduced from Table C-2 of the Spent fuel report. Activity per
average canister based on the total number of 6,103 canisters.
NuclideTotal activity ofspent fuel (Bq)

Activity per average
canister (Bq/canister)

NuclideTotal activity ofspent fuel (Bq)

Activity per average
canister (Bq/canister)Ac-227

8.67·109

1.42·106

Pa-231

1.46·1010

2.39·106

Ag-108m

6.86·1016

1.12·10

13

Pa-233

1.78·1014

2.92·1010

Am-241

1.72·1018

2.82·10

14

Pa-234m

1.30·1014

2.13·1010

Am-242

4.33·1015

7.09·10

11

Pb-2104.67·1087.65·104

Am-242m

4.34·1015

7.11·10119.75·109

Am-243

1.40·1016C-14

5.10·1014

Cd-113m

4.57·1015

Cl-36

2.34·10

12

Cm-242

3.58·1015

Cm-243

4.88·1015

Cm-2445.09·1017

Cm-245

2.19·1014

Cm-2464.14·1013

Cs-1352.36·1014

Cs-1372.34·1019

Eu-152

5.05·1014H-3

3.85·1016

Ho-166m

4.53·1013I-129

1.39·1013

Mo-93

3.24·10

12

Nb-93m2.50·1017

Nb-94

9.32·1014

Ni-59

1.73·1015

Ni-63

1.85·1017

Np-237

1.78·1014

Np-2381.96·1013

Np-2391.40·10

16

TR-10-52

Pd-1075.95·1013

2.29·10

12

Pu-2381.34·1018

2.20·1014

8.36·1010

Pu-2391.41·1017

2.31·1013

7.49·10

11

Pu-2402.54·1017

4.16·1013

3.83·108

Pu-2411.05·1019

1.72·1015

5.87·10

11

Pu-242

1.13·1015

1.85·1011

8.00·10

11

Ra-226

1.53·1092.51·1058.34·1013

Se-79

3.73·1013

6.11·1093.59·1010

Sm-1511.40·1017

2.29·1013

6.78·109

Sn-121m

1.04·1016

1.70·1012

3.87·1010

Sn-126

2.73·1014

4.47·1010

3.83·1015

Sr-90

1.56·1019

2.56·1015

8.27·10

10

Tc-99

6.86·1015

1.12·1012

6.31·10

12

Th-2291.50·108

2.46·104

7.42·10

9

Th-2301.88·1011

3.08·1072.28·10

9

Th-2322.59·1054.24·101

5.31·10

8

Th-2341.30·1014

2.13·1010

4.10·1013U-233

4.84·10107.93·1061.53·1011U-234

5.97·1014

9.78·1010

2.83·1011U-235

6.61·1012

1.08·1093.03·1013U-236

1.35·1014

2.21·1010

2.92·1010U-237

2.51·1014

4.11·1010

3.21·109U-238

1.30·1014

2.13·1010

2.29·1012

Zr-93

1.04·1015

1.70·1011

59

Inventory per type canisterIn the Spent fuel report, in Tables C-6 to C-13, the inventories of eight different type canisters are
given in terms of activity per canister. In addition to giving the total activity per canister, the activity
is distributed among different sources for each type canister (cf. Figure 3-5). The different sources
are the UO2 spent fuel, the construction material, and the crud. In addition, in the case of PWR typecanisters, inventory data for the control rods are given.
To avoid unnecessary duplication of data, we have chosen to not reproduce the data here, but toinstead refer to the Spent fuel report. The referenced table associated with each type canister isgiven in Table 3-4. All data of the referenced tables are considered as qualified.
Table 3-4. Sources for where inventory data for type canisters are found.
Type canister

Short description

Table in the Spent fuel report.

BWR I

12 BWR assemblies
Burnup 40.4 MWd/kgHM*
Age 37 years

Spent fuel report

BWR II

12 BWR assemblies
Burnup 47.8 MWd/kgHM
Age 48 years

Spent fuel report

BWR III

9 BWR assemblies
Burnup 47.8 MWd/kgHM
Age 32 years

Spent fuel report

BWR MOX

11 BWR assemblies
Burnup 37.7 MWd/kgHM
Age 43 years, and1 MOX assembly
Burnup 50 MWd/kgHM
Age 50 years

Spent fuel report

PWR I

4 PWR assemblies
Burnup 44.8 MWd/kgHM
Age 38 years

Spent fuel reportPWR II

4 PWR assemblies
Burnup 57 MWd/kgHM
Age 55 years

Spent fuel reportPWR III

2 PWR assemblies
Burnup 57 MWd/kgHM
Age 51 years, and
1 PWR assembly
Burnup 57 MWd/kgHM
Age 20 years

Spent fuel reportPWR MOX

3 PWR assemblies
Burnup 44.8 MWd/kgHM
Age 32 years, and1 MOX assembly
Burnup 34.8 MWd/kgHM
Age 57 years

Spent fuel report

Table C-6
Table C-8
Table C-9

Table C-10

Table C-7

Table C-11

Table C-12

Table C-13

* HM = Heavy metal i.e. uranium and plutonium.

60

TR-10-52

Half-lives and specific activity of radionuclides

The half-lives and specific activities in Table 3-5 are reproduced from Table C-1 of the Spent fuel report.
It should be noted that there are unsettled issues concerning the half-lives of Ag-108m and Se-79 in
the scientific community. The data given in Table 3-5 for these two radionuclides do not necessarily
correspond to the data finally chosen for SR-Site.
Table 3-5. Half-lives and specific activities of selected radionuclides. Data reproduced
from Table C-1 in the Spent fuel report.
NuclideHalf-life[years]Specific
activity[Bq/mol]

NuclideHalf-life[years]Specific
activity[Bq/mol]

Ac-22721.8

6.07·1014

Pa-2313.28·1044.04·1011

127.0

1.042·10

14Ag-108m

Pa-23327.0

d)1.79·1017

Am-241432.7

3.059·1013m)

5.8·1021

Am-242

16.0

Am-242m

141.0

Am-243h)Pa-234m

1.2

18

Pb-21022.3

9.387·10

7.25·105.94·1014

Pd-107

6.50·101.80·10

12

Pu-23887.7

2.31·10

12

Pu-2399.39·1014

4.40·10102.97·1016

28.5

4.64·1014

Cm-24418.1

Cm-2457.37·103C-14

5.73·103

Cd-113m

14.1

Cl-36

3.01·10

Cm-242

162.9

Cm-2435d)

13

2.04·1096

1.51·1014

2.41·1045.49·1011

Pu-2406.56·1032.02·1012

Pu-241

14.3

Pu-242

9.26·1014

3.73·105

3.55·1010

Ra-2261.60·103

8.27·1012

54.49·1010

7.31·1014Se-79

2.95·108.50·103

1.56·1012

Sm-151

90.0

Cm-246

4.73·1032.80·1012

Sn-121m

55.0

Cs-1352.30·1065.75·109

Sn-1261.00·10

Cs-13730.0

4.41·10

14

Sr-90

28.1

Eu-152

13.3

9.95·10

14

H-3

12.3

Ho-166m

1.47·1014
2.41·1014
1.32·1011

54.71·1014

Tc-99

2.11·1056.27·1010
1.68·1012

1.08·1015

Th-2297.88·103

1.20·103

1.10·10

13

Th-2307.54·104

1.76·1011

I-129

1.57·1078.43·10

8

Th-232

1.40·10109.45·105

Mo-93

3.50·103

3.78·10

12

Th-23424.1

Nb-93m

16.1

Nb-94

d)

2.00·1017

8.22·1014U-233

1.59·1052.03·1046.52·1011U-234

2.46·1055.38·1010

Ni-59

7.50·104

1.76·1011U-235

7.04·108

1.88·107

Ni-63

100.1

1.322·10

7

5.66·108

Np-2372.14·10

Np-23850.8

Np-239

56.5

U-236

2.34·10

6.19·10

9

U-237

6.8

h)

2.28·10

18

U-238

h)

2.05·10

18

Z-r93

6

14

d)

8.32·1010

7.1·1017

4.47·1092.96·1061.53·1068.65·109m) Minutes.
h) Hours.
d) Days.

TR-10-52

61

3.1.11

Judgements by the SR-Site teamSources of information and documentation of data qualification

The supplier suggests that the only source of information, the Spent fuel report, is qualified. The
SR-Site team agree.
Conditions for which data are supplied

The supplier has described different conditions in terms of fuel, burnup, etc. The way of handlingthese different conditions is by defining a number of type canisters. The SR-Site team agree withthis handling.
Conceptual uncertainty

The main conceptual uncertainty identified by the supplier is uncertainty in the SKB spent fuel reference scenario, which can only be handled by performing sensitivity analyses. The SR-Site team agree.
Data uncertainty due to precision, bias, and representativity

The supplier has handled data uncertainty by giving the inventory of eight different type canisters,
together with the inventory of an average canister. The inventories of the type canisters can be used
for estimating the spread in the inventory data of deposited canisters. The SR-Site team agree withthis handling.
Spatial and temporal variability of data

The supplier suggests that spatial variability can be treated as data uncertainty, in case the canisters
are deposited without a trend (i.e. random deposition). It is cautioned that the use of data on inventory
per average canister is restricted, if there is an uneven distribution of type canisters in the repository.
The SR-Site team agree.
Correlations

The supplier has identified correlations in data through the decay chains, as well as a correlation
between half-life and specific activity. The SR-Site team agree.
Result of supplier’s data qualification

The supplier has given total inventories for the average canister (Table 3-3), and references totabulated data for the type canister inventories (Table 3-4). For the type canisters, the referenced
tables include the inventory distribution amongst the UO2 spent fuel, construction material, crud,and control rods. The SR-Site team accept these data. Furthermore, the SR-Site team accept thatdata from both approaches are propagated to SR-Site.
The supplier has given half-lives and specific activities in Table 3-5. The SR-Site team accept thedata for all radionuclides except for Ag-108m and Se-79. For these two nuclides, the most recently
published values of their half-lives are recommended for use in SR-Site. For Ag-108m the recommended half-live is 438 years and for Se-79 the recommended half-live is 3.77·105 years. These
values are published in /Schrader 2004/ and /Bienvenu et al. 2007/ in peer reviewed articles of theopen literature that are considered as qualified. These are also the values on which the Decay data
evaluation project (DDEP) working group base their recommendations (consult e.g. /Helmer et al.
2002/ for participants of DDEP). The recommended DDEP value for the Se-79 half-life (3.56·105
years) is not a measured value but a weighted average of the two latest reported values available in
the open literature. The value used in this report is the latest measured value published in the open
literature.

62

TR-10-52

For Ag-108m, having a relatively short half-life, the estimated inventory at year 2045 depends on
the assumed half-life. This can be exemplified by the PWR I type canister, which is deposited around38 years after being taken out of the reactor (cf. Table C-7 of the Spent fuel report). By assuming
a half-life of 127 years, which is the value used by the code Origen-S, the activity at deposition is
given to 4.55·1013 Bq/canister. However, if assuming a half-life of 438 years, this activity would
increase by about 16%, as the decay rate is lower. This inconsistency is judged as tolerable as Ag-108m
is not one of the dose controlling nuclides, and all inventory data given in the Spent fuel report are
accepted.
For SR-Site radionuclide modelling, the inventory is needed in amount of substance per canister[mol/canister], wherefore the SR-Site team has converted the supplied inventory data. This is doneby dividing the inventory in terms of activity per canister by the specific activity, for each radio­
nuclide. To be consistent, the specific activities given in Table 3-5 are used for Ag-108m and Se-79
for this particular exercise. The result is given in Table 3-7 and Table 3-8.

3.1.12 Data recommended for use in SR-Site modelling
Half-life and specific activity of radionuclides

For all radionuclides, except for Ag-108m and Se-79, the data in Table 3-5 are recommended for use
in SR-Site. For Ag-108m and Se-79, the half-lives and specific activities in Table 3-6 are recommended
for use in SR-Site.
Table 3-6. Half-lives of Ag-108m and Se-79, recommended for use in SR-Site.
NuclideHalf-life [years]

Specific activity (Bq/mol)

Ag-108m

438 a)

3.02·1013

Se-79

3.77·105 b)

3.51·1010a) /Schrader 2004/.
b) /Bienvenu et al. 2007/.

Inventory per average canister

The data in Table 3-7 are recommended for use in SR-Site. The data are produced by dividing theactivity per canister data in Table 3-3 with the specific activity of Table 3-5, for each radionuclide.
The suggested inventory is valid for year 2045.
To illustrate the total inventory of the selected radionuclides in the average canister, the data
in Table 3-7 are illustrated in Figure 3-6.
Inventory per type canister

The data in Table 3-8 are recommended for use in SR-Site. The data are produced by dividing the“total activity per canister” data in Tables C-6 to C-12 of the Spent fuel report with the specificactivity of Table 3-5 of this present report, for each radionuclide. The suggested inventory is valid
for year 2045.
For the inventories in terms of activity per canister for the type canisters, which are distributed
amongst the UO2 spent fuel, construction material, crud, and control rods, Tables C-6 to C-13 ofAppendix C of the Spent fuel report should be used.

TR-10-52

63

Table 3-7. Inventory in mol per average canister at year 2045, recommended for use in SR-Site.
Based on the total number of 6,103 canisters.
Radionuclide

Amount per average
canister (mol/canister)Radionuclide

Amount per average
canister (mol/canister)Ac-227
Ag-108m

2.34·10–9
1.08·10–1

Pa-231
Pa-233

5.92·10–6
1.63·10–7

Am-241
Am-242

9.21
9.79·10–8

Pa-234m
Pb-2103.67·10–12
1.29·10–10

Am-242m
Am-243
C-14
Cd-113m
Cl-36
Cm-242
Cm-243
Cm-244
Cm-245
Cm-246
Cs-135
Cs-137
Eu-152
H-3
Ho-166m
I-129
Mo-93
Nb-93m
Nb-94
Ni-59
Ni-63
Np-237
Np-238
Np-2397.58·10–3
1.27
3.62·10–2
7.97·10–4
8.71·10–3
1.98·10–5
1.72·10–3
1.14·10–1
2.30·10–2
2.42·10–3
6.73
8.69
8.32·10–5
5.84·10–3
6.75·10–4
2.70
1.40·10–4
4.98·10–2
2.34·10–1
1.61
2.29·10–1
4.71
1.41·10–9
1.12·10–6

Pd-107
Pu-238
Pu-239Pu-240Pu-241
Pu-242
Ra-226
Se-79
Sm-151
Sn-121m
Sn-126
Sr-90
Tc-99
Th-229Th-230Th-232
Th-234
U-233
U-234
U-235
U-236
U-237
U-238
Zr-93

4.78
1.45
4.21·101
2.06·101
1.86
5.22
3.03·10–8
1.36·10–1
1.56·10–1
7.07·10–3
3.39·10–1
5.43
1.79·101
1.46·10–8
1.75·10–4
4.49·10–5
1.07·10–7
9.53·10–5
1.82
5.76·101
3.91·101
5.79·10–8
7.20·103
1.97·101

104
10

3

101
1
10

-1

10

-2

Ac-227
Ag-108m
Am-241
Am-242
Am-242m
Am-243
C-14
Cd-113m
Cl-36
Cm-242
Cm-243
Cm-244
Cm-245
Cm-246
Cs-135
Cs-137
Eu-152
H-3
Ho-166m
I-129
Mo-93
Nb-93m
Nb-94
Ni-59
Ni-63
Np-237
Np-238
Np-239Pa-231
Pa-233
Pa-234m
Pb-210
Pd-107
Pu-238
Pu-239Pu-240Pu-241
Pu-242
Ra-226
Se-79
Sm-151
Sn-121m
Sn-126
Sr-90
Tc-99
Th-229Th-230Th-232
Th-234
U-233
U-234
U-235
U-236
U-237
U-238
Zr-93

Amount in average canister (mol/canister)

102

10-3
10-4
10-5
10-6
10-7
10-8
10-9
10-10
10-11
10-12

Figure 3-6. Amount of substance of selected radionuclides in the average canister at year 2045.

64

TR-10-52

Table 3-8. Total inventory of type canisters in mol/canister at year 2045, recommended for use
in SR-Site.
Inventory (mol/canister)
Type
BWR I

BWR II

BWR III

BWR MOXPWR I

PWR IIPWR IIIPWR MOX

Ac-227
Ag-108m
Am-241
Am-242
Am-242m
Am-243
C-14
Cd-113m
Cl-36
Cm-242
Cm-243
Cm-244
Cm-245
Cm-246
Cs-135
Cs-137
Eu-152
H-3
Ho-166m
I-129
Mo-93
Nb-93m
Nb-94
Ni-59
Ni-63
Np-237
Np-238
Np-239Pa-231
Pa-233
Pa-234m
Pb-210
Pd-107
Pu-238
Pu-239Pu-240Pu-241
Pu-242
Ra-226
Se-79
Sm-151
Sn-121m
Sn-126
Sr-90
Tc-99
Th-229Th-230Th-232
Th-234
U-233
U-234
U-235
U-236
U-237
U-238
Zr-93

3.32·10–9
1.01·10–5
1.14·101
1.25·10–7
9.69·10–3
2.11
5.02·10–2
4.71·10–6
1.33·10–2
2.52·10–5
2.09·10–3
1.50·10–1
4.47·10–2
6.85·10–3
9.26
8.62
5.95·10–5
4.10·10–3
9.74·10–4
3.63
1.15·10–4
2.25·10–3
1.62·10–2
2.37
3.18·10–1
6.24
1.79·10–9
1.85·10–6
7.10·10–6
2.16·10–7
4.18·10–12
2.67·10-10
6.84
2.06
4.54·101
2.65·101
1.23
7.87
5.31·10–8
1.76·10–1
1.60·10–1
9.50·10–3
4.63·10–1
5.03
2.29·101
3.10·10–8
2.44·10–4
6.99·10–5
1.21·10–7
1.39·10–4
2.14
4.32·101
4.60·101
3.84·10–8
8.20·1032.56·101

1.86·10–9
7.65·10–6
7.64
1.02·10–7
7.85·10–3
1.58
1.50·10–2
8.04·10–6
9.83·10–3
2.05·10–5
2.36·10–3
2.12·10–1
3.37·10–2
5.16·10–3
6.95
9.50
1.06·10–4
7.72·10–3
7.31·10–4
2.72
2.51·10–5
5.54·10–4
1.66·10–3
2.25·10–1
3.39·10–2
4.45
1.46·10–9
1.39·10–6
4.80·10–6
1.54·10–7
3.14·10–12
6.72·10–11
5.12
1.76
3.41·101
1.98·101
2.07
5.91
1.72·10–8
1.32·10–1
1.36·10–1
3.76·10–3
3.47·10–1
5.68
1.72·101
1.82·10–8
1.13·10–4
3.54·10–5
9.09·10–8
5.92·10–5
1.38
3.25·101
3.44·101
6.46·10–8
6.14·103
1.68·101

2.76·10–9
7.87·10–6
1.47·101
3.20·10–7
2.49·10–2
1.94
4.13·10–2
8.66·10–6
9.79·10–3
6.47·10–5
2.70·10–3
1.57·10–1
9.24·10–2
7.40·10–3
8.86
7.89
9.30·10–5
4.27·10–3
6.78·10–4
2.98
9.16·10–5
2.25·10–3
1.33·10–2
2.00
2.69·10–1
4.93
4.60·10–9
1.70·10–6
6.67·10–6
1.71·10–7
4.20·10–12
2.05·10–10
5.51
1.72
6.02·101
3.19·101
1.82
7.23
4.37·10–8
1.43·10–1
1.90·10–1
8.38·10–3
3.75·10–1
4.73
1.92·101
1.69·10–8
2.21·10–4
5.27·10–5
1.22·10–7
1.09·10–4
2.05
6.81·101
3.96·101
5.70·10–8
8.24·1032.09·1012.72·10–9
4.37·10–1
1.07·101
8.31·10–8
6.45·10–3
1.48
3.07·10–2
3.05·10–3
7.48·10–3
1.68·10–5
1.77·10–3
1.26·10–1
2.47·10–2
2.60·10–3
6.44
9.28
6.94·10–5
5.86·10–3
7.65·10–4
2.97
3.15·10–4
1.85·10–1
9.09·10–1
1.07
1.49·10–1
5.55
1.20·10–9
1.30·10–6
6.77·10–6
1.92·10–7
3.69·10–12
1.84·10-10
5.27
1.62
4.62·1012.02·101
2.02
5.91
4.24·10–8
1.49·10–1
1.79·10–1
3.65·10–3
3.71·10–1
5.76
1.95·101
1.66·10–8
2.37·10–4
5.07·10–5
1.07·10–7
1.10·10–4
2.36
6.44·101
4.31·101
6.31·10–8
7.23·1032.09·101

3.69·10–9
4.24·10–1
1.28·101
1.02·10–7
7.91·10–3
2.82
3.74·10–2
1.32·10–3
9.93·10–3
2.06·10–5
2.16·10–3
1.75·10–1
7.61·10–2
1.22·10–2
8.33
7.88
3.31·10–5
2.97·10–3
1.04·10–3
3.87
3.63·10–4
1.05·10–1
1.05
1.21
1.52·10–1
7.31
1.47·10–9
2.48·10–6
7.43·10–6
2.53·10–7
3.65·10–12
4.43·10–10
7.64
2.34
4.61·101
2.45·101
9.97·10–1
9.27
8.21·10–8
1.83·10–1
1.69·10–1
3.83·10–3
4.98·10–1
4.45
2.36·101
3.79·10–8
3.42·10–4
7.66·10–5
1.06·10–7
1.65·10–4
2.71
3.65·101
4.51·101
3.11·10–8
7.16·103
2.52·101

2.23·10–9
4.50·10–1
8.63
8.22·10–8
6.37·10–3
2.11
2.84·10–2
7.32·10–3
7.43·10–3
1.66·10–5
2.45·10–3
2.68·10–1
5.71·10–2
9.17·10–3
6.28
8.71
7.07·10–5
7.15·10–3
7.87·10–4
2.92
2.81·10–4
1.76·10–1
7.85·10–1
9.28·10–1
1.31·10–1
5.28
1.18·10–9
1.86·10–6
5.15·10–6
1.83·10–7
2.74·10–12
1.91·10-10
5.73
1.97
3.47·101
1.83·101
1.94
6.98
3.80·10–8
1.37·10–1
1.43·10–1
3.51·10–3
3.74·10–1
5.07
1.77·101
2.34·10–8
1.85·10–4
4.33·10–5
7.94·10–8
9.95·10–5
1.81
2.74·101
3.39·101
6.07·10–8
5.36·103
1.89·101

1.84·10–9
4.42·10–1
1.20·101
1.28·10–7
9.98·10–3
1.97
2.89·10–2
4.16·10–3
6.61·10–3
2.59·10–5
2.12·10–3
1.66·10–1
4.33·10–2
4.32·10–3
6.06
8.73
8.07·10–5
6.54·10–3
6.80·10–4
2.69
3.05·10–4
1.63·10–1
8.83·10–1
1.04
1.42·10–1
4.59
1.85·10–9
1.73·10–6
4.98·10–6
1.59·10–7
3.32·10–12
1.00·10–10
5.20
1.60
4.67·101
2.40·101
2.36
7.35
2.55·10–8
1.27·10–1
1.69·10–1
3.46·10–3
3.42·10–1
5.29
1.69·101
1.16·10–8
1.64·10–4
3.37·10–5
9.62·10–8
8.49·10–5
1.90
5.16·101
3.32·101
7.38·10–8
6.50·103
1.77·101

TR-10-52

2.63·10–9
8.52·10–6
9.79
1.02·10–7
7.96·10–3
1.33
4.35·10–2
7.48·10–6
1.05·10–2
2.07·10–5
1.82·10–3
1.16·10–1
2.05·10–2
2.31·10–3
7.74
9.55
9.51·10–5
6.33·10–3
7.41·10–4
3.01
9.57·10–5
2.98·10–3
1.38·10–2
2.06
2.91·10–1
5.11
1.48·10–9
1.17·10–6
6.61·10–6
1.77·10–7
4.21·10–12
1.40·10–10
5.26
1.57
4.59·1012.31·101
1.94
5.59
3.25·10–8
1.51·10–1
1.68·10–1
9.34·10–3
3.77·10–1
5.96
2.00·101
1.64·10–8
1.85·10–4
5.13·10–5
1.22·10–7
1.06·10–4
1.89
6.41·101
4.39·101
6.06·10–8
8.25·1032.22·101

65

3.2

Instant release fraction and corrosion release fraction

The Instant Release Fraction IRF represents the fraction of the total radionuclide inventory that ismodelled to be instantly released to the interior of the canister, upon contact with water. While nuclides
embedded in the fuel matrix (including the higher actinides) will be released at the conversion rateof the fuel matrix, a fraction of the nuclides located in the fuel-clad gap and grain boundaries will be
released more rapidly. On a safety assessment time scale this release is considered to occur instantaneously, as described in the Fuel and canister process report. In addition to gap and grain boundary
inventory, the inventory of the crud4 (a deposit on the outer surface of the cladding) is included in the
IRF. Also, for the particular case of C-14, part of the inventory in the fuel cladding is included in the
IRF. Furthermore, the Ag-108m and Cd-113m inventories in the Ag-In-Cd alloy of the control rods
are included in the IRF.
The Corrosion Release Fraction CRF represents the inventory fraction that is modelled to be released
on a relatively short time scale, as a result of corrosion of construction material upon contact withwater. This corrosion is likely to take place over thousands of years after the canister is breached.
In SR-Site, the construction material includes the fuel cladding, the control rod cladding, and metalparts of the fuel assemblies.
In order to estimate the IRF and CRF, knowledge of the full radionuclide inventory is essential andthis is discussed in Section 3.1.

3.2.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used. Furthermore, for this particular section it falls upon the customer(the SR-Site team) to provide certain data.
Defining the data requested from the supplier

For each radionuclide of the selected inventory, the IRF represents the fraction of the radionuclide
inventory that is assumed to be instantly released. The IRF is defined to be in the interval 0 to 1,
depending on the characteristic of the nuclide. In case IRF = 0, the radionuclide is completely
entrapped in the UO2 spent fuel matrix and/or in the construction material, and no fraction will
be instantly released. In case IRF = 1, the entire inventory of the radionuclide is, or is pessimistically assumed to be, instantly released. In case of 0 < IRF < 1, part of the radionuclide inventory
is entrapped in the UO2 spent fuel matrix and/or the construction material, and part is instantly
released. The IRF is generally calculated according to the following equation:
IRF =( FGGB · IUO 2 ) + I Crud
I Tot

3-1

where IRF (-) is the instant release fraction, FGGB (-) is called the gap and grain boundary fraction
and is the fraction of the UO2 spent fuel inventory comprised of gap and grain boundary inventory,
IUO2 (mol/canister) is the inventory in the UO2 spent fuel, ICrud (mol/canister) is the crud inventory,and ITot (mol/canister) is the total inventory of the canister. In the particular cases of C-14, Ag-108m,
and Cd-113m, an additional term is needed in the numerator of Equation 3-1, stemming from assumed
instant release from the fuel cladding or from the Ag-In-Cd alloy of the control rods. The gap and grainboundary fraction of some volatile radionuclides is correlated to the Fission Gas Release, FGR (-),
while for other radionuclides there is no evident correlation. For most nuclides of the selected inventory, the gap and grain boundary inventory is negligible or non-existing.
It is noted that as a small part of the spent fuel is mixed oxide fuel (MOX), it would be more correct
to use the term UO2/MOX spent fuel. However, we have decided to use the shorter term UO2 spent
fuel and ask the reader to keep in mind that a fraction of the inventory concerns MOX.

4

Originally the abbreviation for Chalk River Unidentified Deposits

66

TR-10-52

The terms IUO2, ICrud, and ITot of Equation 3-1 are delivered from the supplier as part of Section 3.1
and in the Spent fuel report. For this present section the supplier should deliver the:•Fission gas releases, FGR (-), representative for BWR and PWR type canisters.

The SR-Site team should deliver the:•The gap and grain boundary fraction, FGGB (-), either as correlated to FGR or based on leaching
data, representative for BWR and PWR type canisters, and for an average canister.

•

Instant release fraction, IRF (-), as calculated from Equation 3-1, representative for BWR andPWR type canisters, and for an average canister.

The average canister should be based on all eight type canisters given in the Spent fuel report,
or alternatively on the SKB spent fuel reference scenario.
For each radionuclide of the selected inventory, the CRF represents the fraction of the inventory
that is assumed to be released on a relatively short time scale, as result of corrosion of construction
material of the fuel assemblies. (It should be noted that the cast iron insert and the copper canister
is not considered in this section.) The CRF is defined to be in the interval 0 to 1, where the value 0means that the nuclide does not exist in the construction material and the value 1 means that the total
inventory is entrapped in the construction material. In case 0 < CRF < 1, part of the radionuclide
inventory is entrapped in the construction material, and part in the UO2 spent fuel matrix and/or
the IRF. The CRF is calculated according to the following equation:

CRF =I Constr .Mtrl + I Ctrl . Rods
ITot

3-2

Where IConstr.Mtrl (mol/canister) is the inventory of the construction material excluding the control
rods, and ICtrl.Rods (mol/canister) is the inventory of the steel cladding of the control rods, excluding
the Ag-In-Cd alloy inventory. These two inventories are given in Section 3.1 and in the Spent fuelreport. It should be noted that control rods are only included in PWR type canisters. In the particularcase of C-14, only part of the construction material inventory should be accounted for when estimating
the CRF, as part of it is assumed to be instantly released.
The SR-Site team should deliver the:•Corrosion release fraction, CRF (-), as calculated from Equation 3-2, representative for BWR
and PWR type canisters, and for an average canister.

•

The corrosion time, tcorr (years) over which the construction material fully corrodes. This time
may be cautiously set as the minimum time over which full corrosion is possible to occur, or as
a distribution of possible corrosion times.

•

A statement on whether the corrosion rate can be assumed to be uniform in time, or follows somefunctional relationship.

The requested IRF and CRF data should be delivered with uncertainty estimates for all radionuclidesof the selected inventory (see Section 2.2.1), where applicable. It should be noted that these data
are only significant for radionuclides where release due to IRF and/or CRF is not overshadowedby release due to fuel conversion.
SR-Site modelling activities in which data will be used

The instant and corrosion release fractions are used in radionuclide transport modelling in the nearfield to calculate the release rates of radionuclides from the fuel and from failed canisters (see theRadionuclide transport report). In the conceptual model used, it is assumed that the radio­nuclides
may either be released instantly, on a relatively short time scale, or slowly at the dissolution/conversion
rate of the UO2 spent fuel matrix. Due to the complexity of the mechanisms involved, the IRF is pessimistically assumed to be released instantaneously to the intruding water in the canister, in case there is
no solubility limit. For radioelements that are solubility limited, the total IRF is not dissolved instantlyin the intruding water, but the release rate from the canister is also dependent on the water flow in

TR-10-5267

the canister. The CRF is assumed to be released relatively fast on a safety assessment time scale,but not instantaneously. This release is treated in a similar way as release due to fuel conversion,
although the time it takes for the construction material to fully corrode is much shorter than the time
it takes for the spent fuel to fully convert.
In case the corrosion release rate (mol/canister·year) is assumed to be uniform, it is calculated foreach radionuclide by taking the product of the CRF and the total inventory, and dividing it by the
corrosion time. For assessing the release rate of solutes to the intruding water, solubility limits need
to be taken into account.
The conceptual model used in SR-Site differs from that used in SR-Can /SKB 2006a/ and SR 97/SKB 1999a/, in the way that the CRF and IRF are separated in a methodical manner. This has been
made possible as the total inventory given in the Spent fuel report is separated on the basis ofdifferent sources in the fuel assembly.

3.2.2 Experience from SR-Can
Some of the experience gained from previous work with the SR-Can safety assessment is useful forthe data qualification in this Data report. This is briefly summarised below.
Modelling in SR-Can

The modelling activities of SR-Can using the IRF differ from those in SR-Site in that no systematic
distinction between the IRF and CRF was made in SR-Can. In SR-Can, all actinides included in theanalysis, as well as Sm-151 and Zr-93, were regarded to be released with the conversion rate of thefuel matrix (IRF = 0). The full inventory of the activation products Ni-59, Ni-63, and Nb-94 were
together with Ca-41 assumed to be instantly released (IRF = 1). The IRF of all other radionuclides
were given triangular distributions, as suggested in the supporting document /Werme et al. 2004/. Itshould be noted that in SR-Site, the nuclide Ca-41 is not part of the selected inventory (as justified
in the Radionuclide transport report).
Conditions for which data were used in SR-Can

The IRF data used in SR-Can were based on fuel burnups relevant for the April 2003 fuel inventory
in Clab /Werme et al. 2004/. For both BWR and PWR spent fuel, the average burnup was 38 MWd/kg
U and maximum burnup was 60 MWd/kg U.
Sensitivity to assessment results in SR-Can

Depending on the scenario analysed, the IRF was of different importance for the assessed radiologicalrisk. In the SR-Can Main report /SKB 2006a/ the following important scenarios were analysed:
In the SR-Can main scenario, canister failure occurred either through shearing of the canister due
to tectonic activity, or through corrosion due to advective conditions in the deposition hole causedby an eroding bentonite buffer. It was shown that in this scenario, the actinide daughter Ra-226
contributed to over 99% of the total annual effective dose at times where the dose came relatively
close to the regulatory limit /e.g. SKB 2006a, Figure B-5/. In addition to Ra-226, a few actinides of
the 4n+2 decay chain being decay parents to Ra-226 were of relevance for assessment results. For
these actinides the IRF was in SR-Can assumed to be 0, based on conceptual understanding. At earlytimes, the instantly released radionuclides I-129 and Nb-94 dominated the annual effective dose, but
only at very low levels compared to regulatory limits.
The pinhole defect scenario has been the main scenario in earlier assessments, for example SR 97/SKB 1999a/. However, based on knowledge obtained during welding studies, this scenario wasregarded to be less likely in SR-Can. Nevertheless, the pinhole defect scenario was used to show
the importance of different barrier components. It was shown that in this scenario, I-129 significantly
contributes to the annual effective dose, in addition to Ra-226 /SKB 2006a, Figure 10-18/. However,
the total annual effective dose was small compared to the regulatory limit. In SR-Can the IRF for
I-129 ranged from 0 to 0.05.

68

TR-10-52

The SR-Can main scenario was considered more likely than the pinhole defect scenario, and also
gave a higher total annual effective dose. As it was assumed that no actinides are instantly released,
it can be concluded that the IRF had no major effect on SR-Can assessment results.
Alternative modelling in SR-Can

No alternative modelling was performed in SR-Can, with regard to the IRF.
Correlations used in SR-Can modelling

In SR-Can, the instant release fractions of I-129, Cl-36, Cs-135 and Cs-137 were correlated tofission gas release. No other correlation was used.
Identified limitations of the data used in SR-Can modelling

In SR-Can the IRF was for some nuclides calculated from the FGR, while for other nuclides it wascalculated from the inventory of the activation products in the metal parts of the fuel assemblies. How­­ever, there was a lack of information on how this inventory was distributed among the construction
material, crud, and control rods. Altogether, the process of estimating IRFs in SR-Can can be seen
as somewhat inconsistent, and IRFs were not estimated for all of the nuclides of importance for
SR-Site. A more systematic approach is taken in SR-Site where both IRF and CRF are used.
In the SKI report /Stenhouse et al. 2008/ it is cautioned that due to the lack of experimental dataon the instant release fraction, there is a risk that too optimistic IRFs are suggested.

3.2.3 Supplier input on use of data in SR-Site and SR-Can
The instant release fraction, as defined in SR-Site, is based on the gap and grain boundary inventory
and the crud inventory. The fuel cladding and/or the control rods are other sources for the IRF of
Ag-108m, C-14, and Cd-113m. If the radioelement is not solubility limited, the instant release fraction is released as a Dirac pulse in radionuclide transport modelling; as superimposed on the release
due to fuel conversion. If there are solubility limits, all of this inventory cannot be released instantly,
but must be dissolved in sufficient amounts of water.
Concerning the part of the IRF that originates from the crud, the following should be considered:fuel conversion is postulated to take place at all times in the canister, upon contact with water. For
most radionuclides of concern for SR-Site, the inventory stemming from short term fuel conversion
will outweigh the inventory of the crud. This can be demonstrated by comparing the crud inventorywith the “fuel conversion inventory” produced by a short-term fuel conversion over ten years (theradiological risk due to fuel conversion over ten years is insignificant). In this comparison, the fuelconversion occurs at a steady rate of 10–7 year–1 (cf. Section 3.3.12). For the following radionuclides
the inventory of the crud outweighs the inventory of the fuel converted over ten years:•

Ag-108m, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Sn-121m, and Zr-93.

In this comparison the inventories of the type canisters (cf. Tables C-6 to C-13 of the Spent fuel report)
were used. The crud inventory of all other radionuclides falls below the threshold where it is of any
consequence for the safety assessment, and is therefore disregarded.
The corrosion release fraction as defined in SR-Site originates from corroding construction material,
which includes the steel cladding of the control rods (excluding the Ag-In-Cd alloy), the zircaloy
cladding, and other metal parts of the fuel assemblies. If comparing the fuel conversion rate withthe release rate from the corroding construction material, the former outweighs the latter for mostradionuclides. In the following example it is pessimistically assumed that all construction material
is fully corroded after 100 years. For the following radionuclides the release rate due to corrosionof metallic parts outweighs the release rate due to fuel conversion:•

C-14, Cl-36, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Se-79, Sn-121m, Tc-99, U-233, and Zr-93.

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69

In the example, the fuel conversion rate of 10–7 year–1 was used, together with the inventory of the typecanisters (Tables C-6 to C-13 of the Spent fuel report). A uniform corrosion release rate was assumed.
Based on this example, it is suggested that the CRF is set to zero for all other radionuclides.

3.2.4 Sources of information and documentation of data qualification
Both the supplier and customer provide data in this Data report. Therefore, it is noted whether thereferred documents are the supplier’s or the customer’s sources of information.
Sources of information

For the supplier, the main source of information is the Spent fuel report, which provides inventories
and fission gas release data. Background data for the fission gas release are presented in /Oldberg
2009/ and /Nordström 2009/.
For the customer a number of documents are listed as main sources of information in Table 3-9.
Except for the Spent fuel report, these sources include dedicated reports for the safety assessments
SR-Can /Werme et al. 2004/ and SR 97 /Johnson and Tait 1997/, as well as articles and reports fromthe open literature. The main sources are used to estimate the IRF, CRF, and corrosion time.
Table 3-9. Main sources of information used in data qualification.
Spent fuel report, 2010. Spent nuclear fuel for disposal in the KBS-3 repository. SKB TR-10-13, Svensk
Kärnbränslehantering AB.
Oldberg K, 2009. Distribution of fission gas release in 10x10 fuel. SKB TR-09-25, Svensk Kärnbränslehantering AB.
Nordström E, 2009. Fission gas release data for Ringhals PWRs. SKB TR-09-26, Svensk Kärnbränslehantering AB.
Werme L O, Johnson L H, Oversby V M, King F, Spahiu K, Grambow B, Shoesmith D W, 2004. Spent fuel performance under repository conditions: A model for use in SR-Can. SKB TR-04-19, Svensk Kärnbränslehantering AB.
Ferry C, Piron J-P, Poutesquen A, Poinssot C, 2008. Radionuclides release from the spent fuel under disposal conditions: re-evaluation of the Instant Release Fraction. In: Lee W E, Roberts J W, Hyatt N C, Grimes R W (eds). Scientificbasis for nuclear waste management XXXI: symposium held in Sheffield, United Kingdom, 16–21 September 2007.
Warrendale: Materials Research Society. (Materials Research Society Symposium Proceedings 1107), pp 447–454.
Johnson L, Ferry C, Poinssot C, Lovera P, 2005. Spent fuel radionuclide source-term model for assessing spent fuelperformance in geological disposal. Part I: Assessment of the instant release fraction. Journal of Nuclear Materials,
346, pp 56–65.
Johnson L H, Tait J C, 1997. Release of segregated nuclides from spent fuel. SKB TR 97-18, Svensk Kärnbränslehantering AB
Kursten B, Smailos E, Azkarate I, Werme L, Smart N R, Marx G, Cuñado M A and Santarini G, 2004. COBECOMA.
State-of-the-art document on the corrosion behaviour of container materials. Final report. Brussels: European
Commission.
Smart N R, Blackwood D J, Marsh G P, Naish C C, O´Brien T M, Rance A P, Thomas M I, 2004. The anaerobic
corrosion of carbon and stainless steels in simulated cementitious environments: a summary review of Nirex research.
Report AEAT/ERRA-0313, AEA Technology.
Wada R, Nishimura T, Fujiwara K, Tanabe M, Mihara M, 1999. Experimental study on hydrogen gas generation
rate from corrosion of Zircaloy and stainless steel under anaerobic alkaline conditions. In: Proceedings of the 7thInternational Conference on Radioactive Waste Management and Environmental Remediation, ICEM ’99, Nagoya,Japan, 26­–30 September 1999. New York: American Society of Mechanical Engineers.
White J H, Yaniv A E, Schick H, 1966. The corrosion of metals in the water of the Dead Sea. Corrosion Science, 6,
pp 447–460.

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Categorising data sets as qualified or supporting data

Different data sets used can be categorised as qualified or supporting. This is done in Table 3-10.
In Table 3-11 justifications for the sorting are given.
Table 3-10. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Spent fuel report, Tables C-6 to C-13: Inventory data for
type canisters, as distributed on the sources UO2 spent fuelmatrix, construction material, crud, and control rods.
2. Spent fuel report, Section 6.3.1: Average FGR data,with uncertainty estimates, for BWR assemblies and PWR
assemblies of the SKB reference spent fuel scenario. Table
6-16: FGR data for the eight type canisters.
3. /Werme et al. 2004/, Chapter 2: IRF data for C-14, Cl-36,
Se-79, Tc-99, Pd-107, Sn-126, I-129, and Cs-135 for low tomoderately burnup fuel.
4. /Johnson et al. 2004/, Table 6-1: BWR and PWR leachingdata for fuel with known FGR
5. /Johnson et al. 2005/, Table 5: PWR leaching data for fuelwith known FGR
6. /Matsson et al. 2007/, Figure 8: Measured FGR of highburnup BWR fuel7. /Roudil et al. 2007/, Table 2: Leach data for Cs and Sr of
low to high U PWR fuel.
8. /Nordström 2009/: Calculated FGR from PWR fuel. All data.
9. /Oldberg 2009/: Calculated FGR from BWR fuel. All data.
10. /Ferry et al. 2008/, Table IV: IRF best estimates.
11. /Kursten et al. 2004/: Corrosion data for stainless steel
and nickel alloys under anoxic conditions.
12. /Wada et al. 1999/: Corrosion data for stainless steel
under anaerobic conditions at pH 10 and temperature 50°C.
13. /White et al. 1966/: Corrosion data of stainless steel inanoxic seawater.

14. /Johnson and Tait 1997/, Table 3: IRF valuesof key radionuclides.
15. /Smart et al. 2004/: Corrosion data forstainless steel under anoxic conditions in
cementitious environment.

Item number 1, 2, 8 and 9 are data sets provided by the supplier while the rest are data sets provided by the customer.

Table 3-11. Justifications to the sorting of items in Table 3-10.
1–2: Data provided in the Spent fuel report are qualified in the Spent fuel report, in accordance with the SKB quality
assurance system. This qualification is found to be in compliance with the demands of this Data report.
3: /Werme et al. 2004/ was not produced according to the current quality assurance system at SKB. It was, however, theresult of the joint opinion of qualified experts in the field, and the data is therefore judged as qualified. The data in thereport are, however, only valid for low to medium burnup fuel.
4: /Johnson et al. 2004/ is a report of the Spent Fuel Stability (SFS) Project of the 5th Euratom Framework program,
published by Nagra. The authors are all experts in the field and the data is considered qualified. Some of the data isreproduced in the following peer reviewed scientific journal article.
5–7: /Johnson et al. 2004/, /Matsson et al. 2007/ and /Roudil et al. 2007/ are peer reviewed scientific journal articles andthe data presented are be considered to be qualified.
8–9: Both /Nordström 2009/ and /Oldberg 2009/ are produced according to the current quality assurance system at
SKB. The data are applicable for high burnup fuel and spent fuel produced after power upgrade of the Swedish reactors.
Calculated fission gas release data for Ringhals 4 for low linear power also support the estimates of fission gas distribution
made by /Werme et al. 2004/.
10. /Ferry et al. 2008/ is a peer reviewed scientific journal article. Estimates given in Table IV are based on data in /Johnson
et al. 2005/ but modified to take into account the observation that the rim inventory does not contribute to the IRF.
11. This report is produced by the European Commission network COBECOMA, having as a focus to examine corrosionof containers for high-level waste for disposal in various geological formations. The publication is considered as qualified.
12–13: /White et al. 1966/ and /Wada et al. 1990/ are peer reviewed journal or conference articles and the data presented
are considered to be qualified.
14: The data presented in Table 3 in /Johnson and Tait 1997/ are used as supporting data. The document was prepared
for the SR 97 safety assessment.
15. This report is produced outside the SKB framework of qualification and is judged as supporting, partly as the data
concern cementitious conditions.

TR-10-5271

3.2.5 Conditions for which data are supplied
The data are applicable to reactor operations used in the past as well as foreseen future operations inaccordance with the SKB spent fuel scenario (cf. the Spent fuel report). The data are applicable foryear 2045.
For estimating the corrosion time, the conditions in the breached canister are needed. One could expect
elevated temperatures in case the canister is breached relatively early. However, in the more likely
case of late canister breach, the temperature should resemble that of the host rock. The groundwatercompositions should be in line with those given in Section 6.1. However, it is assumed that the corrosion of the cast iron insert and construction materials will assure anoxic conditions in the canister.
Information on the parts comprising the construction material is given in the Spent fuel report.

3.2.6 Conceptual uncertainty
The data of this section, which are used as input to radionuclide transport modelling, are consistent
with the needs of the present modelling approach. However, as there is a large portion of pessimism
included when estimating the data, they are not intended to in detail represent the conceptual viewon these issues.
Instant release fraction, IRF

The radionuclide transport modelling approach concerning the IRF is pessimistic in assuming that
the IRF is released instantaneously. In reality, the release of this fraction of the inventory is likely tooccur during a more or less prolonged time period. In addition to the gap and grain boundary inventory, the IRF includes the crud inventory. The properties of crud are largely unknown, consequently
instantaneous release is assumed. All in all, the conceptual uncertainties associated with the IRF are
handled by pessimistically assuming instantaneous release, which may lead to an overestimation of
the source term.
Corrosion release fraction, CRF

Conceptual uncertainties associated with the CRF include the questions whether all construction
material becomes fully corroded over the repository evolution, and whether corroded construction
material releases its entire radionuclide inventory. Concerning the latter issue it is conceivable that thecorrosion products may sorb or otherwise immobilise a fraction of the corrosion released nuclides.
This uncertainty is handled by cautiously assuming that the full inventory of the construction material
is released to the surrounding water during the corrosion time. This simplified conceptualisation may
lead to an overestimation of the source term.
Corrosion time, tcorr

Conceptual uncertainties associated with the corrosion time include the questions of:•

At what rates do different parts of the construction material corrode?

•

What is the range of conceivable conditions in the canister?

•Will the corrosion itself increase the corrosion time, as corrosion products may pose a transport
resistance for the corrodants?

•

How does one assess a corrosion time from a great number of different corrosion rates andgeometries?

The approach taken in SR-Site is to pessimistically assume that all parts corrode at the same rate as
the fastest corroding material, which is stainless steel. Furthermore, all parts are assumed to be fully
corroded at the time the thinnest part has corroded. This has two consequences. The first is that therelease rates from thick metallic parts become unrealistically high. The second is that the release ratefrom relatively slowly corroding alloys becomes unrealistically high. It is important to notice that thisis due to the pessimistic modelling approach taken, and not a consequence of the conceptual view.
Other uncertainty, such as the corrosion rate at the conditions in the canister, is treated as data uncertainty.

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Fission gas release, FGR

The calculated fission gas release is used to estimate the IRF for some of the radionuclides. The FGR
values are results of first principles calculations based on best available input data, such as diffusion
data of fission gases. Such calculations are currently being performed and validated for the utilities
in order to ensure that fuel failure will not occur and that the reactors can be operated safely. The
calculations show a large span of fission gas releases for different fuel pins in the core. Even though
there is a conceptual element in how to handle such a large span of FGR in safety assessment, this istreated as data uncertainty.
Gap and grain boundary fraction, FGGB

A main conceptual uncertainty in determining the IRF for some nuclides lies in establishing a relation­
ship between the IRF and FGR, via the FGGB. The FGGB relates to the segregation of fractions ofcertain radionuclides in the fuel, during operation, to the fuel-clad gap and grain boundaries. The
concept of a gap and grain boundary fraction that is instantly released is based on the transfer of
certain radionuclides to parts of the fuel from where they can be released much faster than throughfuel conversion or dissolution. For the fuel-clad gap there is no significant uncertainty. However,an element of this uncertainty lies in establishing which part of the spent fuel is easily accessible
to water, thus enabling instant releases of the radionuclides. There are also questions on how tohandle the rim of the spent fuel pellet, as discussed below.
It has been discussed (see e.g. /Johnson et al. 2004, 2005, Ferry et al. 2008/) whether the radio­
nuclide inventory in the rim of the irradiated fuel pellet should be included in the gap and grainboundary inventory. Experimental studies, however, have not shown any preferential release fromthe rim region /Clarens et al. 2008/. In fact several studies have shown that the releases do not increase
with burnup, i.e., within the accuracy of the experiments there is no evidence for an increasing
contribution from the fuel rim /Jégou et al. 2004, Ekeroth et al. 2009, Hanson 2008/. In this section,
the estimations of FGGB include the gap and grain boundary inventory but do not involve the rim
inventory.
For the grain boundaries it is uncertain to what degree they are accessible to water. Furthermore,
the radionuclides associated with the grain boundary inventory is mainly found as insoluble metallic
alloy aggregates and is therefore not likely to be instantly released to water /Ramebäck et al. 2000,
Cui et al. 2004/.

3.2.7 Data uncertainty due to precision, bias, and representativity
Generally the resolution in the data does not allow for separating what part of the data uncertainty
relates to precision, bias, and representativity. Data concerning the inventories of the different
type canisters (that are used as input to this section) are discussed in Section 3.1, where also an
uncertainty discussion is given.
Fission gas release, FGR

This Data report presents FGR estimations for the average canister, as for the BWR and PWR
inventories of the SKB spent fuel scenario that also includes high burnup fuel. Data from highburnup fuel are available for PWR fuel /Johnson et al. 2005, Roudil et al. 2007, Ferry et al. 2008/.
For high burnup BWR fuel, however, there is only a limited set of publicly available FGR data from
Kernkraftwerk Leibstadt (KKL) /e.g. Matsson et al. 2007/. This is due to the fact that BWR reactors
are not licensed for burnup higher than 50 MWd/kgU, with the exception of KKL.
To overcome this shortage of reliable data, fission gas releases were calculated for both PWR and
BWR fuel with burnup up to 60 MWd/kgU. Complete data sets including uncertainties are accounted
for in /Oldberg 2009/ for the BWR cases and in /Nordström 2009/ for the PWR cases. The results of
those calculations are used in the Spent fuel report to calculate an average FGR for the BWR andPWR inventories. These numbers, including uncertainties, are used in the estimations of IRF performedby the customer.

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73

The fission gas release fractions have been calculated for two of the pressurised water reactors,
Ringhals units 2 and 4. The calculations were made for two equilibrium cores with 5% enrichment
and a mean burnup of 60 MWd/kgU /Nordström 2009/. Similarly, calculations were performed forthe fission gas release from boiling water reactor Westinghouse SVEA-96 Optima2 fuel for 5% enrich­ment and average burnup of 60 MWd/kgU /Oldberg 2009/. Examples of the results are shown in
Figure 3-7 and Figure 3-8 below.
16

14

Fission gas release [%]12

2 cycles3 cycles10

4 cycles5 cycles

8

6

4

2

0
0

10

20

30

40

50

60

70

80

Rod average burnup [MWd/kgU]

Figure 3-7. Fission gas release as function of burnup for the different batches at the end of the equilibrium
cycle for Ringhals 2 after power uprate (PWR). Reproduced from Figure 4-1 in /Nordström 2009/.

Fission Gas Release Fraction
Case 2Fission Gas Release Fraction [-]0,12Cycle 1
Cycle 2
Cycle 30,1

Cycle 4
Cycle 50,08

Cycle 5 EOL
Cycle 6
Cycle 6 EOL

0,06

Cycle 7 EOL

0,04
0,02
0

0

10

20

30

40

50

60

70

80

Rod Average Burnup [MWd/kgU]

Figure 3-8. The fission gas release fraction at the end of each cycle for an equilibrium core in Oskarshamn
BWR reactor O3 (109% power), 3,292 MW, ~60 MWd/kgU. Reproduced from Figure 5-5 in /Oldberg 2009/.

74

TR-10-52

As can be seen in the figures, there is a wide spread in the fission gas release within one core. The
high fission gas release starts already during the first cycles in the reactor. The final fission gas release
distribution also differs between reactor types. While the PWR final fission gas release has a symmetric
distribution around an average value, the BWR distribution is somewhat asymmetric with a longer
slope at the high fission gas release end. Therefore, if including all the BWR data for the later cycles
in a histogram (cf. Figure 5-4 of /Oldberg 2009/), the histogram peaks at a slightly lower value than
the arithmetic mean of the data (a difference of 0.5 to 1%). However, as the skewness of the distribution is minor and should not impose a significant impact on assessment results, it is disregarded insubsequent handling.
In the Spent fuel report, Section 6.3.1, the FGR is estimated as following (arithmetic mean ± one
standard deviation):•

For spent BWR fuel: 0.019±0.011•

For spent PWR fuel: 0.043±0.031

These estimations are the results of the calculations presented in (SKBdoc 1222975) in which datafrom all the cases presented in /Nordström 2009/ and /Oldberg 2009/ are used, and not just the ones
shown in Figure 3-7 and Figure 3-8.
As mentioned above, the fission gas release depends on the location of the fuel pin in the reactor
core. This is reflected in the total span of data from the whole core. Furthermore, the FGR depends
on the burnup and the estimated FGR averages given in the Spent fuel report take this, and otheroperation conditions, into account, thereby assuring the representativity of the data.
Gap and grain boundary fraction, FGGBIn SR-Can the term FGGB was not used, but instead the term IRF was used directly. For radionuclides
where the gap and grain boundary inventory is the only significant source of the IRF, the implicationsof these two terms are the same. In such cases, when referring to previous uncertainty discussions/e.g. Werme et al. 2004/ we use the term FGGB even when the cited publications used the term IRF.
The leaching data available in the literature have been used to estimate the gap and grain boundary
fraction of relevant nuclides /Werme et al. 2004 and references therein, Johnson et al. 2004, 2005,
Roudil et al. 2007/. The leaching data indicate variable correlation between FGGB and FGR for different
radionuclides. For some nuclides, no correlation at all could be seen and for these, the FGGB is directly
estimated from available leaching data or from conceptual understanding.
A few radionuclides of the selected inventory are considered to be mobile in the fuel, in a similar
way as the fission gases during reactor operation (high temperature). These are I-129, Cs-135, Cs-137,
Ag-108m, and Cd-113m. Existing leaching data for iodine and caesium indicate that the FGR constitutes an upper limit for FGGB /Werme et al. 2004/; for caesium in particular, correlations of FGR vs.
FGGB have been shown to be 3:1 (Johnson and McGinnes 2002). Thus, for these radionuclides, a 1:1
correlation is used as a bounding case in safety assessment. There is no leaching data for Ag-108m
and Cd-113m; however, their physical and chemical properties have been utilised in model calculations
to show that they are likely to transfer to the fuel-clad gap in a similar way as I and Cs /Cubicciotti
and Sanecki 1978, Johnson and Tait 1997/. In PWR type canisters, the significant sources for Ag-108m
and Cd-113m are the control rods; consequently the gap and grain boundary fraction is negligible.
The existing leaching data for Se-79 is limited to failed attempts to measure Se-79 in solutions/Wilson 1990a, Wilson 1990b/, which resulted in the conclusion that the amount leached was lessthan the detection limit for the method used. This suggests that there is no real evidence for Se-79
segregation to the gap at low to moderate burnup. The dearth of information regarding the behaviourof Se-79 advocates comparisons with other, chemically similar elements. Based on the observationof Te in the fuel-clad gap of a fuel with FGR of 10–13% /Cubicciotti and Sanecki 1978/, one may
suggest that Se-79 could also be somewhat mobile in the fuel at high temperatures. The available
data /Wilson 1990a, b/ indicate that Se release is less than 0.15 times FGR, and the correlation FGGB
= 0.15·FGR is cautiously adopted here. The data uncertainty regarding FGGB (and IRF) for Se-79 isthus related to the uncertainty in the FGR values used in the correlation, as well as the fact that thecorrelation factor 0.15 is based on the upper limit of leached Se-79 given by the detection limit of/Wilson 1990a/.
TR-10-52

75

Two radionuclides are considered to be more mobile than the fission gas in the fuel matrix. The
first is H-3, which is formed in the fuel but where the full inventory is expelled from the fuel matrix
during reactor operation and found in the surrounding material (fuel-clad gap and cladding). The
second most mobile radionuclide is Cl-36, which is expected to be very mobile in the fuel matrix/Pipon et al. 2007/. There are no leaching data for Cl-36 release from light water reactor fuels. The
estimates of the FGGB are therefore based on the CANDU fuel data of /Tait et al. 1997/. A best available
estimate /Tait et al. 1997/ indicates a three-to-one correlation between the FGGB and FGR for Cl-36.
For radionuclides where available data indicate a one-to-one correlation between FGR and FGGB, thevalues and uncertainties in the corresponding FGR are adopted. For Cl-36 and Se-79, the values anduncertainties are multiplied by a factor of three and 0.15, respectively. There is also uncertainty inthe correlation factor through which the FGGB and FGR are correlated. However, this uncertainty is
judged to be small in comparison to the spread in the FGR and is not propagated.
For a number of the concerned radionuclides there is no scientific evidence for a correlation between
the gap and grain boundary fraction and the FGR. These radionuclides are C-14, Sr-90, Tc-99, Pd-107,
Sn-121m, and Sn-126. For these radionuclides, leaching data available in the literature have been
consulted. Since the publication of /Werme et al. 2004/, only one paper has been published whichcontain new FGGB leaching data from UO2 fuel /Roudil et al. 2007/. The data presented by /Roudil
et al. 2007/ do not warrant any change from the previous values recommended for use in SR-Can/Werme et al. 2004/.
Leaching data for C-14 from a few publications are summarised in /Werme et al. 2004/, which basedon the available data recommended FGGB as a triangular distribution in the normal space with 0.05 as
the best estimate value, and with 0.001 and 0.1 as the bounding values. In the more recent publication/Johnson et al. 2005/, an FGGB of 0.1 is recommended for C-14. This latter single point value is pro­pagated in SR-Site on cautious grounds.
The existing leaching data for Sn-126 is based on failed attempts to measure Sn-126 in solution/Wilson 1990a, b/, which resulted in the conclusion that the amount leached was less than the detection limit for the method used. In extension, this information suggests a maximum FGGB of 2.5·10–4/Johnson et al. 2004/. The vapour pressure of tin is relatively low, 10–3.3 atm at 2,000 K /Cubicciotti
and Sanecki 1978/, and a larger release to the gap is, therefore, unlikely. Tin is also expected to form
metallic precipitates /Kleykamp 1985/. In /Werme et al. 2004/ the FGGB for Sn-126 was given as atriangular distribution in the normal space with 3·10–4 as best estimate, with 0.001 as the upper valueand 0 as the lower value. The same estimate can be used for the FGGB of Sn-121m.
There is some existing experimental leaching data for Sr-90. Based on existing experimental dataat the time, /Johnson and Tait 1997/ concluded that the best estimate FGGB for Sr would be 0.0025,
while 0.01 is a pessimistic estimate. Leaching data by /Roudil et al. 2007/ indicate a maximum FGGBof Sr-90 of 0.0025 (for PWR-MOX fuel with 7% FGR). Other PWR-UOX fuel of variable FGR andburnup have lower FGGB for this radionuclide. Therefore, available leaching data indicate an FGGB ofless than 0.01 and no correlation with FGR /Ferry et al. 2008/, for all fuel types. On the other hand,/Johnson et al. 2005/ suggests pessimistically that the Sr-90 grain boundary inventory increases with
FGR and burnup. This suggestion was, however, mainly related to the assumption of contribution of
the rim to the FGR and FGGB at higher burnups. The rim contribution was later shown to not contributesignificantly to the IRF /Ferry et al. 2008/. Thus, Sr-90 is an example where the conceptual uncertainty
regarding the FGGB and IRF results in significant variation in the published recommendations.
Fractions of the inventory of technetium and palladium are present in spent fuel in metallic form asalloy inclusions (also referred to as epsilon particles). These are generally considered to be insoluble,
which is also indicated by the available leaching data /e.g. Gray 1999, Johnson and Tait 1997, Cuiet al. 2004, Fortner et al. 2004/. These metals are known to segregate from the fuel matrix; some ofthese segregations are associated with the grain boundaries of the fuel pellet. However, to argue thatthe full grain boundary inventory of these metals is instantly released when the canister fails is very
pessimistic and probably unrealistic. In order to mobilise the metals in the metallic particles, oxidationis required /e.g. Ramebäck et al. 2000/. The experimentally observed released fractions from leachingexperiments, as summarised in /Johnson and Tait 1997/, have been used in order to estimate the FGGBof Tc-99 and Pd-107. In /Werme et al. 2004/, the recommended distribution is triangular in the normal
space with 0 and 0.01 as the bounding values, and with a best estimate at 0.002.

76

TR-10-52

Instant release fraction, IRF

The sources of the IRF are the gap and grain boundary inventory, crud and, in the case of C-14, thefuel cladding. In addition, for PWR type canisters the Ag-108m and Cd-113m inventories of thecontrol rods are pessimistically assumed to be instantly released.
It should be noted that while FGGB is the fraction of the UO2 spent fuel inventory IUO2 that is comprised of gap and grain boundary inventory, the IRF is the fraction of the total inventory ITot that is
instantly released (cf. Equation 3-1). Therefore, for nuclides where part of the inventory is in theconstruction material, IRF values will not be the same as the FGGB values.
For the following radionuclides, the gap and grain boundary inventory is the only significant sourceof the IRF:•

Cl-36, Cs-135, Cs-137, I-129, Pd-107, Se-79, Sn-126, Sr-90, and Tc-99.

For Cl-36, Cs-135, Cs-137, I-129, and Se-79 the IUO2/ITot ratio is close to unity (> 97%); consequently the values and uncertainties of the FGGB are adopted. For these nuclides, the values anduncertainties of the FGGB are in turn adopted from those of the FGR.
FGR values are only suggested for BWR and PWR type canisters in the Spent fuel report, but notfor the average canister. If combining two populations, and if having their means and variances, themean and variance of the combined population can be calculated:µ XY =σ XY =N X µ X + N Y µY
N X + NY

(

3-3

)

(

)

N X σ X2 + µ X2 + N Y σ Y2 + µY2
2− µ XYN X + NY

3-4

where μXY and σXY2 are the mean and variance of the new distribution, μX and μY and σX2and σY2 are
the means and variances of the distributions to be combined. Furthermore, NX and NY are the numberof data points in the populations to be combined (that is 4,451 BWR type canisters and 1,652 PWR
type canisters).
If using μ = 0.019 and σ = 0.011 for BWR type canisters and μ = 0.043 and σ = 0.031 for BWR typecanisters, the IRF for the average canister becomes distributed according to μ = 0.025 and σ = 0.021.
These values correspond to the data uncertainty for the IRF of Cs-135, Cs-137, and I-129. For Cl-36
these numbers are multiplied by a factor of three, and for Se-79, they are multiplied with a factor of 0.15.

Probability

For Pd-107, Sn-126, Sr-90, and Tc-99 the IUO2/ITot ratio is close to unity (> 99%). The data anduncertainty estimate for the FGGB, is adopted for these nuclides. As the FGGB is estimated based onleaching data, the uncertainty estimates are given as a range and a best estimate value. In the background sources /e.g. Werme et al. 2004/, triangular distributions in the normal space were assigned
based on the range and best estimate value. However, from a risk analysis perspective it may not be
optimal to use an asymmetric triangular distribution (unless this is expressly warranted). In this text
we assume that the best estimate value is set in such manner that it is of equal probability (50%) thatthe “true” value is above as below this best estimate value. Therefore, we recommend the double
triangle distribution /e.g. AACE 2008/. The double triangle distribution is illustrated in Figure 3-9.

50 %

50 %
Value

Figure 3-9. Illustration of the double triangular distribution, where the integral under each triangle holds
50% probability.
TR-10-52

77

The resolution in data from the background sources does not allow for assigning different data setsor distribution to different fuel types or type canisters for these nuclides.
For the following radionuclides, the crud inventory is the only source of IRF:•

Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, and Zr-93.

For these nuclides, the IRF of the average canister is estimated by weighting the IRFs of the eight
different type canisters by the number of each type canister according to:IRF =I Crud ,i

∑N I
i

i

Tot ,i

3-5

∑ Ni
i

Where ICrud,i, ITot,i, and Ni are the crud inventory, total inventory, and number of canisters for thedifferent type canisters, respectively. It is reasonable to assume that the uncertainty range of the IRF
is constrained by the highest and lowest IRF values of the type canisters. However, in doing so caremust be taken when assigning distributions to the data, so that the approach does not give rise torisk dilution. This is achieved by recommending the double triangular distribution. The IRFs of the
discussed radionuclides for the different type canisters are shown in Figure 3-10.
For a few nuclides, individual treatment is required. These are•

Ag-108m, C-14, Cd-113m, H-3, and Sn-121m.

The inventory of Ag-108m differs dramatically between BWR and PWR type canisters, where only
a minute fraction (~10–5) is associated with BWR type canisters (according to the SKB spent fuelreference scenario, see Table C-2 of the Spent fuel report). For PWR type canisters, practicallyall Ag-108m is located in the Ag-In-Cd alloy of the control rods. This inventory is pessimisticallyassumed to be released instantaneously and, as a result, the recommended IRF for PWR typecanisters is one. This pessimistic assumption is adopted due to the lack of qualified data to support
quantification of the expected low silver releases. Available unpublished in-reactor data indicate apotential de-alloying and release of In and Cd. Published data are expected to become available fromstudies on the corrosion of the Cd-In-Ag alloy, with emphasis on silver corrosion and release. For
1

BWR I
BWR II

10-1

BWR III
BWR-MOX

Instant release fraction

10

PWR I

-2

PWR IIPWR III

10-3

PWR-MOX

10-4
10-5
10-6
10-7
10-8
Mo -93

Nb-93m

Nb-94

Ni-59

Ni-63

Zr-93

Figure 3-10. The IRF of some radionuclides for the different type canisters. Plot based on data in Tables C-6
to C-13 of the Spent fuel report.

78

TR-10-52

the BWR type canisters, between 44% and 48% of the total Ag-108m inventory is assumed to becomprised of the crud inventory (cf. Table C-6 to C-13 of the Spent fuel report). The contribution
from the gap and grain boundary inventory is on the order of one percent and can be added. As the
Ag-108m inventory to a great majority exists in PWR type canisters, the recommended IRF of theaverage canister is one, which agrees with the recommendation for PWR type canisters.
A very similar situation as above occurs for Cd-113m, where only about one per mille of the total
inventory is found in the BWR type canisters, and where the rest for all practical purposes is found in
the Ag-In-Cd alloy of the control rods of the PWR type canisters. For the PWR type canisters, as forthe average canister, the IRF of one is recommended on the same grounds as for Ag-108m. For the
BWR type canister, Cd-113m exists in the UO2 spent fuel, and the gap and grain boundary fraction isassumed to have a one-to-one correlation with the FGR. For Cd-113m, the IUO2/ITot ratio is at unity for
BWR type canisters and the best estimate value and uncertainty range are adopted from the FGR.
For C-14, the recommended IRF (FGGB) in /Johnson et al. 2005/ is 10%, as based only on the gap
and grain boundary inventory. This can be seen as cautiously in the light of /Johnson and Tait 1997/
recommending an IRF of 5% as best estimate and 10% as upper value. In the remaining of this text,
FGGB of C-14 is assumed to be 0.1 for all type canisters. In addition to the gap and grain boundary
inventory, C-14 is believed to be instantaneously released from the oxide part of the Zircaloy cladding.
The instant release fraction with respect to the cladding inventory is up to 20% /Johnson and McGinnes
2002, Yamaguchi et al. 1999/. In the remaining of this text we cautiously assume that this value is20%. It is estimated that 40% of the C-14 inventory of the construction material is found in the cladding for BWR and MOX type canisters, while 57% is found in the cladding for PWR type canisters/SKBdoc 1198314/. From these numbers, and from the type canister inventories in Table C-6 to C-13of the Spent fuel report, the IRF of each type canister can be calculated. The weighted average
becomes 0.0925, while the minimum and maximum IRF for any canister becomes 0.085 and 0.11.
For H-3, almost the entire inventory (> 99.6%) originates from the fuel matrix. As the mobility of
H-3 is so high, the full inventory is expelled from the fuel matrix and may therefore be instantly
released. Therefore, for all type canisters and for the average canister, the IRF is set to one.
For Sn-121m, the IRF stems both from the gap and grain boundary inventory and the crud inventory.
As discussed above, the FGGB for Sn-121m is estimated to be within the range 0 to 0.001, with 3·10–4
as best estimate. These numbers can be used to estimate a minimum, best estimate, and maximum
gap and grain boundary inventory for the type canisters. The minimum, best estimate, and maximum
IRF of the different type canisters can then be calculated by using Equation 3-1 (assuming that there
is no uncertainty in the crud inventory). The weighted average of the IRF becomes 1.9·10–4, using thebest estimate FGGB as input, while the minimum and maximum IRF for any type canister and for any
FGGB in the suggested range becomes 3.2·10–7 and 8.7·10–4. To avoid risk dilution the double triangle
distribution is recommended.
Corrosion release fraction, CRF

Based on the reasoning in Section 3.2.3, the CRF is only meaningful for the following radionuclides:•

C-14, Cl-36, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Se-79, Sn-121m, Tc-99, U-233, and Zr-93.

For all other radionuclides, either the release due to fuel conversion will outweigh the release due
to corrosion, even at pessimistic corrosion rates, or the full inventory is in the IRF. The CRFs of the
discussed radionuclides are shown in Figure 3-11 for the different type canisters.
By using Equation 3-5, but exchanging ICrud for IConstr.Mtrl + ICtrl.Rods, the weighted average of the
CRF of the type canisters is obtained. This value is recommended as best estimate for the average
canister. The uncertainty range is assumed to be contained by the highest and lowest CRF of all typecanisters. These CRFs gives the upper and lower limit of the suggested double triangle distribution
for each radionuclide. A special treatment is required for Nb-93m and Nb-94, where 96% of theinventory is found in PWR type canisters. Here, the recommended distributions for the average
canister are based upon PWR type canister data only.
5 	

IFR for BWR calculated by (0.1·IUO2+0.2·0.4·IConstr.Mtrl) / ITot
IFR for PWR calculated by (0.1·IUO2+0.2·0.57·IConstr.Mtrl) / ITot

TR-10-52

79

1

BWR I
BWR II

0.9

BWR III
BWR-MOX

Corrosion release fraction

0.8

PWR I

0.7

PWR IIPWR III

0.6

PWR-MOX

0.5
0.4
0.3
0.2
0.1

Zr
-9
3

3
-2
3
U

Tc
-9
9

1m
-1
2

3

-7
9

Sn

Se

i-6
N

9
i-5
N

b94
N

m
b93
N

6

o93
M

l-3
C

C

-1
4

0

Figure 3-11. CRFs of some radionuclides for the type canisters (based on data in Tables C-6 to C-13 of theSpent fuel report). For C-14, the CRF is corrected for the instant release from the Zircaloy cladding.

Corrosion time, tcorr

There is considerable data uncertainty associated with the corrosion time, as it will take different
times for different construction parts to fully corrode. This is partly due to their different geometries,
where the thickness is an important aspect, partly due to the range of conceivable conditions in thecanister, where the redox conditions and salinity are of particular concern, and partly due to theirdifferent material compositions. The cladding tubes for the fuel are made of Zircaloy, while otherstructural elements in the fuel assembly are made of stainless steel, Inconel, Incoloy, or Zircaloy.
Dimensions and materials of the fuel assemblies are described in Section 2.3.2 of the Spent fuel report.
SKB has not conducted any investigations of the corrosion resistance of the construction materials
under repository conditions, and in general there are very few studies of corrosion rates under theanaerobic conditions, which will prevail inside the canister. In the Fuel and canister process report
it is argued that stainless steel corrodes relatively quickly compared to other construction materials.
There is for example a relatively large literature support for very low corrosion rates of Zircaloy,
which constitutes more than 80% by weight of all construction material in a fuel element (cf. Table 2-4of the Spent fuel report). As stainless steel corrodes relatively fast, it is suggested to use its corrosion rate as a pessimistic proxy for all the construction materials.
A corrosion rate of 0.8 µm/year for stainless steel in anoxic seawater at 24­–40°C is reported by/White et al. 1966/. In a state of the art report of the EU-project COBECOMA, in situ anoxic corrosion
experiments in Boom clay revealed that the average uniform corrosion rates of a series of stainlesssteels at 16–140°C were in the range 0.003–0.15 µm/y. Electrochemical experiments and immersion
tests performed to investigate the influence of various factors on the pitting susceptibility of stainlesssteel and Ni alloys showed that these materials were resistant to pitting under all tested experimental
conditions (Tmax = 140°C, [Cl–]max = 50 g/L, [S2O3]max = 200 mg/L) /Kursten et al. 2004/. No appreciablegeneral corrosion rates (< 0.1 µm/y) nor sensitivity to localised corrosion was observed for stainlesssteel tested in granitic-bentonite water at 90°C during one year /Kursten et al. 2004/.
Under anoxic conditions, /Wada et al. 1999/ measured very low corrosion rates (< 0.01 µm/y) ofstainless steel 304 SS, the material of the hull-end pieces of the fuel elements, in seawater based
groundwater of pH 10. /Smart et al. 2004/ report anaerobic corrosion rates of stainless steel inalkaline environments in the range of 0.01 to 0.1 µm/y. The corrosion rates under anoxic conditions
in alkaline environments were found to be relatively insensitive to pH within the range 6.4 to 13/Blackwood et al. 2002/.

80

TR-10-52

From this limited amount of studies under anoxic conditions, the corrosion rates of stainless steel andnickel-based alloys are estimated to be 0.15 µm/year and contained within the range 0.01–1 µm/year,
where the highest rates correspond to saline groundwaters.
Except for estimating a corrosion rate, there is the matter of finding a representative thickness of theparts of the fuel assemblies. The problem of variable thickness (from less than 0.5 mm to more than
20 mm) is treated pessimistically by estimating the time it takes to fully corrode the thinnest Inconel
spacers, which are as thin as 0.3 mm /SKBdoc 1198314/, by assuming corrosion rates estimated forstainless steel under anoxic conditions. As corrosion can occur from both sides, one may say that asthe corrosion depth becomes 0.15 mm, the part is fully corroded. From this, the corrosion time can becalculated by using the limiting stainless steel corrosion rates 0.01–1 µm/year, and the best estimate
corrosion rate 0.15 µm/year. The corresponding corrosion times become 150–15,000 years, with a best
estimate of 1,000 years. However, considering the uncertainties involved, we propagate only the ordersof magnitudes to safety assessment modelling. For the corrosion time, a triangular distribution issuggested in the log10-space with the lower, best estimate, and upper values of 102, 103, and 104 years,
respectively.

3.2.8

Spatial and temporal variability of data

Spatial variability of data

The IRF and CRF of each canister depend on the spent fuel type of the canisters, and on other circum­
stances, such as whether the canister contains control rods or not. In the Spent fuel report, a simplified case is suggested where the canisters are represented by eight type canisters. From these typecanisters one can assess the variability in the IRF and CRF for each radionuclide. However, as there
is presently no knowledge on how the canisters will be placed in the repository, this variability istreated as data uncertainty.
Furthermore, by taking a pessimistic approach in estimating the corrosion time, potential spatial
variability is dwarfed by the pessimism.
Temporal variability of data

There is no temporal variability of the IRF and CRF data of interest for safety assessment.
For the radionuclide transport modelling, the corrosion rate of the construction material is suggested
to be uniform over time within a canister, although different canisters may have different corrosion
times. This is a simplification where one disregards the fact that corrosion products may pose atransport resistance for corrodants on their way towards the corrosion surface.

3.2.9 Correlations
As discussed in Section 3.1, the radionuclides are correlated through decay chains. As discussed inthis section, the IRF is assumed to correlate with the FGR and/or FGGB for a few nuclides. Functional
relations concerning the parameters of this section are given in Equations 3-1, 3-2, and 3-5.
In terms of the data used as input to probabilistic radionuclide transport modelling, the IRF of Cl-36,
Cs-135, Cs-137, I-129, and Se-79 should be correlated. The IRF of all other radionuclides could be
kept uncorrelated. The CRFs of the different radionuclides do not need to correlate to each other.
However, only one corrosion time should be used per canister.

3.2.10 Result of supplier’s data qualification
The IRF will only be given for radionuclides if the IRF inventory outweighs the easily accessible
inventory formed from ten years of fuel conversion. This applies for the following radionuclides:•

Cl-36, Cs-135, Cs-137, I-129, and Se-79,•

Pd-107, Sn-126, Sr-90, and Tc-99,•

Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, and Zr-93,•

Ag-108m, C-14, Cd-113m, H-3, and Sn-121m.

TR-10-52

81

The bullet list above represents four different categories. The first category features radionuclides
for which the IRF is assumed to be correlated to the fission gas release. For the radionuclides ofthe second bullet the IRF is based on the FGGB, which in turn is assumed to be uncorrelated with
the FGR. For the third bullet, the IRF is solely based on the crud inventory. For the fourth bullet, theIRF is based both on the FGGB, on the crud inventory, and part of the inventory of the construction
material and control rods, as specified for the particular cases.
As justified in Section 3.2.3, a CRF will only be given for radionuclides where the release rate due
to corrosion is larger than the release rate due to fuel conversion, even at pessimistic corrosion times.
This applies for the following radionuclides:•

C-14, Cl-36, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Se-79, Sn-121m, Tc-99, U-233, and Zr-93.

For more detailed discussions and justifications of data and uncertainty ranges, please turn toSection 3.2.7.
IRF : Cl-36, Cs-135, Cs-137, I-129, and Se-79

As discussed above, the IRF of Cl-36, Cs-135, Cs-137, I-129, and Se-79 is equal to the FGGB, which
in turn correlates to the FGR. For Cs-135, Cs-137, and I-129 the IRF equals the FGR, and normal
distributions with the following parameters are recommended:•

For spent BWR fuel: μ = 0.019, σ = 0.011.

•

For spent PWR fuel: μ = 0.043, σ = 0.031.

•

For the average canister: μ = 0.025, σ = 0.021.

For Cl-36, having a three-to-one correlation with the FGR, normal distributions with the following
parameters are recommended:•

For spent BWR fuel: μ = 0.057, σ = 0.033.

•

For spent PWR fuel: μ = 0.13, σ = 0.093.

•

For the average canister: μ = 0.076, σ = 0.064.

For Se-79, the IRF is assumed to be 0.15 times the FGR. Therefore normal distributions with thefollowing parameters are recommended:•

For spent BWR fuel: μ = 0.0029, σ = 0.0017.

•

For spent PWR fuel: μ = 0.0065, σ = 0.0047.

•

For the average canister: μ = 0.0038, σ = 0.0032.

IRF: Pd-107, Sn-126, Sr-90, and Tc-99

The recommended IRF for Sn-126 is based on /Werme et al. 2004/, while the IRF for Pd-107, Sr-90,
and Tc-99 are based on the suggestions in /Johnson and Tait 1997/ and /Werme et al. 2004/, as wellas on results from /Roudil et al. 2007/. It is suggested to use the double triangle distribution in thenormal space and for each radionuclide; lower, upper, and best estimate values are supplied. These
data are recommended for all fuel types and type canisters.
•

Pd-107: lower 0, best estimate 0.002, upper 0.01.

•

Sn-126: lower 0, best estimate 3·10–4, upper 0.001.

•

Sr-90: lower 0, best estimate 0.0025, upper 0.01.

•

Tc-99: lower 0, best estimate 0.002, upper 0.01.

82

TR-10-52

IRF: Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, and Zr-93

For these radionuclides the only source of the IRF is the crud inventory. The recommended data are
based upon inventory data in Table C-6 to C-13 of the Spent fuel report. Double triangle distributionsin the normal space with lower, best estimate, and upper values are recommended. Data are given
for BWR type canisters, PWR type canisters and an average canister. The best estimate values areweighted averages of the IRF of the four BWR type canisters, four PWR type canisters, or all eighttype canisters. The ranges are given by the lowest and highest IRF of the four BWR type canisters,
four PWR type canisters, or all eight type canisters. The data are presented in Table 3-12.
Table 3-12. Recommended IRF for “crud nuclides”.
NuclideBWR type canisters
Lower
Best estimate UpperPWR type canisters
Lower
Best estimate Upper

Mo-93

0.014

0.016

0.018

5.1·10–5

5.5·10–5

5.8·10–5

5.1·10–5

0.012

0.018

Nb-93m

0.020

0.023

0.026

6.5·106.9·107.3·10–7

6.5·10–7

0.017

0.026

Nb-94

0.021

0.024

0.027

6.4·10–7

6.8·10–7

7.1·10–7

6.4·10–7

0.018

0.027

Ni-59

0.013

0.015

0.017

1.6·10–3

1.6·10–3

1.7·10–3

1.6·10–3

0.012

0.017

Ni-63

0.013

0.015

0.017

1.4·10–3

1.5·10–3

1.6·10–3

1.4·10–3

0.012

0.017

Zr-93

1.1·10–5 1.3·10–5

1.4·10–5

6.3·10–8

7.0·10–8

8.5·10–8

6.3·10–8

9.2·10–6

1.4·10–5–7–7

Average canister
Lower
Best estimate Upper

IRF: Ag-108m, C-14, Cd-113m, H-3, and Sn-121m

These nuclides require special treatment, as discussed in detail in Section 3.2.7. In short, the IRF
of Ag-108m for BWR type canisters originates from the crud and gap and grain boundary inventory
while in the PWR fuel assembly, Ag-108m originates from the control rods. For Cd-113m the situation is similar, with the exception that it is not found in the BWR crud inventory. For C-14, the IRF
originates both from the UO2 spent fuel and from the cladding. H-3 is so mobile in the fuel matrix
that the IRF is considered to be unity on conceptual grounds. Finally, Sn-121m exists at similar
amounts both in the gap and grain boundary inventory and in the crud.
Depending on the nuclide, different distributions are recommended. For Ag-108m and Cd-113m for
the PWR type canisters and average canister, a single point value is recommended. The same applies
for H-3 for all canisters. For Cd-113m for the BWR type canisters, the normal distribution is recommended. For all other discussed nuclides and type canisters, the double triangle distributions in thenormal space with lower, best estimate, and upper values are recommended. The data recommended
for BWR type canisters, PWR type canisters, and the average canister are presented in Table 3-13.
Table 3-13. Recommended IRF for Ag-108m, C-14, Cd-133m, H-3 and Sn-121m.
BWR type canisters
Nuclide

Lower

Best estimate Upper

Ag-108m

0.45

0.47

0.49

C-14

0.085

0.086

0.09

H-3
Sn-121m

Average canister

Lower

Lower

Best estimate Upper

0.085

0.092

5.0·10–5 1.6·10–4µσ

Cd-113m

0.019

0.011

Best estimate Upper

1.0
0.11

1

NuclideTR-10-52

PWR type canisters

0.11

1.0
0.11

1
4.2·10–4

3.2·10–7 2.6·10–4
Lower

8.7·10–4

Best estimate Upper

1

0.11

1
3.2·10–7 1.9·10–4
Lower

8.7·10–4

Best estimate Upper

183

CRF: C-14, Cl-36, Mo-93, Nb-93m, Nb-94, Ni-59, Ni-63, Se-79, Sn-121m, Tc-99, U-233,
and Zr-93

The CRF is based on the inventory of the construction material and control rods, with the exceptionof the Ag-In-Cd alloy, as reported in Tables C-6 to C-13 in the Spent fuel report. Double triangle
distributions in the normal space with lower, best estimate, and upper values are recommended.
Data are given for BWR type canisters, PWR type canisters and an average canister. The best estimate
values are weighted averages of the CRF for the four BWR type canisters, four PWR type canisters,or all eight type canisters. The ranges are given by the lowest and highest CRF of the four BWR typecanisters, four PWR type canisters, or all eight type canisters. The data are presented in Table 3-14.
Table 3-14. Recommended CRFs.
NuclideBWR type canisters
Lower
Best estimate

Upper

PWR type canisters
Lower
Best estimate Upper

Average canister
Lower
Best estimate Upper

C-14

0.65

0.66

0.68

0.57

0.60

0.62

0.57

0.64

Cl-36

1.4·10–2 0.015

0.016

0.014

0.016

0.018

1.4·10–2

0.015

0.018

Mo-93

0.80

0.81

0.81

0.82

0.83

0.80

0.81

0.83

0.80

Nb-93m

0.88

0.92

0.93

1.0

1.0

Nb-94

0.97

0.97

0.98

1.0

1.0

0.68

Ni-59

0.98

0.99

0.99

0.90

0.91

0.92

0.90

0.96

0.99

Ni-63

0.98

0.98

0.99

0.91

0.92

0.93

0.91

0.97

0.99

4.6·10–4 4.8·10–4

5.5·10–4

0

1.3·10–4

5.5·10–4

0.66

0.13

0.15

0.13

0.52

0.66

4.1·10–5

1.1·10–4 1.2·10–4

1.3·10–4

4.0·10–5

6.1·10–5

1.3·10–4

Se-79

0Sn-121m

0.65

Tc-99

4.0·10–5 4.0·10–5

0.65

0.14

U-233

0.221

0.28

0.29

0.13

0.18

0.24

0.126

0.25

0.29

Zr-93

0.134

0.136

0.14

9.3·10–2 0.096

0.11

0.093

0.125

0.14

3.2.11 Judgements by the SR-Site team
Only part of the data and discussion delivered in this section are produced by the supplier, while
the SR-Site team has produced the remaining part. In this section the judgment is limited to the part
supplied by the supplier, which is the FGR. The supplier has also supplied the inventory on whichcalculations are based. However, judgment on these inventories has been delivered in Section 3.1.11.
Sources of information and documentation of data qualification

The SR-Site team agrees with selection and categorisation of the different sources of informationand data sets made by the supplier.
Conditions for which data are supplied

The conditions for which the data are supplied have been described as in accordance with the SKB
spent fuel reference scenario. This is accepted by the SR-Site team.
Conceptual uncertainty

The supplier argues that there is little conceptual uncertainty concerning the calculations on which
the FGR are based, and that the existing uncertainty in the FGR should be handled as data uncertainty.
The SR-Site team agrees.
Data uncertainty due to precision, bias, and representativity

The description of the FGR has been much improved since SR-Can and allows for interpreting the
spread in the data. The SR-Site team accept the supplier’s interpretations.

84

TR-10-52

Spatial and temporal variability of data

The supplier suggests that there is significant spatial variability of the FGR within a canister. How­ever, for SR-Site the spatial variability of concern is between canisters. This must be handled as data
uncertainty, as it is not known how different canisters will be placed in the repository. As the IRF is
assumed to be instantly released, there is no temporal component of the FGR of interest (after thefuel is taken out of the reactor).
Correlations

The supplier gives no correlation related to the supplier data. Correlations given between theparameters of this section are suggested by the customer.
Result of supplier’s data qualification

The supplier has only provided a limited part of the data delivered in this section, which is data anduncertainties of the FGR for BWR and PWR fuels. The SR-Site team agree with these data.

3.2.12 Data recommended for use in SR-Site modelling
The IRF data presented in Table 3-15 to Table 3-17 are recommended to be used in SR-Site modelling.
Where normal distributions are suggested, they should be truncated at zero and one. For all radionuclidesof the selected inventory that are not displayed in these tables, the IRF is insignificant and is recommended to be set equal to zero.
Table 3-15. IRF recommended for the average canister.
Radionuclideµσ

Distribution

Cl-36

0.076

0.064

Normal

Cs-135

0.025

0.021

Normal

Cs-137

0.025

0.021

Normal

I-129

0.025

0.021

Normal

Se-79

0.00380.0032

NormalRadionuclide

Lower limit

Best estimate

Ag-108m
C-14

Upper limit

1.0
0.085

0.092

Cd-113m

1.0

H-3

1.0

Distribution

Single point value
0.11

Double triangle, normal spaceSingle point value
Single point value

Mo-93

5.1·10–5

0.012

0.018

Double triangle, normal space

Nb-93m6.5·10–7

0.017

0.026

Double triangle, normal space

Nb-94

6.4·10–7

0.018

0.027

Double triangle, normal space

Ni-59

1.6·10–3

0.012

0.017

Double triangle, normal space

Ni-63

1.4·10–3

0.012

0.017

Double triangle, normal space

Pd-107

0

0.002

0.01

Double triangle, normal spaceSn-121m

3.2·10–7

1.9·10–4

8.7·10–4

Double triangle, normal spaceSn-12603.·10–4

0.001

Double triangle, normal space

Sr-90

0

0.00250.01

Double triangle, normal space

Tc-99

0

0.002

0.01

Double triangle, normal space

Zr-93

6.3·10–8

9.2·10–6

1.4·10–5

Double triangle, normal space

TR-10-52

85

Table 3-16. IRF recommended for BWR type canisters.
Radionuclideµσ

Distribution

Cl-36

0.057

0.033

Normal

Cs-135

0.019

0.011

Normal

Cs-137

0.019

0.011

Normal

I-129

0.019

0.011

Normal

Cd-113m

0.019

0.011

Normal

Se-79

0.00290.0027

NormalRadionuclide

Lower limit

Best estimate

Upper limit

Distribution

Ag-108m

0.45

0.47

0.49

Double triangle, normal space

C-14

0.085

0.086

0.086

H-3

1

Double triangle, normal spaceSingle point value

Mo-93

0.014

0.016

0.018

Double triangle, normal space

Nb-93m

0.020

0.023

0.026

Double triangle, normal space

Nb-94

0.021

0.024

0.027

Double triangle, normal space

Ni-59

0.013

0.015

0.017

Double triangle, normal space

Ni-63

0.013

0.015

0.017

Double triangle, normal space

Pd-107

0

0.002

0.01

Double triangle, normal spaceSn-121m

5.0·10–5

1.6·10–4

4.2·10–4

Double triangle, normal spaceSn-126

0

3.·10

0.001

Double triangle, normal space

–4

Sr-90

0

0.00250.01

Double triangle, normal space

Tc-99

0

0.002

0.01

Double triangle, normal space

Zr-93

1.1·10–5

1.3·10–5

1.4·10–5

Double triangle, normal space

Table 3-17. IRF recommended for PWR type canisters.
Radionuclideµσ

Distribution

Cl-36

0.13

0.093

Normal

Cs-135

0.043

0.031

Normal

Cs-137

0.043

0.031

Normal

I-129

0.043

0.031

Normal

Se-79

0.00650.0047

NormalRadionuclide

Lower limit

Best estimate

Ag-108m
C-14

Upper limit

1.0
0.11

Single point value

0.11

Cd-113m

1.0

H-3

1.0

Distribution

0.11

Double triangle, normal spaceSingle point value
Single point value

Mo-93

5.1·10–5

5.5·10–5

5.8·10–5

Double triangle, normal space

Nb-93m6.5·10–7

6.9·10–7

7.3·10–7

Double triangle, normal space

Nb-94

6.4·10–7

6.8·10–7

7.1·10–7

Double triangle, normal space

Ni-59

1.6·10–3

1.6·10–3

1.7·10–3

Double triangle, normal space

Ni-63

1.4·10–3

1.5·10–3

1.6·10–3

Double triangle, normal space

Pd-107

0

0.002

0.01

Double triangle, normal spaceSn-121m

3.2·10–7

2.6·10–4

8.7·10–4

Double triangle, normal spaceSn-126

0

3.·10

0.001

Double triangle, normal space

–4

Sr-90

0

0.00250.01

Double triangle, normal space

Tc-99

0

0.002

0.01

Double triangle, normal space

Zr-93

6.3·10–8

7.0·10–8

8.5·10–8

Double triangle, normal space86

TR-10-52

The CRF data presented in Table 3-14 are recommended to be used in SR-Site modelling. For all
radionuclides the double triangle distribution (see Figure 3-11) is recommended. For all radionuclidesof the selected inventory that are not displayed in Table 3-14, the CRF is insignificant and is recommended to be set equal to zero.
As the corrosion rate is assumed to be uniform, the corrosion release rate for each radionuclide
should be calculated by taking the product of the CRF and total inventory and dividing it by the
corrosion time. The corrosion time distribution is shown in Table 3-18. Each canister should begiven a single corrosion time in radionuclide transport modelling.
Table 3-18. Distribution of corrosion time tcorr
Lower limit(yr)Best estimate(yr)Upper limit(yr)

Distribution

102

103

104

Triangular distribution, log10-space

3.3

Fuel conversion

In case of canister failure, groundwater may come in contact with the UO2 spent fuel matrix inside
the canister. If so, this will cause the fuel matrix to dissolve or become otherwise altered. As a result,uranium and other radionuclides embedded in the fuel matrix will be released as solutes in the
intruding water. The process is controlled primarily by the chemical environment of the intruding
water and by the fuel composition (see the Fuel and canister process report).
In case there is no significant transport of uranium species out of the canister, the intruding water
will with time reach its saturation limit of uranium species. This would prevent a net dissolution of
the UO2(s) fuel matrix. Even so, radionuclides in the fuel matrix may be released in a process called
fuel conversion, caused by the oxidative dissolution of the fuel matrix. As the fuel matrix continues
to dissolve, reduction and precipitation of the dissolved uranium species in the canister interior
occurs at a matching rate, preventing the intruding water from becoming oversaturated. This offers
a way for the embedded radionuclides to become released into the intruding water.
In the conceptual model for fuel dissolution, when the transport rate of uranium species out of thecanister is low, only the fuel conversion component contributes to fuel dissolution and, for thesecases, the fuel dissolution rate is practically equal to the fuel conversion rate. For larger out flows,
the component for upholding the solubility limit of uranium may become important, as the UO2(s)dissolution component increases and the total fuel dissolution rate will be larger than the fuel con­version rate.

3.3.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The supplier is to deliver fuel conversion data for use in radionuclide transport modelling.
Specifically, the supplier should deliver:•

The fuel conversion rate of the UO2 spent fuel matrix under reducing conditions, in fractions per
year (yr–1). The fuel conversion rate should, if possible, be given with uncertainties, implicitly
encompassing different relevant conditions, such as water and fuel compositions.

To clarify the expression “fuel conversion rate in fractions per year”, this is the amount of UO2
converted per year, divided by the total amount of UO2 present in the matrix at that time.

TR-10-52

87

SR-Site modelling activities in which data will be used

The fuel conversion rate is used in SR-Site radionuclide transport modelling to calculate theconcentrations of radionuclides in the water that has intruded into the canister (see the Radionuclidetransport report).
The near-field radionuclide transport code used within SR-Site is Compulink /Vahlund and Hermansson
2006b/, which is a Matlab implementation of COMP23 /Romero et al. 1999, Cliffe and Kelly 2006/.
Fuel dissolution has been implemented in the above mentioned transport code by using two components. One component represents fuel being mobilised at a rate described by the fuel conversion
rate. In the model the fuel conversion rate is constant and independent of the degree of saturation of
uranium species in the intruding water. The other component represents fuel dissolution due to theupholding of the uranium concentration at the solubility limit. If the fuel conversion rate is larger
than the transport rate out of the canister, the solubility limit will be reached and, consequently,
uranium precipitates will form. In this situation, the component representing fuel dissolution due tothe upholding the uranium concentration will be negligible. If the transport rate out of the canister islarger than the fuel conversion rate, the component representing fuel dissolution due to the upholdingof the uranium concentration at its solubility limit will become more important in dissolving the fuelmatrix. The fuel dissolution rate is set to balance the fuel conversion and transport rates in the model.
The conceptual model agrees with that used in SR-Can /SKB 2006a/ and previous assessments /SKB
1999a, 2004a/.

3.3.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The handling of fuel conversion and fuel dissolution in SR-Can agrees with the handling in SR-Site.
In SR-Can the fuel conversion rate was given as a triangular probability density function in the log10
space with the central value at 10–7 yr–1, and minimum and maximum values at 10–8 yr–1 and 10–6 yr–1,
respectively.
Conditions for which data were used in SR-Can

The data provided in the literature review /Werme et al. 2004/ is based on the April 2003 inventoryof fuel stored at the Clab interim storage facility. Besides the different waste types, also linear power
rating and burnup were considered. The experimental data presented in /Werme et al. 2004/ wereobtained in a reducing environment.
Sensitivity to assessment results in SR-Can

The radiological risk was, for important SR-Can scenarios, dominated by Ra-226 at times where thetotal annual effective dose came relatively close to the regulatory limit. For these times, the release
rate of Ra-226 was assumed to be directly dependent on the fuel conversion rate.
Alternative modelling in SR-Can

No alternative modelling of fuel conversion was used in SR-Can.
Correlations used in SR-Can modelling

The fuel conversion rate was not directly dependent on any other data in SR-Can. However, therelease rate of matrix bound radionuclides was correlated to the fuel conversion rate.

88

TR-10-52

Identified limitations of the data used in SR-Can modelling

The fuel conversion data delivered to SR-Can modelling could be used for all required conditions
and modelling activities. Concerning the data in SR-Can, the regulatory authorities found that “…theproposed release fractions – between 10–6 and 10–8 per year for spent fuel matrix after disposal – arereasonable and realistic. Conversely, the rationale concerning uncertainty management and thereasons for this choice are relatively weak” /Stenhouse et al. 2008/.

3.3.3 Supplier input on use of data in SR-Site and SR-Can
For the safety assessment SR-Can, a supporting document /Werme et al. 2004/ reviewed all available
experimental studies carried out under reducing conditions. Based on this review, fuel dissolution rates
for the inventory of fuel stored at the Clab interim storage facility on the April 2003 were proposed.
As discussed in the Fuel and canister process report (Section 2.5.5), several experimental andmodelling studies on both relatively fresh spent fuel and α-doped UO2 in the presence of dissolvedhydrogen or actively corroding iron have been published recently. As discussed in the Fuel andcanister process report, the results of these recent studies support the recommended fuel conversion
rates and would even justify lower ones.
Furthermore, it is suggested that as the alpha radiation of the fuel decreases below a certain threshold,no measurable effect of the α-radiolysis on the oxidative fuel dissolution can be observed /Rondinella
et al. 2004, Muzeau et al. 2009/. Such a threshold, which is cautiously neglected when recommending
data for use in SR-Site, would effectively halt fuel conversion after a few ten thousand years /Poinssot
et al. 2007/, whereafter a solubility limited model could be used. This is pointed out also in the reviewof the regulatory authorities “…it now appears that the effect of alpha radiolysis of water on oxidisingdissolution of the fuel matrix will have little or no impact on alteration at low flux levels (after
10,000 years)” /Stenhouse et al. 2008/. From these perspectives, the fuel dissolution rates proposed
to be used in SR-Site are cautious.

3.3.4

Sources of information and documentation of data qualification

Sources of information

The main source of information is /Werme et al. 2004/, from the point of view that the same conversion
rates are used in SR-Site. In the Fuel and canister process report (Section 2.5.5), recent publications
supporting the chosen conversion rates, or even suggesting lower conversion rates, are discussed.
The main sources of information are tabulated in Table 3-19. Some of the supporting publications
discussed in the Fuel and canister process report are given in Table 3-20.
Table 3-19. Main sources of information used in data qualification.
Fuel and canister process report, 2010. Fuel and canister process report for the safety assessment SR-Site.
SKB TR-10-46, Svensk Kärnbränslehantering AB.
Werme L, Johnson L H, Oversby V M, King F, Spahiu K, Grambow B, Shoesmith D W, 2004. Spent fuelperformance under repository conditions: A model for use in SR-Can. SKB TR-04-19, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting data

The main source of information /Werme et al. 2004/ is produced prior to the implementation of thecurrent data qualification framework. However, upon examining the report with the guidelines given
in Section 2.3 in mind, it is judged that the report can be considered as qualified. Furthermore, theFuel and canister process report (Section 2.5.5) is considered as qualified. Main references within/Werme et al. 2004/ and the Fuel and canister process report are either considered to be qualified
or supporting. Data sets that are sorted as qualified or supporting are summarised in Table 3-20. The
rationale for the sorting is discussed below the listed items.

TR-10-52

89

Table 3-20. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Werme et al. 2004/: Fuel dissolution rates for reducing
conditions.
2. Fuel and canister process report, Section 2.5.5: Review
of recent publications supporting the chosen fuel conversion range.
3. /Muzeau et al. 2009/: Experimental data on threshold
alpha activity and H2 role in absence of ε-particles.
4. /Carbol et al. 2009b/: Below detection uranium releases
from α-doped pellet even under very low H2 concentrations;
reduced UO2 surface at test end.
5. /Jégou et al. 2004, Hanson and Stout, 2004, Hanson
2008, Ekeroth et al. 2009, Clarens et al. 2008/: Experimental
data on the influence of high burnup on oxidative fueldissolution.
6. /Fors et al. 2008, 2009/: Very low dissolution rates for
HBU fuel in presence of H2.
7. /Carbol et al. 2009a/: Very low dissolution rates for MOX
fuel in presence of H2.
8. /Eriksen et al. 2008/: Support for low spent fuel dissolution
rates in the presence of only radiolytic H2. Data modelledusing steady state model.
9. /Jonsson et al. 2007/: Support for very low fuel dissolution
rates using steady state model. Includes modelling of theinfluence of Fe(II) and H2.
10. /Ollila and Oversby 2006/: Very low dissolution rates ofalpha doped pellets in presence of corroding iron.

10. /King and Shoesmith 2004/: Fuel dissolution rates fromelectrochemical model.
11. /Ollila and Oversby 2005, Ollila 2006/: Fuel dissolution
rates from α-doped samples under reducing conditions(1 ppm sulphide or iron).
12. /Carbol et al. 2005/: Fuel dissolution rates for spent fueland alpha doped samples under various H2 conc.

1: /Werme et al. 2004/ is considered as qualified as the report is transparently written, is scientifically sound, andmainly reviews peer-reviewed articles from the open literature. Especially the suggested fuel conversion rate given as
a “triangular distribution with a range from 10–6 to 10–8 per year with a peak at 10–7 per year” is considered as qualified.
2: The Fuel and canister process report is produced within the current SKB framework for data qualification.
3–9 : /Muzeau et al. 2009, Carbol et al. 2009a, b, Jégou et al. 2004, Hanson and Stout, 2004, Hanson 2008, Ekeroth et al.
2009, Clarens et al. 2008, Fors et al. 2008, Eriksen et al. 2008, Jonsson et al. 2007, Ollila and Oversby 2006/ are all
peer reviewed journal or conference articles. Furthermore, they are all considered as relevant for the fuel in the Swedish
programme, and for Swedish repository conditions.
10–12: These are peer reviewed SKB Technical reports published before the qualification system was set in place.

Excluded data previously considered as important

No data previously considered as important has been excluded.

3.3.5 Conditions for which data are supplied
The fuel conversion rate suggested in this section, with its uncertainty range, should encompass all
relevant fuel types and water compositions under reducing conditions, expected in a KBS-3 repository.
The spent fuel to be deposited is described by the SKB spent fuel reference scenario (cf. the Spentfuel report) and consists predominantly of UOX spent fuel but to some extent also of MOX spent
fuel. The burnup ranges from somewhat above 10 MWd/kgHM to somewhat below 60 MWd/kgHM(cf. Figures 2-3 and 2-4 of the Spent fuel report).
The water composition influences both the radiolytic reactions and the kinetics of matrix dissolution.
The most important factors are the redox conditions and the composition of the groundwater; including
its pH, ionic strength, and the concentration of major components (such as carbonates, sulphates,
chloride, etc.) and minor components (such as trace metal ions, bromide, etc.), which affect theradiolytic reaction chains and the dissolution process. In addition, the temperature may affect thefuel conversion rate.

90

TR-10-52

Fuel type and burnup

The analysis of data from high temperature oxidation of spent fuel (or doped UO2) as a functionof burnup shows that the increase of doping level with burnup makes fuel oxidation more difficult/Hanson 1998, Cobos et al. 1998/. The available fuel leaching studies /Jégou et al. 2004, Hanson
2008, Clarens et al. 2008, Ekeroth et al. 2009/ which include a large range of fuel burnups show noremarkable influence of the burnup on the oxidative dissolution of the UO2 fuel matrix. They indicate that the increase of UO2 doping levels by fission products and actinides seems to compensate
successfully for the increased radiation field and surface area of high burnup fuel. Dedicated studies
of HBU (High Burn Up) UO2 fuel or MOX fuel under reducing conditions show no measurable
influence of the increased burnup. For all these reasons the data provided in the literature review
by /Werme et al. 2004/ are considered to be valid also for the higher burnup spent fuels consideredin the spent fuel inventory for the SR-Site. The data provided in /Werme et al. 2004/ were based on
the April 2003 inventory of spent fuel stored at the Clab interim storage facility. Besides the different
spent fuel types, also linear power rating and burnup were considered.
Redox conditions

In case of early canister failure, the relatively high radiation field of the fuel could potentially influence the redox conditions. However, the effect of radiolytic oxidants are, in this case, counteracted
by the high pressures of hydrogen that are expected to build in the canister, from anoxic corrosion of
the iron insert. This will assure reducing conditions in the case of early canister failure. A prerequisite
for high hydrogen pressures is that the canister is surrounded by the buffer, exerting a transport
resistance that limits the transport of dissolved hydrogen out of the canister /Liu and Neretniks 2002/.
At early times, the buffer is not expected to have been compromised by, for example, buffer erosion.
At later times, canister failure may occur as a consequence of removed buffer, followed by copper
corrosion. In such a case, dissolved hydrogen may be transported out of the canister at higher rates,
wherefore the hydrogen concentration in the canister is expected to be relatively low. However, insuch time frames (typically hundreds of thousands of years after deposition) the alpha radiation fieldof the spent fuel will have decreased to such low levels that there will be no measurable effect ofalpha radiolysis on fuel dissolution /Poinssot et al. 2007, Rondinella et al. 2004, Muzeau et al. 2009/.
The hydrogen is produced as iron corrodes; a process that is expected to continue for tens of thousandsof years after water intrudes into the canister. As long as there is corroding iron, both hydrogen andFe(II) concentrations will be sufficient to assure reducing conditions. In the longer perspective, after
all metallic iron has corroded, the Fe(II) concentrations and the redox potential of groundwaters
inside the canister will be determined by equilibrium with magnetite. At the same time, the hydrogenconcentration is expected to be low, as only radiolytic hydrogen is produced.
The reducing conditions may be disturbed in the case of penetration of oxidising water down torepository depth. Such an event is postulated to occur during glaciation, as melt water from the ice
cap penetrates the host rock, but only during short time periods as the ice front is directly above therepository (cf. Section 6.1).
If, on the other hand, pessimistically assuming that the water intruding the canister is in equilibriumwith the oxygen of the atmosphere, the amount of oxygen contacting the fuel may be so plentiful thatthe kinetics will determine the fuel dissolution rate. In such a case, the long term dissolution rates
measured under oxidising conditions in /Forsyth and Werme 1992, Forsyth 1997/ may be used. These
measurements suggest dissolution rates in the interval 10–4–10–5 yr–1. Other environmental parameters(T, pH, etc.) affect in various degrees the dissolution rate under oxic conditions, but they are discussed
below only for reducing conditions.
Even if it is postulated that a pulse of oxidising water may reach repository depth, it is unlikelythat it will be sustained for a significant period of time. It is also important to note that the host rockground­water may not represent the water contacting the spent fuel, as oxygen is likely consumed
in the engineered barrier. Therefore, this section focuses on supplying fuel conversion rates under
reducing conditions.

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pHThe pH of the groundwater affects the solubility of UO2(s), which is the main component of the fuelmatrix. The solubility of UO2 increases at low pH, especially at pH values lower than 3. Spent fuelexperiments carried out in the interval 4 < pH < 9.6 show that in this interval, the dissolution rate islargely independent of pH /Röllin et al. 2001/. Below pH 4, an increase of the dissolution rate wasobserved, due to the increased solubility of UO2. Very few data are available for the high pH range.
Simple thermodynamic considerations /Berner 2002/ indicate that at pH values higher than 11, theredox potential has to be much lower than the one expected in the repository, in order to maintainuranium in the four valence state as UO2(s). However, recent measurements in fresh concrete water(pH 13.5) in the presence of Bure clay indicate a limited (if any) effect of high pH waters on the fueldissolution rate /Loida et al. 2006/. Leaching experiments with UO2 at high pH /Cui et al. 2003a/
indicate no loss of stability for uranium dioxide at pH values in the interval 12.5–13.5.
In view of the scarcity of data at high pH, the fuel conversion rates suggested in this section are
considered to be valid for pH values in the interval 4 < pH < 11.
Ionic strength, major and minor groundwater components

Most of the spent fuel dissolution data produced by SKB and other organisations are obtained indilute groundwaters. An increase of the ionic strength affects, in principle, solubilities and otherthermodynamic data, as well as kinetic constants. The effect on thermodynamic data is outlined
in Section 3.4, and here only kinetic factors are discussed.
Recent data from leaching of alpha doped pellets under anoxic and reducing conditions in 0.5 and1 M NaCl solutions, as well as (Na,Ca)Cl solutions of ionic strength 0.625 M, show no influence of
the increased salinity on the dissolution rates /Ollila 2008/. The data from the German program in
5 M NaCl solutions /Grambow et al. 1996, 2000/ indicate similar fuel dissolution rates under oxidisingconditions as in dilute groundwater. Furthermore, during spent fuel leaching in the presence of metallic
iron or hydrogen, very low dissolution rates were measured, without any marked influence of the highionic strength /Grambow et al. 1996, 2000, Carbol et al. 2005/. In view of the above discussion, thefuel conversion rates suggested in this section are considered to be valid in groundwater of up to 1 M
ionic strength, and no major influence is expected even at higher ionic strengths.
Carbonate, and to a lesser extent sulfate, forms strong U(VI) complexes and enhances fuel oxidative
dissolution rates /Shoesmith 2000, de Pablo et al. 1999/. On the other hand, U(IV) carbonate complexes form at much higher carbonate concentrations than those of typical granitic groundwaters/Ciavatta et al. 1983, Guillamont et al. 2003/. Experimental data under reducing conditions show nomarked influence of carbonate up to concentrations of 0.01 M /Röllin et al. 2001/. No experimental
data are available for higher carbonate concentrations. Some precaution is necessary at carbonate
concentrations higher than 0.1 M, in view of the potential solubility increase of UO2 due to the formationof U(IV) tetra and penta carbonate complexes. The fuel conversion rates suggested in this section
are considered to be valid for HCO3– concentrations below 0.01 M, and some caution is necessary
at higher carbonate concentrations.
In all SKB spent fuel leaching tests under reducing conditions, the presence of major groundwater
cations such as Ca and Mg has been avoided. Their presence in the groundwater is expected to decrease
the rates of oxidative dissolution of spent fuel /Santos et al. 2006, Cui et al. 2003b/ and is for thisreason not discussed further.
Radiolysis of water caused by low LET (Linear Energy Transfer) radiations as β- and γ- radiation is
influenced very much by the presence of groundwater components in trace concentrations, including
strongly hydrolysing metal ions (e.g. Ni) and chloride and bromide ions. In recent works from theGerman program /Metz et al. 2007, Kelm and Bohnert 2005/, the addition of bromide to 5 M NaClsolutions substantially decreased the beneficial effect of the presence of dissolved hydrogen in theradiolytic oxidant production caused by γ- radiation. On the other hand, spent fuel leaching in thesame solutions indicates for the absence of radiolytic oxidants and very low U concentrations. In
this case the presence of bromide inhibits the effect of H2 and the production of oxidants in bulk
solution continues. In spite of this, U concentrations decrease which indicates that surface processes
also consume most of the oxidants produced in the bulk solution. In gamma radiolysis experiments
in hydrogen saturated granitic groundwater, no enhancing effect of bromide on UO2 dissolution isobserved /Quiñones et al. 2004/.
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For the case of α-radiolysis, which is the dominant type of radiation in spent fuel older than 1,000 years,
the effect of trace groundwater components is expected to be negligible /Pastina and LaVerne 2001/
due to the formation of mainly molecular radiolysis products.
Temperature

The temperature affects radiolytic and dissolution/precipitation reactions. Dissolution of fuel athigher temperatures is only an issue in case of very early canister failure. As discussed above, very
early canister failure is associated with a high hydrogen partial pressure, ensuring reducing conditions. At later times, the temperature in the canister is expected to have decreased to near ambient.
Available data with fresh fuel at 70°C in the presence of 1, 5, and 50 bar H2 /Cui et al. 2008, Spahiu
et al. 2000, 2004, Carbol et al. 2009a, Fors et al. 2009/ do not indicate elevated dissolution rates, butrather higher rates of reduction and precipitation of U(VI) and other oxidised radionuclides originatingfrom a pre-oxidised fuel layer immediately after test start. There are no published data from fuelleaching at temperatures over 70°C under reducing conditions, and thus the fuel conversion rates
suggested in this section are considered to be valid for temperatures up to 70°C. In general a tempera­ture increase causes faster kinetics and may increase the rate of oxidative fuel dissolution, whichshould be kept in mind if modelling radionuclide release under oxidising conditions.

3.3.6 Conceptual uncertainty
The model proposed in /Werme et al. 2004/ is based on the analysis of experimental data performed
under redox conditions similar to those expected in a repository at the time when water may comein contact with the spent fuel. The analysis of a number of experimental data obtained afterwards
confirms the consensus of opinion that the fuel dissolution will be extremely low under conditions
foreseen in the repository, i.e., in the presence of the corroding iron and hydrogen overpressure.
Progress has been made in the understanding of the role of metallic particles for fuel surface reduction
and also in the kinetic modelling of the role of Fe(II) and H2 for fuel dissolution. The use of therecently developed steady state model /Jonsson et al. 2007/ with kinetic constants determined inseparate experimental work /e.g. Trummer et al. 2008/ would provide justification for even lower
dissolution rates than those recommended for SR-Site, as discussed in the Fuel and canister processreport. Potential mechanisms have been proposed also for the threshold of alpha activity or theinteraction of alpha emitting UO2 surfaces with hydrogen, but they remain still at the level of hypo­thesis and the exact mechanisms are still not sufficiently understood. Studies on the interaction of
metal oxides with water and radiation /Petrik et al. 2001/ have only recently been extended with studies
dedicated to the interaction of actinide oxide surfaces with water and radiation /LaVerne and Tandon
2003, Icenhour et al. 2004/. Further progress in this complicated scientific field is expected to contri­bute to the mechanistic understanding of the processes at the fuel-water interface.
In order to compensate for all these conceptual uncertainties, the model chosen for use in SR-Site is
not a model in the mechanistic sense, although it rests on a consistent and carefully measured bodyof experimental data and synthesis of experiments relevant to repository conditions. The wide spreadin the proposed dissolution rates is intended to compensate for conceptual and other uncertainties.
Uranium oxide in nature, i.e. natural uraninite, has been extensively studied in several natural analoguesites. These natural analogue sites contain uraninite which has persisted for many millions of years/Ruiz López et al. 2004, Miller et al. 2000/. Studies in natural settings, concerning not only uraniumoxide but also other minerals, indicate that dissolution rates derived from laboratory studies arehigher than those derived from studies based on observations in nature /White and Brantley 2003/.
This is an indication of a conceptual uncertainty which is subject to on-going investigation; in any
case, the approach to focus on the experimental data derived from laboratory studies is, in this
aspect, a pessimistic approach.

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3.3.7 Data uncertainty due to precision, bias, and representativity
The uncertainties in the experimental data analysed in /Werme et al. 2004/ or in the more recent
works, reviewed either here or in the Fuel and canister process report, are discussed in the original
publications, which are generally peer reviewed journal articles. However, as discussed in /Werme et al.
2004/, the model is based on total uranium releases and cautious assumptions considering conversion
rates. It disregards a large body of experimental data which show decreasing release rates, or alternatively no release, as well as modelling results that suggest lower releases. The large uncertainty (a spanof two orders of magnitude) in the dissolution rates suggested for SR-Site is intended to capture all
uncertainties, including conceptual uncertainties.
Data uncertainty due to precision

As the chosen range of fuel conversion rates is cautious (as discussed above), data uncertainty due toprecision issues in measurements or models is judged as subordinate.
Data uncertainty due to bias

There are experimental results suggesting much lower fuel conversion/dissolution rates than thosesuggested for SR-Site. In this sense the degree of pessimism in SR-Site could be seen as a bias. Below,
results from a few experiments suggesting lower, or no, fuel conversion/dissolution rates are summarised.
In /Werme et al. 2004/ the dissolution rates of α-doped pellets under reducing conditions were estimated by analysing the total amount of released uranium during the test, including the part in vessel
rinse. In a leaching test the dissolution rate is usually proportional to the increase of the concentrationof uranium caused by the oxidative dissolution of UO2(s) and the production of soluble U(VI) species.
No increase of uranium levels in solution could be detected (they remained extremely low during thewhole test), indicating the absence of any oxidative dissolution of the pellet.
In /Carbol et al. 2005, 2009b/ an extensive analysis of the autoclave after the test was carried out
and only detached particles from the pellet could be observed. In the other α-doped test reported in/Carbol et al. 2005/, no dissolution rate could be calculated because the concentration of uranium
decreases slightly with time, instead of increasing. In the electrochemical tests a dissolution rate wascalculated from impedance measurements (rendered difficult by the high impedance of the pellet),but the evolution of uranium concentrations during this same test shows a decrease of uranium
levels, instead of the expected increase. The same holds for the data of /Muzeau el al. 2009/; thedecreasing uranium concentrations with time make it difficult to report any dissolution rate.
Similar trends are observed for the dissolution rates of spent fuel measured under 5 bar or 50 bar H2/Cui et al. 2008, Spahiu et al. 2000, 2004/; the concentrations of all redox sensitive elements decrease
instead of increase. The same holds for the releases of non-redox sensitive elements such as Cs andSr during each interval; they decrease by more than two orders of magnitude during one year. The
decreasing FIAP (Fraction of Inventory in the Aqueous Phase) or IFR (Incremental Fractional Releases)
values with time indicate very low dissolution rates which decrease with time. Finally, the data obtainedwith spent fuel in sealed ampoules with initial Ar atmosphere /Eriksen et al. 2008/ have been modelledusing the steady state model (kinetic constants determined in dedicated experiments). They showthat even the radiolytic hydrogen produced during one year decreases the dissolution rate of the fuelmarkedly, especially when any air contamination is avoided by the sealed ampoules.
Data uncertainty due to representativity

The experimental data upon which the analysis is based have been obtained both with relatively fresh
spent fuel and α-doped UO2. Spent fuel represents the real properties of the fuel matrix, but has an
unrealistically high radiation field with large contribution of β- and γ- radiations. For this reason it
is tested under relatively high hydrogen concentrations (> 1 mM). On the other hand, α-doped UO2
represents better the radiation field of the spent fuel expected to contact groundwater, but does not
represent other properties of the spent fuel matrix. In this case the data have been obtained withlow concentrations of reductants, or no reductants present. Most of the spent fuel leaching tests
have been carried out in bicarbonate containing solutions, known to enhance dissolution. Cations
such as Ca or Mg, which are reported to decrease dissolution rates /Wilson and Gray 1990, Santos
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et al. 2006/, have often been excluded in order to obtain cautious rates. The analysis includes dataobtained in solutions with average or high chloride concentrations, variable pH, as well as synthetic
groundwaters. Thus the rates analysed should be cautious and representative for all possible conditions in the repository.

3.3.8 Spatial and temporal variability of data
No spatial or temporal variability is considered. The fuel dissolution rates are assumed constant intime and valid for all canisters.
Spatial variability of data

As the burnup and linear power for the fuel is increasing (due to the increased power outtake in the
power plants), it is not unlikely that the deposition tunnels built in the later part of the deposition period
will contain fuel with higher burnup than that deposited in the initial part. However, as discussed
above, several studies have shown that the releases do not increase with burnup, i.e. within the accuracyof the experiments there is no evidence for increased releases from high burnup fuel. For this reason,
spatial effects on fuel conversion rates are disregarded. They are assumed to be captured by theuncertainty range in the fuel conversion rates.
Temporal variability of data

As discussed previously, the driving force for the oxidative dissolution of spent fuel is its radiation
field, which decreases with time due to radioactive decay. This decrease is taken into account in mostother fuel dissolution models /Johnson and Smith 2000, Andra 2005, Martínez Esparza et al. 2005,
Jonsson et al. 2007/ leading to a lower production of radiolytic oxidants with time and decreasing
dissolution rates. Even though there is hardly any uncertainty that the radiolytic dissolution rateof spent fuel will decrease with time, the SR-Site model pessimistically neglects this decrease andassumes a constant dissolution rate, which varies within broad ranges in order to compensate forconceptual and other uncertainties. The rationale for this is that during the first ten thousand years,
the amount of hydrogen and iron will be very large and compensate for the higher radiation fieldof the fuel. At longer times, the α-activity of the fuel reaches such low levels that even radiolytichydrogen would be sufficient to counteract oxidative fuel dissolution.

3.3.9 Correlations
No correlations were mentioned in /Werme et al. 2004/ for the proposed fuel conversion model.
Based on this and the results from recent studies, it is concluded that fuel dissolution is not a functionof fuel burnup and no correlation with the instant release fraction exists.
It should be noted that the release rate of matrix bound radionuclides is correlated to the fueldissolution rate.

3.3.10 Result of supplier’s data qualification
As pointed out previously in this section, there are experimental results suggesting very low fuelconversion/dissolution rates. In recent publications several studies have indicated the existence of athreshold of the α-activity, below which no measurable effect of the α-radiolysis on fuel dissolution
can be observed, even in carbonated de-aerated water /Rondinella et al. 2004, Muzeau et al. 2009/.
Even so, the mechanisms that control fuel dissolution at very low alpha doses are not fully understood /Cui et al. 2008/. Thus we are compelled to include a degree of caution in the data suggested
for SR-Site.
Based on the experimental data in the review of /Werme et al. 2004/ and the review of more recent
experimental data discussed here and in the Fuel and canister process report, a dissolution ratewith a triangular probability density function in the log10-space is suggested. The lower limit, best
estimate, and upper limit are suggested to be 10–8, 10–7, and 10–6 yr–1. The fuel conversion rate issuggested not to vary in time. Below, a few publications supporting this range are given.

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The electrochemical model /King and Shoesmith 2004/ proposes slightly lower dissolution rates,
10–7–10–8 yr–1. Practically the same rates (10–6 to 10–8 yr–1, with a best estimate at 4·10–7 yr–1) areproposed in /Carbol et al. 2005/, based on the experimental data obtained under the EU-project SFS.
In /Ollila and Oversby 2005/ and /Ollila 2006/ dissolution rates based on total uranium releases
similar to, or lower than, those suggested above are reported for reducing conditions.

3.3.11 Judgements by the SR-Site teamAccording to the instruction given in Section 2.3, the SR-Site team should comment on Sections 3.3.4
to 3.3.10.
Sources of information and documentation of data qualification

The supplier considers that the two main sources of information used are qualified. The SR-Siteteam agrees. The supplier has relied on a number of peer-reviewed articles, which are judged to bequalified. The SR-Site team supports this judgment. The SR-Site team judge that the referencing tosupporting publications is sufficiently precise.
Conditions for which data are supplied

The supplier has discussed the conditions that are of main concern for data quantification, suchas the fuel type, burnup, groundwater composition, and redox conditions. It is suggested that therecommended uncertainty range (with its included pessimism) should encompass a likely rangeof such conditions, as long as reducing conditions prevail. The SR-Site team agree.
Conceptual uncertainty

The supplier has discussed conceptual uncertainty and argues that the recommended uncertainty
range, with its included pessimism, should encompass conceptual uncertainty. The SR-Site teamagree.
Data uncertainty due to precision, bias, and representativity

The supplier has referred to numerous experiments suggesting much lower dissolution rates than thoserecommended for SR-Site. One could interpret the degree of caution in the recommended data as a bias.
The SR-Site team suggest that as the recommended uncertainty range is rather based upon the degree of
pessimism than on experimental data, the discussion on data uncertainty becomes less important.
Spatial and temporal variability of data

The supplier has argued that even though there may be spatial and temporal variability, this should
be encompassed by the range of uncertainty in suggested data. The SR-Site team agree.
Correlations

The supplier suggests that no correlation has been used in obtaining the fuel dissolution/conversion
rate, but that the release of matrix bound radionuclides is correlated to the fuel dissolution/conversion
rate. The SR-Site team agree.
Result of supplier’s data qualification

Throughout this section, the supplier has referred to experimental studies suggesting that for all practical
purposes, there will be no fuel dissolution/conversion, or that the fuel dissolution/conversion rates aremuch lower than those recommended for SR-Site. Even so, the supplier recommends relatively large
dissolution rates based on cautious considerations. In making such cautious considerations, one shouldbe very careful in assessing whether or not “cautious data” are indeed cautious in all possible scenarios.
For the fuel dissolution/conversion rate, it is judged by the SR-Site team that a larger fuel dissolution/
conversion rate would be cautious for all possible (but still reasonable) scenarios.

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The supplier has argued well that the dissolution/conversion rate will not be higher than that suggested,
and the SR-Site team judges that the suggested probability density function will suffice for SR-Sitemodelling.

3.3.12 Data recommended for use in SR-Site modelling
The probability density function of the fuel conversion rate recommended for use in SR-Site is given
in Table 3-21. The distribution should apply for all types of fuel, and for all reducing conditions andtime periods.
Table 3-21. Distribution of fuel conversion rates suggested for use in SR-Site.
Lower limit(yr–1)

Best estimate(yr–1)

Upper limit(yr–1)

Distribution

10–8

10–7

10–6

Triangular distribution, log10-space.

3.4

Solubility data

In case of canister failure, groundwater may enter the void inside the canister and come in contact
with the spent fuel and metal parts inside the canister. As a result, radionuclides may be released
into this water as dissolved species. If the concentrations of the dissolved species get high enough,radionuclides will precipitate as solid phases. The solubility of the precipitated phases will determine
the maximum concentration of a radioelement inside the canister. These concentrations are used as
the source term for the radionuclide transport calculations.
The solubility concentration in the assessment is radioelement specific and dependent on:
1. The assumed solubility limiting phase.
2. The geochemical conditions inside the void.
3. The thermodynamic database used.
The selection of solubility limiting phases is done for the purpose of safety assessment.

3.4.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The output from this section should include solubility limiting phases and associated reactions
assumed to occur in the aqueous phase inside a failed canister, as well as associated thermodynamic
data. The supplier should deliver:•

A list of solubility limiting phases and associated reactions including the elements requested in
Section 2.2.1, as well as relevant groundwater species.

•

Thermodynamic data for each of the listed reactions, including uncertainty estimates. The data
should be delivered in form of equilibrium constants, log10(K0r) ± Δlog10(K0r).

The elements for which data are requested (see Section 2.2.1) are: H, C, Cl, Ni, Se, Sr, Mo, Zr, Nb,
Tc, Pd, Ag, Cd, Sn, I, Cs, Sm, Eu, Ho, Pb, Ra, Th, Ac, Pa, U, Np, Pu, Am, and Cm. The choice of
relevant groundwater components should be justified. The groundwater composition is handled in
Section 6.1 of this report. As part of the uncertainty discussion, supporting data from natural systems,laboratory experiments, and previous assessments may be useful.
In SR-Can the process of co-precipitation was disregarded, which should be considered as pessimistic.
For the radionuclide Ra-226, which dominated the annual effective dose of the important SR-Can
scenarios /SKB 2006a/, efforts have been undertaken to scrutinise whether disregarding co-precipitation
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is overly pessimistic /Grandia et al. 2008, Bosbach et al. 2010/. In view of the favourable thermo­
dynamics and relatively fast kinetics, it was decided to consider the co-precipitation process for
Ra-Ba sulphate in SR-Site. Thus, the supplier should deliver:•

A formula for the (Ra,Ba)SO4 co-precipitate, as well as guidance on how to incorporate
co-precipitation in the safety assessment.

SR-Site modelling activities in which data will be used

The reactions and thermodynamic data delivered in this section are inputs to the Simple Functions
spreadsheet /Grivé et al. 2010a/, which is used to assess the solubility limits of radioelements in thecanister. As a second step of the SR-Site radionuclide transport modelling, the assessed solubility
limits are used to determine the concentration of dissolved radioelements inside the canister, andthus to determine the source term (see Radionuclide transport report).
Except for the data delivered here, the Simple Functions spreadsheet requires input data on the
groundwater composition, in terms of the major ions, pH, redox potential, and ionic strength. Suchdata are presented in Section 6.1. From the data delivered in this present section and groundwatercomposition data, the code calculates the concentration of dissolved radioelements in equilibriumwith the solubility limiting phase, as well as the concentration’s associated uncertainty. The codeworks with equilibrium constants at 25°C and uses an ionic strength correction similar to the DebyeHückel approach /Oelkers and Helgeson 1990/, strictly valid for ionic strengths below 0.2 M.

3.4.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The SR-Can radionuclide transport modelling activities using solubility data generally agree withthose of SR-Site. However, in the SR-Can the Data report supplied calculated distributions for thesolu­bilities, while this present Data report only supplies the selected solubility limiting phases andassociated reactions, as well as associated thermodynamic data.
Co-precipitation was not generally incorporated as a process in SR-Can; it was only considered in
the sensitivity analysis (or what-if cases).
Conditions for which data were used in SR-Can

In broader terms, conditions for SR-Can agree with those of SR-Site, even though some conditions
such as groundwater composition, temperature, etc. may slightly deviate. In the SR-Can radionuclidetransport calculations, future groundwater compositions were calculated based on the hydrogeologicalmodelling tasks reported in /Auqué et al. 2006/. Solubility limiting phases were selected according to
an expert judgement strategy, as presented in /Duro et al. 2006a/.
Sensitivity to assessment results in SR-Can

The release of radionuclides from the canister depends on the concentrations of the dissolved
radioelements inside the canister. However, the solubility limit will only be reached for certain
radioelements and under certain conditions since:•

Some radioelements have high solubilities and will not be solubility limited under any conditions(e.g. iodine and caesium).

•

For some radioelements, the total inventory at the time of canister failure is so low that the solubility
limit will never be reached. These elements may co-precipitate with other components, although
this process was not considered in SR-Can and will for most elements not be considered in SR-Site.

•

Also elements with relatively low solubilities may not precipitate, if the release from the spent
fuel matrix is so slow that the solubility limit is not reached within the void of the canister.

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The two radionuclides contributing to the great majority of radiological risk were in SR-Can I-129
and Ra-226. I-129 was not considered to be solubility limited, while Ra-226 was limited by thesolubility limiting phase RaSO4(s).
In cases where fuel dissolution outweighs fuel conversion (see Section 3.3), the solubility limit ofuranium governs the stability of the UO2 spent fuel matrix, which in turn may be important for the
release of radioelements incorporated in the spent fuel matrix. In the case of very limited removal of
uranium from the canister, which was the case in SR-Can, fuel conversion is the governing process
while the uranium solubility limit is of negligible importance for assessment results. A process that
potentially increases the importance of the uranium solubility limit, which was not considered in SR-Can
but will be considered in SR-Site, is enhanced removal of uranium from the canister, as result of buffer
erosion products entering the canister. These erosion products may then function as sorption surfaces.
Alternative modelling in SR-Can

No alternative model focusing on solubilities was used in SR-Can.
Correlations used in SR-Can modelling

As solubility data are based on chemical equilibrium calculations for given groundwater compositions,the data are inherently correlated.
Identified limitations of the data used in SR-Can modelling

In SR-Can, the uncertainties in the thermodynamic database were not considered in the calculationsof elemental solubility. This resulted in very narrow distributions of calculated solubilities for certain
radioelements.
In SR-Can, co-precipitation of radium-barium was generally not taken into account, which was
identified as overly pessimistic.
In the SKI report by /Stenhouse et al. 2008/ it was identified as a limitation that solubilities were given
for 15°C, while the temperature inside the canister may be considerably higher. In /Stenhouse et al.
2008/, it was furthermore suggested that the solubility of Pu(III) phosphate should be given more attention, as well as the effect of polymer/eigencolloid formation on solubilities of tetravalent actinides.
Not all radioelements included in SR-Site were included in SR-Can.
Only groundwater compositions were considered in the SR-Can assessment. The possible effect ofmodification to the groundwater composition due to reaction with the bentonite was neglected. This
will also be the case in SR-Site.

3.4.3 Supplier input on use of data in SR-Site and SR-Can
The supplier will not deliver solubility data or reactions for the radioelements Mo, Cd, Eu, and Ac.
These elements are not included in the Simple Functions Spreadsheet and no solubility assessment
has been made.
The supplier agrees with the use of data in SR-Site. It is noted that the exclusion of other co-precipitates
than (Ra,Ba)SO4 from the safety assessment is pessimistic, but that it is up to the safety assessment
team to choose the degree of pessimism. With the chosen degree of pessimism in SR-Can (the exclusionof all co-precipitates) the supplier agrees with the use of data.

3.4.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information on solubility limits are /Duro et al. 2006a, b/, which are reports
dedicated for SR-Can. The solubility limiting phases and reactions are taken from /Duro et al. 2006a/,
while the thermodynamic database (TDB) is taken from /Duro et al. 2006b/. A supplement to /Duroet al. 2006a/ has been recently produced /Grivé et al. 2010b/. A major reference in /Duro et al. 2006b/
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is the NAGRA-PSI 01/01 TDB, reported in /Hummel et al. 2002/. For data on Pb, an element thatwas not included in /Duro et al. 2006a, b/, the main reference is /Grivé et al. 2010b/. The main sourceof information on the performance of the Simple Functions spreadsheet is /Grivé et al. 2010a/. The
main sources of information on (Ra,Ba)SO4 co-precipitation are /Grandia et al. 2008, Bosbach et al.
2010/. All these sources of information are shown in Table 3-22.
Table 3-22. Main sources of information used in data qualification.
Duro L, Grivé M, Cera E, Gaona X, Domènech C, Bruno J, 2006a. Determination and assessment of the concentration
limits to be used in SR-can. SKB TR-06-32, Svensk Kärnbränslehantering AB.
Duro L, Grivé M, Cera E, Domènech C, Bruno J, 2006b. Update of a thermodynamic database for radionuclides to assist
solubility limits calculation for performance assessment. SKB TR-06-17, Svensk Kärnbränslehantering AB.
Hummel W, Berner U, Curti E, Pearson F J, Thoenen T, 2002. Nagra/PSI chemical thermodynamic data base 01/01.
Boca Raton: Universal Publishers.
Grivé M, Domènech C, Montoya V, García D, Duro L, 2010a. Simple Functions Spreadsheet tool presentation. SKB TR-10-61,
Svensk Kärnbränslehantering AB.
Grivé M, Domènech C, Montoya V, García D, Duro L, 2010b. Determination and assessment of the concentration limits to
be used in SR-Can. Supplement to TR-06-32. SKB R-10-50, Svensk Kärnbränslehantering AB.
Grandia F, Merino J, Bruno J, 2008. Assessment of the radium-barium co-precipitation and its potential influence on thesolubility of Ra in the near-field. SKB TR-08-07, Svensk Kärnbränslehantering AB.
Bosbach D, Böttle M, Volker M, 2010. Experimental study of Ra2+ uptake by barite (BaSO4). Kinetics of solid solution
formation via BaSO4 dissolution and RaxBa1-xSO4 (re) precipitation. SKB TR-10-43, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting data

The different data sets used as inputs to this section are categorised as qualified or supporting in
Table 3-23. The reasoning behind the sorting is given in Table 3-24.
Table 3-23. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Duro et al. 2006a/: Selected solubility limiting phases andrationale for their selection.
2. /Duro et al. 2006b/: Thermodynamic database used in SR-Can(tables in Chapters 2 to 18). Formation reactions of aqueous
species and solubility constants for solid phases. One chapter of thereport is dedicated to each element or group of elements.
3. /Hummel et al. 2002/, NAGRA-PSI 01/01 thermo­dynamic database: Reference thermodynamic database modified for the SR-Can
solubility assessment.
4. /Grivé et al. 2010b/: Updated thermodynamic database, as supplement to SKB TR-06-32. Includes the element Pb.
5. Original thermodynamic database published by the OECD
NEA-TDB project and discussion on the selection of thethermodynamic data.

9. Figure 3-2 in /Grandia et al. 2008/ and conclusions
stating that the relative amount of Ra with respect to
Ba in the precipitating system will be at least 1,000 times
lower (e.g. page 33 of /Grandia et al. 2008/).
10. /Duro et al. 2006a/, Chapter 4.1 and Appendix A:
Data of measured radionuclide solubilities fromlaboratory experiments.
11. /Duro et al. 2006a/, Chapter 4.2 and Appendix B:
Data of measured radionuclide solubilities from natural
systems.
12. /Duro et al. 2006a/, Appendix C: Data ofrecommended solubility limits from other national
performance assessments exercises.

/Rand et al. 2009/ TDB of Thorium.
/Brown et al. 2005/ TDB of Zirconium.
/Olin et al. 2005/. TDB of Selenium.
/Gamsjäger et al. 2005/. TDB of Nickel.
/Guillaumont et al. 2003/. TDB of Uranium, Neptunium,
Plutonium, Americium and Technetium.
/Lemire et al. 2001/. TDB of Neptunium and Plutonium.
/Rard et al. 1999/. TDB of Technetium.
/Silva et al. 1995/. TDB of Americium.
/Grenthe et al. 1992a/. TDB of Uranium.
6. References (Chapter 19) in /Grivé et al. 2010b/: Papers andreports used to update thermodynamic data included in the Simple
functions spreadsheet.
7. Results in /Bosbach et al. 2010/ showing that re-crystallisation
kinetics of barite as well as (Ra, Ba) sulphate co-precipitation are
relatively fast processes.
8. Inventory data in Appendix C, Table C-15 of the Spent fuel report.

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Table 3-24. Justifications to the sorting of items in Table 3-23.
1: /Duro et al. 2006a/ was written and reviewed according to the SKB quality assurance system.
It is considered as qualified, as it is a dedicated SR-Can document and produced in accordancewith the current framework for data qualification. In /Duro et al. 2006a/, the solubility limiting phases
and associated reactions are selected with an expert judgement methodology. The selection of theaqueous species considered in the solubility calculations for each radio­element is based on thesensitivity analyses presented in Chapter 6 of /Duro et al. 2006a/.
2: /Duro et al. 2006b/ is considered as qualified, as it is a dedicated SR-Can document and producedin accordance with the current framework for data qualification. The report delivers equilibrium constants as well as enthalpy data for the chemical reactions of interest. The aim of that report was tomodify and/or update, as well as to complete if considered appropriate, the NAGRA-PSI 01/01 TDB/Hummel et al. 2002/ database. Several changes, updates and checks for internal consistency andcompleteness to the NAGRA-PSI 01/01 database were conducted when needed. Most modifications
incorporated to the NAGRA-PSI 01/01 database are reported in /Duro et al. 2006b/.
3: The thermodynamic database “NAGRA-PSI 01/01 database” was prepared by PSI and NAGRA
and reported in /Hummel et al. 2002/. This work has undergone a peer review by an independent
reviewer, according to NAGRA’s QA procedures. Furthermore, the data are relevant for the Swedish
repository concept and can therefore be considered as qualified.
4: Since the publication of /Duro et al. 2006b/ several experimental programmes and scientific literature
works have been devoted to the improvement of the thermodynamic databases for the elements ofinterest. All modifications needed for the implementation of the newly appeared databases until 2009
are reported in /Grivé et al. 2010b/. Most modifications reported in /Grivé et al. 2010b/ refer to theimplementation of the thermodynamic data published in the “Chemical Thermodynamics” series ofbooks from the OECD NEA. Thermodynamic data for Pb system has been included in the Simple
Functions spreadsheet in 2009.
5: The OECD NEA “Chemical Thermodynamics” series of books, as well as articles and reports
from the scientific literature, were used to update the thermodynamic data included in the Simple
Functions spreadsheet and are properly referenced in /Grivé et al. 2010b/. These data are consideredas qualified given that they are peer reviewed and constitute the highest quality thermodynamic
databases on radioelements relevant for the Swedish repository.
6: Papers and reports used to update thermodynamic data included in the Simple Functions
spreadsheet are qualified, given that they have been submitted to a peer review process before
considering them. References are given in /Grivé et al. 2010b/.
7: The SKB report /Bosbach et al. 2010/ is written and reviewed in accordance with the SKB quality
assurance system.
8: The Spent fuel report is written and reviewed in accordance with the SKB quality assurance
system. The data are used for calculating the Ba/Ra ratio in the canister for BWR and PWR spent fuel.
9: The SKB report /Grandia et al. 2008/ is written and reviewed in accordance with the SKB quality
assurance system. However, the spent fuel for which numerical data are given is ATM-104 spent
fuel /PNL 1991/, which is not identical with the Swedish spent fuel (but very similar). These data aresorted as supporting, and it should be noted that recommended data are based on similar calculationsfor Swedish fuel (cf. Figure 3-13).
10: Data of measured radioelement solubilities from laboratory experiments are reported in Section 4.1
and Appendix A of /Duro et al. 2006a/. These are supporting data used in the expert judgement forthe solubility assessment presented in /Duro et al. 2006a/.
11: Data of measured radioelement solubilities from natural systems are reported in Section 4.2 andAppendix B of /Duro et al. 2006a/. These are supporting data used in the expert judgement for thesolubility assessment presented in /Duro et al. 2006a/.
12: Data of recommended solubility limits from other national performance assessment exercises
are included in Appendix C of /Duro et al. 2006a/. These data are used as supporting information tocompare the current solubility assessment with other assessments of the same type, conducted bydifferent nuclear waste management organisations.

Excluded data previously considered as important

No such data has been identified.

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3.4.5 Conditions for which data are supplied
The conditions under which the radioelement solubility assessment has been done are described in/Duro et al. 2006a/.
They are briefly summarised as follows:•

The assessment of radioelement concentration limits considers the vicinity of the spent nuclear
fuel, in the void between the spent fuel and the canister. The solid phases, selected as likely to
precipitate, apply only to the conditions in the void between the spent fuel and canister (Chapter 5
of /Duro et al. 2006a/).

•

The temperature for which solubility limits are assessed, by using the Simple Function spreadsheet,is 25°C. The code is currently not designed to return solubility limits for any other temperatures.
For the time periods where solubility limits are most likely to be of consequence for the safetyassessment, that is when the canister has failed and water has intruded, the ambient temperature
is expected to be lower than this.

•The selected solubility limiting phases and thermodynamic data support calculations of solubilities
for all relevant redox conditions, i.e. from pH2 = 10 MPa (100 atm of H2 derived from steel anoxic
corrosion) to pO2 = 20 kPa (equilibrium with atmospheric pO2). Inside the canister, corrosion of
the cast iron insert and stainless steel construction material will give rise to production of H2 gas,
wherefore reducing conditions are expected.

•

Sensitivity analyses of radioelement concentration limits have been done to study both the influence of the groundwater composition on the geochemistry of the selected radioelements, and to
identify the main solid phases thermodynamically able to precipitate in the system.

•

The selection of thermodynamic data and solubility limiting phases has been done for a pH rangeof 6 to 11. The assumptions may not be valid outside of this range.

•

The activity correction used (Debye-Hückel theory) is not valid for an ionic strength of > 0.2 M
and it would have been preferable to use the Specific Interaction Theory instead, since the ionicstrength of the waters considered may be as high as 2 M, or more. However, this approach has
not yet been implemented in the geochemical codes that are available. Comparisons between
both approaches have been conducted, when possible, and show small differences.

Concerning (Ra,Ba)SO4 co-precipitation, sufficient amounts of Ba2+ and SO42–, or BaSO4, is generally
expected to exist for the co-precipitate to form in the void between the spent fuel and canister. The
co-precipitate is also expected to form within the expected temperature range and range of watercompositions /Grandia et al. 2008/.

3.4.6 Conceptual uncertainty
The solubility assessment reported in /Duro et al. 2006a/ is focused on the very-near field, whenwater enters the canister void and interacts with the spent fuel.
The most significant conceptual uncertainties concerning to the capacity of the data and model used
to represent the reality are presented below.
The composition of the interacting groundwater

Although this could be seen a priori as data uncertainty, there is a non-trivial conceptual component
associated with the water composition. Is very likely that groundwater interacts with the engineeringbarriers before contacting the spent fuel. In SR-Site the reaction between the groundwater and thebuffer is suggested to be neglected in the solubility calculations. The justification for this is:•

Elemental solubilities are active for very long time frames. “Conditioning” of the groundwater byaccessory minerals (carbonates) in the buffer is expected have relatively short duration, or to havesmall impact on the overall composition.

•

Ion-exchange reactions will have limited impact on solubilities.

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The reaction between the cast iron insert and the groundwater is considered throughout the assessment. This is done by an adjustment of the redox conditions in the interior of the canister, based on
the equilibrium of magnetite/goethite. The corrosion products of the cast iron insert can be expected
to control the redox potential for all timescales.
Precipitation of pure solid phases and mixed solid phases

Except for (Ra,Ba)SO4 co-precipitate, calculations implying the formation of mixed solid phases
have not been conducted. This approach may be seen as non-realistic given the expected low concentrations of the elements originating from the spent fuel matrix, but it is based on the following facts:•

The solubility assessment applies to the void between the spent fuel and the canister which implies
low water volumes; so that even limited radioelement release can result in concentrations highenough as to reach the solubility limit with respect to pure solid phases.

•

No presence of major minerals frequently occurring in geochemical systems is expected inside
the canister, except for minerals produced by corrosion. This a priori excludes the possibility thatcalcite and fracture filling material can act as substrates for radionuclide co-precipitation.

•

Although phases produced by the canister corrosion may act as substrates for retention and coprecipitation of radionuclides, the uncertainty associated with assessing the formation of mixed
solid phases is much higher than the uncertainty associated with the assumption of pure solid
phases governing the individual radioelement concentrations.

The co-precipitation of Ra-Ba sulphate is a very well-known process. The affinity of radium tobarium sulphate was first reported by Marie Curie in her radium discovery work /Curie et al. 1898/.
The fact that the process occurs in nature is undisputed /Grandia et al. 2008/. There are two main
potential scenarios for (Ra,Ba)SO4 co-precipitation in the vicinity of the spent fuel.
•Co-precipitation of primary Ra-226 as generated and released contemporaneously with Ba fromspent fuel and the sulphate present.

•

Surface precipitation of Ra-226 on existing BaSO4(s). This initial surface precipitate would
evolve towards a Ra(II)-Ba(II) sulphate solid solution by re-crystallisation and/or Ra2+ diffusion
into the BaSO4(s) lattice.

Within the canister, the second scenario is primarily of interest. Although the thermodynamics
favours co-precipitate, there is still the question of kinetics. /Grandia et al. 2008, Section 4.5/ statesthat even if all the direct and indirect evidence points towards a fast incorporation of Ra(II) onto
an existing Ba(II) sulphate structure, there is a need to perform a devoted mechanistic study to fullyback up the macroscopic findings. Such a study was carried out by /Bosbach et al. 2010/ and theresults indicate for a relatively rapid incorporation of Ra in the barite structure.
Crediting amorphous over crystalline phases

Amorphous or less crystalline solid phases have been credited over crystalline phases. This assumption
may not necessarily be true for high temperatures and long time periods, when crystalline phases
may form. As crystalline phases have lower solubility than amorphous ones, this assumption is
pessimistic.
Metallic and native phases

No metallic phases have, in principle, been considered, given the very slow kinetics of precipitationof this type of phases under the conditions of interest. This approach is, nevertheless, associated witha certain level of uncertainty and in some cases (e.g. Pd, Ag, Se, and Tc) the role of a metallic phaseas a solubility limiting phase has been discussed in /Duro et al. 2006a/.

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103

The process of reduction of sulphate to sulphideThis process has not been considered in the reference case. The abiotic (thermochemical) reductionof sulphate to sulphide is such a slow process that it has not been observed at temperatures below~200°C /e.g. Goldstein and Aizenshtat 1994/. The reduction is accelerated with the presence ofsulphate reduction bacteria. Having sulphate reducing bacteria present will assist this reaction andwill have impact on the solubility limit for elements limited by sulphate solids. The rate of sulphate
reduction will, however, be limited by the supply of electron donors, most likely methane and hydrogen. Since concentrations of these are much lower than the sulphate concentration, sulphate concentration will not be much influenced by the reaction. Although sulphide phases could give very low
solubilities for certain radioelements (e.g. nickel), no sulphide phases are considered in the solubility
assessment.
The process of reduction of carbonate to methaneThis process has not been considered in the reference case. However, it is thermodynamically plausible
if temperature increases and/or biological activity is present. /Kudo and Komatsu 1999/ studied the
reduction of metal carbonates to methane and concluded that the yield of methane generation reacha maximum (only of 17%) at 400°C and does not occur in the absence of biological activity if thetemperature is below 150–200°C.
Summary

All these uncertainties do not affect the solubility assessments of the different radioelements inthe same degree. Table 3-25 summarises the conceptual uncertainties identified for each individual
radioelement, as further discussed in Chapter 5 of /Duro et al. 2006a/.
Table 3-25. Sources of conceptual uncertainty identified for each one of the radioelements understudy /Duro et al. 2006a/. No uncertainties of this type have been identified for radioelements notincluded in the list.
Element

Associated uncertainty

C

Reduction to CH4(g).

Sr

SO42– to HS– reduction.
Possibility of co-precipitation with other elements’ carbonates.

Ra

SO42– to HS- reduction.
Possibility of co-precipitation with other elements’ carbonates.

Sn

SO42– to HS– reduction.

Pb*

SO42– to HS– reduction.

Se

Formation of native Se0.
SO42– to HS– reduction.

Zr

Crystallinity of the solid phase.

Tc

Formation of metallic Tc0.

Ni

SO42– to HS– reduction.

Pd

Formation of metallic Pd0.

Ag

Formation of metallic Ag0.
SO42– to HS– reduction.

Th

Crystallinity of the solid phase.

U

Silicate solid precipitation.

Np

Crystallinity of the solid phase.

Pu

SO42– to HS– reduction.

*Uncertainty concerning Pb is discussed in /Grivé et al. 2010b/.

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3.4.7 Data uncertainty due to precision, bias, and representativity
In Chapter 6 of /Duro et al. 2006a/ a discussion on data uncertainty is given, where the contributions
from precision, bias, and representativity to the overall data uncertainty have been identified.
Concerning the matter of (Ra,Ba)SO4 co-precipitation, this is discussed under a separate heading at
the end of this section.
Precision

Precision of solubility data reported from laboratory and field measurements is described in theoriginal sources from which data have been extracted. The influence of the precision of the measurements is included in the sensitivity analyses conducted for the solubility assessment of each radio­
element in Chapter 6 of /Duro et al. 2006a/.
The uncertainty of solubility limits, as calculated in the SR-Site safety assessment, is affected by theprecision of the input data used in the calculations. This mainly concerns groundwater compositions
and SKB-TDB thermodynamic data. Precision of groundwater data compositions is discussed in
Section 6.1. Precision of thermodynamic data is specifically reported in /Duro et al. 2006b/ and in/Grivé et al. 2010b/.
Bias

Data on concentrations of elements obtained from spent fuel dissolution experiments, gathered in
Chapter 4 of /Duro et al. 2006a/, are biased due to the variability in the experimental conditions andto the difficulty of maintaining reducing conditions in the experiments. This would result in reported
measured values in different publications that are higher than the actual solubility values expected
to prevail under reducing conditions. This is especially relevant for the concentrations measured for
uranium, due to its redox sensitivity. Experimental data of solubilities for other radioelements can be
more importantly biased due to the high ionic strengths used in some of the experiments. Bias due tothe data scarcity must be also considered.
Representativity

The data presented should be representative for a canister in a KBS-3 repository, under ambient conditions. In /Duro et al. 2006a/ different categories of data have been used as sources of information:•

Radioelement inventory and occurrence in rocks and minerals.

•

Radioelement concentrations from laboratory spent fuel experiments.

•

Radioelement concentrations in natural waters.

•

Radioelement inventories and aqueous concentrations in natural analogue sites.

The way these data have been acquired varies and depends on several factors: the study performed;
the institution doing it; the measured radioelement; natural variability; etc. The individual acquisition
and treatment procedure is described in the original source. However, these sets of data have been
selected after a critical revision of both the quality of data and their usefulness for the solubility
assessment of the very-near field of a KBS-3 repository. Data of poor or uncertain quality, or dataobtained under conditions far from those expected, have not been used.
Each category of data relies on data compiled from several authors, obtained at different conditions
and for different purposes. Data selection has been done, thus, with the aim to cover the whole rangeof conditions expected in the studied scenarios, ensuring the representativity of each of these categoriesof data.

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105

Different groundwater compositions have been selected. Besides the values of the master parameters,
Eh and pH, and the analytical error in the concentrations, there is a specific uncertainty affecting
their representativity:•

Lack of phosphate concentration: It affects the prediction of the solubility of some elements, mainlyrare earth elements and trivalent actinides. In most of the groundwater compositions used, a zero
phosphate concentration in solution has been considered because of the lack of reliable data on
phosphate concentrations in the analysed groundwaters at the time the study was conducted. The
choice of excluding phosphates in the calculations is a limitation and introduces uncertainties
relating to effects of complexation and precipitation of solid phosphates. In the background reportto the solubility assessment /Duro et al. 2006a/ it was found that inclusion of phosphate could
lower the solubility of rare earth elements and Pu under some circumstances. Phosphate complexes
were however never important for the speciation. Since it is very doubtful whether phosphate
phases could control solubility, based on mass balance considerations, the phosphate system
was excluded from the solubility assessment.

In /Duro et al. 2006b/ the procedure for both logK selection and uncertainty assignment is fully
documented. The database update has been done considering that it must be used in the assessmentof radioelement solubility in a KBS-3 repository environment, that is, considering the most relevantaqueous species and solid phases under these conditions.
A major representativity issue is the effect of temperature on the stability of aqueous species andsolid compounds. The input data to, and output data from, the Simple Functions spreadsheet apply
at 25°C. Due to the presence of the spent fuel, it is foreseen that the temperature initially can reachup to 100°C. However, after longer time periods, when canister failure may occur, the activity of the
spent fuel has substantially decreased wherefore elevated temperatures are not expected. Therefore,
the ambient temperature is likely to be lower than 25°C. At temperate climate the ambient temperature is expected to be around 15°C, while at periglacial conditions the temperature may drop to justabove freezing (cf. Section 7.1).
It would have been preferable to be able to correct all logK data, so that they represent a rangeof temperatures. However, due to lack of enthalpy data this has not been possible in the Simple
Function spreadsheet. To investigate the data uncertainty introduced by this limitation, a sensitivity
study was performed in /Grivé et al. 2010a/ including the radioelements Ni, Np, Se, Th, and U, forwhich reliable enthalpy data exist. The data delivered from the Simple Functions spreadsheet wascompared with data delivered from the program HYDRO-MEDUSA /Puigdomenech 2002/, at 0°C,
10°C, 25°C, 40°C, 70°C, and 100°C. A comparison is shown in Figure 3-12, in terms of dissolved
radioelement concentration at the solubility limit.

0ºC
25ºC
70ºC
Simple Functions

log [RN] (mol dm-3)

-2

10ºC
40ºC
100ºC

-4

Forsmark

-6
-8
-10

UO2.2H2O

ThO2:2H2O

Se(s)FeSe2(s)NpO2:2H2O

Ni(OH)2(beta)-12

Solubility limiting phases

Figure 3-12. Results from a sensitivity study on the influence of temperature on the solubility limit. Image
reproduced from Figure 4-5 of /Grivé et al. 2010a/.

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The (limited) sensitivity study indicates that for most radioelements, the solubility limits deliveredby the Simple Functions spreadsheet are adequate or overestimated for temperatures at or below
2°C. However, nickel shows that there are exceptions where the solubility increases with decreasing
temperature.
Ionic strength activity corrections have been considered in the calculation of radioelement solubility.
Although the most appropriate procedure to conduct activity corrections in the ionic strength range
studied (10–3 to 2 mol/dm3) is the Specific Interaction Theory (SIT), as recommended by the NEA
guidelines, this approach is not yet implemented in the geochemical codes used in /Duro et al. 2006a/.
The codes used in /Duro et al. 2006a/, which are HYDRA-MEDUSA and PhreeqC, both use
the extended Debye-Hückel approach. Therefore, Debye-Hückel is used in the calculations ofsolubility (cf. Section 3.3.1 of /Duro et al. 2006a/). Nevertheless, calculations have been done withboth approaches whenever possible and results consistently compare. The largest differences arefound in solubilities calculated at saline water composition, and in the case the aqueous speciation
is dominated by highly charged species which stability is much dependent on the ionic strength,e.g. silver chlorides.
Table 3-26 summarises sources of representativity uncertainties identified for each individual
radioelement. These uncertainties are discussed in Chapters 5 and 6 of /Duro et al. 2006a/.
Table 3-26. Sources of the representativity uncertainties for each of the radioelements understudy /Duro et al. 2006a/. No uncertainties of this type have been identified for radioelements
not included in the list.
Element

Associated uncertainty

Nb

Scarcity of data in TDB.

Pb*

Effect of phosphates in water.

Sm

Effect of phosphates in water.
Stability of the solid hydroxo-carbonate.

Ho

Effect of phosphates in water.
Stability of the solid hydroxo-carbonate.

Th

Uncertain thermodynamic data for aqueous carbonates.

Pa

Lack of thermodynamic data.

U

TDB data on solid stability.

Pu

Effect of phosphates in water.
Stability of the solid hydroxo-carbonate.

Am/Cm

Effect of phosphates in water.
Stability of the solid hydroxo-carbonate.

*Uncertainty concerning Pb is discussed in /Grivé et al. 2010b/.

Radium barium co-precipitation

Special attention is given to the matter of co-precipitation, as new data are presented in this Data reportthat has not been presented in supporting documents. The approach taken to account for co-precipitation
is simplistic and delivers for most time periods pessimistic results. In essence the Ra/Ba ratio inside of
the canister is estimated based on inventory data and radioactive decay calculations. The calculation
carried out in Chapter 3 of /Grandia et al. 2008/ for ATM-104 spent fuel was repeated for the spent
fuels of the two type canisters PWR I and BWR I. In doing this, inventory data were taken from
Table C-15 of the Spent fuel report. For these fuels, the Ra/Ba ratio varies with time from extremely
low values (~10–9 after about 40 years when the fuel is placed in the canisters) to a peak value ofabout 3·10–4 years after 100,000 years, as illustrated in Figure 3-13.
A safety margin is added to this peak value and the Ra/Ba ratio of 10–3 is pessimistically assumed
during the entire repository evolution period. This ratio is recommended to be used as a correction
factor that is multiplied with the Ra2+ concentration, as calculated from the Simple Function spreadsheet when disregarding co-precipitation, using RaSO4(s) as the solubility limiting phase.

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107

10–3
10–4

mol Ra / mol Ba

10–5
10–6
10–7
10–8
BWR
10

–9

PWR

10–10
1

10

102

103

104

105

106

year

Figure 3-13. Time evolution of the molar ratio between Ra and Ba in unaltered spent fuel calculated for
BWR I and PWR 1 type canisters.

3.4.8 Spatial and temporal variability of data
Natural heterogeneity is considered by determining the solubility limits and solubility limiting
phases for the entire range of different groundwater compositions, which in turn represent different
spatial and temporal conditions expected in the repository over long timescales.
3.4.9 Correlations
Geochemical calculations are by themselves a set of closely related correlations between different
parameters, usually named primary parameters. The simple calculation of the aqueous speciation ofa chemical element is thus the solution of a set of equations in which different parameters, such as
pH, redox conditions, or ligand concentrations are correlated. Because of that, geochemical codesthat are especially devoted to solve this type of complex equations have been used. Therefore, it isevident that dependences between parameters have been used to identify the solid phases controllingthe elements’ solubility under the selected conditions.
The main correlations used are:•Equilibrium reactions between aqueous species.

•

Equilibrium reactions between solid phases and aqueous solutes.

•

Calculation of the ionic strength corrections by means of the extended Debye-Hückel reaction.

More information about the treatment of these correlations can be found in the code manuals
and descriptions /Parkhurst and Appelo 2001, Puigdomenech 2002/ and in general chemical andgeochemical literature.
Not all the correlations between parameters have the same importance. Table 3-27, which includes
Table 3-4 of /Duro et al. 2006a/, indicates the main parameters affecting the behaviour of the studied
radioelements. Special attention was paid to these parameters in /Duro et al. 2006a/.

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Table 3-27. Main geochemical parameters affecting the geochemical behaviour of each radio­
element under study. Shadowed cells indicate that the element in the row is sensitive to pH and/or pe, within the range of variability of the parameter studied. All information from /Duro et al.
2006a, Grive at al. 2010b/, except for Ra-Ba co-precipitation where information is taken from
/Grandia et al. 2008/.
Element a)pHpe

Ligand/species

C

Ca2+

Cs

Cl–

Sr

CO32–, SO42–

Ra

CO32–, SO42–, Cl–, Ba2+, BaSO4 b)

Sn

Ca2+

Pb

CO32–, Cl–

Se

Fe(II)

Zr

–

Nb

–

Tc

–

Ni

–

Pd

Cl–

Ag

Cl–

Sm

CO32–

Ho

CO32–

Th

CO32–

Pa

–

U

CO32–, Ca2+, Si(OH)4

Np

CO32–

Pu

CO32–

Am and Cm

CO32–a) The excluded elements H, Cl, and I are suggested to have no solubility limit in safety assessment modelling.
b) The two components Ba2+ and BaSO4 are not directly included in the assessment. They are only included by
way of determining the correction factor for co-precipitation.

3.4.10 Result of supplier’s data qualification
The customer has requested that a list of relevant solubility limiting phases and associated reactions
are supplied, together with thermodynamic data in form of equilibrium constants. These data are
inputs to the Simple Functions spreadsheet.
In addition, the customer has requested that an expression for (Ra,Ba)SO4 co-precipitate should bedelivered, together with guidance on how to incorporate this information in the safety assessment.

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Solubility limiting phases

The solubility limiting phases for the radioelements, selected according to the expert judgement
procedure presented in /Duro et al. 2006a/, are found in Table 3-28. The solubility assessment for
Pb is reported in /Grivé et al. 2010b/.
Table 3-28. Recommended solubility limiting phases. All data taken from /Duro et al. 2006a/,
except for Pb data taken from /Grivé et al. 2010b/.
Radioelement

Recommended solubility limiting phase

H

Suggested to have no solubility limit in Safety assessment modelling.

C

Suggested to have no solubility limit in Safety assessment modelling.

Cl

Suggested to have no solubility limit in Safety assessment modelling.

Ni

NiCO3(s), Ni(OH)2(s).

Se

FeSe(s), Fe1.04Se(s), Se(s).

SrCelestite (SrSO4(s)), Strontianite (SrCO3(s)).

Zr

Zr(OH)4(s).

Nb

Nb2O5(s).

Tc

TcO2·1.6H2O(s), Tc(s).

Pd

Pd(OH)2(s).

Ag

AgCl(s).

Sn

SnO2(s), Ca[SnO6].

I

Suggested to have no solubility limit in Safety assessment modelling.

Cs

Suggested to have no solubility limit in Safety assessment modelling.

Sm

Sm2(CO3)3(s), Sm(OH)3(am), SmOHCO3(s).

Ho

Ho2(CO3)3(s), Ho(OH)3(am).

Pb

PbCO3(s), Pb3CO3(OH)2(s), PbClOH(s).

Ra

RaSO4(s), RaCO3(s), RaxBa(1–x)SO4(s).

Th

ThO2·2H2O(s).

Pa

Pa2O5(s).

U

UO2·2H2O(s), Coffinite, Schoepite, CaUO4(s), Becquerelite, Uranophane.

Np

NpO2·2H2O(s), Np2O5(s), NaNpO2CO3(s).

Pu

Pu(OH)4(s), Pu(OH)3(s), PuCO3OH(s).

Am

AmOHCO3(s), Am(OH)3(am), Am2(CO3)3(s), Am(CO3)2Na·5H2O(s).

Cm

CmOHCO3(s), Cm(OH)3(am).

Reactions and thermodynamic data

The reactions and thermodynamic data presented in Table 3-29 to Table 3-32 are recommended as
input data to the Simple Function spreadsheet.

110

TR-10-52

Species

Reaction

logK0

DlogK0Groundwater species

CaOH+
FeOH+
Fe(OH)3(aq)
Fe(OH)4Calcite
HCO3CaCO3(aq)
CaHCO3+
NaCO3NaHCO3
FeCO3(aq)
FeHCO3+
HSO4CaSO4(aq)NaSO4FeHSO4+
FeSO4(aq)FeCl+

Ca2+ + H2O = CaOH+ + H+
Fe2+ + H2O = FeOH+ + H+
Fe2+ + 0.25O2(g) + 2.5H2O = Fe(OH)3(aq) + 2H+
Fe2+ + 0.25O2(g) + 2.5H2O = Fe(OH)4- + 3H+
Ca2+ + CO32- = CaCO3
H+ + CO32- = HCO3Ca2+ + CO32- = CaCO3
Ca2+ + CO32- + H+ = CaHCO3+
Na+ + CO32- = NaCO3Na+ + CO32- + H+ = NaHCO3
Fe2+ + CO32- = FeCO3(aq)Fe2+ + CO32- + H+ = FeHCO3+
H+ + SO42- = HSO4Ca2+ + SO42- = CaSO4
Na+ + SO42- = NaSO4Fe2+ + SO42- + H+ = FeHSO4+
Fe2+ + SO42- = FeSO4(aq)Fe2+ + Cl- = FeCl+–12.78–9.50–4.80–13.84
8.48
10.33
3.22
11.44
1.27
10.08
4.38
12.33
1.98
2.30
0.70
3.07
2.25
0.14

0.30
0.10
1.06
0.08
0.02
0.02
0.14
0.09
0.30
0.30
1.31
0.30
0.25
0.30
0.30
0.30
0.05
0.23

Sr

SrOH+
SrCO3(aq)SrHCO3+
SrSO4
SrCl+
SrCO3(strontianite)
SrSO4(celestite)

Sr2+ + H2O = SrOH+ + H+
Sr2+ + CO32- = SrCO3(aq)Sr2+ + CO32- + H+ = SrHCO3+
Sr2+ + SO42- = SrSO4(aq)Sr2+ + Cl- = SrCl+
SrCO3(s) = Sr2+ + CO32SrSO4(s) = Sr2+ + SO42-–13.29
2.81
11.51
2.29
0.32
–9.27
–6.63

0.30
0.05
0.05
0.26
0.12
0.30
0.30

Ra

Ra(OH)+
RaCO3(aq)RaSO4(aq)RaCl+
RaCO3(s)Ra(SO4)(s)Ra2+ + H2O = Ra(OH)+ + H+
Ra2+ + CO32- = RaCO3
Ra+2 + SO42- = RaSO4
Ra+2 + Cl- = RaCl+
RaCO3(s) = Ra2+ + CO32Ra(SO4)(s) = Ra2+ + SO42-–13.50
2.50
2.75
–0.10
–8.30
–10.26

0.25
0.40
0.10
0.30
0.30
0.09

Zr

Zr(OH)4(aq)Zr(OH)4(am,fresh)
Zr(OH)4(am,aged)

Zr4+ + 4H2O = Zr(OH)4(aq) + 4H+
Zr(OH)4(s) + 4H+ = Zr4+ + 4H2O
Zr(OH)4(s) + 4H+ = Zr4+ + 4H2O

–2.19
–3.24
–5.55

1.70
0.10
0.20

Nb

Nb(OH)4+
Nb(OH)5(aq)Nb2O5(s)NbO3- + 2H+ + H2O = Nb(OH)4+
NbO3- + H+ + 2H2O = Nb(OH)5(aq)Nb2O5(s) + H2O = 2NbO3- + 2H+

6.90
7.34
–24.34

0.02
0.02
0.04

Tc

TcO2+
TcO4TcO(OH)+
TcO(OH)3Tc(CO3)(OH)2
Tc(OH)3(CO3)TcO2·1.63H2O

TcO(OH)2 + 2H+ = TcO2+ + 2H2O
TcO(OH)2 +0.75 O2 = TcO4- + 0.5 H2O + H+
TcO(OH)2 + H+ = TcO(OH)+ + H2O
TcO(OH)2 + H2O = TcO(OH)3- + H+
TcO(OH)2 + CO32- + 2H+ = Tc(CO3)(OH)2 + H2O
TcO(OH)2 + CO32- + H+ = Tc(OH)3(CO3)TcO2 1.63H2O =TcO(OH)2 + 0.63 H2O

4.00
32.94
2.50
–10.90
19.30
11.00
–8.40

1.42
2.05
0.30
0.40
0.30
0.60
0.50

Ni

NiOH+
Ni(OH)2(aq)Ni(OH)3NiCl+
NiCO3(aq)Ni(OH)2(s)NiCO3:5.5H2O(s)Ni2+ + H2O = NiOH+ + H+
Ni2+ + 2H2O = Ni(OH)2(aq) + 2H+
Ni2+ + 3H2O = Ni(OH)3- + 3H+
Ni2+ + Cl- = NiCl+
Ni2+ + CO32- = NiCO3(aq)Ni(OH)2(s) + 2H+ = Ni2+ + 2H2O
NiCO3:5.5H2O(cr) = Ni2+ + CO32- + 5.5H2O–9.54–18.00–29.20
0.08
4.20
11.03
–7.52

0.14
0.30
1.70
0.60
0.40
0.28
0.24

Pd

Pd(OH)+
Pd(OH)2
Pd(OH)3Pd(OH)42PdCl+
PdCl2
PdCl3PdCl42Pd(OH)2(s)Pd2+ + H2O = Pd(OH)+ + H+
Pd2+ + 2H2O = Pd(OH)2 + 2H+
Pd2+ + 3H2O = Pd(OH)3- + 3H+
Pd2+ + 4H2O = Pd(OH)42- + 4H+
Pd2+ + Cl- = PdCl+
Pd2+ + 2Cl- = PdCl2
Pd2+ + 3Cl- = PdCl3Pd2+ + 4Cl- = PdCl42Pd(OH)2(s) + 2H+ = Pd2+ + 2H2O

–1.86
–3.79
–15.93–29.36
5.10
8.30
10.90
11.70
–1.61

0.30
0.30
0.30
0.04
0.01
0.04
0.07
0.09
1.16

Ag

Table 3-29. Reactions and equilibrium constants recommended for use in the Simple Functions
spreadsheet, valid at 25°C. (1/4).

AgCl(aq)
AgCl2AgCl32AgCl43AgOH
Ag(OH)2AgOH(s)AgCl(cr)

Ag+ + Cl- = AgCl(aq)Ag+ + 2Cl- = AgCl2Ag+ + 3Cl- = AgCl32Ag+ + 4Cl- = AgCl43H2O + Ag+ = AgOH + H+
2H2O + Ag+ = Ag(OH)2- + 2H+
AgOH + H+ = Ag+ + H2O
AgCl(cr) = Ag+ + Cl-

3.27
5.27
5.29
5.51
–12.00–24.00
6.30
–9.75

0.17
0.37
0.39
1.71
0.30
0.10
0.05
0.04

TR-10-52

111

Species

Reaction

logK0

DlogK0Sn

Sn(OH)+
Sn(OH)2(aq)
Sn(OH)4(aq)
Sn(OH)5Sn(OH)62SnO2(am)Ca[Sn(OH)6](s)Sn4+ + 2H2O = Sn(OH)+ + 0.5O2(g) + 3H+
Sn4+ + 3H2O = Sn(OH)2(aq) + 0.5O2(g) + 4H+
Sn4+ + 4H2O = Sn(OH)4 + 4H+
Sn4+ + 5H2O = Sn(OH)5- + 5H+
Sn4+ + 6H2O = Sn(OH)62- + 6H+
SnO2(am) + 4H+ = Sn4+ + 2H2O
Ca[Sn(OH)6](s) + 6H+ = Sn4+ + 6H2O + Ca2+

–40.28–44.28–0.53–8.53–18.93
–6.77
8.54

0.39
0.39
0.67
0.73
1.00
0.73
0.74

Se

HSeSeO32Se2H2Se
HSeO3H2SeO3
HSeO4CaSeO4
FeSe2(s)Fe1.04Se(s)Se(s)

SeO42- + H+ = HSe- + 2O2
SeO42- = SeO32- + 0.5O2
SeO42- = Se2- + 2O2
SeO42- +2H+ = H2Se + 2O2
SeO42- + H+ = HSeO3- + 0.5O2
SeO42- + 2H+ = H2SeO3 + 0.5O2
SeO42- + H+ = HSeO4SeO42- + Ca2+ = CaSeO4
FeSe2(s) + 3.5O2 + H2O = 2SeO42- + Fe2+ +2H+
Fe1.04Se(s) + 2.02O2 + 0.08H+ = SeO42- + 1.04Fe2+ + 0.04H2O
Se(s) + 1.5O2 + H2O = SeO42- + 2H+–84.61–13.50–99.52–80.76
–5.15
–2.51
1.75
2.00
110.55
82.87
35.44

0.44
0.34
0.77
0.67
0.41
0.43
0.10
0.10
2.80
0.92
0.56

Th

Th(OH)3+
Th(OH)22+
Th(OH)4
Th(CO3)(OH)3Th(CO3)(OH)42Th(CO3)56Th(OH)(CO3)45Th(CO3)2(OH)22Th(SO4)2+
Th(SO4)2
Th(SO4)32ThCl3+
ThO2·2H2O(am, aged)

Th4+ + H2O =Th(OH)3+ + H+
Th4+ + 2H2O =Th(OH)22+ + 2H+
Th4+ + 4H2O =Th(OH)4 + 4H+
Th4+ + CO32- + 3H2O = Th(CO3)(OH)3- + 3H+
Th4+ + CO32- + 4H2O = Th(CO3)(OH)42- + 4H+
Th4+ + 5CO32- = Th(CO3)56Th4+ + 4CO32- + H2O = Th(OH)(CO3)45- + H+
Th4+ + 2CO32- + 2H2O = Th(CO3)2(OH)22- + 2H+
Th4+ + SO42- = Th(SO4)2+
Th4+ + 2SO42- = Th(SO4)2
Th4+ + 3SO42- = Th(SO4)32Th4+ + Cl- = ThCl3+
ThO2·2H2O(am) + 4H+ = Th4+ + 4H2O

–2.50
–6.20
–17.40–3.70–15.60
31.00
21.60
8.80
6.17
9.69
10.75
1.70
8.50

0.50
0.50
0.70
0.70
0.60
0.70
0.50
0.50
0.32
0.27
0.07
0.10
0.90

Pa

PaO2(OH)(aq)
Pa2O5(s)PaO2+ + H2O = PaO2(OH)(aq) + H+
Pa2O5(s) + 2H+ = 2PaO2+ + H2O

–4.50
–4.00

0.20
1.00

U

Table 3‑30. Reactions and equilibrium constants recommended for use in the Simple Functions
spreadsheet, valid at 25°C. (2/4)

UO2OH+UO2(OH)2(aq)UO2(OH)3UO2(OH)42(UO2)3(OH)5+(UO2)3(OH)7UO2CO3(aq)UO2(CO3)22UO2(CO3)34(UO2)2CO3(OH)3UO2+
U(OH)3+
U(OH)4(aq)U(CO3)44UO2·2H2O(am)Coffinite
Schoepite
CaU2O7:3H2O(s)Becquerelite
Uranophane

UO22+ + H2O = UO2OH+ + H+
UO22+ + 2H2O = UO2(OH)2(aq) + 2H+
UO22+ + 3H2O = UO2(OH)3- + 3H+
UO22+ + 4H2O = UO2(OH)42- + 4H+
3UO22+ + 5H2O = (UO2)3(OH)5+ + 5H+
3UO22+ + 7H2O = (UO2)3(OH)7- + 7H+
UO22+ + CO32- = UO2CO3(aq)UO22+ + 2CO32- = UO2(CO3)22UO22+ + 3CO32- = UO2(CO3)342UO22+ + CO32- + 3H2O = (UO2)2CO3(OH)3- + 3H+
UO22+ + 0.5H2O = UO2+ + 0.25O2 + H+
UO22+ + 2H2O = U(OH)3+ + H+ + 0.5O2
UO22+ + 3H2O = U(OH)4(aq) + 2H+ + 0.5O2
UO22+ + 4CO32- + 2H+ = U(CO3)44- + 0.5O2 + H2O
UO2·2H2O(am) + 2H+ + 0.5O2 = UO22+ + 3H2O
USiO4(s) + 2H+ + 0.5O2 + H2O = UO22+ + H4SiO4UO3·2H2O(s) + 2H+ = UO22+ + 3H2O
CaU2O7:3H2O + 6H+ = 2UO22+ + Ca2+ + 6H2O
Ca(UO2)6O4(OH)6·8H2O + 14H+ = Ca2+ + 6UO22+ + 18H2O
Ca((UO2)2SiO3OH)2·5aq + 6H+ = Ca2+ + 2UO22+ + 2H4SiO4 + 5H2O

–5.25
–12.15–20.25–32.40–15.55–32.20
9.94
16.61
21.84
–0.86
–19.30–37.22–42.52
2.60
34.02
31.02
5.96
23.40
29.00
9.42

0.24
0.07
1.05
0.68
0.12
0.80
0.03
0.09
0.04
0.50
0.02
1.00
1.40
0.93
1.09
6.57
0.18
1.00
1.00
5.06

112

TR-10-52

Species

Reaction

logK0

DlogK0Np

Np(OH)3+
Np(OH)4
Np(CO3)44Np(OH)4(CO3)2NpCO3(OH)3Np(OH)2(CO3)22NpO2+
NpO2OH
NpO2(OH)2NpO2(CO3)NpO2(CO3)23NpO2(OH)2
NpO2(CO3)22NpO2(CO3)34NpO2·2H2O(am)NpO2OH (am, aged)
NpO2(CO3)Na·3.5aq

Np4+ + 3H2O = Np(OH)3+ + 3H+
Np4+ + 4H2O = Np(OH)4 + 4H+
Np4+ + 4CO32- = Np(CO3)44Np4+ + CO32- + 4H2O = Np(OH)4(CO3)2- + 4H+
Np4+ + CO32- + 3H2O = NpCO3(OH)3- + 3H+
Np4+ + 2CO32- + 2H2O = Np(OH)2(CO3)22- + 2H+
Np4+ + 0.25O2 + 1.5H2O = NpO2+ + 3H+
Np4+ + 0.25O2 + 2.5H2O = NpO2OH(aq) + 4H+
Np4+ + 0.25O2 + 3.5H2O = NpO2(OH)2- + 5H+
Np4+ + 0.25O2 + CO32- + 1.5H2O = NpO2CO3- + 3H+
Np4+ + 0.25O2 + 2CO32- + 1.5H2O = NpO2(CO3)23- + 3H+
Np4+ + 0.5O2 + 3H2O = NpO2(OH)2(aq) + 4H+
Np4+ + 0.5O2 + H2O + 2CO32- = NpO2(CO3)22- + 2H+
Np4+ + 0.5O2 + H2O + 3CO32- = NpO2(CO3)34- + 2H+
NpO2·2H2O(am) + 4H+ = Np4+ + 4H2O
NpO2OH(am) + 4H+ = Np4+ + 0.25 O2 +2.5H2O
NpO2CO3Na·3.5H2O + 3H+ = Np4+ + 0.25O2 + 5H2O + CO32- + Na+

–2.80
–8.30
36.68
–6.83
3.82
15.17
10.57
–0.73
–13.03
15.53
17.10
–0.45
28.28
31.13
–0.70
–5.87
–21.57

1.00
1.10
1.03
1.13
1.13
1.50
0.12
0.71
0.51
0.13
0.16
1.51
0.74
0.24
0.50
0.23
0.27

Pu

PuOH2+
Pu(OH)2+
Pu(OH)3(aq)PuCO3+
Pu(CO3)2Pu(CO3)33PuSO4+
Pu(SO4)2Pu(OH)3+
Pu(OH)4(aq)Pu(CO3)44PuO2+
PuO2CO3PuO2(OH)2(aq)PuO2CO3(aq)PuO2(CO3)22PuO2(CO3)34Pu(OH)3(s)PuCO3OH(s)Pu(OH)4(s)PuO2(OH)2:H2O

Pu3+ + H2O = PuOH2+ + H+
Pu3+ + 2H2O = Pu(OH)2+ + 2H+
Pu3+ + 3H2O = Pu(OH)3(aq) + 3H+
Pu3+ + CO32- = PuCO3+
Pu3+ + 2CO32- = Pu(CO3)2Pu3+ + 3CO32- = Pu(CO3)33Pu3+ + SO42- = PuSO4+
Pu3+ + 2SO42- = Pu(SO4)2Pu3+ +0.25O2 + 2.5H2O = Pu(OH)3+ + 2H+
Pu3+ +0.25O2 + 3.5H2O = Pu(OH)4(aq) + 3H+
Pu3+ + 0.25O2 + 4CO32- + H+ = Pu(CO3)44- + 0.5H2O
Pu3+ + 0.5O2 + H2O = PuO2+ + 2H+
Pu3+ + 0.5O2 + CO32- + H2O = PuO2CO3- + 2H+
Pu3+ + 0.75O2 + 2.5H2O = PuO2(OH)2(aq) + 3H+
Pu3+ + 0.75O2 + CO32- + 0.5H2O = PuO2CO3(aq) + H+
Pu3+ + 0.75O2 + 2CO32- + 0.5H2O = PuO2(CO3)22- + H+
Pu3+ + 0.75O2 + 3CO32- + 0.5H2O = PuO2(CO3)34- + H+
Pu(OH)3(cr) + 3H+ = Pu3+ + 3H2O
PuCO3OH(s) + H+ = Pu3+ + CO32- + H2O
Pu(OH)4(s) + 3H+ = Pu3+ + 0.25O2 + 3.5H2O
PuO2(OH)2:H2O + 3H+ = Pu3+ + 0.75O2 + 3.5H2O–6.90–15.90–25.30
7.64
12.54
16.40
3.91
5.70
0.79
–5.41
40.09
6.42
11.54
–1.82
20.88
26.08
29.38
15.80
–5.94
–3.89
–5.85

0.30
1.00
1.50
0.86
0.86
1.40
0.66
0.91
0.73
0.84
1.29
0.96
0.97
1.91
1.29
1.29
1.29
1.50
1.26
1.47
1.55

Am

Am(OH)2+
Am(OH)2+
Am(OH)3
Am(CO3)+
Am(CO3)2Am(CO3)33AmHCO32+
Am(SO4)+
Am(SO4)2AmCl+2
AmCl2+Am(OH)3 (am)Am(CO3)(OH)(s)Am2(CO3)3(s)Am(CO3)2Na·5H2O(s)Am3+ + H2O = Am(OH)2+ + H+
Am3+ + 2H2O = Am(OH)2+ + 2H+
Am3+ + 3H2O = Am(OH)3 + 3H+
Am3+ + CO32- = Am(CO3)+
Am3+ + 2CO32- = Am(CO3)2Am3+ + 3CO32- = Am(CO3)33Am3+ + H+ + CO32- = AmHCO32+
Am3+ + SO42- = Am(SO4)+
Am3+ + 2SO42- = Am(SO4)2Am3+ + Cl- = AmCl2+
Am3+ + 2Cl- = AmCl2+
Am(OH)3(am) + 3H+ = Am3+ + 3H2O
Am(CO3)(OH)(s) + H+ = Am3+ + CO32- + H2O
Am2(CO3)3(s) = 2Am3+ + 3CO32Am(CO3)2Na 5H2O(s) = Am3+ + 2CO32- + 5H2O + Na+–7.20–15.10–26.20
8.00
12.90
15.00
13.43
3.30
3.70
0.24
–0.74
16.90
–6.20
–33.40–21.00

0.50
0.70
0.50
0.40
0.60
1.00
0.30
0.15
0.15
0.03
0.05
0.80
1.00
2.20
0.50

Cm

Table 3-31. Reactions and equilibrium constants recommended for use in the Simple Functions
spreadsheet, valid at 25°C. (3/4)

Cm(OH)2+
Cm(OH)2+
Cm(OH)3
Cm(CO3)+Cm(CO3)2Cm(CO3)33CmHCO32+
Cm(SO4)+Cm(SO4)2CmCl2+
CmCl2+
Cm(OH)3 (am)Cm(CO3)(OH)(s)Cm2(CO3)3(s)Cm3+ + H2O = Cm(OH)2+ + H+
Cm3+ + 2H2O = Cm(OH)2+ + 2H+
Cm3+ + 3H2O = Cm(OH)3 + 3H+
Cm3+ + CO32- = Cm(CO3)+
Cm3+ + 2CO32- = Cm(CO3)2Cm3+ + 3CO32- = Cm(CO3)33Cm3+ + H+ + CO32- = CmHCO32+
Cm3+ + SO42- = Cm(SO4)+
Cm3+ + 2SO42- = Cm(SO4)2Cm3+ + Cl- =CmCl2+
Cm3+ + 2Cl- = CmCl2+
Cm(OH)3(am) + 3H+ = Cm3+ + 3H2O
Cm(CO3)(OH)(s) + H+ = Cm3+ + CO32- + H2O
Cm2(CO3)3(s) = 2Cm3+ + 3CO32-–7.20–15.10–26.20
8.00
12.90
15.00
13.43
3.30
3.70
0.24
–0.74
16.90
–6.20
–33.40

0.50
0.70
0.50
0.40
0.60
1.00
0.55
0.15
0.15
0.03
0.05
0.80
1.00
2.20

TR-10-52

113

Species

Reaction

logK0

DlogK0Sm

SmOH2+
Sm(OH)2+
Sm(OH)3
Sm(OH)4SmCO3+
Sm(CO3)2SmHCO32+
SmSO4+
Sm(SO4)2SmCl2+
Sm(OH)3(am)Sm2(CO3)3(s)SmOHCO3(s)Sm3+ + H2O = SmOH2+ + H+
Sm3+ + 2H2O = Sm(OH)2+ + 2H+
Sm3+ + 3H2O = Sm(OH)3 + 3H+
Sm3+ + 4H2O = Sm(OH)4- + 4H+
Sm3+ + CO32- = SmCO3+
Sm3+ + 2CO32- = Sm(CO3)2Sm3+ + CO32- + H+ = SmHCO32+
Sm3+ + SO42- = SmSO4+
Sm3+ + 2SO42- = Sm(SO4)2Sm3+ + Cl- = SmCl2+
Sm(OH)3(am) + 3H+ = Sm3+ + 3H2O
Sm2(CO3)3(s) = 2Sm3+ + 3CO32SmOHCO3(s) + H+ = Sm3+ + CO32- + H2O–7.90–16.50–25.90–36.90
7.80
12.80
12.43
3.50
5.20
0.40
18.60
–34.50
–7.70

0.10
0.20
1.00
1.00
0.50
0.60
0.50
0.20
0.10
0.10
1.00
2.00
0.30

Ho

HoOH2+
Ho(OH)2+
Ho(OH)3
Ho(OH)4HoCO3+
Ho(CO3)2HoHCO32+
HoSO4+
Ho(SO4)2HoCl2+
Ho(OH)3(am)Ho2(CO3)3(s)Ho3+ + H2O = HoOH2+ + H+
Ho3+ + 2H2O = Ho(OH)2+ + 2H+
Ho3+ + 3H2O = Ho(OH)3 + 3H+
Ho3+ + 4H2O = Ho(OH)4- + 4H+
Ho3+ + CO32- = HoCO3+
Ho3+ + 2CO32- = Ho(CO3)2Ho3+ + CO32- + H+ = HoHCO32+
Ho3+ + SO42- = HoSO4+
Ho3+ + 2SO42- = Ho(SO4)2Ho3+ + Cl- = HoCl2+
Ho(OH)3(am) + 3H+ = Ho3+ + 3H2O
Ho2(CO3)3(s) = 2Ho3+ + 3CO32-–7.90–16.10–24.50–33.40
8.00
13.30
12.50
3.40
4.90
0.30
17.80
–33.80

0.20
0.10
0.10
0.20
0.40
0.60
0.50
0.30
0.30
0.50
0.30
1.00

Pb

Table 3-32. Reactions and equilibrium constants recommended for use in the Simple Functions
spreadsheet, valid at 25°C. (4/4)

PbOH+
Pb(OH)2
Pb(OH)3Pb(OH)42PbCO3
PbCl+
PbCl2
PbCl3PbClOH (s)PbCO3 (Cerussite)
Hydrocerussite

Pb2+ + H2O = PbOH+ + H+
Pb2+ + 2 H2O = Pb(OH)2 + 2 H+
Pb2+ + 3 H2O = Pb(OH)3- + 3 H+
Pb2+ + 4 H2O = Pb(OH)42- + 4 H+
Pb2+ + CO32- = PbCO3
Pb2+ + Cl- = PbCl+
Pb2+ + 2Cl- = PbCl2
Pb2+ + 3Cl- = PbCl3PbClOH(s) + H+ = Pb2+ + Cl- + H2O
PbCO3 (s) = Pb2+ + CO32Pb3(CO3)2(OH)2 + 2 H+ = 3 Pb2+ + 2 CO32- + 2 H2O–7.51–16.95–27.20–38.90
7.00
1.55
2.00
2.01
0.62
–13.29–17.91

0.50
0.20
0.70
0.80
0.50
0.30
0.30
0.30
0.30
0.69
1.94

Radium barium co-precipitation

As discussed in Section 3.4.7, the Ra/Ba ratio in the PWR and BWR canister will throughout repository
evolution be below 10–3. It is suggested to pessimistically assume that this ratio is valid during theentire time period, resulting in the formula Ra0.001Ba0.999SO4(s). It is furthermore suggested to handle
Ra-Ba co-precipitation by calculating the Ra solubility limit with the Simple Function spreadsheet,using RaSO4(s) as the solubility limiting phase, and multiplying the outcome by a factor of 0.001.
This treatment is a simplification since it basically means that thermodynamic data for barium is
identical to data for radium, and it is actually the solubility of barium that is calculated.
Recommendations for elements not included in the Simple Functions spreadsheet

The flowing elements are suggested not to be solubility limited, and are thus not included in the
Simple Functions spreadsheet:•

H, C, Cl, I, and Cs.

The following elements of the selected inventory have not been studied by the supplier, and norecommendation is given:•

Ac, Cd, Eu, and Mo.

These elements are not included in the Simple Functions spreadsheet.

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Supporting data

Data from natural systems, laboratory experiments, and previous assessments are documented in/Duro et al. 2006b/. These are supporting data used in the expert judgement for the solubility assessment presented in /Duro et al. 2006a/. A summary of where this information can be found is shownin Table 3-33.
Table 3-33. List of data from natural systems, laboratory experiments and previous assessments
Information

Reference in /Duro et al. 2006a/

Conditions under which radioelement concentrations have been measured
in laboratory spent fuel dissolution experiments.

Table 4-1

Measured radioelements concentrations by the different laboratories.

Figures 4-1 to 4-7 and Appendix A

Range of concentration of the trace elements of interest measured in rocks,
minerals and natural waters.

Appendix B

Results of the solubility calculations obtained in /Duro et al. 2006a/.

Table 8-1

Description of the main conditions under which calculations are done in the
different performance assessments considered in /Duro et al. 2006a/.

Appendix C

Recommended solubility controlling phases and solubility limits proposedin other assessments.

Appendix C, Tables C-1 to C-11

Graphical representation of the recommended solubility limits proposedin other assessments.

Appendix C, Figures C-1 to C-13

Graphical representation of the recommended solubility limits in the reference
groundwater, the solubility limits recommended in SR 97, experimental apparent
solubility limits, and measurements from natural systems.

Appendix D

3.4.11 Judgements by the SR-Site teamSources of information and documentation of data qualification

The supplier has listed a number of sources of information and sorted numerous of data sets as either
qualified or supporting. The supplier has also justified the sorting. The SR-Site team agree with thishandling.
Conditions for which data are supplied

The supplier has listed a number of conditions, such as pH, temperature, pH2, etc., for which data aresupplied. As result of the corrosion of iron inside the canister, the redox conditions of the intruding
water is determined by the magnetite/goethite equilibrium, which is used as a condition in the Simple
Functions spreadsheet. The SR-Site team agree with this handling.
Conceptual uncertainty

The supplier has in detail discussed conceptual uncertainty, and referred to even more detailed
discussions. The SR-Site team agree with this handling.
Data uncertainty due to precision, bias, and representativity

The supplier has in detail discussed data uncertainty, in terms of precision, bias, and representativity,
and referred to even more detailed discussions. The SR-Site team agree with this handling. An identified
limitation of the data is that solubility limits can only be assessed for 25°C, while the ambient tempera­ture of interest most likely is lower. The sensitivity study of Ni showed that for some precipitationdissolution reactions, the shift is towards dissolution at lower temperatures. Therefore, it is recommended
that for the radioelements of greatest importance for radiological risk, one makes at least a qualitative
assessment whether the assumed temperature (25°C) significantly affects assessment results. However,another aspect that needs to be pointed out in relation to uncertainty in temperature is the variability
on log(Ksol), as a result of a 10°C temperature difference, is less than the uncertainty of the constant.

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Spatial and temporal variability of data

For solubility limits, the supplier argues that accounting for various groundwater compositions
handles spatial and temporal variability. The SR-Site team agree. For Ra-Ba co-precipitation, thesupplier suggests a pessimistic approach and argues that this approach makes spatial and temporalvariability subordinate. The SR-Site team agree.
Correlations

The supplier argues that the geochemical calculations by themselves are a set of closely related
correlations. The SR-Site team agree. Because of this, groundwater compositions used as input tothe Simple Functions spreadsheet must be internally consistent.
Result of supplier’s data qualification

The supplier delivers a number of solubility limiting phases, which agree with those given in
SR-Can. In addition, solubility limiting phases for Pb are given. The SR-Site team agree withthe handling.
The supplier delivers numerous of relevant reactions and equilibrium constants, some of which
are updated since SR-Can. For radioelements not included in the Simple Functions spreadsheet,recommendations on how to assess their solubility limit in SR-Site are given. The SR-Site teamagree with the handling.
The supplier gives no data and no recommendation for Ac, Cd, Eu, and Mo. Ac will not be solubility
limited, and the other elements are of minute significance for assessment results, wherefore one canpessimistically assume that they are not solubility limited.
The supplier recommends that Ra-Ba co-precipitation is handled by assessing the Ra solubility
limit using the Simple Functions spreadsheet (with RaSO4 (s) as the solubility limiting phase) andmultiplying the outcome by a factor of 0.001. The SR-Site team agree.

3.4.12 Data recommended for use in SR-Site modelling
The solubility limiting phases recommended for use in the Simple Functions spreadsheet are given
in Table 3-28.
The reactions and equilibrium constants recommended for use in the Simple Functions spreadsheet
are given in Table 3-29 to Table 3-32. Due to the corrosion of iron inside the canister, the redox
condition should correspond to that determined by the magnetite/goethite equilibrium.
The recommended way of handling radioelements of the selected inventory that are not included in
the Simple Functions spreadsheet, which are Ac, C, Cd, Cl, Cs, Eu, Mo, and I, is to assume that they
are not solubility limited.
The recommended ways to assess the solubility limit of Ra is to use the Simple Functions spreadsheetwith RaSO4 (s) as the solubility limiting phase, and to multiplying the outcome by a factor of 0.001.

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4Canister data

4.1

Data of the intact canister

This section concerns non-trivial geometric data associated with the intact canister. These data are
the initial minimum copper thickness and the void volume of the average canister. The former isused in copper corrosion calculations while the latter is used for assessing data relevant for radio­
nuclide transport modelling. Trivial geometric canister data are given in the Canister production
report.
This section also concerns the canister’s resistance to mechanical loads (isostatic load, shear loads
due to shear movement of the rock mass surrounding the deposition hole, asymmetric loads due touneven swelling of the bentonite). Of specific concern is the data for the modelling of the response
to shear loads as this is further modelled within SR-Site.

4.1.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR‑Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The supplier should deliver:•

The initial minimum copper thickness dCu (m) of the installed canister.

•

The void volumes V (m3) for the fully loaded and the average BWR type canister, for the
fully loaded and the average PWR type canister, and for the average canister.

•

Resistance to mechanical loads, in terms of whether the canister complies with the designpremises /SKB 2010a/.

SR-Site modelling activities in which data will be used

The initial minimum copper thickness is used as input to the SR-Site corrosion calculations, as
described in /SKB 2010c/. In the corrosion calculations it is assumed that corrosion may occur
where the copper thickness is the least. The copper shell may have a varying thickness due to thedesign, imperfections induced in the production, and defects induced by the handling up until,
and in, the installation in the repository.
The void volume is used for assessing the delay time (cf. Section 4.2) associated with the onset ofradionuclide transport from a defect canister. This onset occurs as the void volume is filled with water
intruding into the canister. The void volume is also used for assessing the source term (cf. the Radio­
nuclide transport report). In case the concentration of the dissolved radioelement is below its
solubility limit, a larger void volume dilutes the concentration compared to a smaller void volume,
if the same amount of the radioelement is dissolved. And opposite, if the concentration is at its
solubility limit, a larger void volume gives rise to a larger amount of the dissolved radioelement.
The resistance to mechanical loads are input to the choice of scenarios. The resistance to shear movement is the coupling to the seismic modelling (cf. /Munier 2010, Fälth et al. 2010/). In this modelling,
the probability is assessed that the shear magnitude and velocity in the rock volume surrounding thedeposition holes are larger than specified in the canister design premises. In case a seismic event gives
rise to such shear, it is assumed that the affected canisters fail. This probability is used as input in theradionuclide transport modelling (cf. the Radionuclide transport report).

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4.1.2

Experience from SR-Can

Modelling in SR-Can

Concerning the initial minimum copper thickness, the copper corrosion calculations used in SR-Siteagrees with that used in SR-Can. Concerning the void volume, it is used in the same way in SR-Site
as in SR-Can, both for assessing the source term and delay time (Section 4.2 is referred to for further
information on the delay time). The resistance to shear movement was modelled with the ABAQUS
code for a number of different shear cases /Börgesson and Hernelind 2006/.
Conditions for which data were used in SR-Can

The conditions concerning the initial minimum copper thickness and void volume are the same in
SR-Site as in SR-Can, i.e. they apply for the installed but intact canister. The conditions for the resistanceto shear movement generally agree in SR-Site and SR-Can, and correspond to the in situ conditionsof the installed canister at the upper limit of the buffer density. However, the criterion for maximum
shear magnitude is 5 cm in SR-Site, compared to 10 cm in SR-Can.
Sensitivity to assessment results in SR-Can

Concerning the initial minimum copper thickness it was shown in SR-Can that there will be no initialdefects penetrating through the copper shell. For no canister the defects reduce the initial minimum
copper thickness to less than 35 mm (cf. Table 4-1 of /SKB 2006b/). The time needed to corrode throughthe copper shell (approximately) scale inversely proportional to the copper thickness. Accordingly there
is only a difference in corrosion time of a factor of about 1.4 between the 35 mm and 50 mm minimum
copper thicknesses. Therefore, the analysis was rather insensitive to this parameter.
Concerning the void volume, the range of this volume is constrained by the geometries of the castiron insert and the fuel assemblies. A smaller void volume may increase the source term concentration
at relatively early times for some radionuclides, e.g. I-129, if the same amount of the radionuclide isdissolved. The fully loaded PWR canister has the smallest void volume. However, the ratio between
the smallest void volume and a relatively large one is about a factor of two. Accordingly, this parameterhas only a minor impact on the radiological risk. In the SR-Can safety assessment, the radiologicalrisk peaked at late times and therefore the void volume was of little consequence for the assessment.
In /SKB 2006a, Section 9.4.5/ it was concluded that the copper canister could withstand a shear move­
ment with a magnitude of 10 cm, but that for a shear movement of 20 cm, failure of the copper shellcannot be totally excluded. It was recognised that the study was limited and that the creep model
needed improvement. In /SKB 2006a, Section 9.4.9/ it was concluded that no canister failures at the
expected maximum over-pressure at the Forsmark site are expected.
Alternative modelling in SR-Can

No alternative modelling of importance for the initial minimum copper thickness, void volume,or resistance to mechanical loads was performed in SR-Can.
Correlations used in SR-Can modelling

No correlation was used in subsequent SR-Can modelling concerning the initial minimum copper
thickness, the void volume, and the resistance to mechanical loads.
Identified limitations of the data used in SR-Can modelling

No limitation has been identified in the data on, and usage of, the initial minimum copper thickness
and void volume.
Concerning the resistance to shear movement, limitations in the study was recognised in /SKB 2006a,
Section 9.4.5/, giving rise to suggestions for further modelling in SR-Site (cf. /SKB 2006a, Table 9-14/).
The regulatory authorities also recognised limitations in the modelling and found that the arguments
for the 10 cm criterion need to be reinforced in order to demonstrate the validity of SKB’s strategy
for overall handling of the earthquake problem /Dverstorp and Strömberg 2008/.
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4.1.3 Supplier input on use of data in SR-Site and SR-Can
Underlying data on the initial minimum copper thickness and the resistance to shear movement aresupplied by the Canister production report team. Prior to supplying these data there has been muchcommunication with the SR-Site team and as result, the supplier has no further input.
The void volumes supplied in this section have been produced by the SR-Site team; hence no supplierformally exists. None the less, the text is written according to the standard outline of the Data report.

4.1.4

Sources of information and documentation of data qualification

Sources of information

The assessment of the initial minimum copper thickness is based on data in the Canister production
report. The calculation of void volume is based on data in the Spent fuel report and the Canister
production report. The analysis of the canister’s resistance to mechanical loads is made in the Canister
production report and /Raiko et al. 2010/, with especially /Hernelind 2010, Dillström and Bolinder
2010/ as supporting documents. The full references to the main sources of information are given in
Table 4‑1.
Table 4‑1. Main sources of information used in data qualification.
Canister production report, 2010. Design, production and initial state of the canister. SKB TR-10-14,
Svensk Kärnbränslehantering AB.
Spent fuel report, 2010. Spent nuclear fuel for disposal in the KBS-3 repository. SKB TR-10-13, Svensk Kärnbränslehantering AB.
Raiko H, Sandström R, Rydén H, Johansson M, 2010. Design analysis report for the canister. SKB TR-10-28, Svensk
Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

The input data for the assessment on the initial minimum copper thickness mainly come from Chapter 7of the Canister production report. The input data for the void volume calculations mainly come fromthe Canister production report and the Spent fuel report. In addition, some data on densities aretaken from the open literature. For the resistance to mechanical loads, the judgement in the Canister
production report on whether or not the canister can withstand the loads according to the designpremises is used.
These data sets are sorted as qualified or supporting in Table 4‑2. The justification to the sorting ofeach item is given in the table.
Table 4‑2. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Canister production report, Tables 3-3, 3-4, 3‑6: Geometric dataon the BWR type canister, for void volume calculations.
2. Canister production report, Tables 3-3, 3-5, 3‑6: Geometric dataon the PWR type canister, for void volume calculations.
3. Spent fuel report, Table 2-4: Weights of components of fuelassemblies, for void volume calculations.
4. Canister production report, Tables 7-3: Minimum thicknesses of
the copper shell after machining.
5. Canister production report, Sections 7.2.1–7.2.3: Statement thatcanisters manufactured according to the reference design will fulfil thedesign premises related to mechanical loads.

6. Densities of materials in fuel assemblies from
/CRC 2008/ and /Slagle et al. 1994, Appendix A/,
for void volume calculations.

1–2, 4–5: These data have been qualified in the Canister production report, in accordance with the SKB quality assurance
system. This qualification is found to be in compliance with the demands of this data report.
3: These data have been qualified in the Spent fuel report, in accordance with the SKB quality assurance system. Thisqualification is found to be in compliance with the demands of this data report.
6. These density data are taken from the open literature but are judged as supporting as the data may not be representative
for the exact materials used in the fuel assemblies. It is judged, however, that this potential lack of representativeness would
give rise to very minor deviations in the supplied void volume data.

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119

Excluded data previously considered as important

Concerning the void volume, no such data are excluded. Concerning the initial minimum copper
thickness, the concept of an initial pinhole penetrating the copper shell has been abandoned, due
to improved welding methods. Concerning the resistance to shear movement, the design premises
analysis has been set to 5 cm shear , while 10 cm shear magnitude was used in SR-Can (see further
discussion in Section 2.3 of /SKB 2010a/).

4.1.5

Conditions for which data are supplied

Initial minimum copper thickness

The copper shell may have a varying thickness due to the design, imperfections induced in the production, and defects induced by the handling up until, and in, the installation. The entire chain fromproduction to installation is described in the Canister production report. The data applies for the
installed canister, but before corrosion and major mechanical loads influence the copper thickness.
Void volume

The void volume is calculated for the SKB spent fuel reference scenario and for the SKB canisterreference design. Changes in volumes due to varying temperature have been disregarded. The void
volume is calculated for an intact canister, and does not include a potential reduction of the void
volume due to the formation of corrosion products upon water intrusion in a defect canister.
Resistance to mechanical loads

The effect on the canister due to a mechanical loads has been modelled in a 3D finite element modelusing the ABAQUS code /Hernelind 2010, Raiko et al. 2010/. Variables that were varied for the shear
load cases include the angle with which the fracture intersects the deposition hole and the buffer density.
Results corresponding to the shear magnitude of 5 cm are reported in /Raiko et al. 2010/. However,results are available also for larger shear magnitudes in /Hernelind 2010, Dillström and Bolinder 2010/.
The design analysis is valid for canisters that have not been subjected to heavy corrosion. For detailed
conditions of the modelling /Raiko et al. 2010, Hernelind 2010, Dillström and Bolinder 2010/ arereferred to.

4.1.6

Conceptual uncertainty

Initial minimum copper thickness

There is no conceptual uncertainty associated with the initial minimum copper thickness. However,there is some uncertainty associated with how this copper thickness should be used in corrosion
calculations. In case of defects, these occur at a very local area of the copper shell (most importantlyin the welds). In the advection/corrosion case it is assumed that the buffer has eroded at the fractureintersecting the deposition hole, but not that the entire buffer in the deposition hole has been removed.
This means that only a limited area of the copper surface is subjected to increased corrosion rates,
while other copper surface areas are still protected by the buffer. There is only a certain probability
that the area featuring the initial minimum copper thickness is exposed to advection/corrosion.
Void volume

The void volume is calculated using elementary geometrical considerations. Accordingly there isno conceptual uncertainty. In subsequent modelling, however, there is the question of to what degree
the void volume is utilised by water intruding into the canister. There is also the question of how
efficiently radionuclides can be transported from different parts of the void volume.

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Resistance to mechanical loads

An uncertainty discussion concerning the design analysis of the canister is given in Chapter 7 of/Raiko et al. 2010/. An uncertainty discussion is also given in Chapter 10 of /Hernelind 2010/.
Conceptual uncertainty is associated with how well the models represent the intended cases (forexample the size of the mesh in the finite element modelling). Conceptual uncertainty is also associatedwith how the output from one modelling is used as input in subsequent modelling. The nature of
the conceptual uncertainty is not such that it jeopardises the validity of the conclusions from theanalyses.

4.1.7

Data uncertainty due to precision, bias, and representativity

Initial minimum copper thickness

Uncertainties in the initial minimum copper thickness are associated with how well one can produce
the copper shell components, how well it can be welded and the subsequent handling until the canister is installed. This chain of events is discussed at length in the Canister production report. In
Chapter 3 of the Canister production report, the reference design is discussed. In Chapter 7 theprobability of different deviations from this design, as well as of defects in the welds and shell, aregiven. These data and probabilities are used in Section 4.1.10 of this present text to justify an initial
minimum copper thickness for subsequent use in SR-Site corrosion calculations.
Void volume

The void volume is calculated using elementary geometrical considerations (cf. Section 4.1.10).
The tolerances in the canister and cast insert dimensions are very small, wherefore little uncertainty
is induced in the geometrical calculations. The total volume of typical fuel assemblies is calculated
from the weights and densities of different materials of the fuel assembly. In doing this, density dataon similar, but not the exact, materials have been used. However, the uncertainty induced is judged
to be insignificant. When calculating the void volume of the average canister, it is necessary to makea few assumptions regarding this hypothetical average canister (cf. Section 4.1.10 for details on theseassumptions). The uncertainty introduced as result of these assumptions is judged to be minor.
Resistance to mechanical loads

According to the Canister production report the major uncertainties in the shear load case arethat the calculations are based on pessimistic assumptions, mainly on the material properties of thebentonite. Secondly, the knowledge of occurrence of crack-like defects in the insert close to theacceptance level is limited due to the fact that no inspection methods have been available to detect
these relatively small surface defects.
An uncertainty discussion is given in Sections 7.5 and 7.6 of /Raiko et al. 2010/ and Chapter 10 of/Hernelind 2010/, concerning load on the canister due to rock shear movement. Among the mentioned
sources of data uncertainty are uncertainty in the stress-strain relationship of the canister material
and the bentonite; natural scattering in the material data and in the fracture toughness; the fact thatmaterial data for the BWR inserts have been used when modelling PWR inserts; uncertainties in thecopper creep model and in the creep strain rate; and assumption made for the coefficient of friction
between the copper and the surrounding materials. For a full account of the uncertainties /Raikoet al. 2010, Hernelind 2010, Dillström and Bolinder 2010/ are referred to.
In the Canister production report (Sections 7.2.1 and 7.2.3) the major uncertainties for the isostic
load case and the uneven pressure from the bentonite regard the use of material data for BWR insert
when modelling PWR inserts, the fact that only crack-like defects have been analysed (and notvolumetric defects), and the effect of small indentations in the copper shell.

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4.1.8Spatial and temporal variability of data

Initial minimum copper thickness

The recommended initial minimum copper thickness is intended for the general canister, which could
be deposited at any canister position in the repository. In this respect there is no component of spatial
variability. It is noted that the initial minimum copper thickness is more likely to be found in thewelds than in the tube, base or lid, but this should have little consequence for subsequent modelling.
The data applies at the initial state.
Void volume

For the void volume of the average canister, spatial variability is of no concern. For individual canisters the void volume depends on how many channel tubes are occupied by fuel assemblies. Thisis handled by supplying void volumes for canisters filled with different numbers of fuel assemblies(cf. Table 4‑3 in Section 4.1.10). Temporal variability due to changes in temperature is judged as
insignificant and is accordingly disregarded. The void volume is valid for the intact canister, anddoes not account for the formation of corrosion products, upon intrusion of water in a defect canister.
Resistance to shear movement

The design analysis is general and there is no component of spatial variability when (indirectly)
applying its result on different canister positions in the repository. The temporal variability is handled
by accounting for the additional load due to the ice sheet during glaciation. Different sequences ofglacial loads and earthquakes have been analysed /Hernelind 2010/.

4.1.9

Correlations

Initial minimum copper thickness

The supplied data should be used for the general canister and there are no correlations of concern for
subsequent modelling.
Void volume

The supplied data should be used for the general canister and there are no correlations of concern for
subsequent modelling.
Resistance to mechanical loads

The design analysis is general and gives a judgement on whether or not the canister can withstandthe specified mechanical loads. There is no correlation that needs to be used when applying this insubsequent analyses (other than the combinations of mechanical loads already specified in the designpremises /SKB 2010a/).

4.1.10 Result of supplier’s data qualification
Initial minimum copper thickness

In Table 7-6 of the Canister production report the minimum copper thickness after machining at
normal operations, including measurement uncertainties, are given. According to this the minimum
copper thickness is judged to be 48.4 mm for the tube, 48.7 for the base, 48.1 for the lid (including a
1 mm deep identity marking that though only covers about 1% of the lid) and 47.5 mm for the welds.
Table 7-3 of the Canister production report gives probabilities for the ranges of minimum copper
thickness. For the whole surface of the shell it is stated that, for normal operations, > 99% has athickness of 47.5 mm, while a few canisters per thousand could have 45–47.5 mm. The fractionof canisters with < 45 mm is negligible.

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The occurrences of defects in the copper shell are one cause for further reduction of the corrosion
barrier. The local reductions of the copper shell thickness due to defects from the hot-forming andwelding processes are based on SKB’s experience in the pilot production of canisters. The probability
for disturbed operations, i.e. that one or more of the process parameters are outside the process
window is estimated to be low. The probability that the process parameters are such that they cause
defects that exceed 10 mm and that the process and inspection systems fail simultaneously, is at thepresent stage of development judged to be affecting below 0.1% of the canisters. This statement isbased on the fact that the developed welding process is very reliable and reproducible. If disturbed
operations of welding and inspection occur simultaneously, the maximum reduction of the copper
thickness is estimated not to exceed 20 mm. This is based on the presence of a maximum joint linehooking defect of 10 mm in combination with clearly visible wormholes of the same size.
In addition to the reduction of the copper shell thickness due to machining or possible welding
defects, reductions may occur as a result of surface damage during transportation, handling anddeposition of the canister. Available information on the occurrence of surface damage during thesestages is limited since full-scale tests, which focus on this issue using relevant handling equipment
and transport casks, remain to be performed.
In Table 7-3 of the Canister production report the probabilities and ranges for defects during hotforming and welding, and surface damages induced during transportation, handling and deposition,
are summarised. The local reduction due to defects is < 10 mm for > 99.9% of the canister and
10–20 mm for 0.1% of the canisters (the latter occurring only at disturbed operation consideringboth manufacturing processes and inspection). The fraction of canister with defects > 20 mm is
negligible.
To derive a minimum copper thickness to be used in the corrosion calculations the starting point isthat the machined canister surface area has 48.4 mm thickness of copper, while the weld areas haveat least 47.5 mm (for > 99% of the canisters). A cautious statement would thus be to say that a large
majority of the canisters would have at least 47.5 mm, over the entire surface.
The further reductions of this minimum copper thickness regard few canisters and/or small areas.
A few canisters per thousand could have a minimum machined thickness of the welds of 45 mm,covering some percents of the canister surface area. The maximum reduction of the copper thickness due to defects from the hot-forming and welding processes (occurring only under disturbed
operation) is given as 20 mm for 0.1% of the canisters. Also this reduction regards a small area ofthe surface, which could pessimistically be assumed to be the whole weld area. The given values for
local reduction given in Table 7-3 of the Canister production report include surface damages fromtransportation, handling and deposition, implying that no further reduction needs to be considered here.
The absolute lower limit in the ranges given for the welds would be 25 mm (45 mm, lowest value
for machined weld, minus 20 mm, for a maximum defect). It is though totally unrealistic to base thecorrosion calculations that should reflect the status for the whole canister surface, on this value forthe weld areas.
It is suggested that, built on the experience from the pilot production of canister, a copper thicknessof 47 mm (rounded from the 47.5 mm given above) is used as a reference value in the corrosion
calculations.
Void volume

According to the Spent fuel report (Table C-5) the total number of canisters to be deposited is6,103, whereof 4,451 hold BWR and BWR-MOX spent fuel and 1,652 hold PWR and PWR-MOX.
Figure 4‑1 shows the BWR cast iron insert with 12 channel tubes and the PWR cast iron insert with
4 channel tubes.
The length and diameter of both types of cast iron inserts are 4,573 mm and 949 mm, respectively. The
BWR insert holds 12 channel tubes, each 160×160 mm wide and 4,463 mm in length (cf. Tables 3-3 and3-4 of the Canister production report). This gives a BWR insert volume of about 1.87 m3 (if disregarding minor details in the design, such as the curvature of the channel tubes). The PWR insert (with channel
tubes 235×235 mm) has the volume 2.25 m3 (cf. Tables 3-3 and 3-5 of the Canister production report).

TR-10-52123Ø 1,050

Ø 1,050

Ø 949

Ø 949
135

50
235

160

5 cm copper
BWR-type

PWR-type
Nodular cast ironFigure 4‑1. SKB’s reference canister with an outer corrosion barrier of copper and an insert of nodular cast iron.
Basic differences between the BWR and PWR insert design. Figure modified from the Canister production report(Figure 3-2).

A typical BWR fuel assembly holds 198 kg UO2, 78 kg zirconium alloys, 13 kg stainless steel, and
1 kg nickel alloys (cf. Table 2-4 of the Spent fuel report). The corresponding volumes can be calculated from the following densities: UO2 = 1.05·104 kg/m3; stainless steel = 7.9·103 kg/m3; zirconium
alloy = 6.5·103 kg/m3; and nickel alloy = 8.25·103 kg/m3 /Slagle et al. 1994, CRC 2008/. This resultsin the total volume of one typical BWR fuel assembly of 0.033 m3.
A typical PWR fuel assembly holds 526 kg UO2, 129 kg zirconium alloys, 15 kg stainless steel, and
2 kg nickel alloys (cf. Table 2-4 of the Spent fuel report). In addition, in about one fourth of the
PWR fuel assemblies a control rod cluster will be deposited, with a typical weight of 16.3 kg stain­
less steel and 51.4 kg AgInCd alloy6 (Section 2.2.1 and Table B-5 of the Spent fuel report). The
volume of a typical control rod cluster can be calculated to 0.0071 m3. This results in the total
volume of one typical PWR fuel assembly (with one fourth of a control rod cluster) of 0.074 m3.
From the data given in the above paragraphs, the void volumes of BWR and PWR type canisters
with all channel tubes occupied by typical fuel assemblies can be calculated. For the BWR and PWR
type canisters the void volume becomes 1.0 m3 and 0.71 m3, respectively.
As described in the Spent fuel report, there will be canisters deposited with empty channel tubes.
According to the SKB reference scenario, in total 47,904 BWR and BWR-MOX assemblies, as well as
6,049 PWR and PWR-MOX assemblies will be deposited in 4,451 BWR type canisters and 1,652 PWR
type canisters (cf. Tables C-2 and C-5 of the Spent fuel report). This means that on average, a BWR typecanister will have 10.8 occupied and 1.2 empty channel tubes. Each empty BWR channel tube adds
0.11 m3 to the void volume. The average PWR type canister will have 3.66 occupied and 0.34 empty
channel tubes. Each empty PWR type channel tube adds 0.25 m3 to the void volume. If assumingthat the volume of each fuel assembly is described by either the typical BWR or PWR fuel assembly,
described above, the void volume of the average canister can be calculated. Firstly, the void volumesof the average BWR type canister and PWR type canister are calculated to 1.1 and 0.79 m3, respectively.
Based on the weighted average of the 4,451 BWR type canisters and 1,652 PWR type canisters, thevoid volume of the average canister is calculated to 1.0 m3. All the void volumes for the different
canisters are summarised in Table 4‑3. To illustrate extreme void volumes, the half-full BWR andPWR type canisters are also included in the table.
The density of 10.20 g/cm3 of the AgInCd alloy is taken from a product sheet on “Neutron Absorber –AgInCd 80/15/5” from Umicore Technical Materials. For comparison the density of Ag is 10.50 g/cm3.

6

124

TR-10-52

Table 4‑3. Void volume of different canister types.
CanisterVoid volume (m3)Full BWR type canister (12 fuel assemblies)

1.0

Full PWR type canister (4 fuel assemblies)

0.71

Average BWR type canister (10.8 fuel assemblies)

1.1

Average PWR type canister (3.66 fuel assemblies)

0.79

Half-full BWR type canister (6 fuel assemblies)

1.7

Half-full BWR type canister (2 fuel assemblies)

1.8

Average canister

1.0

Resistance to mechanical loads

According to the Canister production report (Section 7.2.2) the strength analysis shows that canisters
manufactured according to the reference design will fulfil the design premises related to shear loads.
However, the damage tolerance analysis gives acceptable defects sizes that put rigorous requirements
on manufacturing and NDT (non-destructive testing) capability. It is further stated that the copper
shell will have sufficient ductility to remain intact also after shear loads as long as the insert remains
intact.
According to the Canister production report (Sections 7.2.1 and 7.2.3) the probability for the canister
not to fulfil the design requirement related to isostatic load and uneven pressure from the bentonite
respectively, is deemed to be insignificant. This also accounts for the load from an ice sheet during
a glaciation.

4.1.11 Judgements by the SR-Site teamThe data on the initial minimum copper thickness and void volume has been produced or modified
by the SR-Site team, based on data from the supplier. The resistance to mechanical loads is justified
by the supplier.
Sources of information and documentation of data qualification

Input data for the assessment of the initial minimum copper thickness and void volume are foundin the Canister production report and the Spent fuel report. The discussion on the resistance tomechanical loads is found in the Canister production report and references therein.
The references cited in the data qualification are judged to be relevant and the citing is judged to be
sufficiently precise. The data sets used are judged to be adequately categorised into qualified andsupporting data sets.
Conditions for which data are supplied

The conditions for which the data are supplied are sufficiently well described, and have been chosen
to represent those of the repository.
Conceptual and data uncertainties

There is little conceptual uncertainty associated with the geometric data on the initial minimum copper
thickness and the void volume. There is detailed information on uncertainties associated with theresistance to mechanical loads in the supporting documents.
Data uncertainty due to precision, bias, and representativity

The data uncertainty concerning the initial minimum copper thickness and void volume is small andbounded in rather narrow ranges by extreme values. The supplier has provided probabilities for minimum copper thicknesses for different canister components. Importantly, the supplies suggest that the

TR-10-52

125

probability is zero that an initial defect penetrates the copper shell. The SR-Site team agree with thishandling. Concerning the resistance to mechanical loads, most of the data uncertainty is dealt withby modelling different isostatic and rock shear cases.
Spatial and temporal variability of data

The canister data supplied for the canister are general and should be applicable to all canisters in therepository. Therefore, in terms of canister positions, the data have no spatial variability. The initial
minimum copper thickness and the void volume data represent the initial state. Temporal variability
associated with the resistance to mechanical loads is handled by the design premises stating thelong-term loads in the repository.
Correlations

There is no correlation that needs to be propagated to subsequent handling.
Result of supplier’s data qualification

Concerning the initial minimum copper thickness and void volume the final data have been producedby the SR-Site team, based on underlying data supplied by the Canister production report and theSpent fuel report. The SR-Site team find these underlying data adequate.
The supplier argues that the canisters that are manufactured according to the reference design will
fulfil the design premises related to mechanical loads. For the shear load case the calculated acceptabledefects sizes in the insert are small and puts rigorous requirements on the manufacturing and NDTcapability.

4.1.12 Data recommended for use in SR-Site modelling
For the initial minimum copper thickness it is recommended to use the thickness 47 mm in SR-Sitecopper corrosion calculations. For SR-Site radionuclide transport modelling, it is recommended thatthe void volume of 1.0 m3 is used for the average canister. Concerning the resistance to mechanicalloads, it is judged that all canisters can withstand the loads according to the design premises, also
accounting for the additional load from an ice sheet during a glaciation. These data are summarisedin Table 4‑4.
Furthermore, for the evaluations of corrosion in the safety assessment, the area exposed to corrosion
and the number of canisters involved need to be taken into account in an evaluation of whether the
low probabilities given in Section 4.1.10 of less than 47 mm copper thickness could influence theresults. Here, also the geometries of the defects need to be considered. For example, while a defectin the form of a small void located near the outer surface of the canister can, for geometrical reasons, beargued to have no impact on the time for breakthrough by corrosion, the same defect in contact with
the inner surface could reduce the effective thickness for a corrosion calculation by the projected
length of the defect along the canister radius.
Table 4‑4. Data recommended for use in SR-Site.
Parameter

Data

Initial minimum copper thickness

47 mm

Void volume

1.0 m3

Resistance to mechanical loads

Canister will withstand the mechanical loads as specified
in the design premises.

126

TR-10-52

4.2

Evolving canister defectThis section concerns data needed in safety assessment modelling regarding potential penetrating
canister defects. Before dissolved radionuclides can migrate from a failed canister, a continuous
water pathway needs to be established between the spent fuel and the canister exterior. This requires
a defect through the copper shell and the size of this defect is of importance for the water intrusionrate, as well as for the radionuclide release rate once the continuous water pathway has formed.
Before the pathway can form, sufficient amounts of water need to intrude the canister so that its
void becomes filled. As water intrudes into the canister, the cast iron insert will corrode, forminghydrogen gas that will limit the pressure gradient constituting the driving force for water to intrude
into the canister. In addition, the corrosion reaction consumes water and corrosion products may
block the void that would otherwise constitute the pathway (cf. the Fuel and canister process report).
The hydromechanical evolution of canisters with a minor initial defect was given substantial
attention in the SR 97 safety assessment /SKB 1999b/. The conclusion was that the processes in
the canister internals add a considerable delay time before a continuous water pathway between
the spent fuel and the buffer is formed. This is mainly related to the slow ingress of water into thecanister, due to the low permeability of the buffer, the limited size of the defect, and the generationof a gas pressure inside the canister. In case of a minor defect, the canister internal would basicallyremain dry until the pressure from the corrosion products would physically damage the copper shell,causing a larger defect /SKB 1999b, Section 9.6/.
This section deals with the time it takes from canister failure, i.e. when a defect penetrating the copper
shell is formed, to the formation of a continuous water pathway. This time is called the delay time.
Furthermore, this section deals with the defect size in the copper shell, which has implications forthe cross-section area over which radionuclide release can occur, as well as for the diffusive transport pattern in the buffer. Finally this section deals with the point in time, denoted tlarge, when thedefect becomes so large that it constitutes no hindrance to mass transfer. Before such unhinderedmass transfer can occur, a continuous water pathway needs to have formed. It should be noted thateven for a large defect, it may take some time to fill the canister with water.

4.2.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplierThis section should supply data for three of the canister failure modes in SR-Site:•

The postulated growing pinhole failure, with an initial defect radius of 2 mm.

•

Canister failure due to corrosion.

•

Canister failure due to shear load.

For these three failure modes, the supplier should deliver the following data:•

The defect radius rdefect (m). In case of a circular defect the defect radius should be given. In caseof a non-circular defect, the equivalent defect geometry should be provided, unless the defect is
so large that the canister poses no transport resistance for escaping radionuclides. If the circularor non-circular defect is so large that the canister poses no transport resistance, rdefect can be set
as unlimited.

•

The delay time tdelay (yr) between canister failure (penetrated copper shell) and the establishmentof a continuous water pathway from the fuel to the exterior of the canister.

•

The time tlarge (yr) from repository closure to when rdefect is set to unlimited.

In case there are large uncertainties in the tdelay and tlarge data, it may be justified to only supply theorders of magnitudes for these data. It may also be justified to supply pessimistic values, where themost pessimistic value of tdelay is zero, and of tlarge is equal to the time for failure.

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127

SR-Site modelling activities in which data will be used

Modelling of radionuclide release in the near-field is done with the transport simulation code
COMP23 /Romero et al. 1999, Cliffe and Kelly 2006, Vahlund and Hermansson 2006b/, as detailed
in the Radionuclide transport report. In this code the canister interior is pessimistically assumed
to possess no transport resistance and no sorbing capacity. Rather, as soon as the canister is filledwith water, a continuous pathway between the spent fuel and the canister exterior is assumed, andthe canister interior is represented as a water volume in which radionuclides are dissolved anddiffuse freely. This occurs after the delay time. Transport resistances or barrier functions of the inner
structural parts of the canister and the fuel, including the fuel cladding, are disregarded once thetransport pathway is established.
In case the canister is surrounded by the buffer, which is assumed in the failures modes growing
pinhole failure and canister failure by shear load, the defect radius and tlarge are input data to modelling diffusive transport of radionuclides through the buffer. The defect radius is used to calculate
both the diffusion resistance in the hole and an additional transport resistance, resulting from the factthat radionuclides exiting a small defect diffuse in a three-dimensional pattern in the buffer. This is
illustrated in Figure 4‑2, showing the copper shell with a minor defect, the buffer, and the surroundingrock where a fracture intersects the deposition hole /Neretnieks et al. 2010/.
In the radionuclide transport model, the additional transport resistance at the canister/buffer interface
is immediately lost as tlarge has been reached. This does not necessarily mean an onset of radionuclidetransport, as this also requires that the canister is filled with water. The additional transport resistancewould in the real cases gradually decrease as the defect radius increases, so this must be compensatedfor by setting the value of tlarge sufficiently small, so that the additional transport resistance is neglected
as soon as the defect radius significantly increases from that of the original defect.
Copper canister shell

Hole incanisterwith porous
infillCompacted
clay as buffer

Fracture
with seeping
water

DiffusingsoluteFigure 4‑2. Illustration of the diffusion profile in the buffer, where radionuclides spread out at the defectand converge at the fracture. Excerpt from Figure 5-1 of /Neretnieks et al. 2010/.

128

TR-10-52

4.2.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The failure modes of SR-Can agree with those of SR-Site. The modelling approach used whensimulating radionuclide release from failed canisters also agrees.
For the growing pinhole failure in SR-Can, the defect radius was set to 2 mm and tdelay was set to
1,000 years. A probability distribution in the log10 space, corresponding to a right triangle, wasassumed for tlarge, within the range 0 to 105 years, with the peak value at 105 years (cf. the SR-Can
Data report /SKB 2006b, Table 4-2/).
For the canister failure due to corrosion, tdelay was pessimistically set to zero. Directly upon canister
failure, the defect radius was pessimistically assumed to be sufficiently large to offer no transport
resistance (rdefect set to unlimited). Accordingly tlarge was set to the time of failure. These data do notreflect on the actual defect evolution in case of corrosion, but rather on the fact that the actual delay
time and tlarge are of little consequence for the assessment results, due to the long time it takes tocorrode through the copper shell.
For the canister failure due to shear load, the delay time was pessimistically set to zero. The reason
was that in SR-Can, it was assumed that no shear movement was possible for the first 1,000 years
after deposition, and as the actual delay time was thought to be small in comparison to these 1,000 years.
Upon canister failure, it was assumed that the transport resistance was immediately lost /SKB 2006a,
Section 10.7/. Accordingly tlarge was set to the time of failure and rdefect to unlimited.
Conditions for which data were used in SR-Can

The conditions affecting tdelay, tlarge, and rdefect are similar in SR-Can as in SR-Site.
Sensitivity to assessment results in SR-Can

Rank regression coefficients were calculated for the SR-Can Interim results /SKB 2004a/. In case of
an initial pinhole, the delay time was shown to be one of the dominating variables. However, for the
probable failure modes (i.e. canister failure due to corrosion and to shear load), the delay time waspessimistically set to zero.
In SR-Can interim /SKB 2004a, Section 12.5.4/, the importance of transport resistance provided by
the canister was investigated. Concerning the defect radius for the growing pinhole failure, goingfrom a defect radius of 2 mm to a complete loss of transport resistance (corresponding to an unlimited
defect radius) implies an increase in releases and doses of about an order of magnitude. This canbe seen from the dose calculation results shown in Figure 4‑3, where different initial and constantdefect radiuses (called rLarge in the figure) have been used as inputs to the calculations.
Figure 4‑3 also reflects on the importance of tlarge. The red curve represents the case where tlarge is
set to zero and where it is assumed that the canister immediately poses no transport resistance. The
turquoise curve represents the case where the defect radius is constant at 2 mm, that is tlarge is set
larger than the assessment time of 106 years.
Alternative modelling in SR-Can

No alternative modelling relevant for these data was performed in SR-Can.
Correlations used in SR-Can modelling

No correlation relevant for these data was used in SR-Can.

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129

Mean Annual Dose; Mire (µSv/year)

0.1

0.01

Instant large, no resistanceBase CaserLarge = 100 mm
rLarge = 10 mm
rLarge = 2 mm

0.001

0.0001
10,000100,000

1,000,000

Time (years)

Figure 4‑3. Probabilistic results of calculations with alternative assumptions related to the internal developmentof failed canisters from the SR-Can interim calculations. Reproduced from Figure 12-18 of /SKB 2004a/.

Identified limitations of the data used in SR-Can modelling

Concerning the delay time for a minor defect, the regulatory authorities consider that “a period
of 1,000 years between a minor penetrating defect in the copper shell and initiated radionuclidetransport is very probably conservative”. “The authorities also consider that SKB should better
justify the distribution of the time interval between an initially started radionuclide transport andthe time when large canister damage arises” /Dverstorp and Strömberg 2008/.
The description in the SR-Can reporting suffered from some unclear statements concerning tlarge,
on the use of a uniform, alternatively a right triangular, distribution. Concerning the right triangular
distribution, the authorities found that “it is difficult to understand why the maximum period is
considered to be the most probable” /Dverstorp and Strömberg 2008/.
The SR-Can Data report /SKB 2006b/ did not clearly explain for which failure modes the given data
were designated.

4.2.3 Supplier input on use of data in SR-Site and SR-Can
The data supplied in this section have been produced by the SR-Site team; hence no supplier formallyexists. None the less, the text is generally written according to the standard outline of the Data report.
In this section, tdelay will pessimistically be set to zero for the canister failure due to corrosion, andtlarge will be set to the time of failure. Because of using this highest degree of pessimism, it is judgedthat there is no need to qualify these data. Accordingly, the standard outline of the Data report will not
be followed for the data for canister failure by corrosion. This failure mode will not be mentioned
until in Section 4.2.10, where the data are tabulated.

4.2.4

Sources of information and documentation of data qualification

Sources of information

For the growing pinhole failure, the sources of information on the delay time and tlarge are theCanister and fuel process report and the SR-Can Data report /SKB 2006b, Section 4.4/. Other
sources of information are the SR-97 Main report /SKB 1999b, Section 9.6/ and its supporting
documents /Bond et al. 1997, Takase et al. 1999/.
For the canister failure due to shear load, the geometry of the defect is discussed in this section, butthere is very limited information on the properties of a shear failure defect. Therefore, pessimistic
defect geometries are propagated, which are used to estimate the delay time. This is done by using
the same approach as in /Bond et al. 1997/, by using an equation describing the water inflow into thecanister /Wikramaratna et al. 1993/. The main sources of information are listed in Table 4‑5.
130TR-10-52

Table 4‑5. Main sources of information used in data qualification.
Fuel and canister process report, 2010. Fuel and canister process report for the safety assessment SR-Site.
SKB TR-10-46, Svensk Kärnbränslehantering AB.
Bond A E, Hoch A R, Jones G D, Tomczyk A J, Wiggin R M, Worraker W J, 1997. Assessment of a spent fueldisposal canister. Assessment studies for a copper canister with cast steel inner component. SKB TR 97-19, Svensk
Kärnbränslehantering AB.
SKB, 1999b. SR 97 – Deep repository for spent nuclear fuel. SR 97 – Post-closure safety. Main report – Vol. II. SKB
TR-99-06, Svensk Kärnbränslehantering AB.
SKB, 2006b. Data report for the safety assessment SR-Can. SKB TR-06-25, Svensk Kärnbränslehantering AB.
Wikramaratna R S, Goodfield M, Rodwell W R, Nash P J, Agg P J, 1993. A preliminary assessment of gas migrationfrom the copper/steel canister. SKB TR 93-31, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting data

The growing pinhole failure is only used as a hypothetical residual scenario, as there will be no
initial pinhole (cf. Section 4.1). Therefore, the data recommended for SR-Site are based upon thecorresponding SR-Can data. However, before qualifying this data set, the argumentation of SR 97
and SR-Can is reviewed. In doing this the argumentation given in /Bond et al. 1997, Wikramaratna
et al. 1993/ is examined and Equation 3-1 of /Bond et al. 1997/ is used for calculating the water
inflow rate into the canister.
For the canister failure due to shear load, little data exist on the extent of the defect. Therefore, it isassumed to be a circumferential crack where the aperture is varied up to pessimistic values. Based onthese apertures the delay time is calculated by using Equation 3-2 of /Bond et al. 1997/, neglecting
any effects of corrosion gases.
The important data sets used in this data qualification are shown in Table 4‑6, where also justifications
to the sorting of the items are given.
Table 4‑6. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /SKB 2006b/, Table 4-2: Radius of initial defect = 2 mm,
tdelay = 1,000 years, tlarge = right triangular distribution in log
space with lower value, upper value, and peak value at 0, 105,
and 105 years, respectively.
2. Equation 3-1 in /Bond et al. 1997/, for calculating the water
inflow rate into the canister in case of a pinhole defect.
3. Equation 3-2 in /Bond et al. 1997/, for calculating the water
inflow rate into the canister in case of a circumferential crack
defect (it is the same as Equation 6.1.2 in /Wikramaratna et al.
1993/).
4. Canister geometry data from Section 4.1 of this Data report(canister radius = 0.525 m, void volume = 1.0 m3).
5. Hydraulic conductivity of the buffer taken from Section 5.2.12of this Data report. Best estimate K = 5∙10–14 m2 m/s, upper
K = 1∙10–13 m2 m/s.

6. Best estimate corrosion rate for the cast iron insert
of 0.1 mm/yr and lower limit corrosion rate of 0.01mm/yr/Bond et al. 1997/.
7. Estimate in /Bond et al. 1997/ that corrosion
consumes intruding water at a matching rate if thedefect radius is at or below 1.62 mm.

1. The data given in the SR-Can Data report is considered as qualified. However, as the authorities found the reportingin Section 4.4 unclear, an effort is made to revisit the data.
2–3. Equation 3-1 in /Bond et al. 1997/ is used for revisiting the delay time for the pinhole case given in SR-Can Data
report. Equation 3-2 in /Bond et al. 1997/ is used for estimating the delay time for canister failure due to shear load.
Although /Bond et al. 1997/ in general is considered as supporting, the equations are judged as qualified.
4–5. These data are used in Equations 3-1 and 3-2 in /Bond et al. 1997/. The geometry data in item 4 are qualified
in the Canister production report (canister radius) and Section 4.1 of this Data report (void volume). The hydraulicconductivity of the buffer is qualified in Section 5.2 of this Data report.
6–7. /Bond et al. 1997/ is generally considered as supporting, as the report is written prior to the implementation of thepresent data qualification framework.

TR-10-52131

4.2.5 Conditions for which data are supplied
The delay time is dependent on the time it takes to fill the canister with water, so that a continuous
water pathway is established between the spent fuel and the canister exterior. The water inflow rate
through a pinhole can be calculated by /Wikramaratna et al. 1993, Bond et al. 1997/:



4‑1

where Q (m3/s) is the water inflow rate, κ (m2) is the buffer permeability, µ (Pa·s) is the dynamic
viscosity of the water, and Δp is the pressure drop over the buffer (i.e. between the rock mass and theinterior of the canister). The hydrostatic pressure in the rock mass at 500 m depth is about 5 MPa.
The equivalent equation for a circumferential crack, as in the shear load scenario, is /Bond et al.
1997/:
4‑2
where rc (m) is the canister radius, b (m) is the buffer thickness, and l (m) is the crack aperture.
As can be seen in Equations 4‑1 and 4‑2 the water inflow rate is proportional to the permeability ofthe buffer, which in turn depends on the degree of compaction of the buffer. The permeability can becalculated from the hydraulic conductivity K (m/s) of the buffer, which is given in Section 5.2:

κ

ρ

μ

4‑3

where ρ (kg/m3) is the density of water and g is the gravity. For a typical hydraulic conductivity forthe buffer of 5·10–14 m/s at 20°C (cf. Section 5.2.12), the corresponding permeability7 is 5.1·10–21 m2.
As can be seen in Equations 4‑1 and 4‑2 the water inflow rate is also proportional to the pressure
drop over the buffer. As water intrudes the canister, the cast iron insert will corrode and hydrogen gas
will form. According to the Fuel and canister process report and /Bond et al. 1997/ this hydrogen
will build up a counter pressure that will more or less match the pressure in the rock, and the water
inflow rate will decrease and eventually become zero. Therefore, the way one accounts for corrosion
and build-up of a hydrogen pressure is an important conditions for the calculation of the delay time.
A simplistic way of calculating the delay time is by dividing the canister void volume V (m3) by thewater inflow rate:
4‑4
For a minor defect this is pessimistic, as water is consumed in the corrosion process as it flows into
the canister. /Bond et al. 1997/ estimated that corrosion consume intruding water at a matching rate
if the defect radius is at, or below, 1.62 mm. In this estimate a cast iron corrosion rate of 0.1 µm/yr
was assumed together with a pressure drop of 5 MPa, which means that the pressure build-up due
to hydrogen gas was neglected. Therefore, using Equation 4‑4 for the growing pinhole failure, where
the initial defect radius is 2 mm, is most likely pessimistic. In case of a major defect, the water inflow
rate may be much higher than the consumption rate (at least if neglecting the hydrogen gas pressurebuild-up).
Concerning tlarge, this parameter is more dependent on the corrosion rate of the cast iron insert thanof the copper corrosion rate. As the cast iron corrodes, the corrosion products will be more voluminous
than the metallic cast iron. This will create a stress from the inside of the canister, which could eventuallyrupture. This was modelled in /Bond et al. 1997/ and it was found that this is a very slow process. In
SR 97 it was assumed that a larger canister failure due to this process would take about 200,000 years/SKB 1999b, Section 9.6.4/. In /Smart et al. 2002a, b/ a series of different corrosion experiments are
7

Calculated by using µ = 1.00·10–3 Pa·s, r = 1.00·103 kg/m3 /Coulson et al. 1990/, and g = 9.81 m/s2.

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reported. Part of the study was to investigate the influence of corrosion products and water chemistryon the corrosion rates. The mean corrosion rates were low and generally less than 1 µm per year. The
uncertainties in the corrosion rates are further discussed in the Fuel and canister process report,
Section 3.5.1.

4.2.6 Conceptual uncertainty
The processes occurring upon canister failure are described in detail in the Fuel and canister processreport. For the delay time, the description of hydraulic processes is essential (Fuel and canister
process report, Section 2.3). For a small defect, inflowing water is to various degrees consumed bycorrosion reactions, which results in a hydrogen pressure build-up that halts further inflow. Accordingly,
further transport of water in the defect can only occur by gas phase diffusion of water vapour. For alarger defect, enough water may have flown into the canister before the hydrogen pressure reaches
the ambient pressure, and a continuous water pathway between the spent fuel and the canister
exterior can be established.
The conceptualisation of the hydraulic situation has very little bearing on the numerical data for thedelay time suggested in this section, as one pessimistically neglects any hindrance of water flow into
the canister due to the pressure build-up in the canister.

4.2.7

Data uncertainty due to precision, bias, and representativity

Delay time

By using Equations 4‑1, 4‑3, and 4‑4, the delay time of the growing pinhole failure can be calculated.
In doing this, one pessimistically neglects that water is consumed in the corrosion reaction. The
hydrogen pressure build-up which would decrease, or even halt, the water inflow is also neglected.
In the following two examples, best estimate and lower delay times are calculated for a 2 mm pinhole, under the prerequisite that realistic buffer properties are used and that the hydrogen pressurebuild-up is pessimistically neglected. In Section 5.2.12, the best estimate hydraulic conductivity ofthe buffer is given as 5·10‑14 m/s, which translates to a permeability of 5.1·10–21 m2. For the pressure
drop over the canister the value 5 MPa is assumed, which equals the external water pressure at repository depth. The void is assumed to be the total void of 1.0 m3, as suggested for the average canister inTable 4‑3. The viscosity depends on the water temperature and in our calculations a relatively highwater temperature of 75°C is assumed, with a corresponding viscosity of 3.8·10–4 Pa·s /Coulson et al.
1990/. By inserting all the above data into Equations 4‑1, 4‑3, and 4‑4, the delay time equals about
3.8·104 years. If, in a second example, instead using the lowest hydraulic conductivity suggested in
Section 5.2 (1·10–13 m/s), and assuming a water temperature of 100°C (thereby decreasing the waterviscosity to the lowest possible), the delay time becomes8 about 1.4·104 years.
In SR-Can, the pessimistic delay time of 1,000 years was suggested for the pinhole case. This low
value would encompass a buffer hydraulic conductivity much lower than the range specified in thedesign premises, alternatively a much larger defect radius than 2 mm (about 70 mm if using the samedata as in the first example above). As seen in Section 4.2.10, this unrealistically low delay time issuggested to be adopted also for SR-Site, justified by the fact that the scenario is only residual andas there in the real case will be no pinhole. Accordingly, the accuracy of the used data is of lesser
concern. In light of this, data uncertainty becomes subordinate.
For the canister failure due to shear load giving rise to a circumferential crack, the delay time canbe calculated by Equations 4‑2, 4‑3, and 4‑4. The pessimism of neglecting the hydrogen pressurebuild-up is adopted from the above case. For the permeability, the best estimate value of 5.1·10–21 m2
is adopted together with the dynamic viscosity at 75°C. The buffer thickness is assumed to be
reduced to 25 cm, as a result of the shear movement.

8

Calculated by using m = 0.287·10–3 Pa·s /Coulson et al. 1990/

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Finally, for the defect aperture the values 1 mm and 10 mm are suggested. For these values the delay
time becomes on the order of 100 years (147 years for the larger aperture and 252 years for the smaller
aperture). Also in this case data uncertainty is overshadowed by the degree of pessimism adopted inneglecting the hydrogen pressure build-up. (If the shear movement instead would induce a circular
defect, the defect radius corresponding to a delay time of about 100 years would be very large; onthe decimetre scale.)
Defect radius and tlarge

For the growing pinhole failure the defect radius is initially set to 2 mm. It is assumed to be of
constant radius until the time tlarge, when the defect radius becomes sufficiently large to offer notransport resistance (rdefect set to unlimited).
In /Bond et al. 1997/, tlarge was assessed for a pinhole case where it was assumed that water vapour
could diffuse into the canister, but that water flow was hindered as the hydrogen pressure in the canister
matches the external water pressure. According to the calculations, it takes at least 100,000 years
from the time of the initial penetration before more extensive damage occurs on the copper canister.
On the other hand, if pessimistically assuming that water can flow into the canister unhindered, thus
neglecting the hydrogen pressure build-up, one could argue for a considerably lower tlarge. In SR-Can
this uncertainty was handled by suggesting a right triangular distribution in the log-space for tlarge,
with one year as the lower limit and 100,000 years as the upper limit and peak value. By setting thepeak value at 100,000 years, instead of in the middle of the range, it was implicitly stated that one
considers it more reasonable, from a process understanding point of view, that the water ingressis diffusion controlled and much less probable than that water flows into the canister. For SR-Sitemodelling, only a single point values is requested. The arithmetic mean of the SR-Can distribution
is around 1.6·104 years. For SR-Site this is suggested to be cautiously rounded to 1·104 years.
For the canister failure due to shear load, the initial circumferential crack is assumed to be so largethat the canister immediately loses its transport resistance. Accordingly it is suggested to set tlarge
equal to the time of failure and rdefect to unlimited.
In the above example, the degree of pessimism overshadows any effect of data uncertainty in input data.

4.2.8

Spatial and temporal variability of data

Spatial variability of data

The description of the canister failure is general and no spatial variability is accounted for.
Temporal variability of data

The temporal variability concerning the onset of radionuclide transport is handled by assuming noradionuclide transport prior to tdelay, while radionuclide transport subsequent to tdelay takes place.
The temporal variability concerning the growing pinhole defect is handled by assuming a constantdefect radius until the time tlarge, when the defect momentarily becomes infinitely large.

4.2.9 Correlations
This section only supplies single point values. Accordingly there is no need to account for correlations
in subsequent modelling.

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4.2.10 Result of supplier’s data qualification
The growing pinhole failure

For the growing pinhole failure, the following pessimistic values are suggested:•tdelay = 1·103 yr•

tlarge = 1·104 yr•

rdefect = 2 mm prior to tlarge and unlimited subsequent to tlarge

The tdelay and tlarge values are justified in Section 4.2.7.
Canister failure due to corrosion

For the canister failure due to corrosion, the most pessimistic values are suggested:•tdelay = 0•

tlarge = same as time of failure (penetrated copper shell)•

rdefect = unlimited subsequent to failure

Canister failure due to shear load

For the canister failure due to shear load, the following cautious values are suggested:•tdelay = 1·102 yr•

tlarge = same as time of failure (penetrated copper shell)•

rdefect = unlimited subsequent to failure

The values are justified in Section 4.2.7. It should be noted that in this case the onset of radionuclidetransport is governed by the time of failure plus the delay time.

4.2.11 Judgements by the SR-Site teamThe data of this section have been produced by the SR-Site team. Therefore no judgement on theabove text is made. However, the general judgment is made that the data presented in this section
are appropriate for use in the assessment and compliance calculations within SR-Site.
4.2.12 Data recommended for use in SR-Site modelling
The following data are recommended for use in SR-Site radionuclide transport modelling.
Table 4‑7. Data recommended for use in SR-Site.
Canister failure due totdelay (yr)tlarge (yr)rdefect (m)Growing pinhole

1·103

1·104

0.002 priori to tlarge
Unlimited subsequent to tlarge

Corrosion

0Same as time of failure.

Unlimited subsequent to failure.

Shear load

1·102

Same as time of failure.

Unlimited subsequent to failure.

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5Buffer and backfill data

5.1

Density and porosity of buffer and backfill

This section concerns the density and physical porosity of the reference buffer and backfill. Examplesof the reference buffer material are MX-80 and Ibeco-RWC (Depontit CA-N) bentonites. The reference
backfill is a low grade bentonite, with Ibeco-RWC-BF as an example. This material is also called
Milos Backill.
For the buffer it is stated in the design premises that the initially deposited buffer mass should be suchthat it corresponds to a saturated buffer density in the interval 1,950–2,050 kg/m3 (Buffer production
report, Table 2-1). The Buffer production report deals with demonstrating that one indeed can
produce and install a buffer that, upon saturation, has a density within this interval.
For the backfill, no target density is stated in the design premises. However, the density is of importancefor other design premises, such as the hydraulic conductivity of the backfill. These other design premises
have been considered in the Backfill production report as a basis for a reference design of theinstalled backfill.
The porosity and density are inputs to Sections 5.2 and 5.3, where hydraulic and solute transport
properties of the buffer and backfill are discussed. The physical porosity can be assessed by knowing
the dry or the saturated density of the clay material, and the density of the solid clay particles.

5.1.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The qualification of buffer density data is performed within the Buffer production report. For the
installed reference buffer, the supplier should deliver:•

Data showing that the installed buffer, upon saturation, has a saturated density ρm (kg/m3) within
the design premises interval 1,950–2,050 kg/m3.

•

It is preferable if information is given on how the saturated density varies. However, only if thedesign premises interval cannot be kept, the supplier is required to carefully describe the density
distribution, with extra focus on the densities outside the interval.

It falls upon the customer to:•

Convert the saturated density to dry density ρd (kg/m3) and physical porosity n (–) (also denoted εin some SKB reports), by use of standard equations.

The qualification of backfill density data is performed within the Backfill production report.
Furthermore, the justification that the reference design complies with the design premises is given in
the Backfill production report. For the reference backfill, the supplier should deliver:•Dry density data ρd (kg/m3) of the installed backfill showing that the backfill can be produced andinstalled in such a way that it conforms to design premises.

•

It is preferable if information is given on how the dry density varies. However, only in the casethe backfill cannot be produced and installed in such a way that its density conforms to designpremises, the supplier is required to carefully describe the density distribution, with extra focus
on the lower density tail.

It falls upon the customer to:•

Convert the dry density to saturated density ρm (kg/m3) and physical porosity n (–), by use ofstandard equations.

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The equation used to convert dry density to physical porosity is:n = 1−rdrs

5-1

Where ρs (kg/m3) is the specific density of solid particles (grain density), where the value 2,780 kg/m3
can be used (Buffer production report, Appendix A and Backfill production report, Appendix A).
The equation used to convert the dry density to saturated density is:ρm = ρd + n·ρw

5-2

Where ρw (kg/m3) is the water density of the typical value 1,000 kg/m3. Equations 5-1 and 5-2 arederivatives of the equations in the Buffer production report (Appendix A)9. In the remaining of thissection, the physical porosity is generally referred to as porosity.
SR-Site modelling activities in which data will be used

The buffer and backfill porosities and densities delivered in this section will be used in a number of
SR-Site modelling activities, including THM modelling, hydrogeological modelling, and modellingof buffer erosion. The THM modelling performed is described in /Åkesson et al. 2010a, b/ and thehydrogeological modelling is summarised in /Selroos and Follin 2010/. The erosion modelling is
described in /Neretnieks et al. 2009/.
It should be noted that the diffusion-available porosity, delivered in Section 5.3, should be used whenmodelling diffusive transport in the buffer and backfill, and not the physical porosity delivered inthis section.

5.1.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

In SR-Can, THM-modelling, hydrogeological modelling, and buffer erosion modelling was performed
in a similar manner as within SR-Site, with regard to the usage of the density and porosity.
Conditions for which data were used in SR-Can

For the buffer, the same reference material was use in SR-Can as in SR-Site. However, for the backfill,
two reference materials were suggested in SR-Can that are of no concern for SR-Site. The two materials
were a bentonite and ballast mixture, and Friedland clay. As different materials were used in SR-Can,
backfill data cannot be directly transferred to SR-Site.
Except for the material itself, the degree of compaction influences the density and porosity data.
Similar degrees of compactions were of concern for SR-Can as for SR-Site. No other condition
significantly affects the data.
Sensitivity to assessment results in SR-Can

As long as the buffer density conforms to the design premises interval, and as long as the backfill
density is high enough so that other design premises (hydraulic conductivity etc.) conform to designpremises, the actual values of the buffer and backfill densities have little effect on repository safety.
In this respect the assumed densities for the buffer and backfill had little influence on assessment
results in SR-Can.

9
In reports dealing with bentonite materials the term bulk density is often used, which is the density at a given
degree of saturation. For a fully saturated bentonite, the bulk density equals the saturated density.

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If the upper limit for the buffer density is exceeded, this may have consequences for the mechanicalimpact on the canister, in case of shearing. On the other hand, if the buffer density is lower thanpermitted by the interval, such a case is anyhow expected in the event of buffer erosion.
If the buffer erodes to the point where advective conditions prevail in the deposition hole, this has
great consequences for assessment results. However, this section concerns the installed buffer andbackfill, prior to potential post-closure buffer and backfill removal. When assessing buffer erosion,
it is rather the composition of the buffer material, as well as the advective flow and the groundwatercomposition, that are of importance, whereas the initial density of the buffer should be of little
consequence.
In the backfill, advective flow was of some importance in SR-Can. The hydraulic conductivity isdependent on the backfill density in a non-linear but monotonic fashion. In case of a too low density,
the hydraulic flow field in the repository may change, which may significantly affect the safetyassessment. This is especially true if also other tunnels than the deposition tunnels are filled witha backfill of too low density. However, the backfill densities considered in SR-Can were not low
enough to substantially impact the assessment results in this way.
Alternative modelling in SR-Can

No alternative modelling of the porosity and densities was used in SR-Can.
Correlations used in SR-Can modelling

The correlations (or functional relationships) between the density and porosity (e.g. Equations 5-1
and 5-2) used in SR-Site and SR-Can agree. A number of parameters of different subject areas werein SR-Can correlated to the density, such as swelling pressure and hydraulic conductivity. Similarcorrelations are used in SR-Site.
Identified limitations of the data used in SR-Can modelling

For the reference buffer of SR-Can, there still remained issues on how it should be produced andinstalled, which affected the data. None of the reference backfills suggested in SR-Can are relevant
for SR-Site.

5.1.3 Supplier input on use of data in SR-Site and SR-Can
The supplier has no additional input on the use of data, but points out that the data in the Buffer
production report and the Backfill production report have been produced in communication with
the SR-Site team.
5.1.4

Sources of information and documentation of data qualification

Sources of information

The data presented in the Buffer production report and the Backfill production report are qualified
in the production reports. Therefore, only these reports are sources of information for the Data report(see Table 5-1). The scrutinising of lower level references is part of the qualification process of the
production reports and is not dealt with in the Data report.
Table 5-1. Main sources of information used in data qualification.
Backfill production report, 2010. Design, production and initial state of the backfill and plug in deposition tunnels.
SKB TR-10-16, Svensk Kärnbränslehantering AB.
Buffer production report, 2010. Design, production and initial state of the buffer. SKB TR-10-15, Svensk
Kärnbränslehantering AB.

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Categorising data sets as qualified or supporting

Table 5-2 shows the data sets of the production reports that are used as bases for this section. The tablealso gives the motivation for the sorting of the data sets as qualified.
Table 5-2. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Buffer production report, Table 6-1: Corresponding saturated buffer density for the
installed buffer, with respect to deposition-hole straightness and at different degrees ofspalling.
2. Buffer production report, Appendix B: Porosity corresponding to the limits in designpremises for the saturated density.
3. Buffer production report, Appendix A: ρs for the clay is around 1,780 kg/m3.
4. Backfill production report, Tables 6-2 and 6-3: Dry density of the installed backfill,at different degrees of tunnel wall and floor irregularities.
5. Backfill production report, Tables 6-2: Porosity of the installed backfill, at different degrees
of tunnel wall and floor irregularities.
6. Backfill production report, Appendix A. ρs for the clay is around 1,780 kg/m3.

–

1–3: Data delivered in the Buffer production report are qualified in the Buffer production report, in accordance with
the SKB quality assurance system. This qualification is found to be in compliance with the demands of the Data report.
4–6: Data delivered in the Backfill production report are qualified in the Backfill production report, in accordance with the
SKB quality assurance system. This qualification is found to be in compliance with the demands of the Data report.

Excluded data previously considered as important

No such data have been excluded for the considered materials.

5.1.5 Conditions for which data are supplied
The reference buffer material is described in Section 3.1 of the Buffer production report. Examplesof the reference buffer material are MX-80 and Ibeco-RWC (Depontit CA-N) bentonites. The reference
backfill material is described in Section 3.1 of the Backfill production report. An example of thebackfill material is Ibeco-RWC-BF, also called Milos Backill.
An important condition is the degree of compaction. Methods of production and installation are
discussed in the production reports.
When the buffer and backfill are installed, they are only partly saturated. As an effect of the saturation,
the buffer and backfill will swell and there should be no gap between the deposition hole or tunnel
wall and the clay material. The data supplied should represent homogenised cross section areas ofthe deposition hole and tunnel, respectively.
The density and porosity are not expected to be significantly affected by groundwater chemistry,
temperature, or other conditions. There may be anion exclusion effects in the porous system, butthese are discussed in Section 5.3. In case the clay material is removed, for example through buffer
erosion, the density and porosity may be altered. However, such post-closure removal is not a focus
of attention in this section.

5.1.6 Conceptual uncertainty
There is no significant conceptual uncertainty regarding the saturated and dry densities, or the physicalporosity, of the installed buffer and backfill. Conceptual uncertainty related to how the porous systemaffects hydraulic properties of, and the migration in, the buffer and backfill is discussed in Sections 5.2
and 5.3.

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5.1.7 Data uncertainty due to precision, bias, and representativity
The data uncertainty associated with the buffer and backfill mainly concerns the degree of accuracy withwhich the buffer and backfill can be produced and installed. The precision, bias, and representativityof experimental measurement add little to the data uncertainty, compared to uncertainties in how well
production and installation can be performed. In this case, the data uncertainty has not been separated
from spatial variability.
Buffer density

In the Buffer production report the production and installation of the buffer are described. Basically,
the average density of the buffer depends on the mass of installed bentonite and the volume of thedeposition hole. The buffer is installed as solid blocks, ring shaped blocks, and pellets. According
to the reference design (Buffer production report, Table 3-4) the bulk density of the ring shaped
blocks should be 2,070±20 kg/m3 and of the solid blocks 2,000±20 kg/m3. The bulk density of theloose fillings (pellets including void) should be 1,035±40 kg/m3. The tolerance for the dimensionsof the blocks should be only 1 mm. The bulk densities are valid for bentonite with the water content
of 17±1% (i.e. the bentonite is only partly saturated).
Results from production of 130 blocks show that it is possible to produce blocks according to thereference design (Buffer production report, Section 5.3.8). Results from the prototype repository(Buffer production report, Table 5-3) suggest that the standard deviation of the dry density of theblocks is less than 20 kg/m3.
The density of the installed buffer is also dependent on variations in the deposition hole geometry andon rock fall out, also called spalling (Buffer production report, Section 6.1.4). In addition, the degreewith which void can be filled with loose fillings is of importance. Based on these considerations,installed densities for a number of different cases are given in Table 6-1 of the Buffer production
report. These are reproduced in Table 5-3.
From the data in Table 5-3 one can say with great confidence that the saturated density of the buffer
will be within the interval given by the design premises, as long as there is not extensive spalling.
The great majority of deposition holes should not be subjected to such extensive spalling according
to the Underground construction report. Here it should be noted that one has the possibility of
discarding deposition holes with extensive spalling, alternatively adjusting the density of the buffer
blocks to compensate for the spalling of the specific deposition hole.
Table 5-3. Different installed buffer densities for different borehole geometries and degreesof spalling. Data reproduced from Table 6-1 of the Buffer production report.
Design parameter

Reference design

Initial state

Saturated density
around the canister

2,000±50 k/m3

With respect to variations in buffer components and deposition hole,
if no spalling occurs:
2,000–2,020 kg/m3 with a 99.9% C.l.
With respect to deposition hole straightness if no spalling occurs:
1,986–2,035 kg/m3 with a 99.9% C.I.
With respect to variations in buffer components if spalling occurs:
Spalling of 10 mm: 1,986–2,003 kg/m3 with a 99.9% C.I.
Spalling of 20 mm: 1,972–1,988 kg/m3 with a 99.9% C.I.
Spalling of 30 mm: 1,959–1,974 kg/m3 with a 99.9% C.I.
Spalling of 40 mm: 1,946–1,961 kg/m3 with a 99.9% C.I.
Spalling of 50 mm: 1,933–1,948 kg/m3 with a 99.9% C.I.

Saturated density
above and below
the canister

2,000±50 kg/m3

2,025–2,045 kg/m3 with a 99.9% C.I.

C.l. is short for confidence limit.

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Backfill density

In Tables 6-2 and 6-3 of the Backfill production report, the installed backfill density has been
estimated from the geometry of the deposition tunnel, the geometry of blocks and density of blocks
and pellets (see the Backfill production report for details). The dry density of the blocks should be
1,700±50 kg/m3 while it should be 1,000±100 kg/m3 for the pellets fillings (including void). There
are special demands on the block constituting the upper part of the deposition hole, which density
should be 1,710±17 kg/m3 (cf. Table 6-1 of the Backfill production report).
What is of great importance is how irregular the tunnel walls and floor are. This impacts the degree ofthe tunnel cross section area that can be filled with blocks. Three installed densities have been estimated:
the lowest installed dry density; the average installed dry density; and the highest installed dry density.
In these alternatives 67.3%, 74.1%, and 78.7% of the tunnel cross section area is filled with blocks,
respectively. The estimated dry densities are 1,458, 1,504, and 1,535 kg/m3, respectively, as averaged
over the entire cross section of the tunnel. In this calculation, the average dry densities for the blocks
and pellet fillings were used, as a great number of randomly selected blocks and pellets are needed tofill the tunnel cross section, and as local variations are likely to even out.
In the unlikely case where all blocks have the lowest density and where the tunnel is filled withblocks to the least degree, the installed dry density would become about 1,408 kg/m3 (Backfill
production report, Table 3-6).
For all these cases, the density of the backfill is sufficient with a wide margin to accommodate
the design premises of hydraulic conductivity; swelling pressure; compressibility; and restrictionof buffer swelling/expansion (see Chapter 4 of the Backfill production report). This margin willassure that the barrier function is maintained in the long-term perspective, even if some backfill
material is lost during or after installation (Backfill production report, Section 4.4).
Buffer and backfill porosity

For the buffer, the porosities corresponding the saturated densities 1,950 and 2,050 kg/m3 are estimatedto 0.46 and 0.41, respectively (Buffer production report, Appendix B). This estimate depends on theassumed density of solid particles ρs. This density is suggested to be within the range 2,750–2,780 kg/m3.
For the backfill, the porosities corresponding to the dry densities 1,458, 1,504, and 1,535 kg/m3 are
estimated to 0.44, 0.46 and 0.48, respectively (Buffer production report, Table 6-2). The densityof solid particles ρs is given to 2,780 in the Buffer production report (Appendix A).
The uncertainty in the porosity is equally affected by uncertainties in the dry and saturated densitiesof the buffer and backfill, as by the uncertainty in the density of solid particles. All in all, the porosity
ranges are narrow, and the overall uncertainty in the porosity is small.

5.1.8 Spatial and temporal variability of data
The spatial variability of the data in the different cross sections of deposition holes and tunnels depends
on the production and installation of the buffer and backfill. In this case, variation in data due to spatial
variability has not been separated from other data uncertainty. Therefore, Section 5.1.7 is referred to.
There will be temporal variability in the data as the buffer and backfill becomes saturated. Furthermore,
erosion and other possible processes of buffer and backfill removal may cause temporal variability.
However, such processes are to be studied within SR-Site and are not accounted for in this Data report.
The data delivered here concern the buffer and backfill at the time of installation.

5.1.9 Correlations
Examples of functional relationships between densities and the porosity used in SR-Site modellingare shown in Equation 5-1 and 5-2. Other functional relationships are found in the Buffer production
report (Appendix A). Other data, for example hydraulic conductivity, swelling pressure, etc. arecorrelated to the density (see e.g. the Backfill production report, Sections 4.1 and 4.2).

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5.1.10 Result of supplier’s data qualification
Buffer

It is suggested that the buffer can be produced and installed in such a way that the design premises
are fulfilled. Thus the saturated density ρm is in the interval:
1,950 kg/m3 < ρm < 2,050 kg/m3
This corresponds to the porosity interval:
0.41 < n < 0.46
Furthermore, experimental data suggest that one can produce and install the buffer in such a waythat it is more likely to obtain buffer densities and porosities in the centre of the interval, than at
the boundaries.
Backfill

The dry density of the installed buffer is given as an average value, and as upper and lower limits,according to Table 5-4. In the table, the corresponding porosities are also given.
Table 5-4. Dry density and physical porosity data for the backfill. Data from Table 6-2 inthe Backfill production report.
Dry density (kg/m3)

Porosity (–)

Lower limit density

1,458

0.48

Average density

1,504

0.46

Upper limit density

1,535

0.44

5.1.11 Judgements by the SR-Site teamAccording to the instruction given in Section 2.3, the SR-Site team should make comments on all
the sections of the supplier.
Sources of information and documentation of data qualification

The supplier considers that the two sources of information used are qualified. The SR-Site teamagree. The SR-Site team judge that referencing is sufficiently precise.
Conditions for which data are supplied

The clay material, the degree of compaction, and the procedures by which the buffer and backfill are
installed are the only significant conditions. The SR-Site team see no controversy in this.
Conceptual uncertainty

The SR-Site team agree that there is no significant conceptual uncertainty associated with these data.
Data uncertainty due to precision, bias, and representativity

The supplier considers that data uncertainty associated with the buffer and backfill mainly concerns
the degree of accuracy with which the buffer and backfill can be produced and installed, and to alesser degree concerns data uncertainty in experimental measurements. The SR-Site team agree. In
the production reports, references are made to experiments that shows that the buffer and backfill
can be produced and installed in such a way that only small uncertainties arise. Due to the natureof the data, the contribution to the given data intervals from data uncertainty and spatial variability
is not separated. The SR-Site team judge that the data intervals are well defended based on existingknowledge and experimental information.
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The SR-Site team note that in the Buffer production report, an uncertainty interval of 2,750–
2,780 kg/m3 is (loosely) given for the density of the clay solids, while the value 2,780 kg/m3 seems
to be used as a single point value in calculations in the appendices. This is judged as acceptable.
Spatial and temporal variability of data

Due to the nature of the data, the supplier has not separated the contribution to the given data intervals
from data uncertainty and spatial variability. The SR-Site team agree with this approach. The SR-Siteteam agree that the density intervals given should encompass spatial variability. Furthermore, the SR-Siteteam judge that it is not part of the supplier’s assignment to account for temporal variability due tosaturation and post-closure buffer and backfill removal.
Correlations

The SR-Site team agree with the functional relationships of Equations 5-1 and 5-2, and in Appendix Aof the Buffer production report. The SR-Site team also agree that other data are correlated to the buffer
and backfill densities.
Result of supplier’s data qualification

The SR-Site team find that the density intervals given are reasonable and well-defended. For the buffer,
the supplier makes no exact suggestion on how the data are distributed within the intervals. However,as the deviation in density of the manufactured blocks is small and measurable, and as there seems to
be means to correct for deviations in deposition hole geometry, it is reasonable to assume that target
value 2,000 kg/m3 can be reached for the saturated density. Furthermore, it is reasonable to assume than
only in exceptions the saturated density will be at the outer limits of the given interval. Based on this,the SR-Site team suggest a triangular distribution in the normal space within the interval 1,950 kg/m3< ρm < 2,050 kg/m3, with the peak value at 2,000 kg/m3. For the dry density, the SR-Site team suggest atriangular distribution in the normal space within the interval 1,484 kg/m3 < ρd < 1,640 kg/m3, with thepeak value at 1,562 kg/m3.
The porosity interval of 0.41 < n < 0.46 is suggested by the supplied. This interval is accepted, eventhough it is noted that the rounding is somewhat odd10. The SR-Site team suggests that the porosity
corresponding to the saturated density 2,000 kg/m3 is 0.435 (the average of 0.41 and 0.46)11.
Based on this we suggest a triangular distribution in the normal space within the interval 0.41 < ρm
< 0.46 kg/m3, with the peak value at 0.435 kg/m3.
Concerning the dry density of the installed backfill, the supplier suggests an average value of 1,504 kg/m3
and lower and upper values of 1,458 and 1,535 kg/m3, respectively. The SR-Site team notes that what
the supplier calls average dry density is rather the best estimate dry density. As the upper and lower
values are described as exceptions, and as the given values are asymmetric, a double triangular
distribution (cf. Figure 3-9) in the normal space with these values is suggested.

If using Equations 5-1 and 5-2, ρs = 2,780 kg/m3, ρw = 1,000 kg/m3, and ρm = 1,950 kg/m3, then n becomes
0.466. A more appropriate rounding would thus be 0.41 < n < 0.47. If using ρs = 2,750 kg/m3, the appropriate
rounding would be 0.40 < n < 0.46.
1011

If using Equations 5-1 and 5-2, ρs = 2,780 kg/m3, ρw = 1,000 kg/m3, and ρm = 2,000 kg/m3, then n becomes 0.438.

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The supplied porosities corresponding to these densities are 0.46 as average value, and 0.44 and 0.48
as bounding values. Again a somewhat odd rounding has been made. However, we accept this andsuggest a triangular distribution in the normal space based on these values. For the saturated density,
a double triangular distribution in the normal space within the interval 1,921 kg/m3 < ρm < 1,983 kg/m3,
with the peak value at 1,963 kg/m3 is suggested. In all calculations performed by the SR-Site teamabove, ρs = 2,780 kg/m3 and ρw = 1,000 kg/m3 has been assumed.

5.1.12 Data recommended for use in SR-Site modelling
Table 5-5 compiles the dry and saturated densities, as well as the porosity, recommended for use in
SR-Site for the reference buffer. Table 5-6 compiles the dry and saturated densities, as well as theporosity, recommended for use in SR-Site for the reference backfill.
Table 5-5. Data for the reference buffer, recommended for use in SR-Site.
Data

ValuesSaturated density ρmLower boundary = 1,950 kg/m
Peak value = 2,000 kg/m3
Upper boundary = 2,050 kg/m3
Lower boundary = 41%
Peak value = 43.5%
Upper boundary = 46%
Lower boundary = 1,484 kg/m3
Peak value = 1,562 kg/m3
Upper boundary = 1,640 kg/m3

Porosity n
Dry density ρdDistribution
3

Triangular distribution in normal space
Triangular distribution in normal space
Triangular distribution in normal space

Table 5-6. Data for the reference backfill, recommended for use in SR-Site.
Data

ValuesSaturated density ρmLower boundary = 1,921 kg/m
Peak value = 1,963 kg/m3
Upper boundary = 1,983 kg/m3

Double triangular distribution in normal space

Porosity n

Lower boundary = 44%
Peak value = 46%
Upper boundary = 48%

Triangular distribution in normal space

Dry density ρdLower boundary = 1,458 kg/m3
Peak value = 1,504 kg/m3
Upper boundary = 1,535 kg/m3

Double triangular distribution in normal space

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Distribution
3

145

5.2

Hydraulic properties of buffer and backfill

This section describes the hydraulic properties of the buffer and backfill, as used in the hydrogeological modelling within SR-Site. It also describes the hydraulic conductivity of the buffer used in
calculations of water ingress into failed canisters.
By necessity the hydrogeological modelling was initiated prior to the final determination of thehydraulic conductivity of the buffer and backfill. Therefore, pessimistic values have been used, basedon the minimum values needed for the safety functions to be up-held according to the design premises/SKB 2009c/. This result in deviations between the used data and the data associated with the actual
properties of the installed buffer and backfill. Such deviations are commented upon by the customerin Section 5.2.11, with focus on the issue whether the used data are pessimistic for all cases studied.
Properties used to assess buffer and backfill saturation and thermo-hydro-mechanical processes in the
bentonite are described in /Åkesson et al. 2010 a, b/, and not in this section. /Åkesson et al. 2010a/
is a complement to this Data report where data corresponding to those of Sections 5.1 and 5.2 of
the SR-Can Data report /SKB 2006b/ are found.

5.2.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in this case also from the customer,
and in what SR-Site modelling activities the data are to be used.
Defining the data requested from the supplier

For the installed and saturated reference buffer, the supplier should deliver:•

The hydraulic conductivity K (m/s) corresponding to the target value of the saturated density
2,000 kg/m3, and its lower boundary values 1,950 kg/m3 (cf. Table 5-5).

For the installed and saturated reference backfill of the deposition tunnels, the supplier should deliver:•

The hydraulic conductivity K (m/s) corresponding to the lower boundary value for the saturated
density of 1,921 kg/m3 (cf. Table 5-6).

For the backfilled tunnels and underground openings the customer (SR-Site team) should supply:•

The hydraulic conductivity K (m/s) used in hydrogeological modelling, including the variant caseaccounting for a crown space.

SR-Site modelling activities in which data will be used

The primary use for the hydraulic conductivity of the buffer is to assess whether diffusion is the dominanttransport mechanism. This is the case when the buffer hydraulic conductivity is < 10–12 m/s (e.g. Table 2-3of the Buffer, backfill and closure process report).
The hydraulic conductivity of the backfill is used in groundwater flow modelling of periods withtemperate climate conditions using the ConnectFlow software /Joyce et al. 2010, Selroos and Follin
2010/. In this modelling the repository with its backfilled deposition tunnels, and other undergroundopenings, is included (cf. Section 6.6). To account for the groundwater flow in these tunnels andunderground openings, the hydraulic conductivity of the backfill is required.
In Section 4.2 of this Data report, the water ingress rate into a failed canister is used to calculate thedelay time, which is the time it takes from canister failure to the formation of a continuous water
pathway between the fuel and canister exterior. This calculation uses the hydraulic conductivityof the buffer as input.

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5.2.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

Backfilled tunnels were explicitly included in the hydrogeological modelling in SR-Can in a similarmanner as in SR-Site. The chosen hydraulic conductivity values were 1·10–10 m/s for the backfill and
1·10–11 m/s for the buffer.
The hydraulic conductivity of the buffer was indirectly used as background information when assigning
the delay time in SR-Can, although the delay time was set pessimistically in a manner that did notreflect on the precise values of the estimated hydraulic conductivity.
Conditions for which data were used in SR-Can

For the buffer, the same reference material with a similar degree of compaction was use in SR-Can as
in SR-Site. For the backfill, two reference materials were suggested in SR-Can that are of no concern
for SR-Site. These two materials were Friedland clay and bentonite and ballast mixture. As different
materials were used in SR-Can, backfill data cannot be directly transferred to SR-Site.
Sensitivity to assessment results in SR-Can

A degraded tunnel backfill case was considered in SR-Can. For this case, the only change was to use
a hydraulic conductivity of 1·10–8 m/s. The sensitivity to the backfill properties was not great since
the system of deposition tunnels was arranged orthogonal to the head gradients.
The hydraulic conductivity of the buffer had little effect on the chosen delay time in SR-Can, asvery pessimistic values were suggested (cf. Section 4.2.2). For the probable failure modes includedin the main scenario, the delay time was set to zero. Accordingly, the hydraulic conductivity was ofno consequence.
Alternative modelling in SR-Can

No alternative way of modelling or representing the hydraulic properties of the buffer and backfill
was used in SR-Can.
Correlations used in SR-Can modelling

Similar correlations and functional relationships were used in SR-Can as are used in SR-Site.
Identified limitations of the data used in SR-Can modelling

There reference backfills of SR-Can differ from that suggested in SR-Site. Accordingly limitations
in SR-Can backfill data have little bearing on the SR-Site backfill data.
No limitation was identified concerning the hydraulic conductivity of the installed and saturated buffer.

TR-10-52147

5.2.3 Supplier input on use of data in SR-Site and SR-Can
The realistic data presented for the buffer and backfill are based on measurements, as compiled in/Åkesson et al. 2010a/ and /Johannesson et al. 2010/. As stated in the Buffer, backfill and closure
process report (Sections 3.3.2 and 4.2.2, subsection “Handling in the safety assessment SR-Site”)
the suggested data on the hydraulic conductivity of the buffer and backfill should be based on
empirical data.
5.2.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information are /Åkesson et al. 2010a, Johannesson et al. 2010/. The recommended data in /Åkesson et al. 2010a/ for the reference buffer are based on /Börgesson et al. 1995,
Karnland et al. 2000, 2006, 2009/. In /Johannesson et al. 2010/ data for the reference backfill aresupplied. The buffer and backfill materials are described in the Buffer production report and the
Backfill production report. The hydraulic conductivities of the buffer and backfill actually used inhydrogeological modelling are compiled in /Joyce et al. 2010/. The full references to /Åkesson et al.
2010a, Johannesson et al. 2010, Joyce et al. 2010/ are given in Table 5-7.
Table 5-7. Main sources of information used in data qualification.
Åkesson M, Börgesson L, Kristensson O, 2010. SR-Site Data report. THM modelling of buffer, backfill and othersystem components. SKB TR-10-44, Svensk Kärnbränslehantering AB.
Johannesson L-E, Sandén T, Dueck A, Ohlsson L, 2010. Characterization of a backfill candidate material,
IBECO-RWC-BF. Baclo Project – Phase 3. SKB R-10-44, Svensk Kärnbränslehantering AB.
Joyce S, Simpson T, Hartley L, Applegate D, Hoek J, Jackson P, Swan D, Marsic N, Follin S, 2010. Groundwaterflow modelling of periods with temperate climate conditions – Forsmark. SKB R-09-20, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

Table 5-8 shows the data sets that are used as bases for this section. The table also gives the justificationof the sorting of the data sets as qualified.
Table 5-8. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Åkesson et al. 2010a/: Experimental data compiled in Figure 10-3 and associatedempirical relations.
2. /Johannesson et al. 2010/: Data from experiments performed in IBECO-RWC-BF.
3. /Joyce et al. 2010/, Table 4-2: Hydraulic conductivities assumed for the buffer andmaterials of backfilled tunnels and underground openings.
4. /Joyce et al. 2010/, Table 5-3: Hydraulic conductivity of crown space in variant case.

–

1: /Åkesson et al. 2010a/ is a complement to this Data report and data are qualified on a similar outline as in thisData report.
2. /Johannesson et al. 2010/ is a dedicated report investigating the properties of the candidate backfill material
IBECO-RWC-BF.
3: The data on hydraulic properties of the buffer and backfill used in hydrogeological modelling are difficult to sort as
qualified or supporting. These data are used as qualified in hydrogeological modelling. However, they are pessimisticand do not reflect on the actual properties of the buffer and backfill.
4. The assumed properties of the crown space given in /Joyce et al. 2010/ are considered as qualified. It should be
noted that the crown space is only included in the hydrogeological variant case, as it is expected that there will always
be a swelling pressure of the backfill and that such a crown space will not develop in reality /Åkesson et al. 2010b/.

Excluded data previously considered as important

No such data have been excluded for the considered materials.

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5.2.5 Conditions for which data are supplied
The conditions of importance can be divided on conditions for the buffer and backfill materials, andconditions for the fluid (groundwater). Conditions for the used materials, including the degree of compaction, are described in Section 5.1 and in the Buffer production report and the Backfill production
report. This manifests in the specified densities and porosities given in Table 5-5 and Table 5-6. Basedon knowledge of the installed buffer and backfill, the permeability can be assessed.
When assessing the hydraulic conductivity also characteristics of the fluid are needed, where thedynamic viscosity is the governing parameter (cf. Equation 4-3). The dynamic viscosity is mainly
affected by the fluid temperature, where a higher temperature increases the viscosity and as a result,also the hydraulic conductivity.

5.2.6 Conceptual uncertainty
There is no conceptual uncertainty concerning the hydraulic conductivity that is of significant consequence
for data and safety assessment calculations. Even though there may be aspects of fluid transport througha highly charged matrix that are not fully understood, data are not based upon modelling. Instead data
are based upon measurements in the reference buffer and backfill materials at relevant conditions (suchas degree of compaction). The data can thus be considered as empirical. If extrapolating data to othertemperatures, minor uncertainty may be introduced as other parameters than those of Equation 4-3 may
affect the hydraulic conductivity. Such other parameters may, for example, be associated with chargeinteractions of the fluid and its solutes with clay mineral surfaces.
5.2.7 Data uncertainty due to precision, bias, and representativity
In /Åkesson et al. 2010a/ experimental results from /Karnland et al. 2000, 2006, 2009/ for the MX-80
bentonite buffer are compiled (cf. Figure 5-1). The measurement data are detailed in the supporting
documents while the empirical relations are described in Section 10.2.2 of /Åkesson et al. 2010a/.
As can be seen from Table 5-5, the dry density range of interest for the installed buffer is 1,484–1,640 kg/m3.
Within this range, measurement results suggest hydraulic conductivities in the range 10–14–10–13 m/s.
Figure 5-2 shows measurement data from the reference backfill material Ibeco RWC-BF, using waterof different compositions /Johannesson et al. 2010/.

Hydraulic conductivity (m/s)

10–9

10–10

10–11

10–12

LOT A1 De-ionized
LOT A1 GW
LOT A0 GW
LOT A2 GW
TR-06-30 De-ionizedTR-95-20 Relation
New Eq 10-4 relation
TR-99-41 Table

10–13

10–14

10–15

400

600

800

1000

1200

1400

1600

1800

Dry density (kg/m3)

Figure 5-1. Hydraulic conductivity versus dry density. Suggested relation shown by black solid line.
Measurement data from LOT A1 /Karnland et al. 2000/, LOT A0 and A2 /Karnland et al. 2009/, TR 06-30/Karnland et al. 2006/. Reproduced from Figure 10-3 of /Åkesson et al. 2010a/.

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149

10–9

IBECO-RWC-BF 1% salt
IBECO-RWC-BF 1% salt ground
IBECO-RWC-BF 3.5% salt

Hydraulic Cond. (m/s)

10–10

IBECO-RWC-BF 3.5% salt ground
10–11

10–12

10–13

10–14
1100

1200

1300

1400

1500

1600

1700

Dry Density (kg/m3)

Figure 5-2. The hydraulic conductivity of Ibeco RWC-BF as function of the dry density. Reproduced from/Johannesson et al. 2010, Figure 6-3/.

As can be seen from Table 5-6, the dry density range of interest for the installed backfill is 1,458–
1,535 kg/m3. Within this range, measurement results suggest hydraulic conductivities in the approximate
range 1·10–13–3·10–13 m/s. As can be seen, the spread in the data is limited, indicating limited data
uncertainty. It is interesting to note that there is a wide margin to the minimum hydraulic conductivity(1·10–10 m/s) of the backfill specified in the design premises.

5.2.8 Spatial and temporal variability of data
The spatial variability of the buffer and backfill properties is handled by the uncertainty ranges
suggested in Section 5.2.7 and in Table 5-5 and Table 5-6.
The data concern the fully saturated system. Accordingly, the temporal variability associated with thesaturation process is of no concern for this section. Furthermore, temporal variability associated with
post-closure removal (erosion) of the buffer and backfill is not handled in this section. Temporal variability due to the changing temperature in the repository can be approximately handled by correcting
for the water dynamic viscosity (cf. Equation 4-3).

5.2.9 Correlations
These data have been estimated from measurements on the concerned materials under appropriate
conditions. Accordingly, no correlation exist that needs to be propagated to subsequent modelling.
5.2.10 Result of supplier’s data qualification
Realistic hydraulic conductivities

Section 5.2.7 provides reasonable ranges for the hydraulic conductivity of the buffer and backfill atroom temperature. From these ranges, the hydraulic conductivity of 5·10–14 m/s is recommended as
best estimate value in subsequent modelling. As an upper value 1·10–13 m/s is recommended. As an
upper hydraulic conductivity for the backfill, 3·10–13 m/s is recommended.
The hydraulic conductivities used in hydrogeological modelling /Joyce et al. 2010, Table 4-2/ aretabulated in Table 5-9. As can be seen, the data pessimistically assumed in hydrogeological modellingare orders of magnitudes higher than those of the actual buffer and backfill. Due to the large discrepancy,
temperature corrections become subordinate.

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Table 5-9. Hydraulic conductivities used in hydrogeological modelling. Data reproduced from
Table 4-2 of /Joyce et al. 2010/.
Underground opening

Material

K (m/s)

Deposition hole

Bentonite

1.0⋅10–12

Deposition tunnel

Low-grade bentonite

1.0⋅10–10

Main tunnel

Low-grade bentonite

1.0⋅10–10

Transport tunnel

Low-grade bentonite

1.0⋅10–10

Ramp

Low-grade bentonite

1.0⋅10–10

Elevator shaft

Low-grade bentonite

1.0⋅10–10

Skip shaft

Low-grade bentonite

1.0⋅10–10

Air intake shaft

Low-grade bentonite

1.0⋅10–10

Air exhaust shaft

Low-grade bentonite

1.0⋅10–10

Deposition area air
exhaust shaft

Low-grade bentonite

1.0⋅10–10

Central area tunnel

Crushed rock

1.0⋅10–5

Top sealing

Rock blocks

0.10

Crown spaceGap

1.0⋅10–3

5.2.11 Judgements by the SR-Site teamAccording to the instruction given in Section 2.3, the SR-Site team should make comments on all
the sections of the supplier.
Sources of information and documentation of data qualification

The supplier has used the compilation of data in /Åkesson et al. 2010a/ as input, together with datafrom /Johannesson et al. 2010/. The data are considered as qualified. The supplier has made detailedreference to supporting document. The SR-Site team agree with this handling.
Conditions for which data are supplied

The supplier has discussed conditions for the buffer and backfill materials, and for the fluid. Given
the specified materials and degrees of compaction, the most important condition becomes the temperature.
The SR-Site team agree with this handling.
Conceptual uncertainty

The SR-Site team agree that there is no significant conceptual uncertainty associated with these data.
Data uncertainty due to precision, bias, and representativity

The uncertainty handling is based on inspection of the displayed measurement data. The SR-Site teamagree with this handling.
Spatial and temporal variability of data

Neither spatial nor temporal variability is of great significance for these data (as they do not accountfor erosion processes). Spatial variability is handled by the uncertainty ranges given. In SR-Site hydro­
geological modelling the temporal variability associated with potential erosion of the backfill ishandled by using pessimistic values.
Correlations

No correlation is suggested to be propagated to subsequent modelling. The SR-Site team agree withthis handling.

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Result of supplier’s data qualification

The suggested hydraulic conductivities for the buffer and (deposition tunnel) backfill are accepted
for used in SR-Site, and should be seen as realistic data.
In the hydrogeological modelling, hydraulic conductivities have been chosen based on the safetyfunction indicator values presented in the design premises /SKB 2009c/. The chosen values correspondto the minimum values needed for the safety functions to be up-held, which are 1·10–10 m/s and 1·10–12 m/s
for the backfill and buffer, respectively. As can be seen, these values are orders of magnitude higher
than realistic data. This affects the hydrogeological modelling by resulting in higher flows in the backfilled repository system than expected in a realistic case. This is thus a pessimistic safety assessment
assumption since the tunnel system, with its prescribed hydraulic properties, will exaggerate connectivity
and flow at repository depths. Furthermore, since retention in tunnels is neglected in the radionuclidetransport calculations, a bias towards longer flow paths in tunnels, and also a bias towards a higher
fraction of flow paths going through tunnels, will result in pessimistic estimates of the flow-relatedtransport resistance, see Section 6.7 for details.

5.2.12 Data recommended for use in SR-Site modelling
For the buffer the best estimate hydraulic conductivity of 5·10–14 m/s is recommended. As an upper
value 1·10–13 m/s is recommended.
An upper realistic value of the hydraulic conductivity of the backfill is 3·10–13 m/s. This value has
not been used in subsequent SR-Site modelling as hydrogeological modelling is based on pessimistic
values. These pessimistic values are tabulated in Table 5-9 and accepted for use in SR-Site hydrogeological modelling.

5.3

Migration data of buffer and backfill

This section concerns migration data for the buffer and backfill, or more specifically the effective
diffusivity, sorption partitioning coefficient, and the diffusion-available porosity. The migration data
are to be used in the SR-Site solute transport modelling.
The data are given for the reference buffer, described in the Buffer production report, and the reference
backfill, described in the Backfill production report. The examples of reference buffer materials
are MX-80 and Ibeco-RWC (Depontit CA-N) bentonites and the reference backfill is a low grade
bentonite, with Ibeco-RWC-BF as an example. This material is also called Milos Backill.
The dry density and physical porosity of the buffer and backfill is given in Section 5.1.

5.3.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The following data should be delivered by the supplier:•Effective diffusivity De (m2/s) for the reference buffer and backfill for the elements of the selected
inventory, dissolved oxygen, sulphide, and sulphate. The supplied effective diffusivities should
be valid for an appropriate range of pore water compositions, and for the appropriate bentonite
density range.

• Sorption partitioning coefficients Kd (m3/kg) for the reference buffer and backfill for the elementsof the selected inventory, dissolved oxygen, sulphide, and sulphate. As sorption partitioning
coefficients are only valid for a particular set of conditions, values should be supplied for an
appropriate range of pore water compositions. In SR-Site, the system is assumed to be open withregard to exchange of CO2. For redox-sensitive elements, all relevant oxidation states should beconsidered for data selection.
•

The diffusion-available porosity ε (–) for all diffusants in the reference buffer and backfill.

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The elements of the selected inventory, for which transport parameters are required, are provided in
Section 2.2.1. For clarification tritium should be assumed to be in the form of tritiated water (HTO),
and data on carbon should be provided for inorganic and organic compounds. The dry densities andphysical porosities are given in Table 5-5 for the reference buffer and in Table 5-6 for the reference
backfill.
In SR-Site, the system is assumed to be open with regard to CO2 exchange, as opposed to being closed.
This assumption results in a calculated pore water pH that is about 0.8 units higher than in the case ofa hypothetical closed system. It is noted that the pore water compositions differ from the groundwatercompositions delivered in Section 6.1, and that it is the pore water composition that is relevant withrespect to all retention processes in the buffer/backfill. Furthermore it is noted that it is too big of atask to model pore water compositions based on all site specific groundwater compositions expectedduring the glacial cycle, and thereafter assessing the associated migration data. Therefore, it is allowed
to use appropriate reference pore water compositions that span the likely conditions. However, theappropriateness of the chosen reference pore waters should be commented upon, in the light of the site
specific groundwater.
SR-Site modelling activities in which data will be used

The modelling of radionuclide transport in SR-Site is described in the Radionuclide transport report.
Radionuclide migration in the buffer and backfill is modelled in the near-field transport simulation
code COMP23 /Romero et al. 1999, Cliffe and Kelly 2006/ as diffusive and advective transport throughthe porous network, in combination with sorption of radionuclides. The modelling parameters that are
not flow related, used to describe these processes, are the effective diffusivity, the diffusion-available
porosity, the sorption partitioning coefficient, and the dry density of the clay material. The magnitudesof these entities depend on physical properties of the buffer and backfill, on the pore water composition, and on the migrating species. From the entities listed above, an apparent diffusivity Da (m2/s) may
be derived /Ochs and Talerico 2004/:
Da =

De

e + K d rd

5-3

Solute transport in bentonite is also modelled to estimate copper corrosion /SKB 2010c/, where thecorrodants are either dissolved oxygen or sulphide (cf. Section 6.1).

5.3.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The modelling performed in SR-Can generally agree with the SR-Site modelling. No significant
difference with bearing to the parameters of this section has been identified.
Conditions for which data were used in SR-Can

In SR-Can the MX-80 bentonite was used as the reference buffer material, and migration parameters
were evaluated based on a buffer dry density of 1,590 kg/m3 and porosity 0.43. While other bentonites(e.g. Deponit Ca-N) were not considered explicitly, the following variations in bentonite composition
were considered:•

Bentonite converted completely to the Ca-form.

•

Bentonite completely depleted of soluble impurities (NaCl, KCl, gypsum).

Two backfills materials, Friedland Clay and a bentonite/ballast mix, were assumed in SR-Can. However,these backfill materials are of no concern for SR-Site. Other conditions used in SR-Can generally agreewith those used in SR-Site.

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Sensitivity to assessment results in SR-Can

In SR-Can it was shown that the scenarios giving the highest dose were associated with a situation
where the buffer had eroded. In such a situation the data delivered in this section lose their importance.
In cases where the buffer remained uncompromised, the general copper corrosion rate was assumed
to increase monotonically with increasing effective diffusivity of dissolved oxygen and/or sulphidein the buffer.
In cases where the buffer remained uncompromised but the canister was breached, the release rate
was assumed to increase monotonically with increasing effective diffusivity, and decrease monotonically with increasing sorption partitioning coefficient. In such case, the annual radionuclide releasefrom the near-field was dominated by the radionuclides C-14, Cl-36, Ni-59, Nb-94, I-129, Cs-135,
Ra-226, and Pa-231 (cf. Figure 10-14 in SR-Can Main report /SKB 2006a/). If putting it into thecontext of radiological risk at ground surface, the mean annual effective dose was overwhelmingly
dominated by Ra-226 in the SR-Can safety assessment (cf. /SKB 2006a, Figure B-5/). Except for
Ra-226, I-129 contributed the most to the radiological risk.
Alternative modelling in SR-Can

No alternative modelling of relevance to this section was used in SR-Can.
Correlations used in SR-Can modelling

The correlations used in SR-Can generally agree with those used in SR-Site (see Section 5.3.9).
Identified limitations of the data used in SR-Can modellingSome of the identified limitations in data used in SR-Can remain for data to be used in SR-Site.
These limitations are highlighted in the remaining of this section. The identified limitations did
not give rise to general changes in the approach of collecting data for SR-Site, but rather to minor
modifications. The limitations in data most likely had a small effect on the assessed radiologicalrisk in SR-Can.
The backfill materials considered in SR-Can are of no concern for SR-Site, and SR-Can data cannot
be transferred to SR-Site.
A few limitations were identified in the SKI report /Stenhouse et al. 2008/. For example, there weredifferences between the reference solid and waters used by /Ochs and Talerico 2004/ and data recommended in the SR-Can Data report /SKB 2006b/. Most significantly concerning the clay material,
there were differences in percentage of minor constituents, for example calcite. However, even tracesof calcite in the bentonite are enough to establish solubility equilibrium between dissolved Ca ionsand calcite, due to the high solid/water ratio in compacted material and the solubility limit of calcite.
The only relevant variation would be complete absence of calcite, which was tested in the pore water
calculations for SR-Can. Furthermore, there were small differences in the CEC of the clay materials.
These may have a small influence on radionuclides sorbing only through ion exchange (Cs, Sr, Ra),but this uncertainty can be assumed to be small compared to the overall uncertainty. In case of radionuclides sorbing by surface complexation, the influence of differences in the CEC is not significant
in comparison to other uncertainties that were considered for SR-Can.
Concerning the groundwater, saline Beberg water (BFI01B) was used as the reference groundwater,
as defined in SR-97 and based on investigations at Finnsjön. This reference water was used as a basis
when modelling the pore water composition.
On a general note, /Stenhouse et al. 2008/ found that a relatively small number of batch sorption
datasets were used as the basis for the different Kd recommendations, and that the treatment of
uncertainty could be improved.
All species dealt with in this section were not included in the SR-Can Data report /SKB 2006b/.

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5.3.3 Supplier input on use of data in SR-Site and SR-Can
As part of SR-Can, a major review was made on migration parameters for the bentonite buffer /Ochs
and Talerico 2004/. As part of SR-Site, a minor update of the data presented in /Ochs and Talerico
2004/ is made. However, the great majority of the data recommended for SR-Can are also recommended for SR-Site.
The following main changes should be noted.
•

Several additional sets of effective diffusion coefficients (De) obtained in through-diffusion
experiments with compacted bentonite and, especially, in argillaceous rocks have become available
since the publication of /Ochs and Talerico 2004/. Therefore, new data are included in the figurescompiling De data from various publications. The new data do not significantly deviate fromthe recommendations given in /Ochs and Talerico 2004/ but are still implemented as a basis forrecommending slightly modified data.

•

In SR-Can, other backfill alternatives were proposed. Therefore, all backfill data are modified.
Concerning De, data based on MX-80 are modified by a scaling for the differences in dry density
between the buffer and backfill. For Kd, data for MX-80 is generally used for the Milos backfill,
but for Cs, Sr, and Ra, a scaling has been performed based on differences in CEC between Milos
Backfill and MX-80.

•

Migration data for a number of new species have been derived, including H-3, Mo, Cd, Ac, HS–,SO42– and dissolved O2.

Concerning the request to comment on the appropriateness of the chosen reference pore water compositions, in the light of the site specific groundwater; such comments are based on groundwaters
given in SR-Can /Auqué et al. 2006/. The reason is that at the time of writing this text, SR-Sitegroundwater modelling was still being performed. For the completeness of this section, the SR-Siteteam should reflect on the appropriateness of the chosen reference pore waters, in the light ofgroundwater composition data for SR-Site presented in Section 6.1.

5.3.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information on migration properties in buffer and backfill are given in
Table 5-10.
Among the references considered to be of major importance in /Ochs and Talerico 2004/ the following
deserves mentioning: A Nagra report on selected Kd values for MX-80 /Bradbury and Baeyens 2003/,
and original data sources cited therein; SKB reports on Kd, De, and Da /Yu and Neretnieks 1997/ andgroundwater composition /Laaksoharju et al. 1998/, and original data sources cited therein; Japan
Atomic Energy Agency (JAEA, formerly JNC) reports and databases on diffusion in Kunigel-V1
and Kunipia-F bentonites as a function of dry density.
Table 5-10. Main sources of information used in data qualification.
Backfill production report, 2010. Design, production and initial state of the backfill and plug in deposition tunnels.
SKB TR-10-16, Svensk Kärnbränslehantering AB.
Buffer production report, 2010. Design, production and initial state of the buffer. SKB TR-10-15, Svensk
Kärnbränslehantering AB.
Auqué L F, Gimeno M J, Gómez J B, Puigdomenech I, Smellie J, Tullborg E-L, 2006. Groundwater chemistryaround a repository for spent nuclear fuel over a glacial cycle. Evaluation for SR-Can. SKB TR-06-31, Svensk
Kärnbränslehantering AB.
Ochs M, Talerico C, 2004. SR-Can. Data and uncertainty assessment. Migration parameters for the bentonite buffer
in the KBS-3 concept. SKB TR-04-18, Svensk Kärnbränslehantering AB.
A number of recent peer reviewed scientific publications and technical report from the open literature, as referred to in
the present text.
A great number of peer reviewed scientific publications and technical report from the open literature, as referred to in/Ochs and Talerico 2004/.

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Categorising data sets as qualified or supporting

A great majority of the data recommended for use is taken directly from /Ochs and Talerico 2004/.
This is a dedicated SR-Can report and the data therein are considered as qualified. However, before
judging the data as qualified, the conditions of the report (buffer and backfill dry density andgroundwater composition) have been compared with SR-Site conditions. Properties of the buffer
and backfill, such as density, CEC, etc., are taken from the Buffer production report and Backfill
production report. These two later reports are judged as qualified.
Migration data for a number of new species not covered by /Ochs and Talerico 2004/, including H-3,
Mo, Cd, Ac, HS–, SO42– and O2, have been estimated. This estimate is based on analogues and data inthe open scientific literature, e.g. /Bazer-Bachi et al. 2007/.
Qualified and supporting transport data sets for the buffer and backfill are displayed in Table 5-11
and numbered from 1 to 9. Detailed comments to the data sets are given in Table 5-12.
Table 5-11. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Ochs and Talerico 2004/, Table in Section 5-4: Summary of
De values.
2. /Ochs and Talerico 2004/, Tables in Section 5-4: Kd data forreference pore water, reference pore water for closed system withrespect to CO2, and highly saline pore water.
3. /Ochs and Talerico 2004/: Reduction factor of 2.5 for diffusionavailable porosity for anions, as compared to the physical porosity.
Text in association with Equation 5.9 (page 52).
4. /Auqué et al. 2006/, Table 3-3: Modelled groundwater compositionsat repository depth at Forsmark at different times.
5. /Bazer-Bachi et al. 2007/: Sulphate sorption on Callovo-Oxfordien
argillites.
6. /van Loon et al. 2003a, b/: The diffusion available anion porosity is
selected based on the findings of /van Loon et al. 2003a, b/, who madean explicit effort to distinguish De from ε in their data interpretation.
7. CEC data for the buffer in the Buffer production report (Table 3-2)
and for the backfill in the Backfill production report (Table 3-2).

8. /Ochs and Talerico 2004/, Appendix A.
Groundwater compositions including thereference groundwater saline Beberg water(BFI01B).
9. A great number of data sets taken fromscientific publications and technical reportfrom the open literature, as referred to in thispresent text or in /Ochs and Talerico 2004/.

Excluded data previously considered as important

Data on the backfill alternatives in SR-Can are excluded, as they are not relevant for SR-Site.
For the buffer, no data previously considered as important is excluded.

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Table 5-12. Justification of the sorting of items in Table 5-11.
1: Concerning De and Kd for the buffer and backfill, this present text can be seen as an updated supplement
to the SR-Can report /Ochs and Talerico 2004/, wherein a full reference list can be found. The SR-Can data
presented in /Ochs and Talerico 2004/ are considered to be qualified, as the report was written on a structure
similarly to the present structure for data qualification. This means that the report is written on a similar outline
as this present text, handling data uncertainty and variability in a robust fashion. Original experimental datareported in a large number of peer-reviewed publications constitute the main data source. Many of thesepublications are cited in a Nagra report on selected Kd values for MX-80 /Bradbury and Baeyens 2003/; in an
SKB report on selection of diffusion data /Yu and Neretnieks 1997/; and reports and databases by JAEA. The
De values are given for the dry density values that correspond well to specifications of the SR-Site reference
buffer and backfill.
2: The Kd values in /Ochs and Talerico 2004/ are given for a range of pore waters corresponding to groundwaters with different salinities based on Beberg water, as given in the SR-97 safety assessment. All Kd values
used were derived on the basis of primary information (original experimental sorption data); i.e., no interpreted
information was used. After having confirmed that these groundwater compositions are in line with site specificgroundwater (see Section 5.3.5 of this present report) the Kd values were judged as qualified.
3: The reduction factor presented in /Ochs and Talerico 2004/ is considered as qualified. Neither the buffer
and backfill dry density or the groundwater composition differs considerable between SR-Site and SR-Can.
4: The modelled groundwater compositions at repository depth in Forsmark at different times /Auqué et al.
2006/ are judged as qualified for this purpose, as they were acquired as part of SR-Can. These compositions
are compared with the groundwaters used in /Ochs and Talerico 2004/.
5–6: /Bazer-Bachi et al. 2007/ and /van Loon et al. 2003a, b/ are peer reviewed scientific journal articles andthe data presented are be considered to be relevant and qualified.
7: Data delivered in the Buffer production report and the Backfill production report are qualified in these tworeports, in accordance with the SKB quality assurance system. This qualification is found to be in compliancewith the demands of the Data report.
8: The saline Beberg water BFI01B presented in /Laaksoharju et al. 1998/ and used as reference groundwaterin /Ochs and Talerico 2004/ is not site specific for Forsmark. It is judged as supporting data.
9: In this text, a number of articles and documents from the open literature are referred to. In addition, in /Ochs
and Talerico 2004/ a great number of articles and documents from the open literature are referred to. These
are generally peer-reviewed and sorting them as qualified or supporting often depends on how representative
data are. It is considered to be too voluminous to handle each article or document in this list. Comments are
instead given, when necessary, where the reference is made.

5.3.5

Conditions for which data are supplied

Buffer and backfill density and composition

Examples of the reference buffer are MX-80 bentonite and IBECO-RWC (Deponit CA-N). An exampleof the reference backfill is Milos bentonite. Their montmorillonite content, accessory minerals content,
granular size distribution, CEC, etc. are given in the Buffer production report (Section 3.1) and the
Backfill production report (Section 3.1), respectively.
In /Ochs and Talerico 2004/ migration data are compiled for MX-80 bentonite of the dry density
1,590 kg/m3. As stated in Section 5.1 of this present report, for SR-Site the buffer dry density should
be within the range 1,484–1,640 kg/m3, with the best estimate somewhere in the centre of that range(set to 1,562 kg/m3). The discrepancy between the SR-Can and SR-Site dry densities is judged to be
insignificant in the light of Kd values, and no correction is needed.
While bentonites other than MX-80 (e.g. Deponit Ca-N and Milos backfill) have not been considered
explicitly in /Ochs and Talerico 2004/, the following variations in bentonite composition wereconsidered:•

Bentonite converted completely to the Ca-form.

•

Bentonite completely depleted of soluble impurities and some accessory minerals(NaCl, KCl, gypsum, calcite).

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With regard to Kd, a difference in bentonite composition (smectite content or measured CEC) would
require a re-calculation on formal grounds (i.e. based on the procedures used for Kd derivationin SR-Can). However, MX-80, Deponit CA-N, and Milos backfill are similar in terms of theseparameters. It can be assumed that the concentration of clay edge sites (which are responsible for thesorption of most radionuclides via surface complexation) are the same for all three materials within
the assumed uncertainties. (It should be noted that it is not finally established whether edge site
density is directly proportional to the measured CEC /NEA 2005/).
Concerning the backfill, the dry density of SR-Site should range between 1,458 and 1,535 kg/m3,
with a best estimate at 1,504 kg/m3. This is somewhat below the dry density of SR-Can /Ochs andTalerico 2004/, but the discrepancy is judged to be small in comparison with other uncertainties
regarding Kd.
In the case of radionuclides sorbing predominantly or exclusively through ion exchange (Cs, Ra, andSr), scaling according to the CEC of the clay can be performed, as sorption of these elements can beassumed to be directly related to this parameter. CEC scaling is also applicable for sulphate, which isassumed to sorb as outer sphere complex. The CEC of the buffer and backfill materials are shown in
Table 5-13.
Concerning the effective diffusivity, a few new data sets have been published since SR-Can. Accordingly,
new De data are supplied. These De data are estimated for the buffer and backfill dry densities of SR-Site.
Also the diffusion-available porosity has been updated, based on the new physical porosity data of
SR-Site (cf. Table 5-5 and Table 5-6).
Table 5-13. CEC of bentonites in SR-Can and SR-Site.
Bentonite

SR-Can
CEC (meq/100g)

SR-SiteCEC (meq/100g)

MX-80

85

75

Deponit CA-N

70

Milos Backfill

73

References: MX-80 and Deponit CA-N, SR-Site (Buffer production report, Table 3-2).
MX-80, SR-Can /Ochs and Talerico 2004, Appendix B/.
Milos Backfill, SR-Site (Backfill production report, Table 3-2)

Groundwater

The data delivered in this section apply at fully saturated buffer and backfill. The buffer and backfill
will be affected by a range of geochemical conditions, such as groundwater composition, redox potential,
and pCO2 (partial pressure of carbon dioxide). The groundwater has an indirect influence on migration parameters, through its interaction with the pore water (see discussion below). While Kd data
are clearly affected by the pore water composition, the effective diffusivity and diffusion-available
porosity are less affected.
In /Ochs and Talerico 2004/ the reference groundwater used for pore water modelling was saline
Beberg water (BFI01B). Variants included a non-saline and highly saline groundwater, as well as
two saline groundwaters having alkaline to highly alkaline pH (up to pH 13.15). None of thesegroundwaters are specific for the Forsmark site.
In /Auqué et al. 2006/, the groundwater composition at repository depth at the Forsmark site at different time periods was modelled. A comparison in terms of the major ions is given in Figure 5-3 forthe three main groundwaters of /Ochs and Talerico 2004, Appendix A/ and three modelled Forsmark
groundwaters during the temperate period /Auqué et al. 2006, Table 3-3/. The error bars for themodelled Forsmark groundwaters represent the 99.9% confidence limits.

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1Concentration (mol/l)

10–1[SO42-] TOT

10–2
10–3

Ionic Strength

Forsmark 2,020 AD[Cl] TOT

10–4

Forsmark 3,000 AD[HCO3-] free

10–5

[K] TOT[Mg] TOT

10–6
10–7

[Na] TOT[Ca] TOT[H+]

Forsmark 9,000 AD
Saline GW (I)
Non-saline GW (II)Highly saline GW (III)

10–8
10–9

Figure 5-3. Concentration of important groundwater constituents of three modelled groundwaters during
the temperate period in Forsmark (squares) /Auqué et al. 2006/ and the three main groundwaters used in/Ochs and Talerico 2004/.

Extracting information by visual inspection of Figure 5-3, with the aim at understanding how Kdvalues will differ for the different groundwaters, would be extremely difficult. This is especially trueas the groundwater only indirectly affects the sorption. The figure should rather be used to demonstratethat the groundwater chemical composition at Forsmark is expected to vary significantly. This isespecially true if taking the entire glacial cycle into account (the Forsmark data in Figure 5-3 only
represent the initial temperate period).
Kd values are conditional data, in other words they only apply at a certain set of conditions. The varying
geochemical conditions over the glacial cycle make it very difficult to estimate a Kd, with its associateduncertainty range, that should encompass the likely geochemical conditions.
The saline Beberg water used in /Ochs and Talerico 2004/ is based upon measurements in Finnsjön,
a site located a few tens of kilometres inland from the Forsmark site (see Figure 5-4).
As the Finnsjön site is located nearby the Forsmark site, and in a broader sense has about the samegeological settings, it is suggested to be acceptable to use the Kd data of /Ochs and Talerico 2004/.
Especially as the variant groundwaters used in /Ochs and Talerico 2004/ span a good portion ofreasonable, and even less reasonable, groundwaters at depth.
One could have chosen to perform new pore water modelling, based on a few site specific groundwatercompositions from arbitrarily chosen points in time of the glacial cycle. However, it is suggested that
performing such a task would not reduce data uncertainty at any greater extent. Due to the conditional
nature of Kd, and the varying geochemical conditions at the site, any approach chosen will be associated with large uncertainties.

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Skutskär
76

Gräsö
Forsmark

Finnsjön
E4ÖsthammarÖrbyhusGimoÖsterbybruk

(km)
0

15

Figure 5-4. The locations of the Finnsjön and Forsmark sites. Reproduced from Figure 2-3 of /SKB 1999a/.

Pore water

The pore water compositions used as the basis for the derivation of Kd data in SR-Can and SR-Site
are the result of thermodynamic modelling of clay-water interactions. The use of models is necessitatedby the experimental and conceptual uncertainties in extracting and determining the composition ofpore water in compacted bentonite. The modelling results depend on the composition of clay materialand contacting groundwater, on assumptions regarding boundary conditions (e.g., imposed redox
conditions, and whether or not the system is open or closed to CO2 gas exchange with the host rock),as well as on the actual modelling approach. This needs to be kept in mind when the influence ofindividual conditions is discussed.
The effect of uncertainties in the pore water compositions differs between the various migration
parameters. Kd depends strongly on the pore water composition and therefore indirectly on the
groundwater composition. De and ε depend comparatively little on the pore water composition,although extremely high salinity can have an influence on anion exclusion effects.
In /Ochs and Talerico 2004, Section 5.4/ Kd values are tabulated for three different pore waters (andvarious conditions in terms of bentonite composition and pCO2) within a reasonable range in termsof salinity and pH. In /Ochs and Talerico 2004, Appendix C/, the modelled pore waters are described.
The different pore waters are: reference pore water (RPW) based on saline groundwater and an open
system with respect to CO2; highly saline pore water (HSPW), based on highly saline groundwaterand an open system; and RPWC, based on saline groundwater but for a closed system. This latter porewater is of lesser concern for SR-Site, where an open system is assumed. No Kd values that formallycorrespond to a non-saline groundwater are tabulated in /Ochs and Talerico 2004/. However, pore waterconditions were calculated by /Ochs and Talerico 2004/ using a non-saline groundwater as well. The
resulting pore water compositions proved to have intermediate values of the most relevant solution
parameters (pH, carbonate concentration) in comparison to the pore waters used for Kd derivation. At
the same time, the resulting ionic strength in the pore water is almost identical in case of the non-saline
and saline groundwater, because the pore water in compacted bentonite is to a large degree controlled
by the soluble constituents of the bentonite itself. Thus, it can be expected that Kd for the non-saline
groundwater will lie within the range given by the saline groundwater.

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As Kd values for many radioelements are very sensitive with regard to pore water composition,
the validity of the selected data is bounded by the ranges of conditions covered by the pore watercompositions considered. It is not advisable to extrapolate Kd values to conditions outside theseranges. Special remarks are made below for carbonate, pH, and salinity:• Carbonate: Different open systems were considered in /Ochs and Talerico 2004/ with a pCO2
of 10–2.6 atm, giving pH values of about 7 to 7.4. A host-pCO2 above 0.1 atm would lead to pH
values below 6.6 and Kd values below the range considered in /Ochs and Talerico 2004/, even if
taken into account the Kd values estimated for the closed system.
•

pH: The groundwater and bentonite compositions that may lead to a low pore water pH have been
investigated in /Ochs and Talerico 2004/. The calculations show that the carbonate buffer system inthe bentonite is the main factor controlling pH. In an open system, as considered for SR-Site, calcite
from the bentonite and pCO2 imposed by the host rock will prevent low pH values. The porewatercompositions are the result of model calculations, where bentonite of a defined composition isequilibrated with a defined groundwater composition and an external (fixed by the host formation)
pCO2 /Ochs and Talerico 2004/. Due to its surface properties and the presence of dissolved impurities, bentonite (and other compacted clays) exerts an important buffer effect. This means that pH
and other solution parameters are buffered towards a common value in case of many different
groundwaters. Still, groundwater with extreme pH values or salinities will strongly influence
porewater composition.

•

Salinity: The salinity does not have a pronounced effect on Kd for most species, because of thecomparative “inertness” of Na+ and Cl–. For species sorbing by complexation it is expected thatan increase of salinity to more than 1 M will have a very small effect on Kd. This effect is likely
encompassed by the uncertainty range of Kd already considered. However, under conditions where
sorption is already very low (e.g. near lower limits and/or at lower end of pH range considered),Kd values could fall outside of the range given in /Ochs and Talerico 2004/ in the case of Cl–
concentrations significantly above 1 M. This is in all likelihood only relevant when the pH isalready low; i.e. if the system is closed with respect to CO2 exchange.
For elements whose migration behaviour is dominated by ion exchange (Cs, Sr, and Ra), an increaseof salinity above the values considered in /Ochs and Talerico 2004/ will lead to a decrease of Kd.
According to /Ochs and Talerico 2004/, an increase in pore water ionic strength from about 0.3 M to
0.8 M leads approximately to a 5-fold decrease of Kd. The lower limits of Kd are a factor of about
5 lower than the best estimates. This means that an increase of ionic strength from about 0.8 M toabout 4 M would be required to give a Kd that falls below the respective lower limit. Based on the
groundwater salinities indicated in /Auqué et al. 2006/, this is not to be expected.
Depending on the type of clay-water model used, an increase of ionic strength due to high salinity
is predicted to cause lower pore water pH, which could lead to Kd values outside of the range
considered. This is in all likelihood only relevant when pH is already low; i.e. if the system isclosed with respect to CO2 exchange.
Double layer effects in the pores of compacted bentonite will be decreased by high pore watersalinity; therefore anion exclusion effects will be less important at high salinity. However, nosignificant effect is expected in comparison to the uncertainties already considered on the basisof the available experimental data.

Concerning the pCO2 of the groundwater, values reported from the Forsmark site investigation in/Gimeno et al. 2008, Figure 3-14/ are either in line with, or lower than, those assumed in /Ochs andTalerico 2004/. In /Auqué et al. 2006, Section 3.4.3/ it is estimated that the pCO2 of the groundwater
will increase with time during the temperate period.
For an approximate estimation of the possible effects of variations in the pore water composition, thediagrams in Section 5.3.5 of /Ochs and Talerico 2004/ can be used. Interpolation between the threereference pore water conditions is easily possible for salinity, but should to be done with care forparameters that are directly linked to others (pH, pCO2).
The influence of the different composition of exchangeable cations between MX-80, Deponit CA-N,
and Milos backfill on pore water composition (and therefore on Kd) is considered to be negligible,
based on the available information.

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Temperature

The selected data are valid for 25°C. An increase to 50°C is expected to lead to a twofold increase of
De. A decrease in temperature to just above freezing is expected to lead to about a twofold decreasein De. The temperature influence on De is also discussed in Section 6.8.5. Based on the data presently
available, it is not possible to assess the influence of temperature on Kd with any certainty. No effecton diffusion-available porosity is expected for this temperature range.

5.3.6 Conceptual uncertainty
For the derivation of buffer and backfill migration parameters, the conceptual uncertainties discussed
below were identified.
•Conceptual uncertainties are introduced when interpreting experimental data into recommended
diffusion and sorption values. Several related conceptual uncertainties exist regarding the inter­
pretation of, and self-consistency among, batch Kd values and diffusivities of sorbing radionuclides
on the one hand, and of diffusivities and diffusion available porosities of anions and certain mobile
cations, such as Cs, on the other hand. Both issues are discussed to some detail in the SR-Can
Buffer and backfill process report /SKB 2006f/. In the case of diffusion coefficients, uncertainties
correspond to the experimental error as well as to any uncertainty introduced in the required
modelling for raw data reduction.

•

For Kd, the most significant conceptual uncertainties, in terms of representing reality, are related tothe description of pore water composition as a function of conditions. There are still some scientificshortcomings regarding the derivation of pore water composition in compacted bentonite (such as
effects of the electrical double layers in the pore space on e.g. the amount of “free” water, water
activity, etc.), and its evolution over time under repository conditions. As the pore water compositionof compacted bentonite cannot be determined experimentally with any certainty, it is calculated
through thermodynamic surface chemical models. Several published models are available for thispurpose. While they are based on the same principles, they differ in a number of details regarding
e.g. the treatment of specific surface chemical equilibria. Because of the limited accessibility ofthe pore space of compacted bentonite, a direct comparison of pore water model and experimental
results is not possible. The comparisons of experimental and model-derived macroscopic migration
parameters in compacted bentonite in /Ochs and Talerico 2004, Ochs et al. 2001/ indicate thatthe available models are sufficient for describing the effect of bentonite and groundwater properties
on pore water composition and radionuclide migration within experimental uncertainties.

•

Conceptual uncertainties are introduced when converting suggested data obtained at a specific setof conditions through models or estimation procedures into data applicable at conditions relevant
for safety assessments. These are largely included in the uncertainty factors applied by /Ochs andTalerico 2004/.

•

If there are significant uncertainties associated with the application conditions themselves, it iscritical to take the conditional nature of the relevant migration parameters into account. Kd values
in particular are highly conditional and need to be derived for each specified set of (expected)
conditions. In /Ochs and Talerico 2004/, variability of geochemical conditions was addressed byderiving Kd values separately for several sets of possible geochemical conditions.

•

There are some open questions regarding the fundamental, underlying chemistry of radionuclides
in aqueous solutions. This is also acknowledged in recent TDB (thermodynamic database) compilations (e.g. /Hummel et al. 2002/). For example, the stability of actinide(III)-silicate or mixed
actinide(IV)–OH-CO3 complexes is not established to date. There are also uncertainties regarding
the solution speciation of many of the less well researched elements, such as Nb, Zr, etc. The TDB
used for the present data derivation is indicated for each element in Appendix F of /Ochs andTalerico 2004/. Use of different TDBs, or future updates in SKB’s reference TDB, could lead todifferent migration parameters.

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5.3.7 Data uncertainty due to precision, bias, and representativity
As discussed in the following (see also /Ochs and Talerico 2004/ for details), the uncertainty of theselected sorption and diffusion parameters depends on uncertainties in the underlying data, but also
on uncertainties that are introduced in the process of synthesizing and scaling these data to the specified safety assessment conditions. Therefore, data uncertainty is discussed together with descriptionsof the derivation of sorption and diffusion parameters, as well as the basic results of data derivation.
The numerical results are summarised in Section 5.3.10.
Effective diffusivity

Diffusion experiments are (in comparison to sorption batch experiments) more difficult to performin terms of controlling chemical and other boundary conditions. On the other hand, the conditions in
diffusion experiments are very similar to the actual conditions anticipated for the buffer and backfill.
Further, effective diffusivities are representative for steady-state conditions; i.e. chemical processes
and in particular sorption do not play a role anymore during the actual data acquisition (or can befactored out). To actually obtain De values requires a relatively complex processing of the raw
experimental data (fitting of models that have to be chosen as a function of boundary conditions).
Because of the effort and time requirements associated with diffusion experiments, De values are
typically available only for mobile but not for sorbing elements, and are only in a few cases available
as a function of systematic variations of chemical conditions.
Following this situation, De values presented here are compiled from various publications, andselected values and uncertainties are evaluated directly from plots of reported De vs. clay dry density.
Uncertainties related to the influence of conditions on diffusion-relevant bentonite characteristics(pore size, etc.) are assumed to be included in the uncertainties given for De in each case, as thesedata are derived based on numerous experimental measurements that already cover a range of different
conditions.
Following the approach taken in /Ochs and Talerico 2004/, De was evaluated separately for neutral
diffusants on the one hand, and anions showing anion exclusion effects on the other hand. In addition,a distinction was made between Cs, which exists practically always as simple cation in aqueous solutions,and all other cations which exist mainly in hydrolysed form or in the form of ion pairs (i.e, where all
species taken together typically give a near-neutral average charge).
Neutral diffusants (HTO)
The effective diffusivity for most elements (hydrolysable cations, cations existing mainly as neutral
ion pairs, neutral molecules) are in this section based on the diffusion of HTO. Many of these elements
are moderately or strongly sorbing, and if any element specific De data exist, they are isolated. There­
fore it is preferred to rely on data for HTO.
Figure 5-5 shows reported De data from a number of publications, obtained on compacted clays of
relevance for the buffer and backfill materials.
The data for HTO and CH4 show good agreement, as do data for MX-80 and the crude bentonite
Kunigel-V1. This means that the effective diffusivity of HTO is consistent among different bentonites
with low to intermediate smectite content. Furthermore, the diffusion behaviours of HTO and CH4
do not appear to differ significantly. On the other hand, it can be seen that (purified) bentonites withhigh smectite content (especially Kunipia-F consisting of almost pure smectite) lead to lower valuesof De for HTO.

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As can be seen in Figure 5-5, the best estimate dry densities of the backfill and buffer are indicatedby the brown and orange lines at 1,504 and 1,562 kg/m3, respectively. For the dry density range ofinterest (1,458–1,640 kg/m3), the experimental De values ranges over about one order of magnitude
for the different clay materials. Some of the spread is due to differences in clay materials, and possibly
also due to differences in conditions (addition of sand, salinity). The variation of conditions does
not appear have a clear effect on De of neutral tracers, however. Experimental uncertainties anduncertainties in the reduction of the raw data will also contribute to the spread in data.
Recommended De values are based on a regression analysis including all HTO data for Kunigel-V1
and MX-80 (Figure 5-6). The standard error of the regression analysis (standard error for predictedvalues) is indicated on the brown and orange lines at the best estimate dry densities of the backfill
and buffer. The regression is not valid at low clay densities, as this would suggest that De becomes
higher than the diffusivity in unconfined pore water Dw (m2/s), which is about 10–9 m2/s.
10–8

10–9

De (m2/s)

10–10

10–11

10–12

10–13

10–14
0

500

1000

1500

Dry density (kg/m3)

2000

2500

HTO, Kunigel-V1 /Sato and Suzuki 2003/

HTO, Kunipia-F /Sato and Suzuki 2003/

HTO, Kunigel-V1 /Sato 1998/

HTO, Kunipia-F /Suzuki et al. 2004/

HTO, Kunigel-V1/30% sand, saline /Suzuki et al. 2007/

HTO, Kunipia-F /Kozaki et al. 1999/

HTO, Kunigel-V1 /Kato et al. 1995/

HTO, MX-80 /Neretnieks and Skagius 1978/

HTO, Kunigel-V1/sand /Kato et al. 1999/

HTO, CO argillite /Bazer-Bachi et al. 2008/

HTO, Ca-Kunigel-V1 /Mihara 2000/

CH4, MX-80, 50°C /Neretnieks and Skagius 1978/

HTO, FEBEX, /García-Gutiérrez et al. 2004/

Specified density buffer

HTO, Avonlea /Cho and Oscarson 1996/

Specified density backfill

Figure 5-5. Effective diffusivities obtained in compacted clays using non-charged tracers (HTO: blue symbols– Kunigel-V1, black – MX-80, green – Kunipia-F, black open – other; grey: CH4/MX-80). Dry densities
specified for the reference buffer and backfill are indicated by the orange and brown line, respectively.
The scale is chosen to allow direct comparison with data for anions and Cs.

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10–8-2.052E-03x

y = 3.476E-09e
2

R = 8.105E-01

De (m2/s)

10–9

10–10

10–11
0

500

1000

1500

2000

2500

Dry density (kg/m3)
HTO, Kunigel-V1 /Sato and Suzuki 2003/
HTO, Kunigel-V1 /Sato 1998/
HTO, Kunigel-V1/30% sand, saline /Suzuki et al. 2007/
HTO, Kunigel-V1 /Kato et al. 1995/
HTO, Kunigel-V1/sand /Kato et al. 1999/
HTO, Ca-Kunigel-V1 /Mihara 2000/
HTO, FEBEX, /García-Gutiérrez et al. 2004/
HTO, Avonlea /Cho and Oscarson 1996/
Limits buffer
Trend HTO-neu (full)

HTO, Kunipia-F /Sato and Suzuki 2003/
HTO, Kunipia-F /Suzuki et al. 2004/
HTO, Kunipia-F /Kozaki et al. 1999/
HTO, MX-80 /Neretnieks and Skagius 1978/
HTO, CO argillite /Bazer-Bachi et al. 2008/
CH4, MX-80, 50°C /Neretnieks and Skagius 1978/
Specified density buffer
Specified density backfill
Limits backfill
Regression HTO

Figure 5-6. Same data as in Figure 5-5. Indicated are the regression analysis of De for HTO (based on
all data for Kunigel-V1 and MX-80) as well as the standard error at the dry densities specified for thereference buffer and backfill. Note that scale is different from in Figure 5-5.

The regression parameters are shown in Equation 5-4.
De = 3.48·10–9·e (–0.00205·ρd)

5-4

The resulting best estimate De values are 1.4·10–10 m2/s for the buffer (ρd = 1,562 kg/m3) and
1.6·10–10 m2/s for the backfill (ρd = 1,504 kg/m3).
Possible additional uncertainties are introduced by assuming that the De values obtained using certain
non-charged tracers also apply for other non-charged tracers. An obvious bias in assuming this isthat differences in diffusivities in unconfined pore water Dw between species are not accounted for.
Theoretically, this bias could be compensated for by scaling the De data with Dw values of all individual
species including the concerned element occurring in the pore water, using a weighted average.
However, this would be very cumbersome, and the usefulness of such a procedure would be limited
due to uncertainties regarding a) the calculated speciation of a given element in the pore water andregarding b) the Dw value of complex species (such as hydrolysed actinide elements). In general,
differences in Dw are considered as small in comparison to the overall uncertainty.

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Anions
The effective diffusivity for anions is estimated from experimental data on compacted clays using anionic tracers and independent model predictions. Figure 5-7 shows compiled De data versus dry density.
Figure 5-8 shows the same data, but includes the regression for HTO from Figure 5-6, as well as
independent predictions for chloride in MX-80 and Tc(VII) in Kunipia-F by /Ochs et al. 2001/.
The effective diffusivity of anions is on average lower than for the non-changed species, indicatinganion exclusion effects. Note that the predicted dependency of De vs. dry density for chloride diffusion
in MX-80 (green line) /Ochs et al. 2001/ corresponds nearly exactly to the slope of the regression for
HTO data (blue line).
The De values close to the dry densities of the reference buffer and backfill ranges over at least twoorders of magnitude. The data points are however obtained on a number of different clay materials.
It can further be seen that the spread appears to increase with the degree of compaction. This may
at least in part be due to basic difficulties of separating the raw diffusion data into De and diffusionavailable porosity in case of anions (see /Ochs and Talerico 2004/ for a more detailed discussion).
The data indicate a spread over at least one order of magnitude for MX-80 bentonite (see forexample data points from /Muurinen et al. 1989/).
10–8

10–9

De (m2/s)

10–10

10–11

10–12

10–13

10–14

0

500

1000

1500

2000

2500

Dry density (kg/m3)
I, MX-80 (Eriksen and Jansson 1996)
I*
I Kunipia-F, /Sato et al. 1992/
I, Avonlea /Cho et al. 1993/
I, Kunigel-V1 /Ishidera et al. 2008/
I, Kunipia-F /Ishidera et al. 2008/
I, Avonlea /Cho and Oscarson 1996/
I, Ca-Kunigel-V1 /Mihara 2000/
I, Avonlea /Oscarson et al. 1992/
I, CO argillite /Descostes et al. 2008/
Cl, Volclay KWK /van Loon et al. 2007/
Cl, FEBEX, /García-Gutiérrez et al. 2004/
Specified density buffer

Tc(VII)*
Tc(VII), Rokle /Večerník and Jedináková-Křížová 2006/
Se(IV)*
Se(IV), CO argillite /Descostes et al. 2008/
SO4, CO argillite /Descostes et al. 2008/
CO3*
SO4, CO argillite /Bazer-Bachi et al. 2008/
Cl, MX-80 /Muurinen et al. 1989/
Cl, Mx-80 /Eriksen and Jacobson 1981/
Cl*
Cl, Kunigel-V1 /Ishidera et al. 2008/
Cl, Kunipia-F /Ishidera et al. 2008/
Specified density backfill

Figure 5-7. Effective diffusivities obtained in compacted clays using anionic tracers. All data marked * arefor Kunigel-V1 /Sato 1998/. The specified reference buffer and backfill dry densities are indicated.

166

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10–8

10–9

De (m2/s)

10–10

10–11

10–12

10–13

10–14

0

500

1000

1500

Dry density (kg/m3)

I, MX-80 /Eriksen and Jansson, 1996/
I*
I Kunipia-F, /Sato et al. 1992/
I, Avonlea /Cho et al. 1993/
I, Kunigel-V1 /Ishidera et al. 2008/
I, Kunipia-F /Ishidera et al. 2008/
I, Avonlea /Cho and Oscarson 1996/
I, Ca-Kunigel-V1 /Mihara 2000/
I, Avonlea /Oscarson et al. 1992/
I, CO argillite /Descostes et al. 2008/
Cl, Volclay KWK /van Loon et al. 2007/
Cl, FEBEX, García-Gutiérrez et al. 2004
Specified density buffer
De TcO4 /Kunipia-F, Ochs et al. 2001/
HTO predicted

2000

2500

Tc(VII)*
Tc(VII), Rokle /Večerník and Jedináková-Křížová 2006/
Se(IV)*
Se(IV), CO argillite /Descostes et al. 2008/
SO4, CO argillite /Descostes et al. 2008/
CO3*
SO4, CO argillite /Bazer-Bachi et al. 2008/
Cl, MX-80 /Muurinen et al.1989/
Cl, Mx-80 /Eriksen and Jacobson 1981/
Cl*
Cl, Kunigel-V1 /Ishidera et al. 2008/
Cl, Kunipia-F /Ishidera et al. 2008/
Specified density backfill
De Cl /MX-80, Ochs et al. 2001/

Figure 5-8. Same data as in Figure 5-7, but including regression for HTO and independent predictions forchloride in MX-80 and Tc(VII) in Kunigel-V1 by /Ochs et al. 2001/.

Because of the scatter of the data, and because of the basic uncertainty of assigning the experimentallyobservable anion diffusion effects to De and available porosity, recommended De is based on thefollowing approach (instead of purely on a regression analysis):•

Accepting the argumentation in /Ochs and Talerico 2004/, it is suggested that the model prediction
by /Ochs et al. 2001/ for the diffusion of chloride in MX-80 is representative for the range of dry
densities considered here. This prediction is used for obtaining best estimates of De for the buffer
and backfill at the dry densities corresponding to the brown and orange lines in Figure 5-8. The
resulting best estimate De values are 1.1·10–11 m2/s for the buffer and 1.2·10–11 m2/s for the backfill.

•

Upper and lower limits are somewhat subjectively based directly on the available experimental data,by drawing an area including the great majority of the data points. This is done in Figure 5-9, where
an upper and lower green line mark the boundaries wherein it is most likely to find experimental De
data. Note that at zero dry density, De equals Dw in accordance with process understanding.

Based on visual inspection, the lower green line is estimated to intercept the buffer dry density at
De = 6·10–13 m2/s and the backfill dry density at De = 1·10–12 m2/s. The upper green line is estimated tointercept the buffer dry density at De = 6·10–11 m2/s and the backfill dry density at De = 7·10–11 m2/s.
By taking this approach, the uncertainty ranges become rather large. It is therefore suggested thatthey also encompasses the minor deviations in dry density estimated for the buffer and backfill.
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167

10–8

10–9

De (m2/s)

10–10

10–11

10–12

10–13

10–14
0

500

1000

1500

2000

2500

Dry density (kg/m3)

Figure 5-9. Subjectively set area, bounded by the green lines, wherein De data are likely to be found.
The data are the same as in Figure 5-7.

Caesium
The effective diffusivity for Caesium is estimated from experimental data on compacted clays using
Caesium as the tracer. Figure 5-10 shows the De data versus dry density.
In contrast with non-charged tracers (see Figure 5-6), it is less clear whether the De values for Cs
correlate with the dry density. While the data by /Sato 1998/ show a clear trend, the De values measured
by different researchers at densities close to the dry densities of the reference buffer and backfill range
over two orders of magnitude. We refrain from speculating whether this spread is due to errors(experimental, raw data interpretation, etc.). It is interesting to note that the range in De values cannot
be entirely attributed to differences in the clay materials, as separate measurements on MX-80 at
the same dry density give very different data.
Most weight is given to the data by /Sato 1998/, because they were measured for a range of dry
densities and the raw data were interpreted without using a separate surface diffusion coefficient.
The value for Cs is evaluated as follows, using again the same reasoning and approach as given in/Ochs and Talerico 2004/. In comparison to the regression line for HTO, the values measured by/Sato 1998/ are at most a factor 3 higher. Therefore, both the best estimate and upper limit of De for
Cs are taken as three times higher the best estimate value for HTO, which becomes 4.2·10–10 m2/s forthe buffer and 4.8·10–10 m2/s for the backfill. The lower limit is set equal to the lower limit for HTO.
Diffusion-available porosity

The diffusion-available porosity for neutral species and cations is suggested to equal the physicalporosity. The diffusion-available porosity for anions is estimated by reducing the physical porosity
by a reduction factor. This factor is based on the findings of /van Loon et al. 2003a, b/, who madean explicit effort to distinguish De from ε in their data interpretation. Their data for chloride suggest
a reduction of the diffusion-available porosity (from HTO to anions) by a factor of 1.8–3.5, if a Kdof zero is assumed. Based on this finding, a reduction factor of 2.5 is proposed. Suggested limiting
values for the reduction factor are 1.8 and 3.5. These reduction factors are found to be reasonable for

168

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10–8

10–9

De (m2/s)

10–10

10–11

10–12

10–13

10–14

0

500

1000

1500

2000

2500

Dry density (kg/m3)
Cs, Kunigel-V1 /Sato 1998/

Cs MX-80, /Eriksen and Jansson 1996/

Cs, CO argillite /Melkior et al. 2007/

Cs, MX-80 /Muurinen et al. 1987/

Cs, Kunipia-F /Kozaki et al. 1999/

HTO predicted

Specified density buffer

Specified density backfill

Figure 5-10. Effective diffusivities obtained in compacted clays using Cs+ as tracer. The specified reference
buffer and backfill dry densities are indicated. The regression for HTO is shown for comparison.

anionic tracers in bentonite, as shown in /Ochs and Talerico 2004, Figure 5-6/ and corresponding text.
The range in the reduction factor is considered to be so large that it encompasses minor uncertainty
in the physical porosity. Thus, for estimating the range of diffusion-available porosity for anions, thelimiting values of the reduction factor can be applied to the best estimate physical porosity.
There is not enough resolution in background data to separately discuss precision, bias, andrepresentativity.
Sorption partitioning coefficient

Overview and general considerations
Sorption batch experiments are comparatively easy to control in terms of the applied boundary
conditions. The interpretation of the experimental raw data is straightforward in well-designed
experiments and Kd values can be obtained by simple arithmetic. As a single experiment requires
relatively little effort, data are often obtained in large series, ideally as a function of an importantparameter such as pH or radionuclide concentration. The resulting trends allow good evaluationof the consistency of various data sets. The embedding in a series of related data lends additional
confidence to the individual data points in terms of both representativeness and absence of bias.
Uncertainties for experimental Kd values, as well as for the related experimental key parameters(pH, CEC), were estimated in /Ochs and Talerico 2004/.

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Batch sorption data obtained in typical laboratory systems need to be interpreted and re-calculated
to correspond to the relevant site-specific conditions, because they are typically not obtained underchemical conditions matching those of the pore water in the bentonite. Because of the pronounced
dependency of Kd on chemical conditions, uncertainties introduced during the transfer of datafrom experimental to site-specific conditions may make up the larger part of overall uncertainties/NEA 2005/. For each step in such data transfer calculations, /Ochs and Talerico 2004/ estimatedthe respective uncertainties. Overall uncertainties were then calculated on the basis of estimateduncertainties for both experiments and data transfer.
Based on the available data, the compositions of Deponit CA-N and Milos bentonites are relatively
similar to that of MX-80. Therefore, it can be expected that calculated pore water compositions will
be similar, in particular under conditions where carbonate equilibria are controlled by an external
pCO2 (open system). Considering the uncertainties in pore water derivation discussed above, themodelling of pore water compositions for Deponit CA-N and Milos backfill is not seen as meaningful,as it would suggest a higher precision than can actually be achieved. Instead, the pore water compositions given in /Ochs and Talerico 2004/ are accepted for the present purpose.
Uncertainty factors
Below, the uncertainty factors introduced in /Ochs and Talerico 2004/ are briefly summarised. Fora full account the reader should turn to /Ochs and Talerico 2004, Section 5.1.4/. The derivation of
Kd values corresponding to the desired conditions in compacted bentonite from experimental dataalmost invariably involves an extrapolation step. Where possible, this extrapolation was done withthe help of an appropriate thermodynamic sorption model. Where such a model is not available,semi-quantitative conversion procedures were applied, based on /Bradbury and Baeyens 2003/.
The conversion procedures include scaling/conversion for differences in CEC of the clay, in pH of
the pore water (which determines surface speciation and radionuclide hydrolysis) and additional
radionuclide complexation effects. In each conversion, uncertainty is introduced, which is handled
by also introducing uncertainty factors. There is no obvious reason for choosing between quantifying
uncertainties as an error on a linear scale (i.e, Kd ± error) versus a logarithmic scale (i.e. log Kd ± log
error). Because several careful sorption studies (e.g. /Bradbury and Baeyens 1997/) report errors thatare symmetric on a log scale, the latter type of representation was adopted for the present purpose.
The following uncertainty factors (UF) are considered:UF-starting Kd: There is always some uncertainty associated with the starting Kd value obtained
through experiments or through modelling. For good quality experimental data an uncertaintyof Kd of ±0.2 log10 units is proposed, based on the findings of /Bradbury and Baeyens 1997/. An
extensive modelling exercise /NEA 2005/ showed that good sorption models are able to reproduce
experimental data very closely, and we propose to use the same uncertainty as for experimental data.
Where a Kd is derived using sorption data for an analogue element, but the speciation is evaluateddirectly for the element under investigation, the UF-starting Kd is set to ±0.4 log10 units. In case of
more questionable analogues, the UF-starting Kd is set to ±0.6 log10 units.
UF-CEC: When scaling the Kd based on different CEC values, uncertainty is introduced. It isestimated that CEC can be measured within an uncertainty of ca. 10%, and that S-OH site density
can be scaled via CEC within an uncertainty of ca. 20%. This gives an overall uncertainty of ±30%,or ±0.15 log10 units.
UF-pH: If performing a conversion based on pH, this introduces uncertainty. When performing such
conversion, two Kd values are needed, one at the source pH and one at the application pH. Because
the conversion contains the uncertainties of two Kd values, UF-pH is set to 0.4 log10 units (two times
UF-starting Kd). Because of the predominance of ion exchange for Cs sorption, the pH-conversion
factor is replaced in this case by a CF-cation conversion factor with a UF-cation conversion of±0.4 log10 units.

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UF-speciation: If performing conversion due to different speciation (pore water composition), uncertainty is introduced. Following the evaluation by /Hummel and Berner 2002/, who propose a factorof 2 between highest and lowest value, a UF-speciation of ±0.3 log10 units is used. Note, however,that this is the uncertainty associated with the use of a given complete TDB. If certain species are
missing or erroneous, uncertainties could be much higher. Also, this uncertainty factor does not take
into account any inappropriate evaluation of the conversion factor for speciation.
UF-batch: The conversion from dispersed batch system to a compacted system does not introduce
significant uncertainty. However, an additional uncertainty factor is introduced to acknowledge
possible uncertainties in the application conditions (i.e. in the pore water composition, resulting
from model uncertainties and effects from variations in the amount of impurities present in bentonite
samples). The uncertainty factor was set to ±0.3 log10 units.
The validity of this approach was in /Ochs and Talerico 2004, Section 5.3.5/ evaluated for each radionuclide by comparing Da values, calculated from Kd values derived for compacted bentonite and De
using Equation 5-3, with independent experimental data for Da. Figure 5-11 shows an example ofsuch a comparison, where the Da data of Am as estimated from thermodynamic modelling for threedifferent pore waters are compared with independent Da obtained from experiments.
In /Ochs and Talerico 2004/ it is judged as extremely likely that that any Kd for Am under the specified
scenario would be within the indicated ranges.
Here a caution is warranted. In this section the recommended uncertainty is distributed symmetricallyaround a central value, which may give rise to higher upper Kd values than experimentally observed inexperiment. When using the data in subsequent radionuclide transport modelling, it must be carefully
considered how the large uncertainty ranges of Kd affect the estimate of radiological risk. Generally
this should not give rise to non-conservatism in probabilistic modelling, as it is the lower tail of the
Kd distribution that affects assessment results.
Am: apparent diffusivity

10–12

Kunigel-V1/Sato et al. 1993/
Kunipia-F/Sato et al. 1992/

Da (m2/s)

10–13

Regression Kunigel-V1/Sato 1998a/
10–14

MX-80
/Torstenfelt and Allard 1986/
SR97

10–15

RPW /CF-total 3/
RPWC /CF-total 3/
HSPW /CF-total 3/

10–16
0

500

1000

Dry density (kg/m3)

1500

2000

Figure 5-11. Example of comparison between modelled Da and Da obtained from experimental values(reproduced from /Ochs and Talerico 2004, Figure 5-8/).

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Elements not considered in SR-Can
For some of the elements of interest for SR-Site, no Kd data was delivered in /Ochs and Talerico 2004/.
These elements are Mo, Cd, Ac, S, dissolved O2, and H (tritium, assumed in the form of HTO). Kd
values for these elements are derived based on the following considerations.
•

HTO corresponds to water in the chemical sense, and is therefore considered non-sorbing.

•

Dissolved oxygen is also considered as non-sorbing (as methane or radon).

•Based on /Brookins 1988/ and /Hummel et al. 2002/, MoO42– and the respective protonated forms(where Mo is present in +VI-valent form) are considered as the only relevant aqueous species.
Therefore, sorption is considered to be identical to that of selenate; i.e. Kd = 0.

•

Ac exists only in +III valent form. It is treated in analogy to the lanthanide elements (Eu, Ho, Sm).
This is pessimistic, since the chemistry of Ac may be closer to that of the actinide elements. To beconsistent with the approach used by /Ochs and Talerico 2004/ an additional uncertainty factor of
2.5 is used to account for uncertainties introduced in the analogy consideration.

•

Based on the systematics regarding hydrolysis discussed in /Baes and Mesmer 1976/, it is assumed
that the main sorption process (surface complexation) is very similar in case of Cd and Ni. Therefore,
Cd is treated by using the analogy with Ni and an additional UF of 4 (see /Ochs and Talerico 2004/
for the selection of UF).

•For sulphur, relevant and stable oxidation states in aqueous solutions include -II and VI, with HS–
and SO42– as the only important species in typical solutions (sulphite is not considered relevant
under the present conditions and timeframes). Following the handling of uncertainties for redoxsensitive elements proposed in /Ochs and Talerico 2004/, if variable redox conditions need to betaken into account, the overall uncertainty for the Kd of S would encompass the combined data
range for all oxidation states predicted to be present.
– For sulphide, no reliable data could be found and a Kd of zero is proposed.
– Relatively little is known regarding the sorption of sulphate on mineral surfaces. Most information is available for the sorption of sulphate on (hydrous) iron oxides (see /Davis and Kent
1990/ and RES3T database /Brendler et al. 2003/). Sorption of sulphate on metal oxides is
weak and strongly influenced by the presence of background electrolyte anions; it is typicallyinterpreted as formation of outer-sphere complexes /Davis and Kent 1990/.
– Regarding sorption on smectite or bentonite under relevant conditions, only one systematic
study has been found. /Bazer-Bachi et al. 2007/ measured sulphate sorption on CallovoOxfordien argillites by batch, column, and diffusion studies. Due to the weak sorption ofsulphate, only one of several batch experiments can be interpreted to show sorption different
from zero. As this result is corroborated by column experiments, it is accepted for the present
purpose. Sorption data derived from diffusion are not used, because they are not consistentand may suffer from uncertainties in the interpretation of diffusion-available porosity. On
this basis, an average Kd of 5·10–4 m3/kg can be extracted, based on both batch and column
experiments. This value has been obtained under conditions that are similar to the calculated
pore water conditions in MX-80. Conversion factors have been applied to account for the
relatively large differences in the CEC of the substrates and the difference in total dissolved[sulphate + chloride] concentration, based on the expected outer-sphere complex sorption ofsulphate. The resulting values and uncertainties are plotted in Figure 5-12. The final results
are compiled in Table 5-20 in Section 5.3.10.

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10–2

10–3

10–4

10–5
6.4

10–1

RPW
RPWC
HSPW

sulphate

Predicted Kd (L/g)

Predicted Kd (L/g)

10–1

6.6

6.8

7.0

pH

7.2

7.4

7.6

RPW
RPWC
HSPW

sulphate

10–2

10–3

10–4

10–5
0.000

0.200

0.400

0.600

0.800

Sum dissolved chloride + sulphate

Figure 5-12. Kd values for sulphate derived for different conditions based on the experimental data by/Bazer-Bachi et al. 2007/.

5.3.8 Spatial and temporal variability of data
The following remarks concern saturated conditions, and may not necessarily apply during thesaturation phase.
Spatial variability of data

On the scale of a deposition hole and tunnel, the buffer and backfill can be considered as homogeneous.
Therefore, spatial variation is not considered relevant.
Temporal variability of data

Temporal variation becomes important when the evolution of clay material and pore water is being
considered. This has to be assessed through models, and is of special importance in light of evolving
geochemical conditions and post-closure removal of the buffer and backfill, e.g. due to erosion.
If the post-closure removal is limited, Kd data reported in this text can be used, and De and ε data
can be scaled to new appropriate clay densities. If the post-closure removal is extensive, which isconceivable in deposition holes, one may have to assume advective conditions where there is limited
or no retention in the buffer. In such case, the data in this section are of little importance (although
Kd principally still applies in advective transport).
In this section, temporal variability due to evolution of the groundwater chemistry is to some extent
dealt with by supplying data corresponding to a few different groundwaters, which may be encountered during a glacial cycle. However, one must be humble to the fact that the temporal variability ingeochemical conditions evokes large uncertainty.

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5.3.9 Correlations
Following their chemical characteristics, the radionuclides considered can be organised into groups(correlation groups) of elements and oxidation states whose migration behaviour will generally show
a similar response to variations in pore water composition, as caused by variations in groundwatercomposition, bentonite evolution, etc. Moreover, elements handled via chemical analogies correlatewith the respective analogues. Overall, the following grouping is used, where analogies are also
indicated in the following way. X/Y: both elements were treated identically in the data derivation;
X(Y): X was derived based on analogy with Y. Groups 1 to 7, as well as 10, should be seen as
correlation groups.
1. Alkaline and alkaline earth elements: Cs, Ra/Sr.
2. Other di-valent elements Pb, Ni, Cd(Ni).
3. Tri-valent elements: Am, Cm(Am), PuIII(Am), Sm/Ho/Ce/Ac(Eu).
4. Tetra-valent elements and Zr: Th, UIV(Th), PuIV(Th), NpIV(Th), TcIV(Th), Zr(Th), Sn(Th). Dueto lack of reliable element-specific data, Tc(IV), Zr, and Sn(IV) were also evaluated on the basisof data for Th, and the derived Kd values show therefore some correlation (because Th is not an ideal
analogue for these elements, additional uncertainties are associated with the resulting Kd values).
5. Penta-valent elements: NpV, PuV(NpV).
6. Hexa-valent cationic elements: UVI, PuVI(UVI).
7. Non-sorbing anions: Cl–/I–/TcO4–/SeO42–/HSe–/HS–/MoO42–/HTO/simple organic anions.
8. Some elements are not known well enough to assess correlations Pa, Nb, Pd, Ag.
9. The weakly sorbing anions SeO3– and carbonate will not correlate. Carbonate is proposed to behandled via isotope exchange. In case of the weakly sorbing anion SO42–, not enough information
is available to exactly define correlations, but a rough correlation with SeO3– is considered morereasonable than assigning no correlation at all.
10. Dissolved gasses: Rn, CH4, O2.
Furthermore /Ochs and Talerico 2004/ points out that:•

Redox-sensitive radionuclides will take on higher oxidation states if oxidising conditions are considered, generally leading to lower Kd values. Notable exceptions are Se(-II→IV) and S(-II→VI).

•

A lower pore water pH (within the range considered) will decrease the Kd of most radionuclides(actinides, lanthanides, transition elements, heavy metals) in a similar way. Exceptions are pentaand hexavalent actinides that form oxo-cations: U(VI), Np(V), Pu(VI/V).

•

An increase in major cation concentration will lower Kd values for alkaline and alkaline earth
elements.

•

A lower density of the buffer will lead to higher De and Da values.

5.3.10 Result of supplier’s data qualification
Concerning solute transport parameters of the buffer, the major work was done in a dedicated SR-Can
report /Ochs and Talerico 2004/, and it has not been an aim of this present work to reproduce thatwork. Only in cases where there are obvious differences in conditions between SR-Can and SR-Site,or where new data have been compiled, the supplied data have been changed from those recommended in SR-Can.
For the backfill, the suggested data are based on data for MX-80 taken from /Ochs and Talerico 2004/,
and modified by performing scaling with regards to dry density (for De and ε) and CEC (for Kd for
Cs, Ra, Sr).

174

TR-10-52

Diffusion-available porosity

For neutral species and cations, the diffusion-available porosity is suggested to equal the physicalporosity.
Concerning the diffusion-available porosity for anions, the reduction factors suggested in /Ochs
and Talerico 2004/ are suggested for use also in SR-Site. The reduction factors are given as a best
estimate at 2.5, an upper limit reduction factor at 3.5 and a lower limit reduction factor at 1.8. The
range in the reduction factor is suggested to be large enough to encompass minor uncertainty in thephysical porosity. Thus, for estimating the range in diffusion-available porosity for anions, the best
estimate and limiting values of the reduction factor can be applied to the best estimate physical porosity.
This is done in Table 5-14.

Parameter

Lower limit

Best estimate

Upper limit

Reference
bufferDiffusion-available porosity ε (%) for non-charged andcationic species (same as physical porosity in Table 5-5)

41

43.5

46

Diffusion-available porosity ε (%) for anions

12.4

17.4

24.1

Reference
backfill

Table 5-14. Diffusion-available porosity, ε.

Diffusion-available porosity ε (%) for non-charged andcationic species (same as physical porosity in Table 5-6)

44

46

48

Diffusion-available porosity ε (%) for anions

13.1

18.4

25.6

Effective diffusivity

In case of the effective diffusivity, new experimental data have warranted an update of the recommended data. The approach taken for the effective diffusivity is to sort the different elements into
three categories: anions; hydrolysable cationic elements existing in the form of different species; andcations with enhanced diffusion due to surface diffusion (Cs), as have been done in SR-Can /Ochs
and Talerico 2004/.
The derivation of the De data in Table 5-15 is described in Section 5.3.7. What can be added is thatthe uncertainty range for non-charged and hydrolysable elements is based on the standard error
shown in Figure 5-6.

Reference
backfill

Reference
bufferTable 5-15. Effective diffusivity, De.

TR-10-52

Parameter

Best estimate

Upper limit

Lower limit

Effective diffusivity De (m2/s) for non-charged andhydrolysable elements (cations except Cs).

1.4⋅10–10

2.1⋅10–10

9.3⋅10–11

Effective diffusivity De (m2/s) for anions.

1.1⋅10–11

6.0⋅10–11

6.0⋅10–13

Effective diffusivity De (m /s) for Cs.

4.2⋅104.2⋅10–10

9.5⋅10–11

Effective diffusivity De (m2/s) for non-charged andhydrolysable elements (cations except Cs).

1.6⋅10–10

2.4⋅10–10

1.1⋅10–10

Effective diffusivity De (m2/s) for anions.

1.2⋅10–11

7.0⋅10–11

1.0⋅10–12

Effective diffusivity De (m /s) for Cs.

4.8⋅104.8⋅101.1⋅10–10

2

2

–10

–10

–10

175

Sorption partition coefficients

For most elements, the Kd for the buffer and backfill is taken directly from /Ochs and Talerico 2004/.
The exceptions are for Cs, Ra, and Sr, which are rescaled to encompass new CEC values of the clay
materials, and elements not reported in /Ochs and Talerico 2004/.
For all species but Cs, Ra, Sr, and S(IV), the same Kd values apply for MX-80, Deponit CA-N, andMilos backfill. These Kd data for the reference pore water, corresponding to saline and non-saline
groundwater in a system that is open with respect to CO2, are shown in Table 5-16. In Table 5-17, theequivalent data for highly saline pore water, corresponding to highly saline groundwater in a systemthat is open with respect to CO2, are shown. Both Kd and log10Kd values are shown. The number ofsignificant digits in the Kd values are taken directly from /Ochs and Talerico 2004/ and does not reflect
the accuracy with which the data are estimated. Two significant digits have been assigned to the log10Kd
values, and this should fairly well reflect on the accuracy with which the data are estimated.
Table 5-16. Recommended Kd values for the reference buffer and backfill (MX-80, Deponit CA-N,
and Milos Backfill) in reference pore water, corresponding to saline and non-saline groundwaters.
CEC sensitive elements are shown in Table 5-18.
Reference pore water (RPW)
Radionuclide(Redox State)

Ac(III)
Ag(I)
Am(III)C, carbonate species
C, methane, organic acids
Cd(II)
Ce(III)Cl(–I)
Cm(III)
Eu(III)
Ho(III)
I(–I)
Mo(VI)Nb(V)
Ni(II)
Np(IV)
Np(V)
Pa(IV)Pa(V)
Pb(II)
Pd(II)Pu(III)
Pu(IV)Pu(V)
Pu(VI)Rn(–)
S(–II)Se(–II)
Se(IV)
Se(VI)
Sm(III)
Sn(IV)
Tc(IV)
Tc(VII)
Th(IV)U(IV)
U(VI)
Zr(IV)

Kd(m3/kg)Upper Kd limit(m3/kg)Lower Kd limit(m3/kg)Value

Log value

Value

Log value

Value

Log value

8
–
61
ie, 0
0
0.30
8
0
61
8
8
0
0
3
0.30
63
0.02
3
3
74
5
100
63
0.02
3
0
0
0
0.04
0
8
63
63
0
63
63
3
4

0.92
n.a
1.8
n.a.
n.a
–0.52
0.92
n.a
1.8
0.92
0.92
n.a
n.a
0.48
–0.52
1.8
–1.6
0.48
0.48
1.9
0.70
2.0
1.8
–1.6
0.45
n.a
n.a
n.a
–1.4
n.a
0.92
1.8
1.8
n.a
1.8
1.8
0.45
0.56

233
15
378
ie, 0
0
13.3
93
0
378
93
93
0
0
45
3.3
1,113
0.2
45
45
457
75
984
1,111
0.2
28
0
0
0
0.4
0
93
1,764
1,764
0
700
1,113
18
103

2.4
1.2
2.6
n.a.
n.a
1.1
2.0
n.a
2.6
2.0
2.0
n.a
n.a
1.7
0.52
3.0
–0.81
1.7
1.7
2.7
1.9
3.0
3.0
–0.62
1.4
n.a
n.a
n.a
–0.39
n.a
2.0
3.2
3.2
n.a
2.8
3.0
1.2
2.0

0.3
0
10
ie, 0
0
0.007
0.8
0
10
0.8
0.8
0
0
0.2
0.03
4
0.004
0.2
0.2
12
0.3
10
4
0.002
0.3
0
0
0
0.003
0
0.8
2.3
2.3
0
6
3.6
0.5
0.1

–0.52
n.a
1.0
n.a.
n.a
–2.2
–0.12
n.a
1.0
–0.12
–0.12
n.a
n.a
–0.70
–1.6
0.55
–2.4
–0.70
–0.70
1.1
–0.48
1.0
0.55
–2.6
–0.54
n.a
n.a
n.a
–2.5
n.a
–0.12
0.35
0.35
n.a
0.75
0.55
–0.34
–0.88

ie = ion exchange.

176

TR-10-52

Table 5-17. Recommended Kd values for the reference buffer and backfill (MX-80, Deponit CA-N,
and Milos Backfill) in highly saline pore water, corresponding to highly saline groundwaters. CEC
sensitive elements are shown in Table 5-19.
Highly saline pore water (HSPW)Radionuclide(Redox State)

Ac(III)
Ag(I)
Am(III)C, carbonate species
C, methane, organic acids
Cd(II)
Ce(III)Cl(–I)
Cm(III)
Eu(III)
Ho(III)
I(–I)
Mo(VI)Nb(V)
Ni(II)
Np(IV)
Np(V)
Pa(IV)Pa(V)
Pb(II)
Pd(II)Pu(III)
Pu(IV)Pu(V)
Pu(VI)Rn(–)
S(–II)Se(–II)
Se(IV)
Se(VI)
Sm(III)
Sn(IV)
Tc(IV)
Tc(VII)
Th(IV)U(IV)
U(VI)
Zr(IV)

Kd(m3/kg)Upper Kd limit(m3/kg)Lower Kd limit(m3/kg)value

log valuevalue

log valuevalue

log value

5
–
24
ie, 0
0
0.07
5
0
24
5
5
0
0
3
0.07
40
0.02
3
3
46
5
43
40
0.02
3
0
0
0
0.05
0
5
40
40
0
40
40
3
5

0.71
n.a
1.4
n.a.
n.a
–1.2
0.71
n.a
1.4
0.71
0.71
n.a
n.a
0.48
–1.2
1.6
–1.6
0.48
0.48
1.7
0.70
1.6
1.6
–1.7
0.45
n.a
n.a
n.a
–1.3
n.a
0.71
1.6
1.6
n.a
1.6
1.6
0.45
0.68

142
15
152
ie, 0
0
3.0
57
0
152
57
57
0
0
45
0.8
702
0.1
45
45
287
75
421
700
0.2
28
0
0
0
0.6
0
57
1,113
1,113
0
442
703
18
134

2.2
1.2
2.2
n.a.
n.a
0.48
1.8
n.a
2.2
1.8
1.8
n.a
n.a
1.7
–0.12
2.8
–0.84
1.7
1.7
2.5
1.9
2.6
2.8
–0.66
1.4
n.a
n.a
n.a
–0.24
n.a
1.8
3.0
3.0
n.a
2.6
2.8
1.2
2.1

0.2
0
4
ie, 0
0
0.002
0.5
0
4
0.5
0.5
0
0
0.2
0.01
2
0.004
0.2
0.2
7
0.3
4
2
0.002
0.3
0
0
0
0.005
0
0.5
1.4
1.4
0
4
2.3
0.5
0.2

–0.74
n.a
0.59
n.a.
n.a
–2.8
–0.34
n.a
0.59
–0.34
–0.34
n.a
n.a
–0.70
–2.2
0.35
–2.4
–0.70
–0.70
0.87
–0.48
0.64
0.35
–2.6
–0.55
n.a
n.a
n.a
–2.3
n.a
–0.34
0.15
0.15
n.a
0.55
0.35
–0.34
–0.77

ie = ion exchange.

TR-10-52

177

For the elements Cs, Ra, and Sr, as well as for S(IV), the sorption depends on the CEC value of theclay material. Therefore, the supplied Kd values differ slightly between different materials. For theseparticular materials, the differences in CEC are so small that the difference in Kd is largely dwarfed
by the general uncertainty. Data for the reference pore water are shown in Table 5-18 and data forthe highly saline pore water are shown in Table 5-19.
For the elements that are included in this section but were not included in /Ochs and Talerico 2004/,
all but one are either suggested to be non-sorbing or analogue to an element handled in /Ochs andTalerico 2004/.
Table 5-18. Recommended Kd values for the reference buffer and backfill (MX-80, Deponit CA-N,
and Milos Backfill) in reference pore water, corresponding to saline and non-saline groundwaters.
Only CEC sensitive elements.
Reference pore water (RPW)
Radionuclide(Redox State)Kd(m3/kg)value

log value

Upper Kd limit(m3/kg)Lower Kd limit(m3/kg)

value

value

log value

Clay material

log value

Cs(I)

0.093

–1.0

0.56

–0.26

0.015

–1.8

MX-80

Cs(I)

0.086

–1.1

0.52

–0.28

0.014

–1.8

Deponit CA-N

Cs(I)

0.090

–1.0

0.54

–0.27

0.015

–1.8

Milos Backfill

Ra(II)

0.0045

–2.3

0.027

–1.6

0.00075

–3.1

MX-80

Ra(II)

0.0042

–2.4

0.025

–1.6

0.00070–3.2

Deponit CA-N

Ra(II)

0.0044

–2.4

0.026

–1.6

0.00073–3.1

Milos Backfill

S(VI)0.00083–3.1

0.0066–2.2

0.00010

–4.0

MX-80

S(VI)

0.00073–3.1

0.0058–2.2

9.1·10–5–4.0

Deponit CA-N

S(VI)

0.00068–3.2

0.0054–2.3

8.5·10–5–4.1

Milos Backfill

Sr(II)

0.0045

–2.3

0.027

–1.6

0.00075

–3.1

MX-80

Sr(II)

0.0042

–2.4

0.025

–1.6

0.00070–3.2

Deponit CA-N

Sr(II)

0.0044

–2.4

0.026

–1.6

0.00073–3.1

Milos Backfill

Table 5-19. Recommended Kd values for the reference buffer and backfill (MX-80, Deponit CA-N,
and Milos Backfill) in highly saline pore water, corresponding to highly saline groundwaters.
Only CEC sensitive elements.
Highly saline pore water (HSPW)Radionuclide(Redox State)Kd(m3/kg)Upper Kd limit(m3/kg)Lower Kd limit(m3/kg)Clay materialvalue

log valuevalue

log valuevalue

log value

Cs(I)

0.031

–1.5

0.18

–0.73

0.0051

–2.3

MX-80

Cs(I)

0.029

–1.5

0.17

–0.76

0.0048–2.3

Deponit CA-N

Cs(I)

0.030

–1.5

0.18

–0.75

0.0050–2.3

Milos Backfill

Ra(II)

0.0012–2.9

0.0070–2.2

0.00020

–3.7

MX-80

Ra(II)

0.0011–3.0

0.0066–2.2

0.00018–3.7

Deponit CA-N

Ra(II)

0.0011–2.9

0.0068–2.2

0.00019–3.7

Milos Backfill

S(VI)0.00022–3.6

0.0018–2.7

2.8·10-5

–4.6

MX-80

S(VI)

0.00021–3.7

0.0017–2.8

2.6·10-5–4.6

Deponit CA-N

S(VI)

0.00022–3.7

0.0017–2.8

2.7·10-5–4.6

Milos Backfill

Sr(II)

0.0012–2.9

0.0070–2.2

0.00020

–3.7

MX-80

Sr(II)

0.0011–3.0

0.0066–2.2

0.00018–3.7

Deponit CA-N

Sr(II)

0.0011–2.9

0.0068–2.2

0.00019–3.7

Milos Backfill

178

TR-10-52

Concerning sulphur, S(II) as sulphide is assumed to be non-sorbing and S(IV) as sulphate is assumed
to be slightly sorbing. The data on sulphate is obtained in Callovo-Oxfordien argillite in synthetic
pore water /Bazer-Bachi et al. 2007/ and rescaling is needed, using correction factors (CF) and
uncertainty factors (UF) in accordance with /Ochs and Talerico 2004/. This is shown in Table 5-20.
In Table 5-20 the correction factor for CEC is obtained from the ratio of application and experimental
CEC. The correction factor for sum of dissolved anions is obtained similarly. The total correction
factor is the product of the two correction factors. No correction is made for the slight deviations in
pH. The best estimate Kd is the product of the total correction factor and experimental Kd. The upperand lower limits for Kd are the best estimate Kd multiplied or divided by the total uncertainty factor.
The above serves as an example on how Kd data and uncertainties are estimated. More can be readin /Ochs and Talerico 2004/.
Table 5-20. Estimation of Kd for sulphate, with uncertainties.
Paramter

Unit

Data source

Application

Solution

Synthetic
pore water

PWR

HSPW

PWR

HSPW

PWR

HSPW

Substrate

K119c

MX-80

MX-80

Deponite
CA-N

Deponite
CA-N

Milos BF

Milos BF

(mol/L)(mol/L)(mol/L)

18.8
7.3
0.072
4.40·10–3
0.076

75
7.4
0.16
0.048
0.208

75
7.0
0.66
0.017
0.677

70
7.4
0.16
0.048
0.208

70
7.0
0.66
0.017
0.677

73
7.4
0.16
0.048
0.208

73
7.0
0.66
0.017
0.677

(m3/kg)5.0·10–4

CEC
pH
Cl dissolved
SO4 dissolved
Sum of anions
dissolved
Experimental Kd(meq/100g)

Correction factor (CF)Solution
Substrate
CEC
Sum of anions
dissolved
CF-total

PWR
MX-80

HSPW
MX-80
3.99

PWR
Deponite
CA-N
3.72

HSPW
PWR
HSPW
Deponite Milos BF Milos BF
CA-N
3.72
3.88
3.88

(meq/100g)(mol/L)

3.99
0.37

0.11

0.37

0.11

0.37

0.11

(m3/kg)

1.46

0.45

1.36

0.42

1.42

0.44

Uncertainty factor (UF)UF-starting Kd
UF-anionconversion
UF-batch ->compacted
UF total

1.6
2.5

1.6
2.5

1.6
2.5

1.6
2.5

1.6
2.5

1.6
2.5

2.0

2.0

2.0

2.0

2.0

2.0

8.0

8.0

8.0

8.0

8.0

8.0

ResultKd best estimate
Kd upper limit
Kd lower limit

TR-10-52(m /kg)(m3/kg)(m3/kg)3

7.3·105.8·10–3
9.1·10–5–4

2.2·101.8·10–3
2.8·10–5–4

6.8·105.4·10–3
8.5·10–5–4

2.1·10–4
1.7·10–3
2.6·10–5

7.1·10–4
5.7·10–3
8.9·10–5

2.2·10–4
1.7·10–3
2.7·10–5179

5.3.11 Judgements by the SR-Site teamAccording to the instruction given in Section 2.3, the SR-Site team should make comments on thesections of the supplier.
Sources of information and documentation of data qualification

The supplier has listed a number of main sources of information, and has also sorted a number ofdata sets as qualified or supporting. The SR-Site team find this handling adequate. In addition thereferencing made throughout the text is sufficiently precise.
Conditions for which data are supplied

The supplier has listed a number of important conditions, which affect the migration data in different
ways. Only for Kd the conditions greatly affects the supplied data. The most important conditions are
the clay material and the pore water composition (indirectly affected by the groundwater composition).
The supplier has argued that performing new modelling of the pore water composition, based on site
specific groundwater, would not decrease the uncertainty to any greater extent. As the groundwaterused is based on measurements in Finnsjön, a site nearby the Forsmark site, the SR-site team finds
the arguments valid. Comparing the reference groundwater with the modelled SR-Site groundwaters
in Section 6.1, instead of with SR-Can groundwaters (as made by the supplier), would not change
the arguments.
Conceptual uncertainty

The supplier has listed a number of conceptual uncertainties. The SR-Site team agree with this handling.
Data uncertainty due to precision, bias, and representativity

The handling of data uncertainty for Kd and ε is almost identical to the handling in SR-Can. This handlingis found adequate for its purpose (it should be reminded that in SR-Can, the radiological risk was mainly
associated with deposition holes where the buffer had been removed by erosion). The estimate of data
uncertainty of De is based on some additional data points, which generally confirms the uncertainty
estimate of SR-Can. The SR-Site team agree with this handling, even though some additional comments
are given on the ranges in the judgement of “Results of supplier’s data qualification”, below.
Spatial and temporal variability of data

The supplier considers the buffer and backfill to be homogenised. Accordingly there is no significant
spatial variability. The SR-Site team agrees. The supplier argues that the temporal variability of Kd
may be significant, as result of changing geochemical conditions. This is to some extent handled bysupplying data for different groundwater compositions. As in the case of data uncertainty, the SR-Siteteam finds the handling adequate for its purpose. Temporal variability of De and ε are associated withclay material removal, which is not a focus of attention of this Data report.
Correlations

The supplier suggests that the effective diffusivity is correlated with the dry density. However, forthe narrow dry density ranges of concern for SR-Site, this correlation is dwarfed by other factors.
Furthermore, the supplier base the diffusion available porosity on the physical porosity, but for anions
the uncertainty in the reduction (correlation) factor is so large that it overshadows the effects ofthe correlation.
For the Kd, elements are sorted in different correlation groups, as in SR-Can. The supplier has alsosorted the new elements into these groups. The SR-Site team agree with this handling.

180

TR-10-52

Result of supplier’s data qualification

The results of the supplier’s data qualification are accepted. However, the supplier has been somewhat
imprecise on how to transfer the data to probabilistic distributions. Therefore, some additional
discussion is needed.
Sorption partitioning coefficient: For Kd data, predictions (best estimates) have been supplied.
In addition, upper and lower limits have been supplied. The data are symmetric in the log10-space.
Accordingly, triangular distributions in the log-space are recommended.
Diffusion-available porosity: For neutral species and cations, the porosity is provided as best estimates,with upper and lower limits. The data are symmetric in the normal space. Accordingly, triangular
distributions in the normal space are recommended. For anions, best estimate, upper, and lower limit
data are asymmetrical. However, the data are more symmetric in the normal space than in the log
space. Thus, double triangular distributions in the normal space are recommended.
Effective diffusivity: For anions, best estimate values and upper and lower limits are supplied. The
data are asymmetric, but more symmetric in the log-space than in the normal space. Accordingly,
double triangular distributions in the log-space are recommended. For neutral species and cations(except Cs), the range is based on the standard error of the regression. This standard error does notencompass all the data (as shown in Figure 5-6). Therefore, basing the limiting data on the standard
error is questionable. However, as the spread in Figure 5-6 is also due to other factors (different
clay materials, content of sand, etc.) this is accepted. When doing this, it is kept in mind that it is theapparent diffusivity (cf. Figure 5-5) that is used in solute transport modelling. For cations, which
sorb at any extent, it is the uncertainty in Kd rather than in De that will give rise to uncertainty in Da.
Based on the best estimate, and the “limiting” values, a triangular distribution in the log10 space isrecommended. For Cs, the upper limit shares the data with the best estimate. Accordingly a right
triangular distribution in the log-space is recommended. It is noted that there are experimental datasuggesting De values higher than the supplied upper limit. However, the supplier has made a judgement that the data of /Sato 1998/ are the most reliable.

5.3.12 Data recommended for use in SR-Site modelling
The following migration data of the buffer and backfill are recommended for use in SR-Site.
Sorption partitioning coefficient

For non-saline and saline groundwaters, the Kd data of Table 5-16 and Table 5-18 are recommended.
The data of Table 5-16 are independent of the bentonite material used (MX-80, Deponit CA-N,
or Milos Backfill). The data of Table 5-18 are dependent on the bentonite material, and it is statedwhich data correspond to which material.
For highly saline groundwater the Kd data of Table 5-17 and Table 5-19 are recommended. The dataof Table 5-17 are independent of the bentonite material used while the data of Table 5-19 are dependent
on the bentonite material, and it is stated which data correspond to which material.
All data are given as best estimate, upper limit, and lower limit. In probabilistic modelling log10 values
should be used in triangular distributions in the log10-space.
Effective diffusivity

The data in Table 5-15 should be used for the buffer and backfill. The following distribution should be
used, based on the best estimate, upper limit and lower limit data.
•

Anions: Double triangular distribution in the log10-space.

•

Neutral and cations (except Cs): Triangular distribution in the log10-space.

•

Caesium: Right triangular distribution in the log10-space.

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Diffusion-available porosity

The data in Table 5-14 should be used for the buffer and backfill. The following distribution should
be used, based on the best estimate, upper limit, and lower limit data.
•

Anions: Double triangular distribution in the normal space.

•

Neutral and cations: Triangular distribution in the normal space.

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6

Geosphere data

6.1

Groundwater chemical composition

The aim of this section is to describe the groundwater chemical data used as input in SR-Site models.
There are two types of modelling activities that deal with groundwater chemical compositions:(1) models that have as main purpose to calculate groundwater chemical compositions; and(2) models that use groundwater chemical compositions as input conditions.
The process of obtaining the groundwater composition data is mainly based on the analysis of presentday groundwater compositions, described in the site-descriptive models, see the Site description
Forsmark and /SKB 2009e/, as well as the results from chemical equilibrium calculations including
water-rock interactions, coupled with the results from the hydrogeological modelling describedin Section 6.6. Additional model calculations are used for specific groundwater components, forexample to evaluate the transport of the reactive gaseous components (such as O2).
Important modelling activities that use chemical compositions as input data concern buffer evolution
and stability, canister corrosion, radionuclide transport, and hydrogeological modelling. From theperspective of assessing the long-term safety of a KBS-3 repository, the most important parameters
are the main chemical components which affect salinity and ionic strength, as well as sulphide, pH,
and redox parameters.

6.1.1 Modelling in SR-Site
This section describes data which are expected from the supplier, and the modelling activities where
the data are used.
Defining the data requested from the supplier

As input to the PhreeqC modelling the supplier should deliver the compositions of the end-member
groundwaters (identified in the Forsmark site), the mass fractions of these end-members (mixing
ratios) and thermodynamic data.
As output from the PhreeqC modelling the supplier should deliver statistics of the:•Sum of the concentrations of main cations, expressed in charge equivalents as Σq[Mq+], where
q is the cation charge number and M is the cation concentration (mol/L).

•

Concentrations (mol/L) of the major cations (Na+, K+, Ca2+, and Mg2+) and anions(Cl−, SO42−, PO43−) and total inorganic carbon.

•

Ionic strength (mol/L).

•

pH.

•

Redox potential Eh (mV).

Furthermore, references to files containing the complete results from the PhreeqC modelling should
be supplied, from which the complete groundwater composition for individual coordinates and times
can be extracted.
Based on observations, and as conceptually estimated from the hydrological evolution of the site,
best estimate data should be delivered for the following components:•

Concentrations (mol/L) of dissolved molecular hydrogen H2, acetate CH3COO–, ammonium
NH4+, nitrite NO2–, dissolved organic carbon DOC, and methane CH4.

•

Concentration (g/L) of natural colloids.

•

Concentration (mol/L) of sulphide HS–.

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Based on oxygen ingress modelling for the most unfavourable situations (infiltration of oxygen-richwaters during glaciation) the supplier should deliver:•

The maximum concentration (mol/L) of dissolved molecular oxygen O2 at repository depth, andthe number of affected canister positions.

All of the above data should to the extent possible reflect on the reference glacial cycle, or appropriate variant cases. The data should reflect the relevant rock volume hosting the repository. Data are
not requested for the excavation/operation period. Instead /Salas et al. 2010/ is referred to, whereinsuch data are reported.
SR-Site modelling activities in which data will be used

Modelling activities resulting in chemical composition data
The chemistry data flow is as follows in the SR-Site model chain. Groundwater chemistry data collected within the site investigation, and analysed in the site-descriptive modelling, are used as input
to the hydrogeological models as a calibration tool. The data are also used to establish the chemicalcompositions of the end-member waters used when calculating the transport of solutes, for example
the composition of the infiltrating meteoric waters, seawater, etc. Collected data also include mineralcompositions, abundances, and physical properties of the rocks minerals and fracture fillings. The
data are used to build up a conceptual model of the past hydrogeochemical evolution of the site andto evaluate the results in SR-Site. Both the conceptual model and the comparison between model
results and analytical data provide confidence in the conclusions of SR-Site.
Groundwater compositions for different periods of interest are calculated using the PhreeqC code
/Parkhurst and Appelo 1999/. This modelling, as described in /Salas et al. 2010/, requires mixing
proportions and compositions of end-member groundwater components. The mixing proportions
are obtained directly from, or are based on the results of, hydrogeological modelling describedin Sections 6.6 and 6.7. The hydrogeological modelling is also described in /Joyce et al. 2010,
Vidstrand et al. 2010, Selroos and Follin 2010/. In addition a thermodynamic database is needed,as discussed in this section and also in Section 3.4.
The concentrations of reactive components (CH4, H2, DOC, etc.) and colloids that cannot be
reproduced by the models of mixing and reactions have been conceptually evaluated, based on
analytical data. In the case of the sulphide concentration, which is affected by bacterial processes,
it can be modelled with PhreeqC using different assumptions to mimic biotic and abiotic processes,
but the different assumptions result in quite different concentration ranges. Therefore, the results
need to be based on measurement data and observations from the site investigation and monitoring
programmes. For some components (e.g. H2) supporting results are compiled from observations
reported in the open literature.
In the case of dissolved molecular oxygen, the maximum concentration at repository depth during
the most unfavourable event of the glacial cycle (when the ice front is over the repository) is estimatedby reactive transport modelling /Sidborn et al. 2010/. This modelling uses transport parameters(cf. Section 6.8) as well as flow related migration properties (cf. Section 6.7) for different flow
paths, based on recharge particle tracks obtained from the hydrogeological models.
Modelling activities using chemical composition data
The chemical data supplied in this section are used by other SR-Site modelling activities either
quantitatively, for example to calculate the extent of copper corrosion, or qualitatively, for example
when motivating why a process can be disregarded in safety assessment modelling. In the following,
the important modelling activities using chemical composition data are outlined.
Buffer stability and erosion: Data on the main components of the groundwater at repository depth areused to evaluate the stability and erosion of the buffer and backfill, as detailed in /Neretnieks et al.
2009/. As input data, statistics on the pH, divalent cations Ca2+ and Mg2+, as well as the monovalentcations Na+ and K+, are needed. In addition, the sum of the concentrations of main cations, expressedin charge equivalents as Σq[Mq+], is needed. According to the design premises, the safety function
indicator criteria is that Σq[Mq+] > 4 mM. Other input data needed are the complete groundwater184

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compositions of the water having the lowest Ca2+/Na+ ratio, the lowest Ca2+ concentration, the lowest
Na+ concentration, the highest and lowest total chloride concentration, the highest and lowest total
bicarbonate concentration, and the highest and the lowest total sulphate concentration. These data are
required for the open repository conditions at 100 years of operation (for which /Salas et al. 2010/ isreferred to); for the temperate period at 2000, 3000, 5000, and 9000 AD; for permafrost conditions;and for the advancement of an ice cap, a period where the repository is fully covered by an ice cap,
and ice cap retreat.
Radionuclide transport modelling: The groundwater composition is used for determining the source
term (that is the radioelement concentrations) inside failed canisters, in case they are solubility
limited (saturated with respect to a thermodynamically stable solid phase), as described in theRadionuclide transport report. The salinity is used when estimating the sorption partitioning
coefficient Kd of certain radionuclides, as outlined in Section 6.8 and detailed in /Crawford 2010/,
and as used in subsequent radionuclide transport modelling. The best estimate and maximum colloid
concentrations are used in radionuclide transport calculations to estimate colloid facilitated transport.
Copper corrosion modelling: For modelling of copper corrosion, as detailed in /SKB 2010c/, statistics
on the sulphide concentration is needed, as well as the maximum concentration of dissolved oxygen.
In addition, statistics on concentrations of chloride, and the redox potential and pH are needed.
Hydrogeological modelling: Hydrogeochemical data (compositions and end-members) fromthe site-descriptive modelling have been used as input in the SR-Site hydrogeological modelling(cf. Section 6.6).

6.1.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

Modelling activities resulting in chemical composition data•

For the initial temperate period, the groundwater compositions were in SR-Can calculated
by coupling the results from hydrogeological calculations to mixing and chemical equilibrium
calculations performed with the PhreeqC code. This approach was deemed satisfactory and, in
SR-Site, a similar procedure is used also for other time periods. For the periglacial and glacial
climate domains the coupling between hydrogeological results and chemical calculations was
not performed in SR-Can.

•

Only a limited amount of sulphide measurements from the site investigations was available for
the SR-Can assessment, as compared to in SR-Site. This was handled by assuming a pessimistic
distribution of sulphide concentrations.

•

The estimation of dissolved oxygen intrusion in fractures was performed using one-dimensional
PhreeqC models. Although an attempt was made to evaluate the decrease of O2 in the rockmatrix, the numerical model was too time consuming, and no results could be reported for
SR-Can. The one-dimensional modelling showed that, for cautiously selected conditions andparameter values, the intrusion of O2 to repository depth was improbable. However this did
not rule out that O2 could reach repository depth for more extreme conditions, as the Swedish
regulatory authorities indicated in their evaluation. In SR-Site the modelling of oxygen transport
is performed taking into account the transport and O2 depletion reactions in the rock matrix at themost unfavourable flow conditions.

Modelling activities using chemical composition data
In SR-Can, bentonite erosion was modelled for deposition holes where the groundwater had a calcium
concentration below the limit set by a safety function indicator S[M2+]GW = 10–3 mol/L. In SR-Site
new information concerning the process of bentonite erosion has been made available and the safetyfunction indicator has been changed. The concentrations of both divalent and monovalent ions(mainly Na+) are used to establish the potential for bentonite erosion within the SR-Site assessment.

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The concentrations of Ca2+, Mg2+, Na+, K+, Fe, Cl–, SO42–, HCO3–, SiO2(aq) and pH and Eh forgroundwater surrounding the repository were used in SR-Can to model the evolution of the properties of the bentonite. Basically the same kind of groundwater chemical data and modelling will be
used in SR-Site.
Radionuclide concentration limits were calculated using a set of solid phases and equilibrium
speciation calculations for a distribution of groundwater compositions. In SR-Site the concentrationof phosphate in the groundwaters will be added to the list of parameters considered. To model thetransport of radionuclides in the buffer, backfill, and geosphere, Kd values for sorption were selectedaccording to the groundwater salinities, carbonate concentrations, pH, and redox potentials. Colloid
facilitated transport of radionuclides was not evaluated in SR-Can, but is part of the SR-Site assessment/Wold 2010/.
In SR-Can, copper corrosion was evaluated using fixed groundwater concentrations of sulphide.
Sulphide either diffused through the bentonite buffer towards the canister or, in the case of deposition boreholes where substantial bentonite erosion had taken place, the sulphide was transportedby the groundwater flow. The reaction between copper and sulphide was assumed to be fast andcomplete, producing Cu2S. A cautious value for the sulphide concentration in groundwater (including reductants such as CH4 and H2 that could be used in microbial sulphate reduction) was set to10–4 mol/L for 10% of the deposition holes, and 10–5 mol/L was assumed for the remaining 90%.
In SR-Site, the modelling of corrosion is performed with the same type of models, but a site specificdistribution of sulphide concentrations is used.
Conditions for which data were used in SR-Can

Groundwater compositions were provided in SR-Can for the Laxemar and Forsmark sites for different
periods from the excavation and operation phases to the end of the first glacial cycle. The groundwatercompositions were estimated in SR-Can using as input the results from hydrological simulations,which in turn were conditioned to a given hydrogeological model and the past historical evolutionof the site. The results were conditioned by the fact that the site descriptions were of provisional
character.
In SR-Can the impacts from the thermal load of the spent fuel, as well as from the temperature
decrease during cold periods, on the calculated groundwater compositions were not evaluatedbecause they were considered negligible.
Sensitivity of assessment results in SR-Can

In SR-Can the groundwater parameters that had the largest impact on assessment results were sulphide(with a major influence on canister corrosion for deposition holes affected by bentonite erosion) andcalcium concentration (when evaluating bentonite erosion). Calcium and magnesium were included
as a safety function indicator: S[M2+]GW > 10–3 mol/L. Bentonite erosion was assumed to take placein deposition holes where groundwaters had a concentration of divalent cations below this limit.
Other groundwater parameters also had a large impact, for example redox conditions. However, theranges and uncertainties in these parameters were such that the uncertainty in assessment results was
not affected.
Sulphide concentration had a direct linear relation to the expected lifetime of the copper canisters(all other parameters being equal). Accordingly, a doubling in the sulphide concentration resulted ina halving of the canister lifetime.
Alternative modelling in SR-Can

No alternative modelling with bearing on the groundwater chemical composition was performed in
SR-Can.

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Correlations used in SR-Can modelling

Groundwater components may be classified as either mainly mixing controlled or mainly reaction
controlled (including microbiologically reaction controlled). The concentrations of groundwatercomponents that are mainly controlled by mixing are highly correlated /Auqué et al. 2006/. Thisis seen in the groundwaters sampled both in the Laxemar and Forsmark areas, for example in thecorrelation between calcium and chloride in the data from the hydrogeochemical evaluation for
Forsmark shown in /Laaksoharju et al. 2008, Figure 4-8a/. For the granitic rocks considered here,plots displaying the variation of concentrations with depth seldom show so clear trends because thehydraulic conditions of the different fractures influence the mixing between different groundwaters.
The correlations between groundwater components are used in the process of evaluating the sitedata, and in obtaining reference water compositions involved in the mixing models. Other trends
may be used to deduce the occurrence of chemical reactions, for example by plotting the data inmineral stability diagrams, which may indicate what solid phases (minerals) are controlling theconcentrations of major components (e.g. calcite for Ca2+ and CO32–).
Chemical reactions will also generate correlations between groundwater parameters. For example,
groundwater equilibrium with respect to Fe(III) oxyhydroxides, according to the reaction:
Fe(OH)3(s) + 3 H+ + e– ↔ Fe2+ + 3 H2Oproduces values for the redox potential that are strongly correlated to pH.
Identified limitations of the data used in SR-Can modelling

For the Forsmark site, within the candidate repository volume, a large part of the fractures havequite low transmissivities, and sampling of groundwater from this rock volume is limited to fracturezones. Sampling of groundwater from volumes with very low hydraulic conductivity has remained
practically not possible, but rock matrix porewater compositions have been made available to the
SR-Site assessment. For the Laxemar site the information on the groundwater compositions in theSouthern part of the study area was not satisfactory at the time of SR-Can.
It was noted by the SR-Can team that sulphide concentrations obtained before the construction ofthe Äspö laboratory were systematically higher than those obtained within the complete chemical
characterisation of the borehole sections studied in the Oskarshamn and Forsmark site characterisations. Several questions regarding the values for sulphide remain in SR-Site.
In SR-Can, estimations of oxygen penetration to repository depth were not fully integrated with thehydrogeological calculations that were carried out. For example, it was not clear how the dischargetransport time for water from the repository to the ground surface, which was estimated in /Jaquet
and Siegel 2006/, is related to the recharge transport time from the bottom of the glacier to therepository /SKB 2006a/. In SR-Site, the hydrogeology calculations are integrated with the oxygen
penetration estimates to make it clear which transport paths (and the associated transport times) areused for the estimates of oxygen ingress /Sidborn et al. 2010, Vidstrand et al. 2010, Joyce et al. 2010/.

6.1.3 Supplier input on use of data in SR-Site and SR-Can
The supplier notes that the main groundwater chemical components and the provided variabilities
of compositions are highly conditioned by the results of the hydrogeological models. However, they
qualitatively reflect the conceptual models of the Forsmark site.
The team supplying groundwater compositions have only limited expertise in the fields covered by
the “non-chemical” safety assessment models that use these data. Nevertheless, the supplier does nothave any reservations concerning the use of the data in SR-Can or SR-Site.

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6.1.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information are: /Salas et al. 2010/, reporting the hydrogeochemical modellingin Forsmark using PhreeqC; /Tullborg et al. 2010/, presenting the selection of representative sulphide
data from the site investigation and monitoring programme; /Sidborn et al. 2010/, reporting theoxygen ingress modelling at Forsmark. The full references to these reports are given in Table 6‑1.
Table 6‑1. Main sources of groundwater chemical information used in data qualification.
Salas J, Gimeno M J, Auqué L, Molinero J, Gómez J, Juárez I, 2010. SR-Site – hydrogeochemical evolution of theForsmark site. SKB TR-10-58, Svensk Kärnbränslehantering AB.
Sidborn M, Sandström B, Tullborg E-L, Salas J, Maia F, Delos A, Molinero J, Hallbeck L, Pedersen K, 2010.
SR-Site: Oxygen ingress in the rock at Forsmark during a glacial cycle. SKB TR-10-57, Svensk Kärnbränslehantering AB.
Tullborg E-L, Smellie J, Nilsson A-C, Gimeno M J, Auqué L F, Brüchert V, Molinero J, 2010. SR-Site – sulphide
content in the groundwater at Forsmark. SKB TR-10-39, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

Most data are taken from /Salas et al. 2010/ wherein two categories of data are reported. In the firstcategory data are derived by PhreeqC modelling, providing concentrations of the major cations
Na+, K+, Ca2+, Mg2+ and anions Cl−, the total inorganic carbon, sulphide, sulphate, and phosphate.
In addition the pH, Eh, ionic strength, and cation charge equivalents Σq[Mq+] are given. Also otherdata not specifically requested in this Data report are provided in /Salas et al. 2010/. The data in thesecond category are not derived by PhreeqC modelling, but are based on observations from the site
investigations, as well as on data in the open literature. These data includes the concentrations of
DOC, acetate, CH4, H2, NO2–, NH4+, and colloids.
The sulphide concentrations are affected by bacterial processes. Although sulphide is modelled with
PhreeqC, different conceptual assumptions provide different ranges of results. The analytical data
and the observations from the site investigation and monitoring programmes are considered morereliable. Such sulphide data are compiled in /Tullborg et al. 2010/.
The ingress of oxygen is modelled in /Sidborn et al. 2010/ based on the flow pattern and properties
provided in /Vidstrand et al. 2010, Joyce et al. 2010/ when the ice front is at its worst position,
which is directly above the candidate repository volume, as well as on recharge particle tracks/Joyce et al. 2010/.
Excluded data previously considered as important

The categorisation of all samples from the monitoring programmes has been revised /Tullborg et al.
2010/ with emphasis on the sulphide and Fe(II) data. In some cases, data previously labelled as
representative when considering only major groundwater components have been deemed as lessrepresentative data for trace elements such as Fe(II), sulphide, Mn(II), dissolved organic carbon, etc.
No other data have been excluded that previously had a significant impact on the perception of theappropriate choice of data values or of modelling approaches.

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Table 6‑2. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Salas et al. 2010/, Table 4-2: Equilibrated end- members
compositions, calculated with the coupled and uncoupled
database used for the geochemical simulations.
2. /Salas et al. 2010/, Appendix 4, Table A4-1: Statisticalresults for the Base case for the temperate (including
submerged) period for pH, Eh, ionic strength and concentrations of Na, K, Ca, Mg, total inorganic carbon, Cl, S(VI),
S(-II), phosphate, and Σq[Mq+].
3. /Salas et al. 2010/, Appendix 4, Table A4-2: Statisticalresults for the hydrogeological variant case for thetemperate period for the same data as in item 2.
4. /Salas et al. 2010/, Appendix 4, Table A4-3: Statisticalresults for the Base case for the glacial cycle for the samedata as in item 2.
5. /Salas et al. 2010/, Appendix 4, Table A4-4: Statisticalresults for the hydrogeological variant case for the glacial
cycle for N-S glacier advance for the same data as initem 2.
6. /Salas et al. 2010/, Appendix 4, Table A4-5: Statisticalresults for the case of glacier advance over a frozen soil
(permafrost) and periglacial period for the same data asin item 2.
7. /Salas et al. 2010/, Table 8-1: Recommended valuesof DOC, acetate, CH4, H2, NO2–, NH4+ and colloids.
8. The complete results from the PhreeqC modelling in/Salas et al. 2010/ are stored in (SKBdoc 1262945).
9. /Tullborg et al. 2010/, set of sulphide concentrations
selected as representative of the present-day groundwatercompositions at Laxemar. Numerical values are also storedin (SKBdoc 1261474).
10. /Sidborn et al. 2010/, Chapter 8: Data on maximum
oxygen concentration and affected canister position for ice
location II and III.

11. Sicada delivery: all groundwater data from
Forsmark containing sulphide analyses (delivery note
Sicada-09-185).
12. Sicada delivery: monitoring data from Forsmark
containing sulphide analyses (delivery note
Sicada-10-126).
13. The ChemNet data, based on the Extended data
freeze 2.3 (December 2007) at the SIMON database(mirror of Modelldatabasen), folder “Forsmark/2.3/
Hydrogeochemistry/Approved”, file “Forsmark_2_3.xls”.

Items 1–10: /Salas et al. 2010, Tullborg et al. 2010, Sidborn et al. 2010/ are SKB reports produced for SR-Site andconcerning the Forsmark site. The reports are produced and reviewed in accordance with the SKB’s quality assurance
system, and these data are judged as qualified. Detailed references to the literature survey conducted to estimateparameters for item 7 is given in Chapter 8 of /Salas et al. 2010/.
Items 11–12 are the complete data sets on which the selection of representative sulphide data in /Tullborg et al.
2010/ is based. These data sets are quality assured but all data points are not necessarily representative for the in situ
conditions (for example as result of disturbance from the measurements). Accordingly the data sets are sorted as
supporting. The data set used for calculating the sulphide distribution in Forsmark was extracted from Sicada (delivery
note Sicada-09-185) and includes all data from the percussion and cored boreholes sections that were analysed forsulphide. Two sources of sulphide data are available: 1) Complete Chemical Characterisation (CCC) data from coredboreholes of the site characterisation programme /Laaksoharju et al. 2008/ that include the earlier established /Smellie
et al. 2008/ categorisation of the samples and 2) data from the monitoring of selected borehole sections in percussion
and cored boreholes initiated at different times during and following the site characterisation programme up to November 2009. An additional data set from the monitoring campaign during the spring of 2010 (delivery note Sicada-10-126)
is used as support for the motivation of the selection of representative samples.
Item 13 is the complete hydrogeochemical data set delivered from Forsmark site investigation. The same considerations
apply as for items 11–12. The data used for the hydrogeochemical modelling and explorative analysis during the site
investigations programme were extracted from Sicada as data freezes 2.2 (October, 2006) and 2.3 (April, 2007) andincluded data from earlier data freezes and newer data stored up to April 2007. Additional data from the monitoring
programme and three deep boreholes were included in the Extended 2.3 data freeze (October, 2007).

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6.1.5

Conditions for which data are supplied

Conditions for hydrogeochemical modelling

Concerning the main geochemical parameters, estimated through PhreeqC modelling, the data arehighly conditioned by the hydrogeological modelling providing mixing proportions /Joyce et al. 2010/or salinities /Vidstrand et al. 2010/. Conditions for the hydrogeological modelling are outlined in
Sections 6.6 and 6.7. The hydrogeological modelling dictates the coordinates for the PhreeqC modelling. However, only subsets of all the coordinates are used in the PhreeqC modelling, representing
three different rock volumes /Salas el at. 2010, Section 3.4.3/:• Complete horizontal slice at the repository depth (440–500 m), with 111,900 and 67,795 points
for the temperate and the glacial modelling, respectively.
• Data points within the candidate repository volume, included inside the following coordinates:(1630.62, 6701.17); (1633.37, 6701.17); (1633.37, 6698.9); (1630.62, 6698.9) with 65,237 and24,425 points for the temperate and the glacial modelling, respectively.
• A vertical slice approximately parallel to the shoreline (NW: 1628519 m / 6702421;
SE: 1636272 m / 6694615), with 25,349 and 20,671 points for the temperate and the glacialmodelling, respectively.
These subsets of coordinates are used to create input files for PhreeqC to obtain the detailed chemicalcomposition at each coordinate (see /Salas et al. 2010, Appendix 1/).
The data are also conditioned by the used end-member compositions. For Forsmark the following
five end-members are used /Salas el at. 2010, Section 4.4/:• Deep saline.
• Old meteoric.
• Glacial.
• Littorina.
• Altered meteoric.
A basis for defining the end-member compositions are the assumptions of with which minerals
are in equilibrium with these waters. Examples of such minerals are calcite, hematite, and quartz.
Conditions for each end-member are discussed in /Salas el at. 2010, Section 4.4/.
The hydrogeochemical modelling results depend on the thermodynamic database (TDB) used.
The thermodynamic database used in the simulations, TDB_SKB-2009_Amphos21.dat (stored
in SKBdoc 1261302), was developed by /Hummel et al. 2002/ with some modifications reported
in /Duro et al. 2006b/12. Some specific phases have been considered in order to establish mineralequilibria with the groundwaters or with the reactive mixing (cf. Table 6‑3). When these species
are not included in the SKB-TDB, they are included as phases in the PhreeqC input file /Salas et al.
2010, Section 4.3/. The reasons for the selection of these phases and their equilibrium constants aredescribed in /Auqué et al. 2006, Gimeno et al. 2009/.
Table 6‑3. Additional equilibrium reactions and constants used in PhreeqC modelling.
Information taken from /Salas et al. 2010, Section 4.3/.
Equilibrium reaction

Log kAlbite: NaAlSi3O8 + 8H2O = Al(OH)4– + 3Si(OH)4 + Na+–19.98

K-Feldspar: KAlSi3O8 + 8H2O = Al(OH)4– + 3Si(OH)4 + K+

–22.62

Fe(OH)3(hematite_grenthe): Fe(OH)3 + 3H+ = Fe3+ + 3H2O

–1.10

FeS(ppt): FeS + H+ = Fe2+ + HS––3.00

Kaolinite_G: Al2Si2O5(OH)4 + 7H2O = 2Al(OH)4– + 2H+ + 2Si(OH)4–37.3

Hydroxyapatite: Ca5(PO4)3OH + 4H+ = 5Ca2+ + 3HPO42– + H2O–3.421

Rhodochrosite: MnCO3 = Mn2+ + CO32––11.13

The thermodynamic database TDB_SKB-2009_Amphos21.dat has recently been modified by M. Grivé,
D. Arcos and co-workers at Amphos21, Barcelona, Spain in reference to thermodynamic data and to specificaqueous species of iron and sulphur. These updates are documented in (SKBdoc 1261302).
12

190

TR-10-52

The redox potential, Fe, sulphide, and pH are sensitive to the mineral assumed to be controlling theequilibrium and, also, to the presence of absence of sulphate-reducing bacteria. Microbial sulphatereducing processes are detailed in /Tullborg et al. 2010, Chapter 2/. The use of Fe(III) oxyhydroxides
or amorphous Fe(II) sulphides as equilibrating minerals represents two alternative assumptions
defining geochemical behaviour of groundwaters. Equilibrium with Fe(III) oxyhydroxides implies asituation where the redox state is not affected by sulphate-reducing bacteria, while equilibrium withamorphous Fe(II) sulphides characterises a situation with significant activity of sulphate-reducing
bacteria. Calculations include alternatively equilibrium with respect to /Salas et al. 2010, Section 1.3.2/:•

Crystalline Fe(III) oxyhydroxide /Grenthe et al. 1992b/ (and redox equilibrium with Fe(II)aqueous species), Fe(OH)3(s) + 3H+ + e– ↔ Fe2+ + 3H2O, where the resulting redox potential
is strongly correlated to pH, and/or•

Amorphous Fe(II) monosulphide, FeS(am) + H+ ↔ Fe2+ + HS–

The fracture mineralogy at the Forsmark site is described in /Sandström and Tullborg 2006, Sandström
et al. 2008/. Based on these descriptions the presence of both mineral phases is deemed possible. Thus,
when evaluating Eh (and iron concentrations) both geochemical cases are included in the so-called“Base Case” and the results from the two cases are pooled when obtaining Eh statistics. For the otherparameters, equilibrium with respect to Fe(III) oxyhydroxides is only assumed in the Base case. In a
third geochemical variant case, a database that uncouples equilibrium reactions between the species
including S(‑II)/S(VI) is used, in order to evaluate the influence of the kinetics of the redox reaction
between the pairs. However, data from this third variant are not propagated to SR-Site.
The different variant cases are outlined in Figure 6‑1 (green boxes). The grey shading indicates
which variant cases are included in the Base case for the different parameters.
The impacts from the thermal load of the spent fuel, and from the temperature decrease during cold
periods within the glacial cycle, on calculated groundwater compositions are not evaluated, but theinduced uncertainty is estimated to be small compared to other uncertainties. Other conditions ofimportant for the hydrogeochemical modelling are discussed in /Salas et al. 2010, Gimeno et al. 2010/.
Base Case

Base Casefor solute concentraons(excep o iron) and pH

Calcite + Quartz +Hydroxyapa te+ Hema teS(VI)-S(-II)
coupled(TDB)

S(VI)-S(-II)
uncoupled(TDB)+ FeS(am)S coupled(TDB)

for iron concentraonand Eh(stascal calculaons)

Calcite + Quartz +Hydroxyapate+ Hemate
S(VI)-S(-II)
coupled(TDB)

S(VI)-S(-II)
uncoupled(TDB)+ FeS(am)S coupled(TDB)

Figure 6‑1. Scheme of the mineral equilibriums assumed in the calculations as geochemical variant cases,
and constrains applied in the so-called Base case for the statistical approach (grey area). Reproduced from
Figure 4-5 of /Salas et al. 2010/.

Conditions for non-modelled components

For a number of requested solutes, the estimated concentrations are based on analytical data. Thisapplies for dissolved molecular hydrogen, acetate, ammonium, nitrite, dissolved organic carbon,
methane, colloids, and sulphide. Observed data are predominantly taken from the Forsmark and
Laxemar site investigations, but also from Olkiluoto in Finland and, to a minor degree, from othersites such as the Whiteshell Underground Research Laboratory site in Canada. Data from the site
investigation and monitoring programmes are obtained at present-day conditions at different depths
and rock volumes at the sites. As the groundwater at different depths and rock volumes comprises
different proportions of end-members, conclusions can be drawn for other time periods than for the
initial temperate. In doing this, governing processes and reactions have been taken into account. In
Chapter 8 of /Salas et al. 2010/ a few conditions are outlined and references to detailed accounts on
the conditions are given for H2, CH3COO–, NH4+, NO2–, DOC, CH4, and colloids.
TR-10-52

191

Concerning sulphide, the complete data set from the Forsmark site has been revised in /Tullborg et al.
2010/ and data deemed to be representative for undisturbed conditions have been selected (while
disturbed data have been discarded). The sampling procedures and associated sampling conditions,as well as the selection criteria, are outlined in Sections 3.1 and 4.5 of /Tullborg et al. 2010/.
Conditions for oxygen ingress modelling

The oxygen ingress modelling is conditioned by the following parameters and processes:•

The transport processes, including advection and diffusion into the rock matrix, and rates ofkinetically controlled reactions /Sidborn et al. 2010, Sections 2.1 and 2.4/. Concerning theadvective transport, properties of the flow paths are taken from /Joyce et al. 2010, Vidstrand etal. 2010/. The most important period of the glacial cycle for the maximum oxygen concentration
is when the ice front is directly above the repository, resulting in increased hydraulic gradients
generating the highest groundwater flow through the candidate repository volume (cf. Sections 6.6
and 6.7). Therefore, the ice front location strongly affects the possibility of oxygen ingress to therepository depth. The main results in /Sidborn et al. 2010/ are given for ice locations II and III.

•

Abiotic oxidation reactions of the fracture filling minerals and minerals of the rock matrix,
chlorite, biotite, and pyrite, and oxidation of dissolved Fe(II) /Sidborn et al. 2010, Section 2.2/.
These minerals and solutes govern the reducing capacity of the rock matrix. In the so-called Basecase a pH of 8 of the intruding glacial melt waters is assumed.

•

Numerous biotic reactions that can scavenge oxygen are described in Section 2.3 of /Sidborn et al.
2010/. In fractured rock, such reactions are predominantly assumed to occur in, or adjacent to, theflow paths. Conditions for the reactions, for example the supply of reductants, are also described.
Biotic reactions are not included in the modelling. However, they are important for the argumentthat the ingress modelling is pessimistic (several biotic reactions are likely to add to the oxygen
scavenging). Therefore, the ingress of oxygen is conditioned by the biotic reactions, but theapplied oxygen ingress modelling is not.

•The oxygen concentration of the intruding glacial melt water (recharge concentration).
For the Base case, the oxygen concentration is assumed to be 1.5 mM O2 /Sidborn et al. 2010,
Sections 5.7 and 7.6/. However, a sensitivity analysis has been performed in order to evaluate
the role of these values.

6.1.6

Conceptual uncertainty

General considerations

The chemical processes controlling the compositions of groundwaters are well known. These include
equilibrium reactions between aqueous species, such as acid-base and complexation, dissolution andprecipitation of minerals, sorption and ion-exchange. This does not mean that the chemical compositionof groundwaters can be provided with a high level of confidence. Several reactions, especially those
involving minerals, have slow rates and their influence on groundwater compositions is dependent
on factors such as reactive surface area, solid to solution ratio, groundwater flow velocity, andturnover time, among others. As a consequence, the composition of groundwaters is influenced byclimatic conditions at the surface, by submerged conditions, etc.
A large degree of the uncertainty in the groundwater compositions is due to uncertainties in hydrogeological models and to the intrinsic exclusion of future climatic conditions and unforeseen conditions.
Some aspects of groundwater compositions need a special discussion, such as redox conditions andparameters affected by microbial process, for example sulphide and dissolved organic carbon.

192

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Redox conditions

It is widely accepted that different redox couples in a groundwater system (for example sulphidesulphate) are not necessarily in equilibrium. Despite of this, PhreeqC is based on overall redox
equilibrium and, consequently, the handling of redox components has an intrinsic uncertainty.
In addition, several assumptions can be made when modelling the reactions governing the overall
redox conditions. In SR-Site two possibilities have been deemed equally probable:•

Equilibrium between aqueous species and Fe(III)-oxyhydroxides.

•

Equilibrium between aqueous species and amorphous Fe(II)-sulphide.

To deal with this conceptual uncertainty, results from calculations using both assumptions have been
supplied to SR-Site.
The results obtained for the flow conditions when an ice sheet advances and retreats over unfrozen
ground (which is one of the cases in hydrogeological modelling) show that glacial melt waters canreach the Forsmark candidate repository, and this could affect the stability of redox sensitive components during the first stages of the glacier advance. The redox conditions are highly conditionedby the hydrogeological modelling, which in turn is conditioned by the estimated properties of the ice
front and its movement. As the modelling to such an extent is dependent on assumptions in earlier
modelling steps, this is considered as a conceptual uncertainty.
Sulphide

The problem with identifying reliable sulphide values was recognised at an early stage, when thediscrepancy between CCC (the initial Complete Chemical Characterisation immediately after
drilling) and monitoring samples was found, and also the large variation within the monitoring timeseries. The parameters that will have largest influence on the sulphide production are SO42–, DOC,
H2, and CH4. The possibility of an increase or decrease in concentrations of these solutes is outlinedin Table 6-1 of /Tullborg et al. 2010/ for the different time periods. It must be assumed that microbial
processes will continue during permafrost and glacial conditions. However, it is difficult to know
which microbial processes will be quantitatively effective. Although many chemical processes are
slowed down by decreased temperatures, it may not necessarily be so for all microbial processes.
Gaseous components

The sources, production rate, and transport mechanisms of geogas are, together with the in situ
sulphide concentrations and redox front variations, aspects of the hydrogeochemical modelling associated with the lowest confidence, see Site description Forsmark. Gases are natural components ingroundwater, but they may also be formed in the repository, mainly as a result of corrosion processes
and radiolysis of water in contact with the spent fuel. Some gases are involved in microbiologicalreactions, e.g. methane, hydrogen, and carbon dioxide /Hallbeck and Pedersen 2008/, and they are
therefore affected by the uncertainties associated with microbial processes.
Oxygen ingress

There some conceptual uncertainty in the way oxygen scavenging is conceptualised. This is discussed
in Chapter 4 of /Sidborn et al. 2010/. In SR-Site, this conceptual uncertainty is handled by taking
a pessimistic approach in limiting the effect of, or totally disregarding, processes that are not wellquantified. As the maximum oxygen concentration is estimated for the time period when the ice front
margin is located right above the candidate repository volume, uncertainty in previous modelling
steps (ice front and hydrogeological modelling) propagates to the oxygen ingress modelling.

TR-10-52193

6.1.7

Data uncertainty due to precision, bias, and representativity

Uncertainty in hydrogeochemical modelling with PhreeqC

Four main sources of uncertainties can be identified in the predictive geochemical modelling
performed for SR-Site:
1. The number and composition of the end-members used for the mixing calculations.
2. Uncertainties arising from the coupling between hydrological modelling (providing mixing
proportions or salinities) and geochemical modelling.
3. The heterogeneous reactions considered to participate in the control of the composition of themixed waters through equilibrium assumptions.
4. The thermodynamic data used for these equilibrium reactions.
Uncertainty in the compositions of end-member groundwaters
Uncertainties in the composition of end-members directly influence the uncertainty of the simulated
groundwater compositions. The chemical compositions of the end-members have been updated in/Gimeno et al. 2010/ using those estimated for SR-Can and reported in /Auqué et al. 2006/ as thestarting point. Estimation of the number of end-member waters involved in the palaeohydrologicalevolution of the sites and their chemical compositions are key parameters in the performed simulations. The effects of the associated uncertainties have been widely discussed in the context of theSwedish and Finnish site characterisation programmes (e.g. /Gómez et al. 2009, Gimeno et al. 2008,2009/ and references therein).
In particular, for SR-Site the original compositions of the end-member waters used in the geochemical simulations have been carefully reviewed in the site-descriptive modelling /Gimeno et al. 2008,2009/. Some of these waters are represented by real samples from the natural systems and others
waters (for the old end-members) are inferred from geological and geochemical information. Thus,
compositional uncertainties in these old end-members are greater. Moreover, in some cases, there
are some unknown fundamental geochemical parameters (pH, Eh, and dissolved Fe(II) and S(-II)
concentrations) that have been obtained assuming a series of mineral equilibrium reactions thought
to be controlling these variables (e.g. equilibrium with calcite, quartz, Fe(III) oxyhydroxides, and/or
amorphous Fe(II) monosulphides). Thus, although the selected end-members are able to simulate theoverall geochemical character of the present groundwaters, some degree of uncertainty remains.
The number of end-members used in simulations depends on the knowledge of the natural system
under study. Thus, it has changed as more information and knowledge has become available fromthe sites. In the SR-Site exercise, a fifth end-member (Old meteoric end-member), not used in SR-Can
calculations, has been included based on the works by /Smellie et al. 2008, Laaksoharju et al. 2009/.
Uncertainties associated with the end-member compositions are discussed in /Salas et al. 2010,
Section 9.5/.
Uncertainty in the mineral/groundwater reactions
The mineral reactions chosen to be in equilibrium with the groundwater mixtures are reasonable, as
they include those with a fast kinetics compared to the simulated time intervals (like calcite) or those
identified in apparent equilibrium situations in the present groundwaters (like quartz). However, they
represent only a limited subset of those minerals present in the fractures and, thus, other mineralssuch as chlorite and illite could participate through dissolution-precipitation reactions. Other solid
phases, such as aluminosilicates, could be stipulated as equilibrium constraints, but their selection
would be difficult to justify because their thermodynamic constants show important uncertainties(as they depend on the particular mineral composition, degree of order/disorder, etc.). Moreover,previous scoping calculations performed for SR-Can /Auqué et al. 2006/ show that including chlorite
and illite equilibrium in the calculations had a negligible effect on the concentrations of Mg and K.
Other processes different from dissolution-precipitation reactions, like cation exchange, could
participate in the hydrochemical evolution of the groundwaters. Cation exchange reactions are
kinetically fast and can exert an important control on the major cationic composition of the ground-194

TR-10-52

waters, cf. /Appelo and Postma 2005, Andersson et al. 2005/ and references therein. These reactions
may be specially activated during the mixing processes as the salinity of the groundwaters exerts amajor control on the intensity and selectivity of the exchangers for the different cations (e.g. /Appelo
and Postma 2005/).
Despite their potential importance, cation exchange processes have not been included in the present
modelling due to two main reasons: a) the available cation exchange capacity (CEC) values for fracture
filling minerals, a necessary parameter to include cation exchange processes in the predictive modelling, are very scarce and uncertain /Selnert et al. 2008/ and b) the thermodynamic database selectedby SKB do not have the possibility to deal with cation exchange processes (see below).
Finally, whereas most equilibrium assumptions are generalised in the present groundwaters over
the whole studied rock volume, it is not the case of the heterogeneous redox equilibria. The work
done for the site-descriptive modelling in Laxemar indicates that in some localised zones of thesystem, equilibrium with hematite is observed whereas in others, waters seem to be in equilibriumwith amorphous Fe(II) sulphides. The approach used for the geochemical calculations simplifies
the behaviour of the system imposing, alternatively, the equilibrium conditions in the whole volumeof rock. This uncertainty has been dealt with by pooling the results obtained from both equilibrium
assumptions in the Base case, giving a wider range of variation for redox parameters but being able
to explain the two possible situations present in the real system.
The uncertainties related to redox disequilibrium situations have also been taken into account.
A modified version of the thermodynamic database is implemented in /Salas et al. 2010/ in order toaccount for the groundwater Eh values controlled by the iron system and the Fe(III) oxyhydroxide/
Fe(II) redox pair. Although it has been chosen not to propagate results from this variant case to theBase case, modelling results that may be used for assessing the uncertainty are discussed in /Salas
et al. 2010/.
Coupling between hydrological and geochemical data
Hydrogeological model results have been provided by the hydrogeological team of SKB throughthe standard QA procedures specified for SR-Site. However, two types of hydrological model results
have been provided: mixing proportions of the considered end-members for the temperate period andalso the submerged under marine water period /Joyce et al. 2010/, and salinities for the periglacial,glacial and submerged under fresh water periods /Vidstrand et al. 2010/. Transformation of salinities(without any other data available) into mixing proportions gives rise different uncertainties andseveral simplified assumptions are made, cf. /Salas et al. 2010, Appendix A.1.3/.
With the applied approach, the groundwater composition at the end of the temperate period, as
calculated from the mixing proportions provided by /Joyce et al. 2010/, does not deviate drasticallyfrom the initial composition of the glacial period, as estimated from the salinities given by /Vidstrand
et al. 2010/. This indicates that there is a certain degree of coherence in the link between the twodifferent hydrogeological models used.
Uncertainties in the thermodynamic database
The thermodynamic data for the mineral phases included in the simulations have been reviewed
in /Auque et al. 2006, Gimeno et al. 2009/. Moreover, these data have been applied, verified,and refined in the WATEQ4F database during the study of the present groundwaters in the sitecharacterisation programmes both in Laxemar and Forsmark.
Consistency between hydrogeochemical modelling and other SR-Site geochemical models using
thermodynamic data is an important issue. This is the reason why, instead of using the same
WATEQ4F database as in previous geochemical calculations for SR-Can /Auqué et al. 2006/ and forthe site-descriptive modelling /Gimeno et al. 2008, 2009 and references therein/, SKB decided touse a new thermodynamic database known as TDB_SKB-2009_Amphos21.dat. This database wasdeveloped by /Hummel et al. 2002/ and was partially modified by /Duro et al. 2006b/, in reference tothe radionuclides thermodynamic data. The use of this database has limited the capacity of simulating processes such as cation exchange. The same problem has been encountered with some specificTR-10-52

195

solubility data already included and verified in the WATEQ4F database /Auqué et al. 2006, Gimeno
et al. 2009/13 for some important phases in the groundwater systems under study /Gimeno et al. 2009/.
Knowing the importance of cation exchange, the University of Zaragoza group has developed themethodology and software needed to deal with cation exchange. Some scoping calculations havebeen performed suggesting that cation exchange would not introduce drastic changes in the results.
However, more work is required to verify the results.
As input to the PhreeqC modelling, solubility data for mineral phases of interest have been provided(cf. Table 6‑3). However, the consistency of this inclusion has not been extensively verified, as wasdone with the previous database used in SR-Can.
Uncertainty for non-modelled components

The estimated concentrations of colloids, dissolved inorganic and total organic carbon, nitrite,ammonia, acetate, methane and molecular hydrogen are based on observations. Most of theseparameters are intimately related to the microbial activity or are being conditioned by different
redox processes. This fact makes their evaluation a difficult task due to the complexity and lackof knowledge of microbial processes in the groundwaters of crystalline systems, as well as in the
bentonite. Thus, all proposed values have uncertainties and, in some cases, they are especially large/Salas et al. 2010, Chapter 8/. This is the case for DOC, acetate, CH4, H2, NO2–, and NH4+ for the
excavation/operation period (not requested in this Data report), of CH4 and H2 for the glacial period,and of DOC for the submerged period.
The values proposed for CH4 and H2 during the glacial period are extrapolated from the highest
contents in present groundwaters. The estimation of CH4 and H2 concentrations in this period iscomplicated by the existence of additional processes and different boundary conditions to thosepresent and/or expected in the other periods. The concentration of these gases will be controlled
by their production and flow from the deep bedrock and by the active microbial metabolisms(acting as sources and sinks), but also by the impervious frozen layers at the top of the site and by
the formation/dissociation of clathrates during permafrost advance and decay. From the preliminary
estimations on the fluxes and maximum productions of methane and hydrogen for the Forsmark and
Laxemar sites /Delos et al. 2010/, low contents of these components are expected (e.g. increased
sulphide production under ice sheets is not expected). However, more data and studies are necessary
and, also, the microbiological CH4 production and consumption rates must be assessed.
DOC values during the submerged period have been extrapolated from the highest DOC values inpresent-day Forsmark groundwaters with an important marine contribution. The use of present-day
groundwaters with a clear Littorina contribution as an expected analogue situation for this period
would be reasonable. However, the measured DOC values (up to 2.92 mM) are still subjected todifferent uncertainties (e.g. contamination during drilling/sampling) and, therefore, they remain
uncertain. Table 6‑4 shows statistics on observed data as taken from the Forsmark and Laxemar site
investigations. This indicates the spread at present-day conditions due to both data uncertainty andnatural variability. Details on the uncertainty of the individual components are given in Chapter 8 of/Salas et al. 2010/.
Uncertainty in oxygen ingress modelling

There is major data uncertainty in the oxygen ingress modelling relating to the results from thehydrogeological models and, to a minor extent, to the migration properties of the rock matrix.
There are also uncertainties related to the rates of abiotic and, in particular, biotic oxygen
scavenging. Uncertainties in biotic scavenging rates are handled by pessimistically neglecting
biotic scavenging in the modelling. The input data used in the modelling are described in detail in
Chapter 5 of /Sidborn et al. 2010/. Lack of data or knowledge on data variability, and incomplete
process understanding, have been handled by pessimistic assumptions.

Appendix A in /Auqué et al. 2006/ contains a detailed discussion of the main difficulties encountered whenworking with these type of phases, the range of solubility values found in the literature, and the values selected
for the SR-Can modelling. See also Appendix C in /Gimeno et al. 2009/.
13

196

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Table 6‑4. Main statistics for the values of the analysed parameters, as measured in the presentday groundwaters at Forsmark, and at Laxemar in the case of acetate and DOC. Data reproduced
from Tables 8-2 and 8-3 of /Salas et al. 2010/.
Forsmark

Laxemar

TOC(mg/L)

DOC(mg/L)

NO2(mol/L)NH4(mol/L)CH4 g(mol/L)H2 g(mol/L)

Acetate(mol/L)

DOC(mol/L)N total

56

62

16

60

16

12

16

20

Minimumbdl

bdl

1.43·10–8

0

1.07·10–6

bdl

0Median2.55

1.95

2.50·10–8

3.22·10–5

2.46·10

–6

Mean

4.85

3.5

3.08·10–85.42·10–5

1.55·10–5Standard
deviation

6.21

5.51

2.44·10–85.62·10–55.07·10–5

Maximum

40.3

35.7

1.07·10–7

1.86·10–4

2.05·10–4

P0.1

bdl

bdl

1.43·10–8

0

1.07·10–6

P5

bdl

bdl

1.43·10–8

2.13·10–6

1.07·10

–6

bdl

P95

13

13

1.07·10–7

1.70·10–42.05·10–4

1.92·10

P99.5

40.3

35.7

1.07·10–7–+

1.86·10–4

2.05·10–4

6.03·10–8

3.37·10–66.09·10–6

1.66·10–4

1.49·10–54.79·10–4

7.55·10–52.92·10–3

1.73·10–47.04·10–3

1.92·10–5

6.87·10–4

3.06·10–2

bdl

01.66·10–4

0–5

1.92·10–5

6.87·10

1.83·10–4–4

0.02136

6.87·10–4

0.03056

bdl: Below detection limit.

Uncertainty in site investigation data

Available data concerning groundwater chemical composition from the site investigations are used tocalibrate the hydrogeological models, as well as for estimating the composition of the groundwater
end-members, which are in turn used to assess the distribution of the present-day groundwaters andthe future groundwater compositions. Therefore, such uncertainty is propagated to the output of
PhreeqC modelling. These data are also used for estimating the concentration of components thatcannot be modelled.
There is a bias introduced in the data set since sampling has been performed on fractures with highenough transmissivity, meaning that there practically exists no chemistry data for hydraulically very
low and non-conductive rock volumes. Exclusion of samples due to technical difficulties may also
result in a bias. See /Nilsson 2008/ for a detailed description of precision in the analyses.
The relative charge balance gives an indication of the quality and uncertainty of the analyses of the
major ions. Together with the chloride/electrical conductivity correlation, the charge balance was used
to verify that the analytical concentrations of the most dominating ions are consistent. The errors,
as calculated for each one of the 1,790 samples in SKB database SIMON, very seldom exceed
the acceptable limits ±5% (8 samples) and ±10% (11 samples) for groundwater and dilute surface
waters.
The sulphide data set is based on selected sulphide concentrations obtained during the site investigations, which means that they should represent present-day conditions. There is a representativity
issue concerning whether they may be used for entire safety assessment period, which is further
discussed in /Tullborg et al. 2010/. In the process of selection, some data points have been discarded,as they are judged to be contaminated or, otherwise, non-representative. As some of these discarded
data points show higher sulphide concentrations than those finally selected, a detailed discussion on
the selection procedure is given in /Tullborg et al. 2010, Section 4.5/. It is judged that this selection
procedure should constrain the degree of bias in the data, and assure that they accurately represent
the in situ groundwater.
The reliability and quality of the analytical data derived from the monitoring boreholes may be
influenced negatively by lack of time series measurements, and uncertainties whether the sample
has achieved hydrogeochemical stability and therefore is representative. This became obvious whenanalysing the sulphide concentration in all five section volumes that normally are discarded before asample is taken. In many borehole sections, many more than five section volumes are needed to bepumped away in order to achieve a stable chemistry. This has been evaluated in detail in /Tullborg
et al. 2010/, wherein sampling uncertainties are discussed in Chapter 3.

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6.1.8Spatial and temporal variability of data

Spatial variability

The spatial variability of the concentrations of main components is handled by modelling
groundwater compositions for tens of thousands of coordinates in the rock volume surrounding
the repository /Salas et al. 2010/. This modelling is performed by using PhreeqC and the mixing
proportions, the compositions of end-member groundwater components, and the selection of themineral phases chosen to be in thermodynamic equilibrium with the mixed solutions as input data.
The grid providing the coordinates is determined by the hydrogeological modelling /Joyce et al.
2010, Vidstrand et al. 2010/. Table 6‑5 shows an excerpt of the result file of a PhreeqC simulation,
where selected data for seven, out of the tens of thousands, modelled coordinates are displayed. The
upper left field shows the coordinates and the upper right field the mixing proportions supplied fromthe hydrogeological modelling. The lower field shows concentrations of the some of the requested
main component (in this example Ca2+, Cl−, Na+, HS− and total inorganic carbon are shown), as wellas the pH, Eh and ionic strength. The complete result file includes many more columns than shownin Table 6‑5, cf. /Salas et al. 2010/.
Table 6‑5. Example of results from PhreeqC calculations, showing groundwater data for sevenout of tens of thousands of coordinates. The complete results include more columns with otherdata not specifically requested in this report.
Row

X(km)

Y(km)

Z(km)

Brine

Littorina

DGW

Glacial6.34·102.15·10–4

11,630.62

6,698.9–0.46

0.9065–0.46

6.96·102.60·10–4

2

1,630.64

6,698.9
6,698.9–0.46

7.35·102.97·10–4

3

1,630.66
1,630.68

6,698.9–0.46

7.58·10

3.25·10–4

4
5

1,630.7

6,698.9–0.46

7.75·103.51·10–4

6

1,630.72

6,698.9–0.46

7.90·103.96·10–4

7

1,630.74

6,698.9–0.46

7.95·10

6.15·10

–4

mCa

mCl

mNa

mHS–

–2
–2
–2
–2
–2
–2
–2

PoreWater

7.23·10–42.92·10–2

0.8972

8.46·10–43.22·10–2

0.89119.45·10–43.42·10–2

0.88751.02·10–3

3.54·10–2

0.8846

1.10·10–3

3.65·10–2

0.88191.21·10–33.75·10–2

0.8798

1.53·10–3

3.86·10–2

mC

pH

Eh

IonicStr1

3.07·10–2

9.15·10–2

3.53·10–2

4.80·10–20

5.46·10–3

6.56–196.8250.127

23.37·10–2

9.98·103.76·102.24·10–20

5.38·10–3

6.54–196.825

0.139

33.56·10–2

0.10513.90·10–2

1.44·10–20

5.34·10–3

6.53–196.8820.146

43.68·10–2

0.1083

3.99·10–2

1.13·10–20

5.31·10–3

6.53–197.0540.150

53.76·10–2

0.11064.05·10–2

9.44·10–21

5.29·10–3

6.52–197.340.153

63.84·10–2

0.1127

4.11·10–2

8.19·10–21

5.28·10–3

6.52–197.740.156

73.86·10–2

0.11344.13·10–2

8.50·10–21

5.26·10–3

6.52–198.541

0.157

–2

–2

X (km), Y (km), Z (km) are the coordinates for simulated groundwater composition in Forsmark at 9000 AD.
Brine, Littorina, DGW (altered meteoric), Glacial, PoreWater (old meteoric) are the mixing proportions of theend-member groundwaters.
mCa, mCl, mNa, and mHS- are the concentrations (mol/L) of the solutes. mC is the concentration of total
inorganic carbon (HCO3–+CO32–).
pH, Eh, and IonicStr are the pH, redox potential (mV), and ionic strength (mol/L).

The chemical compositions of groundwaters in fractured rock may show a strong spatial variability
over short distances. However, over larger distances overall patterns may appear, for example thegeneral salinity increase with depth. Figure 6‑2 shows the distribution of total dissolved solids (TDS)
at the Forsmark site for different time periods. The repository is indicated in black enlarged squares.
As shown, the variation in TDS is quite large within the candidate repository volume, with a trend of
more saline waters at the repository location. Numerous of similar images of pH, Eh, total inorganiccarbon, among others are found in /Salas et al. 2010/.
The sulphide concentrations being selected as representative in /Tullborg et al. 2010/ indicate asignificant variability, over three orders of magnitude. Figure 6‑3 shows the selected measured sulphide concentration vs. elevation for percussion and core drilled boreholes. No correlation has been
deduced, reflecting that the processes controlling the sulphide concentrations in the groundwaters arewidely distributed.
198

TR-10-52

Figure 6‑2. Distribution of the modelled salinity (TDS in mg/L) for Forsmark in vertical slices at times
equal to 2000 AD, 3000 AD, 5000 AD, and 9000 AD. Reproduced from Figure 6-1 of /Salas et al. 2010/.

Figure 6‑3. Dissolved S(−II) in groundwaters from percussion and core drilled boreholes from CCC andmonitoring sampling versus elevation. The dotted line indicates the detection limit. Image reproduced from
Figure 5-5a of /Tullborg et al. 2010/.

TR-10-52

199

Temporal variability of data

Hydrogeochemical modelling has been performed for the time periods 2000, 3000, 5000, and 9000 AD,
as well as for periglacial and glacial periods. During the glacial cycle, there are events of dilution andsaline upconing, affecting the concentration of the main components within the candidate repository
volume. This is illustrated by the distribution of TDS, in Figure 6‑2 for the temperate period, andin Figure 6‑4 for the glacial cycle (showing the TDS calculated at glaciation at different ice front
locations). Similar images for other parameters are provided in /Salas et al. 2010/.
The temporal variability of estimated components (but not modelled) is discussed in /Salas et al. 2010,
Chapter 8/. Concerning the sulphide concentration, modelling results indicates that the concentration
becomes more diluted with time, wherefore using data measured at present-day conditions is justified.

Figure 6‑4. Changes in the distribution of TDS (total dissolved solids, mg/L) shown in vertical slices whenan ice sheet advances and retreats over the unfrozen Forsmark area. The figure shows results during theglacial sheet advance (1 to 6) and the glacial retreat (7 to 10) calculated using the model described in/Salas et al. 2010, Chapter 3/. When the ice sheet retreats the area is covered by a 100 m deep glacial
melt water lake. Reproduced from /Salas et al. 2010, Figure 7-5/.

200

TR-10-52

6.1.9 Correlations
The concentrations of chemical components are correlated through the mixing proportion and thestoichiometric coefficient of the equilibrium reactions, as described in Section 6.1.2. Such reactions
and the reaction constants are compiled in the thermodynamic database used in the PhreeqC modelling(TDB_SKB-2009_Amphos21.dat). In subsequent modelling (cf. the Radionuclide transport report)solubility limited concentrations of radioelements in the canister is calculated using a similar approach.
It is important that there is consistency in the thermodynamic data used in the two modelling steps.
Except for this, there is no correlation that needs to be propagated to subsequent copper corrosion orradionuclide transport modelling.
6.1.10 Result of supplier’s data qualification
End-members

The end-member compositions used for the Forsmark site are justified in /Salas et al. 2010/ andshown in Table 6‑6. The data are reproduced from Table 4-2 of /Salas et al. 2010/, wherein also
end-member compositions for the uncoupled database are found.
Table 6‑6. Equilibrated end-members, calculated with the coupled database used for the geochemical
simulations. Units in molal (mol/kg) except for pH, temperature, and pe. Data are reproduced from
/Salas et al. 2010, Table 4-2/.
DeepSaline

Old Meteoric

GlacialLittorina

Altered Meteoric

T (°C)

15

15

15

15

15

pH

8.000

8.500

9.300

7.951

7.314

pe

–4.449–4.925–5.260–4.4220.554

Al7.371·10–9

9.141·10–7

5.205·10–6

3.247·10–7

7.719·10–8

Br

4.156·10–3

7.163·10–6–

2.812·10–4

7.166·10–6

Ctot

3.678·10–5

5.222·10–4

8.515·10–5

1.627·10–3

7.230·10–3

Ca

4.940·10–1

1.184·10–3

7.180·10–5

3.865·10–3

4.702·10–4

Cl

1.366·100

5.109·10–3

1.410·10–5

1.856·10–1

5.111·10–3

F

8.644·10–5

8.427·10–5–

2.610·10–5

8.431·10–5

Fetot

2.525·10–7

8.744·10–9

8.002·10–7

8.262·10–6

1.793·10–6

K

8.222·10–4

1.433·10–4

1.023·10–5

3.469·10–3

1.434·10–4

Li

6.861·10–4

2.018·10–6–

1.021·10–5

2.019·10–6

Mg

8.952·10–5

3.088·10–4

4.114·10–6

1.865·10–2

3.089·10–4

Mn

2.615·10–6

–

–

–

–

Na

3.801·10–1

1.193·10–2

7.395·10–6

1.617·10–1

1.193·10–2

Stot

1.000·10–4

8.854·10–4

5.305·10–6

9.385·10–3

8.858·10–4

Si

8.808·10–5

1.396·10–4

1.665·10–4

1.282·10–4

1.342·10–4

Sr

3.947·10–3

4.340·10–6–

3.096·10–5

4.341·10–6

Main chemical components

The PhreeqC results from the numerous modelling cases presented in /Salas et al. 2010/ are stored
as tabulated data in files in (SKBdoc 1262945). Table 6‑5 shows an excerpt of a resulting filefrom PhreeqC modelling. Statistical results for the thousands of coordinates for different cases aretabulated in Appendix 4 in /Salas et al. 2010/. Based on the statistical results, box-and-whisker plots
can be made. Figure 6‑5 shows the sum of concentrations of the main cations expressed as charge
equivalents Σq[Mq+] for different time periods and events of the glacial cycle. The indicated 4 mM
line corresponds to the criterion for the safety function indicator for SR-Site (Σq[Mq+] > 4mM). As
shown, only for the glacial cases this criterion fails, and only for the 5th percentile and lower.

TR-10-52

201

Na +K + 2*(Mg + Ca) (mM)

Na +K + 2*(Mg + Ca) (mM)
Na +K + 2*(Mg + Ca) (mM)

1,000

100

10

4 mM
1

0.1

2000 AD

3000 AD

9000 AD

5000 AD

1,000

100

10

4 mM
1

0.1

Temperate

Periglacial

1,000

100

10

4 mM
1

0.1

T-0

Ia

IIa

IIIa

IVa

Va

Vr

IVr

IIr

0r

Figure 6‑5. Box-and-whisker plots showing the statistical distribution of the calculated safety function
factor ∑q[Mq+] for the coordinates located within the candidate repository volume at Forsmark. Upper
left figure shows statistics for the temperate period, upper right a comparison between temperate andperiglacial period, and lower figure different ice front locations. The statistical measures are the median,
the 25th and 75th percentile (box), the mean (square), the 5th and 95th percentile (whiskers), the 1st and99th percentile (crosses) and the maximum and the minimum values (bars). Excerpts of Figures 6-3, 7-1,
and 7-7 of /Salas et al. 2010/.

The underlying data to Figure 6‑5 are given in Table 6‑7 to Table 6‑9, which are reproduced from
Appendix 4 of /Salas et al. 2010/. Table 6‑7 shows the statistics for the total concentrations of the
major cations Na, K, Ca, and Mg and chloride, ionic strength, pH, Eh, and for sulphate, sulphide,phosphate, and total inorganic carbon. The data are for the temperate and submerged period.
Statistics for the same parameters are shown for the glacial period for different ice front locationsin Table 6‑8 and Table 6‑9. In Appendix 4 of /Salas et al. 2010/, statistics are also given for hydrogeological variant cases and for the case where the ice advances over frozen grounds/permafrost.
Furthermore, in /Salas et al. 2010/ numerous of box-and-whisker plots illustrating the data in Table 6‑7
to Table 6‑9 are found.

202

TR-10-52

TR-10-52

Table 6‑7. Statistics for temperate and submerged seawater period for the hydrogeochemical Base case. Reproduced from /Salas et al. 2010, Appendix A4-1/.
Temperate period

2000 AD

pH
7.07
7.09
7.21
7.44
7.74
7.89
8.05

Eh
-260
-250
-242
-221
-208
-198
-196

Temperate period

3000 AD

pH
6.73
6.91
7.14
7.42
7.73
7.89
8.00

Eh
-256
-250
-241
-220
-202
-184
-141

Temperate period

5000 AD

pH
6.54
6.61
6.77
7.33
7.52
7.63
7.72

Eh
-243
-241
-231
-214
-179
-119
-83

Temperate period

9000 AD

pH
6.48
6.49
6.57
7.07
7.51
7.56
7.57

Eh
-238
-234
-226
-198
-119
-62
-57

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

0.1%
5%
Median=
95%
99.9%
Max=

TDS (g/L)
6.88
7.40
8.67
10.40
12.89
17.66
19.88

Ionic Str
0.15070.16380.18970.22490.2946
0.41830.4730Na
4.09E-02
4.45E-02
5.14E-02
7.21E-02
1.42E-01
1.52E-01
1.56E-01

K
1.57E-04
1.65E-04
1.86E-04
7.39E-04
2.98E-03
3.25E-03
3.34E-03

Ca
2.86E-03
3.69E-03
5.73E-03
4.92E-02
7.42E-02
1.06E-01
1.21E-01

Mg
1.29E-04
1.40E-04
1.80E-04
3.34E-03
1.60E-02
1.75E-02
1.79E-02

Fe
1.34E-07
3.10E-07
7.94E-07
8.75E-06
1.88E-05
2.55E-05
2.68E-05

Alk
2.15E-04
2.18E-04
2.67E-04
5.08E-04
1.70E-03
2.14E-03
2.43E-03

C
2.02E-04
2.06E-04
2.60E-04
5.25E-04
1.70E-03
2.16E-03
2.46E-03

Cl
1.14E-01
1.23E-01
1.44E-01
1.69E-01
2.18E-01
3.05E-01
3.45E-01

S(VI)
3.37E-04
3.50E-04
4.69E-04
2.00E-03
8.10E-03
8.82E-03
9.05E-03

S(-II)
5.05E-09
1.07E-08
3.36E-08
1.41E-06
7.88E-06
8.65E-06
8.89E-06

Si
1.19E-04
1.21E-04
1.25E-04
1.28E-04
1.29E-04
1.30E-04
1.31E-04

P
1.43E-08
1.47E-08
2.09E-08
6.29E-08
6.19E-07
9.04E-07
1.11E-06

Σ qM q+121.92130.94
153.75183.24227.91
312.73351.94

TDS (g/L)
6.49
7.51
8.81
10.43
14.21
18.96
20.18

Ionic Str
0.12200.15480.18630.2234
0.33520.45060.4747Na
4.30E-02
4.68E-02
5.42E-02
8.26E-02
1.41E-01
1.51E-01
1.54E-01

K
1.64E-04
1.73E-04
1.98E-04
9.87E-04
2.97E-03
3.22E-03
3.30E-03

Ca
2.86E-03
3.48E-03
5.42E-03
4.69E-02
8.54E-02
1.16E-01
1.21E-01

Mg
1.34E-04
1.54E-04
1.96E-04
4.74E-03
1.59E-02
1.73E-02
1.77E-02

Fe
1.77E-07
3.44E-07
7.53E-07
9.76E-06
2.00E-05
3.33E-05
4.74E-05

Alk
2.16E-04
2.23E-04
2.72E-04
6.15E-04
1.80E-03
2.94E-03
3.97E-03

C
2.04E-04
2.11E-04
2.66E-04
6.43E-04
1.81E-03
3.05E-03
4.12E-03

Cl
9.49E-02
1.21E-01
1.44E-01
1.70E-01
2.44E-01
3.29E-01
3.48E-01

S(VI)
3.44E-04
3.68E-04
4.86E-04
2.66E-03
8.06E-03
8.72E-03
8.95E-03

S(-II)
4.02E-11
2.72E-09
2.90E-08
1.94E-06
7.85E-06
8.55E-06
8.77E-06

Si
1.19E-04
1.20E-04
1.24E-04
1.28E-04
1.29E-04
1.30E-04
1.31E-04

P
1.45E-08
1.52E-08
2.13E-08
8.74E-08
6.89E-07
1.42E-06
2.30E-06

Σ qM q+111.20132.83155.60183.46251.82335.57356.77

TDS (g/L)
1.12
1.29
2.90
9.29
13.18
17.03
17.80

Ionic Str
0.01710.02050.0524
0.19880.3096
0.40550.4260Na
1.28E-02
1.50E-02
3.61E-02
7.52E-02
1.09E-01
1.24E-01
1.28E-01

K
1.64E-04
1.92E-04
2.51E-04
9.37E-04
2.13E-03
2.55E-03
2.64E-03

Ca
7.92E-04
9.89E-04
1.85E-03
3.49E-02
7.74E-02
1.05E-01
1.11E-01

Mg
1.60E-04
1.99E-04
4.81E-04
4.51E-03
1.13E-02
1.36E-02
1.41E-02

Fe
5.99E-07
8.55E-07
1.32E-06
1.35E-05
3.30E-05
5.38E-05
6.57E-05

Alk
2.31E-04
2.46E-04
3.00E-04
1.09E-03
5.82E-03
6.87E-03
7.09E-03

C
2.20E-04
2.36E-04
2.97E-04
1.18E-03
6.23E-03
7.31E-03
7.48E-03

Cl
6.21E-03
9.25E-03
3.61E-02
1.51E-01
2.26E-01
2.95E-01
3.10E-01

S(VI)
4.05E-04
4.50E-04
6.27E-04
2.65E-03
5.85E-03
6.96E-03
7.22E-03

S(-II)
1.52E-17
2.32E-14
4.86E-09
1.57E-06
5.31E-06
6.68E-06
6.93E-06

Si
1.21E-04
1.22E-04
1.25E-04
1.28E-04
1.33E-04
1.34E-04
1.34E-04

P
1.62E-08
1.81E-08
2.53E-08
2.39E-07
3.93E-06
4.87E-06
5.12E-06

Σ qM q+
15.43
18.52
47.31
163.34233.60301.75315.47

TDS (g/L)
1.06
1.07
1.21
6.94
11.13
14.31
15.54

Ionic Str
0.01580.01600.01890.14700.25710.33390.3620Na
1.20E-02
1.21E-02
1.38E-02
6.00E-02
8.36E-02
1.01E-01
1.08E-01

K
1.44E-04
1.47E-04
1.66E-04
7.09E-04
1.40E-03
1.96E-03
2.19E-03

Ca
7.72E-04
7.93E-04
9.61E-04
2.23E-02
6.12E-02
8.15E-02
8.70E-02

Mg
2.96E-04
3.00E-04
3.69E-04
3.24E-03
7.17E-03
1.03E-02
1.16E-02

Fe
6.98E-07
7.31E-07
1.31E-06
1.36E-05
5.14E-05
7.15E-05
7.59E-05

Alk
2.66E-04
2.95E-04
7.03E-04
2.67E-03
6.89E-03
7.10E-03
7.13E-03

C
2.58E-04
2.89E-04
7.55E-04
3.24E-03
7.33E-03
7.51E-03
7.52E-03

Cl
5.16E-03
5.36E-03
7.87E-03
1.10E-01
1.89E-01
2.45E-01
2.66E-01

S(VI)
6.05E-04
7.11E-04
8.90E-04
2.08E-03
3.92E-03
5.39E-03
6.00E-03

S(-II)
1.01E-20
2.22E-20
3.17E-17
6.53E-07
2.81E-06
4.78E-06
5.58E-06

Si
1.22E-04
1.23E-04
1.26E-04
1.30E-04
1.34E-04
1.35E-04
1.35E-04

P
2.06E-08
2.45E-08
1.07E-07
1.12E-06
4.87E-06
5.10E-06
5.13E-06

Σ qM q+
14.29
14.49
17.02
120.38196.27252.98274.70TDS (g/L)
5.33
5.78
7.34
9.69
11.99
14.78
15.89

Ionic Str
0.10780.12700.16260.21800.28650.35350.3803Na
3.37E-02
3.66E-02
4.31E-02
5.67E-02
1.40E-01
1.57E-01
1.59E-01

K
1.22E-04
1.39E-04
1.54E-04
2.05E-04
2.95E-03
3.37E-03
3.40E-03

Ca
3.04E-03
3.80E-03
5.64E-03
5.39E-02
7.45E-02
9.21E-02
9.91E-02

Mg
1.14E-04
1.17E-04
1.35E-04
2.79E-04
1.58E-02
1.81E-02
1.83E-02

Fe
3.16E-07
4.50E-07
6.04E-07
8.67E-06
2.12E-05
2.51E-05
2.60E-05

Alk
2.04E-04
2.05E-04
2.27E-04
2.81E-04
1.62E-03
1.79E-03
1.81E-03

C
1.90E-04
1.91E-04
2.15E-04
2.75E-04
1.62E-03
1.77E-03
1.79E-03

Cl
8.50E-02
9.53E-02
1.22E-01
1.63E-01
2.08E-01
2.56E-01
2.76E-01

S(VI)
3.06E-04
3.12E-04
3.59E-04
5.37E-04
8.00E-03
9.13E-03
9.22E-03

S(-II)
5.14E-09
1.36E-08
4.24E-08
1.02E-05
1.59E-05
2.01E-05
2.09E-05

Si
1.23E-04
1.24E-04
1.26E-04
1.28E-04
1.30E-04
1.32E-04
1.33E-04

P
2.65E-08
2.69E-08
3.23E-08
4.67E-08
1.16E-06
1.46E-06
1.51E-06

Σ qM q+94.53
102.17130.06171.53212.98262.19281.81Submerged seawater 3000 BC
Min=

203

0.1%
5%
Median=
95%
99.9%
Max=

pH
7.22
7.24
7.34
7.57
7.77
7.93
8.03

Eh
-259
-253
-247
-229
-215
-208
-206

Table 6‑8. Statistics for glacial period (ice location 0–IVa) for the hydrogeochemical Base case. Reproduced from /Salas et al. 2010, Appendix A4-3/.

204

Temperate
Min=

0.1%
5%
Median=
95%
99.9%
Max=

0
pH
6.44
6.46
6.50
6.67
7.32
7.53
7.53

Eh
-231
-231
-211
-172
-62
-49
-44

Glacial (advance)
Min=

0.1%
5%
Median=
95%
99.9%
Max=

pH
6.37
6.38
6.41
6.55
6.98
7.38
7.43

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

TR-10-52

0.1%
5%Median=
95%
99.9%
Max=

K
1.44E-04
1.44E-04
1.45E-04
1.66E-04
1.99E-04
2.18E-04
2.25E-04

Ca
7.98E-04
7.98E-04
1.44E-03
1.60E-02
3.99E-02
5.31E-02
5.86E-02

Mg
2.83E-04
2.85E-04
2.91E-04
3.02E-04
3.09E-04
3.09E-04
3.09E-04

Fe
7.79E-07
7.79E-07
1.67E-06
7.24E-06
6.69E-05
7.78E-05
8.21E-05

Alk
3.37E-03
3.43E-03
3.60E-03
4.24E-03
6.54E-03
7.03E-03
7.03E-03

C
5.17E-03
5.25E-03
5.44E-03
5.95E-03
7.22E-03
7.48E-03
7.48E-03

Cl
1.76E-04
1.76E-04
2.64E-03
4.58E-02
1.13E-01
1.49E-01
1.64E-01

S(VI)
7.91E-04
8.00E-04
8.21E-04
8.60E-04
8.84E-04
8.86E-04
8.86E-04

S(-II)
4.27E-22
1.31E-21
2.86E-20
5.48E-06
6.43E-05
7.52E-05
7.93E-05

Si
1.26E-04
1.27E-04
1.29E-04
1.32E-04
1.34E-04
1.35E-04
1.35E-04

P
3.95E-06
4.08E-06
4.44E-06
5.52E-06
8.71E-06
9.80E-06
9.80E-06

Σ qMq+
9.20
9.20
11.17
52.07
118.21154.56169.54

TDS (g/L)
0.81
0.83
1.35
5.44
11.71
15.02
15.87

Ionic Str
0.01180.01230.02390.12120.27250.35230.3729Na
7.14E-03
7.28E-03
1.00E-02
3.02E-02
6.07E-02
7.67E-02
8.08E-02

K
1.28E-04
1.31E-04
1.48E-04
1.86E-04
2.41E-04
2.70E-04
2.78E-04

Ca
1.07E-03
1.21E-03
4.35E-03
3.03E-02
7.05E-02
9.17E-02
9.72E-02

Mg
2.51E-04
2.55E-04
2.76E-04
2.94E-04
3.07E-04
3.09E-04
3.09E-04

Fe
1.10E-06
1.35E-06
1.60E-06
1.78E-06
8.37E-05
9.55E-05
9.84E-05

Alk
3.06E-03
3.09E-03
3.26E-03
3.77E-03
5.44E-03
6.70E-03
6.81E-03

C
4.71E-03
4.77E-03
5.02E-03
5.58E-03
6.65E-03
7.31E-03
7.36E-03

Cl
1.23E-03
1.76E-03
1.21E-02
8.58E-02
1.97E-01
2.56E-01
2.71E-01

S(VI)
7.16E-04
7.26E-04
7.68E-04
8.33E-04
8.79E-04
8.85E-04
8.85E-04

S(-II)
6.02E-24
1.27E-23
2.75E-22
8.32E-08
8.10E-05
9.28E-05
9.56E-05

Si
1.22E-04
1.22E-04
1.25E-04
1.30E-04
1.34E-04
1.34E-04
1.35E-04

P
3.20E-06
3.29E-06
3.69E-06
4.75E-06
7.11E-06
9.01E-06
9.23E-06

Σ qMq+
10.03
10.45
19.41
91.60
202.51260.93276.00TDS (g/L)
0.30
0.33
2.26
8.79
15.31
19.30
19.91

Ionic Str
0.00670.00740.04820.20240.35950.45590.4705Na
1.47E-03
1.61E-03
1.30E-02
4.65E-02
7.81E-02
9.74E-02
1.00E-01

K
1.66E-05
1.72E-05
6.39E-05
2.15E-04
2.73E-04
3.08E-04
3.13E-04

Ca
1.73E-03
1.92E-03
1.15E-02
5.19E-02
9.36E-02
1.19E-01
1.23E-01

Mg
1.25E-05
1.37E-05
6.86E-05
2.77E-04
2.99E-04
3.05E-04
3.05E-04

Fe
8.14E-09
1.39E-08
1.17E-06
2.30E-06
9.51E-05
1.04E-04
1.05E-04

Alk
3.01E-04
3.27E-04
1.32E-03
3.27E-03
4.07E-03
4.75E-03
4.87E-03

C
2.65E-04
2.93E-04
1.46E-03
5.03E-03
5.80E-03
6.27E-03
6.33E-03

Cl
4.59E-03
5.11E-03
3.24E-02
1.46E-01
2.61E-01
3.32E-01
3.42E-01

S(VI)
2.91E-05
3.23E-05
1.87E-04
7.71E-04
8.50E-04
8.71E-04
8.73E-04

S(-II)
8.23E-25
1.36E-24
1.44E-23
1.05E-06
9.23E-05
1.01E-04
1.03E-04

Si
1.18E-04
1.19E-04
1.22E-04
1.27E-04
1.34E-04
1.41E-04
1.42E-04

P
6.01E-08
6.83E-08
6.13E-07
3.73E-06
5.21E-06
6.22E-06
6.38E-06

Σ qMq+
4.96
5.49
37.05
151.09266.17336.62347.32

TDS (g/L)
0.11
0.12
0.25
1.26
8.68
16.02
17.30

Ionic Str
0.00230.00250.00550.02910.20040.3766
0.4074Na
4.37E-04
4.92E-04
1.19E-03
6.50E-03
4.56E-02
8.13E-02
8.77E-02

K
1.18E-05
1.20E-05
1.49E-05
3.80E-05
2.08E-04
2.77E-04
2.91E-04

Ca
5.95E-04
6.55E-04
1.41E-03
7.45E-03
5.15E-02
9.82E-02
1.06E-01

Mg
5.92E-06
6.20E-06
9.62E-06
3.98E-05
2.86E-04
3.02E-04
3.04E-04

Fe
5.03E-10
7.06E-10
7.86E-09
1.73E-06
6.32E-05
9.61E-05
1.01E-04

Alk
2.00E-04
2.03E-04
2.39E-04
8.58E-04
3.56E-03
4.29E-03
4.63E-03

C
1.37E-04
1.40E-04
1.97E-04
8.82E-04
5.32E-03
5.97E-03
6.15E-03

Cl
1.42E-03
1.60E-03
3.71E-03
2.04E-02
1.44E-01
2.73E-01
2.96E-01

S(VI)
1.04E-05
1.12E-05
2.07E-05
1.06E-04
8.04E-04
8.60E-04
8.68E-04

S(-II)
2.81E-24
1.06E-23
3.74E-20
4.52E-07
6.08E-05
9.33E-05
9.83E-05

Si
1.20E-04
1.21E-04
1.27E-04
1.36E-04
1.45E-04
1.62E-04
1.65E-04

P
2.72E-08
2.78E-08
4.06E-08
2.92E-07
4.29E-06
5.57E-06
5.96E-06

Σ qMq+
1.65
1.83
4.08
21.58
149.49278.69301.30TDS (g/L)
0.05
0.05
0.16
0.99
3.66
7.66
8.96

Ionic Str
0.00070.00070.00330.02290.08520.18110.2120Na
6.71E-05
7.91E-05
7.21E-04
4.98E-03
1.90E-02
3.86E-02
4.51E-02

K
1.06E-05
1.07E-05
1.33E-05
2.87E-05
9.01E-05
1.40E-04
1.52E-04

Ca
2.07E-04
2.09E-04
8.39E-04
5.92E-03
2.20E-02
4.73E-02
5.55E-02

Mg
4.61E-06
4.85E-06
7.77E-06
2.33E-05
1.11E-04
1.82E-04
2.50E-04

Fe
3.04E-10
3.61E-10
5.06E-09
4.66E-06
2.70E-05
5.11E-05
5.55E-05

Alk
1.87E-04
1.98E-04
2.23E-04
5.22E-04
1.91E-03
2.84E-03
3.78E-03

C
1.31E-04
1.33E-04
1.69E-04
5.05E-04
2.30E-03
3.69E-03
5.05E-03

Cl
1.90E-04
1.90E-04
2.12E-03
1.62E-02
6.08E-02
1.31E-01
1.54E-01

S(VI)
6.71E-06
7.39E-06
1.56E-05
5.88E-05
3.06E-04
5.12E-04
7.10E-04

S(-II)
8.93E-20
6.65E-19
5.63E-13
3.66E-07
1.96E-05
4.34E-05
4.74E-05

Si
1.27E-04
1.28E-04
1.32E-04
1.37E-04
1.52E-04
1.97E-04
2.01E-04

P
2.47E-08
2.56E-08
3.44E-08
1.36E-07
1.22E-06
2.60E-06
4.30E-06

Σ qMq+
0.51
0.51
2.48
16.95
63.19
133.50156.34

IIIa
Eh
-348
-342
-311
-233
-65
-29
-24

Glacial (advance)
pH
6.67
6.70
6.93
7.87
8.99
9.60
9.63

Na
6.85E-03
6.85E-03
7.52E-03
1.93E-02
3.76E-02
4.75E-02
5.16E-02

IIa
Eh
-302
-293
-205
-165
-30
-21
-19

Glacial (advance)
pH
6.36
6.37
6.48
7.57
8.74
9.21
9.27

Ionic Str
0.01070.01070.01320.06760.15750.20710.2275

Ia
Eh
-224
-220
-192
-166
-42
-30
-27

Glacial (advance)
pH
6.34
6.35
6.38
6.46
7.16
8.49
8.54

TDS (g/L)
0.76
0.76
0.87
3.20
6.94
9.00
9.85

IVa
Eh
-372
-366
-327
-254
-156
-88
-80

TR-10-52

Table 6‑9. Statistics for glacial period (ice location Va–0r) for the hydrogeochemical Base case. Reproduced from /Salas et al. 2010, Appendix A4-3/.
Glacial (advance)
Min=

0.1%
5%
Median=
95%
99.9%
Max=

pH
7.15
7.21
7.40
7.98
8.99
9.56
9.71

Va
Eh
-377
-365
-327
-262
-215
-182
-168

Glacial (retreat)
Min=

0.1%
5%
Median=
95%
99.9%
Max=

pH
6.97
7.04
7.22
7.69
8.42
9.20
9.42

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

0.1%
5%
Median=
95%
99.9%
Max=

Min=

205

0.1%
5%
Median=
95%
99.9%
Max=

K
1.02E-05
1.09E-05
1.36E-05
2.58E-05
4.56E-05
5.86E-05
6.51E-05

Ca
1.67E-04
2.52E-04
9.08E-04
5.28E-03
1.08E-02
1.39E-02
1.41E-02

Mg
4.11E-06
4.80E-06
7.59E-06
1.94E-05
5.11E-05
8.01E-05
1.03E-04

Fe
2.60E-10
4.21E-10
5.19E-09
4.41E-06
1.72E-05
2.22E-05
2.46E-05

Alk
1.54E-04
1.58E-04
1.97E-04
4.33E-04
1.09E-03
1.67E-03
2.10E-03

C
1.01E-04
1.10E-04
1.48E-04
4.11E-04
1.15E-03
1.80E-03
2.33E-03

Cl
1.41E-05
3.66E-04
2.30E-03
1.44E-02
2.96E-02
3.82E-02
3.88E-02

S(VI)
5.31E-06
7.20E-06
1.47E-05
4.76E-05
1.38E-04
2.22E-04
2.88E-04

S(-II)
8.61E-12
1.09E-10
2.48E-08
3.57E-07
9.94E-06
1.48E-05
1.71E-05

Si
1.34E-04
1.34E-04
1.35E-04
1.37E-04
1.52E-04
1.92E-04
2.14E-04

P
1.54E-08
1.71E-08
2.64E-08
1.01E-07
4.34E-07
8.73E-07
1.31E-06

Σ qM q+
0.36
0.66
2.68
15.01
30.65
39.43
40.13

TDS (g/L)
0.10
0.16
0.81
2.62
3.82
4.42
4.53

Ionic Str
0.00200.00340.01890.06220.09050.10500.1081Na
3.50E-04
6.66E-04
3.93E-03
1.29E-02
1.90E-02
2.19E-02
2.24E-02

K
1.10E-05
1.20E-05
2.12E-05
4.42E-05
6.56E-05
7.88E-05
8.48E-05

Ca
5.42E-04
9.03E-04
4.95E-03
1.63E-02
2.37E-02
2.75E-02
2.84E-02

Mg
4.19E-06
4.96E-06
8.83E-06
2.10E-05
5.19E-05
8.03E-05
1.02E-04

Fe
4.96E-10
1.23E-09
4.55E-08
4.43E-06
2.22E-05
2.86E-05
3.28E-05

Alk
1.22E-04
1.24E-04
1.64E-04
4.06E-04
1.01E-03
1.51E-03
1.85E-03

C
8.28E-05
8.64E-05
1.30E-04
3.93E-04
1.09E-03
1.70E-03
2.17E-03

Cl
1.25E-03
2.30E-03
1.36E-02
4.49E-02
6.54E-02
7.59E-02
7.83E-02

S(VI)
5.39E-06
7.39E-06
1.61E-05
4.88E-05
1.37E-04
2.20E-04
2.84E-04

S(-II)
4.98E-15
7.40E-14
3.16E-11
3.80E-07
1.50E-05
2.12E-05
2.53E-05

Si
1.32E-04
1.32E-04
1.33E-04
1.35E-04
1.40E-04
1.61E-04
1.77E-04

P
9.94E-09
1.03E-08
1.83E-08
8.55E-08
3.88E-07
7.75E-07
1.13E-06

Σ qM q+
1.45
2.49
13.89
45.69
66.58
77.14
79.31

TDS (g/L)
0.59
0.62
1.41
3.68
7.65
9.76
10.31

Ionic Str
0.01390.0146
0.03360.08760.18310.23370.2472Na
2.81E-03
2.96E-03
6.90E-03
1.81E-02
3.77E-02
4.79E-02
5.06E-02

K
1.64E-05
1.68E-05
2.69E-05
5.54E-05
1.03E-04
1.26E-04
1.28E-04

Ca
3.68E-03
3.87E-03
8.84E-03
2.30E-02
4.82E-02
6.16E-02
6.52E-02

Mg
5.04E-06
5.44E-06
8.98E-06
2.13E-05
5.31E-05
8.06E-05
1.02E-04

Fe
3.08E-09
4.17E-09
6.71E-08
4.73E-06
2.67E-05
3.56E-05
4.35E-05

Alk
1.07E-04
1.19E-04
1.50E-04
3.81E-04
9.52E-04
1.40E-03
1.61E-03

C
6.24E-05
7.83E-05
1.19E-04
3.70E-04
1.04E-03
1.63E-03
1.98E-03

Cl
1.00E-02
1.06E-02
2.43E-02
6.35E-02
1.33E-01
1.70E-01
1.80E-01

S(VI)
6.33E-06
7.92E-06
1.57E-05
4.76E-05
1.36E-04
2.16E-04
2.74E-04

S(-II)
5.79E-18
1.67E-16
1.25E-13
7.11E-07
1.95E-05
2.82E-05
3.59E-05

Si
1.27E-04
1.28E-04
1.30E-04
1.34E-04
1.39E-04
1.47E-04
1.48E-04

P
6.34E-09
9.12E-09
1.54E-08
7.61E-08
3.54E-07
7.06E-07
9.32E-07

Σ qM q+
0.04
10.72
24.59
64.28
134.24171.38181.15

TDS (g/L)
0.09
0.11
0.37
2.30
7.54
10.08
11.11

Ionic Str
0.00180.00220.0086
0.05480.18030.24190.2665Na
3.06E-04
4.09E-04
1.74E-03
1.13E-02
3.71E-02
4.95E-02
5.45E-02

K
1.10E-05
1.12E-05
1.44E-05
3.84E-05
9.92E-05
1.25E-04
1.35E-04

Ca
4.82E-04
6.00E-04
2.28E-03
1.44E-02
4.75E-02
6.38E-02
7.03E-02

Mg
4.19E-06
4.40E-06
5.60E-06
1.53E-05
4.53E-05
6.50E-05
8.51E-05

Fe
4.78E-10
6.05E-10
7.37E-09
4.39E-06
2.37E-05
3.18E-05
3.69E-05

Alk
1.16E-04
1.17E-04
1.37E-04
2.76E-04
8.18E-04
1.14E-03
1.43E-03

C
7.56E-05
7.86E-05
9.29E-05
2.53E-04
8.76E-04
1.30E-03
1.69E-03

Cl
1.07E-03
1.42E-03
6.17E-03
3.96E-02
1.31E-01
1.76E-01
1.94E-01

S(VI)
5.39E-06
5.90E-06
8.83E-06
3.24E-05
1.13E-04
1.71E-04
2.28E-04

S(-II)
1.00E-16
1.43E-15
2.69E-12
3.81E-07
1.66E-05
2.46E-05
2.94E-05

Si
1.27E-04
1.28E-04
1.30E-04
1.36E-04
1.50E-04
1.72E-04
1.78E-04

P
8.63E-09
8.89E-09
1.22E-08
4.51E-08
2.72E-07
4.92E-07
7.38E-07

Σ qM q+
1.29
1.63
6.33
40.15
132.29177.18195.23

TDS (g/L)
0.41
0.51
1.33
2.93
4.32
5.16
5.26

Ionic Str
0.00960.01200.0316
0.06990.10310.12320.1256Na
1.93E-03
2.41E-03
6.45E-03
1.43E-02
2.13E-02
2.54E-02
2.59E-02

K
1.45E-05
1.56E-05
2.48E-05
4.42E-05
6.44E-05
7.97E-05
8.75E-05

Ca
2.54E-03
3.17E-03
8.33E-03
1.84E-02
2.71E-02
3.24E-02
3.31E-02

Mg
4.69E-06
4.97E-06
6.74E-06
1.45E-05
3.67E-05
5.75E-05
7.32E-05

Fe
1.97E-09
2.74E-09
2.51E-08
4.59E-06
1.91E-05
2.64E-05
3.19E-05

Alk
1.03E-04
1.05E-04
1.16E-04
2.48E-04
7.09E-04
1.05E-03
1.29E-03

C
6.25E-05
6.55E-05
7.95E-05
2.25E-04
7.37E-04
1.16E-03
1.48E-03

Cl
6.87E-03
8.63E-03
2.29E-02
5.08E-02
7.48E-02
8.95E-02
9.13E-02

S(VI)
5.94E-06
6.58E-06
1.00E-05
2.93E-05
9.26E-05
1.51E-04
1.96E-04

S(-II)
3.99E-15
2.38E-13
1.85E-09
6.02E-07
1.20E-05
1.91E-05
2.45E-05

Si
1.32E-04
1.32E-04
1.33E-04
1.35E-04
1.42E-04
1.50E-04
1.52E-04

P
6.19E-09
6.64E-09
8.73E-09
3.75E-08
2.13E-07
4.17E-07
6.09E-07

Σ qM q+
7.02
8.78
23.17
51.21
75.60
90.37
92.12

IIr
Eh
-360
-354
-325
-260
-175
-137
-120
0r

Submerged glacial lake

pH
7.01
7.11
7.36
7.91
8.57
8.93
8.99

Na
7.40E-06
1.20E-04
7.81E-04
4.38E-03
8.97E-03
1.15E-02
1.18E-02

IVr
Eh
-327
-321
-288
-241
-159
-124
-103

Glacial (retreat)
pH
6.92
7.02
7.18
7.91
8.92
9.36
9.43

Ionic Str
0.00060.00090.00360.02040.04150.05350.0543

Vr
Eh
-359
-343
-293
-244
-183
-148
-133

Glacial (retreat)
pH
6.82
6.94
7.11
7.64
8.32
8.82
8.88

TDS (g/L)
0.04
0.06
0.17
0.88
1.78
2.28
2.32

Eh
-334
-329
-304
-260
-207
-159
-136

The maximum and minimum Ca/Na ratios are compiled in Table 6‑10 for different climatic periods,
together with the maximum and minimum values of a few other parameters of interest for the buffer
stability. Many of the data are the same as in Table 6‑7 to Table 6‑9.
Table 6‑10. Minimum and maximum values of a few main geochemical buffer parameters
obtained for the Base case over the complete Glacial Cycle in Forsmark. Data reproduced
from Table 9-1 of /Salas et al. 2010/.
Modelling case

Max Cl(mol/L)

Max
Ca/Na

Min
Ca/Na

Max
pH

Min
pH

Max1
carbonate(mol/L)

Min1
carbonate(mol/L)Max Ionic
strength(mol/L)

Temperate (2000 AD)

3.45·10–1

1.22

0.02408.05

7.07

2.46·10–3

2.02·10–4

0.473

Temperate (9000 AD)

2.66·10–1

1.13

0.0496

7.57

6.48

7.52·10–3

2.58·10–4

0.362

Glacial (stage IIa)

3.42·10–1

1.23

0.600

8.54

6.34

6.33·10–3

2.65·10–4

0.471

Glacial (stage Vr)

7.83·10–2

1.55

1.02

9.42

6.97

2.17·10–3

8.28·10–5

0.108

Permafrost (before onsetof glaciation)

9.94·10–2

1.04

0.398

7.01

6.52

6.71·10–3

5.52·10–3

0.140

Submerged glacial lake

9.13·10–2

1.32

1.20

8.99

7.01

1.48·10–3

6.25·10–5

0.126

Submerged seawater

2.76·101.23

0.02418.03

7.22

1.79·101.90·10–4

0.380

1

–1

Max and min carbonate, also called total inorganic carbon, includes HCO3 +CO3
−

–3

2−

Best estimate data for non-modelled components

In Chapter 8 of /Salas et al. 2010/ the concentration of solutes than cannot be modelled using PhreeqC
are discussed. Based on analytical data and observations, best estimate data are suggested, as shownin Table 6‑11. The data are reproduced from Table 8-1 of /Salas et al. 2010/, where also data for the
excavation/operation period can be found.
Table 6‑11. Recommended values of DOC, acetate, CH4, H2, NO2–, NH4+, and colloids for the different periods during the evolution of the Forsmark site. Data reproduced from Table 8-1
of /Salas et al. 2010/.
Time periods

DOC(mol/L)

Acetate(mol/L)CH4(mol/L)

H2(mol/L)NO2–(mol/L)NH4+(mol/L)

Colloids(μg/L)

Temperate

1.25·10–3

mM range

2·10–4

1.92·10–5

1.07·10–7

4.0·10–5< 180

Glacial4.2·10–5

mM range

2·10–4

1.92·10–5< 1.07·10–7< 4.0·10–5

20,000Submerged (marine)

2.9·10mM range

2·10

1.92·101.07·101.9·10

< 180

–3

–4

–5

–7

–4

Best estimate data for sulphide

Hydrogeological modelling indicate that present-day sulphide concentrations at depth are relatively
high compared to those of the remaining glacial period (cf. columns with S(-II) in Table 6‑7 toTable 6‑9). As PhreeqC modelling is unable to fully reproduce the microbial influence on the sulphide
concentration, the values of the measured sulphide concentrations selected in /Tullborg et al. 2010/
are recommended for use in SR-Site modelling. Figure 6‑6 shows the cumulative distribution function of these selected sulphide concentrations. Practically all sulphide concentrations at the Forsmark
site are below 10−4.9 mol/L (~1.3·10–5 mol/L or 0.4 mg/L). It is, however, possible that a few percentsof the deposition location in the repository are surrounded by groundwaters that have sulphide
concentrations as high as 10–3.9 mol/L (~1.3·10–4 mol/L or 4 mg/L). The numerical data of the CDF
are stored in (SKBdoc 1261474).

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Figure 6‑6. Cumulative distribution function showing the selected sulphide values for groundwaters,
recommended for use in SR-Site. The blue curve shows the values used in SR-Can. Reproduced from
Figure 7-1 of /Tullborg et al. 2010/.

Maximum dissolved oxygen concentration

The oxygen concentration at the repository depth depends on the oxygen concentration of therecharge waters and on the flow related transport resistance, the F-factor (yr/m), for the entire
recharge flow path, among other things. Figure 6‑7 shows the oxygen concentration reaching thedeposition holes as a function of the F-factor, for the two worst ice locations (II and III). If theF-factor is 1,000 yr/m, the oxygen concentration (blue curve) reaching the canister position is~10–3 mol/L. However, as shown by the red CDF for the F-factor, no canister position has such alow F-factor for ice location II. Approximately 99% of the canister positions are reached by flow
paths with F-factors larger than 50,000 yr/m. However, for such a large F-factor all oxygen has
been scavenged. For ice location III one can see that at CDF = 0.001 (about 6 canister positions),
the oxygen concentration in the range 10–4 to 10–3 mol/L.

10-3

0.16
0.14
0.10
0.08
0.06

10-6

0.04
0.02

10-7
102

0
103

104

105

106

107

108

Flow related transport resistance, F (yr m-1)(a) Ice location II

0.16
0.14

10-4

CDF

0.12

10-5

0.18

0.12
0.10

10-5

0.08
0.06

10-6
10-7
102

CDF

0.18

10-4

0.20

Concentration (mol Lw-1)

Concentration (mol Lw-1)

0.20

10-3

0.04
0.02
103

104

105

106

107

0
108

Flow related transport resistance, F (yr m-1)(b) Ice location III

Figure 6‑7. Superpositioned plots of the oxygen concentration profile (blue curve) along flow paths for theoxygen ingress Base case with a 1.5 mM O2 recharge concentration and CDF (dashed red curves) of flowpaths obtained from hydrogeological modelling for (a) ice location II and (b) ice location III. Reproduced
from Figure 8-7 of /Sidborn et al. 2010/.

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207

The following is concluded in /Sidborn et al. 2010, Section 8.2/.
•

At a flow path distance corresponding to an F-factor of ~5,400 yr/m the oxygen concentration
has decreased to 0.3 mM under these conditions. Applying the hydrogeological model results forthe worst-case ice location III, 14 canister positions in the repository would be exposed to thisconcentration or higher. For ice location II none of the canister positions would be exposed to thisconcentration. At a flow path distance corresponding to an F-factor of ~9,400 yr/m the concentration has decreased to 3 μM. This concentration or higher would affect 25 canister positions for
ice location III. For ice location II only 6 canister positions would be exposed to this concentration or higher.

It should be noted that these results are valid for a recharging melt water oxygen concentration of 1.5 mM,
which is deemed to be a pessimistic value. Also, the results are valid provided that the ice front remains
stationary at the most unfavourable position over some time so that steady state hydraulic conditions
are established. It is pointed out in /Sidborn et al. 2010/ that for canisters surrounded by the buffer,much lower concentrations will reach the canister, as result of the transport resistance of the buffer.

6.1.11 Judgements by the SR-Site teamAccording to the instruction (cf. Section 2.3), the customer should make judgment on the sections
provided by the supplier.
Sources of information and documentation of data qualification

The sources of information are referenced in detail, and the categorisation of data sets into qualified
and supporting is judged to be adequately performed. It is noted that the choice of underlying data is
generally made in the background reports /Salas et al. 2010, Tullborg et al. 2010, Sidborn et al. 2010/.
Conditions for which data are supplied

The conditions for which data are supplied are judged to be adequate for the use in SR-Site. The conditions are also adequately outlined or, alternatively, references are made to supporting documents
detailing the conditions.
Conceptual and data uncertainties

The conceptual uncertainty is judged to be sufficient well described and references for further reading are provided. However, it is evident that these conceptual uncertainties have induced uncertainty
in the recommended data values.
The data uncertainty is sufficiently well described, both for modelled and non-modelled parameters.
The total uncertainty (conceptual and data uncertainty, as well as natural variability) remaining in
the recommended modelled data is handled by studying groundwater compositions at coordinates
producing extreme values.
For the non-modelled components, except for sulphide, only a best estimate value is recommended(cf. Table 6‑11) while some information on the variability is given in Table 6‑4. Therefore, the data
uncertainty may be greater for these components than for the modelled components. For sulphide a
CDF is given describing the total uncertainty (cf. Figure 6‑6).
The uncertainty in oxygen ingress modelling is quite large, which is handled by using pessimistic
assumptions and modelling the ingress at the worst possible ice front locations.
All in all the data uncertainty is judged to be adequately handled, in the perspective that this is acomplex subject area with large uncertainties.
Spatial and temporal variability of data

The spatial and temporal variability it adequately handled by providing, where possible, data for a
multitude of coordinates and time periods.
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Correlations

There are numerous correlations within the subject area that are adequately handled. No correlationneeds to be propagated to subsequent modelling, but it is important that the thermodynamic databaseused in radioelement solubility limit calculations are consistent with that used here.
Result of supplier’s data qualification

The SR-Site team judge that the data supplied are adequate and can be used in subsequent SR-Sitemodelling.

6.1.12 Data recommended for use in SR-Site modelling
For the Forsmark end-member groundwaters, the data given in Table 6‑6 are recommended. For
modelled components, the data given in Table 6‑7 to Table 6‑10 are recommended. The complete
data set from the PhreeqC modelling is stored in (SKBdoc 1262945).
For non-modelled data the best estimate values of Table 6‑11 are recommended. For sulphide
concentrations the CDF in Figure 6‑6 is recommended. The underlying sulphide data set is storedin (SKBdoc 1261474).
Concerning oxygen ingress, on the order of 10 canister positions may be affected by elevated levelsof oxygen, up to concentrations of 1.5 mM O2, during time periods on the order of a few hundredyears per ice front passage.

6.2

Bedrock thermal properties

In the present section, thermal properties of the rock, that is rock thermal conductivity, rock heat
capacity, thermal diffusivity, and thermal expansion coefficient, as well as the temperature at repository depth, are presented. These data are mainly needed for evaluating the impact of the thermal loadfrom the spent fuel, which has bearing on the spacing between canister positions in the repository.
The bentonite buffer temperature should not exceed 100°C for any canister deposited. A temperature
margin including uncertainties is therefore also presented; see further description in Section 6.2.1.
The thermal properties of the rock are also needed when assessing the long-term thermal evolutionof the repository, caused by climate changes. Modelling of the long-term thermal evolution is further
discussed in Section 7.1. However, in this present section, data on thermal rock properties, involving
geothermal gradient, geothermal surface heat flow, and internal heat generation, will be also
discussed in this context.
Furthermore, thermo-hydro-mechanical (THM) analyses of the THM evolution of the repository
require thermo-mechanical parameters, where the coefficient of thermal expansion is given in thissection but where other mechanical input data are discussed in Section 6.4.

6.2.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR‑Site modelling
activities the data are to be used.
Modelling the thermal evolution of the repository is in SR-Site focused on assessing the impact of
the heat generated by the spent nuclear fuel. Of particular concern is to assess the peak temperatureof the buffer, since it must not exceed 100°C. There are uncertainties associated with calculationof the peak buffer temperature, meaning that a margin to the design threshold (100°C) must be
established. Establishing a safe, yet not overly pessimistic, margin is an important issue.
The thermal properties are also used to assess the overall thermal evolution of the repository thatis used as input to thermo-hydro-mechanical analyses. Furthermore, the thermal properties are also
used to assess the large-scale long-term thermal evolution caused by climate changes. Note that

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thermal data for the latter are required for a much larger area and at a larger scale compared to modelling of the peak buffer temperature in the repository. The demands, e.g. on describing the spatial
distribution, are on the other hand much lower. Only mean and uncertainty estimates are required.
Defining the data requested from the supplier

The following data should be delivered for the repository area by the supplier:•

Spatial distribution of the thermal conductivity λ (W/(m·K)) and its potential anisotropy, withcorrection for temperature and with uncertainty estimates. Different data sets may have to begiven for different rock volumes. Especially the lower tail of the distribution is of concern since
this is of central importance for canister spacing with respect to the temperature criterion. The
temperature field around a canister is dependent on the rock thermal conductivity. Rock having
low thermal conductivity will require larger distances between canisters than high conductivity rock.

•Spatial distribution of heat capacity C (J/(m³·K)), with correction for temperature and withuncertainty estimates. Different data sets may have to be given for different rock volumes.

•

In situ temperature T (°C) at repository depth, with uncertainty estimates.

•

Thermal expansion coefficients α (m/(m·K)) for significant rock types, with uncertainty estimates.

•

Site-specific temperature margin (°C) data (margin up to 100°C) in the thermal dimensioning of
the repository. The margin constitutes of both general and site-specific uncertainties. Different
data sets may have to be given for different rock volumes. The value given constitutes theuncertainty.

In addition, the following data should be delivered for the long-term evaluation caused by climatechanges by the supplier:•Mean value of the thermal conductivity λ (W/(m·K)) and its potential anisotropy, with correction
for temperature and with uncertainty estimates. Different data sets may have to be given fordifferent rock volumes, inside and outside the target area.

•

Mean value of the heat capacity C (J/(m³·K)) with correction for temperature and with uncertainty estimates. Different data sets may have to be given for different rock volumes, inside andoutside the target area.

•

Mean value of the thermal diffusivity κ (m2/s). Different mean values may have to be given fordifferent rock volumes.

•

Mean value of the internal heat generation (µW/m³) with uncertainty estimates. Different data
sets may have to be given for different rock types.

•

Mean value of the geothermal gradient (°C/m).

•

Mean value of the geothermal surface heat flow (mW/m2) with uncertainty estimates.

The data should originate from the site investigations and from the assessment of the site investigation data presented in Chapter 6 of the Site description Forsmark and its lower level references.
Note that pessimistic considerations taken when assessing the peak temperature of the buffer are notnecessarily pessimistic when assessing the in situ temperature as impacted by climate change.
SR-Site modelling activities in which data will be used

The SR-Site modelling of the thermal evolution of the near-field is based on a combination ofanalytical and numerical approaches as further described by /Hökmark et al. 2009/. The peak buffertemperature is calculated as function of the canister spacing, the tunnel spacing, and the rock thermal
properties, whereas a set of fixed assumptions are made for the conditions in the interior of the deposition holes. These assumptions are relevant for dry deposition holes in which the temperatures willtend to be particularly high. This modelling approach has been used for dimensioning the repository,
as further described in the Underground openings construction report and in the Site engineering
report Forsmark /SKB 2009d/. However, in SR-Site the objective is to assess the thermal evolution
for this selected design. The modelling tools will be the same, whereas the application of the tools
may differ somewhat.
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The internal solution, i.e. the heat transport in the interior of a dry deposition hole, is practically,
but not completely, independent of the host rock properties and the repository layout. In other words
the heat transport from the canister through the buffer is for practical purposes independent of thedeposition hole rock wall temperature. The solution gives the difference between the rock wall
temperature and the maximum bentonite temperature /Hökmark et al. 2009/. To find the peak buffertemperature, that temperature difference must be added to the rock wall temperature. This can bedone in two ways:•

Adding the temperature difference to analytically calculated rock wall temperatures.

•

Adding the temperature difference to numerically calculated rock wall temperatures.

The analytical way (cf. /Claesson and Probert 1996a, Hökmark and Fälth 2003, Hökmark andClaesson 2005/) is fast but based on idealised assumptions of the rock heat transport properties.
The analytical solution cannot account explicitly for the spatial variability of the rock heat transport
properties. However, by knowing the distribution of the rock thermal conductivity, reasonablevalues of the dimensioning rock thermal conductivity selected from the low-conductivity tail ofthe distribution (which represent rock having the least favourable thermal properties) are used in
the dimensioning to estimate trial values of the canister spacing required to ensure that the 100°C
criterion will be met for all canisters. For SR-Site, the results of the calculations will be combinedwith supplementary calculations using the numerical as well as the analytical model, for the actually
selected canister spacing in the design.
The numerical way is used to account directly for the spatial variability of thermal conductivity andheat capacity in the rock mass. First a number of realisations of the spatial distribution of thermal
conductivity in the 1–2 m scale are selected. This is a scale that corresponds to the lower limitof spatial influence on the canister/buffer temperature according to Chapter 3 in /Sundberg et al.
2005b/. The selected realisations are the ones with the lowest thermal conductivity in the influence
volume to the potential canister positions. Subsequently the peak buffer temperature is calculatedwith the numerical simulation model for each canister in the selected realisations. This should ensurethat the buffer peak temperature is safely bounded. In reality, the majority of the canisters will beplaced in much more favourable positions, i.e. in rock portions with higher thermal conductivity
resulting in lower buffer temperatures. The numerical simulation of the thermal process in therepository is performed using the explicit finite difference method (FDM) /Eftring 1990/. The
repository and a certain part of the surrounding rock are described by dividing the region into a gridof sub volumes with different thermal properties from the thermal realisations. The subdivision ismade with smaller cells around the canister in order to describe details of the near-field and to obtaina better resolution of large temperature gradients. The cell size is increased the further away the cells
are located from the canister. The simulated region consists of a segment including one tunnel withnine deposition boreholes in a row. This region is bounded by symmetry planes in the horizontal
directions. In the vertical direction it extends to a sufficient distance, beyond which the thermal
properties of the rock have an insignificant effect on the maximum buffer temperature. Only the five
central canisters are used in the evaluation of the peak buffer temperature to avoid boundary effects.
There are uncertainties in input data as well as systematic over and underestimates associated withboth the local canister/buffer solution and the calculation of the rock wall temperature. Therefore, thedimensioning of the repository, i.e. establishing canister spacing for the different rock domains at agiven site, must be made with a margin to the 100°C threshold. In /Hökmark et al. 2009/ the different
parts that constitutes the margin are described. The margins are optimised for the numerical method.
The overall thermal evolution is in the THM-analyses assessed by the 3DEC code, as further
described by /Hökmark et al. 2010/. The thermal property input needs to this modelling is coveredby the input needs of the buffer peak temperature modelling.
The thermal evolution of plugs, the central area, shafts, ramps, top seals and borehole seals areassessed by the analytical solution, using mean values of the site-specific thermal properties givenin the Site description Forsmark.
Thermo-mechanical models used in SR-Site are described in Section 6.4 and models used for assessing
the thermal evolution of the repository, as impacted by climate changes, are discussed in Section 7.1.

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6.2.2 Experience from SR-Can
This section briefly summarises experience from the SR‑Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

Thermal modelling with similar scope as in SR-Site was also conducted in SR-Can, focusing on
assessing the impact of the heat generated by the spent nuclear fuel. Such thermal modelling was in
SR-Can performed using a semi-analytical model /Hedin 2004/, based on the methods of /Hökmarkand Fälth 2003/ where the temperature in the fuel, the cast iron insert, the copper canister, the buffer,and the host rock was determined based on canister spacing as well as thermal properties of the canister, the buffer, and the geosphere. Compared to SR-Site, this semi-analytical modelling approach
could not explicitly handle the spatial variability of the thermal properties and instead, a key aspectof the SR-Can Data report /SKB 2006b/ was to justify upscaled values of the thermal properties. The
temperature margin up to 100°C was not in detail evaluated.
The thermal modelling also concerned the overall thermal evolution as part of an assessment of thethermo-hydro-mechanical evolution /Hökmark et al. 2006/. Furthermore, the long-term thermal
evolution caused by potential climate change was assessed.
Conditions for which data were used in SR-Can

Data used in SR-Can for the thermal properties of the bedrock, provided in the two preliminary
site-descriptive model reports /SKB 2005b, 2006g/, concern the situation at the sites at present day
conditions. These properties directly depend on the distribution of rock types and on the mineralcomposition of the rock types, and are thus not expected to change in the future.
Sensitivity to assessment results in SR-Can

The peak buffer temperature is quite sensitive to the thermal conductivity and initial temperatureof the rock, as demonstrated already in the SR-Can Interim Main report /SKB 2004a, Section 7.3/.
The peak value is reached after a few years (in the order of 7 to 14 years) implying that the thermal
pulse has not migrated very far when the peak temperatures are reached. Thus, it is the local
value of the rock thermal conductivity in the vicinity of each canister, and the canister separation
distance, that will determine these peak temperatures. The impact of the rock heat capacity is judged
comparatively small, as noted by /Hökmark and Fälth 2003, Section 5.2.2/, since a quasi-equilibriumsoon establishes between the heat output and the thermal transport in the near-field, resulting in a
non-changing thermal gradient.
Alternative modelling in SR-Can

There was no alternative modelling of thermal properties in SR-Can.
Correlations used in SR-Can modelling

The most important aspect of correlation of thermal properties is related to the spatial correlation,
as this would affect the upscaling of thermal properties measured on core samples. In SR-Can,
the spatial variability was estimated by assuming a correlation between thermal conductivity androck density, as obtained from detailed density loggings along boreholes, and the upscaling was
performed by calculating geometric means for different scales on this data. Various approaches,
described in the preliminary site-descriptive model reports /SKB 2005b, 2006g/, were used todescribe this spatial variability and to upscale the measured values into deposition hole scale. These
approaches have been replaced in SR-Site by a new approach for modelling the spatial variability ofthe thermal properties.
Furthermore, the thermal conductivity and the heat capacity have slight temperature dependence andthis was also considered in the modelling.

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Identified limitations of the data used in SR-Can modelling

A new strategy for describing and predicting the spatial distribution of the thermal properties wasused in the SDM-Site modelling for both Forsmark and Laxemar /Back and Sundberg 2007/. The
previous methodology for thermal modelling /Sundberg 2003, Sundberg et al. 2005b/, used for data
in SR-Can, did not take the spatial correlation fully into account during simulation. The result wasthat the variability of thermal conductivity in the rock mass was not sufficiently well described.
Experience from earlier thermal SDM versions indicated that development of the methodology was
required in order describe the spatial distribution of thermal conductivity in the rock mass in a sufficiently reliable way, taking both variability within rock types and between rock types into account.
A good description of the thermal conductivity distribution is especially important for the lower tail.
This tail is important for the design of a repository because it affects the canister spacing.

6.2.3 Supplier input on use of data in SR-Site and SR-Can
The supplier broadly agrees with the use of data in SR-Can as described in Section 6.2.2, withthe limitations noted above which have lead to modifications in the strategy of SR-Site thermal
modelling. Furthermore, the supplier broadly agrees with the use of data in SR-Site, as describedin Section 6.2.1.
6.2.4

Sources of information and documentation of data qualification

Sources of information

Main sources of information on thermal properties used in data qualification are described inTable 6‑12. In the thermal site-descriptive modelling, a large number of realisations of thermal
properties are produced from stochastic modelling. These realisations are the basis for the description of thermal properties on domain level in the site-descriptive modelling reports, see Table 6‑12.
However, the thermal property realisations (see Table 6‑19) are directly used in the thermal
dimensioning, as described in Section 6.2.1.
Within the publications listed in Table 6‑12, references to relevant lower level documents canbe found.
Table 6‑12. Main sources of thermal property information used in data qualification.
Site description Forsmark, 2008. Site description of Forsmark at completion of the site investigation phase. SDM-Site
Forsmark. SKB TR-08-05, Svensk Kärnbränslehantering AB.
Sundberg J, Wrafter J, Back P E, Ländell M, Rosén L, 2008a. Thermal properties Forsmark. Modelling stage 2.3.
Complementary analysis and verification of the thermal bedrock model, stage 2.2. SKB R-08-65, Svensk Kärnbränslehantering AB.
Back P E, Wrafter J, Sundberg J, Rosén L, 2007. Thermal properties. Site descriptive modelling Forsmark –stage 2.2. SKB R-07-47, Svensk Kärnbränslehantering AB.
Sundberg J, Back P-E, Ländell M, Sundberg A, 2009a. Modelling of temperature in deep boreholes and evaluationof geothermal heat flow at Forsmark and Laxemar. SKB TR-09-14, Svensk Kärnbränslehantering AB.
SKB, 2009d. Site engineering report Forsmark. Guidelines for underground design, step D2. SKB R-08-83, Svensk
Kärnbränslehantering AB.

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Categorising data sets as qualified or supporting

Qualified and supporting thermal data sets are displayed in Table 6‑13 and numbered from item
1 to 15. Detailed justifications to the sorting of the data sets are given in Table 6‑14 and Table 6‑15.
Where different data sets are required for different rock volumes, these are described for the rockdomains RFM029 and RFM045. Data outside the target area are described for a number of different
rock domains.
Most data sets have a priori been considered to be qualified. They have thereafter been judged
to be qualified based on the criteria in the instruction for supplying data to this Data report (seeSection 2.3). The exception is the supporting data set accounted for in item 15.
Excluded data previously considered as important

No important data have been excluded from the analysis. Non-used thermal data are described in
Section 2.2.3 in /SKB 2008/.
Table 6‑13. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Thermal conductivity: RFM029: Table 6-3 in /Back et al.
2007/. RFM045: Table 7-1 in /Sundberg et al. 2008a/.
2. Heat capacity: RFM029, RFM045: Table 7-2 in /Sundberg
et al. 2008a/.
3. Spatial distribution of thermal conductivity and heat capacityin each rock domain and thermal subdomain. Up to 1,000
realisations for each rock domain (SKBdoc 1271438).
4. Thermal conductivity and heat capacity for rock domains
outside target area: Properties based on modelling results
and data for different rock types in the thermal site-descriptivemodel version Site /Back et al. 2007, Sundberg et al. 2008a/
and version 1.2 /Sundberg et al. 2005a/ and geology fromsite-descriptive model version 1.2 /SKB 2005b/ and 2.1/SKB 2006g/.
5. Thermal diffusivity: Calculated from thermal conductivity
and heat capacity above.
6. Anisotropy in thermal conductivity: Section 6.3.1 in /Backet al. 2007/. Orientation in Chapter 6 in the Site description
Forsmark.
7. Temperature dependence in thermal conductivity and heat
capacity: Table 2-3 and 2-4 respectively in /Sundberg et al.
2008a/.
8. Pressure dependency in thermal conductivity: Section 2.5
in /Sundberg et al. 2008a/.
9. Thermal expansion coefficient: Table 3-19 in /Back et al.
2007/.
10. Temperature at repository level: Table 2-6 in /Sundberg
et al. 2008a/.
11. Geothermal gradient: Figure 2-4 in /Sundberg et al. 2008a/.
12. Heat generation: Section 6.1.3 in /Sundberg et al. 2009a/.
13. Heat flow: Mean in Section 6.2.2 with uncertainty estimatesin Section 8.1 in /Sundberg et al. 2009a/.
14. Temperature margins are based on the principals in/Hökmark et al. 2009/ and site-specific data in /Back et al.
2007, Sundberg et al. 2008a/.

15. Temperature dependences for some rock types
is taken from the literature, see Table 2-3 and 2-4
respectively in /Sundberg et al. 2008a/.

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Table 6‑14. Justification to the sorting of items 1–10 in Table 6‑13.
1: The relevant thermal conductivity data as reported in /Back et al. 2007/ and /Sundberg et al. 2008a/ are basedon data acquired and refined within the Forsmark site description and site investigation phases. These phases havebeen conducted in conformance with relevant quality assurance routines. Lower level references are carefully listed,thus facilitating traceability (see e.g. Chapter 3 in /Back et al. 2007/). Through lower level references the traceability isensured down to relevant quality assurance documents such as method descriptions, task descriptions, etc. In /Backet al. 2007/ and /Sundberg et al. 2008a/ issues such as data quality, variability, and representativity are discussed.
Nonconformities and uncertainties are also discussed. Much of the thermal data delivered are derived from modelling.
In /Back and Sundberg 2007, Back et al. 2007, Sundberg et al. 2008a/, the modelling strategy, the modelling tools, andvalidity of the modelling are described. It is judged that the modelling approach is adequate.
2: The relevant heat capacity data reported in /Back et al. 2007, Sundberg et al. 2008a/ are based on data acquired andrefined within the Forsmark site description and site investigation phases. The same considerations as for item 1 apply.
3: The spatial distribution of thermal conductivity and heat capacity are described in a large number of realisations foreach rock domain, and based on data acquired and refined within the Forsmark site description and site investigation
phases. Item 1 and 2 above are based on these realisations. The same considerations as for item 1 apply.
4: The thermal conductivity and heat capacity data for rock domains outside target area are based on thermal propertiesof different rock types in the site-descriptive model /Back et al. 2007, Sundberg et al. 2008a/. Similar considerations as
for item 1 apply on the data for different rock types/TRCs (Thermal Rock Classes). However, the requirements are much
lower compared to those on data from the target volume. The mean thermal conductivity and heat capacity may be
calculated from the proportions of different rock types or based on the dominant rock type /SKB 2005b, 2006g/.
5: The thermal diffusivity data are calculated, in this present section, based on the data set for thermal conductivity (item1) and heat capacity (item 2 and 4). As a consequence, the same considerations as for item 1 and 4 apply.
6: The anisotropy in thermal conductivity reported in /Back et al. 2007/ are based on data acquired within the Forsmark
site investigation phases. The same considerations as for item 1 apply. However, no advanced data refinement has
been made. Focus has been on providing a relevant mean value for areas where the anisotropy is pronounced. The
orientation and strength of anisotropy is further described in Chapter 5 in the Site description Forsmark.
7: The temperature dependences of thermal conductivity and heat capacity reported in /Sundberg et al. 2008a/ aremainly based on data acquired within the Forsmark and Laxemar site investigation phases. The same considerations as
for item 6 apply. The description has focused on mean value for the dominant granite. Data for subordinate rocks havebeen obtained from similar rock types in Laxemar /Sundberg et al. 2008b/ and from literature data given in /Sundberg
et al. 2008a/. This approach has been judged to be adequate since the temperature dependence is moderate and has
only minor influence on the maximum bentonite temperature.
8: The pressure dependence in thermal conductivity is described in /Sundberg et al. 2008a/. The pressure dependenceis small and has been neglected in the thermal conductivity modelling. Neglecting the pressure dependence has aslightly pessimistic effect on the results of the thermal conductivity modelling.
9: The thermal expansion coefficient reported in /Back et al. 2007/ are based on data acquired within the Forsmark site
investigation phases. The same considerations as for item 6 apply. Focus has been on providing a relevant mean value.
10: The temperature at repository level reported in /Sundberg et al. 2008a/ is based on data acquired within the
Forsmark site investigation phases. Similar considerations as for item 1 apply. In the thermal modelling, the reliabilityof temperature logging data has been evaluated in relation to calibration errors and disturbances from drilling. Only
approved data has been used in the site-descriptive model. Focus has been on providing both a relevant mean value
for repository depth and a measure of variability.

Table 6‑15. Justification to the sorting of items 11–15 in Table 6‑13.
11. The geothermal gradient data reported in /Sundberg et al. 2008a/ are based on the temperature logging data
described in item 10. As a consequence, the same considerations as for item 10 apply.
12. The internal heat generation data are reported in /Sundberg et al. 2009a/. It is based on data acquired within the
Forsmark site investigation phases. Similar considerations as for item 1 apply. However, no advanced data refinement
has been made. Focus has been on providing a relevant mean value.
13. The site-specific geothermal surface heat flow is reported in /Sundberg et al. 2009a/. The heat flow is based ondata acquired within the Forsmark site investigation phases together with data on the past climate evolution /Sundberg
et al. 2009a/. The heat flow is determined indirectly from geothermal gradient (item 11), thermal conductivity (item 1),
thermal diffusivity (item 5), internal heat generation (item 12) and past climate evolution. The uncertainty estimates havebeen evaluated from uncertainties in the above mentioned parameters together with the influence from e.g. temperaturedependences (item 7) and anisotropy (item 6). Similar considerations as for the mentioned items apply (climate evolution and permafrost modelling is further discussed in Section 7.1). Focus has been on providing both a relevant mean
for the site and an uncertainty estimate.
14. The temperature margin data are based on the principles and general data in /Hökmark et al. 2009/ and site-specificdata in /Back et al. 2007, Sundberg et al. 2008a/. The temperature margin consists of both uncertainties and under- andoverestimates in the calculation of the peak buffer temperature. The uncertainties are carefully discussed in /Hökmark et
al. 2009/ and quantified in general terms with example values that can be relevant for the different rock domains. In the
Site engineering report /SKB 2009d/, site-specific data are used to define a margin for each rock domain.
15. The supporting data on the temperature dependence (item 7) for subordinate rock types is provided by articles in
reputable scientific journals, which have been subjected to the customary peer-review process before publication.

TR-10-52215

6.2.5 Conditions for which data are supplied
The thermal conductivity and heat capacity in the thermal properties realisations are provided at
20–25°C and at water saturated conditions. The natural temperature at repository depth is slightly
lower. After deposition of the canisters the temperature is going to increase, as a consequence of theheat generation in the canisters. However, the temperature dependence for the thermal conductivity
and heat capacity is described in the thermal site-descriptive models in the Site description Forsmark.
The thermal conductivity measurements are made on stress released samples. However, the pressure
dependence is low when the samples are water saturated.
Further, the thermal transport is assumed to be conductive only. This is a relevant assumption
since the thermal transport in the rock mass surrounding the canisters should have an insignificant
contribution from convective heat transport.
The thermal expansion coefficients for different rock types are provided at 20–25°C.
The temperature loggings in water filled boreholes are assumed to correctly describe the temperaturein the rock mass.

6.2.6 Conceptual uncertainty
The major conceptual uncertainty of the thermal rock properties concerns correlations with density.
Different correlations with density have been used in the thermal site-descriptive modelling in order
to: 1) define the spatial correlation structure for thermal conductivity, 2) subdivide certain TRCs
(Thermal rock classes /Back et al. 2007/), and 3) strengthen the thermal conductivity /heat capacity
relationship. Both the density and the thermal conductivity of rock is related to the mineralogicalcomposition /Sundberg et al. 2009b/. The thermal modelling mentioned in this section refer to thethermal site-descriptive model (Site description Forsmark), based on modelling in /Back et al.
2007, Sundberg et al. 2008a/.
It has been assumed that thermal conductivity exhibits a similar correlation structure to density.
The variogram models, used to describe the spatial correlations structure of the thermal conductivity
in the thermal modelling, are primarily based on density loggings (approximately 0.1 m scale) inboreholes, supported by TPS (Transient Plane Source) data if a sufficient amount of data were available. A relationship between thermal conductivity and density has been established (see Section 6.2.2
in the Site description Forsmark). Even for rock types where such a relationship is not evident,
density logging data are used as input to the variogram modelling. It is reasonable to assume thatany spatial dependence in density, as indicated by a variogram, also reflects spatial dependence inthermal conductivity /Back et al. 2007/. However, the associated uncertainty is not known. This
uncertainty is handled by using slightly pessimistic variogram models in the stochastic modelling.
The relationship between thermal conductivity and density has also been used in order to subdivide
different TRCs when necessary.
Referring to the thermal conductivity/density relationship, it is reasonable to assume a corresponding
relationship between density and heat capacity. Such a relationship has been found previously /Sundberg
et al. 2003/, although weaker than that for thermal conductivity versus density. In /Sundberg et al.
2008a/ a relationship between heat capacity from direct measurements and thermal conductivity is
described. The relationship has been used to calculate heat capacity from the modelling results onthermal conductivity in order to complete the thermal conductivity realisations with heat capacity.
Uncertainties associated with the thermal conductivity modelling are also present in the modelledheat capacity distribution. In addition, uncertainties related to the relationship between thermal
conductivity and heat capacity are added. However, these uncertainties in heat capacity are judged
to have only minor results on the calculated maximum buffer temperature (see Section 6.2.1).
The conceptual uncertainty in other parameters is judged to be low.

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6.2.7 Data uncertainty due to precision, bias, and representativity
The data uncertainty can be subdivided into precision, bias, and representativity. The data
uncertainty can also be subdivided into uncertainties in primary data and model uncertainties.
The discussion is focused on uncertainties in thermal properties inside the target area but is partly
also applicable to properties outside the target area (mainly used for the thermal evolution of therepository due to climate changes). Uncertainties specifically related to data outside the target area
are explicitly described. The demands on data inside the target area are much higher compared tothose on data for permafrost modelling, see Section 6.2.1.
Data uncertainty due to precision and bias

Primary data in the site investigations
Data uncertainty due to precision is believed to have only minor influences for thermal conductivity
determinations made by the TPS method. Heat capacity values calculated from simultaneously determined thermal conductivity and thermal diffusivity have a rather wide spread due to the combinationof spatial distribution and data precision. Partly the variability is also caused by anisotropy in thesamples, which to some degree is normally the case in the Forsmark area. However, in the thermal
modelling only heat capacity data from calorimetric determinations have been used. These data show
a variability that is believed to be mainly caused by spatial variability.
The temperature loggings in boreholes are disturbed by water movements and the drilling of theborehole. In the SDM-Site model, the reliability of temperature loggings due to disturbance from
drilling has been evaluated. As a result of this only “approved” boreholes have been used in thedescription. However, uncertainties related to water movements are still present, especially at
certain parts of the boreholes. However, the mean temperature at repository depth in different boreholes shows a rather small variability between different boreholes. Uncertainties in the temperature
loggings are transferred into the determination of geothermal gradient and of heat flow.
The reliability of temperature loggings due to calibration error have been evaluated /Back et al.
2007, Sundberg et al. 2008a/. Earlier rather large calibration errors could occur. In the SDM-Siteversion of the thermal site-descriptive model, only temperature logging data that fulfil certain
requirements regarding calibration have been used to describe the in situ temperature conditions/Sundberg et al. 2008a/. The variability between the temperature at the same relevant elevation indifferent boreholes is ±0.4°C about the mean (Table 2-6 in /Sundberg et al. 2008a/). For individual
boreholes, the uncertainty is ±0.25°C (related to equipment).
The heat generation in the rock mass is calculated from the proportions of U, Th, and K /Sundberg
et al. 2009a/. Uncertainties in the proportions in combination with sparse data for some rock types
are transferred to uncertainties in the heat generation. These uncertainties can be rather large.
However, the internal heat generation down to repository depth has only minor influence on thecalculated surface heat flow and the permafrost modelling.
Potential bias in the determination of the thermal expansion coefficient has been discussed in thethermal site-descriptive model version 1.2 /Sundberg et al. 2005a/. The reason for potential bias
was that stress dependence had not been assessed and the measurements are made on stress released
samples. However, through a literature survey the question could be eliminated.
The uncertainties in the orientation of the boreholes and in the orientation of geological objects inthe boreholes, documented by /Munier and Stigsson 2007/, are judged to have little or no effect on
the results of thermal modelling.
Model uncertainties
There are several model uncertainties to consider in the thermal modelling of the spatial distributionof thermal conductivity and heat capacity. The five most important uncertainties identified in sitedescriptive model stage 2.2 /Back et al. 2007/ were associated with 1) the simulation scale, 2) thesimulation volume, 3) the spatial statistical structure of TRCs (lithology), 4) the spatial statistical
thermal models, and 5) the simulation technique. In the complementary site description in modelstage 2.3, rock domain RFM045 was remodelled, and special attention was given to the uncertainties
in the spatial statistical structure of TRCs (lithology) and the simulation volume /Sundberg et al. 2008a/.
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217

A detailed discussion of the above mentioned uncertainties is performed in /Back et al. 2007/ and/Sundberg et al. 2008a/ and a summary is provided in the Site description Forsmark. Uncertainties
associated with the simulation volume and the simulation scale are not believed to have had any
major impact on the thermal modelling results. Geological heterogeneities were dealt with bydividing the domains into subdomains. This is believed to have reduced the uncertainty significantly.
The remaining uncertainty concerning the variability in proportions is believed to be small. Basedon confidence intervals for TRC proportions at borehole scale (see Section 6.1 in /Sundberg et al.
2008a/), this uncertainty has only a minor effect on the lower thermal conductivity tail (the 1-percentile may vary by about 1%). This is also discussed under “representativity”.
Limited amounts of data for some TRCs result in uncertain spatial statistical thermal conductivity
models. The variograms require even more data. It has been assumed that thermal conductivity
exhibits a similar correlation structure to density, see discussion above. The spatial statistical thermal
conductivity models are believed to be more reliable than in previous versions. Uncertainties in thesimulation technique are believed to have only a minor influence on the results.
Uncertainties are also associated in the thermal anisotropic properties of the dominant granite (lineation/foliation), mainly due to few determinations. The mean values of the thermal anisotropic factor
at the investigated site may be quite reliable but the spatial distribution has large uncertainties.
There are a number of uncertainties related to the determination of the site-specific heat flow. These
uncertainties are related to uncertainties in geothermal gradient, thermal conductivity, thermal diffusivity,internal heat generation, and past climate evolution. A detailed discussion of the above mentioned
uncertainties is performed in Section 8.1 in /Sundberg et al. 2009a/.
The temperature margin to the design criteria (100°C) consists of uncertainties as well as underand overestimates in the calculation of the peak buffer temperature. The temperature margin data
are based on the principles and general data carefully elaborated in /Hökmark et al. 2009/ andsite-specific data in /Back et al. 2007, Sundberg et al. 2008a/. The margins are optimised for thenumerical method. Thus, the margin constitutes of both general and site-specific uncertainties.
The uncertainties are discussed in detail in /Hökmark et al. 2009/. A short summary is presented here
and the temperature margin for different rock domains is justified from site-specific data. Non-site
specific uncertainties are not described here. Instead the reader is referred to /Hökmark et al. 2009/.
In Table 6‑16 and Table 6‑17 the different uncertainties in the local solution and the numericallycalculated rock wall temperature are summarised. The uncertainties and biases given in these tables
represent realistic judgements for the individual parameters. In Table 6‑18 the total temperature
margin is presented.
Uncertainty U1 in Table 6‑16 is site-specific and can be interpolated for different conductivities
from /Hökmark et al. 2009/. The thermal conductivity at low percentiles of the thermal conductivity
distribution are approximately 3 W/(m∙K) for rock domain RFM029 and 2.5 W/(m∙K) for rock domain
RFM045. Uncertainties U2–U5 are not site-specific and are discussed in detail in /Hökmark et al. 2009/.
Table 6‑16. Uncertainties in local solution. Modified from /Hökmark et al. 2009/ with site-specificdata from /Back et al. 2007, Sundberg et al. 2008a/.
DTtot, difference between rock wall temperature and maximum bentonite temperature

Margin, °C,
RFM029Margin, °C,
RFM045

Uncertainties related to:

U1

Geometry of air-filled canister/bentonite gap and variations inbarrier conductivity

3.3

3

U2

Moisture redistribution in barrier

0.2

0.2

U3

Spalling

0.1

0.1

U4

Vertical variation of rock conductivity along deposition hole

0.25

0.25

U5

Vertical distribution of heat generation in the canisters

0.2

0.2

4.05

3.75

Sum DTtot

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Table 6‑17. Uncertainties in numerically calculated rock wall temperature, Twall. Modified from
/Hökmark et al. 2009/ with site-specific data from /Back et al. 2007, Sundberg et al. 2008a/.
Twall , Rock wall temperature at canister mid-height at the time ofbuffer temperature peakMargin, °C, RFM029

Margin, °C, RFM045

Uncertainties related to:U6

Anisotropy within rock type

0.7

0.5

U7

Bias in thermal properties

0.5

0.8

U8

Heterogeneity, site models (in the lower tail)

0.1

0.1

U9

Initial temperature

0.65

0.65

U10

Temperature dependence

0

0

U11

Pressure dependence

–0.2

–0.2

U12

Tunnel backfill

0

0

U13

Strategy uncertainties

–

–

1.75

1.85

Sum (uncertainties)
Over/underestimate because of numerical model simplifications

S1

Representation of canister

–0.7

–0.7

S2

Numerical precision

–0.8

–0.8

S3

Boundary conditions

0.2

0.4

Sum (under/overestimates)

–1.3

–1.1

Total uncertainties in Twall

0.45

0.75

Table 6‑18. Total temperature margin in numerical solution to establish a definitive spacing.
Uncertainties related to:

Domain RFM029

Domain RFM045

Local solution (Table 6‑16)

4.05°C

3.75°C

Total Twall. Numerical solution (Table 6‑17)

0.45°C

0.75°C

Total Margin

4.5°C

4.5°C

Uncertainties U6–U10 in Table 6‑17 are site-specific. Item U6 is related to the anisotropy in rock.
It includes the effect of a foliated rock oriented in an unfavourable orientation compared withrepository layout. The anisotropy factor is assumed to be 15%. In rock domain RFM029 the tunnel
orientation is parallel to the subvertical foliation which coincides with the maximum temperature
contribution in /Hökmark et al. 2009/. The temperature contribution is slightly decreased for rockdomain RFM045 compared to /Hökmark et al. 2009/, since the tunnel orientation is not entirely
parallel to the foliation in combination with lower dip. The uncertainty due to potential bias in thethermal properties is discussed in uncertainty U7. The temperature effect of the potential bias isinterpolated from /Hökmark et al. 2009/ using site-specific data (1% percentile). The uncertainty U8,
related to the uncertainty in the lower tail of the thermal conductivity distribution (SDM-Site) due touncertainties in rock type proportions. The temperature effect related to U8 is small.
Uncertainty U9 is related to uncertainties in the in situ temperature. The lowest and highest temperatures measured in different boreholes at 400 m depth vary by ±0.4°C about the mean /Sundberg
et al. 2008a/. In addition there is possible bias in the measurements which is estimated to be on theorder of 0.25°C. The initial temperature relevant for the dimensioning problem may differ from thepresent-day rock temperature because of heat generated during tunnel excavation, ventilation, etc.
At the time of writing this text it is not possible to estimate these effects or even to establish if the
net effect will be a decreased or an increased effective background temperature /Hökmark et al. 2009/.
U10 is related to uncertainties due to the increased rock temperature and the temperature dependence
in thermal properties. The uncertainty is very low and not site dependent when temperature dependent
properties is included in the numerical approach for thermal dimensioning of the repository /Hökmark
et al. 2009/. Data on temperature dependent properties are taken from /Sundberg et al. 2008a/.

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Uncertainties U11–U13 are not site-specific. Over- and underestimations due to the representationof the canister and numerical precision in the numerical model are not site-specific (S1–S2). S3
includes the effect of boundary conditions in the numerical model. Since the dimensioning is madeon the canister surrounded with rock with very low thermal conductivity it is reasonable to believethat the thermal conductivity in the neighbouring tunnels is higher. 10% higher thermal conductivity
in two neighbouring tunnels gives 0.4°C in temperature contribution /Hökmark et al. 2009/. The difference between the mean thermal conductivity and conductivity around a canister in low conductiverock is typically < 5% and 10% for rock domain RFM029 and RFM045 respectively.
Data uncertainty due to representativity

Uncertainties in the proportions of rock types in Boremap (the drill core mapping) have influenceon the overall distribution of thermal conductivities for each domain. In /Sundberg et al. 2008a/
confidence intervals for TRC proportions has been calculated on the basis of the differences between
different boreholes. The uncertainties are relatively small for rock domain RFM029 /Sundberg et al.
2008a, Table 6-3/ but larger for domain RFM045 /Sundberg et al. 2008a, Table 6-4/ because of itslesser degree of homogeneity in geology and thermal properties. However, this uncertainty has only
a minor effect on the lower thermal conductivity tail (the 1-percentile may vary by about 1%).
The samples used for determinations of thermal conductivity and heat capacity for the main rocktypes are from different sections in different boreholes and are judged to be representative for thedifferent rock types. The representativity can be questioned for subordinate rock types with only few
determinations. However, the effect on the domain modelling results from subordinate rock types issmall.
The representativity of samples selected for thermal expansion measurements can be questioned.
The samples are few and focused to certain parts of the rock volume. However, the variability seems
to be quite low.
Also the representativity of the thermal anisotropy can be questioned. The investigations have been
focused at one site but the strength of the foliation varies in different parts of the Forsmark area
(Chapter 5 in the Site description Forsmark).
The thermal data for permafrost modelling include rock domain data outside the target area (cf.
Section 7.1). The uncertainties in the mean and uncertainty estimates for the thermal conductivity,
heat capacity, and heat production are mainly related to the estimates in rock type proportions, if any,
and if thermal data are available for the different rock types. For some rock domains data are basedon the dominant rock type. The uncertainties can be rather large compared to the rock domain data
inside the target area. However, the uncertainties are not believed to have major influence on thepermafrost modelling result since other uncertainties are judged to be larger.
The temperature loggings come from several boreholes and are judged to be representative for thesite. The temperature data and geothermal gradient data are mainly available for the target area but
are judged to meet the needs for the large-scale permafrost modelling, including areas outside thetarget area.
The heat flow data are judged to be representative for the whole site, including areas outside thetarget area used in permafrost modelling.

6.2.8

Spatial and temporal variability of data

Spatial variability of data

The description of the spatial distribution of thermal conductivity and heat capacity has been themain objective in the thermal site-descriptive modelling. Particular emphasis have been made on
describing the lower tail of the thermal conductivity distribution since small uncertainties in thelower tail of the thermal conductivity distributions will have a significant impact on canister spacing
in repository layout D2, in order to fulfil the maximum temperature criteria on the buffer. Also theuncertainty description in /Back et al. 2007/ and /Sundberg et al. 2008a/ is focusing on the lower

220

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tail of the thermal conductivity distributions for the different rock domains. The models presented
in the site-descriptive modelling, SDM Site, are judged to represent the modelled rock domains andtheir variability in an appropriate way (Site description Forsmark). Confidence in the lower tails of
the thermal conductivity distributions is higher for domain RFM029 than domain RFM045. For thelatter, this uncertainty is related to the spatial and size distribution of amphibolite.
As an important part of the thermal modelling, data are upscaled from measurement scale to simulations scale (1 m in Forsmark). The temperature development around a canister is influenced by anumber of different scales, approximately 2–20 m /Sundberg et al. 2005b/. When using the data tocalculate large enough spacing between canisters, the simulation scale is used (1 m) /Hökmark et al.
2009/. This means that an automatic upscaling is provided in the numerical modelling when calculating the maximum temperature from the data provided in the thermal realisations (see Section 6.2.1).
However, in the site-descriptive modelling reports /Back et al. 2007, Sundberg et al. 2008a/ there
is a need to illustrate the upscaling effect. Upscaling is made to 5 m, a scale that approximately is
relevant for the temperature development around the canister, The result for this upscaling is provided
in the thermal site-descriptive modelling reports and used for trial values on canister spacing /Hökmark
et al. 2009/ and uncertainty estimates.
The appropriate scale for the long-term thermal evolution, caused by climate changes, is much larger
compared to the scale appropriate for thermal evolution modelling of the near-field of individual
canisters. Therefore the mean of thermal conductivity, heat capacity, heat generation, anisotropy etc.
is relevant for this long-term thermal evolution.
The description of the anisotropy in thermal conductivity has been focused on determining a relevantmean anisotropy factor for foliated parts the dominant granite. Consequently, there are uncertainties
in the spatial distribution of the anisotropy.
Temporal variability of data

Except for the in situ temperature, none of the data have temporal variability. The temperature loggings
show a significant influence from drilling that is time dependent. If the loggings have been made toclose to the drilling the result can be questioned. However, such temperature logging data have been
excluded from the evaluation during an approval process, see Section 2.7 in /Sundberg et al. 2008a/.
The natural temperature at repository depth has long-term variability in data. However, this is a very
slow process and the natural undisturbed variability is thus insignificant. The long-term thermal evolution of the repository is discussed in Section 7.1. However, the construction of the repository may
influence the temperature at repository depth. This is discussed above in Section 6.2.7 in connection
to the uncertainty discussion of the temperature margin.

6.2.9 Correlations
As described in Section 6.2.6, the spatial correlation structure for the thermal conductivity has been
based on density logging data. Measurements have been used to support the correlation structure
when possible.
A relationship between thermal conductivity and heat capacity is reasonable and have been foundand described in /Sundberg et al. 2008a/, see Figure 6‑8. The relationship has been used to calculate
the heat capacity C from the thermal conductivity for each cell in the produced realisations from thethermal stochastic modelling. A random error component εc has been added to C, see /Sundberg et al.
2008a, Section 2.6/. This means that the correlations structure for the thermal conductivity has also
been used for the heat capacity.

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221

Thermal conductivity vs. heat capacity
101057, 101051, 101058, 102017, 103076 and 111058
Heat capacity, MJ/(m³·K)

2.7
2.6
2.5

TPS

2.4

Direct measurements

2.3

Poly. (Direct measurements)

2.2

y = 0.1311x2 - 1.0368x + 4.0812
R2 = 0.8154

2.1
2.0
1.9
1.8
1.7
2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0

Thermal conductivity, W/(m·K)

Figure 6‑8. Heat capacity vs. thermal conductivity. The second order relationship is based on calorimetricmeasurements only. Reproduced from /Sundberg et al. 2008a, Figure 2-3/.

6.2.10 Result of supplier’s data qualification
The data are presented in the site-descriptive modelling reports /Back et al. 2007, Sundberg et al.
2008a/. Here a summary is made.
Thermal conductivity

The thermal conductivity is mainly measured in laboratory with the TPS-method. Thermal conductivity versus elevation for different rock types is illustrated in Figure 6‑9. From Figure 6‑9 it canbe seen that the thermal conductivity for the dominant granite (101057) is high, but lower for somesubordinate rock types.
The rock type distribution in boreholes together with the distribution of thermal conductivity data
and the correlation structure are used as bases for stochastic modelling of both geology and thermal
conductivity. The main result of the thermal modelling for domain RFM029 and RFM045 is the setof 1,000 realisations (500 for domain RFM045) of thermal conductivity from the simulations in the
1 m scale. These realisations have been used in the design of the repository /SKB 2009d/ and are
input to the SR-Site numerical thermal calculations (see Section 6.2.1). Histograms of the realisations upscaled to 5 m are shown in Figure 6‑10 for domain RFM029 and RFM045. The 5 m scaled
data are judged to be more relevant for the temperature development around the canister and thepeak temperature in the buffer. Summary statistics of the upscaled realisations to 5 m are presented
in Table 6‑19. These values are valid at 20°C.
Table 6‑19. Thermal conductivity (W/(m·K)) at 20°C for domains RFM029 and RFM045 based on
simulations at the 1 m scale and upscaled to 5 m (data reproduced from Table 6-3 in /Back et al.
2007/ and Table 7-1 in /Sundberg et al. 2008a/ respectively). The 0.1–1.0-pecentiles are used as
first guess value for the canister spacing in the numerical modelling in SR-Site.
Statistical parameter

RFM0295 m scale

RFM0455 m scale

Mean

3.57

3.56

Standard deviation

0.13

0.28

0.1-percentile

2.87

2.36

1-percentile

3.12

2.56

2.5-percentile

3.23

2.73

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Thermal conductivity (W/(m·K)) vs. depth(TPS measurements)
0.000-100.000

Diorite, quartz diorite, gabbro 101033
Granodiorite to tonalite 101051-200.000

Tonalite to granodiorite 101054
Granodiorite - 101056-300.000

Granite to granodiorite 101057

Elevation (m)Amphibolite - 102017-400.000
Granite - 101058
Pegmaite, pegmatitic granite 101061-500.000

Granite - 111058
Felsic-intermediate vocanic
rock - 103076-600.000-700.000-800.000-900.000
2.20

2.60

3.00

3.40

3.80

4.20

Thermal conductivity (W/(m·K))

Figure 6‑9. Thermal conductivity versus elevation for different rock types. Samples measured using the
TPS method. Reproduced from /Back et al. 2007, Figure 3-1/.

Figure 6‑10. Histogram of thermal conductivity of domain RFM029 (left) and RFM045 (right) simulated at
the 1 m scale followed by upscaling to 5 m. Reproduced from Figure 6-3 in /Back et al. 2007/ and Figure 5-3
in /Sundberg et al. 2008a/.

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223

The uncertainties in the lower tail of the modelled thermal conductivity distributions due to uncertainties in rock type proportion is estimated to be 1% /Sundberg et al. 2008a, Section 8.2/.
The thermal conductivity is influenced by the temperature, see Figure 6‑11. Thermal conductivity atelevated temperature can be determined from Equation 6‑1.

λ1 = λ0(1+αλ(T1–T0))

6‑1

where, λ0 (W/(m∙K)) is the thermal conductivity at T0 = 20°C, λ1 is the thermal conductivity at thetemperature T1, and αλ (1/°C) is the temperature coefficient for thermal conductivity.
Estimated mean temperature coefficients for thermal conductivity in different rock types arepresented in Table 6‑20 and the variability is indicated in Figure 6‑12. For some rock types thetemperature coefficient is based on similar rock types from the Laxemar site /Sundberg et al. 2008b/.
For example, the mean temperature coefficient for granite to granodiorite is –­ 10% per 100°C. Thismeans that the thermal conductivity at 20°C is going to be reduced with 5% if the temperature in therock mass is raised to 70°C.
Table 6‑20. Estimated mean temperature coefficients for thermal conductivity in different rocktypes inside target area. The thermal rock class (TRC) to which the rock type has been assigned
is also given. Data reproduced from Table 2-3 in /Sundberg et al. 2008a/.
Rock
code

Rock name

TRC

Mean thermal
conduc­tivity at
approx 20°C
W/(m·K)

Mean
temperaturedependence% per 100°C

Temperature
coefficient, αλ1/°CComments

101057

Granite togranodiorite

57

3.68

–10

–1·10–3

Measured

101051

Granite,granodiorite
and tonalite

51

2.85

–5

–5·10–4

Estimated from
Figure 6‑12101058Granite, aplitic

58

3.85

–11

–1.1·10–3

Estimated from
Figure 6‑12101061

Pegmatite

61

3.33

–8

–8·10–4

Estimated from
Figure 6‑12

Amphibolite

17

2.33

–5

–5·10–4

Literature data
/Seipold andHuenges 1998/.

Temperature dependence of thermal conductivity,
granite to granodiorite (101057)

KFM01A 492.39-492.51 m
KFM01A 493.74-493.85 m
KFM01A 493.85-493.97 m

4.1

KFM01A 493.97-494.09 m

Thermal conductivity(W/m·K)

4.0

KFM01A 494.09-494.20 m
KFM01A 494.20-494.32 m

3.9

KFM01A 494.32-494.43 m

3.8

KFM01A 494.51-494.62 m

3.7

KFM01A 494.62-494.74 m
KFM01A 494.82-494.94 m

3.6

KFM02A 528.15-528.27 m

3.5

KFM02A 530.76-530.88 m

3.4

KFM02A 530.88-531.01 m
KFM02A 532.09-532.21 m

3.3

KFM02A 536.56-536.69 m

3.2

KFM03A 527.23-527.30 m
20

30

40

50

Temperature (°C)

60

70

80

KFM03A 527.30-527.36 m
KFM03A 527.36-527.42 m

Figure 6‑11. Example of temperature dependence of thermal conductivity for the rock type granite togranodiorite (101057).Reproduced from /Back et al. 2007, Figure 3-12/.

224

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Temperature influence, %/100°C

Temperature dependence - Thermal conductivity
50Granite to granodiorite (101057)
Ävrö granite (501044-5

Quartz monzodiorite (501036)-10

Fine-grained dioritoid (501030)-15

Para gneiss (Seipold and Huenges 1998)

Amphibolite (Seipold and Huenges 1998)
Amphibolite (Abdulagatov et al. 2006)-20

Granulite (Abdulagatov et al. 2006)

-25
-30
2.0

2.5

3.0

3.5

4.0

4.5

Thermal conductivity at 20-25°C (W/m·K)

Figure 6‑12. Thermal conductivity (W/(m·K)) at 20–25°C versus the temperature coefficient (%/100°C) for
the thermal conductivity in different rock types. Rock types with codes beginning with “50” are found in theLaxemar/Simpevarp area. Reproduced from Figure 2-1 in /Sundberg et al. 2008a/.

Anisotropy in thermal conductivity means that the conductivity (and the thermal diffusivity) is
different in different directions. In a foliated rock the thermal conductivity is commonly higher
parallel to the foliation plane compared to a direction perpendicular to the foliation. In a lineatedrock the thermal conductivity is consequently assumed to be higher in the direction of the lineation.
Anisotropy in thermal conductivity due to foliation/lineation in the dominant granite to granodiorite
has been measured in situ at one location with different methods. Field measurements indicate that
thermal conductivity parallel to the foliation plane is higher, by a factor of approximately 1.15, thanconductivity perpendicular to the foliation /Back et al. 2007, Sundberg et al. 2007/.
It is logical to assume a pressure dependence on the thermal conductivity. However, the pressure
influence is low if the samples are water saturated, approximately 1–2% /Walsh and Decker 1966/,
since any increased micro-cracks porosity as a result of pressure release is water filled. All determinations of thermal conductivity in the site investigation programme have been made on water-saturated
samples. The pressure dependence has therefore been neglected in the evaluation of thermal conductivity/Sundberg et al. 2008a/.
For the large-scale thermal evolution due to climate changes, thermal data are needed for a larger
area, outside the target area. Data on mean thermal conductivity, and the associated standard deviation, are supplied in Table 6‑21.

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225

Table 6‑21. Thermal conductivity (W/(m·K)) and temperature dependence (%/100°C) for a numberof rock domains outside the target area, as well as domain RFM029.
Rock domain

RFM003

Rock code(dominant)Dominant rock type /SKB 2005b, 2006g/

Thermalconductivity(W/(m·K))

Tempera­ture
depen­dence(%/100°C)

mean

Std

mean

1, 7

103076felsic to intermediate volcanic rock, metamorphic

3.02

0.42

–5

RFM0051, 7101033

diorite, quartz diorite and gabbro, metamorphic

2.51

0.21

–1

RFM0071, 7101033

diorite, quartz diorite and gabbro, metamorphic

2.51

0.21

–1

RFM0122, 5101057

granite to granodiorite, metamorphic

3.45

0.28

–8

RFM0181, 7

101054

tonalite to granodiorite, metamorphic

2.73

0.19

–3

RFM0201, 7101058

granite, metamorphic, aplitic

3.83

0.22

–11

RFM0211, 7103076felsic to intermediate volcanic rock, metamorphic

3.02

0.42

–5

RFM0221, 7111058

granite, fine- to medium grained

3.49

0.27

–9

RFM0231, 7101054

tonalite to granodiorite, metamorphic

2.73

0.19

–3

RFM0241, 7101054

tonalite to granodiorite, metamorphic

2.73

0.19

–3

RFM0251, 7101033

diorite, quartz diorite and gabbro, metamorphic

2.51

0.21

–1

RFM0261, 6

101057

granite to granodiorite, metamorphic

3.68

0.17

–10

RFM0293, 5101057

granite to granodiorite, metamorphic

3.58

0.29

–9

RFM0301, 7101054

tonalite to granodiorite, metamorphic

2.73

0.19

–3

RFM0311, 7103076felsic to intermediate volcanic rock, metamorphic

3.02

0.42

–5

RFM0321, 7101058

granite, metamorphic, aplitic

3.83

0.22

–11

RFM0331, 6

101057

granite to granodiorite, metamorphic

3.68

0.17

–10

RFM0401, 61110578

granite to granodiorite, metamorphic, veined to migmatitic

3.68

0.17

–10

RFM0421, 61110519

granitoid, metamorphic

3.12

0.30

–5

RFM0431, 7101058

granite, metamorphic, aplitic

3.83

0.22

–11

RFM0444, 5101057

granite to granodiorite, metamorphic

3.48

–

–9

Rock type proportions not known, thermal conductivity for dominating rock type /Back et al. 2007/.
Thermal conductivity has been modelled in /Sundberg et al. 2005a/.
3
Thermal conductivity has been modelled in /Back et al. 2007/. Data from 1 m scale.
4
Thermal conductivity calculated from rock type proportions based on data for rock types in /Back et al. 2007/, standard
deviation not possible to estimate.
5Temperature dependence calculated from rock type proportions.
6
Temperature dependence for dominating rock type /Back et al. 2007/.
7
Temperature dependence estimated from Figure 6-12.
8
Not investigated, similar to 101057. /Stephens et al. 2007/.
9
Not investigated, assumed similar to 101051.
1
2

226

TR-10-52

Heat capacity

Heat capacity distributions at rock domain level have been integrated in the thermal realisation(see Section 6.2.9), used in SR-Site. The results for the 1 m scale for rock domains RFM029 and
RFM045 are presented in Figure 6‑13 and Table 6‑22.

Figure 6‑13. Histogram of heat capacity for domain RFM29 and RFM045 based on the relationshipbetween heat capacity and thermal conductivity and simulations of thermal conductivity at the 1 m scale.
Reproduced from Figures 5-7 and 5-8 in /Sundberg et al. 2008a/).
Table 6‑22. Mean heat capacity (MJ/(m3·K)) at 20°C for domains RFM029 and RFM045 based on
the relationship between heat capacity and thermal conductivity, and simulations of thermal
conductivity at the 1 m scale. Data from Table 7-2 in /Sundberg et al. 2008a/.
Statistical parameter

RFM0291 m scale

RFM045
1 m scale

Mean

2.06

2.12

Standard deviation

0.10

0.15

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227

The heat capacity is influenced by the temperature. The heat capacity at elevated temperature canbe determined from Equation 6‑2.

C1 = C0(1+ αC(T1–T0))

6‑2

where C0 and C1 (MJ/(m3∙K)) is the heat capacity at room temperature T0 and elevated temperature
T1, respectively, and αC (1/°C) is the temperature coefficient for heat capacity. Estimated mean
temperature coefficients for heat capacity in different rock domains are presented in Table 6‑23,
and the variability in Figure 6‑14.
Table 6‑23. Estimated mean temperature coefficients for heat capacity in different rock types
inside target area. The thermal rock class (TRC) to which the rock type has been assigned is also
given. Data from Table 2-4 in /Sundberg et al. 2008a/.
Rock
code

Rock name

TRC

Mean Heat
Capacity at
approx 20°C
MJ/( m3·K)

Meantemperaturedependence% per 100°C

Temperature
coefficient,αC 1/°C

Comments

101057

Granite togranodiorite

57

2.06

29

2.9·10–3

Measured

101051

Granite,granodiorite
and tonalite

51

2.15

25

2.5·10–3

Estimated from Figure 6‑14101058Granite, aplitic

58

2.01

25

2.5·10–3

Estimated from Figure 6‑14

101061

Pegmatite

61

1.921

25

2.5·10–3

Estimated from Figure 6‑14

Amphibolite

17

2.41

10

1·10–3

Literature data /Seipold andHuenges 1998/¹based on calculations from TPS data. Other based on calorimetric measurements.

Temperature influence, %/100°C

Temperature dependence - Heat capacity
40
35

Granite to granodiorite (101057)

30Ävrö granite (501044)

25

Quartz monzodiorite (501036)

20

Fine-grained dioritoid (501030)

15

Amphibolite (Seipold & Huenges,1998)

10

Paragneiss (Seipold & Huenges,1998)

5
0
1.5

2.0

2.5

3.0

3.5

Heat capacity at 20-25°C (MJ/m ·K)
3

Figure 6‑14. Heat capacity (W/(m·K)) at 20–25°C versus the temperature coefficient (%/100°C) for theheat capacity in different rock types. Rock types with codes beginning with “50” are found in the Laxemar/
Simpevarp area. Reproduced from Figure 2-2 in /Sundberg et al. 2008a/.

228

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For the large-scale thermal evolution due to climate changes, thermal data are needed for a larger
area, outside the target area. Data on mean heat capacity are supplied in Table 6‑24.
Table 6‑24. Heat capacity (MJ/(m³·K)) and temperature dependence (%/100°C) for a number ofrock domains outside the target area, as well as domain RFM029.
Rock domain

RFM003

Rock code(dominant)Dominant rock type /SKB 2005b, 2006g/

Heat capacity(MJ/(m³·K))

Tempera­ture
depen­dence(%/100°C)

mean

Std

mean

1, 7

103076felsic to intermediate volcanic rock, metamorphic

2.26

–

25

RFM0051, 8101033

diorite, quartz diorite and gabbro, metamorphic

2.33

–

10

RFM0071, 8

101033

diorite, quartz diorite and gabbro, metamorphic

2.33

–

10

RFM0122, 6101057

granite to granodiorite, metamorphic

2.17

0.15

28

RFM0183, 7101054

tonalite to granodiorite, metamorphic

2.12

0.20

25

RFM0203, 7101058

granite, metamorphic, aplitic

2.08

0.10

25

RFM0211, 7103076felsic to intermediate volcanic rock, metamorphic

2.26

–

25

RFM0224, 7111058

granite, fine- to medium grained

2.06

0.05

25

RFM0233, 7101054

tonalite to granodiorite, metamorphic

2.12

0.20

25

RFM0243, 7101054

tonalite to granodiorite, metamorphic

2.12

0.20

25

RFM0251, 8101033

diorite, quartz diorite and gabbro, metamorphic

2.33

–

10

RFM0264, 6

101057

granite to granodiorite, metamorphic

2.06

0.06

28

RFM0295, 6

101057

granite to granodiorite, metamorphic

2.06

0.10

28

RFM0303, 7101054

tonalite to granodiorite, metamorphic

2.12

0.20

25

RFM0311, 7103076felsic to intermediate volcanic rock, metamorphic

2.26

–

25

RFM0323, 7101058

granite, metamorphic, aplitic

2.08

0.10

25

RFM0334, 6

101057

granite to granodiorite, metamorphic

2.06

0.06

28

RFM0404, 61110579

granite to granodiorite, metamorphic, veined to migmatitic

2.06

0.06

28

RFM0424, 711105110

granitoid, metamorphic

2.15

0.05

25

RFM0433, 7101058

granite, metamorphic, aplitic

2.08

0.10

25

RFM04411, 6

101057

granite to granodiorite, metamorphic

2.07

–

28

1
Rock type proportions not known, heat capacity (TPS measurements) for dominating rock type /Back et al. 2007/, too
few values to calculate standard deviation.
2
Heat capacity has been modelled in /Sundberg et al. 2005a/.
3
Rock type proportions not known, heat capacity (TPS measurements) for dominating rock type /Back et al. 2007/.
4
Rock type proportions not known, heat capacity (calorimetric measurements) for dominating rock type
/Sundberg et al. 2008a/.
5
Heat capacity has been modelled in /Sundberg et al. 2008a/.
6
Temperature dependence calculated from measurements /Sundberg et al. 2008a/.
7
Temperature dependence calculated from measurements from corresponding rock types at the Laxemar site
/Sundberg et al. 2008a/.
8
Temperature dependence calculated from literature data, too few values to calculate standard deviation/Sundberg et al. 2008a/.
9
Not investigated, similar to 101057 /Stephens et al. 2007/.
10
Not investigated, assumed to be similar to 101051.
11
Heat capacity calculated from rock type proportions and data for rock types in /Back et al. 2007/ and/Sundberg et al. 2008a/. Standard deviation not possible to estimate.

Thermal diffusivity

Thermal diffusivity (m2/s) is a measure of a material’s ability to level out temperature differences.
The thermal diffusivity κ for each domain can be calculated from the thermal conductivity λ and heat
capacity C for each domain by the following equation:
κ = λ/C

6‑3

In Table 6‑25 the calculated mean thermal diffusivity for each domain is given.

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229

Table 6‑25. Mean thermal diffusivity (m2/s) at 20°C of a number of rock domains based on theheat capacity and thermal conductivity in Table 6‑21 and Table 6‑24 respectively.
Rock domain

Thermal diffusivity
m2/s

Rock domain

Thermal diffusivity
m2/s

RFM003

1.34·10–6

RFM026

1.79·10–6

RFM0051.08·10–6

RFM0291.74·10–6

RFM0071.08·10–6

RFM0301.29·10–6

RFM012

1.59·10–6

RFM0311.34·10–6

RFM0181.29·10–6

RFM032

1.84·10–6

RFM0201.84·10–6

RFM033

1.79·10–6

RFM0211.34·10–6

RFM0401.79·10–6

RFM022

1.69·10–6

RFM042

1.45·10–6

RFM023

1.29·10–6

RFM043

1.84·10–6

RFM0241.29·10–6

RFM044

1.68·10–6

RFM0251.08·10–6

Temperature and geothermal gradient

The mean in situ temperature measured at 400 m, 500 m and 600 m depth, based on eight boreholes,
is estimated to 10.5°C, 11.6°C, and 12.8°C respectively, see Table 6‑26 /Sundberg et al. 2008a/. The
gradient is approximately 11–13°C/km at repository depth. The temperature and the geothermal
gradient versus elevation are displayed in Figure 6‑15.
Table 6‑26. Temperature (°C) for the “approved” boreholes at the Forsmark site, at different
levels. Borehole inclinations are also included for the boreholes, given as lowest and highest
angle (modified from Table 2-6 in /Sundberg et al. 2008a/).
Temperature (°C) at depth
400 m

500 m

600 m

Arithmetic mean

10.5

11.6

12.8

Uncertainty

±0.4

±0.4

±0.3

Heat generation

The heat generation based on mean proportions of different rock types in the rock and the content ofU, Th and K is calculated to 3.16 and 3.11 µW/m3 for domain RFM029 and RFM045, respectively(Section 6.1.3 in /Sundberg et al. 2009a/). The reason for the slightly higher values for domain levelis the significantly higher proportions of pegmatite. The uncertainty is judged to be within ±30%
based on ±1 std for the dominant rock types /Sundberg et al. 2009a/.
Heat flow

The palaeoclimatically corrected surface mean heat flow at Forsmark is suggested to be 61 mW/m2
respectively (Section 8.1 in /Sundberg et al. 2009a/). The heat flow determination is judged to bewithin +12% to –14%.

230

TR-10-52

Temperature (°C)
8

12

16

-20

20

0

0

-200

-200

Elevation (metres)

Elevation (metres)

4

Gradient (°C/km)

-400

-1000

20

40

-400

-600

-600

-800

0

KFM01AKFM02AKFM03AKFM04AKFM06CKFM07CKFM08CKFM09B

-800

-1000

KFM01AKFM02AKFM03AKFM04AKFM06CKFM07CKFM08CKFM09B

Figure 6‑15. Temperature (a) and vertical geothermal temperature gradients calculated for nine metreintervals (b) for the eight “approved” boreholes at Forsmark. Results from fluid temperature loggings from
Figure 2-4 in /Sundberg et al. 2008a/.

Thermal expansion coefficient

The mean thermal expansion coefficient for the main granitoid rock types within the target volume
varies between 7.5·10–6 and 7.8·10–6 m/(m∙K), see Table 6‑27. The mean values of measured thermal
expansion for the different rock types are rather similar. Samples of amphibolite (102017) have notbeen investigated.
Temperature margin

There are uncertainties in input data as well as systematic over- and underestimates associated withthe calculation of the peak buffer temperature in the thermal dimensioning of the repository. This is
described in Section 6.2.7. The temperature margin up to the criteria is summarised in Table 6‑28.
The temperature margin is 4.5°C for both rock domains.

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231

Table 6‑27. Measured thermal expansion (m/(m·K)) on samples with different rock types within
the temperature interval 20–80°C. Data from Table 3-19 in /Back et al. 2007/.
Rock code

Rock name

Arithmetricmean

St. dev.

Min

Max

No. of samples

101057

Granite to granodiorite

7.7 ·10–6

2.2 ·10–6

2.1 ·10–6

1.5 ·10–556101056

Granodiorite

8.1 ·10–6

3.4 ·10–6

5.2 ·10–6

1.4 ·10–5

6101054

Tonalite to granodiorite

7.2 ·10–6

1.6 ·10–6

5.3 ·10–6

8.2 ·10–6

3101051

Granite, granodiorite andtonalite

7.8 ·10–6

1.2 ·10–6

6.5 ·10–6

1.0 ·10–5

6101058Granite, aplitic

7.5 ·10–6

5.4 ·10–7

6.9 ·10–6

8.0 ·10–6

3

Table 6‑28. Total temperature margin in numerical solution to establish a definitive spacing.

Total Margin

Domain 29

Domain 45

4.5°C

4.5°C

6.2.11 Judgements by the SR-Site teamThe SR-Site team are in general satisfied with the way data have been presented in the sections
above. Detailed comment on different sections is as follows.
Sources of information and documentation of data qualification

The sources of information are properly referenced and the categorisation into qualified and supporting data is judged to be adequately performed.
Conditions for which data are supplied

The conditions for which data are supplied are judge to be adequate for the use in SR-Site.
Conceptual uncertainty

The conceptual uncertainty in the thermal properties is judged to be sufficient described. The
conceptual uncertainty is mainly related to the correlation between thermal conductivity and density.
The reason for why the correlation exists is explained satisfactorily. The use of density loggings istherefore judged to be relevant to describe the spatial correlation structure for the thermal conductivity and the heat capacity. Consequently it is also relevant for the use of subdividing different thermalrock classes. The conceptual uncertainty in the thermal properties is judged to have only small
influences on the SR-Site modelling results.
Data uncertainty due to precision, bias, and representativity

The data uncertainty in the provided data is judged to be satisfactorily described. The impact of thedata uncertainty on the SR-Site modelling results is in general judged to be small. Concerning thetemperature margin (up to the temperature criteria in the thermal dimensioning), the supplier has
tried hard to establish a safe, yet not overly pessimistic, margin. It can be discussed if one or twoof the underlying uncertainties that constitute the margin are totally on the safe side. On the other
hand the relevance in summing up underlying uncertainties to a total uncertainty can be discussed.
The probability that all underlying uncertainties contributes to a larger temperature margin for thecanister with lowest surrounding thermal conductivity is judged to be quite low. Consequently theestablished temperature margin is judge to be adequate for the use in SR-Site.

232

TR-10-52

Spatial and temporal variability of data

The supplier has put large effort on the description of the spatial variability of the thermal conductivity.
The thermal strategy involves stochastic modelling of both geology and thermal conductivity. A large
number of realisations have been produced for each rock domain in order to describe the total spatial
variability, including the lower tail of the distribution influencing the canister spacing. The strategy
has also been reviewed several times by the SIERG group. Since the thermal realisations are directly
used in the thermal dimensioning of canister spacing, the spatial variability is adequately transferred.
The temporal variability in thermal data is in general small. However, the temperature at repository
depth may be influenced by the construction of the repository. For the moment it is unclear if thetemperature will rise or decline compared with the in situ conditions. During construction it ispossible to maintain the temperature by technical resources. The impact of temporal variability in thetemperature on the SR-Site modelling is judged to be low since it is possible, if necessary, to avoid arise in the temperature during construction by the means of technical efforts.
Correlations

The correlations used in the thermal modelling is already discussed, see conceptual uncertainty above.
Result of supplier’s data qualification

The “Result of the data qualification” is presented scientifically and adequately. The choice of data isalso judged to be sufficient for the use in the SR-Site modelling.

6.2.12 Data recommended for use in SR-Site modelling
All data presented in Section 6.2.10 together with the realisation of thermal properties are recommended to be used in SR-Site modelling. Note that data are found both as tabulated and in the text in
Section 6.2.10.

6.3

Discrete-Fracture Network (DFN) models

The identification, mapping, modelling, and understanding of bedrock fractures at Forsmark are
key components of the site description, which feeds directly into the repository safety assessment.
Fractures are important to both the design (available deposition volume, tunnelling, and excavation
stability) and the long-term performance (groundwater and heat flow, radionuclide transport in theevent that a canister is compromised, and the question of post-glacial seismic safety) of a spent
nuclear fuel repository.
The handling of fracture data from the site investigation can be divided into two general classes:
1. Description of the geometry, geology, morphology, genesis, and deformation history of brittle
structures in the bedrock at Forsmark.
2. Conceptual and mathematical representation of the fracture system in a format amenable to use
in site characterisation, engineering design, and safety assessment efforts.
The description of the geometry and morphology of bedrock fractures includes the assessment ofpatterns of orientation, geometry and spatial arrangement, intensity, and size as functions of depth,
spatial location, and geologic volumes (fracture and rock domains). In addition, properties ofimportance to mechanical modelling, groundwater flow, and contaminant transport have also beenassessed. These properties include aperture, mineral infilling, and fracture surface roughness.
SKB has chosen the discrete-fracture network (DFN) /Munier 2004/ methodology for the conceptualand mathematical representation of bedrock fractures (joints and faults) in repository design andnumerical simulation. In a discrete-fracture network model, bedrock fractures are generally treatedas circular disks in 3D-space, though some applications represent the fractures instead as simple
n-sided polygonal structures. The geometric properties of the fractures can be considered either fixed(deterministic) or variable (stochastic) in space and/or time as probability functions /NRC 1996/.
This is in contrast to the stochastic continuum approach, frequently used in numerical modelling
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of groundwater and heat flow, where the fracture system is treated as bulk volumetric properties(conductivity, storage) of the rock mass and the geometries of individual features are largely ignored.
The DFN methodology is well-suited to groundwater flow, contaminant transport, and mechanicalmodelling in rocks where the fractures dominate the bedrock structure and flow system (such as themassive igneous and metamorphic rocks that make up the bedrock at Forsmark).

6.3.1

Modelling in SR-Site

Elements of Discrete Fracture Network models

As many readers are not likely to be familiar with discrete-fracture network terminology, we haveincluded a brief discussion of terminology relevant to DFN modelling. In a stochastic geological
DFN model, the geometries of fractures in space are described using probability distributions calculated from data recorded during the site investigation. To fully describe the geometries and propertiesof fractures in space, the following parameters must be known:
•
•
•
•
•
•
•

Fracture orientation.
Fracture size (how large the fractures are).
Fracture intensity (how many fractures there are, and how the number changes spatially).
Fracture locations (i.e. spatial model).
Fracture mineralogy (mineral infillings, host-rock alteration).
Fracture hydraulic properties (transmissivity, hydraulic aperture).
Fracture mechanical properties (shear stiffness, normal stiffness).

Fractures represent brittle deformation at points of weakness or stress concentrations in the bedrock;the direction (orientation) of the fractures is generally a function of the in situ stress state at the timeof rock breakage, which in turn can be a function of spatial location. Fractures are rarely randomly
oriented; they generally occur as members of sets, which are generally described as a clustering of
fracture orientations around a common mean direction. Fractures can also be classified into setsbased on other properties, such as fracture mineralogy, structural position (e.g. related to folding),
host lithology, or morphology. However, at the Forsmark site, these other properties are at best athird-order effect; sets are primarily a function of fracture orientation.
The orientation of a fracture in space is described in terms of the trend and plunge, in degrees, ofthe fracture pole (see Figure 6‑16), which is perpendicular to the plane of the fracture and is always
oriented “down”. The trend and plunge of each fracture pole is then plotted on a stereonet drawn
with a lower-hemisphere projection. Fractures with similar orientations will tend to form discrete
clusters on the pole plot. The average (mean) fracture orientation, cluster shape and degree of clustering (dispersion) of these sets of fracture poles can be quantified using a hemispherical probability
distribution, which allows fracture orientations to be represented in numerical models stochastically.
The most commonly used hemispherical probability distribution is the univariate Fisher distribution/Fisher 1953, Fisher et al. 1993/, which, as the spherical equivalent of the normal distribution, willproduce poles that are symmetrically distributed around the mean pole in space. The univariate
Fisher distribution takes the following form:f (θ ) =κ ⋅ sin θ ⋅ e κ cos θ, 0 ≤θ ≤πe κ − e −κ
|

|

6‑4

in which θ (°) is the conjugate angle between the resultant vector rn (–) and a given pole vector,
while κ (–) is the concentration parameter (a measure of the concentration around the central axis)
and N (–) the total number of pole vectors. As the value of κ increases, the degree of clustering
around the mean direction increases. Other hemispherical probability distributions, such as the
Bingham or Bivariate Normal, can also be used to describe more complex patterns of fracturepoles. However, the geological DFN models produced during stage 2.2 site-descriptive modelling at
Forsmark utilise only univariate Fisher distributions; please refer to the geological DFN summary
report /Fox et al. 2007/ for a further discussion on the effects and rationale behind this decision.

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N

ke

Stri

Dip

ri

Ho

D ip

ect

ane
e Pl
ctur

Fra

Dir

ane

l Pl

ta
zon

ion

Dip Direction
Dip

Pole Trend
ge

lun

le P

Po

re

tu
rac

le

Po

F

Figure 6‑16. Illustration of terms used to describe the orientation of fractures in space.

Fracture intensity can be described in several different ways; however, SKB and the DFN modellingteams have adopted the terminology of /Dershowitz 1985/ to describe fracture intensity:•

P10: A measure of lineal fracture intensity in units of (1/m), expressed as the number of fractures
within a given measurement length. P10 is usually recorded on scanlines along outcrops andtunnel walls, or along the centreline of a cored borehole.

•P21: A measure of areal fracture density in units of (m/m2), expressed as the sum of the length of
all fracture traces within a specified area, divided by that area. P21 is generally recorded throughtwo-dimensional surface outcrop or tunnel mapping.

•P32: Volumetric fracture intensity in units of (m2/m3), expressed as the sum of the one-sided
surface area of all fractures in a specified region divided by the volume of that region. It is generally not possible to measure P32 directly; it is usually inferred through empirical relationships toP10 and P21 or calculated through stochastic simulation.

Fracture size is quantified in terms of one-sided surface area, which is in turn defined by a single
variable; the equivalent radius, r (m) of a circular disk fracture. A probability distribution is used
to characterise the distribution of fracture radii, generally as a function of fracture set and fracturedomain. Note that the specification of fracture size as a radius does not mandate that fractures must
be modelled as circular disks; any n-sided polygon is acceptable, so long as the resulting polygon
has the same one-sided surface area as the ‘equivalent’ circular disk fracture using the same value ofr. Past site characterisation efforts /Darcel 2003, Darcel et al. 2003, Hermanson et al. 2005, Darcel
et al. 2006a, b, Fox et al. 2007/ have shown that, at least at the two investigated sites of Forsmark
and Laxemar in Sweden, the Pareto distribution, which represents a power-law behaviour of fracture
size, has been suggested to be the best-fit to observed data.

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The power-law distribution after /Evans et al. 1993/, modified to describe fractures in terms ofequivalent radius r, takes the form of:f (r ) =k r ⋅ r0kr
r0kr +1

r0 ≤ r < ∞6‑5

where r0 (m) represents the mathematical minimum radius (the smallest fracture described by
the power-law), and kr (–) the shape parameter. In practice, however, the size distribution is often
truncated at a maximum size (rmax) that is driven either by the limits of available data or therequirements of the project. At Forsmark, a decision has been made to truncate stochastic fracture
modelling (that is the geological DFN) at the size of structures with a 1 km2 one-sided surface area.
This is equivalent to a square fracture 1,000 m long on a side, with an area equivalent to that of acircular disc fracture with a radius (rmax) of 564.2 m. Structures larger than this are described by thedeterministic deformation zone model (cf. Chapter 5 of /Stephens et al. 2007/).
The geological DFN presents two alternative models for estimating the fracture size distribution
parameters:
1. r0-fixed models: In this suite of model alternatives, fracture intensity (P32) and the minimum
fracture size are fixed (constrained) so that the radius scaling exponent kr can be estimated fromobserved borehole data.
2. kr-fixed models: In this suite of model alternatives, fracture intensity P32 is fixed, while values of
kr are estimated from area-normalised cumulative number plots of outcrop and minor deformation
zone (MDZ) trace data from the ground surface. Once kr and P32 are known, r0 is calculated so as
to simultaneously match borehole and outcrop intensities.
The relationship between fracture size and fracture intensity is quite complex; readers are strongly
encouraged to consult the geological DFN summary report /Fox et al. 2007/ before using any of thegeological DFN tables in downstream modelling.

Figure 6‑17. Example of trace length distribution (red histogram) of fractures whose sizes follow a
power-law distribution (inset DFN model). Note that due to truncation effects and minimum fracture sizes,
the trace length histogram does not show a ‘true’ power law, in which y (frequency) would asymptoticallyapproach infinity as x (trace length) approached zero.

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Spatial modelling in the geological DFN encompasses a wide variety of aspects of the spacing andlocations of the fractures themselves, including:•Termination relationships between fracture orientation sets.

•

Spatial variability of fracture intensity. In the latest version of the geological DFN model, spatial
variability is a function of a generalised Gamma distribution /Evans et al. 1993/, with correction
for variable lithology and fractal intensity scaling.

•

Fracture intensity scaling, which describes whether the intensity of fracturing changes asa function of model scale (that is do big fractures have a different P32 than small fractures).

•

Location of fracture centres in three-dimensional space, including relevant geological or
structural controls.

For further readings on the DFN concept and the vocabulary associated with DFN modelling at
Forsmark, readers are encouraged to consult SKB’s DFN methodology document /Munier 2004/.
Defining the data requested from the supplier

The supplier is expected to provide a complete description of a stochastic model for fractures andminor deformation zones outside of deformation zones and inside fracture domains at Forsmark.
The parameterization of the stochastic model should be based on observed data. The minimum
parameters necessary to recreate a geological DFN model in SR-Site are specified below:•

Domain: Refers to the volume for which the DFN parameters are valid, e.g. fracture domains,rock domains, hydraulic domains, and other relevant volumes as required.

•

Set ID: Refers to the fracture set for which the parameters are valid. It is common practice to use
the mean orientations of the planes, such as “ENE”, “SubH”, etc. to identify sets.

•

Trend (°): Refers to the trend of the mean fracture pole (normal), in degrees.

•

Plunge (°): Refers to the plunge of the mean fracture pole (normal), in degrees.

•κ (–): The Fisher concentration parameter, for the univariate Fisher distributions.

•rmin (m): The minimum fracture radius for which the DFN model is valid. Unless stated differently, rmin = r0.

•

rmax (m): The maximum fracture radius for which the DFN is valid. It has been common practicein the SDM work to use rmax = 564.2 m, which corresponds to a fracture with a one-sided surface
area of 1 km2. At Forsmark, a surface trace length cut-off of 1,000 m was used as the boundary
between stochastic DFN models and deterministic DZ models. As such, rmax corresponds to theequivalent radius of a square fracture 1,000 long by 1,000 m deep.

•

P32 (m2/m3): The intensity of fractures and minor deformation zones in the size range r0 – rmax.

•

Fracture size distribution parameters. For a power-law distribution, this is the scaling exponent
kr and the minimum radius r0 (cf. Equation 6‑5); for a log-normal distribution of fracture size, itwould be the distribution mean radius r (m) and standard deviation σ (–).

SR-Site modelling activities in which data will be used

DFN models will be used in SR-Site to:• Estimate the intersection probabilities of fractures with canisters, deposition holes, and excavation
structures. The fracture intersection probabilities control the degree of utilisation of the repository, which influences the rock volume required for a particular design /Munier 2006, 2007/.
•

Understand the risks of buffer degradation, canister failure, and the compromise of repository
integrity during a seismic event. The Forsmark site is set deep within the Fennoscandian Shield,a large area of Archean to Precambrian crystalline igneous and moderate- to high-grade metamorphic rocks that are extremely tectonically stable; the principal seismic risk is estimated to be from

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faults reactivated through crustal unloading and isostatic rebound after the next glacial cycle/Munier and Hökmark 2004/. The risk is quantified by the computation of the number of canisters
that are intersected by critically-large fractures /Munier and Hökmark 2004, Fälth and Hökmark
2006, Fälth et al. 2007, 2008, Hedin 2008/.
•

Describe the saturated bedrock flow system at site and local scales. The groundwater flow
model is a fundamental input to hydrochemical and transport models, which themselves areintegral parts of the repository safety assessment and long-term safety calculations. Note that thehydrogeological DFN is a separate entity (cf. Section 6.6) from the geological DFN and is notdiscussed further within this section; nevertheless, both conceptualisations are based on similar
fracture data.

6.3.2

Experience from SR-Can

Modelling in SR-Can

Geological DFN models were used in SR-Can to compute intersection probabilities with canisters
and deposition holes /SKB 2006a/. The intersection probabilities, which are largely controlled by thefracture size and intensity distributions, were used to calculate the degree of utilisation available in a
proposed repository for a number of scenarios, which branched both conceptual and data uncertainty
space. SR-Can used the geological DFN model parameterisation from stage 1.2 preliminary site
description modelling at Laxemar /Hermanson et al. 2005/ and Forsmark /La Pointe et al. 2005/.
Conditions for which data were used in SR-Can

Geological DFN model parameterisations used in SR-Can were valid for the Local model volume
at both sites; as of stage 1.2 modelling fracture domains were not established at either site. In stage
1.2 models, fracture intensity was modelled as a function of rock domain /La Pointe et al. 2005,
Hermanson et al. 2005/, while all other parameters (fracture orientation, size, and spatial arrangement) were a global function of the Local model volume.
Changes in fracture properties as functions of future processes (underground construction, de-watering,thermal loading, climate change, or additional mechanical loading from future glacial cycles) were
not assessed by the geological DFN team. Instead, these processes were handled as part of the
scenario analysis of SR-Can /SKB 2006a/.
Sensitivity to assessment results in SR-Can

The intersection probability after /Hedin 2005/ is largely a function of the geometry of the fractures,and their relationship to the geometry and size of the canisters and the repository deposition holes
and tunnels. The following conclusion arose out of the SR-Can assessment:
1. If a univariate Fisher orientation distribution (as used for all fracture sets in the Laxemar stage 1.2
model, and for three of the five fracture sets in the Forsmark stage 1.2 model) is assumed, thecanister intersection calculation is relatively insensitive to fracture orientation /SKB 2006b/.
2. Stage 1.2 modelling results at both Laxemar and Forsmark recommended that, though a weakfractal clustering relationship was visible in observed data, a Poisson point process should be
used to describe fracture centres in space. All other factors being equal, a Poisson location model
will slightly increase the number of affected canisters /SKB 2006b/.
3. Fracture size, and, to a lesser extent, fracture intensity, has the largest potential effect on canisterand deposition-hole intersection probability. In particular, the population of fractures within
the size (radius) range of 50–250 m are critical to the degree of repository utilisation. Fractures
within this size range represent a threat to both the hydraulic stability of the bentonite buffer/SKB 2006a/ and to canister shear failure and rupture during seismic events /Munier and
Hökmark 2004/. All other parameters being equal, larger values of the radius scaling exponent
kr will lead to a smaller number of large (≥ 50m) fractures and minor deformation zones, while alarger minimum radius r0 will lead to a bigger number of large fractures and minor deformation
zones.

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Correlations used in SR-Can modelling

The fracture size model parameterisation was fundamentally coupled to the fracture intensity model
parameterisation in the preferred geological DFN alternative models at both Forsmark and Laxemar
during SR-Can. Both outcrop fracture intensity P21 and percentile values of P32 intensity from coredboreholes as a function of fracture set, rock domain, and model sub-area (Forsmark) were used tocalibrate the minimum radius r0 in the model parameterisation at Laxemar /Hermanson et al. 2005/
and in the “Regional Deterministic Sets” alternative at Forsmark /La Pointe et al. 2005/. As such,
fracture size and fracture intensity can be said to be correlated for the purpose of SR-Can modelling.
The geological DFN analyses at both Forsmark and Laxemar during stage 1.2 geological modellingsuggested no correlation between open and sealed fractures as a function of depth below the groundsurface. However, during the preparation of the SR-Can Data report /SKB 2006b/, it was recognisedthat the intensity of hydraulically-active fractures (assumed to be a sub-population of open fractures)
did show a distinct depth-dependency. This correlation was not propagated to geological-DFN
related scenarios; however, it was included in scenarios and processes related to site hydrogeology
and groundwater evolution.
Identified limitations of the data used in SR-Can modelling

It was recognised during SR-Can that, owing to the relatively early stage of site modelling, relativeto Forsmark, only limited data existed on fractures within the Laxemar sub-area, which substantially
increased the uncertainty in the understanding of the fracture system /SKB 2006a/. Second, it wasrecognised that at both Forsmark and Laxemar, only sparse information on fractures and minor
deformation zones in the 50–250 m size range was available /SKB 2006b/. Finally, it was recognisedthat the size distribution of subhorizontally-dipping fractures at both sites was significantly more
uncertain than of the subvertically-dipping fracture sets /La Pointe et al. 2005, Hermanson et al. 2005/.

6.3.3 Supplier input on use of data in SR-Site and SR-Can
Supplier input to the SR-Can Data report /SKB 2006b/ consisted of the recommendations and usage
presented in the geological DFN summary reports /La Pointe et al. 2005, Hermanson et al. 2005/
completed during stage 1.2 of the site-descriptive modelling efforts. Additional review and feedback
on model processes and inclusion/exclusion of data was performed by the SR-Can team at SKB.
For SR-Can calculations based on the Forsmark site, the supplier to SR-Can recommended:•

Five fracture orientation sets (four subvertical and one subhorizontal) described using a combination of bivariate Bingham /Bingham 1964/, bivariate Fisher /Dershowitz 1979/ and univariate
Fisher /Fisher 1953/ hemispherical probability distributions.

•

No consistent variation of fracture intensity with depth was noted; therefore, fracture intensity
should be represented as a function of rock domain or model region.

•

Poissonian spatial arrangement of fracture centres.

•

Euclidean fracture intensity scaling.

•

A power-law (Pareto) distribution of fracture radii, with the radius scaling exponent kr for
lineament-related sets calculated using the tracelength-scaling method /La Pointe et al. 2005/
with fracture traces from outcrops and combined topographic and geophysical lineaments.

•

That Local fracture sets were a mix of exponential and power-law distributions.

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Supplier input to SR-Site consists of the detailed DFN parameterisations presented in the stage 2.2
Forsmark geological DFN summary report /Fox et al. 2007/, tempered by the additional guidance
towards model selection suggested in the Site description Forsmark. For SR-Site calculations at
the Forsmark site, the supplier recommends the following:•

Fracture orientation should be simulated as a function of fracture domain, using the average
parameters for each Global and Local set. Fracture set orientation is specified in terms of thetrend and plunge of the univariate Fisher distribution mean pole, as well as the average Fisher
concentration parameter κmp.

•

We recommend that, if at all possible, Local orientation sets be simulated using a geocellularstyle approach with the conditional probabilities presented on worksheet “Spatial Model”.
If that is not an option, the adjusted bulk domain intensity values (Local set P32 multipliedby the probability of occurrence) should be used.

•

Due to the large conceptual uncertainties inherent in the coupled fracture size-intensity models,
the TCM (Tectonic Continuum model), OSM+TFM (Outcrop Scale + Tectonic Fault models),
and r0-fixed models should be carried through all SR-Site modelling activities. Both fracture
intensity and fracture size should be simulated on a fracture domain basis. The differences
between TCM and TCMF (Tectonic Continuum model assuming fractal intensity scaling) are
judged to be minimal.

•

Fracture intensity P32 should be modelled on a domain-by-domain basis using the values for eachsize-intensity model alternative. We recommend the use of the average P32 value to which thesize-intensity distribution is coupled, rather than using a Gamma distribution; this is conceptuallya simpler method to implement, and the conceptual uncertainty in the size model dwarfs theparameter variability of intensity in space within a single domain. In addition, it was not possible
to fit a Gamma distribution to two of the Global sets in both domain FFM01 and FFM02.

•

Within a given fracture domain, fracture centres should be distributed in space according to a 3D
Poisson point process. In addition, fracture intensity scaling should be treated as Euclidean for all
fracture domains.

•

We recommend the use of termination matrices from fracture domain FFM02 (worksheet“Termination Matrices” at SKB's model database Modelldatabasen14) to describe fracture
termination relationships in domains not exposed at the ground surface (FFM01, FFM06).

6.3.4

Sources of information and documentation of data qualification

Sources of information

The geologic discrete-fracture network models derived as a component of the Forsmark site investigation are presented as stand-alone project reports for each modelling stage. The last geological DFN
model was completed during Forsmark stage 2.2 site-descriptive modelling /Fox et al. 2007/. Summary
tables of relevant geologic DFN parameters, including fracture orientation, size, and intensity statistical
distributions, are archived as Microsoft Excel workbooks in SKB’s model database Modelldatabasen.
A brief summary of the geological DFN, along with descriptions of fracture geometric properties, ispresented in the Forsmark stage 2.2 site-descriptive modelling Geology report /Stephens et al. 2007/.
A list of the main sources of information used in data qualification is presented as Table 6‑29. With
respect to the development of the stage 2.2 geological DFN parameterisation, references to delivery
dates and source tables for primary data (SICADA, SDE, SIMON, and external data sources) aredescribed in detail in Section 2.1 of /Fox et al. 2007/.

Modelldatabasen 2007. Model: PFM DFN 2.2.xls. Version 0.6. Approved 2007-11-29, Modified 2009-05-18.
Modeller: A. Fox. Simon ID: GEO_WTAGLLAA. https://service.projectplace.com/pp/pp.cgi/r232241793(access might be given on request).
14

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Table 6‑29. Main sources of information used in data qualification.
Site description Forsmark, 2008. Site description of Forsmark at completion of the site investigation phase. SDM-Site
Forsmark. SKB TR-08-05, Svensk Kärnbränslehantering AB.
Olofsson I, Simeonov A, Stephens M, Follin S, Nilsson A-C, Röshoff K, Lindberg U, Lanaro F, Fredriksson A,
Persson L, 2007. Site descriptive modelling Forsmark, stage 2.2. A fracture domain concept as a basis for the statistical modelling of fractures and minor deformation zones, and interdisciplinary coordination. SKB R-07-15, Svensk
Kärnbränslehantering AB.
Fox A, La Pointe P, Hermanson J, Öhman J, 2007. Statistical geological discrete fracture network model. Forsmark
modelling stage 2.2. SKB R-07-46, Svensk Kärnbränslehantering AB.
Stephens M, Fox A, La Pointe P, Simeonov A, Isaksson H, Hermanson J, Öhman J, 2007. Geology Forsmark.
Site descriptive modelling Forsmark stage 2.2. SKB R-07-45, Svensk Kärnbränslehantering AB.
Fox A, Hermanson J, 2006. Identification of additional, possible minor deformation zones at Forsmark through a review
of data from cored boreholes. SKB P-06-293, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

Geological discrete-fracture network models at Forsmark were constructed exclusively from qualified data extracted from SKB quality-assured databases (SICADA and SDE). Both the geological
DFN summary report and the fracture domain identification report /Olofsson et al. 2007/ present
detailed lists of SICADA, SDE, and qualified SKB data delivery references (Section 2.1 andChapters 3 and 4, respectively). However, as noted in the method-specific summary report /Fox
et al. 2007/, early data deliveries of GIS shapefiles did not possess sufficient metadata to refer backto layers within SDE. Qualification of these deliveries is ensured through a reference to the datadelivery number, which internally at SKB refers back to a stored SQL query.
With respect to official SKB sources for geological DFN parameters, the Microsoft Excel tables
contained in the database SIMON (a mirror of Modelldatabasen) from Forsmark stage 2.2 modellingare considered authoritative. In terms of published references, the tables presented for fracturedomains FFM01 and FFM06 in the Site description Forsmark are considered the latest qualified
geological DFN parameterisation. For all other fracture domains and alternative model cases thatare not recommended for use in SR-Site (e.g. intensity in domains labelled ‘Affected by DZ’), thegeological DFN summary report /Fox et al. 2007/ is considered the authoritative source.
The deformation zone (DZ) model at Forsmark was undergoing constant evolution while thegeological DFN model was built during stage 2.2 modelling. As such, the DZ geometries used in
the geological DFN models represent interim versions rather than final, fully-qualified data. The DZ
model was primarily used to exclude data from cored borehole sections inside deformation zones;
the borehole/deformation zone intersections were well-established and controlled before the start ofDFN modelling. The differences between the interim DZ and final DZ models presented in /Stephenset al. 2007/ were in the geometries of zones away from borehole control and, as such, have no effecton the geological DFN parameterisation.
In Table 6‑30 qualified data sets of special importance are listed. No data set was considered as
supporting.
Excluded data previously considered as important

The paradigm for geological DFN modelling changed during stage 2.2 of the site description process
at both Forsmark and Laxemar. In particular, the introduction of the fracture domain concept /Olofsson
et al. 2007/ limited the spatial extent of DFN validity. The geological DFN is parameterised only fordomains in which sufficient three-dimensional information was available: FFM01, FFM02, FFM03,
and FFM06 (cf. /Fox et al. 2007, Figure 1.2/). The previous geological DFN at Forsmark (stage 1.2
modelling) presented DFN properties as a function of rock domains RFM029, RFM018, RFM017,
and RFM012 (cf. /La Pointe et al. 2005, Figure 6-1/). In addition, the size of the Forsmark local
model domain changed significantly between stage 1.2 and stage 2.2 (see Figure 6‑18).
The end result is that stage 2.2 geological DFN models use only minimal data from rock domains
RFM017 and RFM012, and exclude data from rock domain RFM018 entirely (cf. Figure 6‑19).
Fracture domain FFM06 is defined based on the contact at depth between rock domains RFM028
and RFM045, the latter of which was not included in stage 1.2 geological DFN modelling.
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Table 6‑30. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. All data sets, including related SICADA and SDE tables, referenced in Section 2.2 ofstage 2.2 site-descriptive modelling report /Fox et al. 2007/.
2. All data sets, including related SICADA and SDE tables, referenced in Sections 3.1,
3.2, 4.1, 6.1, 7.1, 8.1, and Appendix 1 of stage 2.2 site-descriptive modelling report/Olofsson et al. 2007/.
3. PFM DFN 2.2.xls, version 0.6, SIMON database, folder /Forsmark/2.2/Geology/
Approved/DFN Models/.
4. Section 5.6 of the Site Description Forsmark.
5. Tables 5-5 through 5-8 of the Site Description Forsmark.
6. Tables 5-3 and 5-4, published in the Erratum to /Fox et al. 2007/.
7. Geological DFN Model implementation: Tables 7-1 through 7-10 in /Fox et al. 2007/.
8. Sections 3.6, 3.12, and Chapter 6 of Forsmark stage 2.2 Geology summary report/Stephens et al. 2007/.
9. All data sets referred to in Chapter 2 of /Fox et al. 2007/.
10. WellCAD summary plots and BIPS image logs described in Section 3.1 of /Fox andHermanson 2006/.
11. Figures 5-34 and 5-36 of the Site Description Forsmark.
12. Interim DZ geometries (.dxf export), stage 2.2 Forsmark site-descriptive modelling.
See Section 2.1.4 of /Fox et al. 2007/ for further discussion.

None.

All data sets in this table are considered as qualified with the justification that they are published as part of the site
investigation and site characterisation, in conformance with relevant quality assurance routines.

Figure 6‑18. Map of the Forsmark site, with references to stage 1.2 and stage 2.2 model boundaries andthe stage 2.2 rock domains, which did not change appreciably from stage 1.2.

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Figure 6‑19. Fracture domains and deterministic deformation zone traces at Forsmark. Note that theDZ traces represent the mid-plane of the zone, which are implemented as complex volumes in RVS. Rockdomains are included as numbered polygons. Note that FFM04 and FFM05 are not part of the stage 2.2
geological DFN parameterisation.

6.3.5 Conditions for which data are supplied
Geological DFN parameters are supplied for rock volumes within fracture domains FFM01, FFM02,
FFM03, and FFM06 inside the Forsmark local model volume /Fox et al. 2007/. In addition, thegeological DFN describes fractures and minor deformation zones outside the mapped limitsof deterministically-modelled deformation zones /Stephens et al. 2007/. The geological DFN
parameterisation is valid for fractures and minor deformation zones within a size range of r0 m and564.2 m; the latter is the equivalent radius of a square fracture with an area of 1 km2, and represents
the transition from stochastic fracture modelling to deterministic deformation zone modelling /Stephenset al. 2007/. The geological DFN is defined for all fractures; no distinction is made between open,
sealed, or hydraulically-significant fractures.
Changes in fracture properties as functions of future processes (underground construction, de-watering,thermal loading, climate change, or additional mechanical loading from future glacial cycles) were
not assessed by the geological DFN team, and are assumed to represent scenario cases in SR-Site.

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It is extremely important to note that the stage 2.2 geological DFN report /Fox et al. 2007/ presented
not one but three alternative models to describe the coupled distributions of fracture size and fracture
intensity at Forsmark. All three of the model alternatives fall into the category of ‘kr-fixed’ models(cf. Section 6.3.1).
1. The Tectonic Continuum (TCM) concept, in which fractures at the outcrop and borehole scale(which are thought to largely consist of joints) are part of a larger population of brittle deformation features that also include the faults and minor deformation zones identified as geophysicaland topographic lineaments. Fundamental tenets of the tectonic continuum hypothesis arethat fracture size and fracture intensity are coupled (one cannot change without affecting theother), that fracture size is scale-invariant, and that it is consistent with the understanding ofbrittle deformation zones as intense clusters of connected smaller fractures. The TCM assumes
Euclidean intensity scaling.
2. The Tectonic Continuum model assuming fractal intensity scaling (TCMF). This alternative isconceptually the same as the TCM, but utilises pseudo-areas (cf. Equation 3-10 in Section 3.2.3
of /Fox et al. 2007/) and the fractal mass dimension /Feder 1988/ instead of outcrop and model
region areas to account for scale-dependent fracture intensity.
3. The Outcrop Scale (OSM) and Tectonic Fault (TFM) concept, in which it is assumed that outcrop
and borehole fractures are not part of the same population as minor deformation zones, and thatboth populations exhibit different size distribution characteristics. Outcrop-scale fractures are
assumed to consist predominantly of joints, while minor deformation zones and large fractures
are assumed to be predominantly faults. Both models (OSM and TFM) are required to produce
a complete description of fracturing in the required scale range (0.5–564.2 m).
During the review of the geological models for the preparation of the Site description Forsmark,an uncertainty case from the stage 2.2 DFN summary report was promoted to a full alternative model
based on feedback from the Forsmark hydrogeology team and SKB internal reviewers. The fourthalternative model is:
4. The r0-fixed concept, in which instead of calculating the radius scaling exponent kr fromfracture trace data (which exists for only two of the four fracture domains at Forsmark), the size
distribution is inferred from fracture intensity data recorded in cored boreholes. In this alternativemodel, fracture size is assumed to follow a power law, and the minimum fracture size r0 is fixedat the radius of the cored boreholes (generally double- or triple-tube NQ/NX core drills, which
cut a hole approximately 76 mm in diameter). The ratio of borehole fracture intensity (P32BH)
to deformation zone intensity (P32DZ) is inverted to solve for the radius scaling exponent, using
Equation 3-11 of /Fox et al. 2007/.
The Tectonic Continuum model (TCM) was submitted as the recommended size-intensity model
alternative for use by the geological DFN team (cf. /Fox et al. 2007, Section 7.2/).

6.3.6 Conceptual uncertainty
A formal uncertainty analysis performed as a component of the stage 2.2 geological modelling(cf./ Fox et al. 2007, Chapter 5/) identified the following conceptual uncertainties in the geological
DFN parameterisation:•

Validity of the Tectonic Continuum hypothesis as opposed to the OSM+TFM or r0-fixed conceptualmodels.

•What is the cut-off between large fracture and minor deformation zone (affects the OSM+TFM
alternative.

•

Which is a more appropriate description of fracture traces in outcrop: linked or unlinked(cf. /Fox et al. 2007, Section 3.2.3/).

•

Does fracture intensity follow Euclidean or fractal scaling? This uncertainty is discussed in detailin the SR-Can Data report /SKB 2006b/ and in the stage 2.2 geological DFN summary report/Fox et al. 2007/. The general recommendations has been that since the departure from Euclidean
scaling is inside the estimates of error of the mass dimension, that the assumption of Euclidean
scaling is a reasonable one (cf. /Fox et al. 2007, Section 7.4/).

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•Can the fracture size model, and, in particular, the radius scaling exponent kr, for fracturedomains not exposed at the ground surface (FFM01, FFM06) be assumed identical to the values
established from outcrop trace data in domains at the surface (FFM02, FFM03).

In addition, two mathematical uncertainties /Bechtel-SAIC 2001/ were identified:•

Uncertainty in the depth correction for fracture intensity.

•

Uncertainty in the lithology correction for fracture intensity.

Finally, there are several aspects of fracture geometry and geology that the geological DFN treats as“assumptions” that could also be considered conceptual uncertainties. These assumptions, which aredetailed in Section 3.2.1 of /Fox et al. 2007/ and are again summarised in Section 6.2 of /Stephenset al. 2007/, include:•

The length of a deformation zone trace, geophysical lineament, or linked/unlinked fracture tracein outcrop is an accurate and appropriate measure of the trace length of a single feature, for the
purposes of deriving the distribution of fracture radii.

•

Fracture sets can be usefully identified based solely on orientation.

•

The lack of clustering (i.e. Poissonian spatial arrangement) observed in outcrop and borehole datacarries over to the large fracture and MDZ-size range within the Forsmark local model volume.
Note that this assumption was implied, but not explicitly stated, in the geological DFN summary
report /Fox et al. 2007/ and was raised as a valid conceptual uncertainty during the finalisation of
the site-characterisation efforts (cf. the Site description Forsmark).

The magnitude of the conceptual and mathematical uncertainties were described and quantified
in Section 5.1.2 of /Fox et al. 2007/ as a component of stage 2.2 geological DFN model reportingin terms of the P32 ratios between pairs of alternative models within two different size ranges(0.5–564.2 m and 28–564.2 m). No effort was made to analyse or quantify uncertainty with respect
to geological DFN assumptions (using only univariate Fisher distributions; ground magnetic lineaments as fractures, etc.).
The P32 ratios, which are summarised below in Table 6‑31, serve to bracket the uncertainty in boththe size and intensity models, as all the alternatives utilise coupled size-intensity models. By far thelargest conceptual uncertainty in terms of its effect on P32 is the method by which the fracture size
distribution is parameterised, with the question of the validity of using size data from the groundsurface to parameterise rock volumes at depth a lesser but still important concern. The other remaining conceptual uncertainties are of second-order importance.
Table 6‑31. Summary of key conceptual uncertainties and their expected impacts on downstream
models. Modified after Table 5-7 of /Fox et al. 2007/.
Uncertainty

Magnitude

Comments

Fracture size conceptual model

0.3–3.0

Varies significantly as a function of fracture set andfracture domain

Boundary between OSM and TFM

0.6–0.9

Specifying a boundary between OSM and TFM reduces
the total fracture intensity by approximately 1/3

Euclidean versus Fractal intensity scaling

0.82–1.16

Minor impact, especially on subhorizontally-dipping
fractures and MDZ

Use of size data from surface (FFM02) toparameterise domains at depth

0.3–0.7

This uncertainty case promoted to full-fledged model(r0-fixed); results in significantly lower intensity in the
MDZ size range (28–564.2 m)

Fracture intensity as function of rock domain

0.5–2.0

Impact is greatest for minor rock types such asamphibolite and pegmatite

Fracture intensity as a function of depth~ 0.9–1.05

Variable as function of fracture set and fracture domain;
ignoring depth-dependency ‘slightly’ increases mean
fracture intensity

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6.3.7 Data uncertainty due to precision, bias, and representativity
Past modelling efforts at both Forsmark and Laxemar have suggested that conceptual or mathematicaluncertainties dominate the geological DFN uncertainty space. However, additional model uncertainties with respect to geological and fracture data do exist; these uncertainties are largely due to limitsof the accuracy and coverage from site characterisation efforts. As with the conceptual uncertainties,
the biggest effect of data uncertainty is on the coupled size-intensity model. However, no formal
quantification or ranking of data uncertainties exists for the stage 2.2 Forsmark geological DFN.
Data uncertainty due to precision

With respect to uncertainty in fracture orientations in base data, the geological DFN models
parameterised during Forsmark site description stage 2.2 are fairly well-constrained. Data analysis
followed the recommendations of /Munier and Stigsson 2007/ in only using orientation data forwhich BIPS logs were available, and avoiding the use of any fracture data for which the overall
uncertainty Ω was greater than 10°. In addition, spatial variability of fracture orientations is encompassed by the use of probability distributions for fracture set mean poles and Fisher concentration
parameters (cf. “Parameter variability”, Section 4.1.3 of /Fox et al. 2007/). The principal remaining
uncertainty in fracture orientations is due to the precision of the strike and dip measurements madeon detail-mapped fracture outcrops at Forsmark (cf. /Fox et al. 2007, Section 4.1.5/); the accuracyof these measurements is estimate to be +/– 10°.
As fracture intensity is largely a derived parameter, the precision of its estimate depends on theconfidence of the identification of fractures in outcrop and in core. Presently, no formal estimateof the probability of fractures 'missed' during the core logging exists. The uncertainty in Wang's
analytical conversion factor /Wang 2005/ for borehole fracture intensity (P10) to volumetric fracture
intensity (P32) was indirectly evaluated through Monte Carlo simulation. Though no quantified error
estimate exists, the Monte Carlo simulation results, cf. /Wang 2005, Figure 3.10/, suggest that theuncertainty is quite small. However, Wang’s conversion engenders an additional conceptual uncertainty; it assumes that fractures follow a univariate Fisher distribution, and is therefore not a valid
estimator for fracture intensity for fractures with orientations better described by other hemisphericalprobability distributions (Bingham, Kent). Neither the model parameterization nor the uncertainty
analyses completed in the DFN model during Forsmark site description stage 2.2 made any estimatesof the magnitude of the uncertainty in P32 should the fractures at Forsmark not follow a univariate
Fisher distribution.
For the OSM, TCM, and TCMF size model alternatives, kr is estimated from the trace length scaling
exponent kt, which is calculated by manually fitting a straight line to trace data on an area-normalised
CCN (complementary cumulative number) plot. As there are multiple outcrops in the surficial fracturedomains (two outcrops in FFM02 and three outcrops in FFM03) with trace length distributions that
do not overlap, it is possible to pick multiple values of kt depending on which outcrop (see Figure 6‑20)
is used. This uncertainty was addressed in the SDM-Site Laxemar geological DFN analysis /La Pointe
et al. 2008/, but has not been accounted for in the stage 2.2 Forsmark modelling.
Though no formal uncertainty quantification exists, similar work done during Laxemar stage 2.2
geological DFN modelling /La Pointe et al. 2008/ suggests that the uncertainty in the estimation of
the trace length scaling exponent is by far the most important uncertainty due to precision.
Data uncertainty due to bias

Data uncertainty due to bias is caused by truncation effects inherent in sampling fracture properties
over a variety of scales. The distribution of trace lengths (from which the radius scaling exponent
kr is estimated in the TCM, TCMF, and OSM models) shows a strong censoring effect at twice theminimum trace length mapped (0.5 m); the censoring effect is visible as a flattening of the area-normalised CCN plots, of which an example is presented in Figure 6‑20. There is also a censoring effecton the upper end of the outcrop trace data (cf. the Site description Forsmark); the largest fracture
detectable is limited by the size of the outcrops (~10 m) or the minimum resolution of LIDAR andground magnetic lineaments (~50–100 m). The result of this censoring is that a scale (10 m–100 m)
exists within which very little information on fracture size is available. The availability of LIDAR
and high-resolution ground magnetic lineament data only partially ameliorates this uncertainty.
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1.00E+01

Area-Normalized Cumulative Number

PFM 2.2 DZ Model, Clipped

1.00E+00

PFM 2.2 DZ Model, Regional
Ground Magnetic Lineaments
AFM000053 (Linked)

1.00E-01

AFM001243 (Linked)
AFM001244 (Linked)

1.00E-02

TCM
P32 Fit to Trace Data

1.00E-03
1.00E-04

kt = 1.95

1.00E-05
1.00E-06
1.00E-07
1.00E-08
1.00E-09

NE Global Set (Euclidean scaling), Domain FFM03

0.1

1

10

100

1000

10000

100000Tracelength (m)Figure 6‑20. Example area-normalised complementary cumulative number (CCN) plot used to analysefracture trace length scaling for NE global set in fracture domain FFM03. This style of plot is used to
calculate the radius scaling exponent kr for the TCM, TCMF, and OSM-TFM alternative models. Figure
reproduced from Appendix A of /Fox et al. 2007/.

Bias due to the orientation of sampling structures (boreholes, outcrops) relative to the orientation of
fracture sets has been minimised through the use of the Wang C13 (–) conversion factor; this allows
for all fracture intensity. Wang’s C13 does not exist for outcrop data; as such, the detail-mapped
fracture outcrops exhibit a bias towards vertically- to subvertically-dipping fractures. The paucity
of data on trace lengths of subhorizontally-dipping fractures results in increased uncertainty in theradius distribution for these features. It is difficult to impossible to reduce this uncertainty without
additional information, which is not likely to be available until the start of underground construction.
Data uncertainty due to representativity

Between the completion of the fracture domain report for Forsmark /Olofsson et al. 2007/ andthe completion of geological DFN modelling, a number of additional analyses performed by the
deformation zone and geology teams suggested that the breakdown of fractures into “inside DZ”
and “outside DZ” was insufficient to characterise the bedrock at Forsmark. An additional category,“Affected by DZ”, was adopted by the SKB/SGU geological modelling teams to describe rockvolumes in which fracture frequency was increased due to the presence of a nearby deformation
zone, but none of the other morphological characteristics of DZ “transition zones” were observed/Stephens et al. 2007/.
In the stage 2.2 geological DFN model, there are three alternative models (OSM+TFM, TCM, andTCMF) that rely on the length and intensity of the deterministically-modelled deformation zones andhigh-resolution lineament maps as a component of the size model. The traces of deformation zones
in the Forsmark 2.2 DZ model and the lineaments identified in the high-resolution ground-magnetic
surveys were divided into orientation sets, and their trace lengths were plotted on complementary
cumulative number (CCN) plots, with lengths normalised by observation area (or pseudo-area, forthe fractal scaling case). The clipping of these lineaments against fracture domain or rock domainboundaries would introduce unacceptably large truncation effects and mass defects into the geological model. As such, the parameterisation of the TFM, TCM, and TCMF alternative models uses all
structures inside the Forsmark local model area (cf. Figure 6‑18) and is the same for all fracturedomains. Therefore, the uncertainty associated with this question of representativity is constantacross all of the size-intensity model alternatives.

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This is an appropriate assumption if both the density of lineaments and the relative intensity ofeach orientation set are roughly uniform over the local model area, and does not differ significantly
between fracture domains. However, at Forsmark the relative intensity of NW-striking lineaments
is much lower in domain FFM02 (and also presumably in FFM01, which lies directly underneath
FFM02 and is ‘inside’ the tectonic lens) than in the other fracture domains. In other words, there
is significant anisotropy in the density of certain lineament sets at Forsmark. Differences in theintensity of MDZ-sized lineaments between fracture domains introduces additional uncertainty into
both the fracture intensity and fracture size models, due to the fact that the slope of the power-law
exponent (kr) is partially dependent on the shape of the CCN curve for MDZ-sized lineaments.
Uncertainty in the spatial anisotropy of aspect of MDZ-sized lineaments and DZ surface traces was
not explored during the Forsmark 2.2 geological DFN modelling, though it is dealt with briefly in an
Erratum published to the stage 2.2 geological DFN summary report /Fox et al. 2007/. The questionof MDZ anisotropy across the Forsmark local model volume is probably the largest representativity
uncertainty, though no formal quantification exists.

6.3.8

Spatial and temporal variability of data

Spatial variability of data

Though the subdivision of the Forsmark local model volume into explicit fracture domains hasdone much to constrain fracture geometries and properties, significant spatial variability in fractureorientation, the number of fracture sets, and fracture intensity still exists.
During stage 2.2 modelling, the orientations of fractures recorded at outcrops or in borehole data
are described in terms of univariate Fisher distributions. Geological DFN model users can choose todescribe the fracturing on a domain-by-domain basis using the average mean pole and concentration
parameter for all data in that domain (the recommended approach), or by treating both the meanpole orientation and Fisher κ as stochastic parameters following normal distributions (cf. /Fox et al.
2007, Section 4.1.4/). However, no spatial correlation of mean pole orientation or Fisher κ inside an
individual fracture domain is used.
Fractures are divided into Global sets, which are seen in nearly all parts of a fracture domain,and Local sets, which are seen in only a subset of outcrops and boreholes. No analysis of spatial
correlation within a given fracture domain was performed for the Local sets. The recommendationfrom the DFN modelling team was to treat the local sets as a conditional probability at a given scaleusing geocellular-style generation; that is if the conditional probability for a given Local set was
0.2 at 30 m scales, then 20% of the cells making up a given fracture domain would include fracturesfrom that Local set, generated at full intensity. Alternatively, the local sets can be treated as a bulk
property of the fracture domain, with the fracture intensity multiplied by the conditional probability.
This approach was not recommended by the DFN teams.
The coupled fracture size-intensity models utilise the mean fracture P32 calculated across 6 m longsections of the Forsmark cored borehole array, with adjustments for host lithology and fractal dimension(cf. /Fox et al. 2007, Section 4.4.4/). However, it is also possible to partially capture the spatial variability of fracture intensity within a given fracture domain through the use of a probability distribution
for a given scale. Analyses conducted during stage 2.2 modelling (cf. /Fox et al. 2007, Section 4.4.3/)
indicate that at scales of 30 m and larger, the spatial variability of all fracture sets can be describedusing a generalised Gamma distribution /NIST 2007/. By describing fracture intensity in this fashion,
however, one automatically assumes no spatial or lithological correlation within a fracture domain.
As previously mentioned during the discussion on data uncertainty due to representativity, anissue exists with respect to the spatial variability of large fractures and MDZ. The MDZ size range(28–564.2 m) is described in terms of all structures in the Local model area, and not divided into
fracture domains. As such, for some MDZ/lineament sets (in particular, NW-striking MDZ), theexisting geological DFN models may not accurately describe spatial variability.
Temporal variability of data

Temporal variability of fracture data has not been taken into account during site-characterisation
efforts or reporting.
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6.3.9 Correlations
The following general correlations (most of which have been thoroughly discussed in the previous
sections) exist in the geologic DFN models parameterised for use at the Forsmark site:••

Fracture geometric properties (orientation, size, and intensity) are correlated to fracture domain.
Fracture location, in terms of the fracture centre, is uncorrelated in space, and follows a 3D
Poisson point process.
• Modelling results from the stage 2.2 geological DFN analysis indicate a weak correlation
between fracture intensity and bedrock lithology (cf. /Fox et al. 2007, Section 4.4.4/). The
correlation is strongest for subordinate rock types such as pegmatite and amphibolite. Should
lithological correlation be desired during SR-Site modelling, Table 4-99 of /Fox et al. 2007/
should be used.
• Fracture size and intensity are correlated (coupled) in all recommended models due to the useof power-law relationships. The effects and consequences of this are summarised in previous
sections of this document and analysed in detail in /Fox et al. 2007, Stephens et al. 2007/ and theSite description Forsmark.
• No significant (that is predictable in a global or domain-specific fashion) correlation between
fracture intensity and true vertical depth exists at Forsmark. This is not to say that there is novariation in fracture intensity with depth; rather, that there is no useful relationship to describe it.

6.3.10 Result of supplier’s data qualification
The authoritative source for all parameters necessary to describe the geological DFN at Forsmark is
the quality-assured Microsoft Excel workbook “PFM DFN 2.2.xls”, contained within SKB's database
Modelldatabasen/SIMON. References to tables in published reports linked in previous sections of
this report are provided as a courtesy only; the Modelldatabasen/SIMON database is considered theprimary authority. Summary tables of the size-intensity model cases are also presented as Tables 5-5
through 5-8 of the Site description Forsmark. Table 6‑32 presents an example of the format of thegeological DFN model parameterisation tables as contained in SIMON.
Table 6‑32. Example of geological DFN parameter table from SIMON database. This table is forthe “tectonic continuum” (TCM) conceptual model alternative.
Fracture Domain FFM01, TCM Alternative (‘kr-fixed’), rev 0.6, 2009-05-18.
Frac.
Set

Set
Type

Orientation Model
Trend
Plunge κ

Size Model
rmax
r0

kr

Intensity model between (r0–rmax)
P32
Cond.* Global P32
Prob.

NE

Global314.90

1.30

20.94

0.659

564.2

3.020

1.733

1.00

1.733

NS

Global270.10

5.30

21.34

0.059

564.2

2.780

1.292

1.00

1.292

NW

Global230.10

4.60

15.70

0.594

564.2

2.850

0.948

1.00

0.948

SH

Global

0.80

87.30

17.42

0.816

564.2

2.850

0.624

1.00

0.624

ENE

Local

157.50

3.10

34.11

0.325

564.2

3.250

2.742

0.09

0.256

EW

Local

0.40

11.90

13.89

0.170

564.2

3.100

1.117

0.15

0.169

NNE

Local

293.80

0.00

21.79

0.039

564.2

3.000

4.395

0.15

0.658

SH2

Local

164.00

52.60

35.43

0.039

564.2

2.610

0.918

0.09

0.082

SH3

Local

337.90

52.90

17.08

0.039

564.2

2.610

0.837

0.08

0.067

* Local fracture sets, i.e. those that do not exist everywhere within the model volume, can either be modelled
deterministically through spatial bootstrapping, or stochastically through adjusting the fracture intensity by theconditional probability of their existence in the target volume. See Section 3.2.5 of /Fox et al. 2007/ for moredetail on how to treat local fracture sets in DFN models.

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Based on the results of analyses performed during site characterisation, the outcome of discussions
with reviewers associated with the site-descriptive modelling, and the professional judgement of
the geological DFN team, the following data is recommended for use in the stochastic simulation of
fractures and minor deformation zones during SR-Site modelling:
1. Fracture set orientations for Domains FFM01 and FFM06 are provided in the worksheet“SR_Site_DataReport” as a function of fracture domain. If orientation parameters are requiredfor other fracture domains, they can be obtained from worksheet “Orientation”; the columns“Trend”, “Plunge”, and “Distribution of Fisher K (Mean)” should be used.
2. The geological DFN is described for all fractures (open, partly open, and sealed) within a size
range (equivalent radius) of 0.5–564.2 m; the DFN parameterisation is not valid outside those limits.
3. Fracture set size distribution models for Domains FFM01 and FFM06 can be obtained fromworksheet “SR_Site_DataReport” as a function of fracture domain and conceptual model. Notethat neither the TCMF alternative nor the other fracture domains at Forsmark are included on
this sheet; should parameters for that conceptual model be required, they can be obtained fromworksheets “Size_TectonicContinuum” or “Size_OSM+TFM”.
4. Fracture set intensities for Domains FFM01 and FFM06 can be obtained from worksheet“SR_Site_DataReport” as a function of fracture domain and conceptual model. If intensity distribution parameters are required for other fracture domains, they can be obtained from worksheet“Intensity”; the columns “Mean P32: OSM, TCM, TCMF and TFM”, should be used. Note that
this worksheet divides the rock mass into volumes “Affected by DZ” and “Not Affected by DZ”;
the geological DFN team recommends only using the values for “Not Affected by DZ”.
5. Fracture set terminations for all fracture domains should be taken from worksheet “Termination
Matrices”.
To illustrate the differences between the size-intensity conceptual alternatives at Forsmark, a numberof stochastic DFN realisations of each case have been constructed for what is viewed as the mostimportant of the six fracture domains at Forsmark (domain FFM01). This domain was chosenbecause it represents rock volumes that are most likely to play host to repository deposition holes
and access tunnels. Each model was intersected with a 25 m long rectangular tunnel with a squarecross Section 5 m on a side (as illustrated in Figure 6‑21). The traces of all fractures intersecting the

Figure 6‑21. Illustration of the simulation region and sampling tunnel used to produce the DFN trace
illustrations in Figure 6‑22 to Figure 6‑24. The yellow transparent box represents the simulation region,
while the white rectangular prism represents the sampling tunnel. This illustration shows fractures from the
NW Global Set in domain FFM01, assuming the TCM conceptual alternative. Only fractures intersecting
the sampling tunnel that are larger than 0.5 m (equivalent radius) are shown.

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simulated tunnel were then recorded. In each tunnel, within a 5 m section, traces for all fractures
down to the distribution minimum radii were recorded. Outside of this strip, only traces longer than
0.5 m were recorded, to be consistent with outcrop mapping. The results are presented as Figure 6‑22
through Figure 6‑24. Note that the TCMF alternative was not simulated; functionally, it is almost
identical to the TCM model. Simulations of Local orientation set use domain bulk values (intensity
multiplied by probability of occurrence) rather than conditional probability. Though this is inconsistent with the recommendations previously described in Section 6.3.3, the use of bulk domain values
is consistent with the likely methodology that the SR-Site modelling team will take to dealing withlocal fracture sets. As the simulations were designed for use as simple visualizations of the relative
differences between conceptual alternatives, and given the relatively low intensity of the Local sets
relative to the Global set intensities, this is an acceptable simplification.

Figure 6‑22. Example tunnel trace map for fracture domain FFM01 using the r0-fixed fracture size-intensity
model alternative. The top figure shows all fractures, while the lower figure includes only fractures from the
Global sets.

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Figure 6‑23. Example tunnel trace map for fracture domain FFM01 using the TCM fracture size-intensity
model alternative. The top figure shows all fractures, while the lower figure includes only fractures from the
Global sets.

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Figure 6‑24. Example tunnel trace map for fracture domain FFM01 using the OSM+TFM fracture sizeintensity model alternative. The top figure shows all fractures, while the lower figure includes only fracturesfrom the Global sets.

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6.3.11 Judgements by the SR-Site teamSources of information and documentation of data qualification

The sources quoted in Table 6‑29 and Table 6‑30 are judged by the SR-Site team to be adequate forthe intended purpose.
Conditions for which data are supplied

The prerequisites listed in Section 6.3.5 are in accordance to the directives given to the supplier.
As far as the SR-Site team can judge, these prerequisites have not been violated.
Conceptual uncertainty

The target volume at Forsmark, which consists of Fracture Domains FFM01 and FFM06 /Olofsson
et al. 2007/, is nowhere exposed at the ground surface; it is entirely overlain by fracture domain
FFM02. Though it is likely that structures with a radius in the tail of the size distribution are as
likely to occur in FFM01 (at depth) as at the ground surface (FFM02, FFM03), the nature of thesize-intensity relationship of smaller-scale fracturing in the target volume is more uncertain as itneeds to be largely inferred from surface data using various assumptions. Additionally, the Forsmark
site is covered with a fairly thick section of Quaternary sediments; this restricts the possibilities for
direct studies of the rock. With few exceptions, the outcrops used for gathering fracture data mainly
represent the exhumed foundations of drill sites. The inability of being able to study rock of thetarget volume directly (and, in particular, the size distribution of fractures in three dimensions), hasresulted in a number of alternative DFN models which, as an ensemble, are likely to span the fulluncertainty space.
Though the TCM model was presented as main alternative by the supplier, neither the DFN- nor
SR-Site teams have, with sufficient credibility, been able to favour one alternative over the otherand it is therefore the opinion of the SR-Site team, that the DFN alternatives should be viewed upon
as different aspects of one and the same fracture description and, accordingly, all DFN alternatives
should to be forwarded to the seismic risk calculations in SR-Site.
Data uncertainty due to precision, bias, and representativity

The argumentation brought forward in Section 6.3.7 is judged by the SR-Site team to be wellfounded and the conclusions are, essentially, supported by the SR-Site team.
We accentuate, however, that the intricate interplay between various uncertainties, and their propagation through the DFN modelling process, cannot readily be summarised in this Data report without
extensive repetition of texts in the modelling report. The reader is therefore kindly referred to /Fox et al.
2007/ for a thorough discussion on sources of uncertainties and their handling in DFN modelling.
Additionally, the data uncertainties are partly coupled to conceptual uncertainties. For instance, thenecessity of trying different modelling approaches, “r0-fixed models” vs. “kr-fixed models” wasdriven by e.g. the lack of data in the size range intermediate between outcrop and deformation zones.
The SR-Site team judge that the data uncertainties are overshadowed by the conceptual uncertainties,
and that the alternative DFN models span a sufficiently wide uncertainty space as to cover the data
uncertainties.

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Spatial and temporal variability of data

The supplier recommends making use of a geocellular approach (Sections 6.3.3 and 6.3.8) for
simulating the so-called “Local fracture sets” /Fox et al. 2007/ rather than using bulk probabilities of
their occurrence. The SR-Site team do, however, not intend to follow this particular recommendation
because:
1. A geocellular approach will, by definition, tend to increase the clustering of fractures. This meansthat fracture intensity is locally increased and that this local intensity increase impacts fewer
canisters compared to an averaging of intensity over larger scales. From a seismic risk calculation
point of view, geocellular modelling is less cautious.
2. The seismic risk calculations are based on simulations that provide average values of intersection
probabilities. Using a geocellular approach would require a dramatic increase in the number ofrealisations for the statistics to stabilise.
3. A geocellular modelling approach would require a radical modification of existing numericalcodes.
4. The impact on the seismic risk calculations of a geocellular approach is anticipated to be far lessthan the impact due to propagation of uncertainties and alternative models.
In essence, therefore, though we recognise the importance of fracture clustering and, naturally,
appreciate the potential to decrease the degree of pessimism by using a geocellular approach, it is thejudgement of the SR-Site team that, simply speaking, the introduction of geocellular modelling is not
worth the effort required.
Correlations

The SR-Site team agree with the supplier on this issue. We do, however, not intend to explore thefaint correlation between intensity and lithology as these effects are largely overshadowed by various
sources of uncertainties.
Result of supplier’s data qualification

The SR-site team agree with the description provided by the supplier. The DFN models for Forsmark
have been thoroughly reviewed both internally and externally. These quite extensive reviews haverendered revision of the modelling methodology and considerable improvement of the DFN models.
Flaws and errors that were detected after the publication of the modelling reports were handled byerratum.
It is the judgment of the SR-Site team that the geological DFN modelling has been driven to its limit.
We intend to use the DFN models as described in /Fox et al. 2007/ with no modifications.

6.3.12 Models recommended for use in SR-Site modelling
The DFN models recommended for use in SR-Site modelling are stored in SKB's database formodels, Modelldatabasen/SIMON, to ensure adequate QA and version handling. The data in the
below tables are reproduced from Modelldatabasen15.

Modelldatabasen 2007. Model: PFM DFN 2.2.xls. Version 0.6. Approved 2007-11-29, Modified 2009-05-18.
Modeller: A. Fox. Simon ID: GEO_WTAGLLAA. https://service.projectplace.com/pp/pp.cgi/r232241793(access might be given on request).
15

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255

Table 6-33. DFN model “r0 fixed” for fracture domain FFM01, version 0.6 from Modelldatabasen.
Fracture Domain FFM01, r0-fixed Alternative
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global314.90

1.30

20.94

0.039

564.200

2.718

1.733

NS

Global270.10

5.30

21.34

0.039

564.200

2.745

1.292

NW

Global230.10

4.60

15.70

0.039

564.200

2.607

0.948

SH

Global

0.80

87.30

17.42

0.039

564.200

2.579

0.624

ENE

Local

157.50

3.10

34.11

0.039

564.200

2.972

0.256

EW

Local

0.40

11.90

13.89

0.039

564.200

2.930

0.169

NNE

Local

293.80

0.00

21.79

0.039

564.200

3.000

0.658

SH2

Local

164.00

52.60

35.43

0.039

564.200

2.610

0.081

SH3

Local

337.90

52.90

17.08

0.039

564.200

2.610

0.067

Table 6-34. DFN model “TCM” for fracture domain FFM01, version 0.6 from Modelldatabasen.
Fracture Domain FFM01, TCM Alternative (’kr-fixed’)
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global314.90

1.30

20.94

0.659

564.200

3.020

1.733

NS

Global270.10

5.30

21.34

0.059

564.200

2.780

1.292

NW

Global230.10

4.60

15.70

0.594

564.200

2.850

0.948

SH

Global

0.80

87.30

17.42

0.816

564.200

2.850

0.624

ENE

Local

157.50

3.10

34.11

0.325

564.200

3.250

0.256

EW

Local

0.40

11.90

13.89

0.170

564.200

3.100

0.169

NNE

Local

293.80

0.00

21.79

0.039

564.200

3.000

0.658

SH2

Local

164.00

52.60

35.43

0.039

564.200

2.610

0.082

SH3

Local

337.90

52.90

17.08

0.039

564.200

2.610

0.067

Table 6-35. DFN model “OSM+TFM” for fracture domain FFM01, version 0.6 from Modelldatabasen.
Fracture Domain FFM01, OSM+TFM Alternative
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global314.90

1.30

20.94

0.039

28.000

2.640

1.709

NS

Global270.10

5.30

21.34

0.039

28.000

2.900

1.290

NW

Global230.10

4.60

15.70

0.039

28.000

2.440

0.898

SH

Global

0.80

87.30

17.42

0.039

28.000

2.610

0.615

ENE

Local

157.50

3.10

34.11

0.039

28.000

2.200

0.188

EW

Local

0.40

11.90

13.89

0.039

28.000

3.060

0.168

NNE

Local

293.80

0.00

21.79

0.039

28.000

3.000

0.657

SH2

Local

164.00

52.60

35.43

0.039

28.000

2.610

0.080

SH3

Local

337.90

52.60

17.08

0.039

28.000

2.610

0.066

NE

Global315.30

1.80

27.02

28.000564.200

3.000

0.029

NS

Global

92.70

1.20

30.69

28.000564.200

2.200

0.0003NW

Global

47.60

4.40

19.67

28.000564.200

2.060

0.0003SH

Global347.40

85.60

23.25

28.000564.200

2.830

0.029

ENE

Global157.90

4.00

53.18

28.000564.200

3.140

0.087

EW

Global186.30

4.30

34.23

28.000564.200

2.850

0.0014

256

TR-10-52

Table 6-36. DFN model “r0 fixed” for fracture domain FFM06, version 0.6 from Modelldatabasen.
Fracture Domain FFM06, r0-fixed Alternative
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global125.70

10.10

45.05

0.039

564.200

2.785

3.299

NS

Global

91.00

4.10

19.49

0.039

564.200

2.780

2.150

NW

Global

34.10

0.80

16.13

0.039

564.200

2.662

1.608

SH

Global

84.30

71.30

10.78

0.039

564.200

2.582

0.640

ENE

Local

155.40

8.30

20.83

0.039

564.200

2.865

0.194

SH2

Local

0.00

47.50

12.71

0.039

564.200

2.610

0.429

Table 6-37. DFN model “TCM” for fracture domain FFM06, version 0.6 from Modelldatabasen.
Fracture Domain FFM06, TCM Alternative (’kr-fixed’)
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global125.70

10.10

45.05

0.351

564.200

3.020

3.299

NS

Global

91.00

4.10

19.49

0.039

564.200

2.780

2.150

NW

Global

34.10

0.80

16.13

0.319

564.200

2.850

1.608

SH

Global

84.30

71.30

10.78

0.793

564.200

2.850

0.640

ENE

Local

155.40

8.30

20.83

0.740

564.200

3.250

0.194

SH2

Local

0.00

47.50

12.71

0.039

564.200

2.610

0.429

Table 6-38. DFN model “OSM+TFM” for fracture domain FFM06, version 0.6 from Modelldatabasen.
Fracture Domain FFM06, OSM+TFM Alternative
Set
Type
Trend
PlungeKr0 (m)

rmax (m)

kr

P32

NE

Global125.70

10.10

45.05

0.039

28.000

2.640

3.252

NS

Global

91.00

4.10

19.49

0.039

28.000

2.900

2.146

NW

Global

34.10

0.80

16.13

0.039

28.000

2.440

1.522

SH

Global

84.30

71.30

10.78

0.039

28.000

2.610

0.630

ENE

Local

155.40

8.30

20.83

0.039

28.000

2.200

0.142

SH2

Local

0.00

47.50

12.71

0.039

28.000

2.610

0.423

NE

Global315.30

1.80

27.02

28.000564.200

3.000

0.029

NS

Global

92.70

1.20

30.69

28.000564.200

2.200

0.0003NW

Global

47.60

4.40

19.67

28.000564.200

2.060

0.0003SH

Global347.40

85.60

23.25

28.000564.200

2.830

0.029

ENE

Global157.90

4.00

53.18

28.000564.200

3.140

0.087

EW

Global186.30

4.30

34.23

28.000564.200

2.850

0.0014TR-10-52

257

6.4

Rock mechanics

This section presents the mechanical properties of the rock and fractures (for fractures also hydromechanical properties), as wells as the stress conditions needed for the assessment of the thermohydro-mechanical (THM) evolution of the rock on different scales. These quantities are assessedfrom the site-descriptive model reports (SDM-Site) and the version D2 repository layout.

6.4.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR-Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The modelling requires the following geosphere input data with estimates of spatial and temporalvariability, as well as associated uncertainties.
•
•
•
•
•
•
•
•

•

Rock mass elastic properties for the different scales of importance (Young’s modulus E (GPa)and Poisson ratio υ (–)).
Rock mass density ρ (kg/m3).
Uniaxial compressive strength UCS (MPa) and spalling strength (% of UCS).
Fracture shear stiffness KS (GPa/m) and fracture normal stiffness KN (GPa/m).
Fracture friction angle φ (°) and fracture cohesion c (MPa).
Fracture dilation angle ψ (°).
Principal in situ stress magnitudes σ1, σ2, and σ3 (MPa) and orientations of stress components interms of plunge and trend (°).
Evolution of stress additions during glacial cycle (glacially induced stresses), principal stressmagnitudes σ1, σ2, and σ3 (MPa) and orientations of stress components in terms of plunge andtrend (°).
Stress-transmissivity relations– Parameter values for the continuously-yielding joint model /Itasca 2007/, that is JKN(MPa/mm) and JEN (–).
– Joint roughness coefficient JRC (–).
– Parameters for exponential expression of hydraulic apertures /Liu et al. 2003/, that is er(μm), emax (μm), and α (–).

SR-Site modelling activities in which data will be used

SR-Site assesses the THM-evolution of the rock during the construction and operational phase,during the initial period of temperate climate after closure, and during the remaining part of thereference glacial cycle. The main results of these analyses concern:
1. Estimates of mechanically induced changes of the transmissivity of fractures that intersect thenear-field, as well as fractures in the far-field, due to excavation, thermal effects, and effects of
ice load.
2. Estimates of the scope and extent (time-frames and location) of fracturing around deposition
holes and tunnels (spalling), and based on this, a qualitative assessment of the potential hydraulicimpact of this fracturing.
3. Evolution of pore pressures induced by glacial loads and potential for hydraulic jacking at different depths. The modelling work on hydraulic jacking is conducted both by use of closed form
solutions and by use of numerical methods.
The mechanical and thermo-mechanical modelling explicitly conducted for SR-Site, relevant topoints 1 and 2 above, is done using version 4.1 of the extensively tested 3DEC code /Itasca 2007/.
3DEC is a distinct element code, particularly developed to analyse the mechanical and thermomechanical behaviour of jointed media such as fractured rock. Furthermore, the large-scale effects ofparameter variations, deposition sequences, and optimisation are investigated by use of an analytical
thermo-mechanical solution /Claesson and Probert 1996b/.
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The modelling work on point 3 above, evolution of pore pressures and hydraulic jacking, isconducted both by use of specifically for the purpose derived closed form solutions and by use ofnumerical methods (UDEC /Itasca 2005a/, FLAC /Itasca 2005b/ and Code_Bright /CIMNE 2004/
models). Results from the hydraulic jacking assessment are presented in a separate report /Lönnqvist
and Hökmark 2010/.
The results of the rock mechanics modelling, evolution of the glacially induced pore pressures anda summary of the findings from the study on hydraulic jacking are presented in the THM-report/Hökmark et al. 2010/.
In addition to the mechanical properties given in this section, the modelling also needs the following
thermal/thermo-mechanical (cf. Section 6.1), hydraulic (cf. Section 6.6), and non-flow related (cf.
Section 6.8) properties of the rock mass, as well as fracture orientations (cf. Section 6.3): The dataneeded are listed below.
1. Rock mass thermal conductivity λ (W/(m∙K)) and heat capacity C (J/(m3·K)).
2. Rock mass thermal expansion coefficient α (m/(m∙K)).
3. In situ temperature at repository depth T (°C).
4. Rock mass hydraulic conductivity K (m/s).
5. Rock mass porosity n (–).
6. Fracture transmissivity T (m2/s).
7. Fracture orientations.
8. Temperature evolution during permafrost (cf. Section 7.1 and /Hartikainen et al. 2010/).
Furthermore, the modelling needs:
9. Repository design, that is the geometry and dimensions of deposition tunnels and deposition
holes /SKB 2007/.
10. Layout, that is the coordinates of individual canister positions /SKB 2009a/.
11. Initial canister power and decay-rate of the spent nuclear fuel /Hökmark et al. 2009/.

6.4.2 Experience from SR-Can
SR-Can assessed the THM-evolution during the operation phase /SKB 2006a, Section 9.2.2/, during
the initial period of temperate climate after closure /SKB 2006a, Section 9.3.5/, and during theevolution for the remaining part of the reference glacial cycle /SKB 2006a, Section 9.4.4/.
Modelling in SR-Can

The rock mechanics modelling conducted for SR-Can was done using version 3.0 of the extensively
tested 3DEC code /Itasca 2003/.
The objective of the modelling was to estimate the disturbance caused by mechanical processes(such as glaciation/deglaciation) and thermo-mechanical processes on the hydraulic conditions in thenear-field and in the far-field, and to estimate the risk for spalling in the walls of the deposition holes
during excavation and the thermal period.
There were separate 3DEC near-field models for Forsmark site conditions, Simpevarp site conditions, and for Laxemar site conditions based on input data determined during different stages of thesite-descriptive modelling work, and on general layout and design guidelines. The fracture geometry,however, was generic and without links to the site models.
The results of the modelling as well as input data used in the modelling were presented in tworeports /Hökmark et al. 2006, Fälth and Hökmark 2007/.

TR-10-52259

Conditions for which data were used in SR-Can

In SR-Can all data supplied from the sites relate to present-day conditions. None of the geosphereproperty data used as input in the thermo-mechanical models is sensitive to changes in temperature,or is in any way likely to change over time. Initial stresses were based on the, at the time, knowledgeof the site conditions.
The representation of the stress additions due to a glacial load was based on preliminary ice/crust/
mantle 2D calculations.
Sensitivity to assessment results in SR-Can

Values of intact rock and rock mass deformability parameters typically vary within tens of percentsrather than within orders of magnitude. Because of this, the upper bound estimates of transmissivity
changes and the spalling risk estimates are not very sensitive to input data variations within theranges suggested by the probability distributions given in the preliminary site descriptions /SKB
2005a, b, 2006d/ for these parameters. Fracture strength parameter values and in situ stresses arepotentially more important.
/Hökmark et al. 2006/ did not make any systematic check for sensitivity to stress assumptions.
However, the high-stress Forsmark model gave the largest fracture shear displacements, whereas thelow-stress Simpevarp and Laxemar stress domain II model (presented in the preliminary site descriptive reports /SKB 2005a, 2006d/) did not give shear displacements sufficiently large that the fracturetransmissivity would change (according to the experimental data used to evaluate the modelling
results). On the other hand, transmissivity changes induced by normal stress variations were at maximum in the low-stress Simpevarp stress domain II model. For the bounding and general estimatesof the effects on transmissivity, the net effect of in situ stress variations between the Forsmark meanvalues and the Simpevarp stress domain II mean values are unimportant.
All analyses were conducted using schematic fracture geometry with a small number of fractures
located in a region of potential importance in hydro-models. The fracture orientations were selected
to give a high sensitivity to mechanical disturbances. Yet, the possibility that other fracture geometries could have given different results cannot be excluded.
The 3DEC results were checked with additional analyses using the minimum and maximum valuesof fracture friction given in the Forsmark preliminary site description /SKB 2005b/. The minimum
value of friction did not give more slip on the particular fracture that gave the largest slip in the basecase analysis, that is using the mean value of the Forsmark preliminary site-descriptive report. Thismeans that the upper bound estimate of effects on fracture transmissivities would not change, or besensitive to fracture strength reductions. The maximum friction gave approximately half the slip on
all fractures. Strength variations appeared to have insignificant effects on transmissivity changes
related to normal stress variations.
In the 3DEC model, mean values of fracture shear stiffness and fracture normal stiffness were usedthroughout without sensitivity tests, while the site models give standard deviations, corresponding toabout 40% of the mean values. Variations within these ranges are not judged important to any of theresults.
Alternative modelling in SR-Can

There was no alternative modelling of rock mechanics conditions in SR-Can.
Correlations used in SR-Can

No correlations among the rock mechanics properties were considered in the modelling for SR-Can/Hökmark et al. 2006, SKB 2006b/, see also subsection on “Conditions for which data were used in
SR-Can”.

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Identified limitations of the data used in SR-Can modelling

The following limitations of the data used in SR-Can were identified.
•

The possibility of ‘long-term time-dependent degradation of rock-strength parameters’ was notaddressed in SR-Can. In the SKI report /Rutqvist and Tsang 2008/ it is suggested that the timedependence of mechanical properties needs to be addressed in the safety assessment.

•

The stress-transmissivity relation used in SR-Can was based on data from one rock sample of
Sellafield volcanic tuff (rather than granitic rock) and the residual hydraulic aperture was fixedat 10 μm. /Rutqvist and Tsang 2008/ suggest that stress-transmissivity relations ideally should bebased on in situ experiments.

6.4.3 Supplier input on use of data in SR-Site and SR-Can
The supplier broadly agrees with the handling of data in SR-Can. However, the following issues arerecommended to be addressed in SR-Site.
•

The 3DEC code uses a built-in analytical solution, based on a grid of point sources representing
the heat generating nuclear waste, to calculate temperatures and temperature increments at eachgrid point in the numerical model. Depending on the required level of detail, the point sources inselected parts of the repository region can be substituted for vertical line sources or combinationsof line sources to better reflect the temperature distribution around individual canisters (see /Fälthand Hökmark 2007, Hökmark et al. 2009/). The analytical solution is relatively fast and thus
provides the opportunity to simulate the thermal and thermo-mechanical evolution over longtimes in an efficient way. Based on this it is recommended that 3DEC is used also in SR-Site fornear-field and far-field analyses.

•

In the SR-Can modelling, a generic layout, with rectangular deposition areas parallel to eachother, was applied. For the different models the layout was made site-specific by adjustment of
the canister spacings according to preliminary data from the design project. For SR-Site there aremuch more elaborated site-specific layouts depending on the expected loss of potential canister
positions /SKB 2009a/. It is recommended that one of these layouts should be used as basis forthe thermal models applied in the SR-Site modelling.

•

The near-field modelling conducted for the SR-Can safety assessment concerned only centrally
located canister within the repository region. In SR-Site, the influence of different locations
within the repository region should be investigated.

•

In SR-Can, only the mean value of the thermal conductivity was used. This is appropriate formodelling the thermal and thermo-mechanical evolution on a large scale (e.g. repository-scale).
The temperature distribution around an individual canister is, during the first few years, to alarge extent determined by its own heat contribution and the local thermal properties /Hökmark
et al. 2009/. Therefore, the spatial distribution of the thermal conductivity (particularly the lower
tail of the distribution) needs to be considered when specifying the minimum canister spacing,cf. Section 6.1. However, 3DEC cannot account for spatial variations in the thermal properties.
Given the canister spacing, a global value of the rock mass thermal conductivity can be obtainedthat reproduces the threshold value of the peak buffer temperature (cf. Section 6.1).This value isknown as the dimensioning value of the thermal conductivity and is usually within the 0.1–2 percentile of the low tail of the thermal conductivity distribution /Hökmark et al. 2009/. Using mean
the mean value of the thermal conductivity may therefore underestimate the temperature at earlytimes in the near-field. Methods for obtaining the dimensioning value of the thermal conductivity
are described in /Hökmark et al. 2009/. For the near-field modelling in SR-Site, for example
modelling of stresses in the walls of a deposition hole, it is recommended that the dimensioning
value of the thermal conductivity is also considered.

•Generally, the influence of uncertainty spans in parameters judged to have impact on safety
should be investigated.

•

It is recommended that the fracture geometry used in the SR-Site modelling is also generic, thatis circular, and that, in addition to generically oriented fractures, site-specific data for the orientation of fractures should also be used (e.g. /Fox et al. 2007/). Furthermore, near-field models
should be made larger, such that larger fractures (≥ 50 m radius) can also be accommodated.

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261

•In SR-Can, the representation of the glacial load was based on preliminary ice/crust/mantle 2D
calculations. Since then, the technique for performing this type of analyses has been developed
significantly, and now includes 3D representations of the crust/mantle-system and variationsof the lithosphere stiffness /Lund et al. 2009/. It is likely that a 3D-model is capable of making
better estimates of the stress evolution during a glacial cycle than the 2D-model used in the
SR-Can safety assessment. Thus, results from such 3D-models should be used as input to the
3DEC analyses for SR-Site.

•

Based on the comments by /Rutqvist and Tsang 2008/, it is recommended that stress-induced
transmissivity changes are addressed by relations based on site-specific fracture normal-stiffness
data and hydraulic aperture data.

6.4.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information used in the data qualification of rock mechanics data are presented
in Table 6‑39. It should be noted that data delivered in this section are used as input to the “THM
report” /Hökmark et al. 2010/. Accordingly, this report is not considered to be a main source of
information.
Table 6‑39. Main sources of information used in data qualification.
Site description Forsmark, 2008. Site description of Forsmark at completion of the site investigation phase. SDM-Site
Forsmark. SKB TR-08-05, Svensk Kärnbränslehantering AB.
Andersson J C, 2007. Äspö Hard Rock Laboratory. Äspö Pillar Stability Experiment, Final report. Rock mass response tocoupled mechanical thermal loading. SKB TR-07-01, Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2003a. Forsmark site investigation. Borehole: KFM01A. Results of tilt testing. SKB P-03-108,
Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2003b. Forsmark site investigation. Boreholes: KFM03A and KFM03B. Tilt testing. SKB P-04-178,
Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2004a. Forsmark site investigation. Borehole: KFM02A. Results of tilt testing. SKB P-04-08,
Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2004b. Forsmark site investigation. Borehole: KFM04A. Tilt testing. SKB P-04-179,
Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2004c. Forsmark site investigation. Borehole: KFM05A. Tilt testing. SKB P-04-205,
Svensk Kärnbränslehantering AB.
Chryssanthakis P, 2006. Forsmark site investigation. Borehole: KFM09A. Tilt testing. SKB P-06-25,
Svensk Kärnbränslehantering AB.
Follin S, Hartley L, Jackson P, Roberts D, Marsic N, 2008. Hydrogeological conceptual model development and numerical modelling using CONNECTFLOW, Forsmark modelling stage 2.3. SKB R-08-23, Svensk Kärnbränslehantering AB.
Glamheden R, Fredriksson A, Röshoff K, Karlsson J, Hakami H, Christiansson R, 2007. Rock mechanics Forsmark.
Site descriptive modelling Forsmark Stage 2.2. SKB R-07-31, Svensk Kärnbränslehantering AB.
Jacobsson L, Flansbjer M, 2005a. Forsmark site investigation. Borehole KFM05A. Normal stress test with direct andindirect deformation measurement together with shear tests on joints. SKB P-05-141, Svensk Kärnbränslehantering AB.
Jacobsson L, Flansbjer M, 2005b. Forsmark site investigation. Borehole KFM06A. Normal loading and shear tests on
joints. SKB P-05-122, Svensk Kärnbränslehantering AB.
Jacobsson L, Flansbjer M, 2005c. Forsmark site investigation. Borehole KFM07A. Normal loading and shear tests
on joints. SKB P-05-213, Svensk Kärnbränslehantering AB.
Jacobsson L, Flansbjer M, 2005d. Forsmark site investigation. Borehole KFM08A. Normal loading and shear tests
on joints. SKB P-05-218, Svensk Kärnbränslehantering AB.
Jacobsson L, Flansbjer M, 2006. Forsmark site investigation. Borehole KFM09A. Normal loading and shear tests
on joints. SKB P-06-29, Svensk Kärnbränslehantering AB.
Lund B, Schmidt P, Hieronymus C, 2009. Stress evolution and fault stability during the Weichselian glacial cycle.
SKB TR-09-15, Svensk Kärnbränslehantering AB.
Martin D, 2005. Preliminary assessment of potential underground stability (wedge and spalling) at Forsmark,Simpevarp and Laxemar sites. SKB R-05-71, Svensk Kärnbränslehantering AB.
SKB, 2009d. Site engineering report Forsmark. Guidelines for underground design, step D2. SKB R-08-83,
Svensk Kärnbränslehantering AB.

262

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Categorising data sets as qualified or supporting

The qualified and supporting data sets are presented in Table 6‑40. Comments regarding each item
are given in Table 6‑41. Data sets taken from the site-descriptive modelling /Glamheden et al. 2007/
and the Site description Forsmark (i.e. points 1, 2, 4, 6, 7, 8 and 9) can a priori be categorised as
qualified data. They have subsequently been judged to be qualified according to the instructions for
supplying data to this Data report (see Section 2.3).
For the qualified data sets in Table 6‑40, more extensive discussions on the uncertainties, variability,
and methodology used in the acquisition of data associated with each set of parameters are provided
by the references given in Table 6‑39 and Table 6‑40; a summary is provided in the following sections. For the supporting data sets (with the exception of rock mass density), discussions regarding
uncertainties and variability for each set of parameters are provided in the following sections.
Excluded data previously considered as important

No important data have been excluded from the analyses.
Table 6‑40. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Intact rock deformation properties: Sections 3.3.1
and 3.3.2 /Glamheden et al. 2007/ and Table 7-3 in
the Site description Forsmark
2. Intact rock uniaxial compressive strength: Section
3.2.1 /Glamheden et al. 2007/; Section 7.2.1 in the Site

9. Spalling strength: Tables 3-2 and A.1 /Martin 2005/;
Sections 8.2 and 11 /Andersson 2007/.
10. Rock mass density: Table 3-4 /Stephens et al. 2007/
11. Stress-transmissivity relations: Model parameters JKN
and JEN for the continuously-yielding joint model:· Chapter 5 (normal stress-normal displacement curves
based on direct measurements) in each of /Jacobsson
and Flansbjer 2005a, b, c, d, 2006/.
· Joint roughness coefficient: Table 5-1 and appendix ineach of /Chryssanthakis 2003a, b, 2004a, b, c, 2006/.
· Initial (or residual) hydraulic aperture: Table 10-25/Follin et al. 2007b/

description Forsmark

3. Rock mass deformation properties: Table 5-9/Glamheden et al. 2007/
4. Fracture shear and normal stiffness: Table 4-15
/Glamheden et al. 2007/
5. Fracture friction angle and cohesion: Table 4-14
/Glamheden et al. 2007/
6. Fracture dilation angle: Table 4-16 /Glamheden et al.
2007/
7. Initial pre-mining stress magnitudes and orientations:
Tables 6-7 and 6-8 /Glamheden et al. 2007/; Table 2-11
/SKB 2009d/
8. Evolution of stress additions during glacial cycle:
Figure 7-17 /Lund et al. 2009/ and underlying data.

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263

Table 6‑41. Justification to the sorting of items in Table 6‑40.
1–2. Primary data for intact rock are obtained by laboratory (small) scale testing, see the Site description Forsmark. Lower
level references to test methods as well as discussions on uncertainties are provided by /Glamheden et al. 2007/. The values of Young’s modulus presented in /Glamheden et al. 2007/ is based on the tangent Young’s modulus evaluated at 50%of the compressive strength and determined from uniaxial and triaxial compressive tests /Glamheden et al. 2007/. Poisson’s ratio is evaluated from these test results. No triaxial tests were performed on samples from rock domain RFM045.
3. The deformation properties of the rock mass are obtained from two different modelling approaches /Glamheden et al.
2007/:– An empirical approach, which is based on classification systems and empirical relationships.
– A theoretical approach, which is based on numerical modelling.
The final estimate of the rock mass deformation properties is made by weighting the results together – a process known
as “Harmonisation”. More detailed information regarding the process is provided by /Glamheden et al. 2007/.
4–6. The strength and deformation properties of fractures reported in /Glamheden et al. 2007/ are based on laboratory
results from tilt tests and direct shear tests of discrete fractures. The fracture samples are taken from the target volume
in fracture domains RFM029 and RFM045, as well as from adjacent fracture domains RFM012, RFM034, and RFM044.
Lower level references to test methods as well as discussions on uncertainties are also provided by /Glamheden et al. 2007/.
– The fracture normal stiffness, KN, is presented as the secant stiffness evaluated between the unloaded state and fullloading of the second load cycle.
– The fracture shear stiffness, KS, is presented at the secant shear stiffness evaluated between 30% and 50% of thepeak shear stress at each level of normal stress (0.5, 5, and 20 MPa).
– The fracture dilation angle, ψ, is presented as the secant dilation angle at three levels of normal stress. It is evaluatedbetween 0.3 and 1.3 mm of shear deformation at the normal stress level 0.5 MPa, between 0.5 and 1.9 mm of shear
deformation at the normal stress level 5 MPa, and between 0.7 and 2.1 mm of shear deformation at the normal stresslevel 20 MPa.
– The Mohr-Coulomb fracture strength properties (peak cohesion and peak friction angle/residual cohesion and residual
friction angle) are determined by a least squares fit to the measured peak/residual shear stresses at the three levels of
normal stress (0.5, 5, and 20 MPa).
7. The in situ stress model for Forsmark, i.e. initial pre-mining stress magnitudes and orientations, are based on overcoringdata from the site investigations as well as results from numerical modelling /Glamheden et al. 2007/. Further discussions
on the methods of acquisition of data as well as uncertainties are provided by /Martin 2007/. An upper limit of stressmagnitudes at repository depth is provided in Table 2-11 /SKB 2009d/. These values should only be used for assessmentsof the risk of spalling due to elevated stress magnitudes /SKB 2009d/.
8. The stress additions during a glacial cycle are based on 3D simulations of the glacial isostatic adjustment /Lund etal. 2009/ due to a Fennoscandian ice model by Näslund /SKB 2006c/. The numerical model used to calculate glaciallyinduced stresses has been extensively tested and verified against other, independent, codes as well as compared withGPS measurements of glacial isostatic adjustments (GIA) data /Lund et al. 2009/. Therefore, this data set can be judged
to be qualified according to the instructions for supplying data to this Data report.
9. No field experiments of the spalling strength have been performed on the rock types at Forsmark. However, /Andersson
2007/ suggested that for sites lacking in situ data, lower bound estimates of the crack initiation stress based on the strain
gauge method may be used to assess the rock mass spalling strength. The spalling strength is expressed as the range
52–62% of the uniaxial compressive strength (UCS) in each rock domain based on the findings from AECL’s Mine-by
experiment (e.g. /Martin 2005/), the Äspö Pillar Stability Experiment (APSE) /Andersson 2007/ and the ratio between
crack initiation stress and UCS at Forsmark, see the Site description Forsmark. The spalling strength should therefore beconsidered a supporting data set.
10. The value of the rock mass density is generic (2,700 kg/m3) and used to calculate the vertical stress component.
It should therefore be considered a supporting data set.
11. No field experiments have been carried out at Forsmark to determine the relationship between stress and transmissivity during loading or unloading of fractures. Instead, stress-induced transmissivity changes are based on the continuouslyyielding joint model (CY model) /Itasca 2007/, using lab-scale stress-stiffness data and average hydraulic apertures at
repository depth, and an exponential relation /Liu et al. 2003/ to account for a residual aperture at high normal stress.
Therefore, the stress-transmissivity relations should be considered a supporting data set.

6.4.5 Conditions for which data are supplied
All data supplied are valid at present-day conditions at the site and based on current knowledge ofthe site conditions. The rock volumes the data concern are clearly indicated throughout this section.
The stress additions during a glacial cycle (cf. Section 7.1) provided by /Lund et al. 2009/ are valid
at 500 m depth, corresponding to the repository depth.

6.4.6 Conceptual uncertainty
The following conceptual uncertainty applies to all rock mechanics parameters. As the availablesite-specific data are collected from a limited area of the site, the mechanical properties are evaluatedon the assumption that the rock volume within each geological domain has similar mechanicalcharacteristics, see the Site description Forsmark.
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Deformation and strength properties of intact rock

Other than the conceptual uncertainties that apply for all rock mechanics parameters, the conceptual
uncertainty of the deformation and strength properties of intact rock are judged to be low.
Factors contributing to the conceptual uncertainty of the spalling strength are:• Relative humidity: Reduced humidity seems to increase the spalling strength /Glamheden et al. 2010/.
• Stress path: The Äspö Pillar Stability Experiment (APSE) /Andersson 2007/ was designed togive high tangential stresses in the floor region and some spalling was observed in the hole walls
already after excavation (cf. Figure 6‑33). In the KBS-3 repository, the deposition tunnels aredesigned and oriented such that the stresses in the floor region are minimised. For a more gentle
stress path than in APSE, that is if the tangential stresses after excavation are well below thenominal spalling strength, and followed by a slow stress increase towards the nominal spallingstrength during the subsequent heated phase, the spalling strength may be different.
Deformation properties of rock mass

The main conceptual uncertainties associated with the empirical approach of estimating the deformation properties of rock mass are listed below /Glamheden et al. 2007/:• As well as uncertainty in input data (see Section 6.4.7), the size of sample populations andsubjectivity in the characterisation of the rock mass will contribute to the conceptual uncertainty.
• No explicit stress dependence in any of the parameters is assumed. The empirical relationships
were developed for conditions at shallow depths and low confining pressure. As the empirical
relationships will be used to estimate properties at approximately 500 m depth, this will introduce
some uncertainty.
• It is assumed that the rock mass is isotropic.
• The applicability of the empirical relationships for high-quality rock mass at a depth of around
500 m is somewhat uncertain and may result in an overestimated value of Young’s modulus.
In addition to the conceptual uncertainties that apply for all rock mechanics parameters, a measureof the conceptual uncertainty can be found by examining the differences in results from the empiricaland theoretical approaches. The mean values of Young’s modulus obtained from the theoretical andempirical approaches deviate by 2–3 GPa from each other in both fracture domains FFM01 andFFM06 /Glamheden et al. 2007/. The corresponding difference in Poisson’s ratio is 0.01 in fracturedomain FFM01 and 0.07 in fracture domain FFM06 /Glamheden et al. 2007/.
Fracture mechanical properties

Other than the conceptual uncertainties that apply for all rock mechanics parameters, the conceptual
uncertainty of the mechanical and strength properties of fractures is judged to be low. No samples
were taken from fracture domain FFM06, but visual inspection of the fractures in that fracturedomain suggests that their properties are similar to those in fracture domain FFM01, see the Sitedescription Forsmark.
In situ stresses

The main conceptual uncertainties associated with hydraulic fracturing and hydraulic testing of
pre-existing fractures (HTPF) methods, as well as the overcoring method /Martin 2007/ are:
• In the small-diameter boreholes (used by SKB), only the orientation of the fracture at the
injection-borehole wall can be determined. It is assumed that this orientation is the same as
the fracture orientation at the end of the test. Note that results from the hydraulic fracturingand HTPF methods are not used in the stress model, which results in a more cautious estimateof the state of in situ stress (see the Site description Forsmark), cf. Section 6.4.7.
• The overcoring method relies on elastic theory. In the stress regime at Forsmark, microcracking
may occur during drilling and induce a non-linear stress-strain response, which makes the resultsdifficult to interpret. There is significant scatter in the measurement data, which can be attributed
to both uncertainties in the measurement methods as well as spatial variability /Martin 2007/(see also Section 6.2.8). /Martin 2007/ further states that this type of scatter is more likely to berelated to measurement technique than actual variability. However, it is practically impossible toseparate the two sources of scatter /Martin 2007/.
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265

Evolution of stress additions during a glacial cycle

The main sources of conceptual uncertainty stem from three assumptions:•

The modelling approach.

•

The ice model.

•

The Earth model.

The conceptual uncertainty is judged to be large and also the source of the largest uncertainty.
Modelling approach

The main conceptual uncertainties associated with the modelling approach by /Lund et al. 2009/ ofthe glacially induced stress additions are listed below. The first two bullets together will amount to at
most 1 MPa.
•

The influence of tectonic strain is not considered, i.e. the possibility that the stabilising ice coverinhibits incremental strain energy release. A horizontal tectonic strain rate of 1·10–11 per year, assuggested by for example /Muir Wood 1995/, would correspond to a horizontal stress increase by
0.06 MPa during a glacial cycle.

•

The shear impact of the ice is not considered. /Wu 2009/ concluded that the shear impact is lessthan 1 MPa at the surface and decreases with depth.

•

Thermo-mechanical effects during the glacial cycle are not considered, i.e. in particular the
reduction of the horizontal stresses due to the temperature reduction of the rock during permafrost conditions. These effects are handled in the THM report /Hökmark et al. 2010/ and notquantified here.

Ice models

SKB’s reference ice model by Näslund /SKB 2006c/ is based on a reconstruction of the Weichselian
glaciation, cf. Figure 6‑25. The thickness (~3 km) of Näslund’s ice sheet is generally consideredrather large /Lund et al. 2009/. The ice thickness at Forsmark using a different ice model by /Lambeck
2005/ is also presented in Figure 6‑25. Lambeck’s ice is significantly thinner (maximum thickness ofaround 2 km) and covers the site for a much longer period in time than Näslund’s ice. As seen in the
figure, there are significant differences between the two models and the stress evolution due to thetwo ice models would also be expected to be different. Note that the vertical glacially induced stresscomponent reflects the ice load (e.g. /Lund et al. 2009/).
Figure 6‑26 shows the temporal evolution of the glacially induced stresses at a depth of around
5 km due to the two ice models shown in Figure 6‑25. The higher stress magnitudes (up to 40 MPa)obtained from Lambeck’s model are likely to be a combination of thinner lithosphere, lower upper
mantle viscosity and longer duration of ice coverage /Lund et al. 2009/.

Figure 6‑25. Evolution of ice thickness at Forsmark from two different models. Reproduced from
/Lund et al. 2009, Figure 4-5 left/.

266

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σH
σh
σv

Stress (MPa)

20

0

-20

-40

-60
150

100

50

0

Time BP (ka)Figure 6‑26. Left: Temporal evolution of the glacially induced stresses (model T9) at 5.5 km depth
obtained from ice-crust-mantle analyses performed by /Lund et al. 2009/ using Näslund’s /SKB 2006c/
ice model. Right: Modelled stresses at 5 km depth due to Lambeck’s ice model, reproduced from /Lambeck
2005, Figure 7/. Here, compression is negative. The scale of the right figure is the same as in the left figure.

Earth models•

Three types of Earth models were considered by /Lund et al. 2009/.

•

Models with a 100 km thick uniform compressible elastic plate overlying an incompressible
visco-elastic half-space (Model 1).

•

Models with a three-layered 100 km thick compressible elastic plate overlying an incompressible
visco-elastic half-space (Model T9).

•

Models with laterally varying lithosphere (Model T12).

/Lund et al. 2009/ found that the structure of the lithosphere is less important for the fit to GPS
data. Although Model 1 gave the best fit to GPS data (Figure 6‑27, left), the deformation modulus
is considered too high in the upper parts of the rock. The glacially induced stresses at 500 m depth
obtained from this model are 50–90% larger in magnitude during the second glacial maximum thanthe corresponding results from the other two models shown in the figure. By inspection of the stressresults during the last glacial advance and retreat, the glacially induced stresses from all considered
Earth models (except Model 1), appear to be within ±10 MPa of the results from the Earth model(Model T9) preferred by /Lund et al. 2009/.
However, horizontal stratification affects results when relative sea-level data are considered /Lund
et al. 2009/. The horizontally stratified models were found to fit GPS data better than the models
with lithospheric depth variations, cf. Figure 6‑27 (middle and right), mainly due to a poor fit tohorizontal velocities in the models with depth-varying lithosphere. For relative sea-level data thedifference between horizontally stratified models and models with lithospheric depth variations were
smaller, cf. Figure 6‑28.

TR-10-52267

Figure 6‑27. Comparison between data from the Bifrost project (GPS data) and model results. Contours
show differences in vertical velocities, whereas arrows show horizontal velocity components from the Bifrost
project (red) and model results (white). Reproduced from /Lund et al. 2009, Figures 6-2, 6-3, and 6-5/.

Figure 6‑28. Comparison between measured relative sea-level data in northern Uppland and model resultsfrom flat layered Earth models (left) and Earth models with variable layer thickness (right). Excerpt from
/Lund et al. 2009, Figure 6-7/.

Stress-transmissivity relations

The conceptual uncertainty associated with the stress-transmissivity relations is judged to be large
and is also, in comparison with uncertainties due to precision, bias, and representativity, the largest
uncertainty. The conceptual uncertainty is associated with:•

The nature of stress-stiffness relations of individual fractures.

•

Estimates of the variations in mechanical apertures of fractures due to variations in normal stressand, from that, the hydraulic aperture and corresponding fracture transmissivity.

•

Fracture data, that is fracture normal stiffness and hydraulic properties of individual fractures.

Considering the uncertainties associated with the stress-transmissivity relations, it is not feasible
to derive stress-transmissivity relations for individual fractures. Instead, two stress-transmissivity
relations will be given: 1) One based on mean value fracture stiffness data to estimate the average
response to variations in normal stress and 2) one based on fracture stiffness data that give an upperbound estimate of the sensitivity to variations in normal stress.

268

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Stress-stiffness relations of individual fractures

For present-day conditions, only weak correlations between transmissivity and normal stress havebeen observed /Follin et al. 2008/, see Figure 6‑29. However, these correlations may not adequately
describe changes in fracture transmissivity due to the anticipated changes in normal stress during thesafety assessment period. There are several models that describe the relation between fracture normal
stiffness and normal stress (e.g. /Fransson 2009/). For the purpose of estimating the sensitivity offracture transmissivities to variations in normal stress, the stress-dependence of the fracture normal
stiffness is approximated by the continuously-yielding (CY) joint model /Itasca 2007/, Equation 6‑6.
Qualitatively, this model is supported by literature data (e.g. /Fransson 2009/), that is a non-linear
increase in fracture normal stiffness with increased compression.
In the CY model, the relation between fracture normal stiffness and normal stress (σN) is defined as:
K N = JKN ⋅ s N JEN

6‑6

where JKN and JEN are a model parameters. JKN is defined as the tangent normal stiffness evaluated at 1 MPa, whereas JEN is the slope of straight lines fitted to stress-stiffness data in a log-log
plot. Values of the parameters JKN and JEN may be obtained from results of cyclic loading compression tests performed on core samples from the site. Figure 6‑30 shows fracture normal stiffnesses at
1, 5, and 10 MPa estimated from stress-deformation plots in /Jacobsson and Flansbjer 2005a, b, c, d,
2006/ with the CY model (straight lines) fitted to the data points. In the stress-range 1–10 MPa, there
is very good agreement between the CY model and the stress-stiffness data obtained from the normal
loading tests.
1.0

ALL-PFL

Normalised transmissivity T/T 0 (–)

0.9

α = –0.04

0.8

FFM-PFL

0.7

α = –0.08

0.6

ZFM-PFL

0.5

α = –0.28

0.4
0.3
0.2
0.1

α = –2.145

Laboratory

0.0
0

10

20

30

40

50

Normal stress σn (MPa)Figure 6‑29. Fit of transmissivity data and estimated normal stress to T = T0·σnα using all transmissivity
data in the candidate area (ALL-PFL), transmissivity data from fracture domains FFM01, FFM02 andFFM06 (FFM-PFL) and transmissivity data associated with deterministically modelled deformation zones(ZFM-PFL). Reproduced from /Follin et al. 2008, Figure 4-7/.

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269

Specimen ID: KFM06A−117−05
22.5 cm2

Joint area :Normal stress σN (MPa)10

JKN is the tangent
normal stiffness
evaluated at 1 MPa
JEN is the slope of thestraight line fitted totangent normal
stiffness-values atdifferent stress-levels
in a log-log plot

8
6
4

Tangent normal stiffness at 10 MPa
First load-unload cycle
Second loadunload cycle
Tangent normal stiffness at 5 MPa

2Tangent normal stiffness at 1 MPa

0

0

0.005

0.01

0.015

0.02
0.025
0.03
0.035
Normal displacement δN (mm)Joint normal stiffness (MPa/mm)

1E+3

1E+2

KFM05A 117-02
KFM05A 117-05
KFM05A 117-07
KFM05A 117-08

1E+1
1

0.05

1E+4

1E+3
KFM07A 117-01
KFM07A 117-02
KFM07A 117-03
KFM07A 117-04
KFM07A 117-05
KFM08A 117-01
KFM08A 117-02
KFM08A 117-03
KFM08A 117-04
KFM08A 117-05

1E+2

1E+1
1

Normal stress (MPa)

1E+3

KFM06A 117-01

10

KFM06A 117-02

1E+2

KFM06A 117-04
KFM06A 117-05
KFM06A 117-07
KFM06A 117-08

1E+1
1

10

Normal stress (MPa)

Joint normal stiffness (MPa/mm)

Joint normal stiffness (MPa/mm)

0.045

1E+4

1E+4

Joint normal stiffness (MPa/mm)

0.04

10

Normal stress (MPa)

1E+4

1E+3

KFM09A 117-01
KFM09A 117-02

1E+2

KFM09A 117-04
KFM09A 117-05
KFM09A 117-06
KFM09A 117-07

1E+1
1

10

Normal stress (MPa)Figure 6‑30. Example of a cyclic loading compression test (top row), modified from /Jacobsson andFlansbjer 2005b/. CY model (straight lines) fitted to stress-stiffness estimates obtained from stressdeformation curves in /Jacobsson and Flansbjer 2005a, b, c, d, 2006/. The value of JKN ranges between
67 and 1,193 MPa/mm (mean 436 MPa/mm); JEN ranges between 0.26 and 1.69 (mean 0.78).

270

TR-10-52

Fracture apertures and transmissivity

The mechanical aperture E at a normal stress of σ is given by Equation 6‑7, where the initial apertureis E0 at a normal stress of σ0. The hydraulic aperture e is estimated using an empirical relationship(Equation 6‑8) /Barton 1982/ that relates the mechanical aperture E (μm) and hydraulic aperturee (μm) through the laboratory scale Joint Roughness Coefficient (JRC). For naturally loaded joints,
the ratio E/e is usually greater than one /Olsson 1998/. As a joint is opened, the ratio reduces andapproaches one /Olsson 1998/.
E = E0 + DE = E0 −s

1s '− JEN ds '∫JKN s 0E2 
e = min  E ,2.5 
 JRC 6‑7

6‑8

However, the CY model (Equation 6‑7) does not account for a residual aperture, which is important
at high normal stress (e.g. /Rutqvist and Tsang 2008/). This model may therefore overestimate thejoint normal closure at high normal stress. To account for this, an exponential expression suggested
by /Liu et al. 2003/ is fitted to the hydraulic apertures calculated from Equations 6‑7 and 6‑8. The
exponential expression is given by Equation 6‑9:e = er + emax exp(–α·σN)

6‑9Here, er is the residual aperture and σN the effective normal stress, whereas emax and α are model
parameters. The relative transmissivity, T/T0, is subsequently calculated from Equation 6‑9 assuming
the cubic law to hold:T/T0 = (e/e0)36‑10Here, e0 is the hydraulic aperture at the reference effective normal stress σ0.
Fracture data

In order to return a sample to a state that is representative for in situ conditions, several load-unload
cycles are needed /Martin et al. 1990/. /Jacobsson and Flansbjer 2005a, b, c, d, 2006/ performed twoload-unload cycles at normal stress-levels up to 10–20 MPa, cf. Figure 6‑30 (top row). Here, theparameters JKN and JEN are evaluated from results obtained after the second load cycle. By visualinspection of the graphs given in /Jacobsson and Flansbjer 2005a, b, c, d, 2006/, it appears that thetangent normal stiffness at each of the three considered stress-levels does not vary in any significant
way from the first unload cycle through the second load-unload cycle.
The initial mechanical aperture, E0, is estimated from Equation 6‑8 using the mean value of JRC(at joint length tested, cf. Section 6.4.7) /Chryssanthakis 2003a, b, 2004a, b, c, 2006/ and assumingthat the corresponding hydraulic aperture is equal to the residual hydraulic aperture, er. The residual
hydraulic aperture is not a parameter whose value can be estimated from present-day in situ
transmissivity data presented in the site report, e.g. /Follin et al. 2007b/. Here, the residual apertureis taken to be the mean present-day hydraulic aperture at large depths (calculated from transmissivity
values given by /Follin et al. 2007b/ through the cubic flow law). /Hökmark et al. 2006/ concluded
that fractures with high residual, or initial, apertures (i.e. high transmissivity) are less sensitive tovariations in normal stress. This is supported by results from hydraulic jacking tests in borehole
KLX02 at Laxemar described in e.g. /Rutqvist and Tsang 2008/ where it was found that pressure sensitivity was strongly dependent on the initial hydraulic permeability. The most conductive fractures
were relatively insensitive to injection pressure, whereas the least conductive fractures could bestrongly dependent on the injection pressure.

TR-10-52271

6.4.7

Data uncertainty due to precision, bias, and representativity

Data uncertainty due to precision and bias

Deformation and strength properties of intact rock
The uncertainties in deformation and strength properties of intact rock due to precision and bias are
judged to be low based on the following:•

Sampling strategy: In the selection of specimens for mechanical testing, core sections with sealed
fractures or imperfections with an acute angle to the axial load were avoided /Glamheden et al.
2007/. Furthermore, several specimens were taken close to each other /Glamheden et al. 2007/.
The spatial variation must therefore be assumed to be greater than that predicted by the laboratory results /Glamheden et al. 2007/, cf. Section 6.4.8. However, the sampling strategy is notconsidered to have any significant impact on the uncertainties /SKB 2008/.

•

Microcracking: Coring of granite at depths can induce microcracking, which for moderate microcracking can lead to changes in porosity and in case of excessive microcracking also changes
in mechanical properties /Glamheden et al. 2007/. Tests on samples of granite to granodiorite(101057) from boreholes KFM01A and KFM02B showed that microcracking is moderate; themicrocrack volume corresponds to 5–10% of measured mean porosity of the given rock type
/Glamheden et al. 2007/. However, the estimated microcrack volume in the samples is judged
to have an only minor or insignificant impact on the mechanical properties.

•

Interlaboratory differences: As part of the preliminary site description of Forsmark version 1.2,
/Lanaro and Fredriksson 2005/ compared results from laboratory tests performed by the Swedish
National Testing and Research Institute (SP) and Helsinki University of Technology (HUT) on
samples from boreholes KFM01A, KFM02A, KFM03A, and KFM04A. /Lanaro and Fredriksson
2005/ found that the mean value uniaxial compressive strength obtained by HUT (samples
from KFM01A only) was about 5% larger than the corresponding mean value obtained by SP.
Comparisons of results of Young’s modulus obtained from uniaxial testing at the two laboratories
were in very good agreement. Corresponding values of Poisson’s ratio determined on samplesof granite to granodiorite (metagranodiorite) by HUT do not agree with values obtained by SP.
/Lanaro and Fredriksson 2005/ note that the difference in Poisson’s ratio could not be explainedby natural variations in the rock. It should be noted that the results by HUT were obtained fromsignificantly fewer samples. However, in most rock mechanics calculations, the exact value ofPoisson’s ratio does not affect the results very much.

No field experiments of the spalling strength of the rock types at Forsmark have been conducted.
Therefore the uncertainty due to precision and bias cannot be quantified. However, /Andersson 2007/
suggests that for sites lacking in situ data, lower-bound values of the crack initiation stress from uniaxial laboratory test based on the strain gauge method can be used to assess the spalling strength. In
APSE /Andersson 2007/, the spalling strength determined for Äspö diorite was 56–62% of the UCS,
whereas the onset of crack initiation in uniaxial test based on the strain gauge method was found tooccur at 42–48% of the UCS. Figure 6‑31 shows the ratio between mean crack initiation stress andmean UCS for different rock types in two fracture domains (FFM01 and FFM06) and in deformation
zones (DZ and PDZ) at Forsmark. The mean crack initiation stress is about 51–55% of the mean
UCS. Therefore, the assumed lower bound spalling strength (52% of UCS,) appears to be adequate.
Deformation properties of rock mass
The rock at Forsmark is dominated by sealed fractures; open fractures are only a small part of thetotal number /Glamheden et al. 2007/. The empirical approach does not consider sealed or partly
open fractures as it is based on the occurrence of open fractures /Glamheden et al. 2007/. However,
the empirical approach relies on information from databases where sealed and partly open fractures
are considered, meaning that these types of fractures are implicitly analysed /Glamheden et al. 2007/.
This implies that the rock mass quality at Forsmark may be overestimated /Glamheden et al. 2007/.
However, it was found in the theoretical approach that variations in material properties gave a larger
spread in evaluated properties of the rock mass than variations in fracture geometry /Glamheden
et al. 2007/, cf. Figure 6‑41.

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Ratio between crack initiation stress and UCS (%)

100
90

Estimated spallingstrength (52-62%
of UCS)

80
70
60
50

51.3

54.5

53.3

52.5

101061 (FFM01)

101057 (FFM06)

50.9

51.2

101057 (DZ)

101057 (PDZ)

40
30
20
10
0
101057 (FFM01)

101058 (FFM06)

Rock typeFigure 6‑31. Ratio between the mean crack initiation stress and mean UCS for different rock types infracture domains FFM01 and FFM06 and in deterministic deformation zones (DZ) and potential deformation zones (PDZ) compared with the estimated spalling strength. Mean value crack initiation stress and
UCS compiled from Table 7-3 in the Site description Forsmark.

Fracture mechanical properties
As part of the preliminary site description of Forsmark version 1.2, /Lanaro and Fredriksson 2005/
compared results from laboratory tests performed by the Swedish National Testing and ResearchInstitute (SP) and the Norwegian Geological Institute Laboratory (NGI). The fracture stiffness evaluated from normal loading tests was much higher in the tests by NGI. The strength properties evaluated from direct shear tests were also slightly higher in the tests by NGI. /Lanaro and Fredriksson
2005/ attributed the differences to an indication of slightly different accuracy or testing procedures at
the two laboratories. However, the SP results were chosen to represent the properties of fractures at
Forsmark as they were more numerous and agreed well with tilt test /Lanaro and Fredriksson 2005/.
Since version 1.2 of the preliminary site description, the methodology for shear tests has been
modified as it appeared that the deformation of the mould holding the fracture sample affected thedeformation of the sample in the normal direction /SKB 2006b/. /Glamheden et al. 2007/ evaluatedresults from three different test techniques to assess how clamping of the samples in the test apparatus
would influence the measurements. Details regarding the different test techniques can also be foundin /Glamheden et al. 2007/. A measurement technique (denoted Type III) that gives a direct measurementof the normal deformation across the fracture was chosen to be the reference test method. Resultsfrom the other two techniques were converted to Type III. /Glamheden et al. 2007/ found that thevariations in the fracture normal stiffness was high compared with that of the other fracture parameters.
In situ stresses
There is a considerable amount of scatter in the measured stress magnitudes, which may have been
exaggerated due to thermal strains during the overcoring process /Martin 2007/. During overcoring
it is important that the temperature is kept relatively constant. As a result, the calculated stressmagnitudes may also have been influenced by the heat /Martin 2007/.
The estimated uncertainty in the horizontal in situ stress components is ±15–25% /Glamheden et al.
2007/. The uncertainty given for the minor horizontal in situ stress is correlated to the uncertainty
in the major horizontal in situ stress; the ratio of σH/σh is expected to lie in the range 1.4–2.0/Glamheden et al. 2007/. The uncertainty in the trend of the major horizontal in situ stress is ±15°
/Glamheden et al. 2007/.

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273

The estimated uncertainty in the vertical stress component is ±2% in fracture domains FFM01
and FFM06; the corresponding uncertainty in fracture domains FFM02 and FFM03 is ±10%.
The uncertainty span in the vertical stress component is related to the uncertainty in rock massdensity /Martin 2007/.
Estimates of small scale spatial variations due to local fractures and material heterogeneities(denoted local variability in /Glamheden et al. 2007/) are included in the above estimates: 4–12%(uncertainties in stress magnitudes) and ±3–6° (orientation of major horizontal in situ stress),
cf. Section 6.4.8.
Evolution of stress additions during glacial cycle
The following factors are found to be contributing the uncertainties due to precision and bias.
•

The numerical code: The code used in the modelling work has been extensively tested andverified and found to be in good agreement with other independent codes /Lund et al. 2009/.

•

The ice model: SKB’s reference ice model /SKB 2006c/ used in the modelling by /Lund et al.
2009/ is based on a reconstruction of the Weichselian glaciation. It is difficult to assess how
well it would predict a future glaciation, cf. e.g. Figure 6‑25. However, there is no reason tobelieve that future ice sheets will be fundamentally different from previous Late Pleistocene
Fennoscandian ice sheets /Lund et al. 2009/.

•

The Earth model: /Lund et al. 2009/ choose Model T9 (horizontally stratified Earth model)
as their preferred model in terms of fit to GPS data and sea level data and as its lithosphericelastic structure is in agreement with the Fennoscandian seismic investigation, cf. subsectionon uncertainties due to representativity.

Stress-transmissivity relations
The uncertainties due to precision and bias are assumed to be large and stem from uncertainties infracture mechanical and hydraulic data as well as uncertainties in the stress-aperture and transmissivity model. In terms of accurately estimating stress-induced transmissivity changes of individual
fractures, precision is judged to be poor. However, for providing an upper bound estimate as well
as a best estimate of the average sensitivity to stress changes of differently oriented fractures, it isjudged that the stress-transmissivity relations provided by Equations 6‑9 and 6‑10 are adequate.
Equation 6‑8 gives an estimate of the hydraulic aperture as a function of the mechanical apertureand the joint roughness coefficient at laboratory scale (l = 100 mm) (e.g. /Barton et al. 1992/).
/Chryssanthakis 2003a, b, 2004a, b, c, 2006/ remark that due to the small core diameter of the samples(approx. 5 cm), the resulting values of JRC are associated with some uncertainty. Figure 6‑32 shows
a comparison between results obtained at joint length tested (JRC0) and results extrapolated to 100 mm(JRC100). As seen in the figure, there are only small differences between the measured data and theextrapolated data.
Data uncertainty due to representativity

Deformation and strength properties of intact rock
The variability in the evaluated mean values of strength and deformation properties is small between
the different modelling steps, i.e. the increased number of samples since model version 1.2 /SKB 2005b/
has not influenced the mean values. This suggests that the number of tests is sufficient to describe
the deformation and strength properties of intact rock /Glamheden et al. 2007/. However, the numberof tests in fracture domain FFM01 is much larger than in FFM06, which results in larger uncertainties in the properties in fracture domain FFM06 /Glamheden et al. 2007/.

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7.0
6.0

6.2 6.1

6.5
6.0

5.8 5.7

5.8

6.1

6.2

5.8

5.7

6.0

5.9

5.4

JRC (-)

5.0
4.0
JRC0

3.0

JRC1002.0
1.0
0.0
KFM01AKFM02AKFM03A/B

KFM04AKFM05AKFM09A All boreholes

Borehole

Figure 6‑32. Comparison between the joint roughness coefficient JRC0 (at joint length tested) and JRC100(measured data extrapolated to 100 mm), compiled from appendices of /Chryssanthakis 2003a, b, 2004a, b,
c, 2006/.

/Martin 2005/ suggests a spalling strength of 57±2% of the uniaxial compressive strength (UCS)
for coarse and medium grained unfoliated crystalline rocks based on the findings from the ÄspöPillar Stability Experiment (APSE) /Andersson 2007/ and the Mine-by experiment /Martin 2005/,
cf. Table 6‑42. The spalling strength estimated for APSE appears to have been relevant for both theinitial (non-heated) phase and the subsequent heated phase, cf. Figure 6‑33. Therefore, the proposed
range in spalling strength (52–62% of UCS, cf. Table 6‑40) appears to be representative for the
excavation and operational phase and the heated phase of the initial temperate period.
Table 6‑42. Estimates of spalling strength from the Äspö Pillar Stability Experiment /Andersson
2007/ and the Mine-by experiment /Martin 2005/.
Experiment

Spalling strength (%)

Uncertainty span (%)

APSEÄspö Diorite

59

±3

56
65

–
–

Mine-by
Lac du Bonnet granite
Granodiorite

Deformation properties of rock mass
In the empirical approach, characterisation of the rock mass quality was performed on borehole
sections of 5 m in length /Glamheden et al. 2007/. In order to determine the influence of scale,/Glamheden et al. 2007/ compared results of the rock mass quality characterised at 1 m and 5 mlengths /Bäckström and Lanaro 2007/ and found that the difference in scale had an insignificant
impact on the results.
The results from the empirical method in site-descriptive modelling stage 2.2 are in good agreementwith corresponding results from modelling stage 1.2 /Glamheden et al. 2007/. The difference inresults between the empirical and theoretical approaches is also small, cf. Section 6.4.6.

TR-10-52275

Figure 6‑33. Extent of spalling and estimates of the spalling strength before and during heating. Modified
from /Martin 2005/.

Fracture mechanical properties
Fracture samples for direct shear tests were meant to be collected at the same depth intervals as intact
rock. However, the low fracture frequency at depth limited the possibility to collect appropriate
fracture samples for testing /Glamheden et al. 2007/.
It is recommended that the values from the direct shear tests, instead of the results from the tilt test,
are used as they are based on direct measurements with stress magnitudes comparable to those foundat repository depth at Forsmark /Glamheden et al. 2007/.
The variability of selected samples is small and the increased number of tests between modellingversions 1.2, 2.1, and 2.2 has not changed the evaluated mean value significantly /Glamheden
et al. 2007/. Therefore the evaluated mean seems to be representative of the studied fractures andthe number of tests seems to be sufficient /Glamheden et al. 2007/. The number of tests in fracturedomains FFM01 and FFM03 is larger than in the other domains, which implies that the uncertainty
in these two fracture domains is smaller. No sample was taken from fracture domain FFM06, butvisual inspection of the fractures in that fracture domain suggest that their properties are similar to
those in fracture domain FFM01, see the Site description Forsmark.
However, it should be noted that the results presented by /Glamheden et al. 2007/ relate to specimensof 50–60 mm in size. Large-scale properties are likely to deviate from lab-scale properties. It is
likely that the shear strength will be reduced in magnitude compared with lab-results /Glamheden
et al. 2007/. Furthermore, it is possible that the properties of large-scale fractures will differ between
fracture sets, although no such differences were seen in the lab-scale tests /Glamheden et al. 2007/.

276

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In situ stresses
Estimates of the in situ stresses at repository depth have been made by /Ask et al. 2007/ based on
results from hydraulic fracturing and hydraulic tests of pre-existing fractures (HTPF). Estimates havealso been made by /Martin 2007/ based on results from overcoring data and indirect observations.
There are large differences in predicted stress magnitudes based on the two methods. In the proposed
stress model for the Forsmark site /Glamheden et al. 2007/, data from hydraulic fracturing and HTPF
measurements have been excluded for two reasons /Martin 2007/.
•

It is suspected that the hydraulic data does not measure the minimum horizontal stress, butinstead the vertical stress component.

•

The results do not indicate a thrust regime (σH > σh > σv) at Forsmark, which is not in agreementwith the evaluated state of stress in the Fennoscandian shield.

The exclusion of hydraulic data from the stress model results in a more cautious estimate of thestate of in situ stress, cf. Table 6‑43. The magnitude of the horizontal stress components in the stressmodel by /Glamheden et al. 2007/ are approximately twice as large as those of /Ask et al. 2007/.
The majority of the overcoring stress measurements carried out at depths between 150 m and 250 m
were successful, whereas there are very few successful measurements below 250 m /Martin 2007/.
No stress measurements have been carried out in fracture domain FFM06. However, the stresses are
expected to be similar to those in fracture domain FFM01 (see the Site description Forsmark) as:•

The rock mass in both fracture domains have similar stiffness properties.

•

Fracture domain FFM06 is located adjacent to fracture domain FFM01 below the gently dipping
deformation zone ZFMA2.

Table 6‑43. Comparison between stress magnitudes at 400 m and 500 m depth from two stressmodels based on 1) the overcoring method, indirect observations and numerical modelling/Glamheden et al. 2007/ and 2) hydraulic fracturing and HTPF methods /Ask et al. 2007/.
Stress modelσH (MPa)σH, orientation (°)σh (MPa)

σv (MPa)

Glamheden et al. 2007 (400 m)

38.7±5.8145±1520.4±5.1

10.6±0.2

Ask et al. 2007 (400 m)

19.2±0.7124±6

9.3±1.1

10.4

Glamheden et al. 2007 (500 m)

41.0±6.2

145±1523.2±4.6

13.3±0.3

Ask et al. 2007 (500 m)

22.7±1.1124±610.2±1.6

13.0

Evolution of stress additions during glacial cycle
Due to practical reasons, the evolution of stress additions during a glacial cycle has been partly
based on ice sheet modelling results of SR-Can. The corresponding results for SR-Site are found in
Section 7.1.10. There is no reason to believe that future ice sheets will be fundamentally different
from previous Late Pleistocene Fennoscandian ice sheets /Lund et al. 2009/. In the reference scenario
of SR-Can /SKB 2006c/, the maximum thickness at Forsmark was around 3 km, cf. Figure 6‑25.
Figure 6‑34 shows estimates of the maximum ice sheet thickness for different climate scenarios in
SR-Can. Based on modelling of the Saalian ice sheet, the maximum ice sheet thickness at Forsmark(supported by geological observations) was in SR-Can estimated to be 3,200 m; even for more extreme
sensitivity cases it is unlikely to exceed 3,700 m /SKB 2006c/. This corresponds to an increasein thickness by about 10–30% compared with the reference case and an increase in vertical stressby about 2.6–8 MPa, as the vertical glacially induced stress reflects the ice load (e.g. /Lund et al.
2009/). Without explicit modelling work, it is difficult to estimate the impact on the magnitude of
the horizontal stress components due to an ice sheet with larger thickness.

TR-10-52277

Figure 6‑34. Estimated maximum ice sheet thickness for different climate evolutions in SR-Can.
Reproduced from /SKB 2006c, Figure 4-32/. Note that the corresponding figure for SR-Site is foundin Section 7.1.10.

Although Model 1 gave the best fit to GPS data (Figure 6‑27, upper left), the deformation modulus isconsidered too high in the upper parts of the rock. /Lund et al. 2009/ choose Model T9 (horizontally
stratified Earth model) as their preferred model in terms of fit to GPS data and sea level data and as
its lithospheric elastic structure is in agreement with the Fennoscandian seismic investigation, cf.
Figure 6‑35 (middle).
Although there are quantitative differences in resulting stress magnitudes between the Earth models,cf. Figure 6‑35, the temporal variations largely follow those of the ice model (Figure 6‑25). By
inspection of the stress results during the last glacial advance and retreat (without considering resultsfrom Model 1), the glacially induced stresses from the different Earth models appear to be within±10 MPa of those from Model T9. The glacially induced stresses at 500 m depth obtained from
Model 1 are 50–90% larger in magnitude during the second glacial maximum than the corresponding
results from the other two models shown in Figure 6‑35. The vertical stress is identical in all Earthmodels and reflects the ice load, that is the thickness of the ice sheet /Lund et al. 2009/.
Stress-transmissivity relations
It should be noted that the stress-transmissivity relations become very uncertain as the effective
normal stress approaches zero, that is when the transmissivity becomes indefinite. Close to therepository openings, local transmissivity increases could be significant, for example because of shear
displacements along fractures in very low compression. However, it is judged that for fractures incompression of a few MPa, the stress-transmissivity relations are relevant as upper bound estimatesof the sensitivity to variations in effective normal stress.
It can easily be shown that the sensitivity to variations in normal stress is greatest for a combinationof low values of both JKN and JEN. The distribution of JKN values (obtained from Figure 6‑30) is
shown in Figure 6‑36 (left). The values of JKN vary between 67 and 1,193 MPa/mm with a seemingly higher proportion of samples with values in the lower part of the range although the sample
population is quite small (26 samples). However, the results shown in Figure 6‑36 (right) suggestthat the value of JEN increases with decreasing values of JKN.

278

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Figure 6‑35. Left: Schematic representation of Earth model. Right: Example of resulting glacially induced
stresses at 500 m for each type of Earth model. Reproduced/excerpts from /Lund et al. 2009, Figures 5-3
and 7-17/.

2

4.5

KFM05A

4

KFM06A
KFM08A

1.5

KFM09A

3

JEN (-)

Number of samples (-)

KFM07A

3.5

2.5
2

1

1.5
0.5

1
0.5

0
<50
50-100100-150150-200
200-250250-300
300-350
350-400
400-450
450-500
500-550550-600
600-650
650-700
700-750
750-800
800-850
850-900
900-950
950-1000
1000-1050
1050-1100
1100-1150
1150-1200
≥1200

0

0

500

1000

1500

JKN (MPa/mm)

JKN (MPa/mm)

Figure 6‑36. Left: Distribution of JKN values obtained from the stress-stiffness estimates shown in
Figure 6‑30. Right: JEN as functions of JKN for each borehole in Figure 6‑30.

TR-10-52279

Figure 6‑37 (left) shows examples of stress-transmissivity relations obtained from hydraulic jacking
tests in borehole KLX02 at Laxemar. However, the injection tests were carried out near high-flowzones of the borehole /Rutqvist and Tsang 2008/. Therefore, these stress-transmissivity relations may
be representative of relatively permeable fractures /Rutqvist and Tsang 2008/. For comparison, thestress-transmissivity relation used in SR-Can /Hökmark et al. 2006/ in Figure 6‑37 (right). /Rutqvist
and Tsang 2008/ note that the stress-transmissivity relation used in SR-Can gave a similar response
to variations in effective stress, in terms of relative changes from a given stress-level, as the fracturesat 266 m and 267 m. A possible explanation for the sensitivity to variations in normal stress of thetwo fractures at 316 m and 336 m is that mineral filling would tend to clog the fractures at highnormal stress /Rutqvist and Tsang 2008/.

6.4.8

Spatial and temporal variability of data

Spatial variability of data

The rock in the candidate area at Forsmark is subdivided into rock domains and fracture domains asshown in Figure 6‑38. Rock domain RFM029 is made up of Fracture domains FFM01, FFM02 andFFM03, whereas Rock domain RFM045 coincides with fracture domain FFM06 /Glamheden et al.
2007/. The repository is located within fracture domains FFM01 and FFM06, see the Site description Forsmark.
Deformation properties of intact rock
The mean value of Young’s modulus (determined from uniaxial compressive tests) in rock domain
RFM029 is about 75 GPa irrespective of rock type /Glamheden et al. 2007/. For the rock types inrock domain RFM045, the mean value of Young’s modulus varies between 80–83 GPa /Glamheden
et al. 2007, 2008/. A slight reduction in Young’s modulus with depth can be observed in granite
to metamorphic, medium-grained granodiorite /Glamheden et al. 2007/. The reduction is mostpronounced to a depth of 300 m. A similar trend was not observed in Poisson’s ratio.

1E-5

Transmissivity (m2/s)

1E-6

1E-7
e = 10+32 exp(-0.3·σN)/Hökmark et al. 2006/
1E-8

1E-9

1E-10
1E+0

1E+1

1E+2

Effective normal stress (MPa)Figure 6‑37. Left: Example of stress-transmissivity relations estimated from hydraulic jacking tests inborehole KLX02 at Laxemar, from /Rutqvist and Tsang 2008, Figure 5.1-3/. Right: Stress-transmissivity
relation based on the exponential expression suggested by /Liu et al. 2003/ used in SR-Can /Hökmark et al.
2006/ (right).

280

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Figure 6‑38. Three-dimensional view to the East-North-East of the candidate area at Forsmark.
Reproduced from Figure 5-4 of /Olofsson et al. 2007/.

Geological features in the area seem to have little influence on the mechanical properties of intact
rock /Glamheden et al. 2007/. Laboratory results on samples obtained inside or in the near vicinity of
deformation zones have property values in a similar range to those obtained outside the deformation
zones. This indicates that the mechanical properties in the target area are stable and homogeneous.
However, in the selection of specimens for mechanical testing, core sections containing sealed
fractures or imperfections that were likely to shear during the test were avoided. Furthermore,
several specimens were taken close to each other /Glamheden et al. 2007/. The spatial variation must
therefore be assumed to be greater than that predicted by the laboratory results /Glamheden et al. 2007/.
Strength properties of intact rock
The main rock type (101057) shows a gentle trend of decreasing compressive strength with depth,
see the Site description Forsmark. The reduction is in the range 10–15% at depths larger than
500 m /Glamheden et al. 2007/. However, this may be due to stress-induced micro-cracking during
coring. Similar variations with depth are not found in any other rock types.
Stochastic modelling of the uniaxial compressive strength in rock domains RFM029 and RFM045 at
Forsmark /Glamheden et al. 2008/, cf. Figure 6‑39, showed that the spatial distribution of the UCS
makes up a significant part of the total measured variability. However, the lower percentile estimatesof the distribution are rather uncertain /Glamheden et al. 2008/. As the spalling strength is expressed
as a ratio of the uniaxial compressive strength, the spatial variability is likely to follow the distribution of the uniaxial compressive strength.
Deformation properties of the rock mass
The deformation modulus of the rock mass in rock domain RFM029, as determined by the empiricalapproach, cf. Figure 6‑38, varies between 59 GPa (FFM02) and 72 GPa (FFM01) /Glamheden et al.
2007/. The rock mass of rock domain RFM045, that is fracture domain FFM06, have a deformation
modulus of 70 GPa. Estimates of Poisson’s ratio by use of the empirical method gives a value of
0.23 in both fracture domains FFM01 and FFM06. The variations of Young’s modulus and Poisson’s
ratio between fracture domains FFM01, FFM02, FFM03 and FFM06 are shown in Figure 6‑40.
Estimates of the rock mass deformation modulus and Poisson’s ratio by use of the theoretical method
is provided only for fracture domains FFM01 and FFM06 /Glamheden et al. 2007/. The resulting
values are 69 GPa and 0.24 (FFM01) and 68 GPa and 0.3 (FFM06), respectively /Glamheden et al.
2007/.

TR-10-52281

Figure 6‑39. Histograms of the compressive strength simulated at 1 m resolution in Rock Domain RFM029(left) and RFM045 (right). Reproduced from Figures 3-5 and 3‑6 of /Glamheden et al. 2008/.

Figure 6‑40. Estimates of the rock mass deformation modulus (left) and Poisson’s ratio by use of theempirical method in fracture domains FFM01, FFM02, FFM03, and FFM06, cf. Figure 6‑38. Reproduced
from Figure 5-8 of /Glamheden et al. 2007/.

Estimates of the spread in the deformation modulus due to variations of the Discrete Fracture
Network (DFN) model were in the range 64–73 GPa /Glamheden et al. 2007/, cf. Figure 6‑41 (left).
Furthermore, /Glamheden et al. 2007/ evaluated the rock mass deformation modulus using 40
combinations of material properties randomly taken from the distribution of material data for intact
rock and fractures. The influence of variations in material properties was found to be larger thanvariations due to different realisations of the DFN model. The resulting spread in the deformation
modulus was 62–75 GPa, cf. Figure 6‑41 (right).
The rock mass quality in the target area appears to be of high and uniform quality /Glamheden et al.
2007/. There is a slightly higher occurrence of high quality rock in rock domain RFM045 than in
RFM029, but the difference is judged to be insignificant from a rock mechanics point of view
/Glamheden et al. 2007/. Sections with reduced rock mass quality coincide with regions of increased
fracture frequency /Glamheden et al. 2007/. Analyses of rock from adjacent rock domains showthat the rock is of “good competent quality” /Glamheden et al. 2007/. Fracture domains FFM01 andFFM06 therefore appear to have very similar mechanical properties, although the fracture density infracture domain FFM06 is higher than in FFM01 /Glamheden et al. 2007/.

282

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Figure 6‑41. Estimated spread in the evaluated deformation modulus due to the influence of the DFN
model (left) and variations in material properties (right). Reproduced from Figure 5-13 of /Glamheden
et al. 2007/.

Fracture mechanical properties
No clear variation with depth was observed in any of the mechanical properties of fractures/Glamheden et al. 2007/.
A measure on the spatial variability was obtained by analysing the size of the statistical spreadand comparing results from SDM modelling stage 2.2 with results from previous modelling steps;
the small statistical spread and insignificant changes in parameter values between modelling steps
indicate that the conditions are homogeneous /Glamheden et al. 2007/.
In situ stresses
As with the stress magnitudes, there is significant scatter of the orientations of the in situ stresstensor /Martin 2007/. However, the trend of the major in situ stress (σ1) seems to be consistent at
all measured depth-intervals /Martin 2007/, cf. Figure 6‑42.
Numerical modelling of the stress state indicates that the stress field in the target volume is relatively
homogeneous /Glamheden et al. 2007/. There is very little perturbation of the stress field due to steeply
dipping deformation zones. However, the gently dipping deformation zone ZFMA2 perturbs all
stresses in the rock mass above it. Results from the numerical modelling show that below ZFMA2
there is very little disturbance of the stress field (see the Site description Forsmark), although no
successful stress measurements have been performed below ZFMA2 to corroborate this /Martin
2007/. The changes in horizontal stress with depth is thought to reflect the reduced frequency ofopen fractures and improved rock mass quality /Martin 2007/.
Furthermore, /Glamheden et al. 2007/ estimate the small scale spatial variations (denoted “local
variability”) caused by local fractures and material heterogeneities. In fracture domains FFM01 andFFM06 (150–600 m depth) the uncertainty due to local variability is estimated to 4–6%, whereas in
fracture domains FFM02 and FFM03 the corresponding range is 4–12% /Glamheden et al. 2007/.
The uncertainty in the orientation of the major horizontal stress due to local variability is ±3° (fracturedomains FFM01 and FFM06) and ±6° (fracture domains FFM02 and FFM03) /Glamheden et al. 2007/.
These uncertainties are included in the estimate of the total uncertainty /Glamheden et al. 2007/.
Evolution of stress additions during glacial cycle
On a large scale, there are large spatial variations in ice thickness, cf. Figure 6‑43 (left). The spatial
variations also vary with time. However, the ice model has a resolution of 50 times 50 km /Lund
et al. 2009/. Any lateral spatial variations of the glacially induced stresses at the Forsmark site areunlikely to be of any relevance.
Figure 6‑43 (right) shows the temporal evolution (note that the time scale is reversed comparedwith previous figures) of the glacially induced stresses evaluated at two different depths: 500 m
and 1.5 km. In this depth-range the variations with depth are insignificant.

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283

Glacially induced principal stress (MPa)Figure 6‑42. Orientation of the mean stress tensor at four depth intervals based on all overcore measurements in the target area. Reproduced from /Martin 2007, Figure 6-14/.

30

σH (500 m)
σh (500 m)
σv (500 m)
σH (1.5 km)
σh (1.5 km)
σv (1.5 km)

25
20
15
10
5
0
-5
-10
0

10000 20000 30000 40000 50000 60000 70000

Time (years)

Figure 6‑43. Left: Height of the ice sheet at 18.4 kyr BP when the ice has its largest lateral extent,
modified from /Lund et al. 2009/. Right: Temporal development of the glacially induced principal stresses
at 500 m and 1.5 km depth at Forsmark, obtained from ice-crust-mantle analyses (model T9) performed by/Lund et al. 2009/.

284

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Stress-transmissivity relations
Figure 6‑44 (left) shows values of JKN as functions of depth in boreholes KFM05A, -06A, -07A,-08A, and -09A. The depth is estimated from the average value of the upper and lower (Secup andSeclow) sampling depths /Jacobsson and Flansbjer 2005a, b, c, d, 2006/. The right part of the figure
shows the corresponding values of JEN as functions of depth. No clear trend with depth or between
boreholes can be observed in the two parameters.
/Chryssanthakis 2003a, b, 2004a, b, c, 2006/ classified the samples in boreholes KFM01A, -02A,-03A/B, -04A, -05A, and -09A into three joint sets depending on the angle of intersection with thedrill core.
•

Steeply dipping fractures (set 1).

•

Fractures dipping approximately 45° (set 2).

•

Sub-horizontal fractures (set 3).

Figure 6‑45 (left) shows measured values of JRC subdivided into the three joint sets for eachborehole, whereas in the right part of the figure all measured JRC values are plotted as functionsof depth. No clear trend with joint set, depth, or between boreholes can be observed.
0

0
KFM05AKFM06A100

KFM06A100

KFM07AKFM07A

KFM08A200

KFM08A200

KFM09A

Depth (m)Depth (m)KFM05A

300

400

300

400

500

500

600

600

700

700
0

500

1000

JKN (MPa/mm)

1500

KFM09A

0

0.5

1

1.5

2

JEN (-)

Figure 6‑44. Estimated values of JKN (left) and JEN (right) as functions of depth. Stress-stiffness dataobtained from normal stress/normal deformation plots in /Jacobsson and Flansbjer 2005a, b, c, d, 2006/.

TR-10-52

285

0

8

KFM01AKFM02A

7

100

KFM03A/B
KFM04AKFM05A

200

5

Set 1
Set 2

4

Set 3
3

Borehole av.

2

Depth (m)JRC0(-)

6

KFM09A

300
400
500

1
600

Borehole

ol

es

A

or
eh

M

Al

lb

KF

09

A
M

05

A
KF

04
M

03
M

KF

A/
B

A
02
M
KF

KF

M
KF

01

A

0
700
0

1

2

3

4

5

6

7

8

9

JRC0(-)

Figure 6‑45. Left: Mean value JRC subdivided into fracture sets for each borehole: Set 1 (steeply dipping
fractures), set 2 (fractures dipping approx. 45°), set 3 (sub-horizontal fractures). Right: Joint roughness
coefficient (at joint length tested) as functions of depth. Data for both figures compiled from appendices to/Chryssanthakis 2003a, b, 2004a, b, c, 2006/.

Temporal variability of data

Deformation properties of the rockWithin the temperature range 0–150° the temperature dependence of elastic properties are negligible/Lau et al. 1991/.
The elastic properties are determined from uniaxial compressive tests at 50% of the compressive
strength /Glamheden et al. 2007/. Examples of typical results from tests performed on granitic rocks
are shown in Figure 6‑46. Within the stress-range corresponding to 25–75% of the compressive
strength, the secant Young’s modulus, and Poisson’s ratio do not vary. This indicates that the elastic
properties vary insignificantly with pressure for the stress variations (order of 30 MPa at maximum,
cf. Figure 6‑43, right) projected to occur during the glacial cycle. Furthermore, the differences
between deformation properties of intact rock and the rock mass are sufficiently small that anypressure dependence can be ignored. As the load is increased, the fractures will be compressed
and the elastic properties of the rock mass properties will tend to resemble those of intact rock.
Spalling strength/Damjanac and Fairhurst 2010/ found that the long-term strength of intact rock without any preexisting fractures should be equal to or greater than the crack initiation strength (40–60% of thecompressive strength).

286

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Specimen ID: KFM09A−113−04

Youngs Modulus (E):

79.1

[GPa]

Youngs Modulus (E):

70.4

[GPa]

Poisson Ratio (ν):

0.326

[−]

Poisson Ratio (ν):

0.298

[−]

Axial peak stress (σc):

284.5

[MPa]

Axial peak stress (σc):

192.8

[MPa]

−1.4

−0.6

300

300

250

250

Axial stress σa [MPa]Axial stress σa [MPa]Specimen ID: KFM09A−113−01

200

200

150

150

100

100

50

0

50

−1.4

−1.2

−1

−0.8

−0.6

−0.4

Radial strain ε r [%]

−0.2

0

0.2

0.4

0.6

0.8

1

Axial strain εa [%]

0

−1.2

−1

−0.8

−0.4

Radial strain ε r [%]

−0.2

0

0.2

0.4

0.6

0.8

1

Axial strain εa [%]Figure 6‑46. Examples of stress-strain curves obtained from uniaxial compression tests on intact rocksamples from borehole KFM09A at Forsmark. Reproduced from images in /Jacobsson 2006, Section 5-1/.

Fractures
The fracture normal stiffness, KN, is obtained from cyclic loading compression tests in which thejoints are loaded and unloaded twice up to a normal stress of 10 MPa (or 20 MPa for samples from
borehole KFM05A) /Glamheden et al. 2007/. The results are presented as the secant normal stiffness
evaluated between the unloaded state and full loading of the second load cycle. This quantity does
not vary with time or load. However, in general the fracture normal stiffness varies with normal
stress, for example as in Equation 6‑6.
Direct shear tests were performed at three levels of normal stress, 0.5, 5, and 20 MPa /Glamheden
et al. 2007/.
•

The fracture shear stiffness, KS, is presented as the secant shear stiffness evaluated between 30%
and 50% of the peak shear stress at each level of normal stress.

•

The fracture dilation, ψ, angle is presented as the secant dilation angle at each level of normal
stress.

•

The shear strength of a fracture depends on the normal stress. The Mohr-Coulomb fracture
strength properties (peak cohesion and peak friction angle/residual cohesion and residual friction
angle) are determined by a least squares fit to the measured peak/residual shear stresses at thethree levels of normal stress. The fracture cohesion and fracture friction angle do not vary withtime or load.

In situ stresses
The present-day in situ stresses are taken to be representative of the long-term state of backgroundstress in the Forsmark area. The changes in stress due to the still ongoing post-glacial reboundand the tectonic strain induced by the Mid-Atlantic ridge push /Muir Wood 1995/ are significantly
smaller in magnitude than the thermal stresses induced by the deposited spent fuel and stresses
generated by the ice load during the subsequent glacial phase. Therefore any temporal variationsin the in situ stresses are ignored.
Evolution of stress additions during glacial cycle
The temporal variations of the glacially induced stress additions 500 m below the ground surface at
Forsmark are presented in Figure 6‑43 (right). Although quantitatively the stress magnitudes dependon the Earth model, the temporal variations largely follow those of the ice model /Lund et al. 2009/,
cf. Figure 6‑25.

TR-10-52

287

Stress-transmissivity relations
The stress-transmissivity relations give estimates of changes in transmissivity due to the loading andunloading of fractures that take place during the time-frame of the safety assessment. As there are nodata to suggest otherwise, the stress-transmissivity relations are taken to be relevant for all repository
phases, i.e. the excavation and operational phase, the initial temperate phase, the glacial phase andthe permafrost phase.

6.4.9

Correlations

In situ stresses

A methodology for estimating the stress that depends on trends in the data has been developed by/Martin 2007/. It is based on the:•Principal stress ratio (R): σ1/σ2 = R.

•

Mean principal stress (M): (σ1 + σ2 + σ3)/3 = M.

•

Initiation for spalling expressed as a function of the Spalling Ratio (SR) and uniaxial compressive
strength (UCS): (3 σ1 – σ2)/UCS ≥ SR.

As the vertical stress component can be calculated from the weight of the overburden /Martin 2007/,the expressions for R and M (above) are sufficient to determine the horizontal stress components.
The horizontal stress magnitudes in fracture domains FFM02 (0–150 m) and FFM01 (below 150 m)
are estimated using R = 1.7, cf. Figure 6‑47 (lower right), and M = 12 MPa (150 m), 17.5 MPa (300m) and 24 MPa (400 m), Figure 6‑47 (lower left) /Martin 2007/. The uncertainty in σh is correlated tothe uncertainty in σH, such that the ratio σH/σh at repository depth is in the range 1.4–2.0 /Glamheden
et al. 2007/.
Low K

50

6

40

Low K

4

30
20

2

DBT1
DBT3
KFM01BKFM07C

10

200

400

Depth (m)

600

200

4

50
FFM02

Low K

40

Depth (m)

FFM02

400

600

Low K

3

30

σ1/σ2

Mean stress (MPa)

DBT1
DBT3
KFM01BKFM07C

FFM02

σ1/σ2

Mean stress (MPa)

FFM02

20
Trend

10

2
Trend
1.7

1

Predicted

Mov. Med. = 6

100

200

300

Depth (m)

400

500

100

200

300

Depth (m)

400

500

Figure 6‑47. Left column: Mean stress data (top) and mean stress using a data smoothing technique(bottom) as functions of depth. Right column: Principal stress ratio (top) and trend in principal stressratio (bottom) as functions of depth. Reproduced from /Martin 2007, Figures 6-4 and 6-6/.

288

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Hydraulic diffusivity

The hydraulic diffusivity /Itasca 2005b/ is expressed in terms of the hydraulic conductivity (K) andthe specific storage coefficient (SS)K
SS
where the specific storage coefficient is given by:

k=

6‑11

 n
(1 − 2v)(1 + v) 
S S = ρ ⋅ g +
E (1 − v) 
 Kw

6‑12

Parameters and non site-specific parameter values are explained below. Site-specific parametervalues are provided in Sections 6.4.10, 6.4.12 and 6.8.12.
•

ρ is the density of water (1,000 kg/m3), which is assumed to be pressure-independent.

• g is the acceleration due to gravity (9.81 m/s2).
•

n is porosity (–).

•

Kw is the bulk modulus of water (2.2 GPa).

•

E and ν are Young’s modulus and Poisson’s ratio of the rock mass, respectively.

6.4.10 Result of supplier’s data qualification
The uncertainties associated with the mechanical properties of intact rock, rock mass, and fractures
are expressed statistically as a range of variation in the evaluated mean values and quantified fora 95% confidence interval according to the “Central Limit Theorem” /Glamheden et al. 2007/.
Minimum and maximum values are based on observed minimum/maximum values of the tested
samples /Glamheden et al. 2007/.
Deformation and strength properties of intact rock

Deformation and strength properties of intact rock within fracture domain FFM01 and FFM06 aregiven in Table 6‑44. Young’s modulus is given by the tangent modulus evaluated at 50% of the compressive strength /Glamheden et al. 2007/. Results are presented for the dominant and subordinate
rock types in each fracture domain (see the Site description Forsmark), respectively.
•

Granite to granodiorite, metamorphic, mediumgrained (101057) and pegmatite, pegmatitic
granite (101061) in FFM01.

•

More altered (albitized) variants of granite to granodiorite, metamorphic, mediumgrained(101057) and granite, metamorphic, aplitic (101058) in FFM06.

Table 6‑44. Deformation and strength properties of intact rock in FFM01 and FFM06. Data fromthe Site description Forsmark.
Parameter

Unit

FFM01
101057FFM01
101061

FFM06
101057FFM06
101058Mean/std dev
Min-max
Uncert. of mean

Mean/std dev
Min-max
Uncert. of mean

Mean/std dev
Min-max
Uncert. of mean

Mean/std dev
Min-max
Uncert. of mean

Young’s modulus (E)

GPa

76/3
69–83
±1%

74/4
69–80
±3%

80/1
78–82
±1%

83/3
80–86
±3%

Poisson’s ratio (ν)–

0.23/0.04
0.14–0.30±4%0.30/0.03
0.26–0.35±5%0.29/0.02
0.26–0.31±4%

0.27/0.03
0.25–0.31±8%Uniaxial compressive
strength (UCS)MPa

226/29157–289±4%

214/32.8
158–266±8%373/20
338–391±3%

310/58229–371±16%

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289

Spalling strength

Spalling is assumed to occur when the tangential stresses around an opening reach a certain fractionof the uniaxial compressive strength (UCS). /Martin 2005/ suggested a spalling strength of 57±2% of
the UCS for coarse and medium grained crystalline rocks. Based on the estimates by /Martin 2005/
and values of the crack initiation stress presented in Figure 6‑31, the spalling strength is assumed to
be 57±5% of the UCS. Spalling strength of the dominating rock types in fracture domains FFM01
and FFM06 (cf. Table 6‑44) are presented in Table 6‑45.
Table 6‑45. Estimated spalling strength (57±5% of mean UCS) of the dominating rock types infracture domains FFM01 and FFM06, cf. Table 6‑44.
Rock domain

Rock type

Spalling strength (MPa)FFM01101057129±11

FFM01

101061122±11

FFM06

101057213±19FFM06

101058177±16

Deformation properties of the rock mass

Deformation properties of the rock mass in fracture domain FFM01 and FFM06 are presented inTable 6‑46. The presented values are results from the ’Harmonisation’ process of results from theempirical and theoretical approaches /Glamheden et al. 2007/.
Table 6‑46. Rock mass properties in fracture domains FFM01 and FFM06. Data from /Glamheden
et al. 2007/.
Parameters

Unit

FFM01

FFM06

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

Young’s modulus (E)

GPa

70/8
39–79
±2%

69/12
40–81
±3%

Poisson’s ratio (ν)–

0.24/0.03
0.12–0.33±5%

0.27/0.04
0.12–0.37±4%Fracture mechanical properties

Note that no sample was taken from fracture domain FFM06, but visual inspection of the fracturesin this fracture domain suggest that their properties are similar to those in fracture domain FFM01,
see the Site description Forsmark.
Values of fracture peak and residual friction angles and fracture peak and residual cohesion basedon results from direct shear tests in fracture domains FFM01, FFM02, FFM03, FFM04, and FFM05/Glamheden et al. 2007/ are presented in Table 6‑47.
Values of the secant fracture normal stiffness and secant fracture shear stiffness evaluated at normal
stress levels of 0.5 MPa, 5 MPa, and 20 MPa based on direct shear tests of fractures in fracturedomains FFM01, FFM02, FFM03, FFM04, and FFM05 /Glamheden et al. 2007/ are presented inTable 6‑48.
Values of the secant fracture dilation angle evaluated at normal stress levels of 0.5 MPa, 5 MPa,and 20 MPa based on direct shear tests of fractures in fracture domains FFM01, FFM02, FFM03,
FFM04, and FFM05 /Glamheden et al. 2007/ are presented in Table 6‑49.

290

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Table 6‑47. Fracture peak friction, fracture peak cohesion, fracture residual friction, and fracture
residual cohesion based on results from direct shear tests of fractures in fracture domains
FFM01, FFM02, FFM03, FFM04, and FFM05. Data from /Glamheden et al. 2007/.
Fracture
domainPeak friction, φp(°)

Peak cohesion, cp(MPa)

Residual friction, φr(°)

Residual cohesion, cr(MPa)

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

FFM01

36.6/2.9
29.3–42.0±2.9%

0.8/0.3
0.2–1.3±13.7%

34.9/3.4
27.9–41.9±3.6%

0.3/0.2
0.1–0.8±24.3%

FFM02

36.4/2.5
34.4–40.0±6.7%

0.5/0.4
0.2–1.0±78.4%

34.8/7.3
24.1–40.3±20.6%

0.4/0.6
0.1–1.3±147.0%FFM03

37.0/1.7
34.2–39.0±3.0%

0.6/0.2
0.3–0.9±21.8%

34.2/6.2
25.7–41.5±11.8%

0.5/0.4
0.2–1.1±52.3%

FFM04

32.0/3.3
28.5–35.0±11.7%

0.9/0.4
0.6–1.4±50.3%

32.2/2.5
29.6–34.6±8.8%

0.3/0.1
0.2–0.4±37.7%

FFM05

37.0/1.8
35.7–38.2±6.7%

0.8/0.2
0.7–0.9±34.7%

34.3/3.0
32.2–36.4±12.1%

0.4/0.1
0.4–0.5±34.7%

Table 6‑48. Fracture normal stiffness and fracture shear stiffness evaluated at normal stresslevels of 0.5 MPa, 5 MPa, and 20 MPa based on results from direct shear tests of fractures infracture domains FFM01, FFM02, FFM03, FFM04, and FFM05. Data from /Glamheden et al. 2007/.
Fracture
domainNormal stiffness
KN (GPa/m)Shear stiffness
KS0.5 (GPa/m)Shear stiffness
KS5.0 (GPa/m)Shear stiffness
KS20.0 (GPa/m)

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty. of mean

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

FFM01

656/396
159–1,833±22.0%

10/6
1–23±21.8%

26/9
7–46±12.6%

34/10
18–52±10.7%

FFM02

248/165
115–483±65.2%

8/4
4–12±49.0%

26/4
21–31±15.1%

33/8
25–41±23.8%

FFM03

293/193
152–734±43.0%

8/4
4–15±32.7%

31/7
23–43±14.8%

35/10
20–49±18.7%

FFM04

1,385/283
1,072–1,624±23.1%

8/6
1–12±84.9%

16/5
12–22±35.4%

23/5
18–29±24.6%

FFM05

599/57
559–639±13.2%

6/3
4–8±69.3%

20/7
14–25±48.5%

25/2
23–26±11.2%

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291

Table 6‑49. Fracture dilation angle evaluated at normal stress levels of 0.5 MPa, 5 MPa, and
20 MPa based on results from direct shear tests of fractures in fracture domains FFM01, FFM02,
FFM03, FFM04, and FFM05. Data from /Glamheden et al. 2007/.
Fracture
domainDilation angle, ψ0.5(°)

Dilation angle, ψ5(°)

Dilation angle, ψ20(°)

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

Mean/std dev
Min-max
Uncertainty of mean

FFM01

14.6/4.1
7.8–27.1±10.2%

7.7/2.7
2.5–13.7±12.8%

3.2/2.1
0.2–9.6±23.9%

FFM02

15.2/2.9
11.5–17.6±18.7%

2.2/0.5
1.6–2.6±22.3%

2.1/2.2
0.2–4.3±102.7%FFM03

16.4/2.2
14.0–20.2±8.8%

3.1/2.1
0.5–6.3±44.3%

2.3/1.7
0.2–6.1±48.3%

FFM04

10.1/0.7
9.5–10.9±7.8%

6.6/1.3
5.4–7.9±22.3%

1.3/1.0
0.3–2.2±87.1%

FFM05

14.7/0.4
14.4–15.0±3.8%

8.8/0.1
8.7–8.8±1.6%

2.3/0.5
1.9–2.6±30.1%

In situ stresses

The stress model for Forsmark in fracture domains FFM01, FFM02, FFM03, and FFM06 is presented
in Table 6‑51, cf. Figure 6‑48. The major principal in situ stress (σ1) appears to have only a verysmall plunge (approximately 5° /Martin 2007/), cf. Figure 6‑42. It is therefore judged that the stresscomponents σH (major horizontal stress), σh (minor horizontal stress) and σv (vertical stress) can beapproximated to be principal stresses, i.e. σH = σ1, σh = σ2 and σv = σ3.
The uncertainty spans for the horizontal in situ stresses provided in Table 6‑51 include uncertainties
due to spatial variability /Glamheden et al. 2007/. Note that the uncertainty in σh is correlated tothe uncertainty in σH. The ratio σH/σh, at repository depth, is expected to lie between 1.4 and 2.0/Glamheden et al. 2007/.
The vertical stress is assumed to be equal to the weight of the overburden and is calculated using
a generic value of the rock mass density, cf. Table 6‑50. The uncertainty span for the vertical stressgiven in Table 6‑51 reflects the variability associated with the rock mass density /Martin 2007/.
In the stress calculations the generic rock mass density shown in Table 6‑50 has been used. Thisdensity is consistent with site-specific values given in /Stephens et al. 2007, Table 3-4/.
A ‘Proposed Maximum Stress Model’/SKB 2009d/, valid at repository depth, is provided inTable 6‑52. Note that the stress magnitudes provided in the table should only be used to assess
the risk of spalling due to elevated stress magnitudes at repository level /SKB 2009d/.

292

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Maximum horizontal stress (MPa)
0

0

10

20

30

40

50

60

Minimum horizontal stress (MPa)

70

0

10

20

30

40

50

60

N

70

-100
FFM02
FFM01

FFM02
FFM01

Elevation (m)-200

Elev: 0 -150 m-300

Low confidence

Low confidence-400
Repository
Horizon-500

Most likely

-600

Range

σvert

Most likely

Elev: 150 -400 m

Most likely

N

Range

σvert

Medianbreakouts
046o

KFM01BKFM07BKFM07CKFK001(DBT1)
KFK003(DBT3)

Low
confidence

Alternative stress model based on HF & HTPF
270o

90o

σvert = Calculated vertical stress
Overcoring

σHmax = 124oσHmax = 136oσHmax = 145o±15o

180o

Range

Elev: 400-600 m
HF-HTPF
Borehole
Breakouts

mean σ1mean σ2mean σ3

Figure 6‑48. In situ stress model for fracture domains FFM01 and FFM02, cf. Table 6‑51, with measurement data. Reproduced from Figure 7-18 of Site description Forsmark.
Table 6‑50. Generic rock mass density used to estimate the vertical stress gradient. This value
is consistent with the densities of the most common rock types in rock domains RFM029 and
RFM045 given in /Stephens et al. 2007/.
Parameter

Unit

Value

Density (ρ)

kg/m

3

2,700

Table 6‑51. Stress models for domains FFM01, FFM02, FFM03, and FFM06 at Forsmark/Glamheden et al. 2007/.
DomainσH (MPa)σH, orientation (°)σh (MPa)

σv (MPa)

FFM01 and FFM06 (150–400 m)

9.1+0.074z±15%145±15

6.8+0.034z±25%0.0265z±2%

FFM01 and FFM06 (400–600 m)

29.5+0.023z±15%145±159.2+0.028z±20%0.0265z±2%

FFM02 (0–150 m)

19+0.008z±20%145±15

11+0.006z±25%0.0265z±10%FFM03

5+0.075z±20%145±15

2.5+0.0375z±25%0.0265z±10%

Table 6‑52. Proposed Maximum Stress Model for the repository elevation –450 to –475 m/SKB 2008b/.
Depth range (m)σH (MPa)σH, orientation (°)σh (MPa)σh, orientation (°)σv (MPa)

450–47556±6

145±1535±8550.0265z±0.0005z

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293

Evolution of stress additions during glacial cycle

Model T9, see for example Figure 6‑35 (middle right), is the preferred model in terms of fit to GPS
data, sea-level data and as the lithospheric elastic structure is in agreement with Fennoscandian seismic investigations /Lund et al. 2009/. The shear stresses in the vertical plane at 500 m depth can beshown to be negligible. Therefore, the glacially induced stress components (σH, σh and σv) presented
at repository depth (here 500 m) in Figure 6‑49 are principal stresses. Furthermore, the variations
with depth in the upper 1.5 km of the rock are negligible, cf. Figure 6‑43 (right), this means that thestress additions can be approximated to be constant with depth in the upper 1.5 km of the rock.
Stress-transmissivity relations

For the modelling work in SR-Site, it is recommended that in addition to a stress-transmissivity
model based on mean values of fracture normal stiffness data, a model with low values of JKN andJEN should also be considered. Mean values (and range in parameter values) for the CY model arepresented in Table 6‑53. By inspection of Figure 6‑36 (right), the parameter combination JKN =150 MPa/mm and JEN = 0.78 is judged to be sufficiently low. There are only small variations in themean value of JRC between fractures sets (see Figure 6‑45). It is therefore recommended that themean value of JRC (6.0, cf. Table 6‑53) be used in the models.
The residual hydraulic aperture is taken to be the average hydraulic aperture at large depth. It ishere set to 20 μm based on reported fracture transmissivities below 400 m depth in fracture domain
FFM01 /Follin et al. 2007b/, cf. Figure 6‑50.
Table 6‑53. Model parameters for CY model (JKN and JEN) are derived from cyclic loading
compression tests by /Jacobsson and Flansbjer 2005a, b, c, d, 2006/. Estimates of the joint
roughness coefficient (JRC) at joint length tested are based on results from tilt testing by/Chryssanthakis 2003a, b, 2004a, b, c, 2006/.
Mean

Min

Max

436

67

1,193

0.78

0.26

1.69

JRC (–)

6.0

2.2

8.5

Glacially induced stress (MPa)JKN (MPa/mm)
JEN (–)

35

180

30

160

25

140

20

120

15

100

10

80

5

60

0

Orientation (°)

Parameter

40
sH
sh
sv
Orientation of σH wrt North

-5
-10
0

10000

20000

30000

40000

50000

60000

20
0
70000

Time (years)

Figure 6‑49. Stress additions and orientation of the major horizontal stress at 500 m depth during theWeichselian glacial cycle obtained from of ice-crust-mantle analyses (Model T9) by /Lund et al. 2009/.
Here, 0° represents North and 180° South.

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1E-041000

Min transmissivity
Mean transmissivity
Max transmissivity
Min aperture
Mean aperture

1E-06

100

Max aperture

1E-07

1E-08

10

Hydraulic aperture (μm)

Transmissivity (m2/s)

1E-05

1E-09

1E-10

1
100-200 m

200-400 m>400 mDepth range

Figure 6‑50. Summary of fracture transmissivities at different depth intervals in fracture domain FFM01/Follin et al. 2007b, Table 10-25/. The corresponding hydraulic apertures are calculated from the transmissivity values through the cubic flow law.

Figure 6‑51 (left) shows the hydraulic aperture obtained by the CY model (Equations 6‑7 and 6‑8)
using the two sets of fracture stiffness data described in the text above evaluated for the initialnormal stress σ0 set to the mean effective stress at repository depth (460 m), σ0 = (σH + σh + σv)/3 – p,where σH, σh and σv are the three principal stress components and p is the hydrostatic pore pressure.
The initial effective normal stress is here approximated to be 20 MPa based on the stress modelby /Glamheden et al. 2007/, cf. Table 6‑51 (FFM01). For both sets of fracture stiffness data, an
exponential fit (also shown in Figure 6‑51, left) to the hydraulic apertures (Equation 6‑9) has beenmade. In the right part of the figure, a comparison is made with the in situ stress-transmissivity
correlation for Forsmark /Follin et al. 2008/ and the stress-transmissivity relation used in the safetyassessment SR-Can /Hökmark et al. 2006/. The two models based on fracture stiffness data are moresensitive to normal stress variations than the most cautious in situ correlation (ZFM-PFL) reported
for Forsmark. Parameter values for the two sets of data for the CY model and corresponding values
for the exponential expression are presented in Table 6‑54.
60

1000

Relative transmissivity (-)

Hydraulic aperture (μm)

Exponenal fit (eres = 20, emax = 42, alpha = 0.15)

50
40
30
20
CY (JKN = 150 MPa/mm, JEN = 0.78, E0 = 42 μm, JRC = 6)10CY (JKN = 436 MPa/mm, JEN = 0.78, E0 = 42 μm, JRC = 6)
Exponential fit (eres = 20, emax = 42, alpha = 0.15)
Exponential fit (eres = 20, emax = 13, alpha = 0.13)

0
0

5

10

15

20

25

Effective normal stress (MPa)Exponenal fit (eres = 20, emax = 13, alpha = 0.13)100(σ/σ0)-2.145 (Laboratory) /Follin et al. 2008/
e = 10 + 32 exp(-0.3 σn) /Hökmark et al. 2006/10

1(σ/σ0)-0.28 (ZFM-PFL) /Follin et al. 2008/

0.1
30

0

5

10

15

20

25

30

Effective normal stress (MPa)Figure 6‑51. Left: Hydraulic apertures calculated using the CY model (Equations 6‑7 and 6‑8) compared withexponential fits to the apertures (Equation 6‑9), cf. Table 6‑54. Right: Comparison between in situ stress-transmissivity correlations given by /Follin et al. 2008/ (denoted F), stress-transmissivity relation used in the safetyassessment SR-Can /Hökmark et al. 2006/ (denoted H), and the exponential fits suggested in the left figure.

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Table 6‑54. Recommended parameter values for the CY model (Equations 6‑7 and 6‑8) based on1) average fracture stiffness data, cf. Table 6‑53 and 2) based on lower bound fracture stiffness
data (alternative data set described in the text) with corresponding parameter values for the
exponential expression (Equation 6‑9).
ModelParameter

Average

Alternative

CY-modelJKN (MPa/mm)

436

150

JEN (–)

0.78

0.78

JRC (–)

6.0

6.0

E0 (μm)42

42

Exponential expressioner (μm)20

20

emax (μm)

13

42

α (–)

0.13

0.15

6.4.11 Judgements by the SR-Site teamSupplier input on use of data in SR-Site and SR-Can

In response to the suggestions made in Section 6.4.3, the following has been considered in themodelling work for SR-Site /Hökmark et al. 2010/.
•

The distinct element code 3DEC /Itasca 2007/ is used for all thermal, thermo-mechanical, andmechanical modelling on all scales. However, certain parameter variations and layout assumptions is investigated using analytical thermal /Hökmark et al. 2009/ and thermo-mechanicalsolutions /Claesson and Probert 1996b/. The parameter values used in the modelling (including
uncertainty spans) are presented in Section 6.4.12.

•

The repository layout presented for Forsmark with 13% loss of canister positions /SKB 2009a/
is used in all thermal and thermo-mechanical modelling.
– In the large-scale modelling every 8th canister position is empty to represent the loss ofcanister positions.
– In the near-field models all available canister positions are assumed to be filled.

•

The influence of different locations within the repository region is investigated.

•

In all large-scale thermo-mechanical modelling the mean value of the thermal conductivity is
judged appropriate. In the near-field (thermal and spalling) analyses both mean and dimensioning
values of the thermal conductivity are used. The latter value is derived in the THM report/Hökmark et al. 2010/.

•

Fracture orientations are based on site data /Fox et al. 2007/ as well as generically oriented
fractures on which the potential for slip is large. In the near-field modelling, which includes theeffects of deposition tunnels, all fractures are circular with a radius of 50 m. THM effects onlarger fractures are assessed by use of analytical expressions.

•

Results from the study by /Lund et al. 2009/ are used to represent the stress additions during a
glacial cycle.

•

Stress-transmissivity relations based on site-specific fracture normal stiffness data and hydraulicaperture data have been derived, cf. Figure 6‑51.

Sources of information and documentation of data qualification

The references cited in the data qualification are judged to be relevant and sufficiently exhaustive.
The data sets provided are judged to be adequately categorised into qualified and supporting data
sets.
Conditions for which data are supplied

The conditions for which data are supplied are relevant for SR-Site modelling.

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Conceptual and data uncertainties

The sources of uncertainty of all parameters are adequately described and quantified in Sections 6.4.6
and 6.4.7. Handling of uncertainties in SR-Site is commented below under ‘Results of supplier’s data
qualification’.
Spatial and temporal variability of data

The spatial and temporal variability of all parameters is adequately described and quantified in
Section 6.4.8. Handling of spatial and temporal variability in SR-Site is commented below under‘Results of supplier’s data qualification’.
Correlations

The correlation for the in situ stresses was used to develop the stress model for the Forsmark site.
It will not be used in any further modelling work for SR-Site.
The correlation for the hydraulic diffusivity is used to combine hydraulic and mechanical data to
be used in the assessment of the maximum jacking depth and in the modelling of the pore pressure
evolution during the glacial cycle.
Result of supplier’s data qualification

The SR-Site team finds the supplier’s data qualification presented in Section 6.4.10 fully adequate
for the needs of SR-Site.
The description, provided in Section 6.4.10, of uncertainties and spatial variability in terms of arange of variation of the evaluated mean, standard deviation, and minimum/maximum value ofthe tested population are sufficient for the needs of SR-Site modelling. In SR-Site modelling there
will be no differentiation of the sources of uncertainty as statistical modelling of the distribution ofproperties is not feasible.

6.4.12 Data recommended for use in SR-Site modelling
All data provided in Section 6.4.10 are recommended for use in SR-Site modelling. Reference
parameter values for all models are given by the mean value of each parameter. To assess theinfluence of uncertainties, a small number of parameter values judged to represent the variability are
selected and used in the modelling work. Note that the thermal and thermo-mechanical propertiesof the rock (i.e. thermal conductivity, Table 6‑19, heat capacity, Table 6‑22, and thermal expansion
coefficient, Table 6‑27) are described in Section 6.1. For the thermo-mechanical 3DEC models, thevalue of the heat capacity is based on the dominating rock type in rock domain RFM029 obtained
from stage 2.2 of the site-descriptive modelling for Forsmark /Back et al. 2007/.
Mechanical properties of the rock mass

The mechanical properties of the rock mass at all three scales are provided in Table 6‑55. It should
be noted that here a generic rock mass density is given, which is consistent with site-specific densities.
Parameter variations considered in the modelling work are listed in Table 6‑56.
Table 6‑55. Mechanical properties of the rock mass, cf. Table 6‑46.
Parameters

Large-scale

Medium-scale(FFM01)

Small-scale(FFM01/FFM06)

Generic density, ρ (kg/m3)

2,700

2,700

2,700/2,700

Young’s modulus, E (GPa)

70

70

70/69

Poisson’s ratio, ν (–)

0.24

0.24

0.24/0.27

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Table 6‑56. Uncertainty spans for the mechanical properties of the rock mass, cf. Table 6‑46.
Parameters

Large-scale

Medium-scale

Small-scale(FFM01/FFM06)

Density, ρ (kg/m3)

–

–

–/–

Young’s modulus, E (GPa)

62–78

–

62–78/57–81

Poisson’s ratio, ν (–)

0.21–0.27––/–

Spalling strength

In the near-field spalling assessments, the spalling strength is assumed to be in the range 52–62% of
the uniaxial strength of the dominant rock type in each fracture domain, cf. Table 6‑57.
Table 6‑57. Strength properties of intact rock, cf. Table 6‑44 and Table 6‑45.
Parameter

Unit

FFM01

FFM06

Uniaxial compressive strength (UCS)

MPa

226

373

Spalling strengthMPa

118–140

194–231

In situ stresses

In situ stresses as function of depth for the large-scale modelling are provided in Table 6‑58.
The most likely in situ stresses at repository depth (here 460 m) for the small- and medium-scalenear-field modelling are provided in Table 6‑59. Five additional stress states (labelled I–V) are also
provided in the table to investigate the influence of uncertainties on the potential for spalling.
Table 6‑58. In situ stresses as functions of depth for large-scale modelling. The orientation of the
major horizontal in situ stress is given with respect to North, cf. Table 6‑51.
Depth rangeσH (MPa)σH, orientation (°)σh (MPa)

σv (MPa)

0–150 m19+0.008z

145

11+0.006z

0.0265z

150–400 m

9.1+0.074z

145

6.8+0.034z

0.0265z

400–1,000 m

29.5+0.023z

145

9.2+0.028z

0.0265z

Table 6‑59. Most likely in situ stresses at repository depth (460 m) for small and medium-scalenear-field modelling. Alternative in situ stresses (I–V) for small-scale near-field modelling. Stressstates I–III are based on the uncertainty spans of the most likely stress model for Forsmark, cf.
Table 6‑51. Stress states IV–V are based on the ‘Proposed Maximum Stress Model’, cf. Table 6‑52.
DescriptionσH (MPa)σH, orientation with respect to north(°)σh (MPa)

σv (MPa)

Most likely

40.08

145

22.08

12.19

I

40.08

130–160, largest possible deviation from tunnel axis

22.08

12.19

II

34.07

130–160, smallest possible deviation from tunnel axis

17.66

11.96

III

46.09

130–160, largest possible deviation from tunnel axis

26.50

12.42

IV

56.00

130–160, largest possible deviation from tunnel axis

35.00

12.42

V

62.00

130–160, largest possible deviation from tunnel axis

43.00

12.42

Fractures

The mechanical and Mohr-Coulomb strength properties of fractures are provided in Table 6‑60.
The fracture cohesion and fracture friction angle are obtained by taking the average value of theirmean laboratory-determined peak and residual values. The fracture normal stiffness, fracture shear
stiffness, and fracture dilation angle are represented by their mean laboratory determined values.
The latter two values are obtained at a normal stress of 20 MPa.

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Table 6‑60. Fracture mechanical and Mohr-Coulomb strength properties, cf. Table 6‑47, Table 6‑48
and Table 6‑49.
Parameters

Large-scale

Medium-scale

Fracture cohesion, c (MPa)

0.5

0.5

Fracture friction angle, φ (°)

35.8

35.8
656

Fracture normal stiffness, Kn (GPa/m)–

Fracture shear stiffness, Ks20.0 (GPa/m)–

34

Fracture dilation angle, ψ20 (°)–

3.2

Glacially induced stresses

The glacially induced stresses (in excess of the present-day in situ stresses) at five selected points
in time during the glacial cycle are presented in Table 6‑61. The stress magnitudes and orientations
given in the table do not vary with depth in the upper 1 km of the rock.
Table 6‑61. Glacially induced stresses (orientation is given with respect to North), cf. Figure 6‑49.
Time

σH (MPa)σH, orientation (°)σh (MPa)

σv (MPa)First glacial maximum (12 kyr)

16.11

99

14.26

18.35

First retreat (15 kyr)

7.27

73

5.04

0.20

Forebulge (39 kyr)

0.02

3

–5.40

0.34

Second glacial maximum (54.5 kyr)

28.90

95

24.76

25.71

Second retreat (58 kyr)

10.95

76

7.64

–0.15

Stress-transmissivity relations

Two stress-transmissivity models (Models A and B) are chosen to estimate stress induced transmissivity changes based on the exponential relation suggested by /Liu et al. 2003/ (Equation 6‑9 ) andthe cubic flow law (Equation 6‑10). The parameter values for each stress-transmissivity model are
given in Table 6‑62.
Table 6‑62. Parameter values for the exponential expression, Eq. 6‑9, cf. Table 6‑54.
Parameter

Model A

Model Ber (μm)20

20

emax (μm)

42

13

α (–)

0.15

0.13

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6.5

Spalling and the excavation damaged zone

Repository induced damages to the rock mass affect the groundwater flow and, as a consequence,the solute transport in the fractured rock. Assessments of these rock damages, in terms of spallingand excavation damaged zone (EDZ), are therefore needed as input for the assessment of radiological risk associated with radionuclide transport from the repository. The input data considered in
SR-Site are the resulting properties from these damages and alterations. These properties, in turn,are assessed from various sources and assessments, including detailed process modelling, as further
described in this section.
The data presented in this section are used as input to Sections 6.6 and 6.7, and associated activities,
discussing hydraulic properties of the rock and flow related migration parameters.

6.5.1 Modelling in SR-Site
The excavation damaged zone is here defined as “the part of the rock mass closest to the underground opening that has suffered irreversible deformation where shearing of existing fractures aswell as propagation and/or development of new fractures has occurred” /Bäckblom et al. 2004/.
In the SR-Site safety assessment, the concern is on the remaining impact from the tunnelling, thatis after re-saturation, on the hydraulic properties. The EDZ basically originates from:•

Excavation damage (i.e. from the blasting or from a Tunnel Boring Machine, TBM, if this is used).

•

The changes of the stress field and associated fracturing (spalling) and fracture dilation effects
resulting from the changed stress boundary conditions compared to the undisturbed situation.

It should be noted that other definitions of the EDZ exist, but the one adopted here pragmaticallyfocuses on the resulting impact on the rock – and not on the various causes for these impacts.
Defining the data requested from the supplier

Depending on the nature of the excavation damaged zone, the data which need to be supplied differ.
If the excavation damaged zone is generally not connected on the tunnel scale:•

The absence of a connected EDZ should be justified based on experimental observations and data.

If the excavation damaged zone is connected on the tunnel scale, giving rise to altered hydraulicproperties compared to the properties of the rock prior to construction, the following should be
supplied:•

The axial transmissivity T (m2/s) of the EDZ along the deposition tunnel, as averaged across thetunnel floor.

•

The axial transmissivity T (m2/s) of the EDZ along other tunnels and shafts, as averaged acrossthe tunnel floor (and corresponding for shafts).

Concerning any volume around the deposition holes with altered hydraulic properties compared tothe properties of the rock prior to construction (referred to as the spalled zone), the following should
be supplied:•

The potential for spalling, with focus on the potential for thermally induced spalling.

If spalling occurs:•

The length Lzone (m) and location of the spalled zone along the deposition hole.

•

The width Wzone (m), and thickness dzone (m) of the spalled zone around the deposition hole.

•

The tortuosity τy (–) and porosity εzone (–) of the spalled zone.

•

The hydraulic conductivity K (m/s) of the spalled zones. If this conductivity cannot be shown
to be small in relation to the transmissivity of the fracture intersecting the deposition hole, orif there is no fracture intersecting the deposition hole, it is sufficient to note that the hydraulicconductivity of the spalled zone is “high”.

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If no spalling occurs:•

The connected effective transmissivity T (m2/s) of the deposition hole EDZ, integrated along
the full length of the deposition hole wall, and as averaged around the hole. If no such fully
connected zone would develop, arguments are needed to state this.

SR-Site models in which data will be used

The data will be incorporated in the groundwater flow models as well as in the migration models
used in SR-Site. These models are further described in Section 6.6 regarding hydraulic properties of
the rock, and in Section 6.7 regarding flow related migration parameters. The potentials for an EDZ
and for spalling are implemented in the following manner:•

The groundwater flow simulations, using ConnectFlow /Serco Assurance 2005/, implement the
EDZ as a continuous fracture, with a prescribed transmissivity as input, under the floor of all
deposition tunnels.
• The source term model, COMP23 /Cliffe and Kelly 2006/ considers the possibility of an EDZ
with enhanced transmissivity intersecting the upper part of the deposition hole and uses anequivalent flow rate Q2, see Section 6.7, to handle the release from the buffer into the EDZ.
• The far-field radionuclide transport model FARF31 /Norman and Kjellbert 1990, Elert et al. 2004/
pessimistically neglects retention in the EDZ.
• For handling spalling in deposition hole walls, a special model was developed for use in SR-Can/Neretnieks 2006/, with an additional equivalent flow rate due to the damaged zone caused byspalling. This model has been updated for use in SR-Site /Neretnieks et al. 2010/. This model isused for solute transport both towards and from the canister.

6.5.2 Experience from SR-Can
SR-Can considered the potential both for an EDZ in the deposition tunnels caused by the excavation,and thermally induced spalling in deposition holes. Data for the assessment were generally obtained
from the coupled thermo-hydro-mechanical analyses and assessments by /Hökmark et al. 2006/.
Modelling in SR-Can

Modelling in SR-Can was essentially the same as in SR-Site:• In the groundwater flow modelling of SR-Can (see SR-Can Main report /SKB 2006a, Section 9.3.6/
and /Hartley et al. 2006/), the EDZ was implemented as a continuous fracture in the tunnel bottom,
with a transmissivity and thickness as input parameters.
• The source term model, COMP23, applied in SR-Can, considered the possibility of an EDZ withenhanced transmissivity intersecting the upper part of the deposition hole and used an equivalent
flow rate Q2 to handle the release from the buffer into the EDZ. The far-field radionuclidetransport model used in SR-Can to handle radionuclide transport in the water phase was FARF31.
However, in the far-field migration calculations, the retention due to sorption and matrix diffusion in the EDZ was pessimistically neglected.
• For handling spalling in deposition hole walls, a special model was developed /Neretnieks 2006/,
with an additional equivalent flow rate due to the damaged zone caused by spalling. This model
needed, among other things, the geometry of the spalled zone, its permeability, porosity, andtortuosity.
Conditions for which data were used in SR-Can

The estimate of the EDZ around repository tunnels concerned the potential impacts from construction of the repository. These were primarily discussed by /Hökmark et al. 2006/, and summarised in
the SR-Can Data report /SKB 2006b, Section 6.5/.
The assessment of disturbances around deposition holes also considered the potential for thermallyinduced spalling due to the heat load, after canister emplacement, see /SKB 2006a, Section 9.3.5/
and /Hökmark et al. 2006/.

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Sensitivity to assessment results in SR-Can

Migration analyses carried out in SR-Can /SKB 2006a, Section 9.3.6, Hartley et al. 2006/ showedthat in analysed cases where the hydraulic conductivity of the tunnel backfill and the EDZ wasincreased by one order of magnitude, this had limited, if any, influence on the performance measures.
In contrast, a spalled zone around the deposition hole could have a profound impact on the mass
exchange between the buffer and the fracture intersecting the deposition hole, as long as the buffer
remains intact. This manifests in an altered Qeq for the Q1 path, see /SKB 2006a, Section 9.3.6/.
In /Neretnieks 2006/ an additional equivalent flow rate due to the damaged zone caused by spalling,
QeqEDZ was derived. It was found to be proportional to the square root of the flow rate in the fractures
around the deposition hole. The results indicate that spalling may increase equivalent flow rates bymore than an order of magnitude, but it was noted that the handling of spalling in deposition holes
was considered pessimistic.
The potential occurrence of spalling depends on the rock stress, the thermal, thermo-mechanical, androck mechanics properties as well as the repository design. In SR-Can, spalling was assessed using
data based on a preliminary site description and repository layout version for Forsmark /Hökmark
et al. 2006/. The results predicted that there would not be any spalling during the construction andoperational phase, but that thermally induced spalling would occur after some time of heating. The
possibility that spalling could be suppressed in deposition holes where the buffer swelling reached asufficient counter pressure before the thermal pulse was discussed, but it was cautiously decided not
to take this possibility into account.
It should be noted that in SR-Can, the radiological risk contribution from canister positions where
the buffer remained intact was very small, compared to the contribution from canister positions were
the buffer had eroded. Spalling had an insignificant impact on repository safety for the cases where
advective conditions prevail in the deposition holes.
Alternative modelling in SR-Can

There was no alternative modelling of spalling or EDZ in SR-Can.
Correlations used in SR-Can

SR-Can assumed the EDZ to be proportional to the hydraulic properties of the host rock. As a basecase the EDZ transmissivities used were adjusted to represent half an order of magnitude increase in
the average hydraulic conductivity along the deposition tunnels. A variant case assumed a 1.5 orderof magnitude increase. However, the assumption on a correlation between the hydraulic propertiesof the EDZ and those of the host rock was not based on any specific evidence.
Identified limitations of the data used in SR-Can modelling

Regarding spalling, it was noted in SR-Can /SKB 2006a, Section 13.6.5/ that procedures forhandling deposition holes with initial spalling needed further specification, and that the potential
for suppressing the thermally induced spalling through the counter pressure, provided by fillingthe buffer-rock clearance with bentonite pellets at the time of deposition, should be assessed.
Furthermore, the potential for spalling depends on the rock properties and on the design, bothof which are updated for SR-Site.
Regarding the EDZ, SR-Can provided reasonable justification that it is possible to drill and blasta tunnel without creation of a continuous EDZ. However, SR-Can also concluded /SKB 2006a,
Section 13.6.6/ that some measures to control the excavation procedures, so that they would notlead to a very extensive and transmissive EDZ, are needed. Consequently, the EDZ properties foruse in SR-Can need to be revised to reflect the planned procedures for tunnel excavation and quality
control, as is provided by the Underground opening construction report for SR-Site.
Furthermore, in the review of SKB’s work on coupled THM processes within SR-Can /Rutqvist andTsang 2008/, a strong potential for tensile failure developing in the tunnel walls after excavation or
during the subsequent heated phase was argued, which could lead to a continuous damaged zone
along the tunnel.
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In addition, in reviewing SR-Can the regulatory authorities noted that /Dverstorp and Strömberg 2008/:•

There is a need to better justify values used for describing variability and uncertainty in the EDZ.

•

The impact of the EDZ should be considered also for the glacial phase.

•

The rock mechanics analyses should be complemented taking new data into account, which has
emerged during the final phase of the site investigations.

6.5.3 Supplier input on use of data in SR-Site and SR-Can
The supplier broadly agrees with the handling of data in SR-Can. However, the following issues
are recommended to be addressed in SR-Site:•

Further enhancing the experimental support for the possibility of excavating the tunnels such as
no continuous EDZ develops.

•

Reassessing the potential for thermally induced spalling based on the current understanding of
the Forsmark site and assessing means of suppressing the spalling by the counter pressure fromthe bentonite pellets in the buffer.

•

Using experimental data in support for the parameter values suggested for characterising thespalled zone.

It is noted that all these issues have been addressed.

6.5.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information used in the data qualification of data on the EDZ and spalling arepresented in Table 6‑63.
Categorising data sets as qualified or supporting data

The qualified and supporting data sets are presented in Table 6‑64. Comments regarding each item
are given in Table 6‑65. The following principles have been applied in this assessment:•

For obvious reasons there are no data from actually constructed tunnels at depth at the Forsmark
site. However, it is judged that the phenomena of forming EDZ and spalling depends on generalrock mechanics processes, which means that data obtained from other sites, such as Äspö HRL,
are applicable.

•

Data obtained from SKB reports being reviewed and approved in accordance with the SKB QA
Plan, can a priori be regarded as qualified data. However, since the report by /Bäckblom 2008/
is a compilation of data from various sources of varying quality, data regarding EDZ in tunnels
presented by Bäckblom may partly be considered to be supporting.

•

The assessment on the EDZ in the Underground opening construction report, is based on thefindings by /Ericsson et al. 2009, Glamheden et al. 2010/. The conclusions reached have beenreviewed and accepted by SKB’s internal and external experts, and it is thus qualified from that
respect.

Excluded data previously considered as important

No important data have been excluded from the analyses.

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Table 6‑63. Main sources of information used in data qualification for EDZ and spalling.
Underground openings construction report, 2010. Design, construction and initial state of the underground openings. SKB TR-10-18, Svensk Kärnbränslehantering AB.
Andersson J C, 2007. Äspö Hard Rock Laboratory. Äspö Pillar Stability Experiment, Final report. Rock mass response
to coupled mechanical thermal loading. SKB TR-07-01, Svensk Kärnbränslehantering AB.
Bäckblom G, 2008. Excavation damage and disturbance in crystalline rock – results from experiments and analyses.
SKB TR-08-08, Svensk Kärnbränslehantering AB.
Ericsson L O, Brinkhoff P, Gustafson G, Kvartsberg S, 2009. Hydraulic Features of the Excavation Disturbed
Zone – Laboratory investigations of samples taken from the Q- and S-tunnels at Äspö HRL. SKB R-09-45, Svensk
Kärnbränslehantering AB.
Glamheden R, Fälth B, Jacobsson L, Harrström J, Berglund J, Bergkvist L, 2010. Counterforce applied to prevent
spalling. SKB TR-10-37, Svensk Kärnbränslehantering AB.
Hökmark H, Fälth B, Wallroth T, 2006. T-H-M couplings in rock. Overview of results of importance to the SR-Can
safety assessment. SKB R-06-88, Svensk Kärnbränslehantering AB.
Hökmark H, Lönnqvist M, Fälth B, 2010. THM-issues in repository rock. Thermal, mechanical, thermo-mechanicaland hydro-mechanical evolution of the rock at the Forsmark and Laxemar sites. SKB TR-10-23, Svensk Kärnbränslehantering AB.
Neretnieks I, Andersson J C, 2009. Characterisation of spalling fragments to obtain data for flow and transport indamaged zones. In: Proceedings of the ISRM-Sponsored international symposium on rock mechanics: “Rock characterisation, modelling and engineering design methods” (SINOROCK 2009) held at the University of Hong Kong, China,
19–22 May 2009.
Olsson R, 1998. Mechanical and hydromechanical behaviour of hard rock joints. A laboratory study. Ph.D. Thesis,
Department of Geotechnical Engineering, Chalmers University of Technology, Sweden.
Olsson M, Niklasson B, Wilson L, Andersson C, Christiansson R, 2004. Äspö HRL. Experiences of blasting of the
TASQ tunnel. SKB R-04-73, Svensk Kärnbränslehantering AB.
Olsson M, Markström I, Pettersson A, Sträng M, 2009. Examination of the Excavation Damaged Zone in the TASS
tunnel, Äspö HRL. SKB R-09-39, Svensk Kärnbränslehantering AB.

Table 6‑64. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. Occurrence, location, and extent of any volume aroundthe deposition tunnels, and other tunnels, with alteredhydraulic properties compared to the properties of therock prior to construction: /Olsson et al. 2009, Figures 7-9
and 7-10/, /Olsson et al. 2004/, Underground opening
construction report.
2. Connected effective transmissivity (m2/s) along thedeposition tunnel, or other tunnels, and averaged acrossthe tunnel floor: /Olsson et al. 2009, Figures 7-9 and 7-10/,/Ericsson et al. 2009, Chapter 4/, Underground opening
construction report.
3. Occurrence, location, and extent of any volume aroundthe deposition holes, with altered hydraulic properties
compared to the properties of the rock prior to construction:
Chapters 8 and 9 in /Hökmark et al. 2010/, /Glamheden
et al. 2010, Andersson 2007/.
4. The connected effective transmissivity (m2/s) integrated
along the full length of the deposition hole wall and as
averaged around the hole: /Bäckblom 2008, Table 3-3/.
Underground opening construction report.
5. Local hydraulic conductivity, tortuosity, porosity, andthickness of the spalled zones, if a spalled zone occurs:/Neretnieks and Andersson 2009, Glamheden et al. 2010/.

6. Published data on characterisation of the EDZ in
drilled and blasted tunnels /Bäckblom 2008, Table 3-3/.

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Table 6‑65. Justifications to the sorting of items in Table 6‑64.
1. /Olsson et al. 2009/ characterised natural and excavation induced fractures in rock blocks removed from a section ofa tunnel wall at the Äspö HRL. /Olsson et al. 2004/ concerns assessment of excavation induced fractures at the TASQtunnel at Äspö HRL. Generally, the Underground opening construction report concludes that it is possible to control the
excavation of a drilled end blasted tunnel such that excavation induced fractures would not form a continuous network.
These reports, apart from /Olsson et al. 2004/, are reviewed and accepted according to the SKB QA-plan. Furthermore,
it is judged that the work of /Olsson et al. 2004/ was carried with a similar quality as their later report. Thus data
presented in these reports are judged to be qualified.
2. The excavation induced fractures in the rock side-walls characterised by /Olsson et al. 2009/ are dominantly radialand does not form a continuous network along the axial direction of the tunnel over any significant distance. /Ericsson et al.
2009/ performed laboratory permeability tests of the fractures in the rock blocks characterised by /Olsson et al. 2009/.
By numerical analyses and analytical expressions, /Hökmark et al. 2010/ assess the change in fracture transmissivity
due to the impact of changing mechanical loads. Generally, the Underground opening construction report concludes
that it is possible to control the excavation of a drilled end blasted tunnel such that the excavation induced fractures
would not form a continuous network. These reports are reviewed and accepted according to the SR-Site QA-plan andthe presented data are thus judged to be qualified.
3. /Hökmark et al. 2010/ performed numerical analyses of the potential for, and the geometrical extension of, thermallyinduced spalling in deposition holes. /Andersson 2007/ presents the Äspö Pillar Stability Experiment (APSE) carriedout to examine the failure process in a heterogeneous and slightly fractured rock mass when subjected to coupled
excavation-induced and thermal-induced stresses. /Glamheden et al. 2010/ present a field experiment at Äspö HRL,
where 0.5 m diameter boreholes are exposed to a thermal load, with and without a confining pressure. These reports,
apart from /Andersson 2007/, are reviewed and accepted according to the SR-Site QA-plan. However, the work of/Andersson 2007/ was part of a doctoral dissertation and has been subject to an extensive scientific review. Thus data
presented in these reports are judged to be qualified.
4. /Bäckblom 2008, Table 3-3/ compiles published data on characterisation of the EDZ in mechanically excavatedtunnels and boreholes. These data are judged to be of more controllable quality and are thus judged to be qualified.
5. /Neretnieks and Andersson 2009/ assessed the tortuosity of the spalled rock fragments obtained from spalling tests
at the Äspö Pillar Stability Experiment (APSE) /Andersson 2007/. /Glamheden et al. 2010/ performed hydraulic testsof the thermally spalled zones in one of their tests. These reports are reviewed and accepted according to the SR-Site
QA-plan and the presented data are thus judged to be qualified.
6. /Bäckblom 2008, Table 3-3/ compiles published data on characterisation of the EDZ in drilled and blasted tunnels.
The data are of various origin and quality, and it is not evident that there is a clear separation between natural andinduced fractures and are thus judged to be supporting.

6.5.5 Conditions for which data are supplied
The field data on the excavation induced fracturing /Olsson et al. 2009, Ericsson et al. 2009,
Bäckblom 2008/, concern conditions resulting after rock excavation. The field data on thermallyinduced spalling /Neretnieks and Andersson 2009, Glamheden et al. 2010/ concern conditions after
heating, with and without a small counter pressure on the rock wall.
The numerical results by /Hökmark et al. 2010/ relate to different conditions, ranging from thesituation after excavation, assessing impact of the thermal load and assessing the impacts of theglacial loading cycle (with accompanying pore pressure variations and temperature reductions in
combination with proglacial permafrost).

6.5.6 Conceptual uncertainty/Hökmark et al. 2010/ base their spalling estimates on results from linear elastic models without
considering the possibility that the counter pressure provided by the swelling buffer or pellet slot
may be sufficient to prevent or limit the spalling process. Spalling is said to have occurred whenthe modelled tangential stresses in the walls of a deposition hole or deposition tunnel exceed thenominal spalling strength (52–62% of the laboratory-determined uniaxial compressive strength, cf.
Section 6.4). In reality, the spalling strength will be “reached” rather than “exceeded” and thereafter,
when the failure is in progress, the linear elastic models are not strictly valid, cf. /Hökmark et al.
2010/. Therefore, the results can only be used to predict in which parts of the deposition holes ortunnel walls spalling may occur. The following sources of conceptual uncertainty have been identified:•Exceeding the nominal spalling strength does not necessarily mean that spalling will occur.
/Hökmark et al. 2010/ suggest that if the bentonite buffer has had time to take up water and begin
to close the buffer-rock gap before the thermal stresses have reached levels that may initiate thespalling process, then the bentonite support pressure may be sufficient to prevent spalling or tolimit the growth of failed rock regions. /Cho et al. 2002/ and /Andersson and Eng 2005/ haveconcluded that confining stresses in the order of tens of kPa are sufficient to control spalling.

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•The shape and the size of the spalled zone cannot be assessed from the calculations performedby /Hökmark et al. 2010/. However, experience from the Äspö Pillar Stability Experiment (APSE)/Andersson 2007/ indicates that the failures will be notch-shaped and that the notch will selfstabilize at some depth that depends on the stress that prevailed at the time of the failure. Oncethe notch is stable, subsequent increases in stress will not significantly increase the depth of the
failure. The notch developed at APSE was typically 0.5 m wide and 0.1 m deep /Andersson 2007/.

Changes in the hydraulic conditions of fractures intersecting the repository region result from variations
in normal stress and shear displacements induced by the various load scenarios during the repository
phases. /Hökmark et al. 2010/ estimate variations in transmissivity by applying stress-transmissivity
relations derived for the Forsmark site (see Section 6.4 or this report) and results from laboratoryscale shear-flow tests of fracture samples in granitic rock /Olsson 1998/ to the modelled fracturenormal stresses and shear displacements. The following sources of conceptual uncertainty relating
to the estimates of load-induced variations in fracture transmissivities have been identified:•

As noted in Section 6.4, the stress-transmissivity relations become very uncertain as the effective
normal stress approaches zero, i.e. at which the transmissivity becomes indefinite. However,/Hökmark et al. 2010/ argue that, for fractures in compression of a few MPa, the stress-transmissivity relations are relevant as upper bound estimates of the sensitivity to variations in effective
normal stress.

•

According to /Hökmark et al. 2010/, there are no corresponding relations that describe transmissivity changes due to shearing. /Hökmark et al. 2010/ base their transmissivity estimates on
results from coupled shear-flow tests on joint samples of Ävrö granite performed by /Olsson
1998/. Laboratory-scale experiments performed at low normal stress (e.g. /Olsson 1998, Koyama
2007/) indicate that significant increases in transmissivity occur for shear displacements exceeding a few millimetres. However, this transmissivity increase appears to be sensitive to variations
in normal stress /Hökmark et al. 2010/. /Olsson 1998/ demonstrated that raising the initial normal
stress from 2 MPa to 4 MPa may suppress the potential transmissivity increase almost completely
for shear displacements of 4–5 mm. /Hökmark et al. 2010/ estimate that shear displacements
exceeding about 4 mm that take place under low normal stress, e.g. close to the repository openings, may result in local transmissivity increases of between one and two orders of magnitude.
Furthermore, /Hökmark et al. 2010/ argue that any transmissivity increase due to shearing thattakes place under normal stress-levels greater than 6–7 MPa can be ignored. However, theyemphasize that this is uncertain, as there are no field data to corroborate the assumption.

6.5.7

Data uncertainty due to precision, bias, and representativity

Data uncertainty due to precision

The extent of the EDZ around the tunnels excavated using careful drill-and-blast techniques at ÄspöHRL was found to vary from a few cm to a few tens of cm /Ericsson et al. 2009/. /Ericsson et al.
2009/ observed a distribution of discontinuous fractures and microcracks and a slightly increased
matrix porosity within this zone. Given the heterogeneous nature of this zone it is difficult to know
if the transmissivity values reported by /Ericsson et al. 2009/ for the fractures in individual coredsamples taken from this zone are representative, or represent an upper bound value. In addition thetransmissivity values of the very small discontinuous fractures were not measured. The precisionof the reported transmissivity values of the individual fractures is judged to be adequate but may
overestimate the transmissivity of the EDZ and may hence not be representative.
/Hökmark et al. 2010/ considers a number of cases regarding magnitudes and orientations of thein situ stresses and variations in the thermal properties of the rock (cf. Section 6.4 of this report).
Results from all models indicate that spalling during the excavation and operational phase would
not take place other than by exception. For the heated phase of the initial temperate period, theresults predict that there will be thermally induced spalling in the majority of the deposition holes(cf. sections on conceptual uncertainty and spatial variability). However, the extent of the spalling
along the deposition hole wall depends on the assumed state of in situ stress and the thermal, thermomechanical, and elastic properties of the rock. In terms of agreement with the results obtained from
SR-Can /Hökmark et al. 2006/, and the fact that models give similar results regarding the potential
for spalling, precision is judged to be adequate.

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For the estimates of transmissivity changes in the near-field, precision is judged to be poorer mainly
based on the uncertainties relating to the stress-transmissivity relations (cf. Section 6.4) and assumptions regarding transmissivity increases due to shearing (cf. Section 6.5.6).
Data uncertainty due to bias

There is no known bias in the data characterisation by /Olsson et al. 2009/. The methodology
developed for the fracture characterisation was specifically designed to eliminate the bias createdby two-dimensional fracture mapping typically carried out in tunnels. Extracting large blocks andslicing the blocks into 10 cm slabs (see Figure 6‑52) to develop a three-dimensional fracture pattern
eliminated any mapping bias. The wire saw cutting of the blocks and their removal, and the slab
cutting likely allowed closed fractures to open and caused some fractures to extend. Therefore the3D mapped fractures likely overestimate the fracture pattern in situ. It is believed that they havecarefully characterised all fractures in the excavated rock blocks and have created a new standard for
fracture mapping in the EDZ. Any bias introduced by this new technique is judged to be very small.
/Ericsson et al. 2009/ used well-established laboratory methods to measure the transmissivity of
discrete fractures. The bias associated with such laboratory techniques is not considered significant.
The selection of fractures for testing can be biased since closed or partially closed fractures are notsampled. Therefore the fracture sampling produces a bias that results in upper bound transmissivity
values. This bias is considered acceptable.

Figure 6‑52. Schematic view of the process of extracting blocks from the tunnel wall, sawing the block into
slabs and making a digital 3D model of the tunnel. Reproduced from /Olsson et al. 2009, Figure 4-6/.

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307

/Glamheden et al. 2010/ concluded that the hole scale used in CAPS project (Counterforce Applied
to Prevent Spalling) was too small to be fully representative for the heterogeneous rock conditions
encountered at Äspö. The depth of spalling is dependent on hole size, this means that the larger thehole, the greater the depth for given stress conditions, and therefore the conclusions by /Glamheden
et al. 2010/ may be biased by this factor.
CAPS hydraulic testing was carried out using standard testing procedures used for borehole testing.
Complete saturation during testing could not be ensured. However, it is unlikely these conditions
affected the conclusions by /Glamheden et al. 2010/ regarding the relative transmissivity of thespalled zone. The absolute transmissivity values for the spalled zone may be biased by the test
conditions, but the uncertainties due to this bias are judged to be small.
The uncertainties due to bias associated with the numerical models analysed in /Hökmark et al. 2010/
are judged to be small. However, a potential source of bias relating to the modelling approach has
been identified. /Hökmark et al. 2010/ assess the four repository phases (excavation and operation,
heated phase of initial temperate period, permafrost, and glaciation) independently of each other,although in reality they will overlap:•/Hökmark et al. 2010/ does not consider any potential thermo-mechanical effects due to alreadydeposited canisters during the construction of the repository, that is the transition from in situ
conditions to excavated state is assumed to take place in undisturbed rock. For the heated partof the temperate phase, all thermal and thermo-mechanical calculations are performed with theassumption that all canisters are deposited simultaneously. /Hökmark et al. 2010/ demonstrates
that these approximations are valid, unless very specific deposition sequences are used, forexample starting and finalising the deposition in neighbouring tunnels or very nearby depositionareas. In particular, the approach appears to be valid for the deposition sequence /Hansson et al.
2009/ projected for the Forsmark repository.

•/Hökmark et al. 2010/ does not consider thermal stresses during the glacial phase. /Hökmark
et al. 2010/ presents thermally induced rock stresses for the first 10,000 years of the heated periodof the temperate phase. The authors show that, after 10,000 years, the stresses are within a few
MPa of the present-day in situ levels. According to the Climate report for SR-Can /SKB 2006c,
Figure 4-4/, the next major glacial cycle is not projected to occur until about 50,000 years after
repository closure. This is in line with the findings in SR-Site (cf. Section 7.1). Therefore, it isjudged that that the approach taken in /Hökmark et al. 2010/ is justified. Furthermore, /Hökmark
et al. 2010/ assumes that the permafrost scenario coincides with the forebulge stress regime
associated with the second major glacial advancement, that is the point in time with the greatest
reduction in horizontal stresses. For the purpose of providing upper bound estimates of transmissivity increases for the permafrost phase, it is judged that the approach taken by /Hökmark et al.
2010/ is justified.

Data uncertainty due to representativity

The results documented in /Olsson et al. 2009/ were obtained from a tunnel excavation in fractured
rock that utilised careful-blasting techniques. While the data produced by /Olsson et al. 2009/ represent
conditions of a carefully blasted tunnel wall, it must be recognised that there are few as carefully
conducted characterisations from other underground excavations. It has been demonstrated by/Kuzyk and Martino 2008/ that careful blasting techniques can essentially eliminate the connected
fracture characteristics that are frequently associated with an EDZ created using traditional drill andblast techniques. Therefore, while the /Olsson et al. 2009/ data set is unique, it is believed that theirfindings are applicable to hard rock masses where careful blasting techniques are applied.
As already stated above, it is difficult to know if the transmissivity values reported in /Ericsson
et al. 2009/ for the fractures in individual cored samples taken from this zone are representative, or
represent an upper bound value. In addition the transmissivity values of the very small discontinuous
fractures were not measured.
The spalling criteria and EDZ data have been obtained from Äspö HRL. The rock mass at Äspö HRL
is considered typical of the rock masses found in Fennoscandia. The EDZ fracturing is composed ofnatural fracturing and excavation-induced fractures. The excavation-induced fractures are a function

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of the blast design and are hence controlled. The natural fractures are a characteristic of the rockmass and may locally vary. However, at the repository depth this variability is not judged to besignificant. The spalling strength developed by /Anderssson 2007/ at Äspö HRL has been normalised
to the laboratory uniaxial compressive strength, which has been measured for all the major rocktypes at Laxemar and Forsmark.
The results from the rock mechanics modelling performed by /Hökmark et al. 2010/ are based on
idealised descriptions of the rock mass and fractures. The following issues have been identified tobe contributing to the uncertainty due to representativity:•

Estimates of the spalling potential are based on results from linear elastic models /Hökmark
et al. 2010/ without considering structurally controlled failures. As part of the safety assessment
SR-Can, /Fälth and Hökmark 2007/ found that slipping low strength-fractures intersecting thedeposition hole tend to increase the tangential stresses between the intersection and the floor andto reduce them below the intersection. However, /Hökmark et al. 2006, 2010/ concluded that
sporadic local, structurally controlled failures should not be considered important for the overall
assessment of the spalling potential. This means that, for sparsely fractured rock, the linear elastic
models analysed by /Hökmark et al. 2010/ are judged to be adequate.

•

The parameter values chosen to represent the fracture properties are based on results of laboratoryscale tests (cf. Section 6.4) and then applied to perfectly planar fractures with a radius of 50 m.
/Hökmark et al. 2010/ states that the effects of large-scale undulations and in-plane asperities
may prevent large shear displacements, which means that the largest shear displacements foundin their analyses would potentially be overestimated. However, /Hökmark et al. 2010/ concludes
that, for the purpose of obtaining realistic estimates of transmissivity changes due to shearing,
this is not very important. /Hökmark et al. 2010/ founds that the largest shear displacements took
place under normal stresses that are judged to be sufficient to suppress dilation and transmissivity
increases, see Section 6.5.6.

6.5.8

Spatial and temporal variability of data

Spatial variability of data

The spatial variability of spalling and transmissivity changes depends mainly on the spatial variabilityof the thermal, thermo-mechanical, and mechanical properties of the rock and fractures. These variations are assessed in Sections 6.2 and 6.4 of this report. The following additional factors contributeto the spatial variability of the potential for spalling:•

With the exception of the uniaxial compressive strength of intact rock, no systematic large spatial
variation in the rock mechanical or heat transport properties, which could have a significant
impact on the modelled results, has been observed (cf. Sections 6.2 and 6.4). /Hökmark et al.
2010/ found that the tangential stresses in the walls of the deposition holes are of a similar
magnitude in both fracture domains considered for the location of the repository (FFM01 andFFM06), suggesting that the potential for spalling is significantly reduced in fracture domain
FFM06. However, in the layout version considered by /Hökmark et al. 2010/ the majority ofcanisters (approximately 80%) will be deposited in fracture domain FFM01.
• Repository layout, that is tunnel orientation with respect to the major horizontal in situ stress:
It is well known that the tangential stresses in the walls of a deposition hole are minimised if thedeposition tunnel is approximately aligned with the major horizontal in situ stress. In the layout
version considered by /Hökmark et al. 2010/, the deposition tunnels deviate by 3–22° from themost likely orientation of the major horizontal in situ stress, which has a moderately large impact
in terms of increasing the stresses around the tunnel (cf. Figures 9-5, 9-6, and 9-9 in /Hökmark
et al. 2010/).
• The location within the repository region (applies for the heated part of the temperate period):
Canister positions located near a tunnel end or beginning have lower temperatures than more
centrally located canister positions /Hökmark et al. 2009/. Consequently, the stresses in the wallsof the former deposition holes would also be lower. However, the potential for spalling in thewalls of such deposition holes has not been analysed by /Hökmark et al. 2010/. Furthermore, thethermally induced stresses around deposition holes located close to rejected canister positions
will also be lower. For the purpose of providing upper bound estimates of the potential for
spalling, /Hökmark et al. 2010/ assumes that no potential canister positions are rejected.
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In addition to the spatial variations of the mechanical and hydraulic properties of the fractures, thespatial variations of transmissivity changes depend on the fracture orientations and proximity to, orintersections with, repository openings.
Temporal variability of data

With the exception of the glacially induced stress (and accompanying increase in pore pressure), thethermal, thermo-mechanical, and mechanical properties of the rock mass and fractures are assumed
not vary with time (cf. Sections 6.2 and 6.4).
The thermo-hydro-mechanical impact on the rock mass and fractures will vary with the induced
loads during the different repository phases /Hökmark et al. 2010/:
Excavation and operational phase•

Transmissivity changes: The range of the stress-induced hydraulic disturbance generated by the
excavation is small and the results are independent of the size of the fractures that intersect thenear-field.

•

Spalling potential: Spalling in the walls of deposition holes during the construction and operational phase will occur only by way of exception.

Heated period of the temperate period•

Transmissivity changes: The thermal load, induced by the decaying spent fuel, will increase thecompression of fractures of almost all orientations, possibly with exception for portions that are
directly affected by the proximity to the repository openings. For horizontal fractures there is,however, a modest loss of compression between tunnels in the early stages of the heated period.
The magnitude of fracture shear displacements is only locally affected by the proximity to therepository openings.

•

Spalling potential: The nominal spalling strength is likely to be exceeded in a large majority ofthe deposition holes at some point in time during the heated phase (see also subsection on spatial
variability). For a discussion on the temporal evolution of the spalled zone, see the Section 6.5.6.

Glacial and periglacial period•

Transmissivity changes: With the exception of the time-period leading up to a glaciation, during
which the crustal flexure tends to reduce the horizontal stresses (forebulge), the compression of
the rock increases during the glacial period /Lund et al. 2009/. Therefore, the magnitude of theglacially induced pore pressure is decisive of the potential for increased fracture instability or
increased transmissivity. /Hökmark et al. 2010/ show that there are only moderate increases inrelative transmissivity (except for limited areas around the repository openings) caused by variations in normal stress. Any additional increase in transmissivity caused by shearing is also likely
to be modest.

•

Spalling potential: /Hökmark et al. 2010/ does not consider the potential for spalling in the wallsof deposition holes during the glacial phase. By the time of the next glacial period, the state ofstress in the repository region will (approximately) be equal to that after excavation, cf. subsection on data uncertainty due to bias. The glacially induced stresses are of similar magnitudes tothose occurring during the time of the thermal load, and there is likely to be a significant swellingpressure in all deposition holes apart from those where the buffer is lost due to erosion /Hökmark
et al. 2010/. Therefore, it is judged that if spalling does not take place during the temperate period
it will not happen during the remaining of the glacial cycle. Furthermore, if the buffer is lost, theadditional impact spalling may have on repository safety is insignificant.

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6.5.9 Correlations
The potential for spalling depends on the properties of the rock mass (elastic properties, heattransport properties, and the coefficient of thermal expansion), the magnitude and orientationsof the in situ stresses, as well as the near-field design of deposition tunnels and deposition holes.
The potential for spalling is assessed by comparison of the calculated tangential stresses /Hökmark
et al. 2010/ with the estimated spalling strength. The parameter values used in the modelling work
are described in Section 6.4.12 of this report. Similarly to the assessment for SR-Can /Hökmark et
al. 2006/, the possibility that the swelling pressure from the bentonite buffer may suppress spallingis ignored.
Transmissivity changes due to variations in normal stress are calculated using the stress-transmissivity relations described in Section 6.4. Corresponding estimates of transmissivity changes due toshearing are made based on the work by /Olsson 1998/, see Section 6.5.6. /Hökmark et al. 2010/
estimates that shear displacements exceeding about 4 mm under low normal stress may result in atransmissivity increase by 1–2 orders of magnitude. However, the authors assume that increasinglevels of normal stress will suppress the transmissivity increase such that any increase due to shearing that takes place under normal stress-levels greater than 6–7 MPa can be ignored.

6.5.10 Result of supplier’s data qualification
Data for the tunnels

In this section, the absence of an EDZ that is connected on the tunnel scale is argued, as motivatedbased on experimental observations and data. This does not necessarily mean that transmissivity inthe EDZ is as low as in the undisturbed rock. Accordingly, the axial transmissivity T (m2/s) of the
EDZ along deposition tunnels, other tunnels, and shafts is given.
As stated in the Underground opening construction report it is possible to control the drill andblasting of the tunnels such that a continuous fracturing along the axial direction of the tunnel will
not develop. This was stated already in SR-Can, based on experiences from the excavation of the
TASQ tunnel at ÄSPÖ HRL /Olsson et al. 2004/, and has been further confirmed by the intermediate
results from the demonstration trial of smooth blasting techniques at the ÄSPÖ HRL /Olsson et al.
2009, Ericsson et al. 2009/. These indicate that blast induced fractures in the rock side-walls are
dominantly radial and that such fractures will not be continuous along the axial direction of thetunnel over any significant distance, see Figure 6‑53.
Furthermore, available literature suggests that the hydraulic conductivity in drilled and blasted
tunnels is on the order of 10–8 m/s /Bäckblom 2008/ although these conductivities could possibly
be very local and may not necessarily be created by the excavation activities.
/Hökmark et al. 2010/ assessed the potential for tensile stresses developing in the walls of the deposition tunnel. Figure 6‑54 shows contours of the vertical stress component in a vertical cross-section
perpendicular to the tunnel axis after excavation, and at two different times during the heated phase.
/Hökmark et al. 2010/ did not confirm the assessment made by /Rutqvist and Tsang 2008/ that tensile
failure would develop along tunnel walls. The differences in results can be attributed to the differences in the estimated in situ stress-field at repository depth and modelling approach.
The stress redistribution resulting from the tunnel excavation will reactivate some existing near-field
fractures. The process has been modelled in a set of numerical analyses /Hökmark et al. 2010/,
building on the experiences with a similar approach used for SR-Can /Hökmark et al. 2006, Fälthand Hökmark 2007/. In short the 3DEC code is used to determine stress redistribution effects andshear displacements in fractured near-field rock, and then the results are used to estimate possible
permeability changes. The numerical analyses cover a series of events ranging from excavation oftunnel, through the heated period of the initial temperate period, to the effects of a glacial cycle withan accompanying increase in pore pressure. Relevant changes, that is changes that extend more thana couple of metres from the openings, only occur after the thermal load is applied, starting from theinitial temperate period.

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Figure 6‑53. Top: Boreholes, slabs, and all interpreted fractures. Bottom: Ditto but only with blast andblast-induced fractures. The colours indicate different fractures according to: Green = natural, red = blast,and yellow = blast induced. Reproduced from /Olsson et al. 2009, Figures 7-9 and 7-10/. The length of thetest area is 8 m and the height is 1.5 m.

Figure 6‑54. Contours of the vertical stress component after excavation (left), 50 years (middle) and
500 years (right) Stresses are plotted in the range from –40 MPa (compression) to 0 MPa. Note thatstress magnitudes that fall outside the range are marked in black for compression and light grey fortension. Modified from /Hökmark et al. 2010, Figure H-9/.

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The analyses made by /Hökmark et al. 2010/ confirm the results obtained for SR-Can /Hökmark
et al. 2006/, that is an increase in relative transmissivity by about two orders of magnitude within
a distance of 1–2 m from the tunnel openings and only marginal changes elsewhere as shown in
Figure 6‑55 (cf. subsection on temporal variations).
An increase of the transmissivity by two orders of magnitude, as compared to the pristine rock witha transmissivity of about 10–10 m2/s, would suggest an axial transmissivity of about 10–8 m2/s in the
EDZ.
Very close to the periphery of the tunnel, the normal stress could be low enough that, theoretically,
the transmissivity increase could be even larger than that suggested in SR-Can for fractures that arealmost parallel to the tunnel axis. However, in reality fractures will not be persistently parallel to thetunnel where there are deposition holes, especially since deposition holes intersected by fracturesintersecting more than four deposition holes will be rejected according to the EFPC criterion. Thismeans that this effect can be discarded, or at least captured within the EDZ assumption of T = 10–8 m2/s
along the tunnel.

Fractures

Nr

Dip range

Strike range#1

No change intransmissivity

65-900±22.5,180±22.5#2

Two orders ofmagnitude increase at
the height section
opposing the tunnel, nochange elsewhere.

65-90

90±22.5,270±22.5#3

Two orders ofmagnitude increase at
up to 1 m distance fromthe tunnel periphery

65-90

45±22.5,270±22.5#4

Two orders ofmagnitude increase inregion shown in left
figure, no change
elsewhere.

0-65

#5

Two orders ofmagnitude increase inregion shown in left
figure, no change
elsewhere

0-65

#6

Two orders ofmagnitude increase inregion shown in left
figure, no change
elsewhere

0-65

-

-

-

Figure 6‑55. Estimates of transmissivity increases for different fracture orientations based on results from thenear-field modelling for the safety assessment SR-Can. Reproduced from /Hökmark et al. 2010, Figure 8-3/.
The grey-shaded areas around the tunnel represent regions with increased transmissivity as given in the rightcolumns.

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/Hökmark et al. 2010/ also assess the major principal stress in the centre of the tunnel roof. As
opposed to the stresses in the deposition hole walls, the stresses in the centre of the tunnel roof
will not exceed the spalling strength during the heated phase. However, stress concentrations in
the tunnel roof corners may be high enough to cause spalling.
Data for the deposition holes

The data that should be supplied are listed in Section 6.5.1. This is addressed in the following.
Potential for spalling

The potential occurrence of spalling is site and repository design specific, as it depends on the in situ
stress, the intact rock mechanical strength, and on the repository layout (i.e. orientation of deposition
tunnels and near-field design of deposition tunnels and deposition holes). For the heated part ofthe initial temperate period, additional parameters such as the elastic properties, the heat transport
properties, and the thermo-mechanical properties of the rock mass become important. Spalling is the
only fracturing mechanism identified as relevant during the initial temperate period.
/Hökmark et al. 2010/ has revised the assessment of potential for thermally induced spalling presented in SR-Can, using a detailed near-field model geometry that incorporates one tunnel segmentwith seven canisters, three of which have explicitly modelled deposition holes. The model calculates
the tangential stress in the wall of the deposition hole resulting from the excavation, and the subsequent thermal load. The calculated stress is in turn compared with the spalling strength of the rock,which is assumed to be in the range 52–62% of the uniaxial compressive strength (UCS) of intact
rock. /Hökmark et al. 2010/ studied a number of cases with different assumptions regarding the valueof the thermal conductivity, and magnitudes and orientations of the in situ stress. A summary of theseare provided in Table 6‑66.
Table 6‑66. Summary of stress and thermal conductivity assumptions used in the near-field modelling. Reproduced from /Hökmark et al. 2010, Table 9-1/. Box A1 and Box B1 relate to different
locations within the repository region where the local tunnel orientation deviates the most andthe least from the most likely orientation of the major horizontal in situ stress, respectively.
Thermal conductivity

0
Most likely stress magnitudes, most likely
stress orientation

I
Most likely stressmagnitudes, mostunfavourable stressorientation

II
Lower limit stressmagnitudes, mostfavourable stressorientation

III
Upper limit stressmagnitudes, mostunfavourable stressorientation

Mean

C (Box B1)
D (Box A1)

F

A

H

Dimensioning

E (Box B1)
G (Box A1)

I

B

J

Table 6‑67 shows the depth-intervals along the deposition hole wall in which the mean nominal
spalling strength (57% of UCS) is exceeded after excavation, as well as in given time-intervals of
the heated phase (time zero represents deposition of the canisters). For the reader who is not familiar
with the way of presenting assumptions and results in Table 6‑66 and Table 6‑67, /Hökmark et al.
2010, Section 9.5/ is recommended.
As can be seen from Table 6‑67, spalling after excavation will only occur by exception, whilethermally induced spalling will generally occur during the heated phase. Therefore, the answer tothe question of the potential of thermally induced spalling would be that it is likely to occur in mostdeposition holes.

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Table 6‑67. Depth intervals along the deposition hole wall in which the nominal spalling strength(57% of UCS or about 130 MPa) is exceeded after excavation and during given time-intervals after
canister deposition. Reproduced from /Hökmark et al. 2010, Table 9‑2/.
Depth (m)Excavation0 – 1 years

J

D

1–2

0–1

F

H

H

I

J

D

2–3

H

J

3–4

E

F

G

H

I

J

F

G

H

I

J

D

E

F

G

H

I

J

D

E

F

G

H

I

J

4–5

E

F

G

H

I

J

5–6

E

F

G

H

I

J

H

I

J

6–7
7–8
Depth (m)J
1 – 5 years

0–1
1–2

B

C

5 – 50 years

D

E

F

G

H

I

J

D

E

F

G

H

I

J

B
A

B

C

D

E

F

G

H

I

J

D

E

F

G

H

I

J

2–3

B

C

D

E

F

G

H

I

J

A

B

C

D

E

F

G

H

I

J

3–4

B

C

D

E

F

G

H

I

J

A

B

C

D

E

F

G

H

I

J

A

4–5

B

C

D

E

F

G

H

I

J

B

C

D

E

F

G

H

I

J

5–6

B

C

D

E

F

G

H

I

J

B

C

D

E

F

G

H

I

J

C

D

E

F

G

B

C

6–7
7–8

H

I

J

H

I

J

D

E

F

G

H

I

J

D

E

F

G

H

I

J

Geometry and location of the spalled zone
An example of the magnitude of the major principal stress and its location on the deposition holeperimeter at stress maximum is provided in Figure 6‑56 for cases D and G in Table 6‑66.
As can be deduced from Figure 6‑56, for these cases representing the most likely stress magnitudes
and orientation (D and G), spalling is likely to occur along the great majority of the deposition hole.
Therefore it is reasonable to assume a length of the spalled zone Lzone of 8 m, located along the entire
length of the deposition hole.
According to /Hökmark et al. 2010/ there is, at present, no way of directly calculating the actual
shape or depth of thermally induced failures. Experience from the APSE /Andersson 2007/ indicatesthat the failures will be notch-shaped and that the notch will self-stabilise at some depth that depends
on the stress that prevailed at the time of the failure. Once the notch is stable, subsequent increases
in stress will not significantly increase the depth of the failure. The notch developed at APSE wastypically 0.5 m wide /Andersson 2007, Figure 6-17/ and 0.1 m deep (thick) /Andersson 2007,
Figure 6-18/. Based on this Wzone = 0.5 m and dzone = 0.1 m is suggested.
Tortuosity and porosity of the spalled zone
/Neretnieks and Andersson 2009/ assessed the tortuosity of the spalled rock fragments obtained fromspalling tests at the Äspö Pillar Stability Experiment, carried out to examine the failure process in aheterogeneous and slightly fractured rock mass when subjected to coupled excavation-induced andthermal-induced stresses /Andersson 2007/. After the experiment all the rock fragments resulting
from the spalling were collected. The rock fragments range in size from less than mm size particles
up to large plate like thin irregular sheets. The fragments were photographed and measures of thickness, length, and widths were collected. They typically are 10 times as long as they are thick. Thismeans that in the radial direction the tortuosity, in this case the increase in distance a solute has totravel to move one length unit in the radial direction, is about τy= 10. This is because the shape of therock fragments forces a solute to move in sharp zigzag paths whereas in the tangential-direction, thepaths are nearly straight (see Figure 6‑57).
According to /Neretnieks et al. 2010/ there are no data on porosity of the zone, but since the spalling
occurs after the deposition hole has been filled with buffer, the expansion cannot be larger than thevolume of the gap between the rock wall and the buffer, and a porosity value of εzone = 0.02 can thus
be justified.
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315

0

0

Box A1 (Exc)Box A1 (1a)Box A1 (1b)

-1
-2

-2

-3

Depth (m)Depth (m)Box A1 (Exc)Box A1 (1a)Box A1 (1b)

-1

-4
-5

-3
-4
-5

-6

-6

-7

-7

-8
-250 -240 -230 -220 -210 -200 -190 -180 -170 -160 -150 -140 -130 -120 -110 -100 -90 -80 -70 -60 -50

Major principal stress (MPa)

-8
0

90

180

270

360

Angle (°)

Figure 6‑56. Left: Simulated maximum tangential stress along the deposition hole wall for the most likely
in situ stress magnitudes and orientations after excavation (Exc) and after 50 years of heating for two
assumptions regarding the thermal conductivity – mean value (1a) and dimensioning value (1b). This
example corresponds to cases D and G in Table 6‑66 and Table 6‑67. Blue area represents spalling strengthin fracture domain FFM01 (52–62% of UCS). Right: Location of maximum stress on deposition holeperimeter. Both figures are obtained from Figure 9-8 in /Hökmark et al. 2010/.

Radial, y-dir

Vertical ortangential, x-dir

Figure 6‑57. Arrangement of the rock fragments and flow directions. Reproduced from /Neretnieks andAndersson 2009/.

Hydraulic conductivity/Neretnieks and Andersson 2009/ addressed whether the hydraulic conductivity of the damaged zone
could be so small that the water flowing in the intersecting fracture would prefer to flow around thedamaged zone instead of flowing through it. Their results suggest that the water would prefer to flow
through the zone unless the conductivity could be made one or more orders of magnitude smaller.
SKB has conducted field tests at Äspö HRL /Glamheden et al. 2010/ to assess the potential for
mitigating spalling by adding small counter pressures. However, while the project supports thepossibility that the counter pressure exerted by bentonite pellets in the gap between buffer and rockwall may suppress the spalling, or at least keep the spalled slabs in place and by this minimize thehydraulic conductivity of the spalled damaged zone, current results are inconclusive.
Based on current knowledge and techniques, the hydraulic conductivity of the spalled zone is suggested to be “high”.

316

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The connected effective transmissivity T (m2/s) of the deposition hole EDZ
Findings from the comprehensive literature study /Bäckblom 2008/ support that for mechanicalexcavation techniques in elastic rock conditions, that is full face down-hole drilling, the depth ofdamaged zone (EDZ) is limited to a few centimetres in the rock surrounding the deposition hole.
As a result, connected effective transmissivity will be governed by the natural fractures intersecting
the deposition holes, and by the fact that the intersecting fractures have a sufficiently low connectedtransmissivity.
As stated in the Underground opening construction report, there is high confidence that elasticrock conditions prevail for the reference design, and consequently that the transmissivity associatedwith EDZ in deposition holes would be less than 10–10 m2/s. This is under the pre-requisite that nospalling occurs.

6.5.11 Judgements by the SR-Site teamSources of information and documentation of data qualification

The references cited in the data qualification are judged to be relevant and sufficiently exhaustive.
The data sets provided are judged to be adequately categorised into qualified and supporting data sets.
Conditions for which data are supplied

The conditions for which (modelled) data are supplied are relevant for SR-Site modelling.
Conceptual and data uncertainties

The sources of uncertainty of all parameters are adequately described and quantified.
Spatial and temporal variability of data

The spatial and temporal variability of all parameters are adequately described and quantified.
Correlations

The correlations used in the data qualification are adequately described. Uncertainties regardingthese are described in Section 6.4.
Result of supplier’s data qualification

Generally, the SR-team agrees with the suppliers input. However, some additional reasoning isneeded in order to complete the data set needed. This is provided in the following.
Hydraulic properties along tunnel walls and floor
The information presented in previous sections show there is ample evidence that the fracturesinduced by the construction work will not result in a connected zone along the tunnel, with transmissivities above the maximum allowed transmissivity, as set out by the design premises. In fact, datasuggests that a continuous EDZ would not develop at all. However, given that the occurrence of the
EDZ currently can only be assessed by indirect measurements, it seems justified to consider an EDZ
according to the design premises, that is with an axial transmissivity of 10–8 m2/s as a basic assumption for further analyses. This is suggested for deposition tunnels, as well as for other tunnels.
It also seems justified to explore how transmissive an EDZ need to be in order to significantly
impact other safety functions, as well as exploring the impact of no axially continuous EDZ at all.
Therefore, the following variants values on axial transmissivity are suggested for the EDZ: 0, 10–7,
and 10–6 m2/s. There variants are suggested for the deposition tunnels, as well as for other tunnels.

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317

The analyses of /Hökmark et al. 2010/ shows that reactivation of fractures caused by the stressredistribution only results in insignificant increases of transmissivity in near-field fractures, apartfrom very locally close to the tunnel. These limited fields of increased transmissivity will have little
importance, unless the fracture is located close to the tunnel floor and approximately parallel to it.
However, in reality fractures will not be persistently parallel to the tunnel where there are utilised
deposition holes, as result of the EFPC criterion. The SR-Site team agree with the supplier that thiseffect can be discarded, or at least captured within the EDZ assumption of 10–8 m2/s along the tunnel.
Hydraulic properties in deposition hole wall
If spalling of the deposition hole would not occur, available data and experience described in previous sections support that using mechanical excavation for the deposition holes would at most resultin excavation induced fracturing with a zone of enhanced conductivity in the order of 10–10 m/s,
/Bäckblom 2008/. However, the analyses presented by /Hökmark et al. 2010/ suggest that fracturingof the rock, that is thermally induced spalling, is likely to occur. Furthermore, since their modellingshows that the spalling strength would be exceeded to a maximum of 7.9 m depth (cf. Figure 6‑56),
setting the length of the spalled zone Lzone to 8 m seems to be a valid overestimate. The dimensionsof the notch could be taken from the APSE values Wzone = 0.5 m wide and dzone = 0.1 m thick.
While the study by /Glamheden et al. 2010/ suggest that counter pressure exerted by bentonite
pellets in the gap between buffer and rock wall may suppress the spalling, or at least keep the spalledslabs in place, and by this minimize the hydraulic transmissivity of the spalled damage zone, there
currently seems to be too little support to suggest that the groundwater will not preferentially flow inthe spalled zone. This will thus be a base case, although a variant case should consider that the counter pressure is sufficient to make it possible to discard the impact of spalling. For the spalling case,data on tortuosity can be taken from /Neretnieks and Andersson 2009/, that is τy = 10 and τy2 = 100(the squared value should be used in Fick’s second law of diffusion in solute transport modelling).
The SR-Site team also agrees with the judgement made by /Neretnieks et al. 2010/ that a porosity
value of εzone = 0.02 for the spalled zone can be justified.
In short, for SR-Site, it is judged necessary to assume that thermally induced spalling is likely tooccur, and this needs to be considered when assessing the migration across the rock-buffer interface.
This does not mean that efforts to handle and mitigate thermally induced spalling should not continue,
since suppressing the spalling will limit potential radionuclide transport from the buffer to thefracture water.

6.5.12 Data recommended for use in SR-Site modelling
Hydraulic properties along tunnel walls and floor

Table 6‑68 lists the recommended axial transmissivity value along tunnel walls. The value is basedon the value given as a design premises. However, some variant transmissivity values are also suggested
as input for exploring how transmissive an EDZ need to be in order to significantly impact othersafety functions, as well as exploring the impact of no axially continuous EDZ at all.
Table 6‑68. Recommended axial transmissivity data along deposition tunnels, other tunnels,
and shaft.
Parameter

Best estimate value

Variant values

Tunnel and shaft EDZ transmissivity

10−8 m2/s

0, 10–7, 10–6 m2/s

Hydraulic properties in deposition hole wall

Table 6‑69 lists recommended hydraulic data along deposition hole walls, and properties of the spalledzone. Two variants are recommended, assuming no spalling or spalling in all deposition holes.

318

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Table 6‑69. Recommended data for deposition hole walls.
Parameter

Best estimate value

Variant values

Potential for spallingHigh

No spalling

Length of spalled zone, Lzone

8m

No spalling

Thickness of spalled zone, dzone

0.1 m

No spalling

Width of spalled zone, Wzone

0.5 m

No spalling

Tortuosity of spalled zone, τy

10

No spalling

Spalled zone porosity, εzone

0.02

No spalling

Hydraulic conductivity of spalled zone, K

High

No spalling

Connected effective transmissivity of deposition hole wall
EDZ, in holes where no spalling occurs

10–10 m2/s–

6.6

Quantities for groundwater flow modelling

The use of hydrogeological models in SR-Site allows for simulations of groundwater flow from a
deep disposal facility to the biosphere. It also allows for the calculation of performance measuresthat will provide an input to the safety assessment.
This section provides site-specific data of the quantities associated with the stochastic fracture
network realisations for use in the Discrete Fracture Network (DFN) and Equivalent Continuous
Porous Medium (ECPM) groundwater flow modelling. It also provides the homogeneous flow andtransport properties for use in the Continuous Porous Medium (CPM) groundwater flow modelling.
This section only concerns quantities and properties of the rock mass volumes found in between thedeterministically modelled deformation zones. The flow and transport properties of the latter, as well
as of the regolith found on top of the rockhead, are given in /Selroos and Follin 2010/. The usage offlow related migration parameters in SR-Site is described in Section 6.7.

6.6.1 Modelling in SR-Site
In SR-Site, groundwater flow in the repository host rock is conceptualised to occur in a discrete
fracture network. Hydrogeological DFN models explicitly model the fractures through which thegroundwater flows and are characterised by quantities associated with these fractures such as orientation, size, intensity, transmissivity, and aperture. Hydrogeological DFN modelling invokes Monte
Carlo simulations (multiple realisations) as the fracture quantities are described statistically.
In order to assess the implications of DFN models on flow and transport on scales larger than thatencompassed by the repository host rock, it is often necessary for practical reasons to convert DFN
models to models with appropriate continuum flow and transport properties such as hydraulicconductivity, fracture kinematic porosity, and flow wetted fracture surface area per unit volume of rock.
•On a regional model scale, that is in rock mass volumes far away from the repository, the classic
continuous porous medium approach with homogeneous flow and transport properties is used.

•

On an intermediate model scale, that is on a site scale, the equivalent continuous porous medium
approach with heterogeneous flow and transport properties is used in SR-Site. The ECPM properties are derived by means of up-scaling the underlying DFN realisation /Jackson et al. 2000/.
Since each ECPM model is based on a particular underlying stochastic DFN realisation, the flow
and transport properties of the ECPM models are also stochastic. That is, uncertainties relating to
the spatial variability are quantified by means of multiple realisations.

In conclusion, in SR-Site, groundwater flow modelling on the repository and site scales is performed
using the DFN and ECPM approaches, respectively. On a regional scale, groundwater flow modelling is performed using the CPM approach. The rational for using this mixture of flow concepts in
SR-Site is summarised in /Selroos and Follin 2010/.

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319

Defining the data requested from the supplier

The following quantities associated with the generation of stochastic fracture network realisations
for use in the DFN and ECPM groundwater flow modelling are requested from the supplier:•

The fracture orientation model statistics for each suggested fracture set, in terms of the Fisher
distribution mean pole trend (°), plunge (°) and concentration κ (–).

•

The fracture size model statistics for each suggested fracture set in terms of the Pareto distribution location parameter r0 (m) and shape parameter kr (–).

•

Statistics for the fracture intensity model, in terms of the open fracture surface intensity per unit
volume of rock P32,o[r0,564 m] (m2/m3), for each suggested fracture set. The largest fractureradius considered for these data should be 564.2 m (cf. Section 6.3).

•

The transmissivity model constants a, b, and σlogT, for a semi-correlated transmissivity model,
constants a and b for a correlated transmissivity model, and constants μlogT and σlogT for an uncorrelated transmissivity model.

•

The aperture model constants a and b for a correlated aperture model.

All these constants should be given for each suggested fracture set. Judged as appropriate by the supplier, different fracture sets should be suggested for different rock mass volumes of the host rock. In
addition, the repository system should be taken into account, in case it affects how discrete fractures
are connected.
The following quantities for use in the CPM flow modelling are requested from the supplier:•

The hydraulic conductivity K (m/s) of the rock mass volume.

•

The kinematic porosity ne (–) of the rock mass volume.

•

The fracture surface area per unit volume ar (m2/m3) of the rock mass volume.

Judged as appropriate by the supplier, different data should be given for different rock mass volumes
and may include potential depth trends.
SR-Site models in which data will be used

Groundwater flow modelling in SR-Site is performed with the purpose of:•Estimating the inflow rate to the repository and the potential for upconing of saline groundwater
during open repository conditions.

•

Estimating buffer and backfill saturation times after closure of open repository conditions.

•

Assessing exit locations of downstream flow paths as well as entrance locations of upstream flow
paths starting at different deposition hole positions, and their associated flow-related transport
properties such as Darcy fluxes q at repository depth and advective travel times tw and transport
resistances F during saturated temperate, periglacial, or glacial conditions.

•

Delivering input data to hydrogeochemical modelling during saturated temperate, periglacial, andglacial conditions. In particular, assessing the potential for increased flow rates in the fractured
bedrock at repository depth and upstream transport of dilute and/or oxygenated water fromground surface to repository depth during periglacial and glacial conditions.

The following groundwater flow modelling studies are performed in SR-Site:•

Excavation and operation phases: During the excavation and operation phases of the repository,
the system is characterised by the tunnels being at atmospheric pressure. Also, the saturation
phase of the repository is included in this period, when the backfilled tunnels go from unsaturated
to fully saturated conditions. It should be noted that saturation will also occur after repository closure. Accordingly, this period is somewhat extended in hydrogeological modelling, as compared
to what is described as the excavation and operation periods in other disciplines. Simulationsof the excavation and operation periods are performed by /Svensson and Follin 2010/ using
the DarcyTools code /Svensson et al. 2010/. For the inflow calculations, saturated groundwaterflow below the groundwater table, and a simplified description of unsaturated flow above the320

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groundwater table, are used. The DarcyTools code can also handle the saturation process, i.e.,
two-phase (air-water) flow, in a simplified manner /Enssle and Poppei 2010/. Additional simulations of near-surface effects are done using the hydrogeological modelling tool MIKE-SHE/Mårtensson and Gustafsson 2010/. The objective of these latter studies is primarily to assess theinteraction between the near-surface and deep rock groundwater systems. Also, the near-surface
modelling provides information to Environmental Impact Assessment studies conducted outsideof the SR-Site modelling.
•

Periods with temperate climate conditions: The simulations that deal with temperate climate
conditions address an approximately 10,000 year long period, extending from repository
saturation up till the initiation of the next permafrost-glaciation event. Saturated groundwaterflow is modelled including the shoreline displacement as a transient boundary condition. The
backfilled tunnels are explicitly included in the models that are based on a nesting of different
scales (from regional to site to canister scales) using a mixture of continuum and discrete fracture
network representations of the rock mass. Simulations of the temperate period are performed
by /Joyce et al. 2010/ using the ConnectFlow code /Hartley and Holton 2004/. The strategy for
identifying the most important and/or critical hydrogeological variants to be addressed in thetemperate period simulations is to propagate a number of SDM-Site variants to the regional scale
simulations. These SR-Site simulations differ from the SDM-Site calculations in that they cover
the time period from the end of the last glaciation up till the onset of the next glacial period,
whereas the SDM-Site models end at present day conditions. The variants with greatest impact on
performance measures are propagated to detailed site and repository scale calculations involving
an explicit hydrogeological DFN representation.

•

Periods with periglacial and glacial climate conditions: The main objective of the periglacial
and glacial simulations is to assess the groundwater flow pattern during periods when the upper
part of the geosphere may be frozen, thus restricting flow, and/or when a glacial load (glaciation
advance and retreat) may imply different recharge and stress conditions. Input is obtained froman ice-sheet model that provides the glacial thickness (cf. Section 7.1). A second objective is
to study the possible movement of salt due to up-coning during glacial conditions. Simulationsof the permafrost and glacial conditions are performed by /Vidstrand et al. 2010/ using theDarcyTools code. The modelling strategy is to identify important parameter combinations ofclimatological and rock property conditions, and then implement 3-D models on a large, supraregional scale. Initial and boundary conditions from these simulations have later been transferredto the site and repository scale ConnectFlow models for detailed analyses of flow and solutetransport.

6.6.2 Experience from SR-Can
This section briefly summarises experiences from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The hydrogeological modelling performed as part of SR-Can was very similar to what is outlined
above. The main difference concerns the periglacial and glacial simulations. Due to the limited scopeof SR-Can, no complete permafrost/glaciation simulation was performed. Only one DarcyTools
application addressing freeze-out processes of salt, and one ConnectFlow application addressing atransient ice sheet advance and retreat, were studied. These applications were based on the preliminary site-description model versions Simpevarp 1.1 and 1.2 /Follin et al. 2004, Hartley et al. 2005/,
which were earlier versions than those used in SR-Can in the remaining applications.
The suggested approach for SR-Site is intended to rectify these shortcomings associated with
SR-Can.

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Conditions for which data were used in SR-Can

In SR-Can, data were supplied for the present day conditions prevailing at the sites, as described in
the preliminary site investigation reports /SKB 2005b, 2006/. The data were thus relevant for the
excavation and operation periods and the following temperate period. However, for the anticipatedperiglacial and glacial periods, the SR-Can team had to hypothesise if, and how, hydrogeologicalconditions and properties were to be modified. Furthermore, note that the two special periglacial andglacial modelling applications referenced above were based on the earlier preliminary site-description model versions Simpevarp 1.1 and 1.2. Thus, the use of this data was less straightforward.
Sensitivity to assessment results in SR-Can

Sensitivities were explored by assessing a number of variants. These variants were primarily basedon the variants identified in the preliminary site-descriptive modelling, and were then propagatedthrough SR-Can. In addition, new variants deemed important for the safety assessment, but notnecessarily originating from the SDM, were added. The variants addressed both issues related toconceptual understanding and issues related to lack of knowledge (uncertainty). An example of theformer was the additional Forsmark case where the rock mass was described as a low permeable
homogeneous continuum (CPM model) within large sub-volumes (the base case utilised a DFN
description characterised by a poorly connected fracture network with low fracture frequency
but fairly high-transmissive features). Examples of the latter variant were cases where additional
deformation zones were included.
The conceptual choice of a CPM or DFN representation of the bedrock in Forsmark had the greatest
impact (sensitivity) on end points (dose) in the safety assessment. Within the DFN conceptualisation,
the assumption concerning relationship between fracture size and transmissivity had a large impact
on performance measures.
Alternative modelling in SR-Can

For the Forsmark site, a multi-component CPM model (instead of the explicit DFN representation)
was used as an alternative conceptual description of the site. This model was justified by the fact thatthe HRD bedrock is very sparsely fractured, and thus was hypothesised to be well represented bycontinuum properties. It is noted that this hypothesis later has been abandoned.
Correlations used in SR-Can modelling

The main correlations utilised were the correlations between fracture size and fracture transmissivity(uncorrelated, semi-correlated, and perfectly correlated), and the correlation (functional relationship)
between fracture aperture and transmissivity.
Identified limitations of the data used in SR-Can modelling

A major limitation with the data used in SR-Can relates to paucity of data; that is, the data stemmed
from the initial site investigation phase only, and were thus by necessity limited in amount.
In the regulatory authorities review of SR-Can /Dverstorp and Strömberg 2008/, issues specificallyconcerning hydrogeological modelling are discussed in Sections 7.4 and 12.3. Out of the issues
discussed, the following deserves mentioning (with direct bearing on this present section). The
authorities requested a more detailed discussion on the relations between fracture transmissivity
and fracture size and aperture.
In more general terms concerning hydrogeological modelling, the regulatory authorities requested
a better link between modelled and measured data (including a more careful description on how themodels are calibrated). Examples given of measured and modelled data that can be compared are
groundwater flow, pressure, and salinity. Also more detailed descriptions of modelling at permafrost
and glaciation, and associated conditions, were requested.

322

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6.6.3 Supplier input on use of data in SR-Site and SR-Can
This section presents data based on site-descriptive modelling activates presented in the Sitedescription Forsmark and references therein. However, hydrogeological DFN data taking therepository into account are requested in SR-Site. As the repository layout was not part of the sitedescriptive modelling, the fracture size distribution has been modified in the groundwater flow
modelling activities being part of SR-Site, in order to account for the repository system impact
on how discrete fractures are connected. Therefore, the separation of the supplier and customer
is not clear-cut. Even so, we use the supplier/customer terminology requested in the instruction(c.f. Section 2.3).
The supplier notes that considerable effort has been devoted to correcting the shortcomings of SR-Can.
Much of the information originating from the site descriptions have been used as inputs for improving
SR-Site models. Thus the input from the supplier has already been implemented. It is also worth
mentioning that an improved description of the climatic evolution (cf. the Climate report) has beenused to improve SR-Site modelling during periods with periglacial and glacial climate conditions.

6.6.4

Sources of information and documentation of data qualification

Sources of information

The main sources of information are listed in Table 6‑70. The publications referred to provide
important input to the groundwater flow modelling studies carried out in SR-Site, see Section 6.6.1.
The Site description Forsmark summarises the results of a chain of modelling activities in several
disciplines. Table 6‑71 details the stages and produced documents within the hydrogeology partof this modelling chain. The upper part of the table shows the stages and the cumulative numberof cored boreholes providing geometrical and hydraulic information about the fractured bedrock
at Forsmark surrounding the future repository (see Section 1.5 for maps that show the locations of
the boreholes). Table 6‑71 also provides references of the background hydrogeological modelling
reports, as well as the reports with geometrical and hydraulic data on the flowing fractures detectedwith the Posiva Flow Log (PFL). The information is shown in relation to the three model versions
and the three modelling stages carried out in preparation of the Site description Forsmark.
/Follin 2008/ is the key reference document on bedrock hydrogeology at Forsmark and summarises
the results of a chain of groundwater flow modelling activities carried out on behalf of the Sitedescription Forsmark, see Table 6‑71.
/SKB 2009a/ provides the geometry of the repository system that is taken into account in the analysesof how discrete fractures are connected to the repository system. /Joyce et al. 2010/ provide a
detailed description of these analyses.
The Climate report provides a description of past and future climate conditions. The understandingof historic data and the suggested reference evolution of the future are used in the groundwater flow
modelling study that handles periods with temperate climate conditions /Joyce et al. 2010/ and in the
study that handles periglacial and glacial climate conditions /Vidstrand et al. 2010/.
/Selroos and Follin 2010/ provides a description of the chosen methodology for groundwater flow
modelling in SR-Site; a specification of the setup of the three groundwater flow modelling studies
described in subsection 6.6.1; and a summary of the simulation results from these studies.
Table 6‑70. Main sources of information used in data qualification.
Climate report, 2010. Climate and climate-related issues for the safety assessment SR-Site. SKB TR-10-49,
Svensk Kärnbränslehantering AB.
Site description Forsmark, 2008. Site description of Forsmark at completion of the site investigation phase.
SDM-Site Forsmark.
SKB TR-08-05, Svensk Kärnbränslehantering AB.
Follin S, 2008. Bedrock hydrogeology Forsmark. SKB R-08-95, Svensk Kärnbränslehantering AB.
SKB, 2009a. Underground design Forsmark. Layout D2. SKB R-08-116, Svensk Kärnbränslehantering AB.
Selroos J-O, Follin S, 2010. SR-Site groundwater flow modelling methodology, setup and results. SKB R-09-22,
Svensk Kärnbränslehantering AB.

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323

Table 6‑71.The cumulative number (and percentage) of cored boreholes (KFM) and their cumulative borehole length (km) providing geometrical and hydraulic information about the bedrock
at Forsmark at the end of each of the three model versions and three model stages carried out
for SDM-Site. Starting with the underlined report numbers, these present correlation analysesof geometrical and hydraulic data of the flowing fractures detected with the Posiva Flow Log
method. The report numbers typed in italics describe the hydraulic data gathered and thehydrogeological modelling undertaken. Finally, the report numbers typed in bold summarisethe development of the hydrogeological modelling achieved at each stage.
Initial site investigation (ISI)

Complete site investigation (CSI)

Desk top
exerciseTraining
exercise

Preliminary
SDM

Feedback andstrategy

Hydrogeologicalmodel

Model verification anduncertainty assessment

Version 0

Version 1.1

Version 1.2

Stage 2.1

Stage 2.2

Stage 2.3

0 KFM (0%)
S length: 0 km

1 KFM (4%)
S length: 1 km

5 KFM (21%)
S length: 5 km

9 KFM (38%)
S length: 7 km

20 KFM (83%)
S length: 15.9 km

25 KFM (100%)
S length: 19.4 km

R-04-15

R-05-18

R-06-38

R-07-49

R-08-95

R-05-32
R-05-60
R-04-77

R-07-20P-06-56

R-07-48
P-07-127

R-08-23
P-07-128

R-02-32

R-04-77

R-02-32: SKB, 2002. Forsmark – site descriptive model version 0. SKB R-02-32, Svensk Kärnbränslehantering AB.
R-04-15: SKB, 2004b. Preliminary site description. Forsmark area – version 1.1. SKB R-04-15,

Svensk Kärnbränsle­hantering AB.

R-04-77: Forssman I, Zetterlund M, Rhén I, 2004. Correlation of Posiva flow log anomalies to core mappedfeatures in Forsmark (KFM01A to KFM05A). SKB R-04-77, Svensk Kärnbränslehantering AB.

R-05-18: SKB, 2005b. Preliminary site description. Forsmark area – version 1.2. SKB R-05-18,

Svensk Kärnbränsle­hantering AB.

R-05-32: Hartley L J, Cox I, Hunter F, Jackson C P, Joyce S, Swift B, Gylling B, Marsic N, 2005. Regionalhydrogeological simulations for Forsmark – numerical modelling using CONNECTFLOW. Preliminary site
description. Forsmark area – stage 1.2. SKB R‑05-32, Svensk Kärnbränslehantering AB.
R-05-60: Follin S, Stigsson M. Svensson U, 2005. Regional hydrogeological simulations for Forsmark – numericalmodelling using DarcyTools. Preliminary site description. Forsmark area – version 1.2. SKB R-05-60, Svensk
Kärnbränslehantering AB.
R-06-38: SKB, 2006g. Site descriptive modelling Forsmark stage 2.1. Feedback for completion of the site investigation
including from safety assessment and repository engineering. SKB R-06-38, Svensk Kärnbränslehantering AB.
R-07-20: Follin S, Johansson P-O, Levén J, Hartley L, Holton D, McCarthy R, Roberts D, 2007a. Updated strategy andtest of new concepts for groundwater flow modelling in Forsmark in preparation of site descriptive modelling stage 2.2.
SKB R-07-20, Svensk Kärnbränslehantering AB.
P-06-56: Forssman I, Zetterlund M, Forsmark T, Rhén I, 2006. Forsmark site investigation. Correlation of Posiva Flow
Log anomalies to core mapped features in KFM06A and KFM07A. SKB P-06-56, Svensk Kärnbränslehantering AB.
R-07-49: Follin S, Johansson P-O, Hartley L, Jackson P, Roberts D, Marsic N, 2007c. Hydrogeological conceptual model
development and numerical modelling using CONNECTFLOW, Forsmark modelling stage 2.2. SKB R-07-49, Svensk
Kärnbränslehantering AB.
R-07-48: Follin S, Levén J, Hartley L, Jackson P, Joyce S, Roberts D, Swift B, 2007b. Hydrogeological characterisationand modelling of deformation zones and fracture domains, Forsmark modelling stage 2.2. SKB R-07-48, Svensk
Kärnbränslehantering AB.
P-07-127: Teurneau B, Forsmark T, Forssman I, Rhén I, Zinn E, 2008. Forsmark site investigation. Correlation of Posiva
Flow Log anomalies to core mapped features in KFM01D, KFM07C, KFM08A, KFM08C and KFM10A. SKB P-07-127,
Svensk Kärnbränslehantering AB.
R-08-95: Follin S, 2008. Bedrock hydrogeology Forsmark. Site descriptive modelling, SDM-Site Forsmark.
SKB R-08-95, Svensk Kärnbränslehantering AB.
R-08-23: Follin S, Hartley L, Jackson P, Roberts D, Marsic N, 2007. Hydrogeological conceptual model development andnumerical modelling using CONNECTFLOW, Forsmark modelling stage 2.3. SKB R-08-23, Svensk Kärnbränslehantering AB.
P-07-128: Forssman I, Forsmark T, Rhén I, 2008. Forsmark site investigation. Correlation of Posiva Flow Log anomalies
to core mapped features in KFM02B, KFM08D and KFM11A. SKB P-07-128, Svensk Kärnbränslehantering AB.

324

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Categorising data sets as qualified or supporting data

The data requested in this section were produced during the chain of site-descriptive modelling
activities detailed above, but also in SR-Site activities taking into account the repository layout.
Most data sets are reproduced from tables or equivalents of background reports, which arereferenced in Table 6‑72. All data sets that are produced for the Forsmark site within site-descriptivemodelling or SR-Site activates are considered as qualified. The sorting of the data sets into qualified
or supporting is justified in Table 6‑73.
Excluded data previously considered as important

Since SR-Can, no data have been excluded which have had a significant impact on the perceptionof the appropriate choice of data value, or modelling approach, of the rock mass volumes found in
between the deterministically modelled deformation zones.
Table 6‑72. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Selroos and Follin 2010/, Table 2-2: The fractureorientation, size, and intensity statistics, as well as thefracture transmissivity model constants for the fractionof the repository rock mass volume coinciding with thefracture domains FFM01 and FFM06
2. /Selroos and Follin 2010/, Table 2-3: As in 1, but for
the fraction of repository rock mass volume coinciding
with the fracture domain FFM02
3. /Selroos and Follin 2010/, Table 2-4: As in 1, but forthe site-scale rock mass volume coinciding with thefracture domains FFM03, FFM04 and FFM05.

4. /Selroos and Follin 2010/, Figure 5-19: The fracture
aperture model constants for the fraction of the repositoryrock mass volume coinciding with the fracture domains
FFM01–06
5. /Selroos and Follin 2010/, Table 2-6: The hydraulicconductivity, the kinematic porosity, and the flow wetted
fracture surface area per unit volume of rock of the rockmass volumes far away from the repository host rockmass volume (outside FFM01–FFM06)

Table 6‑73. Justification of the sorting of items in Table 6‑72.
1–3: For the repository host rock mass volume and the site-scale rock mass volume, the fracture orientation model statistics, the fracture size model statistics, the fracture intensity model statistics, and the fracture transmissivity model constants
coinciding with fracture domains FFM01 through FFM06 were derived and applied within the site-descriptive modellingthrough a chain of different modelling activities that were each peer reviewed. These modelling activities comply with the
SKB quality assurance system and thus the data are judged as qualified.
4: For the repository host rock mass volume and the site-scale rock mass volume, the fracture aperture model constants
coinciding with fracture domains FFM01 through FFM06 were derived at Äspö /Dershowitz et al. 2003/ and applied within
the site-descriptive modelling through a chain of different modelling activities that were each peer-reviewed. Although thesemodelling activities comply with the SKB quality assurance system, the model parameters are not based on investigations
at Forsmark. These data are therefore sorted as supporting.
5: For the bedrock outside the repository host rock mass volume and the site-scale rock mass volume, approximate CPM
values of the hydraulic conductivity were taken from hydraulic single-hole tests in deep boreholes at Finnsjön /Andersson
et al. 1991/ and applied in the site-descriptive modelling. By the same token, approximate CPM values of the kinematic
porosity and the flow-wetted fracture surface area per unit volume of rock are based on an expert judgement and appliedin the site-descriptive modelling /Follin et al. 2007c/. These data are therefore sorted as supporting.

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325

6.6.5 Conditions for which data are supplied
Six fracture domains, FFM01–FFM06, are defined in the geological DFN modelling based on
spatial differences in the fracture frequency of all fractures /Olofsson et al. 2007/. The six fracturedomains are refined in the hydrogeological DFN modelling based on the differences observed in thefrequency of flowing fractures versus depth, see /Follin 2008/ for details. In summary, three of thesix fracture domains were split into two sub-units each (FFM03–FFM05) and two fracture domains
were split into three sub-units each (FFM01 and FFM06). One fracture domain was left unchanged(FFM02). Figure 6‑58 shows two cartoons of the fracture domains at Forsmark that constitute therock mass volumes on the repository and site scales.
Structural-hydraulic data from twelve cored boreholes gathered during the site investigations at
Forsmark /Follin et al. 2007b/ are used in the hydrogeological DFN modelling in SR-Site. The
twelve boreholes have been drilled at different locations and in different orientations in the rockmass volumes surrounding the repository as shown in Figure 6‑59 and Figure 6‑60.
The structural data gathered in the twelve boreholes are analysed for each fracture domain sub-unit
based on the analyses of fracture frequency, the spatial arrangement of fractures, fracture orientation,and fracture size described in the geological DFN modelling:•

The Terzaghi corrected frequency P10,o,corr (L–1) and orientation strike, dip (°,°) of open fractures(including fractures mapped as partly open).

•

A Poisson process is assumed for the 3-D spatial arrangement of open fractures in each fracturedomain sub-unit. This assumption implies a Euclidian scaling of the fracture intensity within eachfracture domain sub-unit.

•

Five, global, Fisher orientation distributions f(mean pole trend, mean pole plunge, concentration)(°,°,–) are assumed for the open fractures.

•

The sizes r (L) of all fractures and open fractures are assumed to be Pareto distributed f(r0, kr)(L,–). The value of the location parameter r0 (L) is fixed to the borehole radius (rBH = 0.038m),
whereas the value of the shape parameter kr (–) varies between the fracture sets and the fracturedomains; for FFM02 one set of kr values is used, for FFM01 and FFM06 another set of values isused, and for FFM03–FFM05 a third set of values is used.

It is recommended to read Section 6.3 of this Data report, where many issues of relevance for thequantities listed above are detailed. In addition to the analyses of the structural data, the hydraulicdata gathered in the twelve boreholes are analysed for each fracture domain sub-unit with regard to:•

The Terzaghi corrected frequency P10,pfl,corr (L–1) and orientation strike, dip (°,°) of flowing
fractures detected with the Posiva Flow Log method.

•

The specific capacity Q/Δh (L2T –1) of the flowing fractures detected with the PFL method during
pumping (Q is the inflow rate (L3T–1) and Δh is the applied drawdown (L), that is, change inpressure head Δp/(ρg), during the pumping).

326

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FFM02
below ZFMA2

1
file
2
file
Pro

Pro

Profile 1 (drill site 6)
Start of channeleast of nuclear
reactors 1-2

130 (SE)FFM02 (RFM029/RFM045)

34–500 m elevation

FFM06 (RFM045)

FFM03 (RFM029)

ZFMA

2

A
ZFMENE0062

32

M0

RF

M0

RF

FFM01
(RFM029)

E0060A

0m

Drill site 6

ZFMEN

310 (NW)

ZFMF1

FFM01 (RFM029)

FFM01 (RFM029)-1100 mProfile 2 (drill sites 8 and 2)
310 (NW)

Drill site 8

Drill site 2
130 (SE)FFM02 (RFM029)–500 m elevation

A

FFM03 (RFM029)
ZFMENE0062

0060A

32
M0
RF
34
M0
RF

FFM01 (RFM029)

ZFMENE0m

Channeleast ofnuclearreactors 1-2

ZFMA

2
ZFMF1

FFM01 (RFM029)-1100 mFigure 6‑58. Cartoons showing the fracture domains at Forsmark that constitute the bedrock surrounding
the repository. The key fracture domains forming the repository host rock are labelled FFM02, FFM01, andFFM06. Labels beginning with ZFM are names of major deformation zones. Reproduced from Figure 3-11
in /Follin 2008/ and Figure 5-4 in /Olofsson et al. 2007/.

TR-10-52327

Figure 6‑59. Three-dimensional view towards the east-north-east showing the cored boreholes drilled into
the fracture domain model. The key fracture domains are labelled FFM02, FFM01 and FFM06. Labels beginning with ZFM are names of major deformation zones. Reproduced from Figure 5-7 in /Olofsson et al. 2007/.

Figure 6‑60. X-ray view through the regolith above the rock mass volumes surrounding the repository. The
boreholes shown are all hydraulically investigated with the Posiva Flow Log method. The labels represent
drill site numbers (1–10), fracture domains (FFM01 and FFM06) and deformation zones (NE0060A andNE0062A). Here, fracture domain FFM01 is made transparent, whereas FFM06 has a brownish colour.
Reproduced from Figure 4-1 in /Follin et al. 2008/.

328

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6.6.6 Conceptual uncertainty
With reference to the geological and hydraulic data analyses presented in Section 6.6.5, several
working hypotheses are invoked in the subsequent hydrogeological DFN modelling, see /Follin
2008/ for details. Three key assumptions in SR-Site are:
1. The fracture surface area per unit volume of rock of all fractures greater than the boreholeradius P32,a[r ≥ rBH] can be estimated from the Terzaghi corrected linear (1‑D) borehole fracture
frequency of all fractures:
P32,a[r ≥ rBH] ≥ P10,a, corr

6‑13

By the same token, it is assumed that for the open fractures, that:
P32,o[r ≥ rBH] ≥ P10,o,corr

6‑14

2. The fracture surface area per unit volume of rock (fracture intensity) of all fractures (a) is greaterthan the fracture intensity of open fractures (o), which in turn is greater than the fracture intensityof connected open fractures (cof), which in turn is greater than the measured intensity of flowing
fractures detected with the PFL method (pfl):
P32,a ≥ P32,o ≥ P32,cof ≥ P32,pfl

6‑15

3. The sizes of all and open fractures, from the smallest fractures to the largest, are power-law
distributed (tectonic continuum).
The second and third working hypotheses are illustrated in Figure 6‑61. It is noted that it is theintensity of open fractures (P32,o) that is used in the hydrogeological DFN modelling to generate
the fracture networks.
Another key assumption in SR-Site is the assignment of fracture transmissivity. The quantitative
calibration of fracture transmissivity is attempted for three different size-transmissivity correlation
models, T = T(r), see Table 6‑74. Ten Monte-Carlo realisations are studied for each set of parametervalues as a mean to address variability between realisations. For each transmissivity model andhydrogeological DFN realisation a pumping test simulation is carried out.
As a means to assess the ‘goodness of fit’ for the tested fracture transmissivity models, the following
statistics are calculated:•

Average total flow rate to the simulated abstraction borehole over ten realisations.

•

Histogram of log(Q/Δh) to the simulated abstraction borehole as an average over ten realisations.

•

Bar and whisker plot of minimum, mean minus standard deviation, mean, mean plus standard
deviation, maximum of log(Q/Δh) to the simulated abstraction borehole within each fracture set
taken over all realisations.

•

The average numbers of flowing fractures within each fracture set giving specific capacities tothe simulated abstraction borehole above the measurement limit of the PFL method.

Table 6‑74. Transmissivity models used for hydrogeological DFN modelling.
Type

Description

Relationship

Parameters

Correlated

Power-law relationshiplog(T) = log(a·r )a,bUncorrelated

Log-normal distribution about
a specified meanlog(T) = μ log(T) + σ log(T) N(0,1)

μ log(T) , σ log(T)Semi-correlated

Log-normal distribution abouta power-law correlated meanlog(T) = log(a·rb) + σ log(T) N(0,1)

a , b, σ log(T)TR-10-52

b

329

all

P32,o[r>rZFM] ≤P32,a[r>rZFM] →(kr,o)min

Feature intensity
log(P32[r>ri]) [m2/m3]

P32,cof[r>rBH] ≥P32,pfl[r>rBH] →(kr,o)max

open
1
cof

(k )

r,o 1

2
(k )

r,o 2

Lower (Q/s)lim

pfl

kr,o

Higher (Q/s)lim

rBH

kr,a

Feature radiuslog(r) [m]rZFM

Figure 6‑61. Cartoon showing the working hypothesis of tectonic continua for all fractures and for open
fractures, respectively. Given this hypothesis, the possible minimum and maximum limits of the shapeparameter for the open fractures, (kr,o)min and (kr,o)min, can be defined as shown. The notation used is
explained in the text.

6.6.7 Data uncertainty due to precision, bias, and representativity
The heterogeneous nature of the flowing fractures in cored boreholes creates uncertainties in thehydrogeological understanding and modelling. To mitigate these uncertainties, a large numberof cored boreholes were drilled at different locations and orientations in the fractured bedrock at
Forsmark surrounding the future repository as shown in Figure 6‑59 and Figure 6‑60. Most of thecored boreholes were hydraulically investigated with two types of test methods, in order to better
understand the spatial differences in the near-field and far-field hydraulic properties. The two test
methods employed were:•

Difference flow logging with the Posiva Flow Log method during long-term pumping conditions.

•Short-term double-packer injection tests with the Pipe String System (PSS) method.

The shortest length of the borehole interval tested is 0.1 m with the PFL method and 5 m with the
PSS method. This difference in spatial resolution affects the interpretation of the measured frequencyof flow anomalies, P10,pfl, in particular where this frequency is high (small spacing). This is the mostimportant reason why the PFL method was selected as the major single-hole hydraulic test method in
SDM-Site.
Data uncertainty due to precision

The cored boreholes have length markers in the borehole wall every 50 m. The maximum uncertainty in position along the borehole of a flow anomaly detected with the PFL method varies withthe distance from the length markers. Close to the length markers, the maximum error is c. ±0.2 m.
Between the length markers, the maximum possible uncertainty in position is c. ±0.3 m /Forssman
et al. 2004, 2006, 2008, Teurneau et al. 2008/. The uncertainty in position affects the certainty in theassignment of geometrical properties to the flow anomalies, see below.
The lower detection limit of the specific capacity Q/Δh (L2T –1) is of the order of 1·10–9 m2/s (for bothtest methods) /Ludvigson et al. 2002, Follin et al. 2007b/. The magnitude of the lower detection limit
affects the measured frequency of flow anomalies, P10,pfl, thus the position of the blue point shown in
Figure 6‑61.
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Data uncertainty due to bias

The uncertainty in determining the exact positions along the borehole of the flow anomalies detectedwith the PFL method causes uncertainties in the determination of their geometrical properties (strikeand dip). The determination reported to SKB’s database Sicada honour these uncertainties. Thatis, the flow anomalies that have several options are assigned a Best Choice orientation and one (ormore) Alternative Choice orientations. The hydrogeological DFN modelling presented in the Sitedescription Forsmark was based on the Best Choice orientations.
Some examples of reasons that give rise to uncertainties in the geometrical property assignment are:•Sometimes several open fractures with different orientations are within ±0.3 m of the flow
anomaly detected with the PFL method.

•

Two or more flow anomalies detected with the PFL can sometimes be correlated to a single
geological feature, in particular if this is sub parallel with the borehole trajectory.

•

Occasionally, a partly open fracture is closer to the detected flow anomaly than the closest fully
open fracture. The orientation of a partly open fracture is considerably much more uncertain, inparticular if it is a partly intersecting open fracture.

The methodology used in SDM-Site to determine the geometrical properties of flow anomalies
detected with the PFL method in cored boreholes is described in detail for all investigated boreholes/Forssman et al. 2004, 2006, 2008, Teurneau et al. 2008/.
Data uncertainty due to representativity

In the general case, the constituent parameters measured during single-hole hydraulic tests in coredboreholes are the flow rate, Q (L3T–1), and the fluid pressure, p (ML–1T–2). Since these are correlated,
the parameter measured is the specific capacity, Q/Δp, which has the same dimension as transmissivity, T (L2T–1), if pressure is expressed as a hydraulic head, h (L).
The envisaged test conditions of the single-hole hydraulic testing with the PFL and PSS methods areshown in Figure 6‑62. The measured specific capacity is dependent on several important aspects,
among which the following ones are particularly noted:•

Qlimit (L3T–1); the lower measurement limit of the test method.

•

TBH (L2T–1); the transmissivity of the tested fracture intersecting the borehole. TBH can be affectedduring the drilling operations. For instance, the fracture can be clogged (positive skin) or stimulated(negative skin).

•C (–); the connectivity of the tested fracture to other fractures away from the borehole. Somefractures are isolated, or are a part of an isolated cluster of fractures. Others are well connected
and a part of the overall hydrological system.

•

T/S (L2T–1); the hydraulic diffusivity of the fracture system within the radius of influence.

•t (T); the duration of the hydraulic testing, i.e. the test time.

•ΔL (L); the length of the test interval (test section).

The PFL method uses a short test interval (0.1 m) under approximately steady state flow conditions(the pumping time before testing is 5,000–10,000 minutes). The spatial resolution of the PFL
method is suitable to study the frequency and magnitudes of discrete values of the specific capacity.
However, the long-term pumping implies that the data obtained from the PFL method can only be
used to evaluate the conductive fracture frequency of continuously flowing networks, for example
network situations like cases C–F in Figure 6‑62. Indeed, the PFL method cannot detect any flow
rates for situations such as in cases A and B, which represent single dead-end open fractures or
isolated clusters of connected open fractures (compartments). The “hydraulic choke” (or bottleneck)phenomenon shown in case C is important as it affects the magnitude of the specific inflow ratemeasured at the intercept. If the impact of the “hydraulic choke” is such that the flow rate into theborehole falls below the lower detection limit, the “hydraulic choke” affects the measured numberof flow anomalies contributing to the frequency, P10,pfl.

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331

Figure 6‑62. Cartoon showing a borehole with six different symbolic fracture network situations. These
are described in the text and referred cases A–F. The specific capacity, Q/Δp, measured in the borehole isdependent on several factors that are also described in the text. For the sake of clarity, the overall hydrogeological system is here indicated by a constant head boundary (CHB) suggesting a pseudo steady stateflow regime at long test times. Further, the borehole is rotated 90° to improve the readability. Reproduced
from Figure 2-5 in /Follin 2008/.

The PSS method uses a longer test interval (> 5 m) and a much shorter pumping time (20 minutes).
In effect, the PSS method has greater problems in distinguishing network situations like cases A–Bfrom network situations like cases C–F. This means that using data from the PSS method alone forthe hydrogeological DFN modelling could easily result in an over prediction of fracture connectivity
in the sparsely fractured bedrock in between the deformation zones.
Due to the differences in spatial resolution and test time, the hydrogeological DFN modelling carriedout for SDM-Site was based on data acquired with the PFL method. The results from the PFL measurements reported to Sicada are sometimes called fracture transmissivities. The conceptualisation of
the specific capacity of each flow anomaly detected with the PFL method as a fracture transmissivity
is based on the short test interval and Thiem’s classic formula for steady-state, saturated, radial
groundwater flow to a well in a porous medium /Thiem 1906/:
T = (Q/Δh) [ln(re/rBH) / (2π)]

6‑16where re is the radius of influence of the hydraulic test and rBH is the radius of the cored borehole.
The results from the PFL measurements reported to Sicada are based on the assumption that the ratioof re/rBH is constant along the borehole. The value of the ratio is chosen such that:
T = Q/Δh

6‑17

In other words, the values reported to Sicada are not fracture transmissivities of the intersecting
fractures, but test section specific capacities, where the intersecting flowing fractures are the last
fractures in a network of flowing fractures.
The “hydraulic choke phenomenon” is likely to affect the PFL measurements more than the PSS
measurements for the reasons described above. Hence, cross-plots of the specific capacities from thetwo test methods should allow for a judgment of how common the “hydraulic choke phenomenon”
is. Figure 6‑63 shows a cross-plot of PSS data versus PFL data from the hydraulic testing of borehole
KFM06A (the location of this borehole is shown in Figure 6‑60, drill site 6).
The specific capacities shown in Figure 6‑63 are in general lower for the PFL method than for PSS
method. Noteworthy, the look of the cross-plot shown in Figure 6‑63 is repeated for almost every
borehole investigated, which implies that the “hydraulic choke phenomenon” is quite common/Follin et al. 2007b/.
332

TR-10-52

Q/∆h PSS 5m interval data vs. Σ Q/∆h PFL-f in KFM06A

PSS Log(Q/∆h) at t ~ 20 min [m2/s]

-3.0
-4.0
-5.0
-6.0
-7.0
-8.0
-9.0
-10.0
-11.0
-12.0
-13.0
-13.0

-12.0

-11.0

-10.0

-9.0

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

Σ PFL-f Log(Q/∆h) at t ~ 10,000 min [m2/s]

Figure 6‑63. Cross-plot showing specific capacities after 20 min of injection (PSS) vs. specific inflow rates
after one week of pumping (PFL) in borehole KFM06A. Data from PSS test sections without PFL data areplotted to the left for an arbitrary low value on the abscissa. Reproduced from Figure 4-11 in /Follin et al.
2007b/.

In conclusion, the specific capacities of the intersecting features determined with the PFL method are
not fracture transmissivities, but specific capacities of “hydraulic chokes” in the connected networkof flowing fractures.
This characteristic affects the interpretations of the inferred orientations of the flowing fracturesintersecting the borehole detected with the PFL method, since the fractures causing the “hydraulicchokes” may have different “orientations” than the flowing fractures intersecting the boreholes.
Although the importance of hydraulic chokes for the distribution of measured specific capacities
is not looked at in detail, this important phenomenon is honoured in the methodology used in thehydrogeological DFN modelling, see Section 6.6.5. That is, the measured values of Q/Δh are notinterpreted as transmissivities of the intersecting fractures, but as test section specific capacities (cf.
Equation 6‑17. A forward modelling approach is used in the calibration process, where T values are
generated and assigned to the stochastic network of features according to the three transmissivity
models shown in Table 6‑74. The different parameters associated with each transmissivity model arethen altered until an acceptable match is reached for ten consecutive realisations using four different
objective functions, see the four bullets shown at the end of Section 6.6.6.

6.6.8

Spatial and temporal variability of data

Spatial variability of data

Figure 6‑64 shows Terzaghi corrected frequencies of open fractures and of the flowing fracturesdetected with the PFL method. The data come from the cored drilled boreholes at different locations
and orientations in the target volume, that is, FFM02, FFM01, and FFM06, see Figure 6‑59 and
Figure 6‑60. The thicker lines represent average values over all boreholes and the dotted lines represent the spread between individual boreholes, that is, the minimum and maximum values observedin any borehole. Figure 6‑65 shows specific capacities of the flowing fractures detected with the
PFL method. Again, the thicker line represents the geometric mean over all boreholes and the dottedlines represent the spread between individual boreholes. In conclusion, Figure 6‑65 implies that the
average Terzaghi corrected frequency of flowing fractures detected with the PFL method is very low
at repository depth. Below –400 m elevation, this frequency is approximately 0.036 fractures per
metre (36/km). Likewise, Figure 6‑65 implies that the average specific capacity at repository depth islow, approximately 6.5·10–9 m3/s per metre of head change.

TR-10-52

333

Terzaghi corrected fracture frequency [m-1]
0.001

0.01

0.1

1

10

0

Elevation [m RHB 70]

FFM02
-200

-400
FFM01 &
FFM06

-600

Open

-800

PFL
-1000
2/km

5/km

36/km

260/km615/km 1470/km

Figure 6‑64. Terzaghi corrected frequencies of open fractures and of the flowing fractures detected with the
PFL method. The thicker lines represent average values over all boreholes and the dotted lines represent
the spread between individual boreholes, i.e., the minimum and maximum values observed in any borehole(cf. /Selroos and Follin 2010, Figure 2-9/).

Specific capacity of flowing fractures [m2/s]
1E-10

1E-09

1E-08

1E-07

1E-06

1E-05

0

Elevation [m RHB 70]

FFM02
-200

-400
FFM01 &
FFM06

-600

-800

PFL

-1000
6.2E-10m2/s

6.5E-9m2/s

8.9E-8m2/s

Figure 6‑65. Specific capacities of the flowing fractures detected with PFL method. The thicker line represents
the geometric mean over all boreholes and the dotted lines represent the spread between individual boreholes,
i.e., the minimum and maximum values observed in any borehole (cf. /Selroos and Follin 2010, Figure 2-9/).

Temporal variability of data

The geometrical and hydraulic properties reported in the Site description Forsmark describe thepresent-day hydraulic properties. It is advocated that the present-day properties are reasonable forthe groundwater modelling of the excavation and operation periods as well as for the temperate
period. For the groundwater flow modelling of the periglacial and glacial periods, temporal changes
in the hydraulic properties seem more plausible, for example due to the potential impact of freezing
and mechanical deformation. The temporal changes in the hydraulic properties to be used for
SR-Site are described in /Selroos and Follin 2010/.

334

TR-10-52

6.6.9 Correlations
As previously mentioned, the hydrogeological DFN modelling carried out in SDM-Site considers
three types of correlation models between fracture transmissivity and fracture size, that is correlated,uncorrelated, and semi-correlated, see Table 6‑74. As explained in Section 6.6.7, the use of different
correlation models complies with the conclusion that the specific capacities measured at the boreholewith the PFL method are not fracture transmissivities of the intersecting fractures but specific capacities of “hydraulic chokes” in the connected network of flowing fractures.
The hydraulic calibration (parameterisation) of the generated DFN realisations reveal that all of thetested transmissivity models give reasonable matches against the four ‘goodness of fit’ statistics
discussed in Section 6.6.6. Hence, none of the tested transmissivity models is discarded in SR-Site.
However, the semi-correlated transmissivity model is advocated to be the most realistic of the threemodels attempted. For SR-Site, it is suggested that the correlated and uncorrelated transmissivity
models should be tested as variants.
Particle transport calculations were not carried out in SDM-Site. In SR-Site, particle transport
calculations are made in the study dealing with groundwater flow modelling during periods withtemperate climate conditions /Joyce et al. 2010/ and in the study dealing with groundwater flow
modelling during periglacial and glacial climate conditions /Vidstrand et al. 2010/. These flow
simulations employ a mixture of DFN, ECPM, and CPM representations of the rock mass volumes
depending on scale and computer code, see Section 6.6.1 for details.
With respect to the flow related migration parameters discussed in Section 6.7, in particular theflow-related transport resistance (F [TL–1]), the following correlations are noted:•

On a repository scale, a DFN representation will be used in the transport calculations. The flowrelated transport resistances along flow paths in a DFN representation is calculated as:
2t 
F = ∑  w f  et  f

6‑18

where tw is the advective travel time [T] in a fracture f along the flow path and et is the fracturetransport aperture [L]. The fracture transport aperture is assumed to be correlated to the fracturetransmissivity T [L2T–1] as:
et = a Tb

6‑19with a = 0.5 and b = 0.5. These values were also used in SDM-Site and originate from the ÄspöTask Force /Dershowitz et al. 2003/.
/Selroos and Follin 2010/ presents a range of fracture aperture relationships that could beconsidered as variants to Equation 6‑19. The relationships are discussed in /Hjerne et al. 2010/,
who compare their relative performance with regard to tracer experiments conducted at several
Swedish sites.
All but one of the cumulative distribution functions shown in Figure 6‑66 represent the different
fracture aperture relationships discussed in /Hjerne et al. 2010/. The cumulative distributions
are derived from transmissivity data of fractures that intersect deposition holes. The intersecting
fractures belong to a fracture network realisation generated by /Joyce et al. 2010/, see /Selroos andFollin 2010/ for details. Here, the empirical mass balance aperture (δm) relationship suggested by/Hjerne et al. 2010/ with a = 0.28 and b = 0.3 can be considered to yield an upper fracture aperture estimate for particle tracking calculations. This is due to the fact that in the conducted tracer
tests, fractures (flow paths) with preferential properties for tracer transport have been chosen.
Hence, the derived mass balance aperture relationship presented by /Hjerne et al. 2010/ should
yield apertures on the larger side and is therefore considered appropriate for use as a bounding
variant in SR-Site in particle tracking calculations.
Appendix A of this report discusses the concept of volumetric fracture aperture, ev and thepink curve in Figure 6‑66 shows the cumulative distribution function to be expected based on
measurements carried out at Forsmark. The volumetric fracture aperture not only represents thepart of the fracture where water flows, but also the part of the fracture holding stagnant water.
Accordingly, it represents the total water filled fracture volume. Therefore, one would expect the
cumulative distribution function of the volumetric fracture apertures (violet curve in Figure 6‑66)
TR-10-52335

to be shifted towards higher values than the cumulative distribution function of the mass balance
aperture (turquoise curve). However, this is not the case, which justifies a questioning of thephysical logic of the mass balance aperture relationship for sparsely fractured rock.
The position of the volumetric fracture aperture values also supports discarding the two relations in the lower range (orange and green) as it would otherwise suggest that less than a few
percents of the water filled fracture volume are available for water flow. Instead, it is suggested
that Equation 6‑19, with a and b = 0.5, is used as a lower aperture relationship in SR-Site, forexample in buffer erosion calculations.
•

On a site scale, the continuation of the DFN representation is upscaled to an ECPM finiteelement representation provided that the distance from the repository is still within the range ofthe hydrogeological DFN modelling of the six fracture domains, FFM01–FFM06. Outside thebounds of these fracture domains, there is no hydrogeological DFN and the CPM approach is
employed. The flow-related transport resistances along flow paths in the ECPM and CPM finiteelement representations is calculated as: a Dl F = ∑  r
q 
l 

6‑20

where ar is the fracture surface area per unit volume of rock (L–1), Δl (L) is a step distance along
the flow path, e.g., through one finite-element, and q (LT–1) is the Darcy flux in the same finiteelement.
Within the range of the ECPM representation, the fracture surface area per unit volume of rock
will be calculated as:ar (x, y, z) = 2 P32,element (x, y, z)

6-21

That is, ar is treated as a stochastic quantity that varies in space in accordance to a particularrealisation of the underlying hydrogeological DFN model. In contrast, ar is treated as homo­
geneous, i.e. statistically uniform (deterministic) quantity within the CPM representation.
1

Cumulative Distribution Function [-]

0,9
0,8

et= 0.5·T0.5
δm= 0.28·T0.3
δh,D= 2·T0.5
δt,D= 0.0575·T0.5
δc= 0.0117·T0.333
δl= 0.00295·T0.388
ev= volumetric

0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
10–8

10–7

10–6

10–5

10–4

10–3

10–2

10–1

Aperture [m]Figure 6‑66. Cumulative distribution functions of different fracture aperture relationships. The black curve
represents Equation 6‑19, the turquoise curve represents the mass balance fracture aperture relationshippresented in /Hjerne et al. 2010/,and the violet curve represents the volumetric (water filled) fracture
aperture data presented in Appendix A. The other curves are discussed in /Hjerne et al. 2010/.

336

TR-10-52

6.6.10 Result of supplier’s data qualification
The hydrogeological DFN parameters to be used for FFM01 and FFM06 are tabulated in Table 6‑75.
Table 6‑76 and Table 6‑77 show the parameters to be used for FFM02 and FFM03–FFM05, respectively.
The values provided for P32,open in these tables represent the Terzaghi corrected linear frequencies ofopen fractures, P10,open,corr.
For the region outside the six fracture domains FFM01–FFM06, that is on a regional scale, there isno site-specific information available in the rock mass volumes between deformation zones fromSDM-Site. Therefore, approximate values of homogeneous CPM properties (hydraulic conductivity
K, kinematic porosity ne, and flow-wetted fracture surface area per unit volume of rock mass ar) are
given in Table 6‑78. A depth dependency is suggested in accordance with the depth zonations used
for fracture domains FFM01 and FFM06. The extended heterogeneity case, described in /Selroos
and Follin 2010/, invokes an ECPM representation on a regional scale and should be considered asa variant to the CPM properties specified in Table 6‑78.
Table 6‑75. Hydrogeological DFN parameters to be used for fracture domains FFM01 and FFM06.
Elevation

Fracture
set name(m RHB 70)> –200–200 to–400< –400TR-10-52

Orientation set
pole: (trend,plunge), conc. kSize model,
power-law(r0, kr)

Intensity, (P32,o), valid
size interval: (r0,564 m)(°,°, – )(m, – )

(m2/m3)

Transmissivity model(cf. Table 6‑74)NS(292, 1) 17.8

(0.038, 2.55)

0.098

NE

(326, 2) 14.3

(0.038, 2.75)

0.391

Semi-correlated:(a,b,σlogT) = (6.3·10–9, 1.3, 1.0);

NW(60, 6) 12.9

(0.038, 3.10)

0.101

EW

(15, 2) 14.0

(0.038, 3.10)

0.099

HZ

(5, 86) 15.2

(0.038, 2.42)

0.619

Uncorrelated:(µlogT, σlogT) = (–7.0, 1.2)
Semi-correlated:(a,b,σlogT) = (1.3·10–9, 0.5, 1.0);

Correlated:(a,b) = (6.7·10–9, 1.4);

NS

As above

As above

0.229

NE

As above

As above

0.432

NW

As above

As above

0.135

EW

As above

As above

0.105

HZ

As above

As above

0.331

Uncorrelated:(µlogT, σlogT) = (–7.5, 0.8)
Semi-correlated:(a,b,σlogT) = (5.3·10–11, 0.5, 1.0);NS

As above

As above

0.122

NE

As above

As above

0.193

NW

As above

As above

0.100

EW

As above

As above

0.056

HZ

As above

As above

0.158

Correlated:(a,b) = (1.6·10–9, 0.8);

Correlated:(a,b) = (1.8·10–10, 1.0);
Uncorrelated:(µlogT, σlogT) = (–8.8, 1.0)

337

Table 6‑76. Hydrogeological DFN parameters to be used for fracture domain FFM02.
Elevation

Fracture
set name

Orientation set
pole: (trend,plunge), conc. kSize model,
power-law(r0, kr)

Intensity, (P32,o),Transmissivity model
valid size interval: (cf. Table 6‑74)(r0, 564 m)(°,°, – )(m, – )

(m2/m3)NS(292, 1) 17.8

(0.038, 2.75)

0.497

NE

(326, 2) 14.3

(0.038, 2.62)

0.533

NW

(60, 6) 12.9

(0.038, 3.20)

0.326

EW

(15, 2) 14.0

(0.038, 3.40)

0.116

HZ

(5, 86) 15.2

(0.038, 2.58)

1.609(m RHB 70)> –200Semi-correlated:(a,b,σlogT) = (9.0·10–9, 0.7, 1.0);
Correlated:(a,b) = (5.0·10–9, 1.2);
Uncorrelated:(µlogT, σlogT) = (–7.1, 1.1)

Table 6‑77. Hydrogeological DFN parameters to be used for fracture domains FFM03–FFM05.
Transmissivity is increased by a factor 2 for fracture domain FFM04.
Elevation

Fracture
set name(m RHB 70)> –400< –400 m

Orientation set
pole: (trend,plunge), conc. kSize model,
power-law(r0, kr)

Intensity, (P32,o),Transmissivity model
valid size interval: (cf. Table 6‑74)(r0, 564 m)(°,°, – )(m, – )

(m2/m3)NS(292, 1) 17.8

(0.038, 2.60)

0.074

NE

(326, 2) 14.3

(0.038, 2.50)

0.279

NW

(60, 6) 12.9

(0.038, 2.55)

0.255

EW

(15, 2) 14.0

(0.038, 2.40)

0.138

HZ

(5, 86) 15.2

(0.038, 2.55)

0.397

NS

As above

As above

0.071

NE

As above

As above

0.307

NW

As above

As above

0.085

EW

As above

As above

0.056

HZ

As above

As above

0.263

Semi-correlated:(a,b,σlogT) = (1.3·10–8, 0.4, 0.8);
Correlated:(a,b) = (1.4·10–8, 0.6);
Uncorrelated:(µlogT, σlogT) = (–7.2, 0.8)
Semi-correlated:(a,b,σlogT) = (1.8·10–8, 0.3, 0.5);
Correlated:(a,b) = (7.1·10–9, 0.6);
Uncorrelated:(µlogT, σlogT) = (–7.2, 0.8)

Table 6‑78. Homogeneous continuum properties to be used for the rock mass volumes outside
the six fracture domains FFM01–FFM06.
Elevation

CPM properties outside FFM01–FFM06(m RHB 70)

K
(m/s)

ne
(–)

ar
(m2/m3)> –2001·10–7

1·10–50.60–200 to –400

1·10–8

1·10–50.30< –4003·10–91·10–50.30

338

TR-10-52

6.6.11 Judgements by the SR-Site teamAccording to the instruction (cf. Section 2.3) the customer should make judgment on the sections
provided by the supplier. However, as part of the work has been done as part of SR-Site, judgment isonly made on work made outside SR-Site (predominantly as part of the site-descriptive modelling).
The SR-Site team also takes the opportunity of clarifying choices on hydrogeological modelling
made in SR-Site.
Sources of information and documentation of data qualification

The references cited in the data qualification are judged to be relevant and sufficiently exhaustive.
The data sets provided are judged to be adequately categorised into qualified and supporting.
Conditions for which data are supplied

The conditions for which (modelled) data are supplied are relevant for SR-Site modelling, with the
exception that no repository system was accounted for in the site-descriptive modelling. This has
been corrected for in the data supplied in this section.
It is also noted that when glacial conditions are considered in SR-Site, data is needed for a larger
model domain than considered in the site-descriptive modelling. The parameterisation of the modelon this super-regional scale is discussed in detail in /Vidstrand et al. 2010/ and summarised in/Selroos and Follin 2010/.
Conceptual and data uncertainties

The sources of uncertainty of all parameters are adequately described and quantified. It is notedthat the hydrogeological discrete fracture network model is associated with conceptual uncertainty
as described above. This relates both to the geometrical description of the network, but maybe
even more importantly to the hydraulic parameterisation, specifically the assumed fracture sizetransmissivity correlation model, see Table 6‑74.
Spatial and temporal variability of data

The spatial and temporal variability of all parameters are adequately described and quantified.
Correlations

The correlations used in the data qualification are adequately described. Specifically the assumed
fracture size-transmissivity correlation model is deemed to be important for assessment calculations.
Hence, all three correlation models are suggested to be assessed within SR-Site.
Result of supplier’s data qualification

The SR-Site team makes the judgement that the model parameterisations for SDM-Site should be
used to the greatest extent possible in the modelling of groundwater flow for SR-Site. Clearly, for themodel application of the glacial period, a model domain much larger than the SDM model is needed,and here the judgement is to follow the approach outlined above, that is an extrapolation of the CPM
properties into the larger domain is utilised.
The SR-Site team also notes that when connectivity analyses within the hydrogeological DFN
modelling are done for the temperate period, the effects of tunnels need to be incorporated in thecalculations.
Furthermore, the SR-Site team makes the judgement that discrete hydrogeological DFN models
should be utilised at as large scales as possible within the SR-Site application. Only at scales
larger than computationally feasible for the discrete DFN approach, or at scales for which a DFN
parameterisation does not exist, should continuum representations be used.

TR-10-52

339

The parameters provided are judged representative for both present day and future conditions except
for the period when permafrost conditions prevail, or during the glacial period when coupled hydromechanical processes, such as hydraulic jacking, may imply changed permeability values within
deformation zones.
The need to assess different fracture size-transmissivity correlation models is recognised.

6.6.12 Data recommended for use in SR-Site modelling
The hydrogeological DFN data recommended for use in SR-Site are listed in Table 6‑75 for fracturedomains FFM01 and FFM06, in Table 6‑76 for FFM02, and in Table 6‑77 for FFM03–FFM05. The
tables include the transmissivity models with their recommended constants. The semi-correlatedtransmissivity model is advocated to be the most realistic of the three models.
Concerning the aperture/transmissivity correlation (cf. Equation 6‑19) the following constants
are recommended. As best estimate values for hydrogeological modelling and buffer erosion
calculations, a = 0.5 and b = 0.5. As upper bounding values for hydrogeological modelling (particle
tracking), a = 0.28 and b = 0.3.
The hydrogeological CPM data recommended for use in SR-Site are listed in Table 6‑78 for rockmass volumes outside fracture domains FFM01–FFM06.

6.7

Flow related migration properties

Several of the parameters controlling radionuclide transport are related to the amount and distribution of groundwater flow. The values of these flow related migration parameters are essentiallyobtained by numerical simulation of groundwater flow using the hydrogeological models describedin Section 6.6. Before reading this section it is recommended to read Section 6.6 where many issuesof relevance for the text in the present chapter are detailed.

6.7.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR‑Site modelling
activities the data are to be used.
Defining the data requested from the supplier

In radionuclide transport modelling, transport from the canister in the near-field is assumed to occur
along three release paths. These are the Q1 path, with transport in a fracture intersecting the deposition hole; the Q2 path, with transport in the excavation damaged zone (EDZ); and the Q3 path, withtransport in the deposition tunnel to an intersecting fracture (cf. Figure 9-16 of the SR-Can Main
report /SKB 2006a/). There are thus three release paths from the near-field into the far-field of thegeosphere; it is the far-field parameters that are dealt with in this section.
The following flow related migration parameters are requested for each deposition hole location
within the repository:•

Darcy flux q (m/yr) for the Q1, Q2, and Q3 release paths. Also equivalent flow rates Qeq (m3/year)
corresponding to the Q1, Q2, and Q3 release paths (Qeq1, Qeq2, and Qeq3) are needed, which inturn are related to the groundwater flow rates for the Q1, Q2, and Q3 release paths. The Darcy
fluxes and flow rates are calculated in fractures intersecting the deposition holes, in the EDZ
in the tunnel floor adjacent to the deposition holes, and in fractures intersecting the deposition
tunnel for the Q1, Q2, and Q3 release paths, respectively. The relation between flow rate, Darcy
flux and equivalent flow rate for the Q1, Q2, and Q3 release paths are detailed in Chapter 3.2.6
and Appendix D of /Joyce et al. 2010/.

340

TR-10-52

•

Recharge and discharge coordinates in the biosphere along flow paths from the Q1, Q2, and Q3
release paths.

•

Advective travel time tw (yr) along flow paths to the recharge and discharge locations for the Q1,
Q2, and Q3 release paths.

•

Flow related transport resistance F (yr/m) along flow paths to the recharge and discharge locations for the Q1, Q2, and Q3 release paths.

In addition the following parameters, which are not related to the paths, should be supplied:•

Measures of longitudinal dispersion Pe (–) or aL (m) along the flow paths.

•

Maximum penetration depth for solute diffusion into the rock matrix LD (m), in case the fracture
spacing is limiting (as opposed to the connectivity of the micoporous system discussed in
Section 6.8).

SR-Site models in which data will be used

The flow related migration parameters described in this section are used in radionuclide transport
calculations and in calculations assessing both the penetration of oxygenated and dilute groundwaterfrom the surface to repository depth.
The radionuclide transport calculations, see the Radionuclide transport report, are performed using
different computer codes for the near-field and far-field, respectively. The near-field code is COMP23/
Compulink /Romero et al. 1999, Cliffe and Kelly 2006, Vahlund and Hermansson 2006a/, and for the
far-field either the code FARF31 /Norman and Kjellbert 1990, Elert et al. 2004/ or MARFA /Painterand Mancillas 2009/ are used. The codes use the following flow related migration parameters:
COMP23:•Equivalent flow rates Qeq1, Qeq2, and Qeq3 for three possible release paths Q1, Q2, and Q3. The
equivalent flow rates are obtained based on the calculated flow rates in the groundwater flow
models described in /Joyce et al. 2010/ and briefly summarized in /Selroos and Follin 2010/.

FARF31 and MARFA:•

Flow related transport resistance F.

•

Advective travel time tw.

•

Peclet number Pe in FARF31 or longitudinal dispersivity aL in MARFA.

•

Maximum penetration depth LD into the matrix if a finite matrix is considered.

Biosphere assessment:• Coordinates (x, y, z) at the end of each flow path connecting canister positions with exit locations.
The calculations of penetration of oxygenated water /Sidborn et al. 2010/ and dilute water are basedon analytical solutions using the flow related transport resistance and advective travel time along
recharge flow paths.
The flow related transport resistance and advective travel time are integrated parameters along flow
paths obtained through particle tracking in the flow models described in Section 6.6. Particles are
released at each of the locations corresponding to the Q1, Q2, and Q3 release paths, see above. It
is noted that FARF31 reads parameters integrated along the complete flow path, whereas MARFA
reads segment wise integrated values; that is, the flow path is divided into a number of segments.
This allows for an assignment of different retention models along the flow path; for example, different retention models may be applied to transport in tunnels and in fractured rock.

TR-10-52341

6.7.2 Experience from SR-Can
This section briefly summarises experiences from the SR-Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The flow related migration properties were obtained from the hydrogeological modelling. The
relevant modelling performed as part of SR-Can is described in Section 6.6.2. From the simulations
carried out the so-called triplets, which are is the equivalent flow rate, advective travel time, andflow related transport resistance (Qeq, tw, and F), were obtained separately for each of the release
paths Q1, Q2, and Q3.
Conditions for which data were used in SR-Can

In SR-Can, data triplets (Qeq, tw, and F) were only supplied for temperate conditions. Specifically,
triplets were supplied for a number of snapshots in time within the temperate period.
Sensitivity to assessment results in SR-Can

Results were most sensitive to the choice of conceptual model where the alternative multi-component continuous porous medium (CPM) formulation provided more favourable results.
Within the discrete fracture network (DFN) conceptual model, results proved to be sensitive to thechosen fracture size-transmissivity model, and to the implementation of a multi-component DFN
representation; that is to a model with different DFN statistics in different parts of the domain.
Alternative modelling in SR-Can

An alternative conceptual model, a multi-component CPM, was used in SR-Can. More details areprovided in Section 6.6.2.
Correlations used in SR-Can

The resulting triplets are correlated to a certain degree since all three entities depend on the
groundwater flow field. A clear cross-correlation was observed between travel time and flow-relatedtransport resistance (in fact, a functional relationship could be approximated as F = aw·tw where aw(m2/m3) is the flow-wetted fracture surface area per unit volume of water), whereas the correlation
between Darcy flux (or equivalent flow rate) at deposition hole locations and F (or tw) was weaker.
Due to the correlations, triplets of values were sampled (i.e. corresponding values of Qeq, tw and F
for the same deposition hole position) for further radionuclide transport calculations. When separate
pathways Q1, Q2, and Q3 were considered, three pairs of triplets for the same canister location were
sampled.
Identified limitations of the data used in SR-Can modelling

The main limitation with the SR-Can data was that triplets only were produced for the temperate
period when clearly assessment results will depend also on triplets corresponding to future climate
conditions. In the regulatory authorities review of SR-Can, summarised in /Dverstorp and Strömberg
2008/, it was asked for more detailed flow and radionuclide transport modelling of specifically theglacial period within future assessments.

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6.7.3 Supplier input on use of data in SR-Site and SR-Can
The data supplied in this section have been produced by the SR-Site team; hence no supplier formallyexists. None the less, the text is written according to the standard outline of the Data report.
The flow related migration properties are results of the groundwater flow simulations presented in
Section 6.6 and in this section. Thus, all data pertinent for defining the groundwater flow simulations
are also relevant for the results presented in this section.

6.7.4

Sources of information and documentation of data qualification

Sources of information

All the data presented in this section result from the groundwater flow simulations. The sourcesof information and data qualification of the data needed for these simulations are described in detailin Section 6.6 and in the individual modelling reports /Joyce et al. 2010, Vidstrand et al. 2010/.
A summary of the hydrogeological models and data used is given in /Selroos and Follin 2010/.
The simulations done are performed as two separate studies, one for the temperate period /Joyce
et al. 2010/, and one for the periglacial and glacial period /Vidstrand et al. 2010/. Full references are
given in Table 6‑79. These reports summarise the different cases modelled and the justification ofthese cases.
The Excavation and Operation period is hydrogeologically analysed in /Svensson and Follin 2010/;
this report is not further discussed in the present context since that analysis does not produce flow
related migration data for use in subsequent assessment calculations. However, all three reports
above are summarised and put in a SR-Site context in /Selroos and Follin 2010/.
Table 6‑79. Main sources of information used in data qualification.
Joyce S, Simpson T, Hartley L, Applegate D, Hoek J, Jackson P, Swan D, Marsic N, Follin S, 2010. Groundwater flow
modelling of periods with temperate climate conditions – Forsmark. SKB R-09-20, Svensk Kärnbränslehantering AB.
Vidstrand P, Follin S, Zugec N, 2010. Groundwater flow modelling of periods with periglacial and glacial climate conditions – Forsmark. SKB R-09-21, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

In this Data report the hydrogeological subject area is divided on two sections, Section 6.6 and thispresent section. This present section gives results from the SR-Site hydrogeological modelling,whereas Section 6.6 supplies the most important inputs to such modelling (although other inputs arealso needed). Because of this, a deliberate deviation from the instruction given in Section 2.3.4 ismade, and no sorting of input data as qualified or supporting is made in this section. What could besaid in general concerning input data to hydrogeological modelling is the following:
The data sets used as input to the groundwater flow simulations are detailed in the individual reports
referenced in Table 6‑79. The main data set is related to the parameterisation of the hydrogeologicaldiscrete fracture network model detailed in Section 6.6. Additional data sets needed for the hydrogeological modelling, for example parameterisation of hydraulic conductor domains (deformation
zones) and hydrogeological soil domains (regolith), as well as formulation of initial and boundary
conditions for both flow and transport of salt and reference waters, are summarised in /Selroos andFollin 2010/.
Excluded data previously considered as important

No data have been excluded that previously have had a significant impact on the perception of theappropriate choice of data values or modelling approach.

TR-10-52343

6.7.5 Conditions for which data are supplied
Data are supplied for temperate period conditions, and for different combinations of periglacial(permafrost) and glacial period conditions, respectively. For the glacial period, also submerged
conditions are included (see the Climate report for details). The temperate period is modelled as acontinuation of today’s conditions including the future shoreline displacement process and projectedchanges in salinity in the Baltic Sea. The present conditions are described in detail in the bedrockhydrogeology report of SDM-Site Forsmark /Follin 2008/. The periglacial and glacial periods are
more hypothetical; rather than trying to predict the future evolution, the models have been set up
to bound the hydrogeological consequences associated with these climatic conditions. The future
evolution of the climate is described in the Climate report.
In the model of temperate conditions /Joyce et al. 2010/, the repository is explicitly included in themodel. The repository description includes deposition holes, deposition tunnels, transport and main
tunnels, as well as the central area including ramp and shafts. Also the excavation damaged zone
(EDZ) is included. All tunnels are assumed to be backfilled. Variants are performed on both EDZ
and backfill parameterisation.
In the model of periglacial and glacial periods /Vidstrand et al. 2010/, the repository is not included.
This is justified by the much larger scale (super-regional) considered in these simulations. However,
it is noted that boundary conditions are transferred from the super-regional scale model to thesmaller scale models of /Joyce et al. 2010/; by using this approach, detailed flow related migration
data incorporating the repository are obtained also for the glacial case.
Conditions in term of geological and other settings governing the hydrogeological data are further
described in Section 6.6.5 and in /Selroos and Follin 2010/.

6.7.6 Conceptual uncertainty
The conceptual uncertainties associated with the hydrogeological modelling are summarised in
Section 6.6 and described in detail in /Selroos and Follin 2010/.
A few uncertainties with specific relevance for the flow-related transport properties are highlighted
here. First, it is noted that the advective travel time and flow related transport resistance are calculated along flow paths in steady-state velocity fields, That is, a snapshot-in-time approach is used.
Clearly, different results would be obtained in transient flow fields, but the resulting uncertainty is
judged small given that steady-state flow fields bounding the transient evolution are used.
Second, the combination of model scales within the hydrogeological applications as detailed in/Selroos and Follin 2010/ implies an uncertainty in some cases. Models in ConnectFlow may be
combined in two ways: First, models can be embedded in each other in which case they are formallynested such that the whole flow system is solved simultaneously using constraint equations on themodel interfaces, resulting in continuity of both pressure and velocity over the interfaces. Second,models may be solved separately using boundary conditions from the larger scale model on theboundaries of the smaller scale model. In this case, velocities are not necessarily continuous, andparticles are manually moved from the smaller scale model to the larger scale model at the boundary.
This implies an uncertainty in the flow path characteristics. Also, boundary conditions from thesuper-regional scale periglacial/glacial case model, developed using DarcyTools, are transferred tothe smaller-scale temperate case model, developed using ConnectFlow, to calculate performancemeasures in the detailed repository scale. The transfer of boundary conditions between not only
scales but also numerical codes implies an additional uncertainty.
An uncertainty stems also from the fact that individual fractures in the hydrogeological DFN model
are modelled as homogeneous, whereas in reality there is some spatial variation of the aperture fieldwithin each fracture. In SR-Can, this uncertainty was dealt with by reducing all calculated flow
related transport resistance values F by a factor of ten. In SR-Site, the F values are left unmodified
based on arguments compiled in the Radionuclide transport report. First, fracture-to-fracture
variability is generally larger than within-fracture variability in aperture. Second, fluid can only enterand leave fractures on a limited area, significantly constraining the meander of flow paths. Third,

344

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substantial portions of the non-contacting fracture surface area outside of the dominant flow channels may still be accessible by diffusion within the fracture pore space and thus provide additionalsurface area for radionuclides to interact with the rock matrix.
Finally, the assumed relationship between fracture transmissivity and aperture will affect the calculated advective travel times. However, the uncertainty is considered small; moreover, the advective
travel times do not have a strong influence on subsequently calculated entities within the compliancecalculations.

6.7.7 Data uncertainty due to precision, bias, and representativity
Since the temperate period model resolves fractures at smaller scales and also has an explicit
representation of the repository structures relative to the glacial period model, one may argue that ahigher degree of precision is present in the former model. There is thus a relative difference in precision between the two models. However, it should be noted that the flow related transport properties
data also for the periglacial/glacial conditions are calculated in the more detailed site-scale andrepository-scale models developed for the temperate modelling, by transfer of boundary conditions
from the super-regional scale glacial model to the site-scale and repository-scale models.
Since the super-regional periglacial/glacial model is set up to bound hydrogeological consequences
for periglacial/glacial climate conditions rather than to provide a detailed prediction of the future,
the results (performance measures) of these analyses are biased towards pessimism relative to thetemperate period results.
There may be judged to be differences between the different performance measures concerning
precision (accuracy) and bias in results. The two flow measures Darcy flux q and equivalent flowrate Qeq depend primarily on the permeability and connectivity of the fractures system and appliedboundary conditions. The accuracy of these measures is thus as good as the underlying descriptionof the bedrock and knowledge of boundary conditions. When it comes to advective travel time,
tw, additional assumptions need to be made concerning porosity and/or transport aperture, both ofwhich are highly uncertain entities. Hence, the advective travel time is associated with much more
uncertainty than the flow measures. The flow related transport resistance F also requires additional
assumptions concerning the flow-wetted surface along the flow path. This entity can be estimatedwith greater certainty than the aperture or porosity, but is still subject to great uncertainty. However,in an explicit discrete fracture network model, the flow-wetted surface is explicitly defined throughthe network properties, and thus the uncertainty in flow-wetted surface is given by the uncertainty inthe discrete fracture network model, see Section 6.6. The resulting uncertainty in F is judged to begreater than the uncertainty in q or Qeq, but smaller than in tw.
The provided values on Peclet number, longitudinal dispersivity, and maximum penetration depth
do not originate from the modelling described above, but are based on judgement and underlying
data. The uncertainty can be judged minor for Peclet number and longitudinal dispersivity; also, thechosen values of these parameters generally have a small effect on calculated radionuclide transportcharacteristics. The uncertainty in maximum penetration depth is related to the uncertainty infracture frequency, and is thus smaller than other uncertainties discussed here.
It is not possible to provide detailed quantitative measures of the uncertainties listed above (combination of conceptual, precision, bias, and representativity). However, it is judged that the greatest
uncertainty is associated with the periglacial/glacial model, followed by the uncertainty discussed inrelation to the performance measures of the temperate model, and finally followed by the uncertainties associated with Peclet number, longitudinal dispersivity, and maximum penetration depth.
Due to the conceptual uncertainty related to understanding of periglacial/glacial processes, several
variants as described in Table 6‑84 in Section 6.7.10 are propagated to the assessment calculations.
Also for the temperate period calculations, see Table 6‑82, uncertainties are handled by propagating
several variants. The key uncertainty is related to the assumed fracture size-transmissivity relationship. The uncertainties related to Peclet number, longitudinal dispersivity, and maximum penetration
depth are so small that no variants are deemed necessary to propagate.

TR-10-52345

6.7.8Spatial and temporal variability of data

Spatial variability of data

The calculated Darcy fluxes are point values in space; one point (three points, if a distinction is madebetween release paths Q1, Q2, and Q3) for each canister deposition hole position is considered.
Depending on the underlying variability of the hydraulic properties and boundary conditions forassumed flow, the Darcy fluxes will display a variation.
The advective travel time and flow related transport resistance are Lagrangian quantities integrated
along flow paths within the FARF31 application. The integration implies an averaging; that is, theorder between high and low values along the path does not matter as long as the cumulative result isthe same. However, it is noted that the integrated values obtained at the end of each flow path willvary between flow paths even though averaging takes place. Specifically, there tends to be large
variability in tw and F values due to the varying nature of the flow path distribution; that is, someflow paths are short and primarily located in high permeability features such as deformation zones,
whereas other flow paths are long and characterised by low permeability conditions.
In Table 6‑80 and Table 6‑81, statistics of the Darcy flux and flow related transport resistance are
exemplified in tabulated form for two central cases; the temperate period at 2000 AD, and the glacial
case without permafrost for an ice front location right above the repository. In Table 6‑81 the flow
paths lengths for recharge and discharge are also given. The tables are based on the hydrogeologicalbase case with stochastic fractures but homogeneous deformation zones (including a depth trend).
The tables thus reflect a single realisation. There is some variability in the entities in Table 6‑81
between realisations (not shown in table), indicating that the spatial variability results in uncertainty.
Multiple realisations are considered within the temperate period only; within the periglacial/glacial
period, the conceptual uncertainties are judged to dominate over the uncertainty implied by spatial
variability.
The advective travel time is generally strongly correlated to the flow related transport resistance.
Also, the equivalent flow rate is directly related to the Darcy flux, so the results shown in Table 6‑80
and Table 6‑81 provide a good overview of the characteristics of the different cases. Furthermore, it
is noted that the flow path length provided in Table 6‑81 for the glacial case is not a formal performance measure, but is provided in the table in order to illustrate differences between recharge anddischarge flow paths. As indicated, recharge flow paths tend to be longer than discharge flow paths
for the glacial conditions. Also advective travel time and flow related transport resistance along therecharge and discharge flow paths, respectively, may thus be quite different.

Table 6‑80. Resulting statistics of Darcy flux (m/yr) at deposition hole locations, and flow-relatedtransport resistance F (yr/m) for the temperate period at 2000 AD for the three release paths
Q1, Q2, and Q3. Fraction of particles in the last row indicates the fraction of released particles
discharging at the top surface of the model; the statistics for F in the table are based on thisfraction.
Log(q), Log(F)q (Q1)q (Q2)q Q(3)F (Q1)F (Q2)F (Q3)

Mean

–5.40

–2.39

–5.37

6.60

6.25

6.18

Median

–5.37

–2.42

–5.34

6.60

6.36

6.28

5th percentile

–7.17

–3.15

–6.61

5.39

4.27

4.34

10th percentile

–6.79

–3.02

–6.35

5.67

5.04

5.03

25th percentile

–6.12

–2.78

–5.86

6.15

5.77

5.72

75th percentile

–4.69

–2.02

–4.87

7.06

6.86

6.77

90th percentile

–4.12

–1.75

–4.49

7.56

7.37

7.25

95th percentile

–3.73

–1.57

–4.26

7.92

7.71

7.56

Std deviation

1.07

5.00·10–1

7.24·10–1

7.67·10–1

9.72·10–1

9.30·10–1

Variance

1.15

2.50·10–1

5.24·10–1

5.88·10–1

9.45·10–1

8.66·10–1

Max value–7.64–5.13·10

–1.98

9.14

9.42

8.84

Min value

–8.61

–4.18

–7.93

3.54

2.95

3.02

Fraction of particles

0.399

1.000

1.000

0.243

0.830

0.682

346

–1

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Table 6‑81. Resulting statistics of path length L (m), flow-related transport resistance F (yr/m),
and Darcy flux (m/yr) at deposition hole locations for the Glacial case without permafrost for an
ice front location right above the repository. Subscripts “re” and “di” represent recharge anddischarge flow paths, respectively.
Log(L), Log(q), Log(F)

Lre

Fre

q

Ldi

Fdi

Mean

4.447

6.101

–3.517

3.085

4.278

Median

4.447

6.093

–3.526

3.071

4.285

5th percentile

4.435

5.794

–4.724

2.874

3.644

10th percentile

4.437

5.855

–4.604

2.906

3.773

25th percentile

4.441

5.982

–4.071

2.979

4.033

75th percentile

4.454

6.236

–3.190

3.180

4.454

90th percentile

4.458

6.373

–2.338

3.273

4.888

95th percentile

4.461

6.418

–1.745

3.345

4.984

Std deviation

0.008

0.187

0.825

0.145

0.402

Variance

0.000

0.035

0.681

0.021

0.161

Max value

4.475

6.463

–0.747

3.606

5.801

Min value

4.430

5.574

–4.998

2.789

2.919

Fraction of particles

0.390

0.390

1.000

0.895

0.895

Peclet number and longitudinal dispersivity are provided as effective values for the whole model
domain; hence judgement concerning spatial variability in these properties is not relevant. Maximum
penetration depth depends on average fracture spacing, see Section 6.7.10 for details. Fracture spacing clearly varies in space; specifically fracture frequency tends to decrease with depth. However, forthe modelling in SR-Site, a single value of maximum penetration depth representative for the rock at
repository depths is chosen.
Temporal variability of data

Temporal variability is handled by using different model set-ups for the different climate domains,that is, temperate and periglacial/glacial, respectively, and also by representing a time evolution
within the different climate domains.
In the temperate domain modelling, the triplets (Darcy flux or equivalent flow rate, advective travel
time, and flow related transport resistance) are calculated at snapshots-in-time. The chosen snapshots
are 2000 AD, 3000 AD, 5000 AD, and 9000 AD. The differences in calculated triplets are due tothe fact that the snapshots-in-time are characterised by different shoreline positions. A more distantshoreline position (lower elevation) results in longer flow paths from the repository and generallyslightly higher advective travel times and flow related transport resistance values. The Darcy flux atthe canister deposition hole positions is not affected as much by the shoreline position.
During the glacial period, both higher and lower flow conditions are experienced. When the ice front
is in close proximity to the repository (located right above the repository), Darcy fluxes are high andadvective travel times and flow related transport resistance values low. However, these conditions
only last for a short time period. Conversely, when the site is completely ice covered, Darcy fluxes
are lower and advective travel times and flow related transport resistance values higher, as comparedto the temperate period. This time period may last for tens of thousands of years (cf. the Climate
report). Also the period with submerged conditions, when the ice sheet has retreated, is characterised by lower Darcy fluxes than during the temperate period. The periglacial/glacial simulations aredescribed in detail in /Vidstrand et al. 2010/ and summarised in /Selroos and Follin 2010/.

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6.7.9 Correlations
The same correlations as described for SR-Can above are relevant for conditions within SR-Site.
However, in SR-Site these correlations are explicitly used in a more rigorous manner than in
SR-Can, as explained below.
Detailed models incorporating the repository structures are only available for the temperate period
modelling utilising combined site-scale and repository-scale models. Thus, transport issues related
to engineered structures such as the EDZ can only be studied in these models. In the super-regional
scale model used for periglacial and glacial conditions, the repository is not included. Two alternative approaches to resolve this shortcoming are utilised in SR-Site. In the first approach, triplets froma given climate period and snapshot-in-time are used directly, or alternatively in the case of the periglacial/glacial domain, boundary conditions from the super-regional scale model are transferred tothe site-scale and repository-scale models for calculation of performance measures. It is recognisedthat the snapshots emanating from the site-scale and repository-scale models have higher resolution
than snapshots from the super-regional scale model.
In the second approach, the values of the triplets from the temperate period model are re-scaled
based on the Darcy flux ratio between temperate period conditions and the other conditions considered (e.g. periglacial, glacial, or submerged). The scaling is argued to be appropriate based on thecorrelation between the triplets. It is fully recognised that flow path characteristics in terms of lengthand discharge locations will vary between different climatic states; hence, an assumption is madethat these different characteristics are of second order relative to the changes implied by the change
in flow magnitude.

6.7.10 Result of supplier’s data qualification
Below, references are made to the resulting triplets for the different cases considered within thetemperate, periglacial, and glacial period simulations. Within each period, the importance of thecases for subsequent assessment calculations is discussed.
At the end of the section, the suggested Peclet number, longitudinal dispersivity, and maximum
penetration depth are presented.
Temperate period

In Table 6‑82 the temperate period cases analysed in /Joyce et al. 2010/ that result in input for theradionuclide transport models are listed. Furthermore, references are made to where tabulated results
can be found for each case. For each modelling case, a data file is delivered for each Q1, Q2, and Q3
release path. Table 6‑83 shows an excerpt of such a data file, displaying four out of 6,916 rows (onerow for each deposition hole).
Table 6‑82. Modelling cases and sources of tabulated results.
Hydrogeological modelling case

Sources of tabulated triplets used by
FARF31

Sources of segment data usedby MARFA

Hydrogeological base case

SKBdoc 1255039

SKBdoc 1256019

Alternative DFN transmissivity-size
relationships

SKBdoc 1255039

SKBdoc 1256019

Inclusion of possible deformation zones

SKBdoc 1255039

Unmodified vertical hydraulic conductivity

SKBdoc 1255039

Extended spatial heterogeneity

SKBdoc 1255039

Tunnel variants

SKBdoc 1255039

348

SKBdoc 1256019

TR-10-52

Table 6‑83. Excerpt from resulting data file from hydrogeological modelling. Four out of 6,916
rows are displayed. The resulting data files also feature other columns than shown in theexcerpt, with data not specifically requested in this present section.
POINT

XS

YS

ZS

XE

YE

ZE

OKFLAG

11631091.11

6700709–473.89

1631609

6701245

–0.63

0

2

1631085.04

6700712–474.01

16316026701257

–0.74

0

3

1631081.05

6700715–474.111631559

6701239

–0.73

0
0

4

1631076.02

6700719–474.2216316076701256

–0.72

POINT

T0

U0

QEQ

TW

F

L

1

2.00·1034.05·10–3

5.32·10–4

3.96·101

1.53·105

1.65·1032

2.00·103

8.37·10–3

7.65·10–4

1.56·101

9.58·104

1.26·1033

2.00·104.34·105.65·10–42.78·1012.09·105

1.52·1034

2.00·1039.90·10–4

2.02·101

9.49·104

1.44·1033–3

8.05·10–3

Point indicates path number
XS, YS and ZS are coordinates for start of flow path
XE, YE and ZE are coordinates for exit location
OKFLAG indicates whether or not a particle reached the model boundary
T0 is release time
U0 is Darcy flux (m/yr) for Q1 and Q2; UR is used for Q3 (not shown in this excerpt)
QEQ is the equivalent flow rate for Q1 and Q2; QEQR is used for Q3 (not shown in this excerpt)
TW is the advective travel time in the rock, i.e. in the DFN (yr)F is the flow related transport resistance for the rock, i.e. in the DFN (yr/m)
L is the path length in the rock, i.e. in the DFN (m)The so-called Hydrogeological base case is a representation of site conditions based on the understanding as expressed in the SDM-Site model /Follin 2008/. The justification for the other variants
are given in /Selroos and Follin 2010/ and the implementation is described in /Joyce et al. 2010/.
It is here argued that the Hydrogeological base case is the central case to propagate to assessment
and compliance calculations. It is the case that best represents perceived site conditions.
In Figure 6‑67, cumulative distribution functions of the Darcy flux, advective travel time, and flow
related transport resistance for the different snapshots-in-time are exemplified.
Within the Alternative fracture size-transmissivity relationships, both completely correlated andun-correlated models have been studied. Both the correlated and uncorrelated models yield lessfavourable results than the semi-correlated model, but are hard to defend from a hydrogeologicalperspective. Nevertheless, it is argued that these cases need to be propagated. Out of the two, thecorrelated model exhibits tails of high Darcy fluxes and low F-values; hence this case is likely more
important to propagate. It is also judged important to analyse multiple realisations of these cases inorder to capture the uncertainty implied by spatial variability.
The Tunnel cases representing a gradual decline in the properties of the EDZ result in correspondingly more unfavourable results. The case with a crown-space, that is a consolidation of the backfill
material in the tunnel resulting in a gap between backfill and tunnel ceiling, also yields unfavourable
results. It is argued that within the tunnel cases, both the crown-space case and the worst EDZ case
(EDZ with T = 1·10–6 m2/s) need to be propagated.
The cases based on inclusion of possible deformation zones, an unmodified vertical hydraulicconductivity, and the extended spatial heterogeneity, do not greatly modify the results and are hence
not further discussed here.

TR-10-52349

Figure 6‑67. Normalised CDF plots of Darcy flux q (denoted Ur in figure caption,) tw, and F (denoted tr,
and Fr in figure captions to indicate that tw and F are calculated in the bedrock described as a DFN only)in the Hydrogeological base case model at 2000 AD, 3000 AD, 5000 AD, and 9000 AD for release path Q1.
The tw and Fr plots are based on particles successfully reaching the model top boundary (cf. /Selroos andFollin 2010, Figure 5-9/).

Periglacial and glacial period

In Table 6‑84 the periglacial and glacial period cases analysed in /Vidstrand et al. 2010/ are listed.
Also, the glacial case based on a transfer of boundary conditions from the super-regional modelof /Vidstrand et al. 2010/ to the smaller scale model of /Joyce et al. 2010/ is included in the table.
Furthermore, references are made to where tabulated results can be found for each case (on thesame form as displayed in Table 6‑83).
Table 6‑84. Modelling cases and sources of tabulated results.
Hydrogeological modelling case

Sources of tabulated triplets

Glacial case without permafrost.

SKBdoc 1255039

Glacial case without permafrost and North-South ice profile. SKBdoc 1255039
Glacial case without permafrost and Distorted permeability
conditions.

SKBdoc 1255039

Glacial case with permafrost – no permafrost tongue.
Glacial case with permafrost – 2 km permafrost tongue.

SKBdoc 1255039

Glacial case without permafrost – boundary conditions
SKBdoc 1255039
transferred from super-regional to site-scale and repositoryscale models.

350

TR-10-52

The variant with a North-South ice profile direction addresses the uncertainty in ice direction,
whereas the variant with distorted permeability conditions addresses an uncertainty in permeability(hydraulic conductivity) related to mechanical effects (see /Vidstrand et al. 2010/ for details).
In addition to the cases listed in Table 6‑84, results can be extracted for a case with permafrost but
no ice (that is, permafrost has developed but the ice sheet has not yet arrived), and for a submergedcase (when the ice sheet has completely retreated and the site is covered by a sea). Results for thesetwo cases are contained within the Glacial case without permafrost for the submerged case, andwithin results for the Glacial case with permafrost for the permafrost only case. The set-up of thesemodelling cases is described in more detail in /Vidstrand et al. 2010/ and summarised in /Selroos andFollin 2010/. It is noted that the Glacial case without permafrost and glacial cases with permafrost
together constitute a base case; that is, during glacial advance permafrost and an ice sheet exist
together, while during retreat no permafrost is present and the glacier is warm based, see the Climate
report for further details.
The glacial case without permafrost, with an ice front location above the repository, yields the mostunfavourable results. This is the case for which boundary conditions are transferred to the site-scaleand repository-scale models incorporating an explicit representation of the repository. For theother ice front locations, the results are much more favourable. Figure 6‑68 shows the normalised
Darcy flux calculated in the super-regional model for the glacial case without permafrost in a few
observation points at repository depth during a glacial advance and retreat. Values are normalized
to the flux value for temperate conditions. It is clearly seen that high groundwater flow conditions
are experienced during the two ice front passages, while during ice coverage the Darcy flux in mostobservation locations are below the corresponding temperate value.
The alternative cases considered, that is the North-South ice profile and Distorted permeability
variant (modified transmissivity for some fractures) do not provide substantially different results
and are not suggested to be propagated.

Normalised Darcy flux q/qtemp (-)

1000

pre-LGM

LGM

post-LGM

100
Glacial casewithout
Permafrost

10

Loc. 1
Loc. 2
Loc. 3

1

Loc. 4
Loc. 5

0.1

0.01
0

2,000

4,000

6,000

8,000

10,00012,00014,00016,00018,00020,000Time (y)Figure 6‑68. Change in Darcy flux ratio, (q/qtemp) during a cycle of approximately 18ka. Between the two
ice front passages, the model domain is completely covered by ice. The time scale represents the model
simulation time rather than calendar time. Reproduced from /Vidstrand et al. 2010, Figure 6-7/.

TR-10-52351

In Figure 6‑69, the median as well as maximum and minimum Darcy flux values representing thefull set of deposition hole locations are shown as obtained in /Vidstrand et al. 2010/. The results
indicate that the situation with permafrost only, that is the period before onset of the glacial advanceprovides more favourable conditions than the temperate case. The Glacial case without permafrost
provides the highest fluxes, while the cases with combined permafrost and ice sheet provide somewhat
more favourable conditions than the pure glacial case. The Glacial maximum case, that is, when thesite is fully covered by an ice sheet, and the submerged case provide more favourable conditions
than the temperate case.
It is suggested that the glacial case without permafrost for the ice sheet location with the front rightabove the repository is propagated to subsequent assessment calculations. This is also the case where
results exist from the detailed model with the repository explicitly included.
If a glacial cycle is to be represented, it is suggested that the Only permafrost (for a site with permafrost but no ice sheet), Glacial case with permafrost (for an advancing ice sheet), Glacial maximum(for a site covered by the ice sheet), Glacial case without permafrost (for a retreating ice sheet), andSubmerged (for a fully retreated ice sheet) are used.
10-7
10-8
10-9

Darcy flux (m/s)

10-10
Maximum value
Median value
Minimum value

10-11
10-12
10-13
10-14

10-15

Temperate

Glacial without
permafrost

Glacialmaximum

Submerged

Permafrost

Glacial with Glacial withpermafrost and permafrost but
2 km tongue
no tongue

Figure 6‑69. Estimated Darcy fluxes for the main climate situations considered in a full glacial cycle.
Minimum, maximum, and median values based on all deposition holes are shown for each situation.
Reproduced from Figure 6-17 of /Vidstrand et al. 2010/.

Peclet number, longitudinal dispersivity, and maximum penetration depth

The Peclet number /Norman and Kjellbert 1990/, which relates to the relative importance of advective versus dispersive transport mechanisms along the individual flow paths through a dimensionlessratio, has not been further addressed since the SR-Can safety assessment. The central value recommended in SR-Can, which is 10, is recommended for use as a single point value in SR-Site. The
justification is twofold:•

Large scale dispersion is handled through multiple flow paths in the groundwater flow models.
Longitudinal dispersion along individual flow paths has a minor effect on breakthrough characteristics. Field evidence from tracer tests suggests that the dispersion length typically is 10 per
cent of the distance of a tracer test; this yields a Peclet number of 10.

•

Since dispersion is more of a model concept than a strict process, it is hard to justify shapes of
distributions.

352

TR-10-52

Given a path length of 500 m, representing a short path in the temperate simulations, a longitudinal
dispersivity of 50 m corresponds to the Peclet number used in FARF31. Thus, in MARFA, a single
value of the longitudinal dispersion, aL = 50 m, is used.
Concerning the maximum penetration depth, it is argued in Section 6.8 that the matrix pore space isconnected over all distances of interest within the assessment. Thus, the maximum penetration depth
is given by the average fracture spacing. This is reported in the site-descriptive model /Follin et al. 2007c,
Table 3-4/ and is approximately 25 m for the rock in the depth interval –200 m to –400 m in fracturedomain FFM01. The maximum penetration depth is half the fracture spacing, i.e., 12.5 m. It is suggested that this value is used as a deterministic value in SR-Site, also for rock at repository depth.

6.7.11 Judgements by the SR-Site teamThe data of this section has been produced by the SR-Site team. Therefore no judgment on the above
text is made. However, the general judgment is made that the data on flow related transport parameters presented in this section, resulting from the hydrogeological modelling described in Section 6.6
and in this present section, are appropriate for use in the assessment and compliance calculations
within SR-Site.
Furthermore, the results presented in this section may be modified due to different criteria of rejecting deposition holes. Discussions on such criteria are not requested in this section, but the criteria
are highly related to the hydrogeological modelling wherein results in this section are produced.
The SR-Site team suggests that the EFPC (FPC and EFPC criterion combined) should be appliedin SR-Site (EFPC is short for Extended Full Perimeter Criterion and FPC is short for Full Perimeter
Criterion). This implies that canister deposition holes failing this criterion will be disregarded.
Hence, in the final data to be used in compliance calculations, the triplet values associated withpositions failing the EFPC should be sorted out.
The SR-Site team does not suggest to apply the inflow criteria elaborated in /Svensson and Follin
2010/ and also discussed and summarised in /Selroos and Follin 2010/. In short, the inflow rejection
criteria disqualify canister deposition holes with too large inflows during the operation phase of therepository. Since a correlation, albeit not perfect, exists between inflows in deposition holes during
operation conditions and Darcy flux through deposition holes during subsequent saturated conditions, the application of inflow rejection criteria will result in the rejection of some unfavourable
deposition holes. Thus, if such criteria were to be used, possibly even more favourable results would
be obtained. However, it is presently judged that the application of inflow criteria is hard to defend
due to their formulation, implying questionable practicability. Also, further studies are needed on
how to implement and evaluate such criteria in numerical models.

6.7.12 Data recommended for use in SR-Site modelling
The SR-Site team recommends that the following cases, out of those tabulated in Table 6‑82 andTable 6‑84, are propagated to SR-Site:
Temperate period:• Hydrogeological base case.
• Alternative DFN transmissivity-size relationships (correlated and uncorrelated).
• Tunnel variants including both EDZ and crown space cases.
Periglacial and Glacial period:• Glacial case without permafrost.
• If a glacial cycle is to be considered, it is suggested to include the following cases: Only permafrost (for a site with permafrost but no ice sheet), Glacial case with permafrost (for an advancing
ice sheet), Glacial maximum (for a site covered by the ice sheet), Glacial case without permafrost(for a retreating ice sheet), and Submerged (for a fully retreated ice sheet). It is further noted thatfor the Glacial case with permafrost, the case with no tongue under the ice sheet is suggested.
References to data files providing Qeq, F, tw, and q are given in Table 6‑82 and Table 6‑84. Furthermore,
the Peclet number, longitudinal dispersivity, and maximum penetration depth recommended for use
in SR-Site are listed in Table 6‑85.

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Table 6‑85. Data recommended for use in SR-Site.
Parameter

Recommended data

Peclet number, Pe (–)

10Longitudinal dispersivity, aL (m)50 mMaximum penetration depth, LD (m)12.5 m6.8

Non-flow related migration properties

Migration of solutes through fractured rock depends on several parameters. The data handled in thissection, concerning the effective diffusivity, diffusion available porosity, and sorption partitioning
coefficient, are related to the rock matrix properties themselves. These data are site-specific, depending on the geological settings and groundwater composition.
Other parameters of importance for solute transport are closely related to the groundwater flow, thatis, they are “flow related”, and are discussed in Section 6.7.

6.8.1 Modelling in SR-Site
This section describes what data are expected from the supplier, and in what SR‑Site modelling
activities the data are to be used.
Defining the data requested from the supplier

The following data should be delivered by the supplier:•

The diffusion available porosity ε (–), also commonly referred to as the porosity, for theundisturbed rock matrix. Different species may experience different diffusion available porosity.
When necessary, different porosities should be delivered for different rock volumes, judged as
appropriate by the supplier.

•

The effective diffusivity De (m2/s) for the elements of the selected inventory and for dissolved
O2 and Fe2+. The De should represent the undisturbed rock matrix. In estimating the effective
diffusivity, it may be necessary to discuss the formation factor Ff (–). When necessary, different
effective diffusivities should be delivered for different rock volumes, judged as appropriate by
the supplier.

•

The scale on which the porous system is connected L (m), thus allowing for matrix diffusion.

•

The sorption partitioning coefficient Kd (m3/kg) for the elements of the selected inventory. The
uncertainty estimate of Kd data should encompass the underlying uncertainties in groundwatercompositions that are likely to be encountered (cf. Section 6.1). When possible and necessary,different Kd data should be delivered for different rock volumes, judged as appropriate by the
supplier.

The elements of the selected inventory, for which transport parameters are required, are: H (assumed
as HTO), C (inorganic and organic compounds), Cl, Ni, Se, Sr, Zr, Nb, Mo, Tc, Pd, Ag, Cd, Sn, I, Cs,
Sm, Eu, Ho, Pb, Ra, Ac, Th, Pa, U, Np, Pu, Am, and Cm (see Section 2.2).
SR-Site modelling activities in which data will be used

Migration of dissolved radionuclides in the rock matrix occurs predominantly by diffusive transport,
which in SR-Site is modelled by using Fick’s laws. The diffusive transport will occur in the porewater that saturates the microporous system of rock at depth. Species will also, to different degrees,interact with the mineral surfaces surrounding the microporous system. Sorption of radionuclides
in the far-field is in SR-Site modelled using a linear relation (justified by a low radionuclide concentration and equilibrium) between sorbed species and solute concentrations. The proportionality
coefficient is the sorption partitioning coefficient Kd.

354

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Radionuclide migration through the far-field is in SR-Site modelled by the transport simulation
codes FARF31 /Norman and Kjellbert 1990, Elert et al. 2004/ and MARFA /Painter and Mancillas
2009/. The general approach taken when modelling radionuclide transport in the far-field is
described in the Radionuclide transport report.
Solute transport modelling of dissolved oxygen in fractured rock is described in /Sidborn et al. 2010/
and requires matrix diffusion data for dissolved oxygen and Fe2+.
As part of the hydrogeological modelling, summarised in /Selroos and Follin 2010/, the salinityof the groundwater is estimated based on solute transport calculations that predominantly include
matrix diffusion of chloride. However, for this modelling it is not necessarily pessimistic to use thematrix diffusivity of the undisturbed rock matrix, as delivered in this section, and additional considerations may be needed. In Appendix A of this Data report, a dedicated text on matrix diffusivities
for use in hydrogeological modelling is given.

6.8.2 Experience from SR-Can
This section briefly summarises experience from the SR‑Can safety assessment, which may be of
direct consequence for the data qualification in this Data report.
Modelling in SR-Can

The modelling performed in SR-Can generally agrees with what will be performed within SR-Site.
The exception is that in SR-Can, the transport code MARFA was not used.
Conditions for which data were used in SR-Can

The conditions for which data applied in SR-Can are similar to those of SR-Site. However, as
SR-Site is based on more site-specific data and more information on the site-specific conditions,
there may be slight deviations in the applied conditions between the two safety analyses. The
specific conditions for which data were delivered are given in /Liu et al. 2006, Section 5.2/ and/Crawford et al. 2006, Section 5.2/.
Sensitivity to assessment results in SR-Can

In SR-Can scenarios including canister failure (by some realistic means) it was shown that at times
where the total annual effective dose comes relatively close to the regulatory limit, it is greatly dominated by the moderately sorbing nuclide, Ra-226 (e.g. /SKB 2006a, Figure B-5 and Figure 10-18/).
Other radionuclides with a minor, but still significant, contribution to the total annual effective dosein SR-Can are I‑129, Nb-94, Th-229, Th-230, and Cs-235.
Concerning the oxygen penetration, the oxygen was generally assumed to be consumed before it
reached repository depth /SKB 2006a, Section 9.4.7/ and thus the effect on assessment results was
small. However, it was recognised that under certain conditions it is conceivable that oxygen may
penetrate down to repository depth at higher rates.
Alternative modelling in SR-Can

No alternative modelling was made in SR-Can, with the focus on non-flow related migration properties.
Correlations used in SR-Can modelling

The correlations used in SR-Can modelling generally agree with those used in SR-Site, as describedin Section 6.8.9.

TR-10-52355

Identified limitations of the data used in SR-Can modelling

The data in SR-Can was based on a limited portion of all data presently available from the site investigations. This especially applies for sorption data, where no site-specific Kd data were available. In SR-Can,
updated recommendations concerning Kd values were given for only eight radioelements (Ni, Sr,
Cs, Ra, Th, U, Np, and Am), based on generic data sets /Crawford et al. 2006/. For the remaining
radionuclides, recommendations were taken from the SR 97 safety assessment. For some of theradionuclides of the selected inventory in SR-Site, no data was given in SR-Can.
In the SKI report /Stenhouse et al. 2008/, the relatively small uncertainty ranges associated with
Kd data were questioned. Furthermore, it was cautioned that the narrow selection of data sets could
cause selection bias.
In /Stenhouse et al. 2008/, a more detailed discussion on uncertainties, for example measurements
errors, was called for concerning ε and De. It was also noted that the in situ electrical method that
De data relied heavily upon is relatively new and not universally accepted. As part of the in situ
electrical method, the in situ porewater electrical conductivity is needed. In SR-Can this conductivity
was estimated based solely on measurements on flowing groundwater as, at the time, no data fromporewater extraction/leaching existed. This had a direct effect on the obtained formation factors.
It is presently recognised that the electrical method used for estimating formation factors is
significantly affected by surface conduction. In SR-Can, surface conduction was not sufficiently
well corrected for.
In the SR-Can Data report there were some confusion concerning the maximum penetration depth
for matrix diffusion, and in different paragraphs different values were given (cf. review comment in/Stenhouse et al. 2008, Section 2.6/).

6.8.3

Supplier input on use of data in SR-Site and SR-Can

Diffusion available porosity

The supplier has no input on the use of the diffusion available porosity in safety assessments. However,there may be a need to clarify the difference between different types of porosities, and also the terminology used. For a more detailed discussion, and for references to the open literature, the Geosphereprocess report is recommended.
The physical porosity n (–), which is also called the total porosity, is comprised of connected andunconnected porosity. The connected porosity is comprised of transport porosity εt (–) and storage
porosity. In essence, the transport porosity it the part of the porosity in which long range transportof solutes can occur through the rock matrix. In the storage porosity, also called dead-end porosity,
no long-range but only short-range transport (typically on the millimetre scale or less) of solutes
occurs. In igneous rock, an electrical double layer at the pore walls (mineral surfaces) hinders anionsfrom occupying all pore space. Normally one considers that non-charged species and cations are notexcluded from the electrical double layer, and thus can occupy the entire pore space. In this text, thediffusion available porosity, out of the connected porosity, is denoted ε (–).
Effective diffusivity and formation factor

Two approaches have been utilised for obtaining the effective diffusivity De of different solutes. The
classical approach is to obtain the De for one or a few solutes in diffusion tracer tests. From the Deof one solute, the De of another solute can be scaled based on the solute’s Dw (m2/s), which is the
diffusivity in unconfined porewater. When performing this scaling, the formation factor Ff (–) isused, which is a factor representing the geometry of the porous system, but is normally defined as
independent of the solute (under the prerequisite that there is no exclusion effect or enhancing effect,
such as surface diffusion):
De = Dw · Ff

356

6‑22

TR-10-52

Obtaining De has been done in laboratory through-diffusion tracer tests within the site investigation
programme. It should be noted that as the laboratory tracer test is performed at different conditions
than in situ, corrections of the obtained De are needed. The most important deviating conditionin laboratory experiments, as compared to in situ, concerns the stress situation. In this text, acomparison of data from electrical resistivity measurements in situ and in the laboratory is used for
estimating a transfer factor for differences in the stress situation. In doing this, the analogue between
electro-migration and diffusion is used.
A second approach used is to first estimate the formation factor of the rock matrix by use of thein situ electrical resistivity method, and as a second step to obtain De by multiplying the formation
factor with the solute’s Dw. Estimating the formation factor by use of the in situ electrical resistivity
method has routinely been done in the site investigation programme.
It has been decided to propagate data from both approaches to SR-Site if necessary, as theapproaches are conceptually different. However, as seen later in this section, the data obtained by
the two approaches are very similar. Therefore, only one set of data will be finally suggested for
SR-Site.
It should be pointed out that since SR-Can, a number of issues that may give rise to uncertainty
in De have been studied in detail. These uncertainties are reported in this text and in backgrounddocuments. The reporting may give the impression that new uncertainties has arisen since SR-Can.
However, the case is rather that known uncertainties of SR-Can have been better described. We areconfident that the overall uncertainty concerning De has been reduced since SR-Can, and that it isrelatively modest compared with many other uncertainties of the safety assessment.
Sorption partitioning coefficient

Kd data recommended for use in SR-Site are based in part on site-specific data obtained during
the Forsmark and Laxemar site investigations, and in part on data contained in the open scientific
literature. Site-specific data are available for the non-redox sensitive radionuclides of Cs(I), Sr(II),
Ra(II), Am(III)/Eu(III), and Ni(II) in contact with different site-specific rock types and groundwatercompositions. The groundwater compositions used in the laboratory investigations can broadly bedescribed as being fresh, saline, marine, and brine in character and represent the approximate rangeof groundwater compositions encountered in situ at the Forsmark and Laxemar sites at the presentday. Site-specific data are also available for the redox sensitive solutes U and Np for the samegroundwater types and at mildly oxidising (atmospheric) conditions. Kd values for other trivalentactinide and lanthanide elements are assumed to be identical to that for Am(III) and Eu(III) sorption
owing to the strong geochemical analogy existing between these solutes.
Kd data ranges for all other radionuclides and redox states are derived from literature sources formeasurements performed using similar geological materials and contact groundwater compositions.
For radionuclides where reliable measurement data was unavailable, geochemical analogies havebeen used to estimate approximate Kd ranges thought to be pessimistic for application within
SR-Site.
Being a surface mediated process, sorption is sensitive to both the available surface area of constituent minerals whereon sorption can occur, as well as their geochemical properties. Because sorption
partitioning data are obtained by laboratory experiments using crushed and sieved rock samples(frequently from different sites), corrections need to be made to extrapolate the data to be applicablefor Forsmark site-specific rock types under in situ conditions. These corrections are referred to astransfer factors in SR-Site and are applied in a multiplicative fashion to the laboratory derived Kd
data to obtain values appropriate for safety assessment calculations. Differences in surface area
between crushed samples and intact rock are accounted for using a mechanical damage transfer
factor fm (–). Mineralogical differences between rock used in sorption experiments and the in situ
rock are accounted for using a cation exchange capacity (CEC) transfer factor fcec (–). For solutes
that sorb principally by ion-exchange, a groundwater chemistry transfer factor fchem (–) is defined toaccount for differences in groundwater composition under application conditions, relative to thoseused in laboratory sorption measurements.

TR-10-52357

6.8.4

Sources of information and documentation of data qualification

Sources of information

Kd, De, and ε data from the Forsmark laboratory site investigation programme is compiled in /Selnert
et al. 2008/ and summarised in /Byegård et al. 2008/. Site investigation data on in situ apparent
formation factors obtained by electrical methods are reported in various site investigation reports by
Löfgren and co-authors (e.g. /Löfgren 2007/). Laboratory and in situ data are subsequently processedas part of Forsmark SDM-Site in /Crawford 2008/. A comparative study on formation factors
obtained by tracer tests and electrical methods is reported in /Löfgren et al. 2009/. All these reports
are written in accordance with the SKB quality assurance system and data therein can be seen as
qualified, unless otherwise stated.
Concerning Kd data, a dedicated SR-Site report has been prepared to support this Data report. In
this report /Crawford 2010/, a detailed account of the various data sources used and specific details
concerning individual radioelements are given. Raw sorption partitioning coefficients and BET
surface area data from the Forsmark and Laxemar site investigations were obtained from the SIMON
database. These data were used as a basis for the derivation of recommended Kd data ranges for sitespecific rock types for the radioelements Cs, Sr, Ra, Ni, Am/Eu, Np(V), and U(VI). It should be notedthat Kd data from the Laxemar site are extrapolated to be applicable to Forsmark metagranite, andare subsequently pooled with the data obtained for actual Forsmark rock samples when estimating
ranges of Kd uncertainty.
Recommended Kd ranges for solutes not among those studied in the site investigations were derived
from sorption data and other supporting data described in the open literature. These data are also
extrapolated to be applicable for Forsmark metagranite. Groundwater compositions during thetemperate phase of repository evolution, as presented in Section 6.1, are documented in (SKBdoc
1262945). The above mentioned publications, and others, are listed in Table 6‑86.
Table 6‑86. Main sources of information used in data qualification.
Crawford J, 2008. Bedrock transport properties Forsmark. Site descriptive modelling, SDM-Site Forsmark.
SKB R-08-48, Svensk Kärnbränslehantering AB.
Byegård J, Selnert E, Tullborg E-L, 2008. Bedrock transport properties. Data evaluation and retardation model.
Site descriptive modelling, SDM-Site Forsmark. SKB R-08-98, Svensk Kärnbränslehantering AB.
Selnert E, Byegård J, Widestrand H, 2008. Forsmark site investigation. Laboratory measurements within the site
investigation programme for the transport properties of the rock. Final report. SKB P-07-139, Svensk Kärnbränsle­
hantering AB.
Löfgren, 2007. Forsmark site investigation. Formation factor logging in situ by electrical methods in KFM01D and
KFM08C. SKB P-07-138, Svensk Kärnbränslehantering AB.
Löfgren M, Pettersson M, Widén H, Crawford J, 2006. Forsmark site investigation. Formation factor logging in situ
by electrical methods in KFM05A and KFM06A. SKB P-06-91, Svensk Kärnbränslehantering AB.
Waber H N, Gimmi, T, Smellie J A T, 2009. Porewater in the rock matrix. Site descriptive modelling, SDM-Site
Forsmark. SKB R-08-105, Svensk Kärnbränslehantering AB.
Waber H N, Smellie J A T, 2005. Forsmark site investigation. Borehole KFM06A: Characterisation of pore water.
Part I: Diffusion experiments. SKB P-05-196, Svensk Kärnbränslehantering AB.
Löfgren M, Vecernik P, Havlova V, 2009. Studying the influence of pore water electrical conductivity on the formation
factor, as estimated based on electrical methods. SKB R-09-57, Svensk Kärnbränslehantering AB.
Löfgren M, 2004. Diffusive properties of granitic rock as measured by in situ electrical methods. Ph. D. thesis. RoyalInstitute of Technology, Stockholm, Sweden.
Crawford J, 2010. Bedrock Kd data and uncertainty assessment for application in SR-Site geosphere transport calculations. SKB R-10-48, Svensk Kärnbränslehantering AB.

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Categorising data sets as qualified or supporting

The most important data sets used in qualifying the porosity, effective diffusivity, and formation
factor are listed in Table 6‑87. The great majority of the data comes from the Forsmark site investigation. It should be noted that the data sets in turn often rely on other measurements, which arereferences in the listed publications. Comments on the sorting of these data sets are given in Table 6‑88.
The Kd data recommended for use in SR-Site represent a mix of data obtained during the site investigations at Forsmark and Laxemar, as well as data obtained from literature sources and deemed of
sufficient quality to be appropriate for use in safety assessment calculations. In the compilation of Kd
data ranges recommended for use in SR-Site, the focus has been primarily on the use of site-specificdata where available. Data obtained from rock samples native to the Laxemar site are pooled withthose obtained for Forsmark site-specific materials, but only after making corrections for differencesin BET surface area and CEC. For radionuclides where site-specific data are not available, qualified
literature sources have been used. In general, the data derived from literature sources are thought
to be associated with larger uncertainties owing to differences in methodology, as well as different
material properties of rock types and contact water compositions used in sorption experiments.
The various sources of information and the data qualification are described fully in /Crawford 2010/,
although some specific comments are given in Table 6‑88.
Table 6‑87. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. /Crawford 2008/, Table 4-6: Porosities of different rocktypes.
2. /Jacobsson 2007/, Appendix B: Micro crack volume induced
in excavation and preparation of samples (implying that only
80% of the porosity seen at the laboratory exist in situ).
3. /Selnert et al. 2008/, Appendix 2: Effective diffusivities fromthrough diffusion measurements. Data used extracted from
Sicada (delivery note Sicada_09_098).
4. /Thunehed 2007/ and corresponding publications. Apparent
formation factors measured by the electrical resistivity method
at the laboratory. Data used extracted from Sicada (delivery
note Sicada_09_098).
5. /Löfgren 2007/: Apparent in situ formation factors for
KFM01D and KFM08C. Data used extracted from Sicada.
6. /Löfgren et al. 2006/: Apparent rock matrix resistivities for
KFM06A. Data used extracted from Sicada.
7. /Waber and Smellie 2005/, Table A7: Porewater chloride
concentrations of KFM06A.
8. /Crawford and Sidborn 2009/, Figure D-5 (and underlying
data by /Ohlsson 2000, Löfgren 2004/): Surface conductivities
vs. apparent formation factor at the Oskarshamn site.
9. /Löfgren et al. 2009/: Surface conductivities vs. apparent
formation factor at the Forsmark site. (Table 6-1, κs DC
average and Table 6-5, Ffapp DC average).
10. /Crawford 2010/: Kd values for radionuclides studied in
Forsmark (Table 6-1) and Laxemar site investigations derived
from raw data sets stored in the SIMON database. Qualified
Kd data sets derived from open literature sources.

11. /Birgersson and Neretnieks 1988/: in situ diffusion
experiment at the Stripa mine with iodide showing
Dp around 10–10 m2/s (e.g. Figure 8.22) and e around
0.3% (e.g. Appendix 5).
12. /André et al. 2009/: Specific surface area determinations on intact drill cores used to estimate transfer
factors for undisturbed in situ rock at Forsmark.
13. /Sidborn et al. 2010/: Fe content of Forsmark and
Laxemar rock types used as a proxy to estimate CEC
transfer factors for conversion of Laxemar site data to
Forsmark rock types.
14. /Vilks et al. 2005/, Tables 5 and 6: De data for HTO
and iodide indicating anion exclusion of about half anorder of magnitude.

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Table 6‑88. Justifications to the sorting of items in Table 6‑87.
1–9/ These are either site investigation reports of site-descriptive modelling reports written in compliance with the
SKB system of quality assurance. All data are judged to be qualified. Unless otherwise stated, data concern relevantrock types of the Forsmark site. Data points non-representative for the undisturbed rock matrix at the site have been
excluded in the data interpretation. It should be noted that in item 8, data from the Oskarshamn site have been used.
These data are still judged as qualified, as they are obtained on drill core samples of similar geology as the rock at theForsmark site.
10/ This is a dedicated background reference for Kd data qualification used in SR-Site. It contains data obtained from theForsmark and Laxemar site investigations. These data are judged as qualified. The reference also contains data derived
from literature sources for solutes not investigated during the site investigations. These data are also judged to be qualified. All Kd data have been extrapolated to be valid for Forsmark metagranite under in situ conditions using multiplicativetransfer factors to account for differences in sorptive surface area and mineralogy.
11/ The report of /Birgersson and Neretnieks 1988/, that is a solid piece of scientific work, is written outside the SKB
data qualification framework. As is performed on rock at the Stripa mine site it is judged as supporting.
12/ BET surface areas of intact monoliths of Forsmark metagranite (SKB rock code 101057) /André et al. 2009/ wereused to derive surface area and mechanical damage transfer factors for undisturbed in situ conditions. Although ofcentral importance for the extrapolation of recommended Kd values these data are judged as supporting.
13/ CEC transfer factors for conversion of Laxemar site-specific data to Forsmark appropriate values were estimatedusing statistical models of Fe content compiled by /Sidborn et al. 2010/ as a proxy for biotite mineral content. Since thedata for both sites are obtained using the same method this is considered to be a reliable means of correcting for the
CEC of different rock types. Although of central importance for the extrapolation of recommended Kd values these data
are judged as supporting.
14. /Vilks et al. 2005/ is produced as part of the LTDE-SD programme at Äspö HRL. As the data concern rock that is notsite-specific, and as the report is produced by an organisation outside SKB, the data are sorted as supporting (althoughstill considered as adequate).

Excluded data previously considered as important

There are publications suggesting a porous system of crystalline rock which is only connected over alimited scale (millimetres to centimetres), (e.g. /Miller et al. 1994/). It is recognised that the extent of
the pore connectivity has been debated (see e.g. /Löfgren and Neretnieks 2006, Benning and Barnes
2009/ for a brief summary). However, based on the present knowledge we consider the suggestion ofvery limited pore connectivity in crystalline rock as obsolete, or at least not applicable for the rocktypes at the Forsmark and Laxemar site investigation areas.
A correlation often referred to (the so-called Archie’s law) is that between the formation factor andconnected porosity. However, this correlation is very weak in the porosity region of interest (ε ~ 0.1
to 0.4%), as shown by /Byegård et al. 2008, Figure 3-5/, and its usefulness is limited. Therefore, nodata that are based on Archie’s law are used in the data qualification.
In some cases literature data previously considered in SR-Can have been disregarded in the current
recommendations on the grounds of a more restrictive set of selection criteria in SR-Site. The
minimum basic requirement for literature data included in the Kd compilation is generally that thecontact solution composition (ionic strength), BET surface area, and CEC (or some proxy indicator
thereof) should be recorded for the material used in the reported studies. This was to enable quantitative transfer factors to be calculated for extrapolation of data to Forsmark site-specific materials.
In a small number of cases this requirement was relaxed when data for the same materials were
documented in other references by the same authors, although not in the primary reference. Expert
judgment was then used to subjectively assess the quality of the reported data before a final decision
was made whether or not to include the data in the compilation. Literature sources were generally
not used for assigning Kd data for radioelements where site-specific data were available, although insome cases they are used as an independent check on the recommended ranges proposed.
Recent results not used

Parallel to the SR-Site project, the Long Term Sorption Diffusion Experiment (LTDE-SD) at theÄspö Hard Rock Laboratory is being finalised. Although the data presented in /Widestrand et al.
2010a, b, Nilsson et at. 2010/ are interesting, it has not been practically possible to include themin this data qualification, due to the timing16. However, data produced earlier in the LTDE-SD
programme /Vilks et al. 2005/ have been used.
16

At the time of finalising this Data report, the LTDE-SD reports are still in the factual review process.

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6.8.5 Conditions for which data are supplied
Part of this text borders on a data uncertainty discussion, and similar issues are also dealt with in
Section 6.8.7.
General in situ conditions

All data delivered should represent in situ conditions, where the most significant conditions arelisted below.
•

Rock type and degree of alteration/disturbance: The rock type is of importance for solute transport. The rock types dominating the target area are metamorphic and medium grained granite togranodiorite (SKB rock code 101057) and pegmatitic granite and pegmatite (rock code 101061).
For a description of the rock types present at the Forsmark site, and their mineralogy, /Stephenset al. 2007, Section 3.4/ and /Selnert et al. 2008, Section 4.2/ are recommended.
Rock adjacent to advective flow paths may be altered and disturbed. This may affect solutetransport and it is generally assumed that radionuclide retention in altered and disturbed rockis increased, compared to in undisturbed rock. Although solute transport properties for theundisturbed rock matrix are requested, the transport consequences of altered and disturbed rockare included in the uncertainty discussions.

•

Groundwater composition: Interactions between solutes and rock mineral surfaces are influenced
strongly by the groundwater composition. The groundwater composition at Forsmark, and its
evolution during the glacial cycle, is described in Section 6.1.

•

In situ temperature: The pore space geometry, interactions between solutes and rock mineralsurfaces, and transport rate of solutes in the pores are to different degree affected by thetemperature. The in situ temperature is dependent on the depth and also on the climate. Presently,
the in situ temperature at 500 m depth is measured to be in the interval 11.2–12.0°C, see the Sitedescription Forsmark Section 6.2.9. However, during permafrost the temperature at repository
depth may decrease to just a few degrees above freezing (see Section 7.1).

•

In situ stress: The pore space geometry is affected by the stress placed upon the rock matrix.
In Forsmark, the maximum horizontal and vertical stresses are estimated to be 41.0±6.2 and13.2±0.3 MPa, respectively, see the Site description Forsmark Section 7.2.4. The stress isthought to substantially increase during glaciation (cf. Section 6.4.10).

Consequence for ε and De

Rock type and degree of alteration/disturbance: The connected porosity, the formation factor, andthe effective diffusivity are very much affected by the rock type, with its specific texture and grain
size distribution. In the site investigations, the porosity and effective diffusivity have been obtainedon hundreds of drill core samples from drill sites DS1 to DS9, ensuring that all important rock types
at the site are included in the laboratory campaign (see e.g. /Selnert et al. 2008, appendices 1 and2/). A great majority of the drill core samples used for the measurements were characterised as freshrock, which means that the rock matrix seems to be undisturbed upon visible inspection. Only afew samples were altered or contained visible microfractures. Generally, altered rock display higher
porosity and effective diffusivity than undisturbed rock (see e.g. /Selnert et al. 2008, Figures 4-6 and4-14/).
Formation factors have been logged by the in situ electrical resistivity method in boreholes on drillsites DS1 to DS8, and tens of thousands of data points have been obtained. The data used in this text
are obtained at locations at least a few decimetres away from the nearest open fracture. Therefore,rock matrix alteration/disturbance adjacent to flow paths is not accounted for. In addition, a great
number of formation factors have been obtained by the laboratory electrical resistivity method.
These measurements were performed on predominantly undisturbed drill core samples from drillsites DS1 to DS6, DS8, and DS9.
Groundwater composition: The porewater composition at depth, which may affect the data deliveredin this section, should to a large extent mirror the groundwater composition and its evolution after
repository closure, with two exceptions. The first is in the electrical double layer at mineral surfaces,
where cations are up-concentrated. The second is in rock volumes many meters away from flow
TR-10-52361

paths, where the pore water may differ from the fracture water as equilibration by means of diffusion
takes such long time. However, such distant rock volumes are of little consequence for the safetyassessment.
The physical porosity is not affected by the porewater composition. However, for the diffusion
available porosity of different solutes, the porewater composition may to a lesser degree be affectedby anion exclusion, in turn affecting the diffusion available porosity.
According to Equation 6‑23, Dw is affected by the viscosity of the porewater:

T hDw,T 2 = Dw,T 1 ⋅ 2 ⋅ 1
T1 h 2

6‑23

where Dw,T1 and D w,T2 are the diffusivities in unconfined porewater at the temperatures T1 and T2(K), and η1 and η2 (Ns/m2) are the porewater viscosity at the two temperatures. Except for being
affected by the temperature, the viscosity is also affected by the porewater composition. Often whenestimating the effective diffusivity, the effect of the porewater composition on the viscosity is notaccounted for. Instead, it is assumed that the Dw in the porewater can be approximated by the Dw at
infinite dilution. /Anderko and Lencka 1998/ studied how Dw is affected by the salinity of different
solutions. For salt concentrations typical of groundwater at repository depth, this should lead to adecrease in Dw of only a few percent, compared to Dw at infinite dilution. Even for solutions similar
to brine, the diffusivity would not decrease by more than about 10%. It is realistic to claim thatthe uncertainty in viscosity from the groundwater composition is overshadowed by uncertainty
introduced by the varying temperature during the glacial cycle.
It is generally thought that the effect of anion exclusion on the effective diffusivity decreases withincreasing salinity (e.g. (Lehikoinen et al. 1992/). A laboratory study on a drill core sample from theForsmark and Laxemar sites /Löfgren et al. 2009/ indicate that the overall effect of anion exclusion
on De is limited to a factor of few, even at very low salinities. As anion exclusion seems to belimited even at very low salinities, the relation between salinity and anion exclusion is of lesser
consequence.
The formation factor is a geometric factor that is unaffected by the porewater composition.
In situ temperature and stress: In the site investigations, the connected porosity, formation factor,
and effective diffusivity have been measured in the laboratory at atmospheric pressure and room
temperature. The increase in temperature, compared to the in situ temperature, is relatively smalland should have an insignificant effect on the porosity but possibly a small effect on the pore spacegeometry (by way of redistribution of mineral grains due to their thermal expansion).
The substantial decrease in ambient stress in the laboratory may somewhat have increased the porevolume, as compared to in situ. In addition, porosity may have been induced in the excavation andsampling procedure. More importantly, changes in the ambient stress may, together with excavationinduced damage, significantly have affected the pore space geometry, resulting in an increased
formation factor and effective diffusivity. The effective diffusivity is normally assumed to be theproduct of Dw and the formation factor (cf. Equation 6‑22), where the formation factor depends
on the pore space geometry:
Ff = e t ⋅

δ
t2

6‑24

where εt (–) is the transport porosity, and δ (–) and τ2 (–) are the constrictivity and tortuosity of theporous system, respectively. Especially the constrictivity/tortuosity factor is expected to be affectedby changes in ambient stress, as pore throats (also called bottlenecks) may become constricted oreven closed off by the increasing stress. Because of the above mentioned issues, data obtained in thelaboratory needs to be reduced by a transfer factor.
Changes in the pore space geometry due to changing ambient conditions were certainly a factor
in the uplift of the bedrock, more than a billion years ago. The changes of in situ stress during
a glacial cycle, mainly induced by the formation and melting of an ice cap, are smaller incomparison but probably not negligible. In /Skagius and Neretneiks 1986/ granite samples fromFinnsjön, a site closely located to Forsmark, were re-stressed in the laboratory with up to 35 MPa

362

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overpressure. Even when going from 30 to 35 MPa overpressures there was a slight decrease in theformation factor /Skagius and Neretneiks 1986, Figure 6/. If extrapolating the data to encompass
an additional 30 MPa overpressure, due to the hydrostatic pressure induced by the thickest ice cap(cf. Section 7.1), it is conceivable that the formation factor would be halved, compared to at 35 MPa
overpressure. The extrapolation in itself is speculative, and it is unknown if samples that have once
been de-stressed can be re-stressed and still represent the in situ rock. The potential effect from theice cap is intended to be encompassed by the data range suggested in Section 6.8.10.
Consequence for the sorption partitioning coefficientRock type and degree of alteration/disturbance: Generally, sorption of solutes on weathered oraltered rock is stronger than on unaltered rock, although the extent to which this occurs depends on
the nature of the alteration and the properties of individual sorbing solutes. In some cases, however,sorptive properties may not be appreciably changed by alteration processes, or at least not nearly tothe same extent as would be predicted on the basis of the typically increased surface area and CECof the altered rock.
Mechanical damage and CEC transfer factors are used to correct laboratory sorption data obtained
from the Forsmark and Laxemar site investigations to Kd values appropriate for Forsmark metagranite (SKB rock code 101057) under in situ conditions. This rock type is assumed as a standard reference material for SR-Site, since its BET surface area data is documented by /André et al. 2009/ forintact monolithic samples. The majority of sorption measurements, BET surface areas, and supporting
geochemical data (specifically iron content) also concern this material, which makes it an obvious
choice for parameterisation of the in situ rock.
The sorptive surface area of the intact rock matrix is not expected to vary greatly amongst thedifferent unaltered rock types. Here, the term “unaltered” is intended to mean with respect to hydrothermal action as distinct from metamorphic alteration. It is noted, however, that the biotite content/­Sandström and Stephens 2009/ for Forsmark metagranite (1.8–12.4% vol. based on 49 samples) issomewhat greater than that for pegmatic granite (0.3–5.2% vol. based on 5 samples) thereby implying a reduced sorptivity for the pegmatic rock type.
Groundwater composition: Key parameters which influence sorption processes are pH and carbonate
in the case of surface complexing radionuclides, as well as the presence of solutes that compete forsorption sites. Various dissolved ligands in groundwater can directly compete to bind radionuclides
at the expense of sorption. In addition to direct competitive effects for radionuclide binding in solution,
carbonate is considered to be particularly important in that it can also form ternary surface complexes
with certain radionuclides. The sorption of, for example, ternary carbonate complexes of uranyl andneptunyl (i.e. U(VI) and Np(V)) is well described in the literature and has been directly confirmedby spectroscopy (e.g. /Bargar et al. 1999, Arai et al. 2007/). For ion-exchanging solutes, direct competition with base cations in the groundwater is expected to have the greatest impact on sorptivity.
A chemistry transfer factor is used to account for differences between contact solution compositions
used in laboratory investigations and groundwater compositions under application conditions (basedon the temperate climate case). For surface complexing solutes, it was not deemed feasible toestimate chemistry transfer factors and this uncertainty was assumed to be part of the overall levelof uncertainty associated with the data.
The sorption of redox sensitive radionuclides is strongly influenced by the redox state (Eh) of thegroundwater, which is determined by the presence of redox controlling pairs such as Fe2+/Fe(OH)3 or
SO42–/FeS(am) and is also related to pH and dissolved carbonate concentration. The redox speciationof radioelements places an important control on chemical behaviour. This is particularly true forelements that switch between anionic (usually non-sorbing) and cationic (usually sorbing) forms
when they change redox state. Other important instances occur when one of the redox states is much
more readily solubilised, or less prone to sorb than the other. In the Kd data compilation, oxidised
and reduced states were considered separately.
In situ temperature and stress: With regard to temperature, solutes that have exothermic sorption
reactions can, on theoretical grounds, be expected to have slightly reduced sorption at higher
temperatures, whereas solutes with endothermic sorption reactions can be expected to have slightly

TR-10-52363

increased sorption. Temperature can also have an indirect impact on sorption by altering the species
distribution of the solutes in groundwater. By altering the thermodynamic conditions for solution
speciation reactions, competitive effects are therefore subtly altered. The general effect of temperature is, however, expected to be vanishingly small relative to the overall uncertainty of the sorption
data and is therefore neglected.
Increased stress conditions may reduce access of solutes to the pore system by compressing theavailable microporous space and by pinching off diffusive pathways, thereby restricting access tosorption sites. It is possible that available sorptive surface areas under in situ stress conditions might
be less than that estimated on the basis of laboratory measurements of BET surface area on intact
core samples. The effect of this, however, is also thought to be small relative to the overall sorption
data uncertainty and has therefore been neglected in the recommendation of Kd data.

6.8.6

Conceptual uncertainty

Conceptual uncertainty of ε and De

There is no significant conceptual uncertainty associated with the physical porosity, or the connectedpart of that physical porosity. However, there is still a lack of knowledge on exclusion effects incrystalline rock where the two major exclusion effects are size exclusion and anion exclusion. In
the site investigation campaign, the connected porosity has been measured by water saturation or
C14-PMMA methods. It is safe to assume that solutes can enter where these fluids have entered.
The negatively charged mineral grains of igneous rock, surrounding the pores, induce a positively
charged electrical double layer in the porewater adjacent to the pore wall (e.g. /Stumm and Morgan
1996/). Generally, the thickness of the electrical double layer is so small that its volume should be
insignificant compared to the connected pore volume.
The effective diffusivity and formation factor are discussed at length, from conceptual points ofview, in the Geosphere process report. Here, only the most important conceptual uncertainty issues
are presented.
The validity of assuming Fickian diffusion can be questioned as it does not address multi-component
mass transfer and interactions between the charged solutes and charged pore walls. The MaxwellStephan diffusion model /Krishna and Wesselingh 1997/ is better equipped to handle this issue, but
has other problems, predominantly associated with a lack of input data for different solutes andsystems. In the Geosphere process report it is suggested that one should continue to use Fickian
diffusion in SR-Site. However, it should be remembered when discussing data uncertainty thatthe conceptual uncertainty is non-trivial. This is of importance when comparing data obtained byconceptually different methods, as the data are evaluated by equations that do not to 100% represent
the studied system.
There are conceptual uncertainties concerning Equation 6‑22. The equation only applies in case there
are no exclusion effects, for example anion exclusion and size exclusion, or enhancing effects suchas surface diffusion. As such effects are present; there is the issue of how to correct for them. Thiswas in SR-Can dealt with by lowering the effective diffusivity of all anions by a correction factor of
10. No correction factor was implemented for non-charged species or cations (even though it wassuspected that there may be a surface diffusion effect for certain cations /Liu et al. 2006/).
When estimating the formation factor or effective diffusivity based on electrical methods, the analogy between ionic mobility and diffusivity is used. With the Einstein relation one can convert ionic
mobility μ (m2/V·s) to diffusivity, as shown by Equation 6‑25:

D=

m RT
zF

6‑25

where z (–) is the charge number of the ionic solute, and R (J/mol·K), T (K), and F (C/mol) are
the gas constant, temperature, and Faraday constant, respectively. There are similar conceptual
uncertainties associated with this analogy, and its applicability in the porous system of crystalline
rock, as with the usage of Fickian diffusion theory. Perhaps one can say that there may be even more
uncertainty associated with this analogy, as relatively few experiments and tracer tests have been
carried out using electromigration as the migration mechanism.
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When estimating formation factors based on electrical resistivity measurements, it is evident that
surface conduction in the electrical double layer introduces uncertainty. Therefore, in this document
we chose to refer to all formation factors measured by electrical resistivity methods as apparent formation factors, unless corrections have been performed for surface conduction effects. It should becautioned that this terminology was not used in site investigation reports (e.g. /Selnert et al. 2008/)
and not consistently in SDM reports (e.g. /Crawford 2008/). By correcting for surface conduction
effects, the formation factor can be estimated from the apparent formation factor (as discussed in
Sections 6.8.7 and 6.8.10).
It is conceivable that anion transport in pore throats or bottlenecks may be limited. In such a bottleneck, a significant portion of the pore cross section area may be occupied by the electrical double
layer. This is illustrated in Figure 6‑70, showing a pore where the bottleneck is almost completely
occupied by the positively17 charged electrical double layer (dashed line). In case the bottle neckis even more constricted due to an increase in ambient stress, anion transport may be completely
hindered while cation transport is allowed for. The consequence for the safety assessment of thisconceivable “membrane” effect is conceptually uncertain.
As there in general is significant conceptual uncertainty regarding De, and as the models used in thesafety assessment only to a certain degree represent the studied system, the uncertainty discussed
in this section may overshadow data uncertainty discussed in Section 6.8.7 and natural variability
discussed in Section 6.8.8. This needs to be reflected upon in the final data selection. We believethat conceptual uncertainty effects are encompassed by the uncertainty ranges suggested in subsection 6.8.10.
Conceptual uncertainty of Kd

The use of a linear model of sorption as implied by the constant Kd concept is a simplification ofwhat in reality is a complex, chemically reactive transport process. Generally speaking, the Kd value
is simply a statement of the expected equilibrium ratio of sorbed and dissolved solute for a given setof conditions, and is not necessarily related to considerations of any specific sorption mechanism or
retention process. The use of a constant Kd value in safety assessment modelling, however, implies
a retention process with particular characteristics that is combined with a model of advective anddiffusive mass transfer to give a quantitative measure of solute transport retardation. For this reason
it is very important that the magnitude of the Kd value selected to represent the retardation process
correctly captures the physics of the postulated retention mechanism. It should be noted that the term
“sorption” is a very broad concept that describes the processes by which dissolved solutes are sorbed(adsorbed or absorbed) on, or in another substance /IUPAC 1997/, which can also be taken to include
processes such as surface precipitation and solid solution formation. In the context of radionuclidetransport (and the sense in which the term is used in this report), however, “sorption” is used strictly
to refer to adsorptive interaction with mineral surfaces by way of electrostatic and covalent chemicalbonding, and specifically excludes other related processes.

Figure 6‑70. Illustration of a pore bottleneck occupied by the electrical double layer.

17
For anions it should be noted that the repulsive force from the negatively charged mineral surface outweighs
the attractive force from the electrical double layer.

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365

The conceptual uncertainty related to the use of a constant Kd, however, extends also to the interpretation of laboratory data used to establish its magnitude. This is a significantly more difficult type of
uncertainty to handle owing to that if processes giving rise to retention in laboratory investigations
are incorrectly identified as sorption, this can invalidate the subsequent modelling of transport
retardation. Although such reaction mechanisms are frequently favourable as additional retardation processes, over and above that provided by purely sorptive interactions, the danger of these
confounding processes is that they scale differently with regard to the physics of sorptive retardation
modelled in safety assessment transport calculations.
Great care is generally taken in laboratory investigations to avoid situations where confounding
factors might skew the interpretation of sorptive retention. This is generally achieved by using
extremely low trace concentrations of radionuclides and designing experiments to avoid situations
where abrupt changes in solution chemistry might occur. Such processes, however, are very difficult
to control and in most cases there is still some residual uncertainty concerning the true nature of theretention process as it is quantified.
The scale on which the porous system is connected

Based on recent research, we suggest that the porous system of the host rock is connected on all
scales relevant for a safety assessment. Furthermore, we suggest that there is little conceptual
uncertainty associated with this view. However, it should be noted that up until recently the issue of
long-range pore connectivity has been debated within the scientific community (see e.g. /Löfgren
and Neretnieks 2006/), and that our view may be challenged.

6.8.7

Data uncertainty due to precision, bias, and representativity

Diffusion available porosity

There are three general steps that need to be taken when estimating the in situ diffusion available
porosity from laboratory measurements, all in which data uncertainty may be induced. The firststep includes estimating the connected porosity of the laboratory samples at hand. The second stepis estimating how much the laboratory porosity overestimates the in situ porosity, as the laboratory
samples are de-stressed. As a third step, one needs to estimate the fraction of the in situ connectedporosity that is diffusion available.
Concerning measurements of the connected porosity in the laboratory, such are made using a water
saturation technique and a C14-PMMA impregnation technique. If the sample is completely saturated in
the water saturation technique, the data uncertainty is small. If the samples are only partly saturated,
the porosity will be underestimated, possibly giving rise to a general bias. However, on average
this effect is not expected to be large (cf. Section 4.3 of /Selnert el al. 2008/). The C14-PMMA
technique is described in /Penttinen et al. 2006/. The rock sample is saturated by a C-14 labelledmonomer which polymerises in the porous system. The connected porosity is calculated by using 2D
autoradiographs of sawn rock surfaces. In the site investigations, the method gave similar results as
the water saturation technique, although it underestimated the porosity values by 10 to 40%, relative
to the water saturation technique /Selnert et al. 2008/.
There are indications from porewater studies (e.g. /Waber and Smellie 2008/) that the laboratorywater (re)-saturation technique may slightly overestimate the in situ porosity, owing to stress releaseand drill core damage effects. In /Waber and Smellie 2009/ iodide was added to the drilling fluid
while core drilling a section of borehole KFM02B. In case of immediate stress release and creationof additional porosity, this iodide spiked drill fluid would (to some extent) enter the induced porosity.
By leaching the samples at the laboratory, the induced porosity could be estimated from the leachediodide. The conclusion was that the additional porosity induced in the drilling was insignificant (onlya fraction of 2.4% of the connected pore volume). However, a recognised limitation to this method isthat the constriction of the drillcore by the drill stem will inhibit significant stress release in the borehole. The effect of potential stress release after the drillcore is taken out of the borehole is missed. If
comparing the results from this study with those of the study discussed in the below paragraph, one
may draw the conclusion that most of the porosity is induced after the drillcore is removed from thedrill stem.

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In a second study, 16 drillcore samples of rock type 101057 from boreholes KFM01A and KFM02B
were subjected to hydrostatic compression tests and subsequent triaxial compression tests /Jacobsson
2007/. The volume of micro cracks, originating from stress relaxation and mechanical damage effects
from the core drilling, was estimated by analysing the volumetric response during the hydrostatic
compression tests. From the measurements the induced micro crack porosity could be singled out.
Based on Table 5‑3 and 5-5 of /Jacobsson 2007/, the mean and standard deviation of the estimatedmicro crack porosity are 0.05%±0.03%. This should be compared with the recommended stressreleased porosity of 0.23%±0.11% for rock type 101057 (Table 4‑6, outside deformation zone, novisible fracture /Crawford 2008/). Based on this comparison, it is reasonable to suggest that thestress-released connected porosity should be multiplied by a factor of about 0.8 to represent in situ
conditions. This factor is also suggested to be propagated to SR-Site.
Concerning the fraction that is diffusion available, out of the connected porosity, we argue that thisfraction is close to unity, even for anions. The thickness of the electrical double layer is very small
compared to the average pore aperture. While the pore aperture on average is on the micrometerscale or somewhat less (see Figure 8-20 of /Birgersson and Neretnieks 1988/ and image in /Möriet al. 2003/ that is reproduced in /Löfgren et al. 2007, Figure 2-6/), the electrical double layer israther on the scale of a few nanometres (e.g. /Hiemenz 1986, Chapter 12/). However, it is noted that
this position may be disputed. For example, in /Lehikoinen et al. 1992/ it is argued that the electrical
double layer plays the role of excluding a significant fraction of the pore space for anions.
All in all, the measured laboratory porosity may be somewhat underestimated in cases where thesaturation is incomplete. On the other hand, due to the fact that rock is de-stressed in the laboratory,
directly applying laboratory data to in situ conditions may give rise to a slight overestimation. With
the accumulated knowledge, it is reasonable to say that the error in the recommended diffusion
available porosity should not be more than 50%.
Effective diffusivity and formation factor

Depending on whether the formation factor and/or effective diffusivity are measured in situ or in thelaboratory the conditions and methods differ, which will in turn affect the data uncertainty. In the
below text, data obtained during the site investigations or other campaigns of interest are divided
into four general categories:•

Through-diffusion measurements in the laboratory•

Electrical resistivity measurements in the laboratory•

Electrical resistivity measurements in situ•

In-diffusion measurements in situ (but not performed at Forsmark)Through-diffusion measurements in the laboratory: The effective diffusivity has been measured in
the laboratory using HTO as the tracer. By evaluating the break-through curves from the experiments
based on Fick’s second law, the effective diffusivity and capacity factor can be evaluated (see /Selnert
et al. 2008, Sections 3.4 and 4.4/ for details). Figure 6‑71 shows an example of breakthrough data
and the associated modelled breakthrough curve.
As can be seen in Figure 6‑71, the model produces a good fit to the data and very limited data
uncertainty is introduced. For more information on through-diffusion measurements /Selnert et al.
2008, Byegård et al. 2008, Crawford 2008/ are recommended.
The recommended way of correcting the data for de-stressed laboratory conditions relies on
information from in situ and laboratory electrical resistivity measurements, as described below
and in Section 6.8.10.

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367

Fit to model
3,50E-03

3,00E-03

exp data
model

2,50E-03

Cr

2,00E-03

1,50E-03

1,00E-03

5,00E-04

0,00E+00
0

50

100

150

200

250

t (d)

Figure 6‑71. Breakthrough curves in through diffusion test. On the y-axis is the tracer concentration andon the x-axis the time. Diamonds represent experimental data and curve the model results. Reproduced from
Figure 4-12 of /Selnert et al. 2008/.

Electrical resistivity measurements in the laboratory: These measurements, described in e.g. /Thunehed2007/, may be used for estimating a transfer factor for de-stressed laboratory conditions, applied to
De data from laboratory through diffusion measurements. By measuring the electrical resistivity ρr(ohm.m) of rock samples saturated by an electrolyte of known electrical conductivity κw (S/m), theapparent formation factor Ffapp (–) can be obtained:
Ff app =1
k w ⋅ rr

6-26

When estimating the formation factor based on the apparent formation factor, one needs to make corrections for surface conduction. The influence of surface conduction on the apparent formation factor
is discussed in /Crawford and Sidborn 2009, Appendix D/ and /Löfgren et al. 2009/. In summary,negatively charged mineral grains surround the pores and induce a positively charged electrical
double layer in the porewater, directly adjacent to the pore wall (see Figure 6‑70). The cations in thiselectrical double layer cannot be removed by for example leaching, as electro-neutrality at the mineral grain/porewater interface must be upheld. In this layer, current can be propagated in a process
called surface conduction (e.g. /Revil and Glover 1997/) at an amount that is suggested to be fairly
independent on the porewater chemical composition. When measuring the formation factor basedon electrical measurements, one wants to study the current propagated in the bulk part of the pore,as this current is directly influenced by the formation factor. Accordingly, one needs to subtract thecurrent propagated in the electrical double layer from the total current running through the porous
system. This is done in Equation 6‑27, in terms of formation factors and electrical conductivities(see derivation in /Crawford and Sidborn 2009/):k
Ff = Ff app − s
6-27
kw
where κs (S/m) is the surface conductivity. In has been shown that there is an empirical relation
between the formation factor and surface conductivity, which is estimated to:k s = 0.0012 ⋅ Ff 0.415

6-28This empirical relation is based on data in /Ohlsson 2000, Löfgren 2004, Löfgren at al. 2009/,
which are shown in Figure 6‑72.

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Surface conductivity (S/m)

1.E-04

1.E-05Laxemar (Löfgren, 2001)

y = 1.20E-03x4.15E-01

Äspö (Ohlsson, 2000)
Forsmark (Löfgren et al. 2009)

1.E-06
1.E-06

1.E-05

1.E-04

1.E-03

Formation factor

Figure 6‑72. Surface conductivity vs. formation factor. Based on measurements on crystalline rock samples
from the Oskarshamn and Forsmark sites.

By combining Equations 6‑27 and 6‑28, Equation 6‑29 is obtained:
Ff = Ff app −0.0012 ⋅ Ff 0.415

kw

6-29

This is the semi-empirical equation suggested for use in SR-Site when performing corrections forsurface conductions, particularly for in situ but also for laboratory apparent formation factors. In thesite investigation laboratory programme, samples have been saturated with a highly saline solution(1 M NaCl) prior to the resistivity measurements. This effectively reduces the magnitude of the correction, as the apparent formation factor only overestimates the formation factor by a few percents.
After performing the correction, the obtained formation factors should be similar to those obtained in
the through-diffusion experiments on the same drill core samples. Figure 6‑73 shows the laboratory
apparent formation factors obtained by electrical methods versus the corresponding formation factors
obtained in through-diffusion tracer tests, using HTO as the tracer.
Although the y-axis shows the apparent formation factor, the appearance of Figure 6‑73 would bevery similar if instead the formation factor was displayed, due to the very limited effect of surface
conduction. The data indicate that the electrical method provides formation factors that, on average,
are about two times larger than the through diffusion formation factors. This factor of about two has
been observed previously (e.g. /Ohlsson 2000/), also on sample on the decimetre scale /Löfgren and
Neretneisk 2006/. A recent study on samples saturated by a solution of a similar electrical conductivity as the in situ porewater confirms this picture (see Figure 7-9 of /Löfgren et al. 2009/, but note thatthe discrepancy partly may be due to anion exclusion). An explanation for this may be that there arelimitations in the applicability of the analogy between diffusion and electromigration in crystalline
rock. Another explanation is that as alternating current is used in the resistivity measurements,
capacitance effects may affect the results. If so, capacitance effects can only partly account for thedeviation, as comparative resistivity measurements using alternating and direct current give similar
results /Löfgren and Nerentieks 2006, Löfgren et al. 2009/.

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Apparent formation factor (-)

1.0E-03

1.0E-04

1.0E-05
1.00E-005

1.00E-004

1.00E-003

HTO formation factor (-)

Figure 6‑73. Comparison between laboratory apparent formation factors and through diffusion
tracer test (HTO) formation factors. Modified from Figure 3-6 in /Byegård et al. 2008/. Data from
/Selnert et al. 2008/ and Sicada (delivery note Sicada_09_098). Data also found in and reports by
Thunehed (e.g. /Thunehed 2007/).

Electrical resistivity measurements in situ: By measuring the in situ electrical resistivity of the rocksurrounding a borehole, and by estimating the electrical conductivity of the porewater, the apparent
formation factor can be obtained (cf. Equation 6‑26). The method and measurements are describedin e.g. /Löfgren and Nerentieks 2002, Löfgren 2007/. The advantage of these measurements is thatthe apparent formation factors are obtained at the in situ stress. The disadvantage is that they areassociated with a number of sources for data uncertainty, where the prominent are listed below:•

The validity of the analogy between ionic mobility and diffusivity, and other unknown sourcesof uncertainty manifesting in a somewhat overestimated formation factor as compared to throughdiffusion formation factors.

•

Surface conduction, giving rise to an enhanced current running through the sample.

•

Water-bearing fractures disturbing the rock resistivity measurements.

•

The electrical conductivity of the porewater, which is estimated from the electrical conductivityof freely flowing water and from data obtained by porewater extraction/leaching of drill coresamples in the laboratory.

Other sources for minor data uncertainty are discussed at length in Appendix H of /Crawford 2008/
and Appendix D of /Crawford and Sidborn 2008/. Concerning the analogy between diffusion andelectromigration (as already discussed above) it is likely that this uncertainty is limited to a factor of
about two, also in situ.
Concerning the effect of surface conduction, this can be handled by use of Equation 6‑29. However,it should be noted that the correction is much more significant at the intermediate electrical conductivities of the in situ porewater, as compared to in the laboratory. To avoid unreasonably large corrections, no in situ formation factors are estimated if the porewater electrical conductivity is below
0.5 S/m. For a typical Ff of 10–5 and a κw of 0.5 S/m, the apparent formation factor overestimates theformation factor by a factor of three, according to Equation 6‑29.

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Concerning the handling of water-bearing fractures intersecting the borehole and disturbing themeasurements, it was decided in the site investigations to propagate two types of formation factors:• The rock matrix formation factor: as valid for the undisturbed and non-fractured rock matrix.
These data are obtained at locations at least 0.5 m away from any open fracture.
• The fractured rock formation factor: as valid for the fractured rock volume, except for that
directly adjacent to detected flow paths. This rock volume is to different degrees intersected byfractures that are open but hydraulically non-conductive (in practice a fracture was assigned as
hydraulically non-conductive if no hydraulic response could be detected by using the Posiva flowlog). These data are obtained at locations at least 0.5 m away from any detected flow anomaly.
By making this division, most of the data uncertainty introduced by fractures intersecting theborehole is avoided and all in all, the data uncertainty is small. This present text relies on the rockmatrix formation factor, as it is requested to supply migration data for the undisturbed rock. Most
likely the capacity for matrix diffusion is enhanced in rock volumes directly adjacent to flow paths,
as compared to the undisturbed rock. Therefore, the data delivered in this section are pessimistic ifused for radionuclide retention calculation. However, in modelling where it is not intuitive that alower effective diffusivity gives rise to a higher radiological risk, using these data is not necessarily
the correct choice. It can for example be argued that when modelling the diffusive transport of salt
(as part of hydrogeological modelling) it may be valid to use the effective diffusivity of the fractured
rock directly adjacent to flow paths (cf. Appendix A of this report).
The electrical conductivity of the in situ porewater has been estimated based on two methods. The
first is by measuring the electrical conductivity on freely flowing groundwater at specific fractures,and using this as an estimate for the porewater of the surrounding rock mass. Such measurements
have been performed in the hydrogeochemical programme /Laaksoharju et al. 2008/ and by the
Posiva flow log (e.g. /Väisäsvaara et al. 2006/). The data uncertainty associated with the actual
measurements is relatively small but larger uncertainty may be introduced when extrapolating theresults to rock volumes tens or of meters away from freely flowing water.
For a few boreholes there are complementary measurements where the porewater of drill cores is
extracted/leached (e.g. /Waber and Smellie 2005/). From the evaluated porewater chloride concentration, the electrical conductivity at the in situ temperature can be calculated. Figure 6‑74 shows thechloride concentration of borehole KFM01D, KFM06A, and KFM08C.
The data uncertainty introduced when estimating the porewater chloride concentration from theextraction/leaching, as well as when assessing the in situ electrical conductivity from the chloride
concentration, is relatively small (the total error is estimated to be within 50%). Part of this uncertainty originates in the stress-release of the samples, giving rise to an additional porosity that may
manifest in a somewhat underestimated porewater chloride concentration.
As becomes evident in Section 6.8.10, the recommended in situ formation factors are only based
upon measurements in boreholes KFM01D, KFM06A, and KFM08C, as porewater extraction/leaching has been performed in these boreholes. In KFM01D and KFM08C, the measured electricalconductivity of the porewater and freely flowing groundwater are similar. Therefore data fromboth methods were used as bases for estimating an electrical conductivity profile in the borehole.
However, for KFM06A the results deviate. Therefore it was chosen to base the electrical conductivity profile only of data from the porewater extraction/leaching. This result in a recalculation of theapparent formation factors, compared to those delivered in /Löfgren et al. 2006/, which is further
described in Section 6.8.10. For these three boreholes, it is considered that the electrical conductivity
profile is not generally overestimated, or underestimated, by more than a factor of two.
In-diffusion measurements in situ: Results from in situ in-diffusion experiments in the intact rockmatrix can more or less be considered to be without artefacts, even though they are associated withdata uncertainty. In Sweden, two such long-term experiments have been performed in crystalline
rock; one at the Stripa mine /Birgersson and Neretnieks 1988, 1990/ and one at the Äspö Hard
Rock Laboratory. In the Stripa campaign – part III, in situ pore diffusivities Dp between 2·10–11 and
5·10–10 m2/s were obtained for iodide (see Figure 8.22 of /Birgersson and Neretnieks 1988/). This
converts to effective diffusivities between about 6·10–14 and 2·10–12 m2/s (using ε = 0.3%). This is
somewhat higher than suggested for Forsmark, but still close enough to suggest that the Forsmark
data are reasonable. In situ results from the Äspö Hard Rock Laboratory campaign LTDE-SD, outlined in /SKB 2009b/, are still being evaluated. Accordingly they are not part of this data qualification.

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371

0

KFM01DKFM06A-100

KFM08C (GW: +8A)

-200
-300

Elevation (m)

-400
-500
-600
-700
-800
-900
-1000
0

5000

10000

15000

Cl in Porewater and Groundwater (mg/kgH2O)

Figure 6‑74. Chloride concentration of porewater and groundwater at Forsmark. Filled legends
represent porewater and unfilled legends represent groundwater. Reproduced from Figure 4-27 of/Laaksoharju et al. 2008/

Anion exclusion

As discussed previously, anion exclusion affects the effective diffusivity by way of lowering it (e.g.
/Liu et al. 2006/). In SR-Can a reduction factor of 10 was applied on De for anions, to account foranion exclusion. This is in line with the reduction factor earlier proposed in the Finnish spent fuelmanagement programme (e.g. /Rasilainen et al. 2001/).
In the Phase I/II laboratory part of the Long Term Sorption Diffusion Experiment (LTDE-SD),
performed on rock samples from the Äspö Hard Rock Laboratory, through diffusion tracer tests on
a number of drill core samples were performed /Vilks et al. 2005/. In these tests, the non-charged
tracer HTO and the anionic tracer iodide simultaneously diffused through the rock samples. The
results indicate that the anionic tracer on average had about a half an order of magnitude (rather
than one order of magnitude) lower effective diffusivity than the non-charged tracer /Vilks et al.
2005, Tables 5 and 6/.
Laboratory results from the Finnish program, comparing Cl-36 and H-3 diffusion in the laboratory/Lehikoinen et al. 1992, Table 2/, can be used in support of a reduction factor somewhere between
half an order and one order of magnitude. However, these tracer tests seem to have been performedon more “tight” rock (lower formation factor) than that at Forsmark.
SKB has recently conducted a laboratory tracer test study using iodide as the tracer in the throughelectromigration method (TEM). In through electromigration tracer tests, an electrical potential
gradient is used as the (main) driving force, as opposed to a concentration gradient in traditional diffusion tracer tests /Löfgren and Neretnieks 2006/. These measurements were performed on the same
Forsmark drill core samples that had previously been subjected to through diffusion measurements
using HTO as the tracer. By comparing the results (cf. samples 2 to 5 in Figure 7-13 of /Löfgren
et al. 2009/) only relatively small deviations could be detected. If taking the ratio of the TEM
formation factors (data from duplicate sampling tabulated in Chapter 7 of /Löfgren et al. 2009/) andthrough diffusion formation factors for the corresponding samples /Löfgren et al. 2009, Table 4‑1/,
this ratio is on average 0.87 with the standard deviation 0.45. Although there are difficulties involvedin comparing data from tracer tests based on different transport mechanisms, the results indicate amodest effect of anion exclusion, and that the reduction factor of 10 used in SR-Can is excessive.
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It is recognised that there is great uncertainty in this reduction factor, especially as it should beapplied to in situ conditions where the porous system to some extent is compressed compared toin the laboratory. However, in view of the results from the in situ diffusion experiments at Stripa/Birgersson and Neretnieks 1988/ using anionic tracers, suggesting that anion exclusion decreases
the effective diffusivity by half an order of magnitude, as compared to De for cations and noncharged solutes, seems reasonable.
In practise, Equation 6‑30 is suggested for use in estimating De for anions from De data for cations
and non-charged solutes:
log10 (De, anions) = log10 (De) – 0.5

6-30

The scale on which the porous system is connected

We suggest that the porous system is connected on all scales relevant for the safety assessment(at least decametre scale), and that there is little uncertainty or variability.
We base our suggestion on three pieces of evidence:•Long-range matrix diffusion has been observed in in situ tracer tests, for example in /Birgersson
and Nerentieks 1988, 1990/ where a diffusion profile of at least 40 cm was obtained in intact rock(40 cm was the scale of the overcored rock in the campaign).

•

Current can be propagated by solutes in the saturated porous system of the undisturbed rock, on
at least the metre scale (e.g. /Crawford 2008, Appendix H/). By using a focused rock resistivity
tool in situ, the current is sent into the rock matrix perpendicular to the borehole axis, at least
three meters into the bedrock (but probably also on a larger scale, e.g. /Löfgren and Neretnieks
2002/).

•

By studying the chemical composition of the porewater of intact rock, and by this way studying
large-scale diffusion profiles, it is suggested that in situ diffusion occurs over at least several tensof metres /Waber et al. 2009/.

Sorption partitioning coefficient

The principal biases and uncertainties which might affect the magnitude of the Kd value have beenbriefly discussed as conceptual uncertainty in Section 6.8.6. These biases concern both the extrapolation of data from laboratory experiments to in situ application conditions as well as the correct
identification of sorptive retention processes in laboratory investigations. Here, the term “bias” isused to describe various epistemic uncertainties relating to a lack of knowledge about the sorption
processes as they are quantified and extrapolated from one reference condition to another.
Measurement of sorption
Laboratory sorption measurements are typically carried out on crushed rock samples for different
contact times and groundwater compositions. The use of crushed materials is dictated by the need tominimise diffusive disequilibrium effects so that sorption measurements can be made in a realistic
time frame. In most cases, the apparent partitioning ratio Rd (m3/kg) is estimated by way of a radiometric mass balance that relates the initial and final activity of the contacting solution to the amountof solute sorbed. Usually the amount sorbed is not measured directly, so the accuracy with which
the initial and final solution activity can be quantified is of great importance for the overall accuracyof the Rd estimate. If the activity variance of the initial blank solutions (i.e. reference solutions
without added geological material) is sufficiently large relative to the final activity of the solution
with geological materials added, then it may not be possible to quantify the existence of sorption ina statistical sense. Similarly, if the strength of sorption is sufficiently high that the remaining solutein the contact solution is at or below the limit of detection, then it may not be possible to quantify anupper limit to the Rd value. In a practical sense, this means that it is very difficult to quantify the Rdof solutes much lower than 10–4 m3/kg or larger than ~1 m3/kg.

TR-10-52373

Extrapolation of data to site-specific conditions, using transfer factors
Given that the sorption of solutes takes place on the surfaces of geological minerals, the sorption
properties of the rock are strongly correlated with the available surface area of mineral grains within
the micro-porosity of the rock matrix. The crushing of rock materials generally results in the creationof new mineral surfaces that are not representative of the undisturbed rock in situ. This may include
both freshly cleaved mineral surfaces as well as surfaces belonging to previously unconnected porespaces in the rock. This needs to be specifically considered when extrapolating laboratory derived
sorption partitioning coefficients to values applicable for the rock matrix under in situ conditions.
The surface area of rock is typically assessed using the BET /Brunauer et al. 1938/ method. This
gives an approximate estimate of the total surface area which can then be used to approximately
relate the sorptive properties of different rock types and crushed size fractions.
Sorption of solutes frequently occurs preferentially on one, or more mineral phases of the rock which
may not comprise more than a few percent of the total mineralogical composition of the rock. In
the cases where preferential sorption does occur, it is typically associated with the so-called darkminerals, biotite (or its alteration product, chlorite) and to a lesser extent hornblende. These minerals
also typically dominate the cation exchange capacity (CEC) of the rock. The cation exchange capacities of rocks used in laboratory investigations are not necessarily representative of the site-specificrock types of importance. This is a particular problem for the extrapolation of literature data to dataapplicable at site-specific conditions. For this reason, the cation exchange capacity of the rock needs
to be considered (for certain elements) when extrapolating laboratory measured sorption properties
to in situ conditions.
Transfer factors are defined to extrapolate sorption data for a particular rock type, crushed size
fraction, and water composition to more accurately represent the sorptive properties of the intact
rock matrix in the repository volume. Since the creation of new surface area relates to both thecrushing process and microstructural damage incurred during bore core sample retrieval, the transfer
factor relating the surface area of crushed rock to in situ conditions is referred to as the mechanicaldamage transfer factor, fm. The transfer factor for extrapolation between rock types with different
cation exchange capacities is referred to as the CEC transfer factor, fcec. For the site-specific data setsfrom Forsmark and Laxemar, where measurements were made on a number of particle size fractions,an additional surface area transfer factor, fA (–) was used to standardise measurements to a givenreference particle size. The transfer factors are multiplicative and relate the apparent partitioning
ratio Rd to the Kd value recommended for intact site-specific in situ rock for the particular laboratory
groundwater composition (here, termed Kd0):

K d0 = ( Rd ⋅ f A ) ⋅ f m ⋅ f cec

6-31

In the background report detailing data selection and extrapolation /Crawford 2010, Section 5.1/,
these transfer factors have been used to predict ranges of Kd values thought to be pessimistic for the
purposes of SR-Site. In certain cases, both surface area and CEC need to be considered, whereas inother cases only surface area effects appear to be important. This is handled on a case by case basis
and is informed as far as possible by available supporting information in the scientific literature.
Since the values derived for fm and fcec are typically based on a small number of measurements froma limited set of bore core samples, the standard errors of the measurement data are used to estimate
an uncertainty distribution which can be used to expand the range of extrapolated Kd values toaccount for uncertainty in these transfer factors. Being defined as ratios of variables that either havenormal or lognormally distributed errors themselves, the uncertainty distributions for the transfer
factors can be assumed to be lognormally distributed (more detailed reasoning for this choice canbe found in /Crawford 2010/).
Since the Kd can be expected to vary in response to different groundwater compositions, an additional chemistry transfer factor, fchem, is defined to correct for differences between the groundwatercompositions used in laboratory investigations and application conditions. Where appropriate for
SR-Site, the fchem transfer factor is calculated on the basis of groundwater compositional statistics ina large control volume surrounding the repository. This is done without consideration of individual
flow path trajectories or spatial correlations. As a result, the overall groundwater compositionalvariability is lumped together as an assumed compositional uncertainty for randomly located flow
paths within the control volume.

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Since the uncertainty distribution of fchem is not necessarily log-normally distributed, this transfer
factor must be applied by convolution of the individual probability density functions for Kd0 andfchem:

K d = K d0 ⊗ f chem

6-32

Generally, the status of the site investigation and literature data is such that it is not possible todefine quantitative chemistry transfer factors to account for groundwater compositions deviating
outside of the ranges which have been investigated in the laboratory. In most cases this means that it
must be assumed that the laboratory derived data represent a sufficiently broad range of groundwatercompositions that all uncertainty related to groundwater composition is implicitly internalised in the
Kd ranges recommended for use in SR-Site.
For radionuclides that sorb by way of a surface complexation mechanism, it is expected that pH
and dissolved carbonate concentration have the largest impact on the sorption magnitude, followedby competition from other groundwater solutes for sorption sites. Competition for sorption sites
by other groundwater constituents is handled by using groundwater compositions in sorption
experiments that broadly approximate those expected under application conditions. The pooling of
Rd data, obtained for a range of different contacting solution compositions, thereby internalises theimpact of uncertain/variable groundwater composition in the final Kd ranges recommended for use in
SR-Site. Although the contact solution compositions used in laboratory investigations do not exactly
span the projected application groundwater conditions, this approach is judged reasonable. Owing
to the typically small liquid to solid ratios and small liquid volumes used in laboratory experiments,
compositional drift of contact solutions occurs, which increases the span of the laboratory compositions. This effect is also internalised in the recommended Kd ranges.
In a small number of cases, the laboratory data are sufficiently detailed that chemistry transfer factors can be defined to account for changing groundwater compositions. Principally, this affects theelements Cs, Sr, Ra, and Ni. In these cases, a combination of empirical and ion-exchange modelling
approaches have been used in an attempt to give more accurate predictions of appropriate Kd ranges
for application groundwater conditions at different times. Since it is not possible to rigorously accountfor temporally variable Kd values in safety assessment calculations, in cases where evolving groundwatercompositions imply a net change in sorptivity over time, Kd ranges are chosen to represent sorptive
properties for the most unfavourable groundwater composition expected to prevail for an extended
period of time (during the temperate climate domain). For ion-exchanging solutes this typically
implies the selection of Kd ranges for the most saline conditions expected.
Redox state under application conditions
The predominance of various oxidised and reduced species of redox sensitive radionuclides underapplication conditions has been addressed by thermodynamic modelling using the PhreeqC code in
conjunction with the SKB thermodynamic database and the groundwater compositions projected for
SR-Site as supplied by the SR-Site hydrogeological modelling team (cf. Section 6.1).
Impact of various biases on data suitability for application conditions
With regard to the scaling of laboratory Rd data to Kd values deemed appropriate for in situ conditions, the corrections implied by the mechanical damage and CEC transfer factors (fm and fcec) areassociated with large conceptual and data measurement uncertainties. Since the sorptivity of therock is assumed to be linearly proportional to total BET surface area and CEC, this can introduce
a considerable bias in data extrapolation. It is clear from both the site investigation data, as well
as some of the literature references, e.g. /Huitti et al. 1996/, that the measured sorptivity of altered
and weathered materials do not scale simply relative to their unaltered counterparts with regard to
surface area and CEC. In many cases the sorptivity is higher than for unaltered rock types, although
not nearly as much as would be expected based on BET surface area and CEC. This is partly due
to the fact that not all mineral phases contribute equally to the overall sorptivity and partly due tothe alteration of strongly sorbing minerals such as biotite to other less strongly sorbing forms suchas chlorite. The extrapolation of sorption data for such materials to make predictions of sorption
properties for unaltered rock types can therefore give rise to excessively over-pessimistic transfer

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375

factors that are not physically meaningful. The same can also be said for rocks with very different
mineralogical compositions, compared to the target rock type (Forsmark metagranite, SKB rockcode 101057) of relevance for safety assessment calculations.
Data for altered materials have been avoided as far as possible in the data selection process,although in some cases it has been necessary to use literature data for rocks taken from sites withsomewhat different geochemical characteristics to Forsmark or Laxemar site-specific rocks. All rocksamples obtained from the Forsmark and Laxemar sites are considered to be sufficiently similargeochemically that the sorption data can be extrapolated using appropriately scaled transfer factors
to represent Forsmark metagranite.
When several data sets are combined for different rock types (and groundwater compositions incases where fchem cannot be estimated), the combined data uncertainty is frequently dominated bydifferences between data sets rather than internal variations of the underlying individual data sets.
This can lead to very broad uncertainty distributions for Kd with 95% confidence intervals ranging
over as much as 2–4 orders of magnitude. Generally speaking, extrapolation of data to site-specificconditions gives uncertainty ranges that appear to increase with the number of data sets used in theassessment.
Although there were close to 950 data points each for Cs, Sr, and Am/Eu sorption at the Forsmark
site, these were based on crushed rock samples from 8 different borehole sections featuring three
distinct rock types. Sorption data for Ni, Ra, Np(V), and U(VI) (roughly 200 data points each) werebased on crushed rock samples from two borehole sections representing a single rock type. The
sorption measurements were made using a variety of contact groundwater compositions described
as fresh, marine, saline, and brine. Roughly the same numbers of data points exist for Laxemar sitespecific rock types. Although the large number of data points gives the impression that there is a very
good statistical basis for parameterisation of site-specific materials, it should also be rememberedthat the total number of data points includes replicates as well as different contact times and particle
size fractions of the same crushed core sample.
Most literature data examined, on the other hand, were based on crushed samples taken from asingle borehole section at a particular site. These generally take the form of replicate samples fora single particle size and contact time (occasionally different spike concentrations of the element
being studied). It is therefore probable that there is some degree of bias in the estimated uncertainty
distributions for literature data since these are based on much smaller sample sets representing a
restricted range of varied experimental parameters.
To account for the sample size biasing effect of the typically small data sets reported in the literaturesources, the uncertainty of the mechanical and CEC transfer factors was propagated in the calculations to give an expanded uncertainty interval in the final data range. This was achieved using a
re-sampling technique based on aggregation of the underlying uncertainty distributions for individual
data points (essentially assuming a Gaussian mixture model for uncertainty aggregation). Since alarge amount of variation was already apparent in the site-specific data sets, this technique was notdeemed necessary in those cases.
A particular bias, that has not been previously mentioned, concerns the interpretation of timedependent behaviour in laboratory measurements. The Forsmark and Laxemar site investigation dataconsisted of Rd values measured on different crushed size fractions and a geometric time schedule
for contact times ranging from 1–180 days. However, since the time dependent behaviour in the raw
data sets for site data appears to be a minor component of the overall variability/uncertainty, the data
sets are considered in their entirety without any filtering with regard to particle size or contact time.
Generally it was found that attempts to fit the data to simplified models of time dependent behaviour(i.e. modelling of diffusive disequilibrium) gave rise to highly non-conservative Kd ranges owing
to the uncertainty of other model parameters required to make the fit. A pronounced pH drift of up
to 1.5 pH units was also documented in a number of (all) control samples at the conclusion of thesorption experiments which confers additional doubt on the assumption of diffusive disequilibrium
being the primary mechanism for the observed time dependent behaviour. It was concluded thatthe most pessimistic assumption with regard to derivation of recommended data for SR-Site was toneglect the time dependent behaviour and assume it as a component of the overall data uncertainty.
Full reasoning behind this choice can be found in /Crawford 2010/.

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6.8.8Spatial and temporal variability of data

Spatial variability of porosity

In Section 4.4.1 of /Crawford 2008/, the porosity measured in the site investigation laboratory
programme is summarised. By comparing the porosity of different rock types, in deformation zones
and non-deformed rock, it can be concluded that the spatial variability is small (cf. Table 4-6 of/Crawford 2008/). For the three dominating rock types of rock domain RMF029, the arithmetic mean
± one standard deviation is 0.23±0.09% for 101051, 0.4±0.23% for 101061, and 0.24±0.13% for101057 for the non-deformed rock matrix. Subordinate rock types in non-deformed rock show very
similar porosities. In deformation zones, the values are somewhat higher. In rock domain RFM045,
rock type 101058 is dominating (see Table 4-1 of /Crawford 2008/). For this rock type, the arithmetic
mean ± one standard deviation of the porosity is 0.22±0.06%.
The porosity should be flow path averaged before use in SR-Site solute transport modelling. Thismeans that all porosities encountered along the flow path should be averaged, by way of taking theirarithmetic mean. As the natural variability of local porosities is so small, the flow path averaged
porosity can for safety assessment purposes be regarded to feature no significant spatial variability.
Spatial variability of effective diffusivity and formation factor

In Section 4.4.2 of /Crawford 2008/, the formation factor as measured by through-diffusion measurements in the site investigation laboratory programme is summarised. By comparing the formation
factor of different rock types it can be concluded that the spatial variability is small (cf. Table 4-9 of/Crawford 2008/). For the rock types dominating the samples, the arithmetic mean ± one standard
deviation was (1.0±0.8)·10–4 for 101051, and (1.6±1.5)·10–4 for 101057 for the non-deformed rockmatrix. Subordinate rock types in non-deformed rock show very similar data. The results suggest
only a small difference between non-deformed rock and rock in deformation zones. It should be
remembered that the laboratory data are believed to be substantially affected by mechanical damage
and stress-release.
In Appendix H of /Crawford 2008/ the in situ apparent formation factors, as measured in the site
investigation downhole programme, are summarised. Figure 6‑75 shows the cumulative distributionfunctions (CDF) for different rock types.
1.01010581020170.9101061101051101057

0.8
0.7

CDF

0.6
0.5
0.4
0.3
0.2
0.1
0.0
-5.5

-5.3

-5.1

-4.9

-4.7

-4.5

-4.3

-4.1

-3.9

-3.7

-3.5

Log 10 Ff

Figure 6‑75. In situ rock matrix apparent formation factors for different rock types in rock domain
RFM029. Reproduced from Figure H-10 in /Crawford 2008/. Note that Ff should be denoted Ffapp according
to the present terminology. The numbers (101058 etc. are SKB rock type codes).
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As can be seen there are relatively small differences between rock types. In /Crawford 2008,
Appendix H/ the correlation length was investigated by means of variograms. It was shown thatroughly 20% of the variance occurs with a correlation length of roughly 1 m or less, while on theorder of 80% is associated with a correlation length of about 5–7 m. As the formation factor andeffective diffusivity should be flow path averaged before used in SR-Site solute transport modelling,
the spatial variability between flow paths is suggested to be minor.
Spatial variability of sorption partitioning coefficient

The Kd for a particular radionuclide can be generally expected to vary along a flow path dependingon the local geochemical composition of the rock, its available sorptive surface area, and local
groundwater composition. In the RETROCK project /RETROCK 2004/ it was demonstrated
formally that, under certain simplifying assumptions, the flow path average product of the bulk rockdensity, Kd, and De is a parameter of central importance for the estimation of solute transport retardation by matrix diffusion and sorption.
In cases where one can differentiate between spatial variability in Kd data and uncertainty it ispossible, in principal, to define a flow path average. If the spatial distributions of De and Kd areassumed to be independent and identically distributed it is relatively straightforward to show thatthe product of mean De and mean Kd is equal to the mean of the product of De and Kd. This suggeststhat the appropriate flow path average measure of Kd along a flow path is the arithmetic mean of the
distribution describing the spatial variability of Kd. In the compilation of Kd values recommended foruse in SR-Site, however, uncertainty is by far the dominating feature of the empirically derived data.
It is therefore not possible to defend the use of a simple arithmetic mean Kd as a flow path averagefor modelling purposes, and the median of the Kd uncertainty distribution must be assumed as thebest estimate of the flow path average.
If the groundwater composition was assumed to be spatially variable although temporally invariant,
it would also be possible to estimate flow path specific (average) chemistry transfer factors (fchem)
for radionuclide transport from the repository. The spatial distribution of groundwater compositions
are, however, supplied without reference to specific flow paths or radionuclide release locations
and therefore this is not strictly possible. Since this information is not available, spatial variationin groundwater composition is therefore treated as a sub-component of overall Kd data uncertainty.
Calculations made for the elements Cs, Sr, Ra, and Ni, for which it is possible to calculate chemistrytransfer factors, indicate that the impact of variable groundwater chemistry is relatively minor incomparison to the overall empirical data uncertainty, and therefore does not need to be considered
in detail.
Temporal variability of porosity, effective diffusivity, and formation factor

The porosity, effective diffusivity, and formation factor do not feature significant temporal variability. In the Geosphere process report it is noted that during glaciation, the additional pressure fromthe ice cap may slightly compress the porous system. Additionally, changes in pore water chemistry
may slightly modify the effective diffusivity and anion exclusion. However, these effects are judged
to be minor and it is recommended not to account for temporal variability in these data. Instead thedata ranges given aim to encompass these effects.
Temporal variability of sorption partitioning coefficient

The use of codes that assume a constant Kd value in transport calculations precludes the possibilityof including the direct impact of temporally variable Kd values in the safety assessment. For the
purposes of safety assessment, however, it is more important to cautiously bound the envelope ofradiological risk rather than simulate reactive transport processes exactly as they would occur. To
account for temporal changes in groundwater chemistry, which might influence sorption, Kd values
are chosen for the most unfavourable hydrochemical conditions expected to prevail for an extended
period of time during the repository groundwater evolution. For ion-exchanging solutes, this means aselection of Kd values for the most saline conditions expected. For other, surface-complexing solutes
it has not been possible to quantitatively assess the impact of changing groundwater compositions,owing to the large uncertainties inherent in the selected Kd data. During the temperate time period,
378

TR-10-52

for example, the gradual freshening of the groundwater due to meteoric intrusion is expected to givea trend towards lower pH and higher carbonate concentrations over time. It is not clear in most cases
whether this should be expected to give rise to higher or lower sorptivity, owing to the complexityof the surface complexation mechanisms and the ambiguous nature of the available measurement
data. This uncertainty has been handled to the extent possible by combining data sets for all relevant
groundwater compositions to provide an overall range of uncertainty that implicitly includes theseeffects. For redox sensitive solutes it is necessary to also consider the consequences of temporalhydrochemical variability on the particular redox species being transported. The gradual fresheningof the groundwater in the repository environment during the temperate climate domain, for example,
suggests that it cannot be guaranteed that uranium will always be in reduced form in the rock.
Full details concerning the handling of uncertainties relating to temporal variability can be found in/Crawford 2010/.

6.8.9

Correlations

Porosity, effective diffusivity, and formation factor

The important functional relationships between the formation factor, effective diffusivity, rockresistivity, etc. are shown in Equations 6‑22 to 6‑29 (although the latter two are semi-empirical
relationships).
If choosing an effective diffusivity from a PDF, the same data should be applied for all cations andneutral species, for all flow paths. This effective diffusivity should be corrected by a reduction factor
to account for anion exclusion, and be applied for all anions and flow paths.
For the porosity range of interest for SR-Site, the porosity should not be correlated to the formation
factor or effective diffusivity (that is, the so-called Archie’s law should not be used).
Sorption partitioning coefficient

The correlations of Kd data are discussed in Section 3.2 of /Crawford 2010/. Correlations in sorptivity are assumed in the assignment of Kd values for elements where no data is available. The useof “analogue” solutes in place of actual data is based on geochemical similarity and considerationof specific reaction mechanisms. Correlations are also considered in the stochastic selection of Kd
values for use in transport calculations. It is expected, for example, that Kd values for ion exchanging
solutes will be mutually correlated with ionic strength. Similarly for cations that sorb by way of asurface complexation mechanism, any shift in hydrochemical conditions should give rise to similar
impacts on sorption (although not necessarily in equal proportionality) for all solutes that have ananalogous reaction chemistry. One exception is made for redox sensitive solutes since the transition
from reducing to oxidising forms occurs at different Eh levels for different elements. Uranium, forexample, could possibly be present in oxidised form while plutonium, neptunium, and technetium
remain in reduced form at the redox potentials projected during the SR-Site temperate climate
domain.
Together with the tabulated recommended Kd data in Table 6‑89 (see Section 6.8.10), each element,or redox speciation, is either assigned a correlation group (1 or 2) or no correlation group (–).
Sorption partitioning coefficient and effective diffusivity

If performing transport modelling where certain solutes that normally sorbs are assumed to be
non-sorbing (e.g. what-if modelling), a rational for this may be that the radioelements predominantly
exist in anionic form. As an example this may be the case for a number or radioelements if oxidisingconditions prevail in the repository. In such a case, it may be prudent to use the effective diffusivity
for anions for these radioelements. In other cases, cationic forms may be (pessimistically) assumed
to be non-sorbing due to competition of other solutes (for example at high salinities). If this is thegrounds for assuming pessimistic Kd values, it may be prudent to use the De of cations.

TR-10-52379

6.8.10 Result of supplier’s data qualification
Diffusion available porosity

As discussed in Section 6.8.8, the porosity obtained in the laboratory for the dominating rock type(101051) of rock domain RDM029 is 0.23±0.09%. For the dominating rock type (101058) of RFM045,
the laboratory porosity is 0.22±0.06%. As the repository is suggested to be predominantly located inrock domain RFM029 we suggest an averaged laboratory porosity of 0.23% for the host rock.
For correction of laboratory porosities to in situ porosity it is recommended to multiply the laboratory porosity by a factor of 0.8 (see discussion in Section 6.8.7). From this, we suggest an averaged
in situ porosity of 0.18%. The porosity should be flow path averaged before use in solute transportmodelling. As the natural variability of local porosities (obtained on cm-scale samples) is so small, it
is considered that there is no significant spatial variability between flow paths. Therefore, the single
point value 0.18% is recommended for the flow path averaged in situ porosity
Concerning the diffusion available porosity, the fraction of the connected pore space that is available
for solutes is close to unity, for anions, cations, and non-charged species. Therefore, the suggested
flow path averaged diffusion available in situ porosity is 0.18% for all solutes. The uncertainty
associated with this single point value is so small that no range is suggested.
Formation factor and effective diffusivity

As discussed in Section 6.8.7, directly basing the recommended in situ formation factors and effective diffusivities on laboratory through diffusion data would lead to overestimated data. Corrections
are needed to reflect the fact that samples in the laboratory are stress released. On the other hand,
directly basing the recommended data on in situ apparent formation factors would also lead to overestimated data, as surface conduction effects need to be corrected for. In addition, there are effects
associated with the electrical resistivity method that are not well understood, which introduce minor
data uncertainty. To derive the data in a more robust fashion, the formation factor and effective diffusivity are estimated from two conceptually different methods, and data from both of these methods
are compared. The two methods are to:
1. Base the recommended data on in situ formation factors, after performing the surface conduction
correction according to Equation 6‑29. The effective diffusivity is then the product of Ff and Dw
in the case of no exclusion effects. For anions, a reduction factor is needed to account for anion
exclusion.
2. Base the recommended data on through-diffusion De data, but only after correcting for stressrelease and mechanically induced damage. This is done by multiplying the laboratory De by atransfer factor, which is based upon differences in the formation factors obtained by electricalmethods in situ and in the laboratory. For anions, a reduction factor is needed to account foranion exclusion.
Method 1: In the Sicada database, apparent in situ formation factors are stored from 10 deep
boreholes in Forsmark. However, only for three of these boreholes there is direct information on
the porewater chemical composition (cf. Figure 6‑74). These boreholes are KFM01D and KFM08C/Löfgren 2007/ and KFM06A /Löfgren et al. 2006/. For boreholes KFM01D and KFM08C, themeasured electrical conductivities of the porewater and freely flowing groundwater generally agree.
Therefore, the suggested electrical conductivity profiles of /Löfgren 2007/ are kept. For borehole
KFM06A, however, the electrical conductivity profile of the porewater disagrees with that of thefreely flowing groundwater. As the data from the porewater extraction/leaching are considered to be
more representative, we chose to base the electrical conductivity profile for the borehole solely on
them. This approach disagrees with that of /Löfgren et al. 2006/, wherein the profile was based on
the electrical conductivity of freely flowing groundwater (to comply with the method description
at the time). Because of this, the apparent formation factors of KFM06A have been recalculated,
as accounted for below.

380

TR-10-52

As a base for the recalculated apparent formation factors in KFM06A, the same rock matrix
resistivities are used as in /Löfgren et al. 2006/. The exception is that no rock matrix resistivity fromabove the borehole length 305 m is used, due to the reassessed pore water electrical conductivity
profile and due to the criterion that κw > 0.5 S/m. The new electrical conductivity profile is based onporewater chloride concentrations given in Table A7 of /Waber and Smellie 2005/. The conversion
between chloride and electrical conductivity is based upon a relation in /Löfgren 2007, Equation 4-4/
(which in turn is based on linear regression of chloride data and electrical conductivity data in borehole
KFM01D). The correction from laboratory temperature to in situ temperature was assumed tomatch that of borehole KFM08C (extrapolated from Table 4-4 of /Löfgren 2007/). In other words
the temperature vs. depth profile in KFM06A was assumed to match that in KFM08C. Figure 6‑76
shows the new electrical conductivity profile for KFM06A.
This electrical conductivity profile was used together with rock matrix resistivities of KFM06A andEquation 6‑26 to obtain recalculated apparent formation factors. It was also used for correcting theapparent formation factors into the formation factor, by use of Equation 6‑29.
Figure 6‑77 shows the cumulative distribution functions of the formation factors of KFM01D, KFM08C,
and KFM06A, after performing the surface conduction correction according to Equation 6‑29. In
addition, the CDF of all 10,130 data points from the boreholes are shown by the yellow curve.
All data points from the three boreholes, in the form of log10(Ff) are shown in a histogram in
Figure 6‑78, together with the best fit normal distribution with μ = –4.71 and σ = 0.16.
As flow path averaged migration parameters should be used in solute transport modelling, we havenot seen it worthwhile to study the skewness and kurtosis of the dataset. Instead, it is recommended
to base the flow path averaged formation factor on the arithmetic mean of all the formation factors.
For the 10,130 formation factors, the arithmetic mean is 2.11·10–5.
2.5

Electrical conductivity (S/m)

Porewater KFM06A2
y = 5.20⋅10-3x - 3.26
1.5

1
y = 8.29⋅10-4x + 0.248
0.5

0
0

200

400

600

800

1000

Borehole length (m)Figure 6‑76. Suggested electrical conductivity profile in borehole KFM06A.

TR-10-52

381

1
0.9
0.8
0.7

CDF

0.6
0.5

KFM01D, 3636 data points
KFM08C, 4049 data points

0.4

KFM06A, 2445 data points
All, 10130 data points

0.3
0.2
0.1
0
1.E-06

1.E-05

1.E-04

1.E-03

Formation factor

Figure 6‑77. CDFs of formation factors for borehole KFM01D, KFM06A, and KFM08C.

3µ = -4.71
σ = 0.16

Normalised frequency

2.510130 datapoints

2

1.5

1

0.5

0
-5.6

-5.4

-5.2

-5

-4.8

-4.6

-4.4

-4.2

-4

-3.8

-3.6

-3.4

Log10(Ff)Figure 6‑78. Histogram of all data from boreholes KFM01D, KFM06A, and KFM08C, together with best
fit normal distribution.

Method 2: Effective diffusivities from through diffusion measurements are tabulated in Appendix 2
of /Selnert et al. 2008/. Most of these data were used as a basis for the recommendations in this text.
However, in small volumes of the bedrock at Forsmark, highly porous episyenite was found andsampled. The samples with a porosity above 10% are considered to be non-representative for the site
and when treating the data statistically, such samples were excluded. A histogram of the 58 included
data points, in the form of log10(De), are shown in Figure 6‑79, together with the best fit normal
distribution.

382

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1.2µ = -12.6
σ = 0.43

Normalised frequency

1

58 data points
0.8

0.6

0.4

0.2

0
-14

-13.5

-13

-12.5

-12

-11.5

-11

Log10(De)

Figure 6‑79. Histogram of all effective diffusivities from laboratory through diffusion measurements, except
for those of samples with a porosity over 10%.

As previously cautioned, these data should not be used without performing corrections for thede-stressed conditions at the laboratory. In performing this correction, we use formation factors
obtained by electrical methods in situ and in the laboratory.
The blue curves of Figure 6‑80 show the CDF of all in situ formation factors obtained in KFM01D,KFM06A, and KFM08C, together with the best fit log-normal distribution (cf. yellow curve of
Figure 6‑77 and Figure 6‑78). In addition, in red curves, the corresponding CDF for data from laboratory measurements is shown together with the best fit log-normal distribution. In the laboratory,
163 drill core samples were studied with the electrical resistivity method. Note that all these data
have been corrected for surface conduction effects by using Equation 6‑29.
As can be seen, the formation factors obtained in the laboratory are about one order of magnitude
higher than those obtained in situ. The ratios between the laboratory and in situ formation factors
at different CDF values are shown in the figure, where at the CDF value 0.5, the ratio is 11.5. The
difference seems to be larger for larger formation factors, and smaller for smaller formation factors.
In spite of this fact, we chose to take a simplistic approach and suggest that all laboratory effective
diffusivities should be divided by a transfer factor of 11.5. After applying this transfer factor, thein situ effective diffusivity is estimated.
Comparison of method 1 and 2: In Figure 6‑81, the CDF of the in situ De from method 2 is shownin red. This is based on 58 data points from laboratory through diffusion measurements, which havebeen reduced by a factor of 11.5. In comparison, the CDF of the in situ effective diffusivities basedon the in situ electrical resistivity method is shown (method 1). Here the effective diffusivity is
obtained from the product of the formation factors and Dw = 1·10–9 m2/s.
As can be seen, the two methods of obtaining in situ De give very similar results, and deviations
are within the data uncertainty of the methods. As so many data points have been obtained with thein situ electrical resistivity method, we chose to propagate results from it to SR-Site. Results fromthe through diffusion measurements are still considered as important and confirmatory.

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383

Formation factors obtained by electrical methods
1
0.9
0.8

log10-distribution

CDF 0.1: Ff lab = 7.1 * Ff in-situµ = -4.71

CDF 0.5: Ff lab = 11.5 * Ff in-situ

σ = 0.16
log10-distribution

CDF 0.9: Ff lab = 15.2 * Ff in-situµ = -3.66

0.7

σ = 0.37

CDF

0.6
In-situ Ff, 10130 data points

0.5

In-situ log-normal distribution

0.4

Laboratory Ff, 163 data points

0.3

Lab. log-normal distribution

0.2
0.1
0
1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

Formation factor (-)

Figure 6‑80. Blue curves: CDF and best fit log-normal distribution of in situ formation factors forborehole KFM01D, KFM06A, and KFM08C. Red curves: CDF and best fit log-normal distribution oflaboratory formation factors obtained by the electrical resistivity method.

1
0.9
0.8

CDF

0.7
0.6

In-situ Ff, electrical resistivity method0.5
In-situ Ff, through diffusion methodwith stress correction

0.4
0.3
0.2
0.1
0
1E-15

1E-14

1E-13

1E-12

In-situ De(m2/s)

Figure 6‑81. Comparison of CDFs of in situ De obtained by the two methods.

384

TR-10-52

Recommended data: For the flow path averaged in situ formation factor, we recommend using thearithmetic mean value of the 10,130 data points from borehole KFM01D, KFM06A, and KFM08C.
This equals 2.11·10–5. If using a Dw of 1·10–9 m2/s, the corresponding De becomes 2.11·10–14 m2/s.
No uncertainty range based on natural variability of the formation factor is assigned to this value,as the deviations between flow paths are considered to be insignificant for safety assessment
calculations. However, as discussed in this text there is significant conceptual and data uncertainty
associated with De that cannot be corrected for, due to lack of knowledge. Furthermore, there is
uncertainty in which Dw should be used for the individual radionuclide, as 1) the temperature andsalinity will vary during the glacial cycle, which would give rise to temporal variation in Dw, and as2) each radionuclide may exist as a number of different species, each having an individual Dw. These
uncertainties may give rise to bias that cannot be reduced by flow path averaging (as De estimates for
all rock volumes along a flow path would potentially suffer from the same bias).
To account for these uncertainties we take on a simplistic approach and suggest that the best estimate
flow path averaged De has an associated error of half an order of magnitude. It seems to be appropriate to distribute this error symmetrically in the log space. The great majority of flow path averaged
De values should be found within this one order of magnitude range. Therefore it is suggested thatthe error corresponds to two standard deviations (i.e. the specified range contains 95.4% of the data
points). The grounds for this approach are the following.
•

The Dw for different species of importance generally differ by half and order of magnitude or less.

•Reasonable porewater temperatures and salinities should not affect Dw by more than a factor oftwo.

•Uncertainty in the analogy between diffusion and electromigration appears to be contained withina factor of two.

•

Uncertainty in correction for surface conduction appears to be contained within a factor of two.

•

Based on expert judgment it seems fair to assume that the great majority of flow paths encounteran averaged value of De varying over one order of magnitude.

Based on this we suggest that the log10-normal distribution with the following parameters should be
used for the flow path averaged in situ De, where there are no exclusion effects:•

μ = –13.7, σ = 0.25.

In case there is anion exclusion, we suggest that the entire distribution is shifted downwards by half
an order of magnitude (cf. Equation 6‑30). This gives a log10-normal distribution with the following
parameters for anions:•

μ = –14.2, σ = 0.25.

Here it should be noted that a radioelement may have a speciation allowing for cationic, neutral, andanionic forms. Therefore, in case of a radionuclide transport modelling code that only allows for
one De per radioelement, one must choose whether the radioelement predominantly is in its cationic,
neutral, or anionic form.
It is cautioned that the suggested standard deviation is due to uncertainty, and not due to naturalvariability. Accordingly this distribution cannot again be averaged.

TR-10-52385

Sorption partitioning coefficient, Kd

Based on the data extrapolation and qualification procedures described in /Crawford 2010/, Kd
data have been estimated for application conditions in SR-Site. The data are based on the use ofForsmark metagranite (rock code 101057) as a reference rock type and consider the impact of
all known sources of uncertainty that can be reasonably quantified. For the impact of temporally
variable groundwater compositions, Kd values have been chosen pessimistically to reflect the least
favourable conditions for transport retardation. A selection of the recommended data are shown in
Figure 6‑82 as box and whisker plots along with a comparison with data previously used in SR-Can/SKB 2006b/. Similar plots depicting the recommended data for trivalent actinides and lanthanides
are given in Figure 6‑83.
210log10 Kd (m3/kg)

-1

-2

-3

-4

-5

-6

-7

-8

Ra(II) Ni(II) Sr(II)Cs(I) Am(III) U(IV) U(VI) Pu(III) Np(IV) Pa(V) Tc(IV) Se(-II) Nb(V) Th(IV) Sn(IV) Zr(IV) Ag(I) Cd(II) Pd(II)Pb(II)

SpeciesFigure 6‑82. Recommended log10Kd ranges for SR-Site temperate climate case application conditions(green box plots). Data previously recommended for use in SR-Can are shown for comparative purposes(purple box plots). The coloured portion of the box plot represents the 25%–75% interquartile range withthe median given as the horizontal dividing line. Whiskers of the box plot represent the 95% confidence
interval. A lower effective bound on relevant Kd values is provided by the storage porosity of the rockmatrix which is shown as the horizontal unbroken reference line in the figure along with a 95% confidence
interval for this estimate (broken lines).

386

TR-10-522

10log10 Kd (m3/kg)

-1
-2
-3
-4
-5
-6
-7
-8
Ac(III)

Am(III)

Cm(III)

Eu(III)

Ho(III)

Pu(III)Sm(III)

SpeciesFigure 6‑83. Recommended log10Kd ranges of trivalent actinides and lanthanides for SR-Site temperate
climate case application conditions (green box plots). Data previously recommended for use in SR-Can are
shown for comparative purposes (purple box plots). Other plot details are the same as in Figure 6‑82.

The data are given in the form of log-normal distributions which implicitly include the combined
impact of uncertainty as well as spatial and temporal variability. The median of the log-transformed
Kd uncertainty may be taken to be the best estimate value for central case deterministic calculations.
Although flow path averaging effects would normally suggest the use of the arithmetic mean Kd
value, the use of the median is deemed a more appropriate choice given that uncertainty (rather thanvariability) is the dominant contribution to the statistical dispersion of the Kd ranges. For stochastic
simulations in SR-Site, it is recommended that the log-normal distributions are sampled uniformly
between the 2.5% and 97.5% percentiles (that is the distribution should be truncated).
The recommended Kd data for the Forsmark site are supplied in Table 6‑89. These data are recommended for all rock volumes throughout repository evolution.

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387

Table 6‑89. Recommended sorption partitioning coefficient, Kd values for use in SR-Site simulations of the Forsmark site. The predominant species for redox sensitive elements are highlighted
in bold text. Values are given for the best estimate (median), parameters for the log-normal
distribution (μ and σ), as well as lower and upper limits corresponding to the 2.5% and 97.5%
percentiles, respectively. For each row a correlation group (1 or 2) or no correlation group (–) isassigned.
Radionuclide(Redox State)

Best estimate Kd(m3/kg)log10Kd – m

log10Kd – sLower Kd limit(m3/kg)Upper Kd limit(m3/kg)

Corr. Group

Ac(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

Ag(I)

3.49·10–4

–3.46

0.51

3.46·10–5

3.52·10–3

1

Am(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

C, HCO3–

0.0

–

–

0.0

0.0

–

C, CH4

0.0

–

–

0.0

0.0

–

C, -CO2H

0.0

–

–

0.0

0.0

–

Cd(II)

1.10·10–3

–2.96

0.65

5.97·10–5

2.04·10–2

1

Cl(-I)

0.0

–

–

0.0

0.0

–

Cm(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

Cs(I)

3.49·10–4

–3.46

0.51

3.46·10–5

3.52·10–3

1

Eu(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

H(I)

0.0

–

–

0.0

0.0

–

Ho(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

I(-I)

0.0

–

–

0.0

0.0

–

Mo(VI)

0.0

–

–

0.0

0.0

–

Nb(V)

1.98·10–2

–1.70

0.64

1.11·10–3

3.53·10–1

2

Ni(II)

1.10·10–3

–2.96

0.65

5.97·10–5

2.04·10–2

1

Np(IV)

5.29·10–2

–1.28

0.65

2.84·10–3

9.84·10–1

2

2

Np(V)

4.13·10–4

–3.38

0.74

1.48·10–5

1.15·10–2

Pa(IV)

5.92·10–2

–1.23

0.48

6.76·10–3

5.18·10–1

2

Pa(V)

5.92·10–2

–1.23

0.48

6.76·10–3

5.18·10–1

2

2

Pb(II)

2.52·10–2

–1.60

0.56

2.05·10–3

3.10·10–1

Pd(II)

5.20·10–2

–1.28

0.83

1.22·10–3

2.21

2

Pu(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

Pu(IV)

5.29·10–2

–1.28

0.65

2.84·10–3

9.84·10–1

2

Pu(V)

9.14·10–3

–2.04

0.60

6.19·10–4

1.35·10–1

2

Pu(VI)

9.14·10–3

–2.04

0.60

6.19·10–4

1.35·10–1

2

Ra(II)

2.42·10–4

–3.62

0.41

3.87·10–5

1.51·10–3

1

S(-II)

0.0

–

–

0.0

0.0

–

Se(-II)

2.95·10–4

–3.53

0.55

2.50·10–5

3.48·10–3–

Se(IV)

2.95·10–4

–3.53

0.55

2.50·10–5

3.48·10–3–

Se(VI)

2.95·10–4

–3.53

0.55

2.50·10–5

3.48·10–3–

Sm(III)

1.48·10–2

–1.83

0.72

5.74·10–4

3.83·10–1

2

Sn(IV)

1.59·10–1

–0.80

0.28

4.51·10–2

5.58·10–1

2

Sr(II)

3.42·10–6

–5.47

0.99

3.84·10–8

3.05·10–4

1

Tc(IV)

5.29·10–2

–1.28

0.65

2.84·10–3

9.84·10–1

2

Tc(VII)

0.0

–

–

0.0

0.0

–

Th(IV)

5.29·10–2

–1.28

0.65

2.84·10–3

9.84·10–1

2

U(IV)

5.29·10–2

–1.28

0.65

2.84·10–3

9.84·10–1

2

U(VI)

1.06·10–4

–3.97

0.66

5.53·10–6

2.05·10–3

2

Zr(IV)

2.13·10–2

–1.67

0.35

4.48·10–3

1.02·10–1

2

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6.8.11 Judgements by the SR-Site teamSources of information and documentation of data qualification

The supplier has listed a number of main sources and data sets. For ε and De the sources predominantly originate from the site investigation and site description. For Kd the main source is a dedicated
SR-Site report /Crawford 2010/, dealing with qualifying data. Furthermore, the supplier has sorted
the data sets as qualified and supporting, with justifications. Here it should be noted that for Kd,
detailed references are not made to the great number of datasets on which the data rely. Instead/Crawford 2010/ is referred to, wherein the information needed to assure traceability is given.
This is judged as sufficient. Furthermore, full documentation of the extrapolation and qualification
procedure for Kd can be found in /Crawford 2010/.
Throughout the text the supplier has made sufficiently detailed references to supporting documents.
The SR-Site team judge that the handling is adequate.
Conditions for which data are supplied

The supplier has listed a number of conditions of different importance for the different parameters.
For the porosity, formation factor, and effective diffusivity, the rock type and in situ stress are themost important conditions. For the sorption partitioning coefficient, the available sorptive surface
area of the rock, its geochemical characteristics, and groundwater chemistry are considered to be
the most important variables. The SR-Site team agree with the supplier’s account.
Conceptual uncertainty

Concerning the porosity, there is little conceptual uncertainty. Concerning the formation factor andeffective diffusivity, the supplier has discussed conceptual uncertainty and argues that the recommended uncertainty range should encompass conceptual uncertainty. The fact that two conceptuallydifferent methods of estimating De data gives about the same values indicates certain robustness inthe approach. The SR-Site team agree with the supplier’s handling.
There are significant conceptual uncertainties regarding the use of constant Kd values in transport
calculations as well as the characterisation of sorption properties in the laboratory and their relation
to retention properties in situ. Interpretation uncertainties are handled by the careful screening ofdata to avoid non-conservative parameterisation of Kd values. Conceptual uncertainties regarding
scaling to site-specific, in situ conditions are handled by the selection of Kd data ranges that pessimistically overpredict radiological consequences where such uncertainty exists. The SR-Site teamfinds the account of conceptual uncertainty regarding Kd as adequate and judge that the conceptual
uncertainty is encompassed by the rather wide Kd ranges supplied.
Data uncertainty due to precision, bias, and representativity

The supplier has discussed different methods and measurements and accounted for potential data
uncertainty induced in different steps. For the porosity the data uncertainty seems to be limited.
For the effective diffusivity measured in the laboratory, the major data uncertainty stems from thede-stressed conditions and from excavation damage affecting the pore space. For in situ formation
factors obtained by electrical methods, the major data uncertainty stems from uncertainties in thepore water chemistry, surface conduction, and uncertainty in the analogy between diffusion and electromigration. However, the supplier argues that overall this uncertainty is limited and encompassedby the data ranges suggested. Concerning the effective diffusivity there is also the uncertainty ofwhich Dw should be used, as a radioelement may exist as a range of different species (where bothanionic and cationic forms are possible). Also the temperature affects the De. The supplier has handled this by assigning a half an order of magnitude uncertainty range on each side of a best estimate
values. The SR-Site team agree with this handling.

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Concerning Kd a major uncertainty originates in the extrapolation of data obtained using crushedrock to values appropriate for intact, site-specific rock types under application conditions. Other
uncertainties concern possible differences in groundwater chemical composition between laboratory
investigations and application groundwater conditions projected for SR-Site. These sources of
uncertainty are quantitatively handled, to the extent possible, by transfer factors. The aim of thistreatment has been to provide cautious data. The SR-Site team note that the detailed account for how
uncertainty is handled is given in /Crawford 2010/. Generally the handling is judged as adequate andshould provide reasonable (sometimes pessimistic) uncertainty ranges.
Spatial and temporal variability of data

Concerning the porosity, formation factor, and effective diffusivity, the supplier has displayed spatial
variability in data but argues that for flow path averaged migration parameters, the spatial variability
is insignificant. The SR-Site team agree. For the effective diffusivity, the supplier has handled
temporal variability by increasing the uncertainty range of the data. The SR-Site team finds thisapproach acceptable.
For sorption properties, spatial variability is expected to make a small, but nevertheless nonnegligible contribution to the flow path average Kd describing a migration path. Since the data are
typically based on a limited number of crushed bore core samples, however, the predominance ofdata uncertainty in the estimation of Kd ranges mean that spatial variability is inconsequential withregard to data selection. Temporal variability in groundwater composition is handled by selection of
Kd data for the most unfavourable hydrogeochemical conditions expected to prevail for an extended
period of time in the repository environment, that is during the temperate climate domain. The
SR-Site team agree with this approach.
Correlations

For the porosity, formation factor, and effective diffusivity, the supplier has given functional relationships in form of equations. The SR-Site team agree. It is important to note that the same flow
path averaged effective diffusivity should be used for all flow paths in probabilistic modelling, as
the given PDF reflects uncertainty and not spatial variability. All cations and neutral species in all
flow paths should be assigned the same De, while for anions this De should be scaled by a reduction
factor.
Correlations between elements in the form of analogue behaviour are used to establish Kd ranges
for elements where sorption data is not available. Similarities in sorption mechanisms also appear
in correlations between elements in the stochastic selection of data from the provided log-normal
distributions of Kd uncertainty. The supplier has tabulated elements (or their redox speciation) for
which data should be correlated in Table 6‑89. The SR-Site team agree with this handling.
Result of supplier’s data qualification

Concerning the porosity, the supplier suggests that the porosity of the most abundant rock type inrock domain RFM029 should be used, but only after correcting for the de-stressed conditions in
the laboratory. It is suggested to base this correction on result from compression testing of drill
core samples. The correction is performed by multiplying the laboratory porosity by a factor of 0.8,
resulting in an in situ porosity of 0.18%. Due to flow path averaging the variability is suggested to be
insignificant. The SR-Site team find this handling adequate.
Concerning the formation factor and effective diffusivity, the supplier uses two conceptually different
methods of obtaining the data. The fact that the two methods give about the same result indicates acertain degree of robustness in the data. The choice of basing the recommended data on the in situ
formation factors, obtained by the electrical resistivity method, is primarily based on the large
amount of data available (over 10,000 data points).
The way of correcting the apparent formation factors into formation factors was not generally usedin the site descriptions, even though it was discussed. This approach appears to have a solid foundation, even though it is associated with a fair amount of uncertainty. It is judged that by performing
this correction, the overall data uncertainty is reduced.
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As the migration parameters should be flow path averaged, the natural variability is suggested to be
insignificant. However, due to uncertainty in obtaining the formation factor, and due to variability
in Dw for different species and temperatures over the glacial cycle, it is suggested that the flow pathaveraged effective diffusivity may range over one order of magnitude. The SR-Site team agree withthis handling.
It is suggested to use a reduction factor for making corrections for anion exclusion. Compared to in
SR-Can the magnitude of suggested reduction factor is decreased. For SR-Site, a half an order ofmagnitude reduction factor is recommended, as compared to the one order of magnitude reduction in
SR-Can. This is based on new measurements on rock samples from Forsmark, but also on measurements on rock samples from the Äspö HRL. Although the effect of anion exclusion may need further
studies, the SR-Site team find this acceptable.
Kd data are supplied based on consideration of both site-specific data, where available, and data
documented in the open literature. Transfer factors based upon consideration of total BET surface
area and cation exchange capacity are used to extrapolate data for crushed materials to values
appropriate for site-specific rocks under in situ conditions. For some species, transfer factors arealso used to correct for differences in groundwater composition. The ranges given are primarily due
to uncertainty, and secondarily due to natural variability. Therefore, the best estimate is taken fromthe median value, and not from the arithmetic mean value. The SR-Site team notes that the data
derivation is largely accounted for in /Crawford 2010/ and only summarised in this present section.
The handling is judged as adequate.

6.8.12 Data recommended for use in SR-Site modelling
Porosity data recommended for use in SR-Site are summarised in Table 6‑90. It should be noted thatthe suggested values are flow path averaged.
The effective diffusivity data recommended for use in SR-Site are summarised in Table 6‑91. Itshould be noted that the probability density function reflects on uncertainty and temporal variability,and not on spatial variability. Spatial variability is already handled by suggesting flow path averaged
values. The De values for different elements should be correlated (cf. Section 6.8.9).
Table 6‑90. In situ diffusion available porosity suggested for use in SR-Site.
Type of soluteε (%)

Cations and non-charged solutes

0.18

Anions0.18

Table 6‑91. Flow path averaged in situ effective diffusivity suggested for use in SR-Site.
Type of solute

Best estimate De(m2/s)

Log10De (m2/s)–µ

Log10De(m2/s) – σ

Probability density
function

Cations and non-charged solutes

2.1·10–14

–13.7

0.25

Log-normal

Anions6.6·10–15

–14.2

0.25

Log-normal

Concerning the scale on which the porous system is connected L (m), it is suggested to be connectedon all scales relevant for the safety assessment (at least on the decametre scale). This means that the
maximum penetration depth LD (m) for matrix diffusion is dependent on half the spacing between
hydraulically conductive fractures, as discussed in Section 6.7.
Sorption data recommended for use in SR-Site are summarised in Table 6‑89. The Kd data are givenin the form of log-normal distributions characterised by a mean (μ) and standard deviation (σ). For
stochastic simulations, it is suggested that the distributions are sampled uniformly between the upperand lower limits defined by the 2.5% and 97.5% percentiles. The best estimate Kd value for use indeterministic calculations is given as the median of the Kd distribution. Kd data of the same correlation group (1 or 2) should be correlated.
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7

Surface system data

7.1

Climate and climate related data

In this section, data on various climate- and climate related processes are described, including air
and ground-surface temperatures, ice sheet thickness, permafrost depth, and shore-level changes.
Many of the data in this section describe reconstructed data for the past 120,000 years, i.e. for the lastglacial cycle. In order to construct a Reference glacial cycle for the future, these reconstructed data
are, as a first step, repeated for the coming ~100,000 years in the safety assessment. In addition to thisReference glacial cycle, complementary cases of future climate developments are constructed, in whichclimate-related processes are modified to describe possible cases with a potentially larger impact on
repository safety than in the reference evolution. The complementary cases describe and cover various
aspects of the large uncertainties that are related to future variations in climate and climate-related
processes on a 100,000 year time scale. The data presented in this section are generally justified in
the Climate report, and lower level references.

7.1.1

Modelling in SR-Site

Defining the data requested from the supplier

The following data should be delivered for the Forsmark area by the supplier:•

Air and ground-surface temperatures (°C) in the Forsmark region for the past 120,000 years.

•

Ice thickness (m) and ice surface gradients (°) over Forsmark for the past 120,000 years.

•

Shore-level changes at Forsmark for the Reference glacial cycle, i.e. for the coming 120,000 years.
The shore-level changes should be given as z-coordinates, giving the elevation of the shore-levelat the repository location at different times.

•

Permafrost and ground-freezing depths (m) at Forsmark for the past 120,000 years, as well as forcomplementary cases with other surface boundary conditions favourable to permafrost development.

The reconstructed data for the past 120,000 years should be repeated for the coming 120,000 years
to construct the Reference glacial cycle for the SR-Site safety assessment. In this process, the time of
120,000 years before present (BP) should be set as the present day.
SR-Site modelling activities in which data will be used

The requested climate- and climate related data are used in a number of different SR-Site modelling
activities. Data on air temperature are needed for simulations of ice sheet and permafrost development,
and for the evaluation of thermal gradients and geothermal heat flow. Data on changing ice sheet
configurations over time are needed for studies of variations in stress in the Earth’s crust, which inthermo-hydro-mechanical (THM) analyses are used for assessing the potential for glacially induced
faulting. Ice sheet data are also needed for simulations of groundwater flow under glacial conditions.
Data from permafrost simulations are needed in order to make appropriate selections of conditions
when studying surface hydrology and groundwater changes associated with colder climates. Dataon shore-level changes are needed for a proper description of the past and future evolution of thelandscape as influenced by the isostatic recovery from the last glacial cycle, for instance in order tomodel future lake formation. These data are also used for studying groundwater flow in potential
future situations when the isostatic uplift has progressed further. Knowledge on permafrost andglacial evolution are also necessary when investigating various THM processes, and for assessing
the potential for freezing of various parts of the repository.
The main flows of data within the climate programme, and between the climate programme andother SR-Site modelling activities, are summarised in Figure 7-1.

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Shore-level
displacement

Landscape modelling(e.g. lake formation)
Crustal stress modelling(glacially induced faulting)

Ice thickness andsurface gradients

THM processes, e.g.
calculation of hydraulicjackling

ClimateMaximum permafrost
depth
Bedrock temperatures
Ground surface
temperature

Ground- and surface waterflow modelling for glacialand permafrost conditions

Geothermal heat flow
Thermal gradients

SR-Site scenario on
repository freezing

Figure 7-1. Main flow of climate- and climate related data in SR-Site. Coloured boxes show data within
the climate programme, described in this section. White boxes show other SR-Site activities, using datafrom the climate programme. Reproduced from Figure 1-4 of the Climate report.

The data flow shown in Figure 7-1 is also characterised in the following bullet list:•

Air and ground-surface temperature data are used for:– ice sheet modelling,– permafrost modelling,– groundwater flow and permafrost modelling,– calculations of thermal gradients and geothermal heat flow.

•

Data on ice thickness18 and surface gradients are used for:– modelling of stress evolution and fault stability during glacial conditions,– groundwater flow and permafrost modelling,– simulation of Glacial Isostatic Adjustment (GIA)19.

•

Data on shore-level changes are used for:– the description of landscape evolution,– permafrost simulations,– modelling of groundwater flow.

•

Data on permafrost, freezing depths, and bedrock temperatures are used in analysis of repository
freezing in the SR-Site freezing scenario. In addition, the general picture obtained from the perma­
frost modelling results will be used to set up simulations on:– surface and groundwater flow, with the modelling code MIKE-SHE,– groundwater flow simulations, with the modelling code DarcyTools,– calculations of hydraulic jacking.

18
These data do not go into the permafrost modelling as they did in SR-Can. For the complementary permafrost
modelling performed for SR-Site, the last glacial cycle simulations have been performed only up to the time of
the arrival of the first ice sheet. Resultat from SR-Can are used to describe permafrost and bedrock temperatures
for subsequent (glacial and non-glacial) periods.

These calculations were performed for SR-Can /SKB 2006c/. The same data are used in SR-Site, complemented
by other recent 3-D GIA results and publications on future sea-level rise.
19394

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7.1.2 Experience from SR-Can
This section briefly summarises experience from the SR-Can safety assessment that may be of directconsequence for the data qualification in this Data report.
Modelling in SR-Can

Air and ground-surface temperatures
In SR-Can, air temperatures were used for permafrost simulations with a permafrost model that was
not coupled to ground water flow /SKB 2006c/. In SR-Site, these data are used both with a permafrost
model that incorporates 2-D groundwater flow /Hartikainen et al. 2010/ and with a coupled 3-Dgroundwater flow/simplified permafrost model (DarcyTools) /Vidstrand et al. 2010/.
Ice thickness and surface gradients
There is no difference between the SR-Can and SR-Site modelling approaches. The same data are used.
Shore-level changes
There is no difference between the SR-Can and SR-Site modelling approaches. The same data areused. For SR-Site, recent 3-D Glacial Isostatic Adjustment (GIA) modelling results /Whitehouse
2009/ are used to evaluate the 2-D GIA results used from SR-Can. For SR-Site, the uncertainty
estimate in the shore-level curve for the Global warming climate case is complemented by recently
published data on this topic, to illustrate the uncertainty.
Permafrost depths
Input data on air temperature for the permafrost simulations are the same in SR-Site as in SR-Can.
Uncertainties in the air temperature curve are better described, analysed and illustrated in SR-Site.
Simulations of permafrost, and bedrock and ground-surface temperatures were made in 1-D for
SR-Can /SKB 2006c/ whereas they are made in 2-D in SR-Site /Hartikainen et al. 2010/. In addition,
ground-surface temperatures are simulated from air temperatures in a better and more detailed way
in SR-Site than they were in SR-Can /Hartikainen et al. 2010/.
Conditions for which data were used in SR-Can

The description of climate and climate-related processes in SR-Can provided external conditions toother safety assessment activities. In SR-Can, the projected climate evolution was based on a reconstruction of the last glacial cycle, including the Weichselian glaciation. Based on variations of boundary
conditions, for example by lowering the air temperature, uncertainties in the data were investigated. In
addition, varying the boundary conditions gave rise a multitude of climate cases, as discussed below.
Sensitivity to assessment results in SR-Can

Air and ground-surface temperatures
These data are important for the other data sets described below, and are discussed there. Uncertainties
in the Forsmark air temperature curve were analysed in the permafrost simulations, see below.
Ice thickness and surface gradients
Sensitivity modelling relating to the maximum ice sheet thickness were performed within the SR-Can
ice sheet model simulations /SKB 2006a, Section 4.4.2/. To investigate the maximum theoretical ice
sheet thickness that may occur over Forsmark, the University of Maine Ice Sheet Model (UMISM)
was run using a set of air temperature evolutions in which local annual air temperatures were decreased
linearly by 1°C per 2,000 years, from present-day temperatures down to various constant levels. In thesesensitivity tests, temperatures were in total lowered between 4 and 16°C.

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The results showed that, as expected, the maximum ice sheet thickness increases with colder climates.
However, the ice thickness will only increase to a certain point, regardless of how much the temperature is lowered. From a temperature lowering of approximately 13°C, colder climates do not generate
thicker ice sheets. In the simulation of last glacial cycle conditions, the maximum ice thickness overthe Forsmark region was 2,920 m. In the sensitivity tests, the maximum theoretical ice sheet thickness
developed at Forsmark was 3,670 m. However, the resulting ice sheet was judged to be unrealisticallylarge /SKB 2006a/. Instead, the maximum ice sheet thickness, judged to be realistic, was based on
the largest ice sheet configuration for the past 2 million years. A simulation of this maximum ice
sheet configuration resulted in a maximum ice thickness of 3,200 m over Forsmark. This value wascomplemented with additional information from peer-reviewed scientific literature suggesting an ice
thickness of 3,400 m over Forsmark during the peak of the Saalian glaciation.
The maximum ice thickness, and its associated additional hydrostatic pressure of 30 MPa, was propagated to the SR-Can scenario Canister failure due to isostatic load /SKB 2006c, Section 12.8.4/. The
corresponding value for the Reference glacial cycle ice sheet was 26 MPa. The combined assessmentof all contributing factors to the Canister failure due to isostatic load scenario, including the maximum
ice thickness, resulted in this scenario being considered as “residual”, meaning that its consequenceswere excluded from the SR-Can risk summation.
Shore-level changes
Sensitivity modelling relating to isostatic changes under various ice-loading models were performedin SR-Can /SKB 2006c, Section 3.3.4/. The following cases were studied:•

Reference glacial cycle.

•

Twice repeated reference glacial cycle.

•

Reference glacial cycle with ice loading only from 40,000 years.

•

Reference glacial cycle, but with the ice thickness over Fennoscandia reduced to 90% of its
reference value.

•

Reference glacial cycle, but with the ice thickness over Fennoscandia reduced to 80% of its
reference value.

•

Reference glacial cycle with timing of deglaciation advanced by 500 years.

•

Reference glacial cycle with ice thickness increased linearly between 60,000 and 20,000 years BP.

•

Four crustal relaxation models.

•Reference glacial cycle with ice sheet development over Fennoscandia excluded from the model.

Sensitivity studies on isostatic changes under various Earth models (i.e. varying lithosphere thickness,upper mantle viscosity, lower mantle viscosity) were also performed /SKB 2006c, Section 3.3.4/.
The following cases were studied:•Thin lithosphere.

•

Thin lithosphere and strong lower mantle.

•

Thick lithosphere.

•

Thick lithosphere and strong lower mantle.

•

Thick lithosphere and strong upper mantle.

The sensitivity studies were performed in order to analyse the effect of uncertainties in input data to
the GIA model, and for setting appropriate values of input parameters when producing the Reference
glacial cycle shore-level curve. Although important for the resulting shore-level changes, none of thesensitivity studies had a direct impact on repository safety in SR-Can.

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Permafrost depths
Sensitivity modelling on permafrost development under a large number of conditions relating toclimate, surface conditions, and bedrock thermal conditions and characteristics were performed in
SR-Can /SKB 2006c, Sections 3.4.4 and 4.4.1/. The following cases were studied:•

Constant ground-surface temperatures of –2, –4, –6, –8, –12 –16, –18, and –20°C.

•

Constant ground surface temperatures combined with various values of bedrock thermal diffusivity.

•

Constant ground-surface temperatures combined with various values on geothermal heat flow.

•

Reference glacial cycle air temperatures, but without presence of winter snow, vegetation, ice
sheets, and sea.

•

Reference glacial cycle air temperatures with various values of thermal diffusivities.

•

Reference glacial cycle air temperatures with various values of geothermal heat flow.

•

Reference glacial cycle air temperatures with presence or absence of heat from the repository.

•

Reference glacial cycle air temperatures, but without presence of winter snow, vegetation, ice
sheets, and sea, and without presence of heat from the repository.

•

Reference glacial cycle air temperatures, but without presence of winter snow, vegetation, ice sheets,and sea, and without presence of heat from the repository, but with various geothermal heat flows.

•

Reference glacial cycle air temperatures, but without presence of winter snow, vegetation,
ice sheets, and sea, and without presence of heat from the repository, but with various thermal
diffusivities.

•

Lowering of the Reference glacial cycle temperature curve until the 0°C isotherm (definition of
permafrost) and the –5°C isotherm (buffer clay freezing temperature used in SR-Can) reachedrepository depth.

•

Talik development for different lake sizes.

Results from the sensitivity case favourable for permafrost growth (with Reference glacial cycle airtemperatures, but without presence of winter snow, vegetation, ice sheets, and sea) were pessimisticallychosen for the SR-Site freezing scenario /SKB 2006c/. In this case, freezing of groundwater at repository
depth in Forsmark could not be ruled out. However, it did not result in freezing of the buffer clay atrepository depth (the criterion for buffer freezing was –5°C in SR-Can). Therefore, none of the sensitivity
studies had an impact on repository safety.
Concerning sensitivity to air temperatures, one sensitivity study conducted was to lower the reference
evolution air temperature curve for Forsmark by 5 and 10.5°C. This corresponded to cases wherethe 0°C isotherm and the –5°C isotherm reached a depth of 400 m, i.e. the repository depth from the
SR-Can repository layout. However, a 10.5°C lowering of the entire reference evolution temperature
curve was considered unrealistic, even given the large uncertainty of the input air temperature curve,
and freezing of the buffer clay was therefore ruled out.
Alternative modelling in SR-Can

In SR-Can, alternative modelling was done within specific fields, which may be exemplified by the
Glacial Isostatic Adjustment (GIA) modelling where e.g. different Earth models and ice load models
were tested and analysed /SKB 2006c/. The same approach is taken within SR-Site (see the Climate
report for details). SR-Can also studied a multitude of variant cases discussed above, using different
boundary conditions but a similar conceptualisation.
Correlations used in SR-Can modelling

The evolutions of climate- and climate-related processes govern the conditions for numerous activities
in safety assessment modelling. In SR-Can the uncertainty in future climate development was handled
by identifying and analysing a Reference glacial cycle plus complementary climate cases that werealso feasible. Data were estimated for each climate case. Instead, the identified climate cases bounded
the uncertainty range in future development of climate-related processes.

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Identified limitations of the data used in SR-Can modelling

Air and ground-surface temperatures
The uncertainty in the future air temperature curve for Forsmark was large /SKB 2006c, Dverstorp and
Strömberg 2008/. However, no major limitations in the data have been identified, given the approachtaken when using the data (such as examining a large range of air temperature sensitivity cases in
the permafrost modelling study).
Ice thickness and surface gradients
No major limitations in the ice thickness data have been identified. However, the frontal-near, steepest,
part of the ice sheet was not included in the ice-surface-gradient data from the ice sheet model.
In SR-Site, other complementary data are used for this frontal-near part.
Shore-level changes
The uncertainty in the shore-level curve in SR-Can was reported as being large /SKB 2006c/. However,no major limitations in the data have been identified, given the approach taken when using them.
Permafrost depths
No major limitation in the permafrost data has been identified. However, the uncertainty in temperature
input data were large, something that was handled by permafrost sensitivity modelling /SKB 2006c/.

7.1.3 Supplier input on use of data in SR-Site and SR-Can
In this section, the supplier cannot be separated from the customer, as the SR-Site team have produced
the data (although based on observations from other groups). Hence, no supplier formally exists.
None the less, the text is written according to the standard outline of the Data report.
7.1.4

Sources of information and documentation of data qualification

Sources of information

The main source of information for the data sets on air temperature, ice thickness, shore-level changes,
and permafrost depths is the Climate report. Within the Climate report, references to relevant
lower-level documents can be found for each data set. The full reference is given in Table 7-1.
Table 7-1. Main sources of information used in data qualification.
Climate report, 2010b. Climate and climate related issues for the safety assessment SR-Site. SKB TR-10-49,
Svensk Kärnbränsle­hantering AB.

Categorising data sets as qualified or supportingAll data sets in the Climate report have a priori been considered to be qualified. They have thereafter
been judged to be qualified based on the criteria in the instruction for supplying data for the Data report(cf. Section 2.3). Qualified climate-related data sets are displayed in Table 7-2 and numbered from 1
to 11. Detailed comments on the items are given in Table 7-3.
Excluded data previously considered as important

No data are excluded that previously were considered important.

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Table 7-2. Qualified and supporting data sets. All data sets are taken from the Climate report,
wherein many underlying data sets are illustrated in figures. Underlying data are stored in(SKBdoc 1265613).
Qualified data sets

Supporting data sets

Air and ground-surface temperatures

None

1. Figure 3-55: Reconstructed air temperature data for the Forsmark region for the past
120,000 years, including submerged periods of the Eemian, Mid-Weichselian, and Holocene.
2. Figure A1-4: Ground-surface temperature data for the past 120,000 years with an ice
sheet and without an ice sheet.
Ice sheet thickness

3. Figure 4-18: Reconstructed last glacial cycle ice sheet thickness data.
4. Figure 5-20: Maximum ice sheet thickness for different schematic temperature evolutions
and for a Saalian ice sheet. Figure 7-7 (black line): Theoretical reference evolution ice sheet
profile data.
6. Figure A2-7(red line): Simulated last glacial cycle evolution ice sheet profile data.
7. Figure A2-5: Ice-surface-gradient data for the reconstructed last glacial cycle.
Shore-level changes

8. Figure 4-26: Shore-level displacement data for the Reference glacial cycle.
9. Figure 5-2 (green line): Shore-level displacement data for the Global warming case.
Permafrost depth

10. Figures 3-70 and 4-20: Data on permafrost development for the reference evolution(including selected bedrock temperatures, permafrost depths, and freezing depths).
11. Figure 5-24: Data on permafrost development for the Severe permafrost case(including selected bedrock temperatures, permafrost depths, and freezing depths).

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Table 7-3. Justification of the sorting of items in Table 7-2.
1. The air temperature data for the last glacial cycle including submerged periods, as reported in the
Climate report, are based on data from a peer-reviewed scientific publication, which, within the safetyassessment work, have been modified by considering the altitude effects of an ice sheet using an ice
sheet model. The method of doing this is described in detail in the Climate report (Appendix 1) and in thelower-level references therein. All relevant lower-level references are listed in the Climate report, facilitating traceability. Lower-level references in the form of peer-reviewed scientific papers are all considered
qualified. In the Climate report, and in lower-level report references therein, issues such as data quality,
variability, representativity, and uncertainties are discussed. Many of the climate data delivered arederived from modelling. In the Climate report, and lower-level references therein, the modelling strategy,
modelling tools, and validity of the modelling are described. It is judged that the modelling approaches
are adequate for the purposes of the SR-Site safety assessment.
2. The ground-surface temperature data for the last glacial cycle with ice sheet and without ice sheet
are based on the air temperature data in item 1 and on numerical permafrost modelling. The sameconsiderations as for item 1 apply.
3. The reconstructed ice sheet thickness data for the last glacial cycle are based on a model reconstructionof the Weichselian ice sheet. The method of doing this is described in detail in the Climate report and in
the lower-level references therein. The same considerations as for item 1 apply.
4. The maximum ice sheet thicknesses data for different schematic climate evolutions and for the Saalian
ice sheet are based on ice sheet model reconstructions and peer-reviewed publications. The sameconsiderations as for item 1 apply.
5. The theoretical ice sheet profile data are based on an equation given in a textbook on glaciology,
referred to in the Climate report (Appendix 2, Equation A.2-2) and a lower-level reference therein. This
ice sheet profile has been selected to represent an advancing ice sheet over Forsmark for the SR-Site
Reference glacial cycle.
6. The simulated ice sheet profile data are based on a model reconstruction of the Weichselian ice sheet.
The method of doing this is described in detail in the Climate report (Section 3.1) and in the lower-levelreferences therein. The same considerations as for item 1 apply. This ice sheet profile (the least steep
simulated profile from 14,300 years BP, see Appendix 1 in the Climate report) has been selected torepresent a retreating ice sheet over Forsmark in the SR-Site reference evolution.
7. The ice surface gradient data for the last glacial cycle are calculated from the data in item 6. The sameconsiderations as for item 6 apply.
9. The shore-level displacement data for the reference evolution are based on Global Isostatic Model
simulations and extrapolations of up-to-present Holocene isostatic uplift rates at the Forsmark site.
The same considerations as for item 1 apply.
10. The shore-level displacement data for the Global warming climate case are based on Global Isostatic
Model simulations, extrapolations of up-to-present Holocene isostatic uplift rates at the Forsmark site, andpeer-reviewed published data on possible future sea-level rise. The same considerations as for item 1 apply.
11. The permafrost development data for the last glacial cycle (including bedrock temperatures, groundsurface temperatures, permafrost depths, and freezing depths) are based on numerical permafrost
simulations. The same considerations as for item 1 apply.
12. The permafrost development data for the Severe permafrost climate case (including bedrock
temperatures, ground-surface temperatures, permafrost depths, and freezing depths) are basedon numerical permafrost simulations. The same considerations as for item 1 apply.
13. The permafrost development data from sensitivity studies (bedrock thermal conditions, surface
conditions, air temperature curve, and presence or absence of heat from the repository) are basedon numerical permafrost simulations. The same considerations as for item 1 apply.

7.1.5 Conditions for which data are supplied
There is a large range of parameters that influence climate and result in changes in climate on a wide
range of time scales. In turn, climate is affected by many of these parameters, resulting in positive or
negative feed-back mechanisms. For a description of these processes, see the Climate report.
7.1.6 Conceptual uncertainty
For the climate and climate-related data it is difficult to distinguish data uncertainty from conceptual
uncertainty. Therefore, the combined uncertainty is discussed in this section, and no additional
information is given in Section 7.1.7.

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The data uncertainties in the reconstruction of parameters for the last glacial cycle are judged to beconsiderably smaller than the uncertainty of to what degree the Reference glacial cycle represents
the conditions for the coming 100,000 years. The latter uncertainty is dealt with by the constructionof several alternative climate cases for Forsmark, see below.
Air and ground-surface temperatures

The air temperature data for Forsmark originate from a reconstruction of a palaeotemperature curvefor the Greenland ice sheet. In this reconstruction, there are various contributions to uncertainty, suchas the transformation of ice core δ18O values to air temperatures. Other uncertainties are introduced
when this proxy palaeotemperature curve is used for the reconstruction of the Weichselian ice sheet
and for the estimation of palaeotemperatures for the Forsmark region. Given these steps, it is obviousthat large uncertainties exist in the resulting palaeotemperature curve for the Forsmark region (cf.
Figure 7-2 in Section 7.1.10). In order to estimate these uncertainties, the temperature curve has
been compared against other reconstructions of temperature from Greenland ice cores and withexisting proxy records and modelling results on Fennoscandian palaeotemperatures (Climate report,
Appendix 1).
Based on how the air temperature curve for the last glacial cycle has been produced in detail, andon the comparison with temperature proxy and model data for periods of the last glacial cycle(cf. the Climate report, Appendix 1), it is estimated that the uncertainty in the temperature curvereconstructed for the last glacial cycle is:•

not larger than 6°C for periods with the largest uncertainties,•

up to c. 4–5°C for the major part of the temperature curve, and•for some parts of the curve, such as the Holocene, noticeably smaller than 4°C.

The temporal data uncertainty that are present in the reconstruction of temperature conditions forthe last glacial cycle are judged to be considerably smaller than the uncertainty of to what degree
the reference evolution represents the conditions for the coming 100,000 years. The latter, larger,
uncertainty is dealt with by the construction of complementary climate cases describing situations
e.g. where a warmer climate than the reference evolution would postpone glacial- and permafrost
conditions into the future, or cases where permafrost might develop earlier than in the Reference
glacial cycle.
In summary, the construction of complementary climate cases (with thicker and thinner ice sheets,
deeper and shallower permafrost, and sea-level developments other than those in the reference
evolution) all take care of, in an indirect way, the large uncertainties in the temperature curve used
to construct the future Reference glacial cycle (cf. the Climate report, Chapter 5).
Ice sheet duration, ice thickness, and ice surface gradients

The uncertainty in ice sheet duration is dealt with by the construction of alternative climate caseswith e.g. longer (Climate report, Section 5.3) and shorter (Climate report, Sections 5.1 and 5.2) ice
sheet coverage over the Forsmark site than in the Reference glacial cycle.
The uncertainty in ice thickness is taken care of by the analysis of cases with a thicker ice sheet thanin the Reference glacial cycle (Climate report, Section 5.4).
The uncertainties in ice surface gradients are taken care of by the inclusion of a range of possible ice
surface profiles (Climate report, Appendix 2). These uncertainties are also addressed by ensuringthat cases are studied in which the ice margin pauses at the Forsmark site.
Shore-level changes

The uncertainty in future shore-level changes is dealt with by the construction of alternative climate
cases with a shoreline affected by a global warming climate (Climate report, Sections 5.1 and 5.2).

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Permafrost depths

The uncertainty in permafrost depth is dealt with by the construction and analysis of alternative
climate cases with surface conditions more favourable for permafrost growth than the Reference
glacial cycle (Climate report, Section 5.5).
The uncertainty in temperature, as such, in the input air temperature curve used for the permafrost
simulations of the last glacial cycle has been estimated to be 6°C or less (see above). This rather
large uncertainty is still considerably smaller than the temperatures range analysed in some of thepermafrost sensitivity model simulations (Climate report, Sections 3.4 and 5.5). When the results arecompared with known levels and variations in temperature for the last glacial cycle, it is concluded
that an unrealistically large lowering of the temperature curve is required in order to force the freezingof groundwater at repository depth (Climate report, Section 5.5).

7.1.7 Data uncertainty due to precision, bias, and representativity
Generally one can say that conceptual uncertainty outweighs data uncertainty. Due to difficulties indistinguishing data uncertainty from conceptual uncertainty, the combined uncertainty is discussed in
Section 7.1.6 for the different data sets. In addition, results from sensitivity assessments that can beused to assess the overall uncertainty are given in Section 7.1.10.
7.1.8 Spatial and temporal variability of data
For the air temperature, ice thickness, ice surface gradients, and shore-level changes, the spatial
variability within the site is not captured in the data presented in this section. Data from the models
are typically of a regional character, from which regional data for the Forsmark site are extracted.
However, in the description of the evolution of climate and climate-related conditions for e.g. theReference glacial cycle, these data are taken to represent the conditions specifically at the repository
location. The spatial representativity of the data described in this section, and the spatial representativity of the resulting development of climate domains (Climate report, Section 1.2) in e.g. the Reference
glacial cycle, is further described in the Climate report (Section 4.5.4, heading “Transitions between
climate domains”).
The permafrost and freezing depths are not only related to the regional climate, but also to local
properties of surface system and geosphere as described from the site investigations. The spatial
variability in permafrost growth is described by the 2-D permafrost model, influenced by e.g.
altitude, vegetation, precipitation and bedrock thermal properties.
This section concerns the climatic evolution, where temporal uncertainty is generally handled bygiving data at different times. An example is the air temperature curve (Figure 7-2) that with hightemporal resolution gives the estimated temperature from 120,000–0 years BP. For other data, forexample the maximum ice thickness, data are given at the point in time when the value is largest (oras otherwise most important for the safety assessment).

7.1.9 Correlations
The evolution of climate and climate-related processes governs the conditions for numerous disciplines
and modelling exercises in safety assessment modelling. In SR-Site, the uncertainty in future climate
development is handled by identifying and analysing a Reference glacial cycle (Climate report,
Section 4.5) with five complementary climate cases that are also feasible (Climate report, Chapter 5).
Data have been estimated for each climate case. No interpolation has been made between climate
cases. The identified climate cases are used to bound the uncertainty ranges in future developmentsof climate-related processes.
As no interpolations are made between climate cases, there is no correlation that needs to be
propagated to other SR-Site modelling activities.

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7.1.10 Result of supplier’s data qualification
The requested data are justified in the Climate report. A brief summary is given here.
Air and ground-surface temperatures

The reconstructed air temperature curves for Forsmark for the last glacial cycle are shown in
Figure 7-2. The blue curve shows the reconstructed last glacial cycle temperatures for Forsmark,including two periods when the site was overridden by the Weichselian ice sheet, and two subsequent
periods of submerged conditions. For the ice-covered periods, the temperature constitutes the basal
ice temperature calculated with the ice sheet model (Climate report, Section 3.1), whereas forperiods with submerged conditions the temperature is set to +4°C. Figure 7-2 also shows what thereconstructed temperatures would look like if the site had not been covered by an ice sheet or been
submerged (red curve that is partly hidden by the blue curve). The curves have been used as input
to the SR-Site permafrost simulations (cf. the Climate report, Section 3.4 and /Hartikainen et al.
2010/). The uncertainty of the temperature curves is discussed in Section 7.1.6 and in the Climate
report (Appendix 1).
Figure 7-3 shows the reconstructed ground-surface temperature for Forsmark for the past 120,000 years.
These data were produced by the 1-D permafrost modelling performed in SR-Can /SKB 2006c/,
using the data in Figure 7-2 as input. As in Figure 7-2, reconstructions with and without the presenceof the Weichselian ice sheet and submerged conditions are shown. An alternative more detailed wayof calculating ground temperatures was applied in the SR-Site 2-D permafrost simulations (cf. theClimate report, Section 3.4 and /Hartikainen et al. 2010/). The uncertainty in the temperature
curves, and the way it is dealt with, is discussed in Section 7.1.6.
References to locations of numerical data characterising the evolution of air and ground-surface
temperatures for different climate cases are given in Table 7-4.

15

Air temperature with ice and submerged conditions
Air temperature without ice and submerged conditions

10

0

-5

Air temperature (°C)

5

-10

-15
120

100

80

60

Time (kyrs before present)

40

20

0

Figure 7-2. Reconstructed air temperature curve for the Forsmark region for the last glacial cycle. The blue
curve includes periods of ice sheet coverage, i.e. it shows simulated basal ice temperatures for glaciatedperiods, air temperatures for ice-free periods, and also a temperature of +4°C for submerged periods. The redline shows reconstructed last glacial cycle air temperatures as they would have been without the presence ofthe Weichselian ice sheet and without submerged periods. The temperature scale shows absolute temperatures.
Reproduced from Figure 3-55 of the Climate report.

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Forsmark Ground surface temperature [C] for the past 120 kyrs

10

Forsmark Ground surface temperature [C] without ice sheet
and sea for the past 120 kyrs

5

0

–5

–10

–120000–100000–80000–60000–40000–200000Ground surface temperature (°C)

15

–15

Time (kyrs BP)

Figure 7-3. Ground-surface temperature for Forsmark for the past 120,000 years with (blue) without (red)ice sheet and submerged conditions (reproduced from Figure A1-4 of the Climate report). Data were produced
within the SR-Can permafrost simulations /SKB 2006c/. The temperature scale shows absolute temperatures.
The underlying data were used as input data in /Sundberg et al. 2009c/ and /Vidstrand et al. 2010/.
Table 7-4. Sources for numerical temperature data.
Parameter – climate case

Document

Air temperature – Reference glacial cycle

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Air temperature – Severe permafrost case

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Ground-surface temperature – Reference glacial cycle

SKBdoc 1265613

Ground-surface temperature – Severe permafrost case

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Ice thickness

The reconstructed ice sheet thickness at Forsmark for the last glacial cycle is shown in Figure 7-4.
The site is covered by the Weichselian ice sheet during two phases, centred around 60,000 and
20,000 years BP. About half of the glacial cycle has passed before the ice sheet reaches the Forsmark
region. In this reconstruction, Forsmark is free of ice during the major part of the so-called Marine
Isotope Stage 3 period (59,000–24,000 years BP), i.e. the period preceding the Last Glacial
Maximum at around 20,000 years BP. This is in line with several recent studies of the Weichselian
glacial history, see discussion and references in the Climate report (Sections 4.2 to 4.4).
It is recommended that these data (Figure 7-4) are repeated for the coming 120,000 years toconstruct the Reference glacial cycle for the SR-Site safety assessment. In this process, the timeof 120,000 years BP reinterpreted as the present day.
The uncertainty of the timing and duration of future glacial phases at Forsmark are taken care of bycomplementary cases with longer and shorter ice sheet coverage than in the reconstructed last glacial
cycle (Climate report, Sections 5.1 and 5.3).
The maximum ice thickness over Forsmark (~2,900 m) occurred, as expected, during the Last Glacial
Maximum, around 20,000 years BP. The uncertainty in future maximum ice sheet thickness isaddressed in a complementary climate case describing larger ice thicknesses (Climate report,
Section 5.4). Figure 7-5 shows maximum ice sheet thicknesses for different assumptions on air
temperature, and also the thickness of the Saalian ice sheet, which was the largest ice sheet during thelast 2 million years. The result regarding a tapering off of the increase in maximum ice thickness withincreasingly colder climates is in line with what is known from studies of the Greenland and Antarctic
ice sheets (cf. the Climate report).
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Ice thickness (m)

3000

2000

1000

0
100

120

80

60

40

20

Time before present (kyrs)0

Figure 7-4. Reconstructed Weichselian ice sheet thickness for the Forsmark region for the past 120,000 years.
Reproduced from Figure 4-18 of the Climate report.

Maximum ice sheet thickness (m)Overall ice sheet
4000

Forsmark

Saalian ice sheet
3000

Reference glacial cycle

2000

1000

0
6

8

10

12

14

16

Temperature lowering from present (°C)

Figure 7-5. Maximum ice sheet thicknesses for different decreases in air temperature. The maximum thickness
for the overall ice sheet is shown (black curve), as well as the maximum thickness at the Forsmark site (bluecurve). The maximum thicknesses at Forsmark for the Saalian ice sheet and for the Reference glacial cycle
are indicated. Reproduced from Figure 5-20 of the Climate report.

Selected numerical data from Figure 7-5 are displayed in Table 7-5, where also the associated additional hydrostatic pressure is given. Just as in SR-Can, the most extreme ice sheet configurations fromthe sensitivity tests are judged to be unrealistically large. Instead, the maximum ice sheet thickness,judged to be realistic for the coming c. 100,000 years, is based on the largest ice sheet configuration
for the past 2 million years. Estimates of this maximum ice sheet configuration give a maximum ice
thickness of 3,400 m over Forsmark (Climate report, Section 5.4). This ice thickness, corresponding
to an additional hydrostatic pressure related to ice thickness of 30 MPa, is propagated to the SR-Sitescenario Canister failure due to isostatic load.

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Table 7-5. Maximum ice thickness over Forsmark and associated additional hydrostatic pressure
for various Fennoscandian ice sheet configurations. Data from the Climate report, Section 5.4.
Ice sheet configuration

Maximum ice
thickness (m)Additional hydrostaticpressure (MPa)Reconstructed Weichselian ice sheet.

2,920

26

Largest Fennoscandian ice sheet during thepast 2 Myrs (Saalian ice sheet).

3,400

30

Extreme ice sheet from climate sensitivity tests.

3,670

32

Ice surface gradients

Figure 7-6 shows the theoretical Reference glacial cycle ice sheet profile data (black line) andthe simulated Reference glacial cycle ice sheet profile (red line). The profiles are described in
the Climate report (Appendix 2). The theoretical profile is steeper than simple parabolic profiles
sometimes used to describe ice sheet profiles. Since this theoretical profile is considered to be more
realistic than simple parabolic profiles, and because it is steeper than the steepest of the simulated
advancing ice profiles (cf. the Climate report, Appendix 2), this theoretical profile is selected torepresent an advancing ice sheet over Forsmark for the SR-Site Reference glacial cycle.
The red line in Figure 7-6 shows a simulated profile from the deglaciation phase of the Weichselian
ice sheet (at 14,300 years BP). For the location of this profile, see the Climate report (Appendix 2).
This profile is the least steep analysed retreat profile from the Weichselian ice sheet reconstruction.
This profile is selected to represent a retreating ice sheet over Forsmark for the SR-Site reference
evolution.
In combination, the two profiles in Figure 7-6, constitute, in their frontal parts, the steepest, and theleast steep profiles of all profiles analysed in the Climate report (Appendix 2), and they are considered to cover a broad enough span of possible profiles to be employed in other SR-Site studies.
Figure 7-7 shows the ice surface gradient data calculated from ice thickness data from the reconstruction of the Weichselian ice sheet. Naturally, ice surface-gradient data are only present for theperiods of ice sheet coverage (cf. Figure 7-4). In Figure 7-7, the gradient for the most frontal-near,and steepest, part of the ice sheet is not shown.

Elevation (m above reference level)

It is recommended that these data are repeated for the coming 120,000 years to construct the reference evolution in the SR-Site safety assessment. The time scale should be projected into the futurein the same way as for the ice thickness data described above. References to locations of numerical
data for the reference ice sheet profiles and surface gradients are given in Table 7-6.

5000
4000

Simulated reference evolution profile

3000

Theoretical reference evolution profile (400 km)

2000
1000
0
0

1000

2000

Distance (km)Figure 7-6. The theoretical reference evolution ice sheet profile data (black) and the simulated last glacial
cycle ice sheet profile from the deglaciation (red). For descriptions of the profiles, see the Climate report,(Appendix 2). Modified from Figure A2-7 of the Climate report.

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0,6Gradient (degrees)

0,5
0,4
0,3
0,2
0,1
0
–80000–60000–40000–200000Time (kyr BP)

Figure 7-7. The ice surface-gradient data calculated from ice thickness data from the reconstruction of theWeichselian ice sheet. Reproduced from Figure A2-5 of the Climate report.
Table 7-6. Sources for numerical ice sheet profiles and surface gradient data.
Parameter

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Reference advancing ice sheet profile

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Reference retreating ice sheet profile

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Reference ice sheet surface gradients

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Shore-level changes

Data on shore-level changes for the SR-Site Reference glacial cycle and Global warming climate caseare illustrated in Figure 7-8. The data are based on Global Isostatic Model simulations, extrapolationsof up-to-present Holocene isostatic uplift rates at the Forsmark site, and peer-review published data on
future sea-level rise (see details in the Climate report, Sections 4.5 and 5.1). The figure shows shorelevel changes from the time of the Weichselian deglaciation of Forsmark (at 8,800 BC), through thepresent time, and into the future. The future shore-level changes are shown both for the Reference
glacial cycle (grey line), and for the Global warming climate case (green line).

Relative shore-level (m)In the Reference glacial cycle, the Forsmark site is submerged by the Baltic Sea following the two
periods of ice sheet coverage (cf. Figure 7-4). In the global warming case, the site is situated above
the level of the Baltic Sea for the majority of the next 120,000 years.
500
400
300
200

Relative shore-level
Global warming case

100

Relative shore-levelreference glacial cycle

0
-100
0

10

20

30

40

50

60

70

80

90

100

110

120

Time (kyrs after present)

Figure 7-8. Shore-level changes data for the Reference glacial cycle (repetition of conditions reconstructed
for the last glacial cycle) and the Global warming climate case. Reproduced from Figure 5-2 of the Climate
report.

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In the Global warming case, a complete collapse of the Greenland ice sheet is assumed in the GIA
simulations. Global mean sea level is raised by 7 m from this assumed melting of the Greenland ice
sheet. However, the gravitational effects associated with the mass re-distribution that follows fromthe ice sheet collapse results in a zero net effect in shore-level change in the Fennoscandian region(see the Climate report, Section 5.1), and is therefore not seen in Figure 7-4. However, there are otheruncertainties related to global warming, such as the response of the West Antarctic ice sheet and also
the themal expansion of ocean water. Due to these uncertainties it is possible that, in contrast to whatis shown in Figure 7-4, there might be initial periods of transgressions at the Forsmark site in the global
warming variant. Such situations are described in the Climate report, Section 5.1 and 5.2. However,after an early phase with these large uncertainties, the results of the isostatic modelling suggest that in
the long run the Forsmark site will be situated above sea level until the end of the 120,000 year period.
In the Global warming case, shorter phases of high water stands during storm events are expected
at the Forsmark site in the near future, but these events are judged not to affect long-term repository
safety.
References to locations of numerical data on shore-level changes for the Reference glacial cycle and
the Global warming case are given in Table 7-7.
Table 7-7. Sources for numerical data on shore-level changes.
Case

Document

Reference glacial cycle.

SKBdoc 1265613

Global warming climate case.

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Permafrost depths

The reconstructed permafrost depth at Forsmark for the last glacial cycle is seen in Figure 7-9 (from1-D modelling performed for SR-Can) and Figure 7-10 (from 2-D modelling performed for SR-Site).
The deepest permafrost occurs prior to the first ice sheet advance over the site, at 70,000 years
BP. The 2-D model in Figure 7-10 only shows the time before the maximum permafrost depth at
70,000 years BP. As can be seen, the results of the two models agree.
The uncertainty of the timing and duration of future permafrost periods at Forsmark is handled incomplementary cases with longer and shorter periods of permafrost coverage than in the reconstructedlast glacial cycle (Climate report, Sections 5.1 and 5.5).
In order to study remaining uncertainties in the permafrost depth, mainly relating to the presence orabsence of an ice sheet at the site and other associated changes, a Severe permafrost case was investi­
gated (cf. the Climate report, Section 5.5 and /Hartikainen et al. 2010/). An assumption of a cold
periglacial climate is here combined with an assumption of a very dry climate, without the presenceof an ice sheet, vegetation, winter snow cover, and post-glacial submerged conditions, in order tofavour permafrost growth. The resulting evolutions of maximum permafrost depth, maximum depthof perennially frozen ground, and maximum depth of −2 and –4°C isotherms over the repository areseen in Figure 7-11.
The underlying data in Figure 7-9 and Figure 7-10 are repeated for the coming 120,000 years toconstruct the Reference glacial cycle for the SR-Site safety assessment. In this process, the time of
120,000 years BP is redefined as the present day. Furthermore, for the Severe permafrost case, thedata underlying the results shown in Figure 7-11 should be used. References to the locations of theunderlying numerical data are given in Table 7-8.
Table 7-8. Sources for locations of the numerical data on permafrost development.
Case

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Reference glacial cycle.

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Severe permafrost climate case.

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Depth (m)

100

150

200

permafrost line
perennially frozen
cryopeg

250

300
120

100

80

60

0

20

40

Time before present (kyrs)

Figure 7-9. Evolution of permafrost and perennially frozen ground depth for the reconstruction of last glacial
cycle conditions for the repository location in Forsmark. The results were obtained by a 1-D permafrost
model /SKB 2006c, Section 3.4/. The permafrost line is defined by the 0°C isotherm, whereas the cryopeg
comprises unfrozen parts of the permafrost due to e.g. high pressure under glacial conditions. Reproduced
from Figure 3-56 of the Climate report.
050Depth below ground surface (m)

100

150

200

250

Maximum permafrost depth
humid variant300

dry variant
SR−Can

350

400

450

500
115

Maximum depth of perennially frozen groundhumid variant
dry variant
SR−CanMaximum depth of −2 °C and −4 °C isotherm−2 °C,−2 °C,
110−4 °C, humid variant−4 °C, dry variant
105

100

95

90

85

Time before present (kyrs)

80

75

70

Figure 7-10. Evolution of maximum permafrost depth, maximum depth of perennially frozen ground andmaximum depths of the −2 and –4°C isotherms over the repository for the reconstruction of last glacial
cycle conditions. The shaded areas in blue and red represent the range when considering dry and humid
climate variants. The lilac colour indicates that the results for permafrost and perennially frozen groundoverlap. Modified from Figures 3-70 and 4-28 of the Climate report.

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409

0
50

Depth below ground surface (m)

100
150
200
250
300

Maximum permafrost depth
dry variant
humid variant

350
400

Maximum depth of perennially frozen grounddry variant
humid variant

450

Maximum depth of −2 °C and −4 °C isotherm

500−2 °C,−2 °C,
0

10

20−4 °C, dry variant−4 °C, humid variant
30

40

50

60

70

80

90

100

120

110

Time after present (kyrs)

Figure 7-11. Evolution of maximum permafrost depth, maximum depth of perennially frozen ground andmaximum depth of −2 and –4°C isotherms over the repository for the Severe permafrost case. The shaded
areas in blue and red represent the range when considering the dry and humid climate variants of the
Severe permafrost case. The darker lilac colour indicates that the results for permafrost and perenniallyfrozen ground overlap. Reproduced from figure 5-24 of the Climate report.

The maximum depths of different isotherms and perennially frozen ground for the two climate cases
are given in Table 7-9. In the table, the uncertainty range is given based on the sensitivity analyses
listed in Table 7-10.The uncertainty intervals are based on the most pessimistic combination of
uncertainties relevant for each case. Several of the combinations are considered quite unrealistic, see
the Climate report Section 3.4.4. and 5.5. In this context it should also be noted that the range of
relevant sensitivity studies are not the same for the Reference glacial cycle and the Severe permafrost case, which is why the uncertainty interval reaches deeper in the Reference glacial cycle thanin the Severe permafrost case, see also Climate report, Section 5.5.
Table 7-9. Maximum depths of permafrost (0°C isotherm), perennially frozen ground, and −2°C and−4°C isotherms for the Reference glacial cycle and for the Severe permafrost case. The uncertainty
intervals, resulting from the sensitivity experiments in Table 7-10, are discussed in the text.
Case

Maximum permafrost
depth (0°C isotherm)[uncertainty interval]

Maximum depth
perennially frozen ground[uncertainty interval]

Maximum depth−2°C isotherm[uncertainty interval]

Maximum depth−4°C isotherm[uncertainty interval]

Referenceglacial cycle

259 m[down to 463 m]

246 m[down to 422 m]

200 m[down to 388 m]

148 m[down to 316 m]

Severepermafrost case393
[down to 456]

359
[down to 408]

311
[down to 359]

234
[down to 268]410

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Table 7-10. Sensitivity analyses made within the permafrost simulations.
Sensitivity experiment

Reference

Constant surface temperatures between –2 and –20°C

Climate report, Figure 3-52

Humid and dry climate variants

Climate report, Section 3.4 and 5.5: all simulations of lastglacial cycle conditions and Severe permafrost case.

Last glacial cycle: uncertainty in geothermal heat flow

Hartikainen et al. 2010, Figure 5-41

Last glacial cycle: uncertainty in bedrock thermal conductivity Hartikainen et al. 2010, Figure 5-42
Last glacial cycle: uncertainty in bedrock thermal diffusivity

Hartikainen et al. 2010, Figure 5-43

Last glacial cycle: mixed thermal diffusivities with the highest
difference for adjacent rock domains

Hartikainen et al. 2010, Figure 5-44

Last glacial cycle: combination of uncertainties in bedrock
thermal properties and geothermal heat flow

Climate report, Figure 3-85
Hartikainen et al. 2010, Figure 5-45

Last glacial cycle: combination of uncertainties in surface
and bedrock thermal properties

Climate report, Figure 3-86
Hartikainen et al. 2010, Figure 5-46

Last glacial cycle: combination of uncertainties in air temperature, surface conditions and bedrock thermal properties

Climate report, Figure 3-87
Hartikainen et al. 2010, Figure 5-48

Last glacial cycle: influence of heat from the repository

Climate report, Figure 3-88
Hartikainen et al. 2010, Figure 5-49

Last glacial cycle: convective heat transfer and salinity
transfer by groundwater flow

Climate report, Figure 3-89
Hartikainen et al. 2010, Figure 5-50

Shifting of the entire air temperature curve reconstructed forthe last glacial cycle by +6, 0, –4, –6, –8, –10, –12, –14 and–16°C

Climate report, Figure 3-79, 3-81, 3-82, 3-83, 3-84
Hartikainen et al. 2010, Figure 4-1, 5-37, 5-38, 5-39, 5-40

Shifting of the entire air temperature curve reconstructed
for the last glacial cycle by +6 and –6°C combined withuncertainty in climate humidity.

Climate report, Figure 3-80
Hartikainen et al. 2010, Figure 5-36

Severe permafrost case: combination of uncertainties insurface and bedrock thermal properties

Hartikainen et al. 2010, Figure 5-47

7.1.11 Judgements by the SR-Site teamAs all these data have been produced within SR-Site, no judgement is made, except that all data anddescriptions are judged to be adequate.
7.1.12 Data recommended for use in SR-Site modelling
References to numerical data on air and ground-surface temperatures are given in Table 7-4. Dataon the maximum ice thickness and associated additional hydrostatic pressure are given in Table 7-5.
References to numerical data on ice sheet profiles and surface gradients are given in Table 7-6.
References to numerical data on the shore-level changes are given in Table 7-7. References tonumerical data on permafrost development are given in Table 7-8. Data on maximum permafrost
and freezing depths are given in Table 7-9.
All of these data are recommended for use in SR-Site.

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7.2

Landscape dose conversion factors

In SR-Site, doses to humans are assessed for a number of scenarios by either multiplying radionuclide
activity release rates, or pulse activity releases, to the biosphere by pre-calculated radionuclide-specificdose conversion factors. The chain of models used to calculate the releases, as well as the applicationof the dose conversion factors in the dose calculations, are described in detail in the Radionuclidetransport report.
Two different types of landscape dose conversion factors (LDF) are discussed in this section. The firstis simply denoted LDF, which is applicable to long-term continuous releases. The second is modified
landscape dose conversion factors for pulse releases, denoted LDF pulse in this report, which areapplicable to releases of radionuclides that reach the biosphere during relatively short time periods(years to hundreds of years).

7.2.1

Modelling in SR-Site

Defining the data requested from the supplier

The LDF for each potentially released radionuclide is defined as the mean annual effective dose toa representative individual of the most exposed group, resulting from a constant activity release rate
to the biosphere of 1 Bq/year of this radionuclide. The LDF pulse, used for pulse activity releasesof specific radionuclides, is defined as the mean annual effective dose to a representative individualof the most exposed group, resulting from a 1 Bq pulse release to the biosphere. For both factors,
the exposure is averaged over the lifetime of an individual (assumed to be 50 years). For a further
discussion of the LDF concept, see the Biosphere synthesis report and details in /Avila et al. 2010/.
The following data should be delivered by the supplier:•Radionuclide-specific LDFs, in units of Sv/y per Bq/y, for temperate conditions, i.e. climate
conditions similar to those of today.

•

Radionuclide-specific LDFs (Sv/y per Bq/y) for periglacial conditions, which represent a colder
climate than today with deep permafrost.

•

Radionuclide-specific LDFs (Sv/y per Bq/y) for glacial conditions, which represent a period
when the considered area is covered by ice. LDFs calculated for submerged conditions during
a temperate climate can be used to represent glacial conditions in the present assessment.

•

Radionuclide-specific LDFs (Sv/y per Bq/y) for the global warming climate case, which represents a case with a prolonged period of temperate conditions.

•Radionuclide-specific values of the modified LDF for pulse releases, the LDF pulse (Sv/y per
Bq). LDF pulse should be delivered for temperate conditions only.

The LDFs should be supplied for the following radionuclides of the selected inventory, as describedin Section 2.2.1 and as justified in the Radionuclide transport report:•

Ac-227, Ag-108m, Am-241, Am-243, C-14, Ca-41, Cl-36, Cm-244, Cm-245, Cm-246, Cs-135,
Cs-137, Ho-166m, I-129, Nb-94, Ni-59, Ni-63, Np-237, Pa-231, Pb-210, Pd-107, Po-210, Pu-239,
Pu-240, Pu-242, Ra-226, Se-79, Sm-151, Sn-126, Sr-90, Tc-99, Th-229, Th-230, Th-232, U-233,
U-234, U-235, U-236, U-238, and Zr-93.

The LDF pulse should only be supplied for following radionuclides, which may be released asa pulse and which are not obviously insignificant for the assessment:•

Ag-108m, Cl-36, Cs-135, I-129, Nb-94, Ni-59, Se-79, Sn-126, and Tc-99.

Radionuclide decay of single radionuclides and in selected decay chains was considered in the SR-Site
LDF calculations. The decay chains included in the assessment are listed in the Biosphere synthesis
report.

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SR-Site modelling activities in which data will be used

The LDFs are used for calculation of maximum annual effective doses to a representative individualof the most exposed group, resulting from long-term continuous releases of radionuclides to thebiosphere. Through a chain of modelling activities within SR-Site, the activity that is assessed to bereleased to the biosphere for each radionuclide per year is calculated for different scenarios. This activity release rate is multiplied with the LDF to obtain a dose rate. In practice, the LDF values obtained fortemperate conditions are used in the majority of the dose calculations, whereas LDFs for other climate
conditions are used in a few sensitivity cases (see the Radionuclide transport report for details).
For a number of radionuclides, associated with the rapid release fraction discussed in Section 3.2,
a short-term pulse release from the canister that is almost instantaneous on safety assessment timescales is possible. Unless the radionuclides are considerably retarded in the engineered barrier and/or geosphere, short-term pulse releases to the biosphere may occur. In this case, the activity of theentire pulse for each radionuclide is multiplied by the radionuclide-specific LDF pulse. The LDF
pulse is used for calculation of maximum annual effective doses to a representative individual ofthe most exposed group.
For each radionuclide and type of LDF, the delivered LDF value is pessimistically chosen to beconstant at the maximum value calculated for the entire climate domain and for all biosphere objects
considered in the biosphere modelling.

7.2.2 Experience from SR-Can
This section briefly summarises the experience from the SR-Can safety assessment, with focus on
experience that may be of direct consequence for the data qualification in this report. For details on
the SR-Can biosphere modelling of Forsmark, the reader is referred to /SKB 2006e/ and the references therein.
Modelling in SR-Can

The LDFs were integrated in the SR-Can radionuclide transport modelling chain in the same way
as is done in SR-Site. However, there are several differences between the modelling in SR-Can and
SR-Site, see /Avila et al. 2010/ and the Biosphere synthesis report.
•

The assumptions of how the released radionuclides reach the biosphere were different in SR-Can
than in SR-Site. In the SR-Can assessment it was assumed that the releases were distributed over
the whole landscape, whereas in SR-Site it is pessimistically assumed that all releases reach eachdischarge area in turn. Also, in SR-Can releases reaching a biosphere object were directed tothe top layer of the deposits (upper layers of the regolith), whereas in SR-Site the releases are
directed to the Quaternary deposits (the lower layer of the regolith).

•

In SR-Can the transport of radionuclides in different ecosystems was modelled with the same
ecosystem models as used in the SR-97 safety assessment, except for the forest ecosystems forwhich a new model was developed. These models have been further developed for the SR-Siteassessment, incorporating the results and new knowledge obtained during the site investigations(see the Site description Forsmark).

•

In the SR-Can assessment, literature values were used for most model parameters, whereas in
SR-Site specific values obtained for the Forsmark site are used.

•

In the SR-Can assessment, two separate dose conversion factors were derived for releases to thelandscape and to wells used for drinking water production. The larger of these dose conversion
factors was used in the dose assessments. In SR-Site, a single LDF value is derived for eachradionuclide and climate domain, wherein the contribution from releases to a drilled well isincorporated where appropriate.

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Conditions for which data were used in SR-Can

The conditions for which the LDFs in SR-Can were used are similar to those for the LDFs in SR-Site.
The LDF values in SR-Can were used for calculation of doses to a representative individual of themost exposed group in scenarios with continuous releases to the biosphere. The LDF values were
derived for the same climatic conditions as in SR-Site and for the same types of potentially affectedareas of the landscape.
Sensitivity to assessment results in SR-Can

The LDFs delivered to the SR-Can radionuclide transport modelling chain had a direct consequence
for the annual effective dose, which was the delivery from SR-Can. Therefore, the assessment was
especially sensitive to the LDFs of Ra-226 and I-129, which were the radionuclides that dominatedthe annual effective dose.
Sensitivity studies showed that the uncertainties in radionuclide specific parameters, such as distribution coefficients (Kd) and concentration ratios (CR) in the biosphere, had a large impact on theuncertainties of the LDFs in SR-Can /Avila et al. 2006/. Furthermore, the derived LDF values weresensitive to the distribution of the radionuclide discharges in the landscape and the type of ecosystem
receiving the discharges.
Alternative modelling in SR-Can

No alternative biosphere modeling was carried out in SR-Can.
Correlations used in SR-Can modelling

The SR-Can LDF values were derived deterministically and therefore parameter correlations were
not considered.
Identified limitations of the data used in SR-Can modelling

In the calculation of the LDF values used in the SR-Can dose assessments, the releases from the geosphere were assumed to be distributed over the whole landscape. This leads to underestimation of the
LDF values for scenarios with releases from only one or a few leaking canisters, since such releases
will traverse a limited number of paths to the surface and these paths will discharge to one or a few
biosphere objects. Another limitation of the SR-Can modelling was that the applied model, and theparameter values used as inputs, did not fully incorporate the data and knowledge obtained from thesite investigation programme. The regulatory authorities’ comments on the biosphere modelling andthe use of LDF data in subsequent radionuclide modelling are summarised in Chapters 9 and 13 of/Dverstorp and Strömberg 2008/.

7.2.3 Supplier input on use of data in SR-Site and SR-Can
LDF values have been delivered by the SR-Site biosphere modelling team to the SR-Site main project,
where the dose assessments are performed. Thus, the SR-Site biosphere modelling team is recognised
as the supplier and the SR-Site main project as the customer. Supplier inputs, partly based on theregulatory authorities’ comments on SR-Can, were given at an early stage and have already been
incorporated in SR-Site.

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7.2.4 Sources of information and documentation of data qualification
The main sources of information on the SR-Site LDFs are /Avila et al. 2010/ and Chapter 10 in the
Biosphere synthesis report, wherein LDF values for the Forsmark site are presented. The scrutinisingof supporting data in lower-level references is part of the qualification process of the Biospheresynthesis report and /Avila et al. 2010/, and is not dealt with in this report. Full references to themain sources of information are given in Table 7-11.
Table 7-11. Main sources of information used in the data qualification.
Biosphere synthesis report, 2010. Biosphere assessment of SR-Site Forsmark – synthesis and summary of results.
SKB TR-10-09, Svensk Kärnbränslehantering AB.
Avila R, Ekström P-A, Åstrand P-G, 2010. Landscape dose conversion factors used in the safety assessment
SR-Site. SKB TR-10-06, Svensk Kärnbränslehantering AB.

Categorising data sets as qualified or supporting

The LDF and LDF pulse delivery for each radionuclide and climate domain consists of the maximum value for all times and biosphere objects. These maximum values are reported as describedin Table 7-12. In a broad sense, the data supporting the LDF calculations include a large amount ofdata from the site investigation, site description, and safety assessment modelling. These supporting
data are discussed in /Avila et al. 2010/ and the Biosphere synthesis report, and their lower levelreferences, and are not further discussed here.
The delivered maximum LDFs (item 1 in Table 7-12) are considered to be appropriate as the approachto derive the LDFs is consistent with recommendations from the ICRP /ICRP 2006/ and with the
SSMFS 2008:37 guidelines /SSM 2008/. In this approach, the dose conversion factors obtained fromthe biosphere assessment are based on best estimates of input parameter values from deterministic
simulations. In addition, probabilistic simulations have been used to quantify the impact of uncertainties
on the model results.
The delivered maximum values of LDF pulse (item 2 in Table 7-12) are considered to be appropriate
as they are derived using the model developed and tested as described above for the LDFs. No
probabilistic uncertainty analysis specifically focusing on the pulse releases has been performed.
However, such an analysis is not considered necessary for the pulse release cases, given their relatively
lower importance in the safety assessment.
Table 7-12. Qualified and supporting data sets.
Qualified data sets

Supporting data sets

1. LDF’s for each radionuclide for the different climate domains presented
in /Avila et al. 2010/ and as illustrated in Figure 10-1 of the Biospheresynthesis report.
2. LDF pulse for each radionuclide for the temperate climate domainpresented in /Avila et al. 2010/ and Table 10-2 of the Biospheresynthesis report.
1–2: /Avila et al. 2010/ and the Biosphere synthesis report are reports produced for the Forsmark site within the
SR-Site project. The data have been produced in accordance with the present data qualification framework and the
SKB quality assurance system.

Excluded data previously considered as important

With reference to the methodological differences between SR-Can and SR-Site, and to the limitations of the SR-Can LDFs (cf. Section 7.2.2), it should be noted that LDFs calculated for SR-Can
should not be used in SR-Site.

TR-10-52415

7.2.5 Conditions for which data are supplied
Figure 7-12 shows the Forsmark site under present-day conditions (2000 AD) and the biosphereobjects used in the SR-Site biosphere modelling. In total, ten biosphere objects, containing adischarge area during any period of the present interglacial, were identified. Five additional biosphere objects located downstream of the discharge areas were also identified. Finally, to represent
discharge directly into a stream or a wetland without an initial lake stage, the basin of one of theoriginal biosphere objects (object 121) was partitioned into three separate objects (denoted 121_1,
121_2 and 121_3).
In the biosphere transport and dose calculations, LDFs are calculated over time for each radionuclide
and each biosphere object. The LDFs are only applicable to continuous, uniform long-term releases.
If applied to scenarios where the system has not reached equilibrium, the calculated doses might besubstantially overestimated. Therefore, modified conversion factors for pulse releases with durationsof years to hundreds of years have also been calculated (LDF pulse). LDFs have been calculated
for the three climate domains of the reference glacial cycle, and also for the global warming climatecase. LDF pulse values have only been calculated for the temperate climate domain (see /Avila et al.
2010/ for details).
Temperate climate domain

The interglacial period, i.e. the period from the deglaciation to the onset of periglacial conditions (cf. theClimate report), is represented by climate conditions similar to those of today and is, in accordancewith the reference glacial cycle, assumed to prevail for 18,400 years (i.e. from –9000 to 9400 AD).
As land has emerged sufficiently from the sea, wetlands are assumed to be converted to arable land.
Drinking water for humans and livestock during the terrestrial stage of this period is assumed to be

Figure 7-12. The Forsmark site under present-day conditions (2000 AD) and biosphere objects used in the
SR-Site biosphere modelling. Excerpt from Figure 7-11 of the Biosphere synthesis report.

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supplied by equal parts from surface water and from a contaminated well drilled into the bedrock(cf. Chapter 8 in the Biosphere synthesis report). The calculation period starts at the time for the
deglaciation around 9000 BC, when the landscape is covered by the sea (i.e. submerged conditions).
The length of the submerged period differs between biosphere objects since it takes almost 10,000 years
from the emergence of the first biosphere object from the sea, until the shoreline has passed over
the whole model area.
The LDFs have been calculated over time for each of the biosphere objects shown in Figure 7-12.
Important inputs to assess the succession of the objects are the shore-level changes and climate data
provided in Section 7.1. Other conditions and inputs are described in /Avila et al. 2010/. An exampleof the variation of LDFs, during the successional development of biosphere object 121_03 throughtime, is shown in Figure 7-15 in Section 7.2.8.
The LDF pulse value for a particular radionuclide has been calculated for the biosphere object that
had the highest LDF for a constant release of that nuclide. Three release times (–9000, 3500, and9400 AD) and for two release durations (one year and 50 years) have been used in the modelling.
It follows that the LDF pulse values are applicable only to release scenarios where the releases tothe biosphere occur during short time periods (years or hundreds of years).
Periglacial climate domain

The temperate domain is followed by a period of periglacial conditions in the reference glacial cycle.
During this period, the climate is colder than today, with episodes of deep permafrost. For the periglacial
LDF calculation, periglacial conditions are modelled to prevail from the emergence of a biosphereobject from the sea, until the onset of the next glaciation around 59,600 AD. When assessing theperiglacial LDFs, the model disregards the fact that biosphere objects will emerge during temperateconditions and instead assumes that they emerge during periglacial conditions. During this period,
it is assumed that agriculture is not possible, and drinking water from a contaminated deep drilled
well is not accessible (cf. Chapter 8 in the Biosphere synthesis report). The modelling approachfor calculating LDFs for periglacial conditions is discussed in /Avila et al. 2010/, where also resultsof supporting simulations are reported.
Glacial climate domain

Exposure of humans under glacial conditions, when the site is covered by an ice sheet, is unlikely.
Nevertheless, if releases occur, humans may be exposed to radionuclides through ingestion of sea
food when the ice margin is situated above or close to the repository. As a cautious estimate of
the exposure from releases during glacial conditions, the LDFs from the open sea stage during atemperate climate (i.e. submerged conditions) are used in the assessment. Thus, LDF values forsubmerged conditions are calculated in order to provide estimates of LDFs for glacial conditions.
Global warming climate caseIn the SR-Site global warming climate case, it is assumed that the temperate climate domain isextended by approximately 50,000 years compared with the reference glacial cycle (i.e. the temperate
climate domain prevails until 59,600 AD, see the Climate report, Section 5.1). The assumptions
made in the global warming climate case affect inputs to biosphere modelling in terms of shore-levelchanges, temperatures, etc. (cf. Section 7.1). Assumptions concerning human usage of the landscapeduring this period are the same as for the interglacial period, i.e. wetlands are converted to arable
land when possible and drinking water for humans and livestock is supplied by equal parts fromsurface water and water from a contaminated well drilled into the rock.

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7.2.6

Conceptual uncertainty

The sources of conceptual uncertainty that contribute to uncertainty in LDF estimates can begrouped as follows:•

Difficulties to make accurate predictions of the long-term development of the climate, the long-term
future landscape configuration, and the future use of the biosphere by humans.

•

Simplified mathematical representations of the conceptual models, e.g. discretisation of the lower
regolith compartment. Such uncertainties have to be evaluated by the use of alternative approaches
and simulation cases, or by discussing the potential implications of simplifying assumptions.

A comprehensive study of conceptual uncertainties affecting the derived LDF values has been carriedout and is fully described in /Avila et al. 2010/ and summarised in the Biosphere synthesis report.
Several cautious assumptions have been adopted in treating conceptual uncertainties.

7.2.7 Data uncertainty due to precision, bias, and representativity
The supplied LDFs are selected maximum values obtained from deterministic simulations, using
best estimate values of a large number of supporting parameters as input data. Thus, the LDFs are
deterministic values obtained from a model, and not measured values, which should be considered
in a data uncertainty discussion. The model input parameters are derived using measured and/or
modelled data, which are associated with uncertainties that may, or may not, be important for theassessment of the calculated LDFs. The overall data uncertainty of the LDFs, arising from uncertainties in the underlying input data, is discussed in the following.
Probabilistic analysis of data uncertainty

The supplied LDFs values are pessimistically chosen to be constant at the maximum value calculated
for the entire climate domain and for all biosphere objects considered in the biosphere modelling.
This approach should encompass most of the associated data uncertainty. To substantiate this, a probabilistic uncertainty analysis has been performed, where the impact of variations and uncertainties
in underlying data has been examined. The analysis has been made for a number of radionuclides;
for the individual biosphere object for which the maximum LDF of each radionuclide was obtained.
This probabilistic analysis included several steps. First, the uncertainty associated with each parameter was characterised based on available site specific and generic data, as well as by elicitation ofexpert judgement. Second, probability distributions were assigned to each parameter used as inputs
to the deterministic LDF model. Third, by a combination of Monte Carlo simulations and systematicstudies of the variation of time-dependent parameters as a group (since these are strongly correlated),
the uncertainty in the deterministically determined LDF could be estimated.
The LDF distributions resulting from the probabilistic analysis were shown to be approximately lognormally distributed, with a 90% confidence interval typically spanning two orders of magnitude (cf.
Figure 7-13). The deterministic value is generally close to the median of the probabilistic distribution,and the LDFs used in the safety assessment thus reflect the central tendency, or the typical outcome,when parameter value uncertainties are considered.
It should be noted that the probabilistic analysis is not fully correlated, which enables combinationsof input data that do not coexist exist in the reality. This may give rise to unrealistic extreme values
manifesting in the large data ranges seen in Figure 7-13. Such extreme values should to a lesser extent
affect the median value than the arithmetic mean. Accordingly, the deterministic value should becompared with the median value. For a few radionuclides, the probabilistic median value is significantly larger than the deterministic value (U-238 and Zr-93). However, by examining these nuclides
it can be concluded that they have a very modest contribution to risk. Accordingly, this discrepancy
should be of little consequence for the safety assessment. This is discussed in more detail in the
Biosphere synthesis report and in /Avila et al. 2010/.
No similar probabilistic analysis has been performed for the LDF pulse. However, it should be notedthat the LDF pulse is derived using the model developed and tested for the LDFs.

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10–8

10–9

Sv/y per Bq/y

10–10

10–11

10–12

10–13

Zr-93

U-238

Th-230Th-229

Tc-99

Sn-126

Se-79

Ra-226

Pu-239Pa-231

Np-237

Ni-59

Nb-94

I-129

Cs-135

Cl-36

C-14

Am-241

10–15

Pu-242

Deterministic
Mean

10–14

Figure 7-13. Variation in interglacial LDFs obtained from probabilistic simulations for a subset of theanalysed radionuclides. The LDF distributions are taken at the time when the median of the probabilistic
output reached its peak. The mean (square) from the simulations are contrasted against the SR-Site LDF(circle). The box represents the 25th percentile, the median and the 75th percentile of the LDF distribution from the simulation, and whiskers represent the 90 percent confidence interval. Reproduced from
Figure 12-6 of the Biosphere synthesis report.

Data uncertainty due to bias

By adopting a methodology of choosing the maximum calculated LDF value for each radionuclide
as representative for all biosphere objects and times within the climate domain, the creates a bias
toward high values. In reality, the releases might be directed to several biosphere objects during
a simulation period. Hence, doses obtained with the delivered LDF values can be considered as
pessimistic estimates.
Data uncertainty due to representativity

For each parameter used in the derivation of LDF values, describing a property or flow component in
the biosphere objects, a best estimate value has been derived from site and/or generic literature data.
The parameter variation has been described by a probability density function (PDF), which has been
also used for obtaining the best estimate values. In deriving PDFs for the parameters, as far as possible,
only natural variations appropriate to a specific climate domain have been considered. However, forsome parameters no site-specific data were available, or the data available were not sufficient tocharacterise the PDFs. In these cases, literature data, or a combination of literature and site data,
have been used for obtaining the characteristics of the PDFs. This was the case for element-specificparameters (distribution coefficients and concentration ratios), for which PDFs were parameterised
from a combination of site-specific and generic data using Bayesian methods /Nordén et al. 2010/.
Biosphere objects develop in time, but for the purpose of the present assessments the properties withina biosphere object are assumed to be homogeneous and to represent yearly averages. Thus, the para­
meter values that are used in the simulations for derivation of LDF values should give representativedescriptions of compartments and flows between compartments within a biosphere object, disregarding
spatial variation within compartments and seasonal variations during a year.
LDF pulse was only studied for one biosphere object. Accordingly the representativity of LDF pulse
is lower than of the LDF.
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7.2.8 Spatial and temporal variability of data
The LDFs represent the highest values for each radionuclide over all biosphere objects during thewhole simulation time period corresponding to different climate conditions. Hence, the delivered
LDF for a specific climate domain or case is constant in time and space. However, the LDF values
are derived on the basis of time-dependent doses per unit release rate, obtained from simulations fordifferent biosphere objects with their different properties. These underlying data for different times andobjects provide an indication of data uncertainties associated with spatial and temporal variations, andare therefore briefly discussed below. For a detailed description of the study of spatial and temporalvariations in calculated LDFs, the reader is referred to /Avila et al. 2010/.
Uncertainty due to spatial variability

The calculated LDFs vary between the biosphere objects (cf. Figure 7-14), but the degree ofvariation depends on radionuclide properties. For radionuclides where exposure from food is thedominant exposure pathway (i.e. C-14, Cl-36, I-129, Nb-94, Np-237, Se-79, Sn-126, and Tc-99),
the LDFs typically vary by two or even three orders of magnitude between objects (excluding
object 105). However, for radionuclides where drinking water is an important exposure pathway(e.g. Am-241, Pa-231, Pu-231, Pu-239, Pu-242, Ra-226, and Th-229) the variation between objects
is typically below a factor of three. Note that the value at the limit of the upper error bar is usedas the deterministically determined LDF (cf. the green dots in Figure 7-13). Biosphere object 105,
which is in the sea stage during the whole temperate period, has consistently lower LDFs for all
radionuclides, by three orders of magnitude or more, and is therefore shown separately by green
squares in Figure 7-14.
In the calculation of the LDF pulse, only one biosphere object (121_3) has been used, which is theobject that generates the maximum LDF in the long-term release simulation. Consequently, the spatial
variability of LDF pulse has not been evaluated.

10−8
10−9
10−10
10−11

Sv/y per Bq/y

10−12
10−13
10−14
10−15
10−16
10−17

Zr−93

U−238

Th−230Th−229Tc −99Se−79

Ra−226Pu−242Pu−240Pu−239Np−237

Ni−59

Nb−94

I−129

Cs−135

Cl−36

C−14

Am−241

10−19

Ac−227

10−18

Figure 7-14. Mean, minimum, and maximum values of LDF’s for a selection of radionuclides across biosphereobjects (excluding object 105) for temperate climate conditions. The LDF values for object 105 are shown
separately (green squares). Reproduced from Figure 10-4 of the Biosphere synthesis report.

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Uncertainty due to temporal variability

The derivation of LDF values included finding, for each biosphere object, the maximum value ofthe time dependent annual dose per unit release rate (Sv/y per Bq/y) for each radionuclide in eachclimate domain or case considered. The time series for unit release rates were obtained from simulations with the radionuclide model for the biosphere and showed a similar pattern for all radionuclides.
Examples of time series of dose rates per unit release rate are presented in Figure 7-15.
The effects of the shoreline regression and the subsequent succession of biosphere objects on LDFs
are similar for many of the dose contributing radionuclides. During the sea stage (1), LDFs increase
continuously with time, reflecting a gradually increasing activity concentration in the sea water.
The LDFs increase further in the transition stage (2). In Figure 7-15, the selected biosphere object
develops directly into a wetland, and the increase in the LDF during the transition stage is a functionof the gradual accumulation of the radionuclides in wetland peat.
In the terrestrial stage (3) the wetland is fully developed. A steep increase in LDFs early in this stage
occurs when it is first possible to transform the wetland into arable land. From this point, further wetland accumulation only marginally affects the LDFs. For some radionuclides, accumulation results in aslight increase of LDFs (e.g. Ni-59), whereas for others (Se-79, I-129) wetland concentrations slightly
decrease under long-term stable conditions. A more detailed description of the temporal variability of
the LDFs, including explanations of the short-term transients displayed in Figure 7-15, is provided in
the Biosphere synthesis report.
In the case of LDF pulse, the duration and the timing of the pulse release might be of importance, as
further evaluated in /Avila et al. 2010/ and the Biosphere synthesis report. The LDF pulse has been
calculated for three release times (–9000, 3500, and 9400 AD) and for two release durations (one and50 years). The results show that the differences due to changes in pulse duration for radionuclides
with high retention in the regolith are insignificant, whereas for radionuclides with low retention(Cl-36 and Tc-99) the LDF pulse values decrease by less than one order of magnitude when the duration of the pulse is extended. Furthermore, the results reported in /Avila et al. 2010/, and summarisedin the Biosphere synthesis report, show that the effect of the time of the release on the LDF pulse
values is insignificant for radionuclides with a high retention in the regolith. For radionuclides withlow retention, such as Tc-99 and Cl-36, the LDF pulse is dependent on the succession stage of thebiosphere object and the present human land use.
10-7

3

10-9

2
1

10-13
10-15

Se
e--79
79

10-17

I-129

10-19

Np-237
Npp 237

10-21
10-23

Ra
a--226
226

10-25

9750

7500

6750

6000

5250

4500

3750

3000

2250

1500

0

750

-750

-1500

-2250

-3000

-3750

-4500

-5250

-6000

-6750

-7500

-8250

-9000

9000

Ni-59

10-27

8250

Sv/y per Bq/y

10-11

Year AD

Figure 7-15. The development of LDF’s for a number of dose-contributing radionuclides during an interglacial
for biosphere object 121_3. Se-79, I-129, Rs-226, and Ni-59 have their maximum LDF’s from this biosphereobject. The objects goes through three development stages: 1) the sea stage (blue), 2) the transitional stage(white), and 3) the terrestrial stage (brown). Note that object 121_3 does not go through a lake stage, but thesea bay develops directly into a wetland. Reproduced from Figure 10-3 of the Biosphere synthesis report.
TR-10-52421

7.2.9 Correlations
The model that has been used for derivation of LDF values, i.e. the radionuclide model described in/Avila et al. 2010/, relies on several element- and radionuclide-specific parameters that are correlatedwith each other. This means that LDFs obtained for different radionuclides might also be correlated.
However, the delivered LDFs have been obtained from independent deterministic simulations foreach radionuclide and parameter correlations have not been considered. Furthermore, parameter correlations have not been considered in the probabilistic simulations carried out within the sensitivity
and uncertainty analyses discussed above. In summary, no correlation needs to be propagated to the
SR-Site main project.
7.2.10 Result of supplier’s data qualification
The LDFs recommended for use in SR-Site are supplied in Table 7-13 for the temperate, periglacial,and glacial climate domains, and for the global warming case.
Table 7-13. LDF’s (Sv/y per Bq/y) for assessment of long-term releases under different climate
conditions: temperate, permafrost, glacial, and the global warming case. Data reproduced from
/Avila et al. 2010/.
Radionuclide

Temperate
LDF

Periglacial
LDF

GlacialLDF

Global warming
LDF

Ag-108m
Ac-227
Am-241
Am-243
C-14
Ca-41
Cl-36
Cm-244
Cm-245
Cm-246
Cs-135
Cs-137
Ho-166m
I-129
Nb-94
Ni-59
Ni-63
Np-237
Pa-231
Pb-210
Pd-107
Po-210
Pu-239Pu-240Pu-242
Ra-226
Se-79
Sm-151
Sn-126
Sr-90
Tc-99
Th-229Th-230Th-232
U-233
U-234
U-235
U-236
U-238
Zr-93

7.05·10–13
1.30·10–11
1.46·10–12
1.53·10–12
5.44·10–12
9.90·10–14
5.84·10–13
8.74·10–13
1.58·10–12
1.55·10–12
3.96·10–14
1.20·10–13
5.90·10–14
6.46·10–10
4.00·10–12
7.39·10–14
1.21·10–15
4.83·10–11
8.10·10–12
5.07·10–12
6.73·10–15
8.86·10–12
1.94·10–12
1.88·10–12
1.89·10–12
3.75·10–12
1.21·10–9
7.16·10–16
2.47·10–11
2.19·10–13
8.98·10–13
3.61·10–12
1.31·10–11
1.72·10–12
2.50·10–12
3.62·10–12
2.76·10–12
1.85·10–12
1.85·10–12
2.77·10–14

8.75·10–15
8.92·10–16
1.10·10–14
1.95·10–13
5.40·10–12
9.25·10–15
4.36·10–13
8.14·10–19
2.20·10–14
1.59·10–14
3.02·10–13
9.47·10–18
8.42·10–16
2.61·10–11
1.06·10–13
1.31·10–15
6.30·10–18
2.21·10–11
1.71·10–13
2.60·10–17
2.68·10–15
3.10·10–20
2.01·10–13
1.25·10–13
2.32·10–13
9.79·10–13
5.79·10–11
1.01·10–20
6.14·10–13
7.18·10–17
2.80·10–13
6.95·10–14
1.50·10–11
4.53·10–13
2.52·10–12
1.06·10–11
1.33·10–13
2.92·10–14
8.05·10–13
6.50·10–16

4.60·10–16
6.44·10–17
1.57·10–17
1.41·10–15
8.51·10–13
1.92·10–16
2.22·10–17
2.18·10–20
3.59·10–16
2.10·10–16
4.33·10–17
3.67·10–20
1.48·10–18
1.70·10–13
2.12·10–17
3.99·10–18
1.86·10–20
8.67·10–15
2.77·10–15
2.19·10–18
4.63·10–18
9.28·10–21
6.35·10–15
4.10·10–15
7.20·10–15
4.46·10–15
9.55·10–13
4.58·10–22
1.55·10–14
1.96·10–19
1.58·10–15
9.58·10–17
1.74·10–14
1.18·10–16
1.96·10–15
4.46·10–15
5.64·10–16
1.93·10–17
1.03·10–16
8.17·10–17

7.05·10–13
1.30·10–11
1.46·10–12
1.60·10–12
5.44·10–12
9.90·10–14
5.84·10–13
8.74·10–13
1.64·10–12
1.57·10–12
2.85·10–13
1.20·10–13
5.90·10–14
6.46·10–10
1.15·10–11
1.99·10–13
1.21·10–15
4.83·10–11
1.27·10–11
5.07·10–12
9.42·10–15
8.86·10–12
2.04·10–12
1.89·10–12
2.17·10–12
3.77·10–12
1.21·10–9
7.16·10–16
1.09·10–10
2.19·10–13
8.98·10–13
3.68·10–12
6.42·10–11
2.59·10–12
1.91·10–11
7.14·10–11
1.99·10–11
1.05·10–11
1.58·10–11
1.06·10–13

422

TR-10-52

As shown in Table 7-13, the LDFs cover a range of several orders of magnitude. Thus, a constantactivity release rate of, for example, Se-79 to the biosphere would result in an eight orders of magnitude
higher exposure than would an equally large release rate of Sm-151, given temperate conditions andsimilar use of natural resources. As explained in Section 7.2.5, LDFs for periods when the biosphereobjects are submerged are used as cautious estimates for the glacial domain.
During the reference glacial cycle, exposure is consistently higher under temperate conditions thanunder the other climate domains. The only radionuclide for which the temperate domain has a much
lower LDF is Cs-135, which due to strong retention (high Kd) and long half-life shows the highest
LDF under periglacial conditions. For all radionuclides, LDFs during submerged conditions are
below those of temperate conditions by two orders of magnitude or more.
LDF pulse values derived for radionuclides that may be released as a pulse to the biosphere arepresented in Table 7-14. The values correspond to the maximum annual doses obtained in simulations
with a pulse release during one year, occurring at different times within the temperate climate domain.
Table 7-14. LDF pulse values recommended for SR-Site. Data reproduced from Table 10-2 of the
Biosphere synthesis report.
Radionuclide

LDF pulse
Sv/y per Bq

Ag-108m

5.1·10–16

Cl-36

4.3·10–15

Cs-1351.8·10–16

I-129

5.6·10–14

Nb-94

3.2·10–16

Ni-59

9.7·10–18

Se-79

9.7·10–14

Sn-1262.3·10–15

Tc-99

2.8·10–15

7.2.11 Judgements by the SR-Site teamAccording to the instruction (cf. Section 2.3) the customer should make judgment on the sections
provided by the supplier. A judgement of the LDF data delivery is provided below.
Sources of information and documentation of data qualification

The references cited in the data qualification are judged to be relevant and sufficiently exhaustive.
Conditions for which data are supplied

The conditions for which (modelled) data are supplied are relevant for SR-Site modelling.
Conceptual uncertainty

The conceptual uncertainty is briefly outlined. This uncertainty is underpinned by detailed informationthat is provided in full in the cited background reports.
Data uncertainties

Generally one can see that the data uncertainty indicated in the probabilistic analysis is quite large(cf. Figure 7-13), compared with the natural variability (exemplified in Figure 7-14 and Figure 7-15).
These large ranges are explained by the fact that the probabilistic analysis is not fully correlated. As
the probabilistic modelling approach may give rise to unrealistic extreme values, the deterministic
value should be compared to the probabilistic median value (and not the arithmetic mean). Generally
the values agree reasonably well. The SR-Site site team agree with this handling.

TR-10-52

423

Spatial and temporal variability of data

The delivered LDFs are constant in space and time, and hence there are no spatial or temporalvariability to describe. However, a brief description of the spatial and temporal variations in the data
set from which the delivered LDFs are selected is provided.
Correlations

There is no correlation that needs to be described.
Result of supplier’s data qualification

The SR-Site team makes the judgement that the supplied LDF and LDF pulse values are adequate
and appropriate for use in SR-Site.

7.2.12 Data recommended for use in SR-Site modelling
The SR-Site team recommends that the LDF values presented in Table 7-13, and the LDF pulse
values presented in Table 7-14, are used in SR-Site modelling.

424

TR-10-52

8

Referenser

SKB’s (Svensk Kärnbränslehantering AB) publications can be found at www.skb.se/publications.
References to SKB’s unpublished documents are listed separately at the end of the reference list.
Unpublished documents will be submitted upon request to document@skb.se.

References with abbreviated names
Backfill production report, 2010. Design, production and initial state of the backfill and plug indeposition tunnels. SKB TR-10-16, Svensk Kärnbränslehantering AB.
Biosphere synthesis report, 2010. Biosphere analyses for the safety assessment SR-Site – synthesis
and summary of results. SKB TR-10-09, Svensk Kärnbränslehantering AB.
Buffer, backfill and closure process report, 2010. Buffer, backfill and closure process report forthe safety assessment SR-Site. SKB TR-10-47, Svensk Kärnbränslehantering AB.
Buffer production report, 2010. Design, production and initial state of the buffer. SKB TR-10-15,
Svensk Kärnbränslehantering AB.
Canister production report, 2010. Design, production and initial state of the canister. SKB TR-10-14,
Svensk Kärnbränslehantering AB.
Climate report, 2010. Climate and climate related issues for the safety assessment SR-Site.
SKB TR-10-49, Svensk Kärnbränslehantering AB.
Closure production report, 2010. Design, production and initial state of the closure. SKB TR-10-17,
Svensk Kärnbränslehantering AB.
FEP report, 2010. FEP report for the safety assessment SR-Site. SKB TR-10-45, Svensk
Kärnbränslehantering AB.
FHA report, 2010. Handling of future human actions in the safety assessment SR-Site. SKB TR-10-53,
Svensk Kärnbränslehantering AB.
Fuel and canister process report, 2010. Fuel and canister process report for the safety assessment
SR-Site. SKB TR-10-46, Svensk Kärnbränslehantering AB.
Geosphere process report, 2010. Geosphere process report for the safety assessment SR-Site.
SKB TR-10-48, Svensk Kärnbränslehantering AB.
Model summary report, 2010. Model summary report for the safety assessment SR-Site.
SKB TR-10-51, Svensk Kärnbränslehantering AB.
Radionuclide transport report, 2010. Radionuclide transport report for the safety assessment
SR-Site. SKB TR-10-50, Svensk Kärnbränslehantering AB.
Site description Forsmark, 2008. Site description of Forsmark at completion of the site investigation
phase – SDM-Site Forsmark. SKB TR-08-05, Svensk Kärnbränslehantering AB.
Spent fuel report, 2010. Spent nuclear fuel for disposal in the KBS-3 repository. SKB TR-10-13,
Svensk Kärnbränslehantering AB.
SR-Site Main report, 2010. Long-term safety for the final repository for spent nuclear fuel at
Forsmark. Main report of the SR-Site project. SKB TR-11-01, Svensk Kärnbränslehantering AB.
Underground openings construction report, 2010. Design, construction and initial state of theunderground openings. SKB TR-10-18, Svensk Kärnbränslehantering AB.

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Appendix A
Using results from in situ electrical resistivity loggings in supportof data needed in hydrogeological modelling
A1
Introduction
Within the site investigations, in situ electrical resistivity loggings have been performed as part ofthe standard geophysical downhole programme. Results from these measurements can be used insupport of two sets of data used in SR-Site hydrogeological modelling:•

The fracture transport aperture et (m), which is discussed in Section 6.6.

•

The effective diffusivity De (m2/s) of salt diffusing to or from the fractured rock surrounding aflow path.

The data presented in this appendix support other data used in SR-Site, and are not themself intended
to be qualified. Therefore, the standard outline for data qualification used in this Data report is notfollowed. The data presented here are not presented elsewhere.
This appendix will begin with outlining the downhole rock resistivity methods, used in the site investi­
gations. It will continue by outlining the methodology used for estimating the volumetric fracture
apertures, and supply some results. Finally the effective diffusivity for use in salt transport modellingis discussed. For the reader who has no background knowledge on these subjects, it may be worthwhile
studying Sections 6.6 and 6.8, where a multitude of references for further reading are given.

A2
Downhole rock resistivity loggings
In downhole rock resistivity loggings, a geophysical tool that emits an alternating electrical current iswired up or down a borehole. In this way the electrical resistance of the surroundings of the borehole ismeasured. The measurements are performed each decimetre along the borehole and data are presented
as the rock resistivity ρ (ohm.m). The current is propagated both in the water filled microporosity of
the non-fractured rock matrix (cf. Section 6.8), and in the stagnant or flowing groundwater of open
fractures. Basically no current is propagated in the rock minerals, due to their high electrical resistivity.
Figure A‑1 illustrates the tool as lowered down a borehole. The red square represents the current
electrode sending out the current normal to the borehole axis into the surrounding rock. What is notshown in the figure is that other electrodes are needed to focus the electric field, so that the current does
not spread out radially but cylindrically from the current electrode (cf. /Löfgren and Neretnieks 2002/).
The orange curve in Figure A‑1 illustrates the resulting rock resistivity log. As the current electrodeis surrounded by non-fractured rock, the emitted current is propagated in the microporous networkof the rock matrix. Heterogeneity in the microporous network manifests in a somewhat varying rockresistivity, which is illustrated by the moderately fluctuating curve. As the current electrode is in frontof the open fracture, current can also be propagated in the fracture water. If the fracture apertureis large enough, the current propagated in the fracture water will outweigh the current propagatedin the surrounding rock matrix. It should be noted that the amount of electrical current that is
propagated in the fracture water is independent of the fracture water flow rate.
In Figure A‑2, a rock resistivity log from borehole KFM10A in Forsmark is shown, from theborehole length interval 330–340 m.
On the x-axis, red diamonds represent locations where open fractures have been identified in the drill
core mapping (cf. /Löfgren 2007/). Blue triangles represent locations where groundwater flow has
been detected in the hydrogeological loggings, using the Posiva flow log (PFL). As can be seen,
open fractures corresponding to flow anomalies, as well as open fracture giving rise to no hydraulicresponse, clearly affect the measured rock resistivity.

TR-10-52449

Rock resistivity (ohm.m)

Lm
Borehole length (m)Downhole toolwith current
electrode in red

Current sent out
normal to theborehole axis into
the rock

Open fracture in
the rock massRock resistivity log

Figure A‑1. Illustration of the downhole tool, emitting an electrical current into the bedrock, and of theresulting rock resistivity log.

80000

Rock resistivity (ohm.m)

70000
60000
50000
40000
30000
20000
10000
0
330

331

332

333

334

335

336

337

338

339

340

Borehole length (m)Figure A‑2. Rock resistivity log of KFM10A and locations of open fractures and PFL anomalies.

450

TR-10-52

The rock resistivity does not only depend on the properties of the rock matrix and of the open fracture,
but also on the electrical resistivity of the porewater and groundwater, respectively. As outlined in
Section 6.8 of this report, the electrical resistivity of the porewater and groundwater has been studied in
the site investigations. Typical values of the electrical resistivity are between 1–2 ohm.m at repository
depth (cf. Section 6.8.10).

A3

Estimating volumetric fracture apertures from rock resistivity loggings

A3.1

Basic theory

Figure A‑1 shows a situation where the borehole is generally surrounded by non-fractured rock, but wherean open fracture intersects the borehole at a 90° angle. In the figure, the thickness of the investigatedsection (i.e., the thickness of the current field sent out into the rock) is denoted by Lm (m). The resistanceR (ohm) of the investigated section in can be calculated by:

1
1
1
=
+
R Rr R f

A‑1

Where Rr (ohm) and Rf (ohm) is the resistance of the rock and fracture, respectively. If the current isassumed to leave the tool in a perfect dish, 1-D equations can be used when comparing the resistances.
This is done in Equation A‑2.

Lpr ⋅ Lm=Lp

r w ⋅ ev+Lp

r r ⋅ (Lm − ev )A‑2

where Lp (m) is the penetration depth of the current into the fracture and rock matrix, ρ (ohm.m) is
the measured rock resistivity, ρw (ohm.m) is the resistivity of the groundwater in the fracture, ev (m)is the volumetric fracture aperture, and ρr (ohm.m) is the resistivity of the non-fractured rock matrix.
If assuming that the penetration depths in the fracture and rock matrix are equal, the term Lp can becancelled. As ev is insignificant in comparison to Lm it can be calculated by:1 1 
ev = r w Lm ⋅  − 
 r rr 

A‑3

In case the fracture plane is not normal to the borehole axis, this must be corrected for. The case isillustrated in Figure A‑3.

aFigure A‑3. Illustration of the downhole tool, emitting current into the rock matrix, and an open fractureintersecting the borehole at an angle α.

TR-10-52451

A somewhat simplified way of correcting for this is by multiply Equation A‑3 by sin(α)‑1, where α isthe angle shown in Figure A‑3:

1 1 
1
ev = r w Lm ⋅  −  ⋅
(α )
r
r
sin
r 


A‑4

Equation A‑4 requires that the shape of the current field is not affected by the fracture intersecting
the borehole at an angle. This may not be entirely true and therefore the results should be seen as
approximate. To not induce too large errors, it is recommended that the angle should be should be atleast 45° (and not exceed 135° in Figure A‑3), otherwise the data point should be discarded.
A3.2

Example of how to estimate the volumetric fracture apertureThis section shows an example of how to practically go ahead when estimating the volumetricfracture aperture. This is exemplified by the fracture at borehole length 335.1 in borehole KFM10A(see Figure A‑2). In the following it is shown how all parameters of Equation A‑4 can be estimatedfrom site investigation data.
The length of the investigated section, Lm
The length of the investigated section Lm depends to a large extent on the configuration of the downhole tool used, but also to a minor extent on the local borehole fluid resistivity, rock resistivity, androck resistivity heterogeneity. For this study, Lm has been obtained empirically, based on observations.
This has been done by assuming that Lm equals half the thickness of a well-defined resistivity dip.
The rationale is that the tool needs to be moved the distance of two investigation sections to capture
the entire decrease and subsequent increase of the rock resistivity, constituting the dip.
In Figure A‑2 the thickness of a resistivity dip is between 0.4 m (cf. borehole length 335.1 m) and
0.5 m (cf. borehole length 330.9 m). Based on the experience from the site investigations (see numerousof site investigation reports, e.g. /Löfgren 2007/) it is judged that these values are representative, andthat a typical thickness of a well-defined resistivity dip is 0.45 m. Based on this, the single point value
for Lm of 0.225 m is assigned.
It is noted that this value is uncertain and can be overestimated or underestimated by a factor judged
to be less than two. It is also noted that estimated volumetric fracture apertures scale linearly withthe assumed value of Lm (cf. Equation A‑4). If again examining Figure A‑2, one can see that between
the open fractures at 334.43 and 335.08 m, three rock resistivity values, one value for each decimetre,match that of the undisturbed rock matrix. This indicates that when the tool is about 18 cm from thefracture, the resistivity is unaffected by it20. On the other hand, if examining the resistivity between
the fractures at 331.70 and 332.02 m, it seems to be affected by the fractures. This indicates that when
the tool about 16 cm from a fracture, the fracture affects the resistivity. For the individual case,circumstances such as the angles of the fractures influence the above discussed distance. In the lightof these two and other observations, an assumed value of Lm of 0.225 m seems reasonable.
The measured rock resistivity at the fracture, ρWhen estimating the volumetric fracture aperture, ρ is the measured resistivity at the open fracture,
which is taken as the minimum rock resistivity value of the corresponding dip. At borehole length335.1 in borehole KFM10A, this value is about 21,200 ohm.m.
The resistivity of the rock matrix, ρrThis is the resistivity of the non-fractured rock matrix. It is assumed that the resistivity of the rockmatrix directly adjacent to the fracture is the same as that at a small distance from the fracture. In
Figure A‑2, the rock resistivity between the borehole lengths 336 and 339 m appears to be unaffectedby open fractures and should therefore represent the non-fractured rock matrix. On average, the rock20

0.18 m = (335.08–334.43–0.3)/2

452

TR-10-52

resistivity of this interval is about 55,900 ohm.m. From the drill core mapping, one can notice thatthe rock type of the studied borehole section in Figure A‑2 is the same (cf. Table A‑1 or page 25 of/Döse and Samuelsson 2006/). In general in this study, care has been taken to estimate the rock matrix
resistivity from data on the same rock type as that surrounding the fracture. When estimating ρr inthis work, a few criteria have been used:•

The mean value of ten rock resistivity data points has been taken.

•

The distance from the data points and any open fracture should be at least 0.5 m.

•

The data points should be located within 5 m of the studied fracture, and the rock type of thestudied section should be the same.

The electrical resistivity of the ground water, ρw
The electrical resistivity of the groundwater has been measured by the Posiva difference flow log, as
well as in the hydrogeological programme. In site investigation reports, the data are often presented
as the electrical conductivity κw (S/m), which is the reciprocal to the resistivity. For example, at theborehole length 315.3 m in KFM10A, the electrical resistivity has been measured to 0.80 S/m at 25°C
/Sokolnicki et al. 2006, Table 6-2/, which corresponds to a ρw of about 1.8 ohm.m at the in situ
temperature.
Generally in this study, interpolations and extrapolations based on the few data points available inthe boreholes have been used to obtain ρw values at different borehole lengths (cf. Figure 6-76 in
Section 6.8.10). At the borehole length 335 m, the interpolation suggests a ρw of 2.0 ohm.m.
The fracture angle, α
The α-value of a fracture is noted in the drill core mapping. Table A‑1 is an excerpt from the multitudeof data obtained in the drill core mapping.
In Table A‑1 “Adjusted Setup” is the borehole length; “Fract Interpret” shows whether the fracture
is judged to be open, partly open, or sealed in situ; “Confidence” indicates with what confidence
this judgment was made, where certain is the highest degree of confidence, followed by probableand possible; “Alpha” is the α-value; and “Best Rock Code” indicates the rock type of the section.
In our example, the open fracture at 335.082 m has the α-value 75.3°. It should be noted that sealed
fractures have little effect on the rock resistivity.
Table A‑1. Excerpt of drill core mapping data taken from Sicada.
Activity
Type

IdcodeAdjusted
Secup

Fract
Interpret

Confidence

Alpha

Best
Rock CodeFeature Id

GE041

KFM10A

330.891

Open

Probable

61.3

1010573CD046D98A150D25

GE041

KFM10A

331.701

Open

Possible

55.3

101057

E25046D98A15104C

GE041

KFM10A

332.023

Open

Possible

35.4

101057FBD046D98A15118D

GE041

KFM10A

332.834

Open

Certain

37.2

101057F21046D98A1514B5

GE041

KFM10A

333.471

Open

Possible

57.8

101057

8A5046D98A151730

GE041

KFM10A

334.430

Open

Certain

44.0

101057

BAD046D98A151AEC

GE041

KFM10A

335.082

Open

Probable

75.3101057

A09046D98A151D76

GE041

KFM10A

337.200

SealedCertain

51.1

101057

CF1046D98A1525B5

GE041

KFM10A

339.916

Open

Possible

57.9101057945046D98A153048

GE041

KFM10A

340.033

Open

Possible

63.2

102017

F75046D98A1530BC

TR-10-52453

The resulting volumetric fracture aperture, ev
If inserting the above data into Equation 4, the following volumetric fracture aperture can becalculated for the open fracture at 335.1 m:
1 
1
 1
ev = 2.0 ⋅ 0.225 ⋅ −= 14 µm⋅ 21,200 55,900  sin (73.5°)

A‑5

As can be seen in Figure A‑2, no hydraulic response was detected at the studied fracture. If instead
performing the same operations for the nearby fracture at 334.4 m (ρ = 894 ohm.m), which is identified
as hydraulically conductive, the obtained volumetric fracture aperture is 710 μm. However, for thisfracture the value is more uncertain, as α = 44°. As a result the data point was discarded from the study.
A3.3

The study and the results
In the study, 160 fractures were investigated, each corresponding to an interpreted PFL-anomaly.
Selected PFL-anomalies from all drill sites at the Forsmark site investigation area, except from DS9
and DS12, were investigated. Fractures from the shallow bedrock were avoiding by only examining
PFL-anomalies from boreholes lengths of 250 m and larger. In addition, sections with many closely
located PFL anomalies were avoided, as they often occur in highly fractured rock making it difficult
to match an anomaly to an individual rock resistivity dip. Except for this, the selection of the PFLanomalies followed no thought-through pattern, which is a weakness of the study. In future workwith this method it would be interesting to assess, if not actual apertures of individual fractures inzones, then at least the combined effect of them as water volumes in zones.

It should be noted that the coordinates of the different logs sometimes mismatch by a few centimetresor even a few decimetres. Therefore, there is a degree of uncertainty induced when assigning an open
fracture to a (nearby) rock resistivity dip. Uncertainty is also induced when assigning a resistivity dip
to an identified PFL anomaly. In this study, which has been a desktop study, only limited certainty canbe achieved when matching the logs. The matching would benefit from making careful judgments ineach case, for example by simultaneously examining the drill core and the anomalies of the logs. For
this study, it is judged that the matching described above only induce minor errors. However, would
one want to take the study one step further, and investigate the relation between the volumetric fracture
aperture and the transmissivity assigned to each PFL anomaly, a more careful methodology would be
required.
The positions of the PFL-anomalies, the rock resistivity data, and the drill core mapping data weretaken from Sicada21. The raw data needed as input to Equation A‑4 are given in Table A‑2 for thestudied fractures.
Out of the 160 studied fractures, 97 had an α value at 45° or above. All other data points werediscarded. Figure A‑4 shows the CDF (cumulative distribution function) of the estimated volumetricfracture aperture for the remaining 97 fractures.
The median volumetric fracture aperture is 0.13 mm, while its arithmetic mean is 0.30 mm, for thedata shown in the figure. Figure A‑4 also shows the CDF of the best fitted log-normal distribution,
for comparison. The distribution parameters are:•Log10(ev(m)): μ = –3.83, σ = 0.51

It should be noted that numerous of data points in Figure A‑4 are obtained in rock volumes that may
not represent the sparsely fractured host rock intended for the repository. Therefore, a special study was
made only including data from boreholes at drill sites DS1, DS5, DS6, and DS8, which are locatedat the intended location for the repository (cf. Figure 1-7 in Chapter 1 for a map of the drill sites).
The resulting CDF for the remaining 27 data points is shown in Figure A‑5.
As can be seen, discarding boreholes outside the target area does not have a major impact on the
resulting CDF, even though the median value is decreased by about a factor of two, to 56 μm, andthe arithmetic mean is decreased to 0.13 mm.
Locations of PFL-anomalies: Data delivery note Sicada_09_169. Rock resistivity data: Data delivery note
Sicada_09_167. Drill core mapping data: Data delivery note Sicada_09_062
21

454

TR-10-52

1
0.9
0.8
0.7

Normal distribution
parameters for log10(ev):µ = -3.83
σ = 0.51

CDF

0.6
0.5
0.4
0.3
0.2
0.1
0
10-6

10-5

10-4

10-3

10-2

Volumetric fracture aperture (m)Figure A‑4. CDF of volumetric fracture apertures associated with 97 PFL anomalies at Forsmark (bluediamonds), and CDF of best fitted log-normal distribution (red curve).

1
0.9
0.8

Normal distribution
parameters for log10(ev):µ = -4.16
σ = 0.47

0.7

CDF

0.6
0.5
0.4
0.3
0.2
0.1
0
10-6

10-5

10-4

10-3

10-2

Volumetric fracture aperture (m)Figure A‑5. CDF of volumetric fracture apertures associated with 27 PFL anomalies at the Forsmark
target area (blue diamonds), and CDF of best fitted log-normal distribution (red curve).

TR-10-52455

Table A‑2. Raw data for use in Equation A‑4, for the studied 160 fractures.
ρ

ρw

ρr

Open/Sealed

α
(°)

5,025
1,048
3,122
3,556
5,752
7,044
2,686
3,618
5,938
1,124

1.27
1.25
1.25
1.24
1.24
1.23
1.23
1.23
1.21
1.17

36,09932,89037,27027,141
33,65946,53037,832
46,83144,73828,728Open
Open
Sealed
Open
Open
Open
Open
Open
SealedSealed

62.4
54.1
50.3
35.6
53.3
54.7
53.3
56.8
74.3
35.1

56
324
110
124
52
42
121
85
42
398

266.6
273
894

81
365
6,263

0.48
0.47
0.46

1,736
1,596
11,092

Open
Sealed
Open

54
13.5
40.4

1,599
1,166
16

KFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02BKFM02B

271
330.7
399.4
410.8
412.2
414.5
419.4
420.5
422.3
423.3
426.1
426.9
428.4
429.6
436.4
497.1
502

4,760
5,911
14,699
2,970
6,060
291
3,634
871
1,465
227
619
1,008
6,169
974
5,043
2,703
1,896

1.24
1.20
1.15
1.14
1.14
1.14
1.14
1.14
1.14
1.13
1.13
1.13
1.13
1.13
1.13
1.09
1.09

29,29538,61842,61230,05623,2607,920
13,677
16,6398,998
12,45931,62918,08918,08920,806
18,951
17,647
8,803

Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open

17.8
39.7
62.5
74.9
73.8
42
75.4
59.9
58.8
41.8
70.7
57.8
76.8
69.1
42.3
66.2
18.2

183
64
14
81
33
1,284
54
324
175
1,663
429
285
28
267
61
85
385

KFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03AKFM03A

314.4
358.5
359.6
362.6
368.6
369.4
372.6
375.1
380.8
381.6
388.6
451.3
454.6
462.4
500.5
515.9
517.7
533.7
642.2
643.9
803.8
813.7
944.2
986.2
992.9

2,139
446
837
1,307
711
429
183
1,648
1,815
934
92
243
977
973
1,213
1,772
1,883
1,681
2,587
125
364
212
299
796
731

0.61
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.60
0.58
0.58
0.58
0.58
0.57
0.57
0.57
0.55
0.55
0.43
0.43
0.36
0.34
0.34

45,262
14,253
12,57011,980
9,494
9,494
5,397
31,08435,028
17,79723,922
25,68024,617
38,16847,55543,85352,862
41,33032,777
21,738
11,51334,89735,67322,902
12,443

SealedSealed
Open
Open
Open
Sealed
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open

59.4
66.1
75.7
73.7
58.2
75.9
41
45
9.5
56.5
72.4
55.9
47.6
61.3
64.3
74.1
64.2
65.8
26.8
52.5
56.1
76.6
74.8
18.7
16.1

72
323
156
97
209
310
1,099
111
445
165
1,519
649
177
150
116
73
74
81
103
1,250
314
463
276
296
368

Borehole

Borehole
length (m)(ohm.m)

KFM01DKFM01DKFM01DKFM01DKFM01DKFM01DKFM01DKFM01DKFM01DKFM01D

264.3
307.4
316.9
353.2
355.2
369.5
377.9
382
431.5
571.2

KFM02AKFM02AKFM02A456(ohm.m)(ohm.m)ev(mm)

TR-10-52

ρ

ρw

ρr

Open/Sealed

α
(°)

ev
(mm)

2,370
1,235
922
3,587
1,681
1,380
2,086
811
1,450
64
570
753
7,418
2,544

0.72
0.72
0.71
0.71
0.70
0.70
0.70
0.70
0.70
0.70
0.68
0.68
0.66
0.41

9,783
13,507
3,037
11,953
2,807
8,111
5,590
4,599
4,384
3,194
5,251
3,782
36,185
15,497

Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open

13.1
39.4
31
38.7
6.4
67.5
69.4
48.5
54.6
56.9
40.4
28
62.6
44.6

285
194
284
58
786
104
52
224
97
2,855
387
394
19
45

702.7
720

1,370
21,8000.99
0.99

24,92022,760Open
Sealed

13
58.3

714
2

KFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06AKFM06A

303
306.2
308.4
321.4
327
329.7
332
338.6
341.7
345.4
354.2
356.6
384.6
392.7
449.4
622.4
653.9
743.3
770.6

6,301
6,068
7,054
2,448
5,797
5,645
1,844
5,751
5,105
2,435
1,926
243
5,754
2,575
4,678
967
8,385
1,390
291

1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
0.86
0.80

41,28044,135
48,958
16,533
15,634
21,0507,559
19,14918,884
14,692
8,874
7,742
32,85023,346
22,62020,15924,807
41,295
11,567

Sealed
Open
Open
Open
SealedSealedOpen
SealedOpen
SealedOpen
SealedSealedSealedOpen
Open
SealedOpen
Sealed

55.1
54.8
58.1
57.4
37.2
32.5
23.1
48.5
70.8
57.5
64.8
30.7
53.7
51.4
51.1
17.6
23.8
31.4
32.2

40
42
35
101
52
67
296
43
37
99
109
1,882
44
108
55
798
60
264
1,149

KFM07AKFM07A
KFM08A
KFM08A
KFM08A
KFM08A
KFM08A
KFM08A
KFM08CKFM08CKFM08CKFM08CKFM08CKFM08CKFM08C

916.3
970
410.1
411.6
431.7
452
480.5
687
454.7
464
470.7
480
499
518.8
683.6

6,981
109
1,574
768
5,457
9,062
1,491
261
1,713
1,970
4,100
3,612
449
3,177
662

0.18
0.17
1.46
1.45
1.41
1.36
1.30
0.99
1.04
1.03
1.03
1.02
1.00
0.98
0.83

13,8052,953
30,4891,861
34,051
33,77923,643
26,46323,456
7,173
13,686
13,471
12,348
7,661
22,641

Open
Open
Open
Open
Open
Open
Sealed
Open
Open
Open
Open
SealedSealed
Open
Open

31.4
13.3
42.1
49.5
61.7
50.2
37.8
49.2
60.2
41.8
70.8
39.1
38.3
49.1
27

8
1,530
300
385
57
35
309
1,122
148
145
43
83
787
63
616

KFM10AKFM10AKFM10AKFM10AKFM10AKFM10A

299.5
308.8
315.3
322
327.3
328.1

1,131
1,851
594
4,399
1,267
4,809

2.04
2.03
2.02
2.01
2.01
2.01

58,76436,03551,83845,225
20,584
26,119

Open
Open
Open
Open
Open
Open

32.3
30.6
34.3
66.8
54
56.9

751
472
1,350
102
418
95

Borehole

Borehole
length (m)(ohm.m)

KFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04AKFM04A

257.6
273.9
297.1
313
338.8
343.4
346
355.5
358.2
359.8
419
421.9
521.5
954.8

KFM05AKFM05A

TR-10-52(ohm.m)(ohm.m)

457

ρ

ρw

ρr

Open/Sealed

α
(°)

ev
(mm)

3,531
3,819
894
5,754
2,554
4,511
212
2,857
2,668
2,493
351
298

2.00
2.00
2.00
1.96
1.95
1.95
1.89
1.88
1.88
1.84
1.83
1.83

29,434
26,914
40,63259,052
29,902
21,192
9,752
34,114
34,114
21,328
9,360
9,360

Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open

47.9
37.2
44
47.1
43.9
49.2
46.4
9.2
37.6
45.7
57.7
84

157
178
712
97
233
108
2,721
915
248
212
1,346
1,349

1,717
1,747
2,084
1,574
1,783
412
2,145
868
1,932
3,098
4,131
1,582
1,310
674
642
2,760
3,088
1,069
1,383
1,894
871
1,694
1,333
316
1,517
1,326
1,619
425
532
878
1,183
1,857
641
1,129
2,045
3,164
1,378

1.31
1.31
1.30
1.30
1.30
1.29
1.29
1.29
1.29
1.28
1.28
1.26
1.22
1.18
1.18
1.16
1.16
1.16
1.16
1.15
1.15
1.15
1.14
1.13
1.13
1.13
1.13
1.13
1.12
1.12
1.12
1.12
1.12
1.11
1.10
1.09
1.09

14,244
14,244
8,159
8,159
4,282
12,633
17,69020,491
15,393
12,21812,047
11,998
16,52814,3692,338
5,570
13,174
21,051
7,314
7,314
10,655
8,863
10,156
8,622
2,830
4,665
2,543
2,807
4,528
4,528
2,276
6,027
18,66017,31722,08628,355
11,072

Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open
Open

49.5
52.4
48.4
51.7
38.2
58.1
58.4
49.5
47.9
38.3
56.7
45.4
38.6
20
38.3
32.3
52.8
51.6
39
38.9
40.4
63.3
29.8
52
48.7
49.1
65.8
58.8
55.9
52.2
38.7
53.4
36.7
37.6
49
19.8
31.6

205
192
152
202
197
807
143
425
183
126
59
228
320
1,134
552
130
86
298
263
183
435
142
362
996
134
200
72
607
519
309
231
127
644
348
149
221
318

Borehole

Borehole
length (m)(ohm.m)

KFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10AKFM10A

328.8
332.9
334.5
368.4
373.6
376
431.9
436.3
437.3
480.3
483.8
484.4

KFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11AKFM11A

256.9
257.4
261.5
262
266.8
271.7
273.4
275
276.5
283.8
285.2
299.5
333.9
376.2
379.3
395.3
397.9
400.8
402.9
403.6
404.5
409.1
420.2
426.1
427.2
428
432.5
433.5
436.2
436.6
438.1
440.7
443.4
452.7
467.6
474.6
475.7

(ohm.m)(ohm.m)It should be noted that the borehole length coordinates of different logs and measurements mismatch somewhat,
which has been corrected before prior to conducting this study. The borehole length shown is for the resistivity
measurement, while the reported borehole lengths for the drip core mapping data may be slightly different.

458

TR-10-52

A3.4

Using ev to support et

It is judged that the entire data set shown in Figure A‑4 represents the study, and may be propagatedas supporting data when estimating the fracture transport aperture. Here a caution is warranted. The
volumetric fracture aperture ev is not easily used for estimating the actual value of the fracture transport
aperture et, but should rather be used for estimating its upper limit. As described in the Geosphereprocess report, channelling is expected in fractures. This means that part of the fracture plane holds
flowing water while part holds stagnant water. While the fracture transport aperture should represent
the part holding flowing water (as averaged over the fracture plane), the volumetric fracture aperture
should represent all water in the fracture. This gives Equation A‑6:
A‑6

ev > etAnother common situation giving rise to ev > et is when fractures are constricted, and where theconstrictions control the water flow. Such a constriction can be located many metres away from theborehole. If so, it does not affect the electrical resistivity measurement, which is only affected by
the local fracture around the borehole (say within one or two meters). Only in the case of parallel
fracture surfaces, with no constriction and no channelling effects, ev would equal et.

A4
The effective diffusivity of salt
In the hydrogeological modelling, the matrix diffusion of salt is an integrated part of the calculations.
To assess the diffusive transport of salt in an out of the rock matrix, the effective diffusivity De (m2/s)
is needed. The effective diffusivity is used both for the paleohydrogeological simulations includingtransport of salt and reference waters, and in the analytical calculations of salt transport.
A4.1

Effective diffusivity of salt in undisturbed rock matrix

The effective diffusivity of the undisturbed rock matrix is discussed in Section 6.8 of this report.
This effective diffusivity is strictly estimated for rock at a distance from any open fracture, and not
for the rock adjacent to the flow paths. This may seem peculiar, as the matrix diffusion of interestfor safety assessment modelling occurs in the rock matrix surrounding flow paths. The explanationis that the effective diffusivities given in Section 6.8 are intended for radionuclide transport modelling,
and that a pessimistic approach is taken. It is a common opinion that the capacity for matrix diffusion is
generally enhanced in the rock adjacent to fractures. Furthermore, results from the site investigations
clearly indicate that if a flow path is intersected by open or partly open fractures, which are water
bearing but hydraulically non-conductive, this extra pore space adds to the diffusion capacity of thesurrounding rock, e.g. /Löfgren 2007/. However, as it is difficult to demonstrate that every flow path
is surrounded by rock of enhanced matrix diffusion capacity, the decision has been taken to pessimistically neglect this additional capacity in radionuclide transport modelling. In this type of modelling
it is straightforward to see that such an approach is pessimistic, as increased matrix diffusion leads toincreased radionuclide retention, which in turn reduces the radiological risk. However, in the case ofsalt diffusion in and out of the rock matrix, one cannot easily draw conclusion on whether a high oflow value of De is the most pessimistic. This can be exemplified by the stability of the buffer, whereat a certain groundwater composition the buffer is stable, but where buffer erosion may occur bothas the water becomes more saline and more diluted. Because of this, a best estimate of the effective
diffusivity in the fractured rock surrounding flow paths is needed (as discussed in section A4.2).
In Section 6.8, the arithmetic mean of the formation factor Ff (–) for the undisturbed rock matrix
at the Forsmark site is given as 2.1·10–5. From the formation factor, the effective diffusivity can beobtained, in case there are no electrostatic effects such as anion exclusion, and in case the solute
exists in trace amounts:
De = Ff ⋅ Dw

A‑7

Where Dw (m2/s ) is the diffusivity in the unconfined pore water, which is often approximated byusing the value for the diffusivity at infinite dilution (cf. Section 6.8).
In the following, salt transport is approximated by NaCl transport. For Na+ the Dw is 1.33·10–9 m2/s
and for Cl– the Dw is 2.03·10–9 m2/s at infinite dilution and at 25°C /Brantley et al. 2008/. Making
a temperature correction to obtain Dw at the in situ temperature, which presently is about 12°C at
TR-10-52459

repository depth, is elementary (cf. Equation 6-23). When using Equation A‑7 for anionic tracers, theresulting effective diffusivity should be reduced by a factor of 100.5 (~3.16), as justified in Section 6.8(cf. Equation 6-30). As stated above, Equation A‑7 is valid for solutes at trace concentrations. In caseof salt transport, one must account for ion-pair diffusion and the harmonic mean of the individual
diffusivities of Na+ and Cl– should be taken:
Dw, salt =(z + z )D
+

−

w, +

⋅ Dw,−

z+ Dw,+ + z− Dw,−

A‑8

where z is the charge number of the anion or cation. As can be seen, Equation A‑8 applies in free waterphase and assuming the equivalent equation for diffusion in the rock matrix, exchanging Dw for De,
is somewhat speculative. As discussed in Section 6.8 and in the Geosphere process report, Fickian
diffusion theory has limitations in the applicability in the microporous system where electrostaticinteractions are of major concern. If anyhow using the harmonic mean for calculating22 the effective
diffusivity of salt at the in situ temperature, De,salt becomes 1.2·10–14 m2/s.
A4.2

Effective diffusivity of salt in fractured rock surrounding flow paths

Under this heading we attempt to motivate a reasonable range of the effective diffusivity for use insalt transport calculation. Here, the fractured rock surrounding the flow paths is the focus of attention,
as opposed to the undisturbed rock at distance from flow paths, as discussed above. The approachof obtaining the data is very similar to that presented in Section 6.8, although the background datasomewhat differ and the scope of the study is limited to one borehole, KFM08C.
Figure A‑6 shows site investigation result from the geophysical downhole tool (cf. Figure A‑1) used
for assessing the in situ effective diffusivity by electrical methods. Here the rock resistivity is shown,which is (approximately) inversely proportional to the effective diffusivity.
As in Figure A‑2, the triangles at the x-axis represent the locations of PFL anomalies, while thediamonds represent the locations of open fractures, as detected in the drill core logging. The solid
grey line is the rock resistivity log obtained in the site investigations. The purple dots show data points
obtained at a distance at least 0.5 m away from any open fracture. These data points are called rockmatrix resistivities, which are used as a basis when assessing the effective diffusivity of the undisturbed
rock (cf. Section 6.8). The combination of grey and purple dots shows all data points obtained at a
distance of at least 0.5 m away from any PFL-anomaly (for further descriptions, please turn to /Löfgren
2007/). These data points are called fractured rock resistivities, and are used as a basis for assessing
the effective diffusivity of salt in this work, for the fractured rock surrounding the flow paths. In doing
this, a PFL-anomaly is assumed to represent a flow path, whereas the water in open fractures where
no hydraulic response has been detected is assumed to be stagnant. This stagnant water is assumed tocontribute to matrix diffusion in addition to the stagnant water of the microporous network of the rockmatrix. It should be noted that we strictly do not know that these open fractures holds stagnant water.
We only know that they are not sufficiently conductive to be detected with the Posiva flow log.
As can be seen in Figure A‑6, the rock resistivity is on average smaller at the PFL anomalies around
520 m, than at the sparsely fracture rock, for example, between 550–560 m. In the borehole lengthinterval 515–525 m the fractured rock resistivity is on average four times smaller than in the interval
550–560 m.
In the study presented in this appendix, the fracture rock formation factor is calculated exclusively
for the rock within 5 m of, and at least 0.5 m away from, a detected PFL anomaly, in the borehole
KFM08C. The value 5 m is set subjectively but should encompass the rock volume that has the greater
impact on hydrogeological results. The locations of PFL-anomalies are taken from Appendix 7 of
/Väisäsvaara et al. 2006/. The formation factors are obtained in the same manner as in Section 6.8,
which includes making corrections for surface conduction. The resulting 982 data points are shownin Figure A‑7, as a CDF.
Data used in temperature correction: Water viscosity h = 1.24·10–3 Ns/m2 at 12°C; m = 0.89·10–3 Ns/m2
at 25°C /Coulson et al. 1990/. Temperature correction based on the Stoke-Einstein equation. Assumed
Ff = 2.1·10–5. De of Cl– calculated by (2.03·10–9 m2/s·Ff ·temperature correction)/100.5. De of Na+ calculated
by 1.33·10–9 m2/s·Ff ·temperature correction.
22

460

TR-10-52

80,000

Resistivity (ohm.m)

60,00040,00020,000

0
500

510

520

530

540

550

560

570

580

590

600

Borehole length (m)Rock resistivity
Fractured rock resistivity
Rock matrix resistivity
Location of broken fracture parting the drill core
Location of hydraulically conductive fracture detected in the difference flow logging

Figure A‑6. The rock resistivity of borehole Section 500–600 m of KFM08C. Reproduced from Appendix A2of /Löfgren 2007/.

1
0.9
0.8
0.7

CFD

0.6
0.5
0.4
0.3
0.2
0.1
0
10-5

10-4

10-3

10-2

Fractured rock formation factor (-)

Figure A‑7. CDF of fracture rock formation factors obtained within 5 m of, and at least 0.5 m away from,any PFL anomaly in borehole KFM08C.

TR-10-52461

The arithmetic mean of the fractured rock formation factors shown in Figure A‑7 is 7.2·10–5, which
is a factor of 3.4 larger than for the undistributed rock matrix given Section 6.8. From this formation
factor one can obtain the effective diffusivity. By performing the same operations as for the undisturbed
bedrock (correcting for temperature and anion exclusion, and using the harmonic mean), the arithmetic
mean of De becomes 4.3·10–14 m2/s for the fractured rock surrounding flow paths.
For the fractured rock, it is uncertain to what degree anion exclusion should be accounted for, as
the apertures of the open fractures likely are much larger than for the pores (cf. discussion on anion
exclusion in relation to constrictivity in Section 6.8). Therefore, the uncertainty range for De mayneed to be chosen so that it encompasses the assumption where anion exclusion is neglected. Whendisregarding anion exclusion, the arithmetic mean of De becomes 8.1·10–14 m2/s for the fracture rock.
It may also be justified to disregard the effect of surface conduction, at least in the fractures. Thismeans that the apparent formation factor would equal the formation factor, and that no correction isneeded (cf. Section 6.6). When disregarding anion exclusion and surface conduction for both the rockmatrix and the fractures, the arithmetic mean of De becomes 1.0·10–13 m2/s for the fractured rock.
Suggested effective diffusivity of rock surrounding flow paths
Based on the information above, a lower limit, best estimate, and upper limit are given for the Deof salt diffusing in the fractured rock surrounding flow paths. For the lower limit, 1·10–14 m2/s issuggested, which is the De for the undisturbed rock matrix.
As best estimate values, 4·10–14 m2/s is suggested. This is the arithmetic mean of the De corresponding
to the data points shown in Figure A‑7. In Section 6.8 it is argued that the flow path averaged formation
factors for the undisturbed rock is obtained by taking the arithmetic mean of available data points.
In this study, formation factors around individual flow paths have been sampled. If the properties (e.g.
the fracture intensity) along the flow path vary, and if one can estimate this variation by samplingrock surrounding many different flow paths, the arithmetic mean would also in this case represent
the flow path averaged De. However, if the properties along the flow paths are relatively constant,
one could argue that the median value would be more appropriate as best estimate. Considering thesetwo choices, and also the fact that groundwater flow is conceptualised to occur in a fracture network,
it seems somewhat more reasonable to propagate the arithmetic mean as the best estimate value.
For the upper limit we acknowledge the fact that the effective diffusivity of the rock surrounding
a flow path may be consistently high. In Figure A‑7, 90% of the data (0 < CFD < 0.9) is contained
within one order of magnitude. Therefore, the upper limit is assigned by multiplying the lower limit
by one order of magnitude, giving an upper De of 1·10–13 m2/s. This upper limit also encompasses
the best estimate De obtained if one disregards anion exclusion, as well as surface conduction (seeabove). The lower, best estimate, and upper limit of De for salt diffusing in fractured rock surrounding
flow paths are summarised in Figure A‑2.
It should be noted that for the rock directly adjacent to the flow path, the De may be even higher thanthis, in case the rock is altered. However, the rock directly adjacent to the flow path (PFL-anomaly) isby necessity excluded in this study, as the fracture water disturbs any attempt to measure the diffusive
properties of the rock by electrical methods (cf. section A3.2).
Table A‑3. Suggested De for fractured rock surrounding flow paths.
Lower limit for De

1·10–14 m2/s

Best estimate of De

4·10–14 m2/s

Upper limit for De

1·10–13 m2/s

462

TR-10-52