Abstraction of Drift Seepage REV 00, ICN 00

MDL-NBS-HS-000019 

August 2003
 
1. PURPOSE 
The purpose of this Model Report is to document the abstraction of drift seepage, conducted to 
provide seepage-relevant parameters and their probability distributions for use in Total System 
Performance Assessment for License Application (TSPA-LA). Drift seepage refers to the flow of 
liquid water into waste emplacement drifts. Water that seeps into drifts may contact waste 
packages and potentially mobilize radionuclides, and may result in advective transport of 
radionuclides through breached waste packages (BSC 2002 [160780], Section 3.3.2). The 
unsaturated rock layers overlying and hosting the repository form a natural barrier that reduces 
the amount of water entering emplacement drifts by natural subsurface processes. For example, 
drift seepage is limited by the capillary barrier forming at the drift wall, which decreases or even 
eliminates water flow from the unsaturated fractured rock into the drift. During the first few 
hundred years after waste emplacement, when above-boiling rock temperatures will develop as a 
result of heat generated by the decay of the radioactive waste, vaporization of percolation water 
is an additional factor preventing seepage. Estimating the effectiveness of these natural barrier 
capabilities and predicting the amount of seepage into drifts is an important aspect of assessing 
the performance of the repository. 
Abstraction is defined as the process of purposely simplifying a mathematical model 
(component, barrier, or subsystem process model) for incorporation into an overall system model 
of the geological repository (BSC 2002 [158794], Section 3.1.1). Thus, the purpose of this 
Model Report is to modify the information generated by the seepage process models into a form 
that can be used in the TSPA-LA. The simplifications and assumptions made in this abstraction 
process must be realistic and appropriate. In particular, uncertainties and spatial variabilities of 
the primary process models for seepage must be represented in the abstraction model and must 
be propagated to TSPA-LA (see Section 4.2). 
A suite of primary process models provides seepage model results used as input to the 
abstraction performed in this report. The following three modelscompatible and consistent in 
their conceptual treatment of flow diversion and capillary barrier behaviorprovide the basis for 
seepage abstraction: 
1. The Seepage Calibration Model (SCM) 
This process model provides the conceptual basis for seepage modeling and derives 
seepage-relevant parameters through calibration of the model against seepage-rate data 
from liquid-release tests (BSC 2003 [162267]). 
2. The Seepage Model for Performance Assessment (SMPA) 
This process model predicts drift seepage rates for long-term ambient conditions at 
Yucca Mountain, for a wide range of seepage-relevant parameters and for potentially 
important perturbing effects. The latter include the effect of rock bolts on flow paths 
and the impact of drift shape changes caused by degradation (BSC 2003 [163226]). 
Drift degradation is expected as a result of thermal stresses, seismic events, and timedependent 
decrease in rock strength.

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3. The Thermal-Hydrological Seepage Model (TH Seepage Model) 
This process model predicts drift seepage during the period when water-flow processes 
in the drift vicinity are perturbed by heating of the rock (BSC 2003 [161530]). 
Additional input from scientific analyses or from process models is required to define probability 
distributions that appropriately cover uncertainty and spatial variability of seepage-affecting 
parameters. In addition to the capillary strength in the fractures close to the driftcalibrated with 
the SCMthe most important parameters defining the amount of seepage are the local 
percolation flux and the formations permeability close to the drifts (BSC 2003 [162267]). 
Information on these parameters is mainly derived from the following sources: 
1. Site-scale and Intermediate-scale UZ Flow Simulations 
The UZ Flow and Transport Model provides three-dimensional site-scale flow fields to 
derive the local percolation flux (BSC 2003 [163045]). In addition, flow focusing 
factors account for intermediate-scale heterogeneity that is not represented in the layeraveraged 
UZ Flow and Transport Model. These are estimated from an intermediatescale 
flow focusing model BSC (2001 [156609], Section 6.4.2). 
2. In situ Testing at Yucca Mountain 
Air-injection tests performed at different scales and locations provide estimates of local 
fracture permeability (BSC 2001 [158463]). 
Finally, results from the coupled thermal-hydrological-mechanical (THM) and thermalhydrological-
chemical (THC) models are utilized to assess whether seepage-relevant parameters 
may be affected by mechanical and/or chemical processes. These processes occur in response to 
the heat generated in the repository (BSC 2003 [162050] and BSC 2003 [162318]). The 
relationship and the information flow between the suite of primary process models important for 
seepage and the seepage abstraction are schematically illustrated in Figure 1-1. A more detailed 
description is given in Section 6.3. 
The seepage abstraction described in this Model Report synthesizes and simplifies the relevant 
input from the above sources, and provides a realistic and appropriate abstraction model for 
TSPA-LA. The scope of this work is to (1) develop an appropriate abstraction methodology for 
drift seepage, considering both the nominal scenario and disruptive scenarios such as seismic and 
igneous events, (2) determine the uncertainty and spatial variability of seepage-relevant 
parameters, (3) provide look-up tables for seepage into nondegraded and collapsed drifts as a 
function of these parameters, (4) evaluate and discuss the impact of additional factors affecting 
seepage, such as THM, THC processes, rock bolts, and igneous events, (5) validate the seepage 
abstraction methodology, and (6) evaluate and discuss all seepage-related features, events, and 
processes (FEPs). The analyses documented in this report were conducted under the Technical 
Work Plan (TWP) for: Performance Assessment Unsaturated Zone, TWP-NBS-HS-000003 REV 
02 (BSC 2002 [160819]). The relevant TWP sections for this work are Section 1.13.5, entitled 
Update Seepage Abstraction Model, and Attachment I, entitled Model Validation Plans, 
Section I-4-3. There are no deviations from the work scope described in the TWP.

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Abstraction of Drift Seepage 
Seepage Model 
For PA 
TH Seepage 
Model 
THC Model 
TSPA
Distributions for Seepage 
Relevant Parameters; 
Seepage Look-up Tables 
for Seepage Rates and 
Their Uncertainty 
Prediction of 
Ambient 
Seepage 
Prediction 
of Thermal 
Seepage 
Conceptual Model; 
Seepage-Relevant 
Parameters 
Seepage 
Calibration 
Model 
Capillary-Strength and 
Permeability 
UZ Flow Model 
Percolation Flux 
THM Model 
Disturbed-Zone 
Permeability 
THM Effects 
THC 
Effects 
Seepage Prediction as a Function of 
Various Parameters 
Estimation of Relevant Parameters 
Qualitative Assessment of THM and 
THC Processes 
Figure 1-1. Relationship and Information Flow between Primary Process Models and Seepage 
Abstraction 
The primary limitations in the scope of this Model Report and its results are as follows: 
 The results of the seepage abstraction model are based on the available data and the 
available analyses/models, as listed above and in Section 4.1 and 6.4. Limitations 
reported in analyses or model reports that directly support this Model Report are 
implicit limitations of seepage abstraction. The limitations of predictive models 
(process models), for example, are defined by the conceptual model, as described in the 
pertinent sections of the respective Model Reports. 
 The predictive seepage models and the seepage abstraction model are probabilistic 
models that provide estimates of seepage fluxes averaged over drift segments. These 
models are not expected to predict individual seepage events or to provide the precise 
spatial seepage distribution along the drifts and within the repository. This is consistent 
with the probabilistic seepage calculation conducted in the TSPA-LA simulation.

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2. QUALITY ASSURANCE 
Development of this Model Report on seepage abstraction and the supporting analyses/modeling 
activities have been determined to be subject to the Yucca Mountain Projects quality assurance 
(QA) program as documented in Technical Work Plan for: Performance Assessment 
Unsaturated Zone, TWP-NBS-HS-000003 REV 02 (BSC 2002 [160819], Section 8.2, Work 
Package AUZM09). Approved QA procedures identified in the TWP (BSC 2002 [160819], 
Section 4) have been used to conduct and document the activities described in this Model Report. 
Electronic management of information was evaluated in accordance with Administrative 
Procedure (AP)-SV.1Q, Control of the Electronic Management of Information and controlled 
under YMP-LBNL-QIP-SV.0, Management of YMP-LBNL Electronic Data, as planned in BSC 
(2002 [160819], Section 8.4). 
This Model Report documents the abstraction of drift seepage in order to provide seepage-related 
parameters and their probability distributions for use in TSPA-LA. It provides insight into the 
performance of natural barriers that are important to the demonstration of compliance with the 
postclosure performance objectives prescribed in 10 CFR 63.113 [156605]. Therefore, it is 
classified as a Quality Level  1 with regard to importance to waste isolation, as defined in 
AP-2.22Q, Classification Criteria and Maintenance of the Monitored Geologic Repository QList. 
The report contributes to the analysis and modeling data used to support performance 
assessment; the conclusions do not directly impact engineered features important to safety, as 
defined in AP-2.22Q.

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3. USE OF SOFTWARE 
Only standard off-the-shelf commercially available software was used for this Model Report. 
These are not subject to software quality assurance requirements of AP-SI.1Q. The software used 
was Microsoft Excel 97 SR-2 for calculations and graphical display, Mathcad 11 for calculations, 
and Tecplot V9.0 for graphical display. All information needed to reproduce the calculations 
using these standard software programs, including the input, computation and output as required 
by AP-SIII.10Q, is included in this report, with references specified (see Attachments I through 
V).

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4. INPUTS 
4.1 DATA AND PARAMETERS 
Table 4.1-1 summarizes the input data and parameters used in this Model Report. All input data 
and parameters needed for seepage abstraction are obtained from the Technical Data 
Management System (TDMS). These data are considered appropriate as input for the seepage 
abstraction model (see Section 6). The parameter values used for abstraction, as well as the 
spatial variability and uncertainty associated with these values, are presented and discussed in 
detail in Section 6. 
Table 4.1-1. Input Data and Parameters Used in This Model Report 
Item Data Name Data Source/DTN 
Roadmap (if applicable) 
Parameters 
Comments 
1 Ambient Seepage 
Prediction Results 
DTN: LB0304SMDCREV2.002 
[163687] 
The data file from the DTN: 
LB0304SMDCREV2.002.zip must 
be unzipped. File Fig6-3toFig6- 
8.dat contains a seepage look-up 
table for non-degraded drifts. 
Another file Fig6-3toFig6-8.xls 
contains seepage results for all 20 
individual realizations. This file 
opens in a subdirectory 
Supporting data for tecplot input. 
Predicted ambient seepage rates and uncertainty 
for suite of simulation cases 
Ambient seepage was predicted by the SMPA. Seepage 
results are given as a function of permeability, capillarystrength 
parameter, and percolation flux. Simulation 
cases cover parameter distributions required for TSPA. 
The SMPA is documented in BSC (2003 [163226]). 
2 Ambient Seepage 
Prediction Results 
for THM Effects 
DTN: LB0304SMDCREV2.004 
[163691] 
The data file from the DTN: 
LB0304SMDCREV02.004.zip must 
be unzipped. File Fig6-22.wmf 
contains a figure comparing 
seepage results for ambient 
conditions versus THM altered 
conditions. 
Predicted seepage rates from ambient model 
compared to model including THM permeability 
changes 
Seepage results were obtained using the Drift-Scale 
THM Model. Two cases are studied and compared. The 
first one calculates seepage using the initial permeability 
field, the second one calculates seepage using the 
altered permeability at 10,000 years after emplacement. 
The two cases are compared. The THM simulation 
model and results are documented in BSC (2003 
[162318]). 
3 Ambient Seepage 
Prediction Results 
for Collapsed Drifts 
DTN: LB0307SEEPDRCL.002 
[164337] 
The data file from the DTN: 
LB0307SEEPDRCL.002.zip must 
be unzipped. File ResponseSurfaceSMPACollpasedDrift.
dat 
contains a seepage look-up table 
for a collapsed drift. 
Predicted seepage rates and uncertainty for suite 
of simulation cases for the collapsed drift scenario 
Ambient seepage was predicted by the SMPA. Seepage 
results are given as a function of permeability, capillarystrength 
parameter, and percolation flux. Simulation 
cases cover parameter distributions required for TSPA.

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Table 4.1-1. Input Data and Parameters Used in This Model Report (Continued) 
Item Data Name Data Source/DTN 
Roadmap (if applicable) 
Parameters 
Comments 
4 Thermal Seepage 
Prediction Results 
(TOUGH2 
Simulation Files) 
DTN: LB0303DSCPTHSM.001 
[163688] 
File Readme.doc explains the 
different simulation cases included 
in the DTN. There are five 
compressed data files that need to 
be unzipped onto a large enough 
disc. A directory structure with 
different result data files opens, as 
explained in the Readme 
document. The temporal evolution 
of relevant TH parameters is given 
in simulation files named 
si_heat.obs. Use the Readme 
document to relate simulation 
cases and subdirectories. 
Thermal seepage modeling results 
The future drift-scale TH conditions were predicted for 
selected simulation cases, using the TH Seepage 
Model. The DTN gives the entire suite of simulation 
input and output files. The thermal seepage model and 
results are documented in BSC (2003 [161530]). 
5 Thermal Seepage 
Prediction Results 
DTN: LB0301DSCPTHSM.002 
[163689] 
File Readme.doc explains the 
different simulation cases included 
in the DTN. The data file 
TH_Seepage_Model.data_ 
summary.tar.gz must be unzipped. 
A directory structure with different 
result data files opens, as 
explained in the Readme 
document. The thermal seepage 
results are given in developed data 
files named seep_relrate.tec. Use 
the Readme document to relate 
simulation cases and 
subdirectories. 
Thermal seepage percentage 
Thermal seepage was predicted for selected simulation 
cases, using the TH Seepage Model. Evolution of 
seepage with time is given for different rock properties 
and two geological units. The thermal seepage model 
and results are documented in BSC (2003 [161530]). 
6 Seepage Calibration 
Results 
DTN: LB0302SCMREV02.002 
[162273] 
File LB0302SCMREV02.002.zip 
must be unzipped. Zip-file contains 
one informational README file 
and one word document that 
contains permeability data in 
Tables 1 and 2, and calibrated 
capillary-strength parameters in 
Table 3. 
Post-excavation air permeability statistics and 
calibrated capillary-strength parameters for niches 
and systematic testing boreholes 
Calibration was conducted by the SCM. The SCM and 
model results are documented in BSC (2003 [162267]). 
800-IED-EBS0-00201-000-00A 
(BSC 2003 [164069]) 
Drift diameter, waste-package spacing 
800-IED-WIS0-00201-000-00B 
(BSC 2003 [164603]) 
Average waste-package length 
7 Drift Design and 
Waste-Package 
Geometry 
800-IED-WIS0-00302-000-00A 
(BSC 2003 [164101]) 
Ground support 
8 Repository Area 800-IED-EBS0-00401-000-00C 
(BSC 2003 [162289]) 
Repository Layout

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Table 4.1-1. Input Data and Parameters Used in This Model Report (Continued) 
Item Data Name Data Source/DTN 
Roadmap (if applicable) 
Parameters 
Comments 
9 DTN: GS960908312232.013 
[105574] 
Data are in Table S01163_001. 
Air permeability data from vertical boreholes 
10 DTN: LB990901233124.004 
[123273] 
Data are in Table S00017_002. 
Pre-excavation air permeability summary statistics 
from niches 
Analysis is documented in BSC (2001 [158463], Table 
6.1.2-3). 
11 DTN: LB0012AIRKTEST.001 
[154586] 
Data are in Table S01048_001. 
Pre-excavation air permeability data from Niche 
1620 (also referred to as Niche 5) 
12 DTN: LB980901233124.101 
[136593] 
Data for Niche 3107 are in Table 
S99469_001. 
Data for Niche 4788 are in Table 
S99469_002. 
Pre-excavation air permeability data from Niches 
3107 (Niche 3) and 4788 (Niche 4) 
13 
Air Permeability 
Data 
DTN: LB0011AIRKTEST.001 
[153155] 
Data for Niche 3650 are in Tables 
S00434_006 through S00434_009, 
S00434_011, S00434_013, and 
S00434_015 (each borehole in 
separate table). 
Data for Niche 3566 are in Tables 
S00434_001, S00434_002, and 
S00434_005 (each borehole in 
separate table). 
Pre-excavation air permeability data from Niches 
3650 (Niche 2) and 3566 (Niche 1) 
14 Flow Field 
Simulations for 
Different Climate 
Scenarios 
DTN: LB0302PTNTSW9I.001 
[162277] 
File LB0302PTNTSW9I.001.zip 
must be unzipped. Zip-file contains 
one informational README file 
and nine percolation flux data files, 
each representing one climate 
period and one climate scenario. 
Percolation fluxes at the PTn/TSw interface 
Fluxes have been predicted by the UZ model. Results 
are given for three climate periods (present-day, 
monsoon, and glacial-transition) and three climate 
scenarios (mean, upper, lower). The simulation model 
and results are documented in BSC (2003 [163045]). 
15 Alternative Flow 
Field Simulations for 
Climate Scenarios 
DTN: LB0305PTNTSW9I.001 
[163690] 
File LB0305PTNTSW9I.001.zip 
must be unzipped. Zip-file contains 
one informational README file 
and nine percolation flux data files, 
each representing one climate 
period and one climate scenario. 
Percolation fluxes at the PTn/TSw interface for 
alternative conceptualization of flow in the PTn 
Fluxes have been predicted by the UZ model. Results 
are given for three climate periods (present-day, 
monsoon, and glacial-transition) and three climate 
scenarios (mean, upper, lower). The simulation model 
and results are documented in BSC (2003 [163045]).

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Table 4.1-1. Input Data and Parameters Used in This Model Report (Continued) 
Item Data Name Data Source/DTN 
Roadmap (if applicable) 
Parameters 
Comments 
16 UZ Model Columns 
Representative of 
Repository Area 
DTN: LB03033DSSFF9I.001 
[163047] 
File xcheckutil.tar.gz must be 
unzipped. One of the extracted files 
is REPO_ZONE.cell. This file 
provides a list of repository zone 
elements. 
Repository Element Names 
The repository element names are used to extract the 
fluxes over the repository area from DTN: 
LB0302PTNTSW9I.001 [162277]. Only the repository 
fluxes are needed for seepage abstraction. 
17 Flow Focusing 
Factor Distribution 
DTN: LB0104AMRU0185.012 
[163906] 
See figures in Sheet 1 of file 
CumuRE_final.xls 
Flow Focusing Factor 
Cumulative probability distribution of flow focusing 
factors is given as a regression curve, derived from a 
stochastic modeling analysis of a vertical cross-section 
of Yucca Mountain. 
18 Degraded Drift 
Profiles 
DTN: MO0306MWDDPPDR.000 
[164736] 
Depending on which scenario 
number is considered, a file named 
scenarioNN.zip must be unzipped 
(NN is the number). A directory 
opens containing an eps plot file 
with a figure of the drift profile. 
Degraded Profiles For Several Scenarios 
Drift profiles have been calculated for several scenarios, 
such as from thermal stresses, seismic events, and 
time-dependent reduction in rock strength..

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4.2 CRITERIA 
Technical requirements to be satisfied by performance assessment (PA) are stated in 
10 CFR 63.114 [156605] (Requirements for Performance Assessment). Technical requirements 
to be satisfied in relation to multiple barriers are given in 10 CFR 63.115 [156605] 
(Requirements for Multiple Barriers). These technical requirements are also identified in the 
Yucca Mountain Project Requirements Document (Canori and Leitner 2003 [161770]). 
The acceptance criteria that will be used by the Nuclear Regulatory Commission (NRC) to 
determine whether the technical requirements have been met are identified in the Yucca 
Mountain Review Plan, Final Report (YMRP; NRC 2003 [163274]). Seepage abstraction is 
based on the current understanding of flow paths in the unsaturated zone (UZ) at Yucca 
Mountain to provide information about the quantity of water seeping into drifts and potentially 
contacting waste packages. Thus the abstraction must meet acceptance criteria in both Section 
2.2.1.3.6.3, Flow Paths in the UZ, and Section 2.2.1.3.3.3, Quantity and Chemistry of Water 
Contacting Engineered Barriers and Waste Forms, of the YMRP (NRC 2003 [163274]). 
Additional acceptance criteria are identified in Section 2.2.1.1.3, System Description and 
Demonstration of Multiple Barriers, of the YMRP (NRC 2003 [163274]). Each of these criteria 
has several subcriteria, not all of which are applicable to seepage abstractions. 
The pertinent requirements and acceptance criteria for this Model Report are summarized in 
Table 4.2-1. Differences between this list and the acceptance criteria identified in the TWP (BSC 
2002 [160819], Table 3-1) are as follows: (1) YMRP section numbers are different as a result of 
the recent revision of the YMRP; and (2) Acceptance Criteria (AC) 1 and 2 from NRC (2003 
[163274], Section 2.2.1.3.6.3), AC 1 to 5 from NRC (2003 [163274], Section 2.2.1.3.3.3), and 
AC 1 to 3 from NRC (2003 [163274], Section 2.2.1.1.3) are considered applicable to seepage 
abstraction and were thus added to the list, i.e., the list discussed in this Model Report is more 
comprehensive than that in the TWP (BSC 2002 [160819], Table 3-1). 
Table 4.2-1. Project Requirements and YMRP Acceptance Criteria Applicable to This Model Report 
Requirement 
Numbera 
Requirement Titlea 10 CFR 63 Link YMRP Acceptance Criteria 
PRD-002/T-014 Performance Objectives for 
the Geologic Repository after 
Permanent Closure 
10 CFR 63.113 
(a) [156605] 
Criteria 1 to 3 for System Description and 
Demonstration of Multiple Barriers d. 
PRD-002/T-015 Requirements for 
Performance Assessment 
10 CFR 63.114 
(a-c,e,g) [156605] 
Criteria 1 to 5 for Quantity and Chemistry of Water 
Contacting Engineered Barriers and Waste Formsb; 
Criteria 1 to 5 for Flow Paths in the Unsaturated 
Zone c. 
PRD-002/T-016 Requirements for Multiple 
Barriers 
10 CFR 63.115 
(b) [156605] 
Criteria 1 to 3 for System Description and 
Demonstration of Multiple Barriers d. 
NOTES: a from Canori and Leitner (2003 [161770]) 
b from NRC (2003 [163274], Section 2.2.1.3.3.3) 
c from NRC (2003 [163274], Section 2.2.1.3.6.3) 
d from NRC (2003 [163274], Section 2.2.1.1.3) 
The main acceptance criteria identified in the YMRP are given below, followed by a list of the 
subcriteria that apply to this Model report, and a discussion on how these selected subcriteria are

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being addressed in the document. This discussion is separate for the three relevant sections of the 
YMRP. Seepage abstraction uses input information from several sources, which provide 
measured data as well as model results. While the validity of these measured data and model 
results is briefly discussed in this abstraction report, we refer to the respective upstream reports 
for a more detailed description of methods, assumptions, initial and boundary conditions, data 
justification, model validation, alternative conceptual models, and sensitivity studies. References 
to upstream reports are given throughout this document. 
. Acceptance Criteria from Section 2.2.1.3.6.3, Flow Paths in the UZ 
Acceptance Criterion 1, System Description and Model Integration Are Adequate: 
 Subcriterion (1): Couplings between thermal, hydrological, mechanical, and chemical 
processes in the fractured rock are incorporated in the abstraction as appropriate (Sections 
6.4.3, 6.4.4, 6.5.1.4, and 6.5.2). 
 Subcriterion (2): All aspects of geology, hydrology, geochemistry, and physical 
phenomena affecting flow paths in the UZ are adequately considered in the seepage 
abstraction. Conditions and assumptions are readily identified and consistent with data 
(Sections 6.3, 6.4, and 6.5). 
 Subcriterion (3): Seepage abstraction uses assumptions, technical bases, data, and 
models that are appropriate and consistent with abstractions of upstream analyses (UZ 
flow paths, climate, and infiltration). Seepage abstraction utilizes results from UZ flow 
path simulations (which are based on climate and infiltration) without any additional 
simplifications (Section 6.6.4) and integrates them with seepage-relevant information 
from drift-scale models. The descriptions and technical bases are transparent and 
traceable to site and design data. 
 Subcriterion (5): Sufficient data and technical bases are provided to assess the degree to 
which features, events, and processes have been included in this abstraction (Section 6.2). 
 Subcriteria (6) and (7): Several process-level models feed into seepage abstraction 
(Section 6.4). Adequate spatial variability of model parameters and boundary conditions 
are employed in these process-level models used to estimate flow paths in the unsaturated 
zone, percolation flux, and seepage flux. Average parameter estimates used in processlevel 
models are representative of the temporal and spatial discretizations considered in 
the model. 
Acceptance Criterion 2, Data Are Sufficient for Model Justification: 
 Subcriteria (1), (2), (5), (6), and (7): These acceptance criteria mainly address the 
justification of upstream models that feed into the seepage abstraction. Adequate 
descriptions of how these upstream models meet the several subcriteria are provided in 
detail in the respective model reports (as referenced in Section 6.4). It is demonstrated in 
these reports that the input parameters used in the models are justified. Accepted and 
well-documented procedures have been used to construct and calibrate the numerical 
models. The models are technically defensible and based on data collected using 
acceptable techniques. Sensitivity analyses have been conducted to assess data 
sufficiency.

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 Subcriterion (2): The main hydrological properties (air permeability and the capillarystrength 
parameters) used in the seepage abstraction are based on adequately designed air 
injection and liquid release tests. From the seepage rates measured in the liquid release 
tests, effective capillary-strength parameters have been calibrated with an appropriate 
inverse model (Section 6.4.1). 
 Subcriterion (3): The percolation flux distributions used in the seepage abstraction are 
based on a technically defensible UZ flow model that reasonably represents the physical 
system and is calibrated using site-specific hydrological, geological, and geochemical 
data (Section 6.6.4.1). These distributions provide the appropriate spatial and temporal 
variability of model parameters, including climate-induced changes in infiltration. 
Percolation fluxes are further adjusted using flow focusing factors to account for 
intermediate-scale heterogeneity (Section 6.6.4.2). 
 Subcriterion (4): Appropriate thermal-hydrological tests have been designed and 
conducted, so that critical thermal-hydrological-mechanical-chemical processes could be 
observed, and values for relevant model parameters estimated (see references in Sections 
6.4.3 and 6.4.4). 
Acceptance Criterion 3, Data Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
 Subcriteria (1) and (2): Seepage abstraction uses parameter values, ranges, probability 
distributions, and/or bounding assumptions that are technically defensible, reasonably 
account for uncertainties and variabilities, and do not result in an under-representation of 
risk (Section 6.5). The technical bases for the parameter values used in this abstraction 
are provided (Section 6.6). 
 Subcriterion (3): Possible statistical correlations between parameters have been evaluated 
in this abstraction. An adequate technical basis or bounding argument is provided for 
neglected correlations (Section 6.5.1.1). 
 Subcriterion (5): The impact of coupled processes is adequately represented in the 
seepage abstraction (Sections 6.5.1.4 and 6.5.2). 
 Subcriterion (6): Uncertainties in the characteristics of the natural system are explicitly 
considered in the seepage abstraction (see summary in Section 6.7.2, detailed discussions 
in Sections 6.5 and 6.6). 
Acceptance Criterion 4, Model Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
 Subcriterion (1): Alternative modeling approaches have been investigated in all upstream 
models that feed into this abstraction (Section 6.4). The results are appropriately 
considered in the abstraction (Section 6.5). 
 Subcriterion (2): The bounds of uncertainty created by the process-level models are 
considered in the abstraction (Sections 6.5 and 6.6). 
 Subcriterion (3): Consideration of conceptual model uncertainty is consistent with 
available site characterization data, laboratory experiments, field measurements, natural 
analog information and process-level modeling studies. The treatment of conceptual

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model uncertainty does not result in an under-representation of risk (Sections 6.5 and 
6.6). 
Acceptance Criterion 5, Model Abstraction Output Is Supported by Objective Comparisons: 
 Subcriteria (1), (2), and (3): The abstraction results implemented in the TSPA-LA are 
based on and consistent with output from detailed process-level models, as demonstrated 
by comparison. For example, results from the process-level model for ambient seepage 
are incorporated in the seepage abstraction without any simplifications. Other 
abstractions of process-level models conservatively bound the process-level predictions 
(Section 6.4 and 6.5). 
. Acceptance Criteria from Section 2.2.1.3.3.3, Quantity and Chemistry of Water 
Contacting Engineered Barriers and Waste Forms 
Acceptance Criterion 1, System Description and Model Integration Are Adequate: 
 Subcriteria (1) and (2): Physical phenomena and couplings are adequately incorporated in 
the seepage abstraction. The assumptions, technical bases, data, and models used in the 
seepage abstraction are appropriate and consistent with the abstractions for UZ flow paths 
and for climate and infiltration (Section 6.6.4) as well as for drift degradation (Section 
6.5.1.5). The descriptions and technical bases are transparent and traceable. 
 Subcriterion (4): Spatial and temporal abstractions appropriately address physical 
couplings from thermal, hydrological, mechanical, and chemical processes (Sections 
6.5.1.4 and 6.5.2). Flow perturbations resulting from these processes, which could 
potentially lead to increased flux of water towards drifts, have been analyzed by 
appropriate testing and modeling studies (Sections 6.4.3 and 6.4.4) and have been 
accounted for in the abstraction (Section 6.5.1.4 and 6.5.2). 
 Subcriterion (5): Sufficient technical data and justification are provided for assumptions 
and approximations regarding the coupled effects on seepage and flow (Sections 6.4.3 
and 6.4.4). The effects of flow distribution on the amount of water contacting waste 
packages are consistently addressed (Sections 6.4 for small-scale variability, Section 
6.6.4 for intermediate-scale variability). 
 Subcriterion (8): Adequate technical bases are provided, including independent modeling, 
laboratory, and field data, or sensitivity studies, for inclusion of any thermalhydrological-
mechanical-chemical couplings and features, events, and processes 
(Sections 6.4.3 and 6.4.4). 
 Subcriterion (9): Performance-affecting processes that have been observed in thermalhydrological 
tests and experiments are included in the seepage abstraction. It has been 
demonstrated that refluxing water will not enhance seepage at Yucca Mountain (Sections 
6.4.3 and 6.5.2). 
Acceptance Criterion 2, Data Are Sufficient for Model Justification: 
 Subcriteria (1) and (2): This acceptance criterion mainly addresses the justification of 
upstream process-level models that feed into the seepage abstraction. Adequate 
descriptions of how these models meet the several subcriteria are provided in detail in the

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respective model reports (as referenced in Section 6.4). It is demonstrated that the 
geological, hydrological and geochemical values used in these upstream process-level 
models are adequately justified. Sufficient data were collected on the characteristics of 
the natural system to establish initial and boundary conditions for these models. 
 Subcriterion (3): Thermal-hydrological tests have been designed and conducted with the 
explicit objectives of observing thermal-hydrological processes for the temperature 
ranges expected for the repository conditions and making measurements for process-level 
models (see references in Section 6.4.3). The data collected are sufficient to verify that 
the thermal-hydrological conceptual models capture the relevant thermal-hydrological 
phenomena (Section 6.4.3). 
Acceptance Criterion 3, Data Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
 Subcriterion (1): Seepage abstraction uses parameter values, ranges, probability 
distributions, and bounding assumptions that are technically defensible, reasonably 
account for uncertainties and variabilities, and do not result in an under-representation of 
risk (Sections 6.5 and 6.6). 
 Subcriterion (2): The parameter values, ranges, probability distributions, and bounding 
assumptions used in seepage abstraction are based on data from the Yucca Mountain 
region, including results from heater tests and niche liquid-release tests, from field 
measurements, and process-modeling studies, corroborated by natural analog research 
(Sections 6.4, 6.5 and 6.6). 
 Subcriterion (3): The input information used in the TSPA seepage calculations is derived 
from and consistent with measured data or parameters provided by process-level models 
(Section 6.4). Possible statistical correlations between input values have been evaluated 
in this abstraction (Section 6.5.1.1). The impact of coupled processes is adequately 
represented in the seepage abstraction (Sections 6.5.1.4 and 6.5.2). Reasonable or 
conservative ranges of parameters or functional relations are established (Section 6.6). 
 Subcriterion (4): Uncertainties in the characteristics of the natural system are explicitly 
considered in the seepage abstraction (see summary in Section 6.7.2, detailed discussions 
in Sections 6.5 and 6.6). 
Acceptance Criterion 4, Model Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
 Subcriteria (1), (2), and (4): Alternative model approaches of features, events, and 
processes have been investigated in all upstream models that feed into this abstraction. 
The results are appropriately considered in the abstraction. The selected modeling 
approaches are consistent with available data and current scientific understanding. 
Alternative approaches not considered in the final analysis, and the limitations and 
uncertainties of the upstream models, are described in the respective Model Reports 
(Section 6.4). Adequate consideration is given to effects of thermal-hydrologicalmechanical-
chemical processes in the assessment of alternative conceptual models. 
 Subcriterion (3): Consideration of conceptual model uncertainty is consistent with 
available site characterization data, laboratory experiments, field measurements, natural

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analog information and process-level modeling studies. The treatment of conceptual 
model uncertainty does not result in an under-representation of risk (Sections 6.5 and 
6.6). 
 Subcriterion (5): The process-level models feeding into the seepage abstraction are based 
on an equivalent continuum assumption for flow and transport in the fractured network 
and in the matrix blocks. As discussed in Section 6.4, these models have been validated 
by comparison with measured data and are appropriate for their use in seepage 
abstraction. Alternative models, such as the discrete fracture models, are briefly discussed 
in Section 6.4.1.2, and references to a more in-depth discussion are given therein. It was 
demonstrated that seepage predictions with a continuum model were consistent with 
seepage predictions from a discrete model. 
Acceptance Criterion 5, Model Abstraction Output Is Supported by Objective Comparisons: 
 Subcriterion (1): The abstraction results implemented in the TSPA-LA are based on and 
consistent with output from detailed process-level models (Sections 6.4 and 6.5). 
 Subcriterion (2): Abstracted models for thermal-hydrological-mechanical-chemical 
effects on seepage and flow are based on the same assumptions and approximations used 
for process-level models (Sections 6.4.4). Note that the ambient seepage process-level 
model results (estimating flow diversion of percolation flux around from drifts) are 
incorporated in the seepage abstraction without any simplifications (Section 6.5.1). Other 
abstractions of process-level models conservatively bound the process-level predictions 
(e.g., abstraction of thermally induced parameter changes discussed in Section 6.5.1.4). 
 Subcriterion (3): Accepted and well-documented procedures have been used to construct 
and test the numerical models that simulate coupled effects of seepage and flow, 
providing the basis for this abstraction (see references in Section 6.4). 
. Acceptance Criteria from Section 2.2.1.1.3, System Description and Demonstration of 
Multiple Barriers 
Acceptance Criterion 1, Identification of Barriers Is Adequate: 
 Subcriterion (1): The unsaturated zone above the repository forms a natural barrier 
important to waste isolation. This Model Report addresses the amount of water seeping 
into drifts relative to the amount of percolation to provide a basis for evaluating the 
barrier capability (see probabilistic seepage evaluation in Section 6.8). 
Acceptance Criterion 2, Description of Barrier Capability to Isolate Waste Is Acceptable: 
 Subcriteria (1) and (2): The capability of the identified barriers to prevent or limit the 
amount of water seeping into waste emplacement drifts is adequately identified and 
described, including changes during the compliance period. The uncertainty associated 
with barrier capabilities is adequately described (see Section 6.8). 
Acceptance Criterion 3, Technical Basis for Barrier Capability Is Adequately Presented: 
 Subcriterion (1): The technical bases for this seepage abstraction are consistent with the 
technical basis for TSPA-LA. The technical basis for assertions of barrier capability is 
commensurate with the importance of the barriers capability and the associated

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uncertainties (see description of technical basis for barrier capability in Sections 6.3 and 
6.4). 
4.3 CODES AND STANDARDS 
No specific formally established standards have been identified as applying to this modeling 
activity.

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INTENTIONALLY LEFT BLANK

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5. ASSUMPTIONS 
The following assumptions pertain to this abstraction model as follows: 
 Assumption 1 
Capillary diversion depends upon the difference in capillary strength (1/a) between the 
interior of the drift and the rock surrounding the drift. For a collapsed drift filled with 
rubble rock material, it is assumed that the capillary strength of the rubble rock material 
is 100 Pa. This assumption is used in Section 6.4.2.4.2. 
Rationale 
The bulk porosity of the rubble rock material in the drift is much greater than the porosity 
of intact rock, because it includes large voids between chunks of fragmented rock. The 
chunks of fragmented rock are expected to have sizes on the order of centimeters to 
decimeters (BSC 2003 [162711], Section 8.1). The voids are similar in size to the chunks 
of rubble and have zero capillary strength. The resulting capillary strength of the rubblefilled 
drift is therefore much weaker than that of the intact surrounding rock. Also note 
that an open space is expected to form between the solid rock at the ceiling of the drift 
and the collapsed rubble material (because of consolidation of the rubble material). This 
open space would even result in a zero capillarity at the drift ceiling, maintaining the full 
diversion potential for percolating water. The value of 100 Pa is therefore chosen as a 
conservative, non-zero value to represent the effective capillary strength of the rubblefilled 
drift with an air gap forming at the ceiling. This assumption is considered adequate 
and requires no further confirmation. 
 Assumption 2 
Seepage into open drifts depends on the fracture permeability and the effective capillarystrength 
parameter of the fractured rock in the drift vicinity. These properties, which are 
influenced by the impact of stress redistribution during drift excavation, have been 
measured and/or calibrated in field experiments and associated modeling work. It is 
assumed for seepage into collapsed drifts that the properties of the intact rock above the 
rubble-filled cave are similar to the properties of the excavation-disturbed zone around 
non-degraded drifts. This assumption is used in Sections 6.5.1.4 and 6.5.1.5. 
Rationale 
This assumption is a reasonable starting point for the analysis of seepage into rubblefilled 
collapsed drifts, but requires further confirmation ([164617]). Supporting analyses 
with the Drift-Scale THM Model are ongoing. 
 Assumption 3 
Analysis of thermal seepage, as described in Section 6.4.3, was conducted for nondegraded 
drifts, and the abstraction methodology for thermal seepage in Section 6.5.2 
was developed based on these non-degraded drift results. It is assumed that the 
abstraction methods are also valid for thermal seepage into collapsed drifts. This 
assumption is used in Section 6.5.2.

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Rationale 
This assumption is a reasonable starting point for the abstraction of thermal seepage into 
rubble-filled collapsed drifts, but requires further confirmation ([164618]). Supporting 
analyses with the TH Seepage Model are ongoing. 
In addition to the above assumptions, several assumptions pertain to the upstream analyses that 
provided input information to the seepage abstraction. These assumptions are explained and 
justified in the following model reports: 
 Seepage Calibration Model and Seepage Testing Data (BSC 2003 [162267], Section 5) 
 Seepage Model for PA Including Drift Collapse (BSC 2003 [163226], Section 5; 
[163017]) 
 Drift-Scale Coupled Processes (DST and TH Seepage) Models (BSC 2003 [161530], 
Section 5) 
 Drift-Scale THM Model (BSC 2003 [162318], Section 5) 
 Drift-Scale Coupled Processes (DST and THC Seepage) Models (BSC 2003 [162050], 
Section 5) 
 In Situ Field Testing of Processes (BSC 2002 [158463], Section 5)

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6. MODEL DISCUSSION 
6.1 OBJECTIVES AND DEFINITIONS 
6.1.1 Objectives 
The following sections describe the development of the abstraction model for seepage of water 
into waste emplacement drifts. The purpose of seepage abstraction is to synthesize and simplify 
the relevant input for the seepage calculations to be conducted in the TSPA-LA. This input stems 
from several different sources, such as drift- and site-scale simulation models, as well as from in 
situ testing conducted in the unsaturated zone (UZ) at Yucca Mountain. 
Seepage is treated as a stochastic process in the TSPA-LA simulations (e.g., CRWMS M&O 
2000 [148384], Section 6.3.1.2). It is recognized that the amount of seepage is sensitive to key 
hydrological parameters that are both spatially variable and uncertain. One of the main tasks of 
this seepage abstraction model is therefore to define appropriate probability distributions that 
represent this spatial variability and uncertainty in a cautiously realistic manner. Using these 
distributions in the Monte Carlo analysis, the probability of seepage will be calculated in the 
TSPA-LA simulations under explicit consideration of spatial variability and uncertainty. 
The TSPA calculations use seepage look-up tables that provide the seepage rate (and related 
estimation uncertainty) as a function of key hydrological properties. These look-up tables have 
been developed from modeling results of the predictive Seepage Model for Performance 
Assessment (SMPA), as described in BSC (2003 [163226]), and from additional studies 
conducted for collapsed drifts (a result of thermal stress combined with joint cohesion changes or 
seismic events). Since this model accounts for seepage at ambient and somewhat idealized 
conditions, the SMPA results may need to be adjusted for the impact of additional factors. These 
factors include thermal perturbation in response to the heat emitted from the radioactive waste, 
transient changes in rock properties as a result of mechanical and chemical processes, and rock 
bolts providing potential pathways for seepage. Based on scientific analyses from several 
sources, seepage abstraction considers the relative importance of these factors and develops 
appropriate and realistic methods for incorporating them into the TSPA-LA simulations. 
The abstraction model is an extension of the previous seepage abstraction model developed for 
Total System Performance Assessment for Site Recommendation (TSPA-SR) (CRWMS M&O 
2001 [154291]). Since then, new data analyses and modeling results have become available. As a 
result, the abstraction model has been substantially revised. Major revisions include the 
incorporation of new seepage calibration data and new predictive modeling results from the 
SMPA, improved treatment of seepage during the period of thermally perturbed flow conditions, 
disposition of mechanical and chemical alterations based on process models, new probability 
distributions for variability and uncertainty, and a revised concept for flow focusing. 
Sections 6.1.2 through 6.1.4 below provide definitions of seepage properties, relevant scales, and 
uncertainties. In Section 6.2, all seepage-related features, events, and processes (FEPs) are 
evaluated, and their disposition for TSPA-LA is discussed. Section 6.3 describes basic processes 
and factors that can be important for seepage. In Section 6.4, several drift-scale process models 
used in the abstraction are introduced and main results are briefly presented. The seepage

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abstraction methodology, developed mainly based on these model results, is explained in detail 
in Section 6.5. Probability distributions covering spatial variability and uncertainty in relevant 
parameters for seepage are generated in Section 6.6. A summary of the proposed seepage 
abstraction methodology is provided in Section 6.7. Finally, using the proposed abstraction 
methodology and the probability distributions for seepage-relevant parameters, a stochastic 
Monte Carlo analysis is conducted to illustrate the probability for seepage into the waste 
emplacement drifts at Yucca Mountain as a function of time (Section 6.8). 
The scientific notebooks listed in Table 6-1 provide details supporting the work discussed in this 
Model Report. 
Table 6-1. Scientific Notebook 
LBNL Scientific Notebook ID M&O Scientific 
Notebook ID 
Relevant Pages Citation 
YMP-LBNL-JTB-2 SN-LBNL-SCI-231-V1 1146 Birkholzer 2003 [164526] 
YMP-LBNL-JTB-3 SN-LBNL-SCI-231-V2 1142 Birkholzer 2003 [164525] 
YMP-LBNL-JTB-4 SN-LBNL-SCI-231-V3 148 Wang 2003 [163702] 
6.1.2 Definition of Seepage Properties 
Seepage is defined as flow of liquid water into an underground opening such as a niche or a 
waste emplacement drift. According to this definition, seepage does not include advective or 
diffusive vapor flow into the opening or condensation of water vapor on surfaces, which may 
lead to drop formation and drop detachment. Some of the water entering an underground opening 
may evaporate or flow along the wall, thus not contributing to seepage in the narrow sense 
defined here. Note, however, that evaporation, condensation, and film flow along the surface of 
the opening affect the moisture conditions in the waste emplacement drift and may thus impact 
repository performance. 
Thermal seepage refers to the seepage of water during the time period when water flow 
processes in the drift vicinity are perturbed from heating of the rock. 
Seepage flux is the amount of water seeping into the opening per unit of time per unit area. 
Seepage rate is the amount of water seeping into the opening per unit of time. A 5.1 m long drift 
section (the approximate length of a waste package plus the 0.1 m spacing between waste 
packages (BSC 2003 [164603] and BSC 2003 [164069])) is used as the reference scale for 
calculating the seepage rate. Thus, the seepage rate refers to seepage per time and waste package. 
Seepage percentage is defined as the ratio of seepage rate divided by percolation rate across the 
reference area multiplied by 100. The reference area is given by the footprint area of a 5.1 m 
long drift section.

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Seepage threshold is defined here as the critical percolation flux below which no seepage occurs, 
i.e., all percolating water is diverted around the opening, evaporates, or flows along the drift 
surface as a thin water film. 
Seepage fraction is defined as the fraction of waste packages affected by seepage. This is 
equivalent to the fraction of 5.1 m long drift sections that exhibit a nonzero seepage percentage. 
6.1.3 Definition of Barriers 
The surficial soils and topography, the rock layers overlying the repository, the host rock, and the 
rock layers below the repository are natural barriers to flow and transport (BSC 2002 [160819], 
Section 2.1.1). The work described in this abstraction report analyzes one of the barrier functions 
associated with the host rock layers, i.e., to reduce the amount of water entering emplacement 
drifts (seepage). These natural processes are (1) capillary forces holding water in the fractured 
rock around drifts, and (2) reduction of flow towards drifts as a result of vaporization. While 
these two processes are not barriers in the terminology of LA, they are referred to in this 
abstraction report as (1) capillary barrier and (2) vaporization barrier, respectively. 
6.1.4 Definition of Spatial Scales 
Seepage abstraction utilizes simulation results from UZ process models that represent different 
spatial model scales. These relevant model scales for seepage abstraction are defined as follows: 
Drift-Scale Model: 
Model that focuses on the processes occurring in the vicinity of waste emplacement 
drifts. A typical model domain includes appropriate rock portions covering a few drift 
diameters. The discretization in the drift vicinity is relatively fine (much smaller than the 
drift diameter), while the geometry of the drift is explicitly accounted for in the model 
discretization. 
Site-Scale Model: 
Model that represents the entire unsaturated zone at Yucca Mountain extending from the 
ground surface to the water table, including several stratigraphic units and major faults. 
Due to the size of the model domain, explicit consideration of individual drifts is not 
taken into account in site-scale models. 
Abstraction needs to consider spatial variability in properties and processes that occur on 
different spatial scales. The relevant heterogeneity scales for seepage abstraction are defined as 
follows: 
Small-Scale Heterogeneity: 
Heterogeneity on a resolution much smaller than the typical drift-scale model domain. An 
example is the small-scale variability of fracture permeability (resolution of about 1 foot) 
considered in the seepage process models.

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Intermediate-Scale Heterogeneity: 
Heterogeneity on a resolution similar to the typical drift-scale model domain. An 
example is the spatial distribution of the calibrated capillary-strength parameter used in 
the drift scale seepage models throughout different sections of the repository. 
Large-Scale Heterogeneity: 
Heterogeneity on a resolution much larger than the typical drift-scale model domain. In 
seepage abstraction, large-scale variability refers to the variability between the different 
stratigraphic units at Yucca Mountain, which is explicitly accounted for by considering 
separate rock property sets for each unit. (Intermediate-scale heterogeneity, on the other 
hand, is heterogeneity within stratigraphic units, on a drift-scale model resolution.) 
6.1.5 Definition of Uncertainty and Spatial Variability 
Seepage is treated as a probabilistic process in this model abstraction. Probabilistic models need 
to account for the variability and uncertainty of parameters (BSC 2002 [158794], Section 4.1): 
Variability, also referred to as aleatory uncertainty, arises due to natural randomness or 
heterogeneity. This first type of uncertainty cannot be reduced through further testing and 
data collection; it can only be better characterized. Thus, this first type of uncertainty is 
also referred to as irreducible uncertainty. It is typically accounted for using geostatistical 
approaches, e.g., using appropriate probability distribution functions. 
Uncertainty, also referred to as epistemic uncertainty, arises from lack of knowledge 
about a parameter because the data are limited or there are alternative interpretations of 
the available data. This second type of uncertainty can be reduced because the state of 
knowledge can be improved by further testing or data collection. As a consequence, this 
second type of uncertainty is also referred to as reducible uncertainty. 
In this Model Report, the term variability is used for aleatory uncertainty, and the term 
uncertainty is used for epistemic uncertainty. 
Uncertainty may have different sources as follows, depending on the respective method of 
deriving the parameter in question (e.g., derived from measurements, analyses, or models): 
Measurement uncertainty refers to the exactness of the actual measurement method and 
related data processing. 
Spatial variability uncertainty refers to the uncertainty in parameters describing the 
spatial variability of data, typically arising from the limited number of samples. 
Conceptual model uncertainty arises when the most appropriate conceptual model for a 
system is uncertain. 
Estimation uncertainty arises if the resulting parameter is estimated from a random 
process (e.g., from noisy data or from a Monte Carlo analysis), giving a range of possible 
results.

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6.2 FEATURES, EVENTS, AND PROCESSES 
Results of this model abstraction provide the technical basis for treatment of features, events, and 
processes (FEPs) that are relevant to this Model Report as discussed in the Total System 
Performance AssessmentLicense Application Methods and Approach (BSC 2002 [160146], 
Section 3.2.2). Table 6.2-1 provides FEPs taken from the LA FEP List (DTN: 
MO0301SEPFEPS1.000 [161496]). The LA FEP List is a revision to the previous project FEP 
list (Freeze et al. 2001 [154365]) used to develop the list of included FEPs in the Technical Work 
Plan for: Performance Assessment Unsaturated Zone (BSC 2002 [160819], Table 2-6). The 
selected FEPs are those taken from the LA FEP List that are associated with the subject matter of 
this report, regardless of the anticipated status for exclusion or inclusion in TSPA-LA as 
represented in BSC (2002 [160819]). 
The table provides the proposed FEPs disposition for TSPA-LA seepage evaluation and lists the 
relevant upstream reports used in the seepage abstraction process. The cross-reference for each 
FEP to the relevant sections of this report is also given in the table. 
The results of this and other model reports are used to fully document the technical basis for the 
include/exclude status of these FEPs for TSPA-LA. The UZ Departments documentation for the 
included FEPs listed in Table 6.2-1 is compiled from this and other model reports, and can be 
found in the model abstraction reports as described in Sections 2.1.2 and 2.4 of the TWP (BSC 
2002 [160819]) and the UZ FEP report as described in Section 1.12.10 of the TWP (BSC 2002 
[160819]). Excluded FEPs are to be documented in the UZ FEP report as described in Section 
1.12.10 of the TWP (BSC 2002 [160819]). 
Table 6.2-1. FEPs Addressed in This Model Report 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
1.1.01.01.0B Influx through 
holes drilled in 
drift wall or 
crown 
BSC (2003 [163226]) Holes may be drilled through the drift walls or crown for a variety of 
reasons including, but not limited to, rock bolt and ground support, 
monitoring and testing, or construction-related activities. These 
openings may promote flow or seepage into the drifts and onto the 
waste packages. 
TSPA Disposition: Excluded 
Detailed simulations were made using the predictive SMPA to study the 
effect of rock bolts in the drift crown. From these results, the presence 
of rock bolts is not considered a relevant factor for seepage into drifts 
(Section 6.4.2.5). The impact of rock bolts is therefore neglected in the 
seepage abstraction (Section 6.5.1.6).

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Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
1.1.02.02.0A Pre-closure 
Ventilation 
BSC (2003 [161530]) The duration of pre-closure ventilation acts together with waste package 
spacing (as per design) to control the extent of the boiling front. 
TSPA Disposition: Included 
Preclosure ventilation in drifts will remove a considerable amount of the 
heat output from the waste canisters. The preclosure period is 50 
years, during which the major fraction of the thermal load is removed 
from the drifts. This effect of preclosure ventilation on the thermal load 
provided to the rock is explicitly simulated with the TH Seepage Model 
that feeds into seepage abstraction, by using time-dependent boundary 
conditions for the thermal load (Section 6.4.3.1). These boundary 
conditions reflect the current emplacement design (waste package 
spacing, average heat output of waste canisters, etc.), as provided in 
design drawings 800-IED-EBS0-00201-000-00A (BSC 2003 [164069]) 
and 800-IED-WIS0-00201-000-00B (BSC 2003 [164603]). Thus, the TH 
modeling results from the TH Seepage Model (DTN: 
LB0301DSCPTHSM.002 [163689]) directly account for the impact of 
preclosure ventilation and waste package spacing on the TH conditions 
in the near-drift rock. As discussed in Section 6.5.2.1, the abstraction of 
thermal seepage utilizes these modeling results to develop an 
appropriate thermal seepage abstraction methodology. Note that preclosure 
ventilation also causes initial rock drying in the drift vicinity as a 
result of evaporation effects. The reduced relative humidity in the 
emplacement drifts leads to evaporation of water at the drift surfaces 
and the development of a small zone of reduced saturation in the drift 
vicinity. This early dryout as a result of evaporation is neglected in the 
TH Seepage Model, because seepage into ventilated drifts is highly 
unlikely (Section 6.5.2.1).

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Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
1.2.02.01.0A Fractures BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
BSC (2003 [162050]) 
BSC (2003 [162318]) 
Groundwater flow in the Yucca Mountain region and transport of any 
released radionuclides may take place along fractures. The rate of flow 
and the extent of transport in fractures are influenced by characteristics 
such as orientation, aperture, asperity, fracture length, connectivity, and 
the nature of any linings or infills. 
TSPA Disposition: Included 
Flow processes in fractures or other channels are important for 
seepage abstraction because the amount of seepage is determined by 
the diversion capacity of the fracture flow in the drift vicinity (Section 
6.3.1). These flow processes are influenced by the fracture 
characteristics, such as orientation, aperture, asperity, length, 
connectivity, and fillings. All the seepage process models that feed into 
seepage abstraction explicitly simulate the flow processes in fractures 
using appropriate continuum properties that represent these 
characteristics (Section 6.4). For ambient seepage, the relevant 
continuum properties are the continuum permeability and the effective 
fracture capillary-strength in the drift vicinity. For seepage abstraction, 
probability distributions describing spatial variability and uncertainty of 
these parameters have been developed in Section 6.8.1, based on airpermeability 
measurements and liquid release tests combined with 
inverse modeling (Section 6.6 and 6.4.1). Ambient seepage 
calculations will be conducted within the TSPA-LA by sampling from 
these probability distributions and interpolating seepage rates from the 
look-up tables given in DTNs LB0304SMDCREV2.002 [163687] and 
LB0307SEEPDRCL.002 [164337]. During the thermal period, the 
ambient seepage rates will be adjusted based on the TH modeling 
results from TH Seepage Model, which explicitly simulates the 
thermally perturbed fracture flow conditions. Results are given in DTN 
LB0301DSCPTHSM.002 [163689]. The abstraction methodology for 
thermal seepage is developed in Section 6.5.2.1.THM and THC effects 
on the fracture characteristics are evaluated with process models that 
explicitly account for fracture flow affected by THM and THC parameter 
alterations (Sections 6.4.4.1 and 6.4.4.2). It was demonstrated that 
these potential alterations can be neglected in the TSPA-LA, because 
the expected changes would lead to less seepage (Section 6.5.1.4). 
1.3.01.00.0A Climate 
change, 
global 
BSC (2003 [163045]) Climate change may affect the long-term performance of the repository. 
This includes the effects of long-term change in global climate (e.g., 
glacial/interglacial cycles) and shorter-term change in regional and local 
climate. Climate is typically characterized by temporal variations in 
precipitation and temperature. 
TSPA Disposition: Included 
Potential effects of climate change on the amount of infiltration and 
percolation at Yucca Mountain are taken into account in the seepage 
abstraction by considering different climate stages and climate 
scenarios (see Section 6.6.4). Seepage is calculated in the TSPA-LA 
using percolation flux distributions based on results from the UZ Flow 
and Transport Model (Section 6.6.4.1), given in DTNs 
LB0302PTNTSW9I.001 [162277] and LB0305PTNTSW9I.001 [163690]. 
These flux distributions include different climate stages and scenarios.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 44 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
1.4.01.01.0A Climate 
modification 
increases 
recharge 
BSC (2003 [163045]) Climate modification (natural or artificial) causes an increase in recharge 
in the Yucca Mountain region. Increased recharge might lead to 
increased flux through the repository, perched water, or water table 
rise. 
TSPA Disposition: Included 
Potential effects of climate change on the amount of flux through the 
repository are taken into account in the seepage abstraction by 
considering different climate stages and climate scenarios (see Section 
6.6.4). Seepage is calculated in the TSPA-LA using percolation flux 
distributions that are based on results from the UZ Flow and Transport 
Model (Section 6.6.4.1), given in DTNs LB0302PTNTSW9I.001 
[162277] and LB0305PTNTSW9I.001 [163690]. These flux distributions 
include different climate stages and scenarios. 
2.1.08.01.0A Water influx at 
the repository 
BSC (2003 [163045]) An increase in the unsaturated water flux at the repository affects 
thermal, hydrological, chemical, and mechanical behavior of the 
system. Increases in flux could result from climate change, but the 
cause of the increase is not an essential part of the FEP. 
TSPA Disposition: Included 
The potential increase in the magnitude of percolation flux at the 
repository, as a result of climate changes or flow focusing effects, is 
accounted for in the seepage abstraction by considering different 
climate stages, climate scenarios, and introducing flow focusing factors 
(see Section 6.6.4). Seepage is calculated in the TSPA-LA using 
percolation flux distributions that are based on results from the UZ Flow 
and Transport Model (Section 6.6.4.1), given in DTNs 
LB0302PTNTSW9I.001 [162277] and LB0305PTNTSW9I.001 [163690]. 
These flux distributions include different climate stages and scenarios. 
The potential focusing of flow towards individual drift sections is 
accounted for by a distribution of flow focusing factors, as discussed in 
Section 6.6.4.2. This distribution is given in DTN 
LB0104AMRU0185.012 [163906]. The local percolation flux distribution 
used for the seepage calculations in the TSPA-LA is derived by 
multiplying the percolation flux values from the site-scale model with the 
randomly sampled flow focusing factors (Section 6.8.1). 
2.1.08.02.0A Enhanced 
influx at the 
repository 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
An opening in unsaturated rock alters the hydraulic potential, affecting 
local saturation around the opening and redirecting flow. Some of the 
flow is directed to the opening where it is available to seep into the 
opening. 
TSPA Disposition: Included 
The impact of an underground opening on the unsaturated flow field 
(including capillary barrier effect and flow diversion around the drifts) 
and its relevance for seepage is explicitly captured in the seepage 
process models used for the seepage abstraction (see Sections 6.4.1, 
6.4.2, and 6.4.3). From these model simulations, seepage predictions 
are available in form of look-up tables in DTNs LB0304SMDCREV2.002 
[163687] and LB0307SEEPDRCL.002 [164337]. These will be used in 
the TSPA-LA to calculate ambient seepage, by sampling parameter 
cases of seepage-relevant parameters from the probability distributions 
that are defined in Section 6.8.1 of this Model Report. These seepagerelevant 
parameters are the effective capillary-strength parameter, the 
permeability, and the local percolation flux. The percolation flux 
distributions include flow focusing effects, as discussed in Section 
6.6.4.2. During the thermal period, the ambient seepage rates will be 
adjusted based on the TH modeling results from DTN 
LB0301DSCPTHSM.002 [163689], using the abstraction methodology 
developed in Section 6.5.2.1.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 45 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.1.09.12.0A Rind 
(chemically 
altered zone) 
forms in the 
near-field 
BSC (2003 [162050]) Thermal-chemical processes involving precipitation, condensation, and 
re-dissolution alter the properties of the adjacent rock. These 
alterations may form a rind, or altered zone, in the rock, with 
hydrological, thermal, and mineralogical properties different from the 
initial conditions. 
TSPA Disposition: Excluded 
The impact of THC processes such as precipitation and dissolution of 
minerals on fracture surfaces is explicitly simulated with a coupled THC 
Model (Section 6.4.4.2). The impact of these alterations on seepage 
may be important, because a precipitation umbrella is expected to 
evolve several meters above drifts, which could divert percolating 
water. However, these potentially beneficial THC effects on seepage 
are not accounted for in the seepage abstraction because of the 
considerable uncertainty related to these predictions (Section 6.5.1.4). 
As indicated by the THC predictions, neglecting THC parameter 
alteration leads to an overestimation of seepage. 
2.2.01.01.0A Mechanical 
effects of 
excavation/ 
construction in 
the near field 
BSC (2001 [158463]) 
BSC (2003 [162267]) 
BSC (2003 [162318]) 
Excavation will produce some disturbance of the rocks surrounding the 
drifts due to stress relief. Stresses associated directly with excavation 
(e.g., boring and blasting operations) may also cause some changes in 
rock properties. Properties that may be affected include rock strength, 
fracture spacing, block size, and hydrological properties such as 
permeability. 
TSPA Disposition: Included 
Excavation effects are taken into account in the seepage abstraction 
through the use of post-excavation air-permeability data (Table 6.6-3) 
and the estimation of a capillary-strength parameter determined from 
seepage tests (Table 6.6-1). These data reflect the impact of 
excavation around a large opening (niche or drift). The measured postexcavation 
air-permeability data are supported by THM modeling 
results (Section 6.6.2.1). The probability distributions for permeability 
and capillary strength given in Section 6.8.1 are based on the values 
given in Tables 6.6-3 and 6.6-1, respectively, and thus account for such 
excavation effects. These distributions will be used in the TSPA-LA to 
calculate seepage from the seepage look-up tables, using the 
methodology defined in Section 6.8.1 of this Model Report. 
2.2.01.02.0A Thermallyinduced 
stress 
changes in the 
near-field 
BSC (2003 [162318]) 
BSC (2003 [163226]) 
Changes in host rock properties result from thermal effects or other 
factors related to emplacement of the waste. Properties that may be 
affected include rock strength, fracture spacing and block size, and 
hydrologic properties such as permeability and sorption. 
TSPA Disposition: Excluded 
The effect of thermal stresses on host rock properties in the drift vicinity 
is explicitly simulated with a coupled THM Model (Section 6.4.4.1). The 
impact of these stress-induced alterations on seepage is rather small, 
however, as described in Section 6.4.4.1. In fact, the seepage rates 
obtained from the SMPA model that neglects thermal expansion are 
slightly higher than those with a THM model (Figure 6.4-17). For 
reasons given in Section 6.5.1.4, effects of THM parameter changes on 
seepage are therefore neglected in the seepage abstraction, leading to 
a slight overestimation of the seepage rates.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 46 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.03.01.0A Stratigraphy BSC (2003 [161530]) 
BSC (2003 [162050]) 
BSC (2003 [162318]) 
BSC (2003 [163045]) 
Stratigraphic information is necessary information for the performance 
assessment. This information should include identification of the 
relevant rock units, soils and alluvium, and their thickness, lateral 
extents, and relationships to each other. Major discontinuities should 
be identified. 
TSPA Disposition: Included 
Ambient seepage as a result of flow diversion around drifts is a local 
process that is simulated by drift-scale models such as the SCM and 
the SMPA (Sections 6.4.1.1 and 6.4.2.1). In these models, the 
stratigraphy below and above the repository unit can be neglected. In 
contrast, the UZ Flow and Transport Model (which provides the 
percolation flux distributions used for seepage calculations) explicitly 
accounts for the various geological units and major faults in the UZ. 
This is because the overall distribution of percolation flux at the 
repository horizon is influenced by stratigraphic layering and by major 
discontinuities. For example, the PTn- unit overlying the Topopah 
Spring welded tuff units can divert a fraction of percolating water to 
intercepting faults and fault zones, thereby changing the spatial 
distribution of fluxes (Section 6.4.1.1). The drift-scale process models 
addressing TH, THM, and THC processes (Sections 6.4.3.1, 6.4.4.1, 
and 6.4.4.2) also represent the stratigraphy in the UZ at Yucca 
Mountain in an explicit manner. This is needed because the thermal 
perturbation of the unsaturated rock extends far into the overlying and 
underlying geological units. Thus, the stratigraphy information is 
automatically embedded in the respective model results from the UZ 
Flow and Transport Model and the TH, THM, and THC drift-scale 
models. 
2.2.03.02.0A Rock 
properties of 
host rock and 
other units 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
BSC (2003 [162318]) 
BSC (2003 [162050]) 
BSC (2003 [163045]) 
Physical properties such as porosity and permeability of the relevant 
rock units, soils, and alluvium are necessary for the performance 
assessment. Possible heterogeneities in these properties should be 
considered. Questions concerning events and processes that may 
cause these physical properties to change over time are considered in 
other FEPs. 
TSPA Disposition: Included 
All the seepage process models that feed into seepage abstraction 
explicitly represent the physical properties of the unsaturated rock and 
their heterogeneity (Section 6.4). Small-scale heterogeneity is 
accounted for by a stochastic continuum representation of fracture 
permeability. Thus, heterogeneity on this scale is implicitly embedded in 
the model output from the SCM, the SMPA, and the TH Seepage Model 
(provided in DTNs LB0304SMDCREV2.002 [163687], 
LB0307SEEPDRCL.002 [164337], and LB0301DSCPTHSM.002 
[163689]). The intermediate-scale spatial variability and uncertainty of 
seepage-relevant rock properties are accounted for by appropriate 
probability distributions that were developed in this abstraction 
(Sections 6.6.1 and 6.6.2). Potential alterations of these properties, as 
a result of THM or THC processes, have been assessed using driftscale 
process models (Sections 6.4.4.1 and 6.4.4.2). It was 
demonstrated that these potential alterations can be neglected in the 
TSPA-LA, as the expected changes would lead to less seepage 
(Section 6.5.1.4). Percolation flux distributions are provided by the UZ 
Flow and Transport Model (Section 6.6.4.1), which accounts for rock 
properties and their variation on a larger scale, e.g., stemming from 
stratigraphy effects.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 47 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.07.02.0A Unsaturated 
groundwater 
flow in the 
geosphere 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
BSC (2003 [162050]) 
BSC (2003 [162318]) 
BSC (2003 [163045]) 
Groundwater flow occurs in unsaturated rocks in most locations above 
the water table at Yucca Mountain, including at the location of the 
repository. See other FEPs for discussions of specific issues related to 
unsaturated flow. See related FEPs 2.2.07.03.0A (capillary rise), 
2.2.07.04.0A (focusing of unsaturated flow), 2.2.07.05.0A (effects of 
episodic infiltration), 2.2.07.07.0A (perched water), 2.2.07.08.0A 
(fracture flow), 2.2.07.09.0A (matrix imbibition), 2.2.07.10.0A (condensation 
zone forms), 2.2.07.11.0A (resaturation of dryout zone), and 
2.2.10.10.0A (two-phase flow/heat pipes). 
TSPA Disposition: Included 
Unsaturated flow processes are accounted for in the seepage 
abstraction by using results from process models that explicitly account 
for various relevant aspects of unsaturated groundwater flow. All the 
seepage process models that feed into seepage abstraction simulate 
groundwater flow processes in unsaturated rock (Section 6.4). For 
ambient seepage, the fracture flow processes in the drift vicinity and the 
resulting seepage rates are predicted by model simulations from the 
SMPA (Section 6.4.2). Results are available as look-up tables in DTNs 
LB0304SMDCREV2.002 [163687] and LB0307SEEPDRCL.002 
[164337]. These will be used in the TSPA-LA to calculate ambient 
seepage, by sampling parameter cases of seepage-relevant 
parameters from the probability distributions that are defined in Section 
6.8.1 of this Model Report. During the thermal period, the ambient 
seepage rates will be adjusted based on the TH modeling results from 
TH Seepage Model, which explicitly simulates the thermally-perturbed 
groundwater flow processes. Results are given in DTN 
LB0301DSCPTHSM.002 [163689]. The abstraction methodology for 
thermal seepage is developed in Section 6.5.2.1.THM and THC effects 
on fracture flow processes are evaluated with process models that 
explicitly account for groundwater flow processes affected by THM and 
THC parameter alterations (Sections 6.4.4.1 and 6.4.4.2). It was 
demonstrated that these potential alterations can be neglected in the 
TSPA-LA, as the expected changes would lead to less seepage 
(Section 6.5.1.4). Percolation flux distributions are provided by the UZ 
Flow and Transport Model (Section 6.6.4.1), which accounts for 
groundwater flow on a larger scale, influenced by climate changes, 
infiltration variability and stratigraphy effects. All related FEPs listed 
above that are important for seepage are included in this FEP table.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 48 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.07.04.0A Focusing of 
unsaturated 
flow (fingers, 
weeps) 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
Unsaturated flow can differentiate into zones of greater and lower 
saturation (fingers) that may persist as preferential flow paths. 
Heterogeneities in rock properties, including fractures and faults, may 
contribute to focusing. Focused flow may become locally saturated. 
TSPA Disposition: Included 
Intermediate-scale focusing of flow from the site scale to the drift scale 
is accounted for in the seepage abstraction by using appropriate flow 
focusing factors (Section 6.6.4.2). Small-scale preferential flow is 
explicitly simulated in the seepage process models that feed into the 
abstraction, by use of heterogeneous fracture permeability fields 
(Sections 6.4.1.1, 6.4.2.1, and 6.4.3.1). Thus, preferential flow is 
automatically embedded in the seepage look-up tables for ambient 
seepage given in DTNs LB0304SMDCREV2.002 [163687] and 
LB0307SEEPDRCL.002 [164337], and in the thermal seepage results 
provided in DTN LB0301DSCPTHSM.002 [163689]. The abstraction 
methodology for both ambient and thermal seepage is described in 
Section 6.8.1 of this Model Report. The possibility of episodic finger 
flow is accounted for with an alternative conceptual model analyzed in 
the thermal seepage model report. Results from this alternative 
conceptual model are consistent with results from the TH Seepage 
Model used for this abstraction (see Section 6.4.3.2). 
2.2.07.05.0A Flow in the UZ 
from episodic 
infiltration 
BSC (2003 [163045]) Episodic flow occurs in the UZ as a result of episodic infiltration. See 
also FEP 2.2.07.02.0A (unsaturated groundwater flow), 2.3.11.03.0A 
(infiltration), 2.2.07.04.0A (focusing of UZ flow), and 1.3.01.00.0A 
(climate change). Episodic flow may affect transport; for example, 
colloidal transport may be enhanced by episodic flow (FEP 
2.2.08.10.0A). 
TSPA Disposition: Excluded 
The process that drives infiltration in the unsaturated zone is 
precipitation, which is episodic in nature. Studies of episodic infiltration 
and percolation have found, however, that matrix-dominated flow in the 
PTn dampens out the transient nature of the percolation such that 
unsaturated zone flow below the PTn is essentially steady (CRWMS 
M&O 1998 [100356], Section 2.4.2.8). Furthermore, the PTn is found 
over the entire repository block (in Underground Layout Configuration 
BSC 2003 [164325]). This damping of transient flow is due to capillary 
forces and high matrix permeability in the PTn that lead to matrix 
imbibition of water from fractures. Therefore, this FEP is excluded, 
because the unsaturated zone flow is steady at the repository and 
along radionuclide transport pathways (BSC 2003 [163045], Section 
6.2.6). Very small amounts of fracture flow do appear to penetrate as 
transients through fault zones between the ground surface and the 
repository elevation, as evidenced by high 36Cl concentrations in 
samples taken from the ESF (Fabryka-Martin et al. 1997 [100145]). 
Higher concentrations of this isotope found in the ESF can only be 
explained through surface deposition of 36Cl from nuclear weapons 
testing and subsequent aqueous transport to certain ESF sampling 
locations over a period of approximately 50 years. However, the flow 
and transport models indicate that the quantity of water and dissolved 
constituents that do penetrate the PTn as flow transients is negligible 
with respect to repository performance (CRWMS M&O 1998 [100356], 
Section 2.4.2.8), generally less than 1% of the total infiltration (CRWMS 
M&O 1997 [124052], Section 6.12.4).

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 49 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.07.08.0A Fracture flow 
in the UZ 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
BSC (2003 [162050]) 
BSC (2003 [162318]) 
BSC (2003 [163045]) 
Fractures or other analogous channels act as conduits for fluids to 
move into the subsurface to interact with the repository and as conduits 
for fluids to leave the vicinity of the repository and be conducted to the 
SZ. Water may flow through only a portion of the fracture network, 
including flow through a restricted portion of a given fracture plane. 
TSPA Disposition: Included 
Flow processes in fractures or other channels are important for 
seepage abstraction because the amount of seepage is determined by 
the diversion capacity of the fracture flow in the drift vicinity (Section 
6.3.1). All the seepage process models that feed into seepage 
abstraction simulate flow processes in fractured rock (Section 6.4). 
Spatial variability in the fracture flow, potentially leading to water flow 
through only a portion of the fracture network, is accounted for by using 
a stochastic continuum representation. For ambient seepage, the 
fracture flow processes in the drift vicinity and the resulting seepage 
rates are predicted by model simulations from the SMPA (Section 
6.4.2). Results are available as look-up tables in DTNs 
LB0304SMDCREV2.002 [163687] and LB0307SEEPDRCL.002 
[164337]. These will be used in the TSPA-LA to calculate ambient 
seepage, by sampling parameter cases of seepage-relevant 
parameters from the probability distributions that are defined in Section 
6.8.1 of this Model Report. During the thermal period, the ambient 
seepage rates will be adjusted based on the TH modeling results from 
the TH Seepage Model, which explicitly simulates thermally perturbed 
fracture flow conditions. Results are given in DTN 
LB0301DSCPTHSM.002 [163689]. The abstraction methodology for 
thermal seepage is developed in Section 6.5.2.1.THM and THC effects 
on fracture flow processes are evaluated with process models that 
explicitly account for fracture flow affected by THM and THC parameter 
alterations (Sections 6.4.4.1 and 6.4.4.2). It was demonstrated that 
these potential alterations can be neglected in the TSPA-LA, because 
the expected changes would lead to less seepage (Section 6.5.1.4). 
Percolation flux distributions are provided by the UZ Flow and 
Transport Model (Section 6.6.4.1), which accounts for fracture flow on a 
larger scale (influenced by climate changes), infiltration variability, and 
stratigraphy effects. Flow focusing effects are included as discussed in 
Section 6.6.4.2. 
2.2.07.09.0A Matrix 
Imbibition in 
the UZ 
BSC (2003 [161530]) 
BSC (2003 [162050]) 
BSC (2003 [162318]) 
BSC (2003 [163045]) 
Water flowing in fractures or other channels in the UZ is imbibed into 
the surrounding rock matrix. This may occur during steady flow, 
episodic flow, or into matrix pores that have been dried out during the 
thermal period. 
TSPA Disposition: Included 
Ambient seepage is mainly governed by flow in the fractures, as 
discussed in Sections 6.4.1.1 and 6.4.2.1. Thus, in the predictive model 
for ambient seepage, i.e., the SCM and the SMPA, matrix imbibition is 
neglected. In contrast, the drift-scale process models addressing TH, 
THM, and THC processes (Sections 6.4.3.1, 6.4.4.1, and 6.4.4.2) 
explicitly account for matrix imbibition using appropriate dualpermeability 
modeling concepts. This is needed because the thermal 
perturbation of the unsaturated rock results in significant transfer of 
liquid and gas from the matrix into the fractures and vice versa. The UZ 
Flow and Transport Model (which provides the percolation flux 
distributions used for seepage calculations) also accounts for the 
impact of matrix imbibition in an explicit manner (Section 6.6.4.1). Thus, 
matrix imbibition effects are automatically embedded in the respective 
model results used for this abstraction.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 50 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.07.10.0A Condensation 
zone forms 
around drifts 
BSC (2003 [161530]) Condensation of the two-phase flow generated by repository heat forms 
in the rock where the temperature drops below the local vaporization 
temperature. Waste package emplacement geometry and thermal 
loading will affect the scale at which condensation caps form (over 
waste packages, over panels, or over the entire repository), and to the 
extent to which shedding will occur as water flows from the region 
above one drift to the region above another drift or into the rock 
between drifts. 
TSPA Disposition: Included 
The coupled processes of vapor condensation forming a condensation 
cap above the drifts and occurrence of shedding between drifts 
(Section 6.3.2) are explicitly simulated with the TH Seepage Model that 
feeds into the seepage abstraction. Using this model, the impact of 
condensation and shedding on seepage is assessed for various 
simulation cases (Section 6.4.3.3). Thus the TH modeling results from 
DTN LB0301DSCPTHSM.002 [163689] automatically include these 
effects. As discussed in Section 6.5.2.1, the abstraction of thermal 
seepage utilizes these modeling results to develop an appropriate 
thermal-seepage abstraction methodology. 
2.2.07.11.0A Resaturation 
of geosphere 
dryout zone 
BSC (2003 [161530]) Following the peak thermal period, water in the condensation cap (see 
FEP 2.2.07.10.0A) may flow downward into the drifts. Influx of cooler 
water from above, such as might occur from episodic flow, may 
accelerate return flow from the condensation cap by lowering 
temperatures below the condensation point. Percolating groundwater 
will also contribute to resaturation of the dryout zone. Vapor flow, as 
distinct from liquid flow by capillary processes, may also contribute. 
TSPA Disposition: Included 
Resaturation of the dryout zone around drifts, and the potential of return 
flow from the condensation zone back to the drifts, are explicitly 
simulated with the TH Seepage Model that feeds into seepage 
abstraction. Using this model, the impact of resaturation and reflux (on) 
seepage is assessed for various simulation cases (Section 6.4.3.3). 
Thus, the TH modeling results from DTN LB0301DSCPTHSM.002 
[163689] automatically include these effects. As discussed in Section 
6.5.2.1, the abstraction of thermal seepage utilizes these modeling 
results to develop an appropriate thermal seepage abstraction 
methodology. The impact of potential episodic flow was addressed with 
an alternative conceptual model for thermal seepage, as discussed in 
Section 6.4.3.2. It was shown that results from this alternative 
conceptual model are consistent with the process model results from 
the TH Seepage Model used for this abstraction.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 51 August 2003 
Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
22.2.07.18.0A Film flow into 
the repository 
BSC (2003 [162267]) Water entering waste emplacement drifts may occur by a film-flow 
process. This differs from the traditional view of flow in a capillary 
network where the wetting phase exclusively occupies capillaries with 
apertures smaller than some level defined by the capillary pressure. As 
a result, a film-flow process could allow water to enter a waste 
emplacement drift at non-zero capillary pressure. Dripping into the 
drifts could also occur through collection of the film flow on the local 
minima of surface roughness features along the crown of the drift. 
TSPA Disposition: Included 
The potential impact of film flow is represented in the SCM results that 
are used for the seepage abstraction; i.e., in the calibrated values of the 
capillary-strength parameter given in Table 6.6-1 (DTN 
LB0302SCMREV02.002 [162273]). If water originating from film flow 
seeps into the opening during a liquid-release test, it is reflected in the 
corresponding seepage data point used for model calibration, i.e., film 
flow is automatically accounted for in the estimated seepage-related 
capillary-strength parameter from the SCM, and thus in the prediction of 
seepage into waste emplacement drifts (Section 6.4.1.1). Note that in 
theory, film flow behavior may be influenced by the elevated 
temperatures in the drift vicinity during the first several thousand years 
after emplacement. This effect is not included in the ambient liquidrelease 
tests. However, the potential changes in film flow as a result of 
temperature increase are not expected to be significant for drift 
seepage. 
2.2.07.20.0A Flow diversion 
around 
repository 
drifts 
BSC (2003 [162267]) 
BSC (2003 [163226]) 
BSC (2003 [161530]) 
Flow in unsaturated rock tends to be diverted by openings such as 
waste emplacement drifts due to the effects of capillary forces. The 
resulting diversion of flow could have an effect on seepage into the 
repository. Flow diversion around the drift openings could also lead to 
the development of a zone of lower flow rates and low saturation 
beneath the drift, known as the drift shadow (see FEP 2.2.07.21.0A). 
TSPA Disposition: Included 
The impact of flow diversion around the drifts and its relevance for 
seepage is explicitly captured in the seepage process models used for 
the seepage abstraction (see Sections 6.4.1, 6.4.2, and 6.4.3). From 
these model simulations, seepage predictions are available in form of 
look-up tables in DTNs LB0304SMDCREV2.002 [163687] and 
LB0307SEEPDRCL.002 [164337]. These will be used in the TSPA-LA 
to calculate ambient seepage, by sampling parameter cases of 
seepage-relevant parameters from the probability distributions that are 
defined in Section 6.8.1 of this Model Report. These seepage-relevant 
parameters are the effective capillary-strength parameter permeability, 
and local percolation flux. During the thermal period, the ambient rates 
will be adjusted based on the TH modeling results from DTN 
LB0301DSCPTHSM.002 [163689], using the abstraction methodology 
developed in Section 6.5.2.1. Note that FEP 2.2.07.21.0A, regarding 
the drift shadow zone, is not discussed in this report.

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Table 6.2-1. FEPs Addressed in This Model Report (Continued) 
FEP No. FEP Name Upstream Reports 
Used for FEP 
Disposition 
FEP Description (from LA FEP List) 
Summary Disposition for TSPA Seepage Evaluation 
2.2.10.04.0A Thermomechanical 
stresses alter 
characteristics 
of fractures 
near 
repository 
BSC (2003 [162318]) 
BSC (2003 [163226]) 
Heat from the waste causes thermal expansion of the surrounding rock, 
generating changes in the stress field that may change the fracture 
properties (both hydrologic and mechanical) of fractures in the rock. 
Cooling following the peak thermal period will also change the stress 
field, further affecting fracture properties near the repository. 
TSPA Disposition: Excluded 
The effect of thermal stresses on fracture properties in the drift vicinity 
is explicitly simulated with a coupled THM Model (Section 6.4.4.1). The 
impact of these stress-induced alterations on seepage is rather small, 
however, as described in Section 6.4.4.1. In fact, the seepage rates 
obtained from the SMPA model that neglects thermal expansion are 
slightly higher than those with a THM model (Figure 6.4-17). For 
reasons given in Section 6.5.1.4, effects of THM parameter changes on 
seepage are therefore neglected in the seepage abstraction, leading to 
a slight overestimation of the seepage rates. 
2.2.10.10.0A Two-phase 
buoyant flow / 
heat pipes 
BSC (2003 [161530]) Heat from waste generates two-phase buoyant flow. The vapor phase 
(water vapor) escapes from the mountain. A heat pipe consists of a 
system for transferring energy between a hot and a cold region (source 
and sink respectively), using the heat of vaporization and movement of 
the vapor as the transfer mechanism. Two-phase circulation continues 
until the heat source is too weak to provide the thermal gradients 
required to drive it. Alteration of the rock adjacent to the drift may 
include dissolution, which maintains the permeability necessary to 
support the circulation (as inferred for some geothermal systems). 
TSPA Disposition: Included 
The coupled processes causing heat-pipe behavior (Section 6.3.2) are 
explicitly simulated with the TH Seepage Model that feeds into the 
seepage abstraction. Using this model, the impact of heat-pipe 
behavior on seepage is assessed for various simulation cases (Section 
6.4.3.3). Thus, the TH modeling results from DTN 
LB0301DSCPTHSM.002 [163689] automatically include the effect of 
heat pipes. As discussed in Section 6.5.2.1, the abstraction of thermalseepage 
utilizes these modeling results to develop an appropriate 
thermal seepage abstraction methodology. 
2.2.10.12.0A Geosphere 
dry-out due to 
waste heat 
BSC (2003 [161530]) Repository heat evaporates water from the UZ rocks near the drifts as 
the temperature exceeds the vaporization temperature. This zone of 
reduced water content (reduced saturation) migrates outward during 
the heating phase (about the first 1,000 years) and then migrates back 
to the containers as heat diffuses throughout the mountain and the 
radioactive sources decay. 
TSPA Disposition: Included 
The coupled processes of vaporization, dryout, and resaturation are 
explicitly simulated with the TH Seepage Model that feeds into the 
seepage abstraction. Using this model, the impact of such coupled 
processes on seepage is assessed for various simulation cases 
(Section 6.4.3.3). Thus, the TH modeling results from DTN 
LB0301DSCPTHSM.002 [163689] automatically include these effects. 
As discussed in Section 6.5.2.1, the abstraction of thermal seepage 
utilizes these modeling results to develop an appropriate thermalseepage 
abstraction methodology.

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6.3 SEEPAGE PHENOMENA AND IMPORTANT FACTORS FOR SEEPAGE 
To help explain seepage phenomena in unsaturated fractured tuff and to identify the factors 
affecting seepage, this Model Report describes the basic processes involved. This description is 
based on and consistent with the related discussion found in the scientific literature (see, for 
example, Philip et al. (1989 [105743]); Finsterle (2000 [151875]) and references therein for the 
capillary barrier effect; Pruess et al. (1990 [100819]) and Birkholzer and Tsang (2000 [154608]) 
for TH processes). The sections below distinguish between seepage at ambient conditions 
(Section 6.3.1) and seepage at thermal conditions (Section 6.3.2). In the framework of seepage 
modeling, ambient conditions refer to the time period where the flow and seepage processes in 
the drift vicinity are not significantly influenced by thermal effects; i.e., the period that rock 
temperature is above boiling has ended and the local flow and saturation perturbations have 
mostly equilibrated. Thermal conditions, on the other hand, are defined as conditions where 
the flow and seepage processes are strongly perturbed in the drift vicinity, because boiling of 
water either occurs or has just recently occurred (BSC 2003 [161530], Section 6.1). Thermal 
seepage refers to seepage during the time period that thermal conditions prevail. Typically, the 
time period with thermal conditions may cover the first 1,000 to 2,000 years after waste 
emplacement. Afterwards, rock temperatures are still higher than ambient as a result of the slow 
heat transfer processes and the slow decay of the radioactive waste, but the local flow conditions 
are essentially back to ambient; i.e., the fractures and the rock matrix at the drift wall have 
saturation levels similar to the ambient state. These conditions can be treated as ambient in the 
seepage evaluation (see Section 6.5). Additional factors for seepage (e.g., stemming from drift 
degradation and ground support), are briefly discussed in Section 6.3.3. 
6.3.1 Seepage Under Ambient Conditions 
Underground openings in unsaturated rock have a tendency to divert water around them because 
of the capillary barrier effect at the interface between the rock and the opening, which retains the 
wetting fluid in the pore space. As a result, a significant fraction of the water that percolates 
down the Yucca Mountain UZ can be prevented from seeping into the emplacement drifts. The 
barrier effect leads to a local saturation build-up and the development of a boundary layer in the 
formation immediately adjacent to the drift. If capillarity and tangential permeability of the 
fracture network within this boundary layer are sufficiently high, all or a portion of the water is 
diverted around the drift under partially saturated conditions. Locally, however, the water 
potential in the formation may be higher than that in the drift, and then water exits the formation 
and enters the drift. At the drift surface, the water either evaporates, or follows the inclined, 
rough wall in a thin film, or forms a drop that grows and eventually detaches (Or and Ghezzehei 
2000 [144773]). Only this last mechanism is considered drift seepage according to the definition 
in Section 6.1.2. 
For an emplacement drift of given shape, the seepage thresholdand the amount of seepage 
once this threshold is exceededdepend on the local flow conditions in the drift vicinity (Figure 
6.3-1). These are mainly influenced by the local percolation flux reaching the drift and by the 
rock properties in the immediate vicinity of the opening. This is because the capillary barrier 
effect occurs in a relatively small region around the opening, with the thickness of this layer 
approximately given by the height to which the water rises on account of capillarity. (This height 
can be estimated by dividing the capillary-strength parameter 1/a by water density and

Abstraction of Drift Seepage U0120 
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gravitational acceleration.) The key rock properties for seepage are (1) the capillary strength and 
(2) the tangential conductivity in the boundary layer near the drift wall. Geological formations 
with strong capillarity and high tangential conductivity exhibit a high seepage threshold (i.e., low 
seepage), whereas a weak capillary barrier effect (i.e., high seepage) is expected if water 
retention is small or if the tangential permeability is insufficient to promote flow diversion. Thus, 
evaluation and description of these local rock properties is essential for seepage calculations. 
Because the matrix permeability is small, the tangential conductivity is governed by the 
properties of the fracture network. For flow diversion to occur, the fracture system must have 
sufficient connectivity and permeability to provide the necessary effective conductivity in the 
tangential direction around the drift. Small-scale heterogeneity increases the probability of 
locally breaching the capillary barrier, because it promotes the existence of flow channeling and 
local ponding (Birkholzer et al. 1999 [105170]). 
In addition to the considerable heterogeneity of seepage-relevant properties that needs to be 
described in appropriate spatial detail, alterations of these properties may occur with time. 
Initially, the undisturbed rock properties around the openings are likely to be altered as a result 
of stress redistribution during drift excavation, which typically leads to local opening of fractures 
and potentially the creation of new microfractures in the drift vicinity (BSC 2001 [158463], 
Section 6.1.2.2; Wang and Elsworth 1999 [104366], pp. 752756). These changes affect 
porosity, permeability, and capillarity of the fracture system in the vicinity of emplacement 
drifts. Later, the heat emanating from the radioactive material will induce thermal-hydrologicalmechanical 
(THM) and thermal-hydrological-chemical (THC) changes (see Section 6.3.2 
below). For example, thermal expansion of the rock matrix induces changes in fracture apertures. 
Also, thermal effects lead to dissolution and precipitation of minerals, again affecting the 
porosity, permeability, and capillarity of the fracture system as well as fracture-matrix 
interaction. The impact of these alterations needs to be assessed in seepage abstraction. 
Another key factor for seepage is the percolation flux arriving at the drifts. Seepage is initiated if 
the local percolation fluxes in individual flow channels and their accumulation near the drift 
ceiling exceed the diversion capacity of the capillary barrier. The fate of water percolating 
through the Yucca Mountain UZ and eventually encountering the immediate vicinity of a waste 
emplacement drift is influenced by various factors. The ultimate source of percolation flux at 
Yucca Mountain is net infiltration at the ground surface, stemming from precipitation events. Net 
infiltration is the fraction of precipitation that moves through the ground surface to a depth where 
the liquid water can no longer be removed by evaporation or transpiration. Net infiltration varies 
in space (as a result of several factors such as vegetation, morphology, and soil and bedrock 
conditions) and in time. Time variations are short-term as a result of daily or seasonal 
fluctuations and long-term as a result of climate changes. As infiltrating water percolates through 
the UZ, driven by gravity and capillary forces, the initial infiltration and flow patterns will 
change depending on the hydrogeologic properties in the UZ and their related heterogeneities. 
On a large scale, several stratigraphic units of volcanic rock can be distinguished at Yucca 
Mountain, with significant differences in fracture frequency, matrix porosity, and lithophysal 
characteristics. Variations between units reflect the type of volcanic eruption, the rate of cooling, 
and the intensity of postdepositional processes. Tilted contacts between hydrogeologic units 
(especially between welded and nonwelded tuffs) can divert flow laterally. Welded units 
typically have lower matrix porosities and high fracture densities, whereas the nonwelded and 
bedded tuffs have relatively high matrix porosities and lower fracture densities (Bodvarsson et al.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 55 August 2003 
1999 [120055], Section 2). These nonwelded units can dampen short-term infiltration patterns, 
homogenizing the unsaturated flow below them. 
The repository would be located about 300 m below the surface in the Topopah Spring welded 
unit (TSw). Unsaturated flow in the TSw is primarily in the fractures, because the small matrix 
permeability in many of the TSw subunits can support flows of only a few millimeters per year. 
Several subunits are defined in the TSw based on the different degrees of lithophysal content. 
According to BSC (2003 [163938], Attachment VIII), where the current repository design was 
analyzed, the Topopah Spring Tuff lower lithophysal unit (Tptpll) is by far the most important 
unit with respect to the part of the repository located in geological units (about 81%), followed 
by the Topopah Spring middle nonlithophysal unit (Tptpmn) (about 12%). Less important are 
two additional geological units, the Topopah Spring Tuff upper lithophysal unit (Tptpul) and 
Topopah Spring Tuff lower nonlithophysal unit (Tptpln). 
On an intermediate scale, there is also considerable heterogeneity within stratigraphic units. This 
kind of heterogeneity can focus water towards one drift location while diverting it away from 
another. Flow focusing and diversion of flow paths also happens within a rough-walled fracture, 
where asperity contacts and locally larger fracture openings lead to small-scale redistribution of 
water within the fracture. In addition, asperity-induced flow instabilities may cause small-scale 
episodic flow within fractures, leading to high-frequency fluctuations. All the above factors 
determining the spatial and temporal variation of local percolation flux need to be considered in 
seepage calculations. 
The overall drift size and geometry may also impact the seepage threshold and the seepage 
amount in the unsaturated fractured rock. As demonstrated by Philip et al. (1989 [105743]) for 
homogeneous media, a large drift exhibits a significantly lower seepage threshold because more 
water accumulates in the boundary layer as it migrates over a longer diversion distance around 
the wide opening. Also shown was that the effectiveness of a capillary barrier is highest if the 
shape of the cavity follows an equipotential surface. Parabolic cavities are more efficient in 
preventing seepage than circular or flat-roofed openings. Small-scale surface roughness on the 
drift wall tends to increase seepage because it may encourage drop detachment.

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Source: Revised from CRWMS M&O (2000 [151940], Figure 3.9-1) 
Figure 6.3-1. Schematic of Phenomena and Processes Affecting Drift Seepage 
It is possible that the initially circular-shaped open emplacement drifts degrade with time as a 
result of thermal stress, seismic ground motion, and time-dependent degradation of rock strength. 
Thermal stresses are caused by the heat generated from the decaying nuclear waste. Significant 
stresses can also be caused by seismically related ground motion. Time-dependent degradation of 
rock strength (joint mechanical properties) may be a result of over-stressing from thermal 
heating and of static fatigue of the rock resulting from stress corrosion mechanisms. All these 
effects may lead to rock mass damage and rock fall in emplacement drifts, changing the drift 
shape and size. Depending on the type of rock, the stress conditions, and the time-dependent rock 
mechanical properties, damage to the drifts may be rather small, with local rock fall at the ceiling 
of otherwise intact drift openings, or, in extreme cases, may result in partial or complete drift 
collapse, with rubble rock material filling the enlarged drifts. These changes affect the potential 
for drift seepage. Local breakouts in the drift ceiling may lead to geometry changes (e.g., 
topographic lows) which can reduce or prevent flow diversion around the opening. The larger 
size and potentially different shape of collapsed drifts can also reduce the potential for flow 
diversion; furthermore, the larger footprint of the collapsed drift leads to an increase in the total 
amount of percolation flux arriving at the drifts, which in turn can affect the total amount of 
seepage. In addition, the capillary-barrier behavior at the drift wall may be affected by the rubble 
rock particles filling the opening, as the capillary strength inside the opening will be different 
from the zero capillarity condition in the initially open drift. Finally, drift degradation may lead 
to fracture dilation or the generation of new fractures in the vicinity of emplacement drifts. 
Fracture dilation would increase the permeability, thereby promoting flow diversion around the

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drift, but at the same time decrease the fracture capillary strength, which could lead to less flow 
diversion around the drift. The generation of new fractures in the drift vicinitywith apertures 
comparable to the existing fractureswould promote flow diversion around the drift opening 
because of the related increase in fracture permeability, but would not affect the fracture 
capillary strength. 
Another factor potentially affecting seepage is the ground support in the emplacement drifts. In 
previous repository designs, the main method of ground support for emplacement drifts at Yucca 
Mountain was grouted rock bolts (BSC 2001 [155187]). Rock bolts are steel rods emplaced into 
a borehole normal to the drift wall (BSC 2001 [155187], Section 6.5.1.2.2). While the new LA 
design is not finalized, it is expected that the updated drift ground support system will use about 
3 m long rock bolts without grout (BSC 2003 [164101]). Both main repository units, the Tptpll 
and Tptpmn, will need ground support. There may be up to 10 such bolts in a drift cross section, 
at a spacing of about 1 m along the drifts. Rock bolts pose a concern with respect to seepage 
because they may provide a direct flow conduit to the drift wall and may increase the likelihood 
of seepage into drifts. 
6.3.2 Thermal Seepage 
The heat generated by the decay of the radioactive waste results in rock temperatures elevated 
from ambient for thousands of years after emplacement. For the current repository design, these 
temperatures will be high enough to cause boiling conditions in the drift vicinity, giving rise to 
local water redistribution and altered flow paths. Key TH processes occurring around a drift are 
shown schematically in Figure 6.3-2, for an idealized circular-shaped drift. The figure indicates 
that heating of the rock causes pore water in the rock matrix to boil and vaporize. The vapor 
moves away from the drift through the permeable fracture network, driven primarily by a 
pressure increase caused by boiling. In cooler regions away from the drift, the vapor condenses 
in the fractures, where it can drain either toward the heat source from above or shed around the 
drift into the zone below the heat source. Condensed water can also imbibe from fractures into 
the matrix, leading to increased liquid saturation in the rock matrix. With continuous heating, a 
superheated dryout zone may develop closest to the heat source, separated from the condensation 
zone by a nearly isothermal zone maintained at about the boiling temperature. This nearly 
isothermal zone is characterized by a continuous process of boiling, vapor transport, 
condensation, and migration of water back towards the heat source (either by capillary forces or 
gravity drainage), and is often referred to as a heat pipe (Pruess et al. 1990 [100819]). 
For the current repository design at Yucca Mountain, the dryout zone around drifts will extend to 
a maximum distance of approximately 5 to 10 meters from the drift wall (BSC 2003 [161530], 
Section 6.2). Boiling conditions in the rock are expected to exist for about 1,000 years after 
emplacement. While these values reflect average conditions, there may be significant 
heterogeneity of the TH conditions within the repository. One factor causing heterogeneity is the 
spatial and temporal variability of the thermal load in different drift sections, stemming from 
heat output variation between individual waste packages and emplacement-time differences. 
Another factor is the variability of the formation properties and the local percolation fluxes. 
Thermal rock properties such as thermal conductivity directly affect the conductive transport of 
heat. Hydrological properties and local percolation fluxes, on the other hand, affect the 
significance of TH coupling as they determine the effectiveness of convective heat transport.

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While heat conduction is the major component of energy transport in Yucca Mountain tuff, the 
impact of TH coupling can be quite large. For example, a large percolation flux above a drift 
segment, combined with relatively high permeability, may cause strong heat-pipe effects that 
give rise to rock temperatures much lower and boiling periods much shorter than at average 
conditions (BSC 2003 [161530], Section 6.2.1.4). 
Heating of the rock in the drift vicinity can affect seepage in two different ways: 
(1) Boiling of rock water will directly affect the seepage-relevant flow processes close to 
the drift wall. For above-boiling conditions, vaporization of percolating water in the 
fractured rock overlying the repository provides an additional barrier to seepage. 
Percolating water is expected to boil off in the superheated rock zone before reaching 
the drift crown. Therefore, thermal seepage is unlikely as long as boiling conditions 
exist. On the other hand, condensed water forms a zone of elevated water saturation 
above the rock dryout zone. Water from this zone may be mobilized to flow rapidly 
down towards the drift, in particular during later heating stages when the effect of 
vaporization has already diminished. This flow mobilization may promote seepage. 
(2) Rock properties relevant for seepage may be affected by reversible and irreversible 
THM and THC effects. Stress-induced THM changes tend to close existing fractures, 
leading to a general decrease in fracture permeability and porosity, combined with an 
increase in fracture capillary strength. Aperture changes that occur during the thermal 
period could be fully reversible, meaning that these properties would recover to preemplacement 
conditions after the temperature has declined to ambient. Thermal stresses 
could also induce permeability enhancement through fracture shear slip with 
accompanying shear dilation opening. Such permeability changes would be irreversible 
and remain after the temperature has declined to ambient. THC processes such as 
mineral precipitation and dissolution in fractures and matrix also have the potential for 
modifying permeability, porosity, and capillary strength of the system. Because the 
molar volumes of minerals created by hydrolysis reactions (i.e., anhydrous phases, such 
as feldspars, reacting with aqueous fluids to form hydrous minerals, such as zeolites or 
clays) are commonly larger than the molar volumes of the primary reactant minerals, 
dissolution-precipitation reactions commonly lead to porosity (and permeability) 
reductions. The extent of mineral-water reaction is controlled by the surface areas of 
the mineral phases in contact with the aqueous fluid, as well as heterogeneity in the 
initial distribution of minerals in the fractures. Therefore, changes in porosity and 
permeability caused by these processes may also be heterogeneously distributed. 
Typically, THC effects on hydrological properties are irreversible. 
Note that during the first 50 years after emplacement, the repository drifts will be ventilated to 
allow access. Ventilation in the drifts will cause initial rock drying in the drift vicinity and will 
also remove a significant amount of heat during this preclosure period.

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Waste 
Package 
Drip Shield 
Waste 
Package 
Drip Shield 
Vapor Transport 
Liquid 
Water 
Flow 
Vapor Transport 
Heat 
Transfer 
Heat 
Transfer 
Drift-Scale 
Condensation 
Zone 
Dryout Zone 
Heat-pipe 
Zone 
Heat-pipe 
Zone 
Source: Revised from BSC (2003 [161530], Figure 6.1-1) 
Figure 6.3-2. Schematic of TH Processes Occurring in the Drift Vicinity as a Result of Repository Heating 
As mentioned in Section 6.3.1, emplacement drifts may collapse in extreme cases as a result of 
thermal and seismic stresses and degradation of joint mechanical properties. The thermal 
conditions in a collapsed drift will be different from those in an open drift, mainly because the 
thermal-hydrological processes in a drift filled with rubbly rock fragments are different from 
those in an open, gas-filled drift. The extent to which these differences can be important for 
thermal seepage is governed by the time at which the drift collapse occurs. Significant 
differences should only be expected when drift collapse occurs during the time period of strongly 
elevated temperatures.

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6.4 PROCESS MODELS PROVIDING INPUT TO SEEPAGE ABSTRACTION 
Seepage abstraction uses modeling results from several upstream drift-scale process models. 
These process models can be categorized into (1) models that directly calculate seepage rates for 
a given set of seepage-relevant properties and boundary conditions, and (2) supporting models 
that provide information about the potential alteration of relevant properties as a result of THM 
and THC processes. These upstream models are introduced in the following Sections 6.4.1 
through 6.4.3 (seepage process models) and 6.4.4 (supporting models), and relevant model 
results are provided. Note that, if not otherwise mentioned, the modeling studies below have 
been conducted assuming open drifts that have not been affected by drift degradation or collapse 
(non-degraded drifts). 
6.4.1 Seepage Calibration Model 
The Seepage Calibration Model (SCM) provides the conceptual basis for modeling of ambient 
seepage processes (BSC 2003 [162267], Section 6.3). It also provides estimates of the seepagerelevant 
capillary-strength parameter through calibration of the model against seepage data 
obtained from in situ liquid-release tests. 
6.4.1.1 Model Description 
The key element of the approach chosen to simulate seepage and determine seepage-relevant 
parameters is to treat seepage as a stochastic process while relying on inverse modeling for the 
estimation of relevant parameters. Given the complexity of the seepage process in a fractured 
porous medium, it was recognized that (1) a detailed deterministic simulation of individual seeps 
is not necessary to estimate average seepage rates into waste emplacement drifts, (2) the impact 
of certain second-order factors affecting seepage can be lumped into an effective parameter, 
making explicit consideration of such factors in the model unnecessary, and (3) calibrating and 
validating the model against data from seepage experiments ensures that the model captures the 
relevant processes for ambient seepage (BSC 2003 [162267], Section 6.3.4). Based on these 
considerations, the SCM was developed as a three-dimensional (3-D) drift-scale calibration 
model that describes the small-scale heterogeneity of the fractured rock using a stochastic 
continuum representation. 
As explained in BSC (2003 [162267], Section 6), the SCM model domain includes appropriate 
rock portions above and sideways of niches in the Exploratory Studies Facility (ESF) and the 
ECRB (a few meters of rock from the niche walls) where liquid-release tests have been 
conducted (see Figure 6.4-1). Different meshes have been generated representing the different 
niches and their respective shapes and test geometries in the Tptpmn and the Tptpll. The smallscale 
fracture permeability variation in each test location was described using spatially correlated 
stochastic fields based on analysis of air-permeability data measured at that location. These 
measurements were taken in close proximity to the drifts after drift excavation; thus the 
generated stochastic fields represent the impact of stress redistribution on permeability. The 
resolution of the stochastic fields is 0.1 m uniform in each direction, slightly smaller than the 
measurement resolution of the air-injection tests (1-foot intervals). Multiple realizations of the 
permeability fields were generated and used for the modeling analysis.

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No attempt was made in the SCM to describe the excavation-induced alterations of rock 
properties in spatial detail. In general, the impact of excavation is largest immediately at the 
opening, and decreases with increasing distance into the rock. However, the extent of the zone 
affected by alterations (e.g., approximately one drift diameter) is larger than the rock portion 
affected by the liquid release above the niches, which approximately corresponds to the extent of 
the model domain. This zone is also much larger than the rather small boundary layer at the drift 
wall that is important for seepage-related flow processes. It is therefore appropriate for seepage 
models to represent the impact of excavation within the model domain with averaged 
excavation-disturbed properties. Thus, the stochastic permeability fields used in the SCM have a 
uniform mean permeability, independent of the distance to the opening; similarly, the calibrated 
capillary-strength parameter is uniform in the model domain. 
Applying the imposed liquid-release rates of each test, inverse modeling runs were conducted 
with the SCM to calibrate the effective capillary-strength parameter of the fractured rock in the 
niche vicinity. Seepage data from multiple test events in one borehole, using different liquidrelease 
rates, were calibrated simultaneously in the inverse modeling approach. Only long-term 
seepage experiments were considered, where near-steady seepage rates are no longer affected by 
storage effects. Therefore, because seepage processes are mainly determined by the properties of 
the fracture system, the SCM is a single continuum model that does not explicitly account for 
imbibition and flow in the rock matrix. Note that evaporation effects, stemming from forced 
ventilation in the Enhanced Characterization of Repository Block (ECRB) cross drift, were 
explicitly accounted for in the model (BSC 2003 [162267], Section 6.6.1.3). 
x [m] 
16 
17 
2
3
4
5
6 
-3 
-2 
-1 
0 
16 
17 
y 
[m] 
2
3
4
5
6
z [m] 
1 
2 
3 
-14 -13 -12 -11 -10 -9 -8 
log(k [m2]) 
Source: BSC (2003 [162267], Figure 15a) 
NOTE: In this visualization, the mesh is split into two parts to expose the boreholes (indicated by 
thick black lines) and the injection interval (thick white line). 
Figure 6.4-1. Example of Numerical Grid and Permeability Distribution used for the SCM Simulation of 
Liquid-Release Tests Conducted in Niche 1620, Showing Injection into Borehole #4

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The calibrated capillary-strength parameter 1/a provided by the SCM is an effective process 
parameter for seepage that is specifically determined for its intended use in drift seepage models. 
This effective parameter not only represents the average capillary characteristics of the fracture 
network, but also accounts for seepage factors that are not explicitly implemented in the 
conceptual model (BSC 2003 [162267], Section 6.3.3). Such processes include effects from (1) 
small-scale roughness within individual fractures, potentially leading to preferential flow and/or 
high-episodicity flow, (2) individual fractures cutting into the opening, (3) small-scale surface 
roughness at the drift ceiling, (4) film flow within fractures and along the drift surface, (5) drop 
formation and detachment, and (6) fracture-aperture changes as a result of excavation effects. 
For the Tptpll unit, the effect of lithophysal cavities is also captured in the effective capillarystrength 
parameter, making the explicit representation of lithophysal cavities into the process 
model unnecessary. While modeling these factors is theoretically possible, the necessary 
characterization data needed to warrant such a detailed simulation are not available. As discussed 
in BSC (2003 [162267], Section 6.3.2), the calibrated capillary-strength parameter is not 
correlated to the local permeability. 
6.4.1.2 Model Validation 
The SCM model was validated in comparison with measured data from liquid release tests (BSC 
2003 [162267], Section 7). Observed and predicted seepage data were compared for various tests 
that had not been used for the calibration of the model. It was demonstrated that the calibrated 
model was capable of reproducing the measured seepage data. As summarized in BSC (2003 
[162267], Section 7.4), the seepage observations (1) fell within the range of pre-test predictions 
of seepage rates in all test cases for the lower lithophysal zone, and in almost all test cases for the 
middle nonlithophysal zone, or (2) were lower than the predicted seepage rates in a few cases, 
i.e., the model prediction was conservative. In addition, alternative conceptual models for 
seepage prediction at Yucca Mountain were qualitatively evaluated. The most important 
alternative conceptual model is a model that simulates flow through discrete fractures rather than 
through a stochastic continuum. This alternative, referred to as the discrete fracture network 
model (DFNM), is discussed in great detail in Finsterle (2000 [151875]), and in lesser detail in 
BSC (2003 [162267], Section 6.4) and BSC (2003 [161530], Section 6.3.5). It has been 
concluded that the full development of a DFNM as a potential alternative to the stochastic 
continuum model is not feasible and not necessary, for the following reasons: 
(1) A continuum representation of unsaturated fracture flow is appropriate when the 
fracture density is high and a well-connected fracture network forms at the scale of 
interest. As evidenced in fracture mapping data from the ESF and the ECRB, the 
repository units at Yucca Mountain have a high fracture density, and these fractures 
form a well-connected 3-D system at the relevant scale (BSC 2003 [163226], Section 
6.3). 
(2) The development of a defensible DFNM requires collecting a very large amount of 
geometric and hydrological data. The databases required to develop a defensible 
DFNM are currently not available and are generally difficult or even impossible to 
obtain for site-specific simulations. As a result, the cumulative effect of all the input 
uncertainties is likely to outweigh the apparent advantage of a detailed representation of

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 63 August 2003 
the fracture network. To reduce prediction uncertainties, the DFNM must be calibrated 
against hydrogeologic dataan approach very similar to that used in the SCM. 
(3) Seepage calculations with a calibrated DFNM are likely to corroborate the results of a 
calibrated stochastic continuum model. For example, Finsterle (2000 [151875]) used 
synthetically generated data from a model that exhibits discrete flow and seepage 
behavior to calibrate a simplified fracture continuum model. The calibrated continuum 
model was used to predict seepage rates. The extrapolated seepage predictions 
performed with the continuum model were consistent with the synthetically generated 
data from the discrete-feature model. 
Three other possible alternative approaches for seepage estimation are discussed in BSC (2003 
[162267], Section 6.4): estimating seepage from the local ponding probability (Birkholzer et al. 
1999 [105170]), estimating seepage from deposition rates of calcite and opal in lithophysal 
cavities, and estimating the seepage threshold directly from the liquid-release tests. These 
approaches are not carried further for the reasons presented in BSC (2003 [162267], Section 6.4). 
Nevertheless, they corroborate the general concept and the findings of the SCM. 
Natural analogues as those reported in TDR-NBS-GS-000027 REV 00 ICN 02, Natural 
Analogue Synthesis Report (BSC 2002 [160405], Section 8) provide additional evidence that the 
concept of seepage exclusion describes a process that actually occurs in caves, lava tubes, rock 
shelters, and buildings. Quantitative seepage measurements from limestone caves in a fractured 
karst terrain have demonstrated that seepage is considerably smaller than the pertinent 
percolation flux, corroborating the seepage testing and modeling results at Yucca Mountain 
(BSC 2003 [162267], Section 7.2.1). Precipitation at these cave locations exceeds current rates at 
Yucca Mountain and those predicted for future climates as well. In addition, various other 
natural analogue sites provide qualitative evidence that most of the infiltrating water is diverted 
around underground openings and does not become seepage. 
It was concluded that the SCM provides a solid conceptual basis and sufficient characterization 
data for predicting seepage into waste emplacement drifts in the repository host rock (BSC 2003 
[162267], Section 8). Specific recommendations were provided concerning the use of the 
conceptual model in predictive seepage models such as the SMPA and the TH Seepage Model. 
These recommendations are as follows (BSC 2003 [162267], Section 8.4): 
(1) Seepage predictions should be conducted with a process model similar to the SCM, 
capable of simulating unsaturated flow under viscous, capillary pressure, and 
gravitational forces. 
(2) A stochastic continuum model should be employed that captures the small-scale 
heterogeneity of permeability on a spatial resolution similar to the SCM. 
(3) The permeability values should capture the impact of excavation effects in the vicinity 
of the emplacement drifts. 
(4) The calibrated capillary-strength parameters derived from the SCM should be used for 
the fractured tuff in the vicinity of emplacement drifts.

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MDL-NBS-HS-000019 REV00 64 August 2003 
(5) Several second-order factors are lumped into the effective capillary-strength parameter. 
These factors should not be explicitly considered in the model. (For example, smallscale 
roughness of the drift wall or lithophysal cavities should not be explicitly 
discretized.) 
(6) A specific boundary condition should be employed at the rock-drift interface, similar to 
the one chosen in the SCM. 
(7) Multiple prediction runs with different realizations of the underlying heterogeneous 
permeability field should be performed. 
6.4.1.3 Model Results 
The calibrated values of the effective capillary-strength parameter provided by the SCM are 
directly used in seepage abstraction. A total of 22 liquid release tests conducted in ten test 
intervals at different locations along the ESF and the ECRB were used in the SCM model 
calibration (13 tests conducted in the Tptpll and 9 in the Tptpmn). The resulting capillarystrength 
parameter values and their associated estimation uncertainty, provided in DTN: 
LB0302SCMREV02.002 [162273], are presented and discussed in Section 6.6.4 of BSC (2003 
[162267]). Parameter values range roughly between 400 and 800 Pa. 
Appropriate probability distributions developed in Section 6.6 of this Model Report cover the 
potential spatial variability and uncertainty of the capillary-strength parameter. A detailed 
discussion of the calibrated parameter values and the derived parameter distributions is presented 
in Section 6.6.1. 
6.4.2 Seepage Model for Performance Assessment 
The Seepage Model for Performance Assessment (SMPA) adopts the conceptual framework 
from the SCM to conduct systematic predictions of ambient seepage fluxes into waste 
emplacement drifts (BSC 2003 [163226]). Isothermal flow simulations are performed for 
selected key parameters that vary over wide ranges. Results are provided in the form of a look-up 
table (DTN: LB0304SMDCREV2.002 [163687]), giving seepage rates and related seepage 
estimation uncertainty as a function of these key parameters. Note that appropriate distributions 
describing the spatial variability and uncertainty of these key parameters are developed in 
Section 6.6 of this model report. The simulation cases studied with the SMPA sufficiently cover 
the parameter range defined by these distributions. 
6.4.2.1 Model Description 
Consistent with the SCM, the predictive SMPA is a 3-D drift-scale model applying a stochastic 
continuum representation of the small-scale heterogeneity of fractured rock in the drift vicinity. 
The modeling framework and the processes studied are identical to the SCM; the 
recommendations for predictive seepage modeling as listed in Section 6.4.1.2 are strictly 
followed in the SMPA. The major difference between the two models is their scope. The SCM is 
applied to simulate field test data for calibration and validation purposes, while the SMPA is 
employed to conduct predictive simulations.

Abstraction of Drift Seepage U0120 
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The 3-D model domain of the SMPA is shown in Figure 6.4-2, representing the fractured rock 
adjacent to the upper left half of a circular-shaped, 8 ft long drift segment with diameter 5.5 m 
(BSC 2003 [164069]). (Potential changes in the drift shape as a result of drift degradation are 
considered with adjusted SMPA grids, as discussed in Section 6.4.2.4.) Based on symmetry 
considerations, the numerical mesh was reduced to a half-drift model, increasing the 
computational efficiency of the simulation. (More than 50,000 simulation runs had to be 
conducted to fully cover the required parameter space for TSPA.) The size of the model domain 
and the discretization were selected according to the following criteria: (1) the model domain 
above the drift should be large enough to allow for flow channeling independent of boundary 
effects, (2) the lateral boundary should cover the region where lateral flow diversion is 
important, (3) the mesh resolution should be similar to the SCM, and (4) the simulation runs 
must be computationally efficient. Satisfying these criteria, the upper boundary of the model 
domain was chosen to be at 10 m above the drift axis. The lateral boundary is at 4 m from the 
drift axis. Grid resolution is identical to the SCM in the plane normal to the drift axis, where flow 
diversion is most important (i.e., 0.1 m). Along the drift axis, grid cells are slightly larger at 1- 
foot length (consistent with the SCM grid design of the ECRB). While the side boundaries have 
no-flow conditions assigned to them, a constant flux boundary is imposed at the top of the 
domain. This flux boundary represents the local percolation flux arriving at the considered drift 
segment. 
The key parameters affecting ambient seepage are the effective capillary-strength parameter 1/ a, 
the statistical parameters defining the small-scale stochastic permeability field, and the local 
percolation flux qperc,ff imposed at the upper model boundary. Only these relevant parameters are 
varied in the predictive SMPA simulation runs, while other model parameters are kept constant, 
with their values similar to the parameter choices made in the SCM (BSC 2003 [163226], 
Section 6.3). Note that the SCM modeling framework calls for the use of an effective capillarystrength 
parameter not correlated to the local permeability. Thus, this parameter is taken to be 
uniform within the model domain. 
Similar to the SCM, no attempt was made in the SMPA to describe the excavation-induced 
alterations of rock properties in spatial detail, despite the fact that the extent of the zone affected 
by alterations (e.g., approximately one drift diameter) is smaller than the vertical extent of the 
model area above the drift. Thus, in theory, hydrogeological parameters representing the 
excavation-disturbed zone should be used within the first diameter from the drift, while 
undisturbed rock properties should be applied outside of this region. For simplification, however, 
the SMPA assumes a uniform mean permeability in the entire model domain, representative of 
the excavation-disturbed zone, and also applies a uniform capillary-strength parameter as 
calibrated from the SCM. This is because it is most important for seepage simulations to 
appropriately represent the conditions in the close vicinity of the drifts. Changes in the 
hydrogeological properties further away from the drifts do not significantly affect the seepage 
results. In fact, the seepage rates would most likely be reduced if the undisturbed rock properties 
were explicitly accounted for outside of the 1-diameter region around drifts. For example, the 
capillary-strength parameters representative of the undisturbed fractured continuum in the 
Tptpmn and the Tptpll, determined in BSC (2003 [160240], Table 11), are much larger than the 
effective capillary-strength parameters calibrated with the SCM (by a factor of 10 or more). 
(Note that tsw34 in BSC (2003 [160240], Table 11) corresponds to the Tptpmn unit when using 
the nomenclature of the Geological Framework Model (BSC 2002 [159124]). Similarly, the

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 66 August 2003 
tsw35 corresponds to the Tptpll unit.) Thus, as a result of capillarity effects in the transition zone 
between undisturbed rock and the excavation-disturbed zone, the percolation flux would partially 
be diverted before reaching the vicinity of the drifts (BSC 2003 [161530], Section 6.2.2.1.4). 
0
1
2
3
4
5
6
7
8
9
10
Z (m) 
0 
1 
2 
3 
4 
X 
(m) 
0 
1 
2 
Y 
(m) 
Source: BSC (2003 [163226], Figure 6-1) 
NOTE: The plot shows the center nodes of grid blocks and the grid block 
connections. The point shown at (z = 0 and x = 0) indicates the drift axis. 
Figure 6.4-2. Model Domain and Mesh Design of the SMPA 
The parameters defining the stochastic permeability fields are the mean permeability S, for 
simplification hereafter referred to as k (in log10), the standard deviation sS (in log10) and the 
correlation length .S. While the mean permeability varies over a wide range in the SMPA 
simulation runs, corresponding to the significant variability of this parameter over the repository 
area, the standard deviation and the correlation length of the small-scale permeability fields are 
kept at constant values of sS = 1.0 and .S = 1 foot, respectively. These parameter choices are 
based on post-excavation air-permeability data measured using 1-foot-injection intervals that are 
appropriate to derive small-scale variability. Standard deviations derived from these data range 
from 0.72 to 1.31, while the correlation structure is described as essentially random (BSC 2003 
[162267], Table 10 and Section 6.6.2.1). Sensitivity analyses were conducted to estimate the 
impact of varying the standard deviation and correlation length of the random fields (BSC 2003 
[163226], Section 6.6.2, Figures 6-12 and 6-13). It was demonstrated that even significant 
parameter variation produced seepage rates comparable to the base case (i.e., for a standard

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 67 August 2003 
deviation of 2.0 in log10) or smaller than the base case (i.e., correlation length of 1.0 and 2.0 m). 
This indicates that the selected constant values of standard deviation and correlation length are 
appropriate, and that seepage can be treated as a function of three rather than five key 
parameters. Note that the SCM modeling framework requires the permeability distribution to be 
representative of the excavation-disturbed zone in the drift vicinity. 
The range of percolation flux qperc,ff imposed at the top boundary needs to cover the potential flux 
variability at Yucca Mountain for present and future climate scenarios. Since small-scale flow 
channeling is explicitly modeled within the SMPA, only the spatial variability on a resolution 
equal to or larger than the model domain needs to be considered. As explained in Section 6.6.4, 
appropriate spatial distributions of percolation fluxes as input to the SMPA can be developed 
using results of the UZ Flow and Transport Model (BSC 2003 [163045]) under additional 
consideration of flow focusing effects. These percolation fluxes are considered constant during 
three distinct long-term climate periods, based mainly on daily or seasonal fluctuations in net 
infiltration being effectively dampened in the overlying PTn nonwelded tuff unit (see Section 
6.2, FEP 2.2.07.05.0A, and Section 6.6.4). Therefore, the SMPA model runs are conducted for 
steady-state flow conditions, applying a constant flux condition at the top boundary. 
As was already mentioned in Section 6.3, the local flow conditions will be strongly altered by 
thermal effects during the first 1,000 to 2,000 years after waste emplacement. In a strict 
interpretation, the derived seepage results from the isothermal SMPA should only be applied for 
late time periods when both the rock temperatures and saturations have returned to ambient 
state. However, results from the TH Seepage Model presented in Section 6.4.3 demonstrate that 
ambient seepage rates from the SMPA are reasonably accurate (slightly conservative), provided 
that the rock temperatures have decreased below boiling and that the fractured rock close to the 
drift wall has resaturated. This means that the SMPA results can be used to abstract seepage 
during most of the 10,000-year compliance period. The potential impact of THM and THC 
effects on the applicability of the SMPA is discussed in Section 6.4.4. 
6.4.2.2 Model Validation 
Since the SMPA and the SCM are similar models that differ only in their scope, validation of the 
SMPA has been mainly achieved by comparison with results from the SCM. In other words, 
confidence in the model results was gained by comparison with another model that is validated 
against field data and other observations (BSC 2003 [163226], Section 7.1). The discussion and 
conclusions regarding alternative conceptual approaches in Section 6.4.1.2 apply similarly to the 
SCM and the SMPA. 
6.4.2.3 Model Results: Systematic Study of Ambient Seepage 
The systematic simulations performed by the SMPA cover a wide range of capillary-strength 
values 1/ a (100 Pa to 1,000 Pa in steps of 100 Pa), mean permeability values k (14 to 10 in 
steps of 0.25, given in log10), and percolation flux values qperc,ff (1, 5, 10, 20, 50, 100 through 
1,000 mm/yr in steps of 100 mm/yr). For each combination of the above values, a total of 20 
realizations of the heterogeneous permeability fields were simulated. It was demonstrated that 
the number of 20 realizations was sufficient to ensure sufficiently stable estimates of the 
simulation outputs (BSC 2003 [163226], Section 7.1). Comparison of selected simulation cases

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 68 August 2003 
conducted with 10 vs. 20 realizations indicated differences of 2% or less in the mean seepage 
rates (BSC 2003 [163226], Table 7-1). 
The resulting seepage values, provided in DTN: LB0304SMDCREV2.002 [163687] in the form 
of a look-up table, are presented and discussed in detail in BSC (2003 [163226], Section 6.6.1). 
Seepage rates and seepage percentages are given for a reference drift section of 5.1 m length, 
corresponding to the length of a waste canister plus the 0.1 m spacing between canisters (see 
definitions of seepage rate and percentage in Section 6.1.2). The SMPA results used in 
abstraction are the seepage mean and the standard deviation calculated over the 20 realizations, 
but values from individual realizations are provided as well. To derive a seepage rate for a 
particular set of key parameters 1/a, ks, and qperc,ff analyzed in TSPA, the corresponding seepage 
results need to be interpolated from the seepage values given in the look-up table (DTN: 
LB0304SMDCREV2.002 [163687]). 
Example results from the SMPA are illustrated in Figures 6.4-3 through 6.4-6. The figures give 
contours of the simulated seepage percentage as a function of the capillary-strength parameter 
and the mean fracture permeability (in log10 space), for selected percolation fluxes of 5, 50, 200, 
and 500 mm/yr. Presenting results in the form of seepage percentages (absolute seepage into a 
drift section relative to the amount of percolation over this drift section) is useful because the 
effectiveness of flow diversion is immediately evident. As expected, the seepage percentage is 
large for small capillary strength, small permeability, and large percolation flux. In these cases, 
seepage may be as high as 100%; i.e., there is no flow diversion at the drift wall, and the entire 
fraction of the percolation flux seeps into the drift. In contrast, the seepage percentage is small 
for all cases with strong capillarity, large permeability, and small percolation flux. In many of 
these cases, there is no seepage at all; i.e., the entire percolation flux is diverted around the drift 
by capillary forces, because the percolation flux is below the seepage threshold for the 
parameters given. 
Note that the resulting seepage percentages in the look-up table are identical for simulation cases 
that have the same ratio of percolation flux qperc,ff over permeability given in m2 (e.g., the 
seepage percentages for cases a permeability of 10-13 m2 and qperc,ff = 5 mm/yr are identical to 
cases with a permeability of 10-12 m2 and qperc,ff = 50 mm/yr and cases with a permeability of 
10-11 m2 and qperc,ff = 500 mm/yr; see Figures 6.4-3 through 6.4-5). This is because the steadystate 
capillary pressure and saturation conditions are determined by the ratio of percolation flux 
over permeability. As a result, it would be possible to reduce the number of key parameters for 
ambient seepage, and thus the size of the look-up table. However, to be consistent with previous 
abstractions, the look-up table is not changed for the TSPA-LA. 
It should be recognized that identical seepage percentages for different percolation flux scenarios 
may correspond to vastly different seepage rates. For example, a seepage percentage of 50% at 
1 mm/yr percolation relates to a seepage rate of approximately 14 kg/yr per waste package. At 
500 mm/yr, the same percentage relates to a seepage rate of 7,013 kg/yr per waste package.

Abstraction of Drift Seepage U0120 
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1 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0304SMDCREV2.002 [163687] (using file Fig6-3toFig6-8.dat) 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-3. Mean Seepage Percentage as a Function of Capillary-Strength Parameter and Mean 
Permeability for a Percolation Flux of 5 mm/yr 
1 
1 
5 
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1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0304SMDCREV2.002 [163687] (using file Fig6-3toFig6-8.dat) 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-4. Mean Seepage Percentage as a Function of Capillary-Strength Parameter and Mean 
Permeability for a Percolation Flux of 50 mm/yr

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 70 August 2003 
1 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 
100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0304SMDCREV2.002 [163687] (using file Fig6-3toFig6-8.dat) 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-5. Mean Seepage Percentage as a Function of Capillary-Strength Parameter and Mean 
Permeability for a Percolation Flux of 200 mm/yr 
1 
1 
5 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0304SMDCREV2.002 [163687] (using file Fig6-3toFig6-8.dat) 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-6. Mean Seepage Percentage as a Function of Capillary-Strength Parameter and Mean 
Permeability for a Percolation Flux of 500 mm/yr

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 71 August 2003 
The range of results observed from the 20 realizations provides information about the estimation 
uncertainty in the predicted seepage rates, on account of uncertainty in the stochastic small-scale 
heterogeneity. As shown in Figures 6-9, 6-10, and 6-11 of BSC (2003 [163226]), the differences 
obtained between realizations of the random permeability field can be quite large. Thus, this 
estimation uncertainty should be included in and propagated through seepage abstraction. This 
can be done using appropriate uncertainty distributions defined on the basis of the standard 
deviations provided in DTN: LB0304SMDCREV2.002 [163687]. Note that these standard 
deviations can be different for each simulated parameter combination; typically, the larger the 
derived seepage rate, the larger the associated standard deviation of the seepage rate. It is not 
evident from the discussion in BSC (2003 [163226], Section 6.6.1) which type of uncertainty 
distribution is best suited to represent the observed statistical spread. Therefore, histograms of 
the distribution of seepage percentage over the 20 realizations have been calculated for selected 
parameter cases, chosen to represent cases with small, average, and large seepage (see 
Attachment I). However, evaluation of these histograms did not reveal a consistent trend (see 
examples of histograms in Figure 6.4-7). It is therefore recommended for TSPA to use a simple 
uniform probability distribution to account for the estimation uncertainty of the SMPA results. 
6.4.2.4 Model Results: Impact of Drift Degradation 
Whereas the systematic study of the previous section assumed a circular drift design, additional 
simulation cases were conducted to analyze the potential impact of changes in the drift shape on 
seepage. Such shape changes, a possible result of thermal stress and joint cohesion degradation 
as well as seismic motion, have been evaluated in different revisions of the Model Report Drift 
Degradation Analysis (Revision 01 in BSC (2001 [156304]), Revision 02 in BSC 2003 
[162711]). Revision 01 of the Model Report Drift Degradation Analysis (BSC (2001 [156304]) 
was based on a Discrete Region Key Block Analysis (DRKBA) to determine the potential rock 
fall at the repository horizon. Key blocks are critical rock blocks in the surrounding rock mass of 
an excavation which are removable and oriented in an unsafe manner so that they are likely to 
fall into the opening unless ground support is provided. It was later recognized, however, that the 
DRKBA analysis presented in BSC (2001 [156304]) needed improvement in several areas. For 
example, the DKRBA could not be used to explicitly apply dynamic loads (due to seismic 
ground motion) or thermal stresses. The DRKBA analysis, which determines structurally 
controlled key-block failure, was also not applicable to lithophysal units, where failure is 
essentially stress controlled (BSC 2003 [162711], Section 1.1). Therefore, an improved 
degradation analysis was presented in Revision 02 of the Model Report Drift Degradation 
Analysis (BSC 2003 [162711]), using additional approaches such as two-dimensional and threedimensional 
discontinuum analysis with explicit representation of seismic and thermal loads. 
Results from this revised analysis indicate more drastic drift shape changes in the lithophysal 
rocks compared to the earlier revision, including cases with partial or complete collapse of drifts.

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Simulation Case: 1/ a = 500 Pa, k = -12, qperc,ff = 50 mm/yr 
0 10 20 30 
0
2
4
6
8 
10 
Seepage Percentage [%] 
Number of Realizations 
sd_hist 1 . . 
sd_hist 0 . . 
Simulation Case: 1/ a = 500 Pa, k = -12, qperc,ff = 500 mm/yr 
40 60 80 
0
2
4
6
8 
10 
Seepage Percentage 
Number of Realizations 
sd_hist 1 . . 
sd_hist 0 . . 
Source: LB0304SMDCREV2.002 [163687] (using file Fig6-3toFig6-8.xls, see Attachment I) 
NOTE: The symbols in the histograms (i.e., sd_hist<0> and sd_hist<1>) denote the variable names 
given in the Mathcad 11 spreadsheet used for the calculation. The histogram ranges are 
derived using the largest and the smallest seepage percentage of the 20 realizations of 
each simulation case, and dividing the difference by the bin number of 5. For qperc,ff = 50 
mm/yr, the largest value is 34.8%, the smallest value is 0.4. For qperc,ff = 500 mm/yr, the 
largest value is 86.9%, the smallest value is 28.3%. 
Figure 6.4-7. Example Histograms of Seepage Percentage from 20 Realizations for Two Selected 
Parameter Cases

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 73 August 2003 
Note that the SMPA simulation cases for drift degradation effects reported in BSC (2003 
[163226], Sections 6.4 and 6.6.3) were based on the previous DRKBA results, as the latest 
Revision 02 of the Model Report Drift Degradation Analysis was not yet available (BSC 2003 
[163226], Section 5; [163017]). Since the studied degradation cases are still useful for 
understanding seepage in moderately degraded drifts, the simulated cases are presented and 
discussed in Section 6.4.2.4.1 below. In response to the new drift degradation results obtained in 
Revision 02 of the Model Report Drift Degradation Analysis (BSC 2003 [162711]), additional 
seepage analyses have been conducted. These analyses are documented in Section 6.4.2.4.2. 
6.4.2.4.1 Seepage Analysis for Revision 01 Drift Degradation Results 
The drift degradation analysis reported in Revision 01 of the Model Report Drift Degradation 
Analysis (BSC 2001 [156304]) considered three different levels of seismic events in conjunction 
with thermal effects and a moderate time-dependent decrease in rock strength. The seismic 
events were defined as follows: Level 1, corresponding to a 1,000-year event with a peak ground 
acceleration of 0.14 g, Level 2, corresponding to a 5,000-year event with a peak ground 
acceleration of 0.30 g, and Level 3, corresponding to a 10,000-year event with a peak ground 
acceleration of 0.43 g (BSC 2001 [156304], Section 4.1, Table 4). None of these levels leads to 
significant drift degradation. Typically, the degraded drift profiles showed indication of local 
rock fall of key blocks at the ceiling of the drift, while the drifts remained essentially intact and 
the horizontal extent was not affected. In general, these results were similar for the Tptpmn and 
the Tptpll. 
Selected drift profile predictions from the drift degradation analyses were adopted for the 
seepage analysis conducted with the SMPA (BSC 2003 [163226], Sections 6.4 and 6.6.3). The 
selected profiles were the 75 percentile profile and the worst-case profile of the seismic Level 3 
case for both geological units, as presented in BSC (2001 [156304], Figures 39 and 40, Table 
43). The 75 percentile profile for a particular unit and seismic event indicates that 75% of the 
drift length within that unit will have less (or no) drift profile deterioration. The worst-case 
profile represents the most severely degraded profile of the probabilistic analysis. The SMPA 
seepage simulations used the selected drift profiles and the fall-off rock volumes to construct 3-D 
numerical grids that explicitly represent the predicted changes in drift shape (BSC 2003 
[163226], Section 6.4). On these discretized drift profiles, seepage was calculated with 10 
realizations of the heterogeneous permeability field. Calculations were carried out for both the 
Tptpmn and the Tptpll units. Seepage simulations were conducted for selected parameter cases, 
using a capillary strength of 600 Pa and a percolation flux of 200 mm/yr. The mean permeability 
value used was -11.86 in the Tptpmn unit and -10.84 in the Tptpll unit (in log10). Nodegradation 
results with the same parameter values were also calculated for comparison, to study 
the impact of drift degradation on seepage. 
The simulation results obtained from the SMPA analysis indicate that the effects of drift shape 
changes are limited for all Revision 01 degradation cases. It was demonstrated that, for both 
considered drift profiles and both geological units, the average seepage rates as well as the 
average seepage threshold calculated over the 10 realizations were almost identical to the nodegradation 
cases (BSC 2003 [163226], Section 6.6.3). This result indicates that the impact of 
geometry changes at the drift ceiling as a result of local breakout of key blocks can be neglected, 
as long as the drifts stay essentially intact and the horizontal extent remains mostly unchanged. 
Thus the seepage look-up table derived for the initially circular drift design should be applicable

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 74 August 2003 
for such moderately degraded drifts. However, note that the statistical spread among the 10 
realizations was considerably stronger than in the no-degradation case. 
6.4.2.4.2 Seepage Analysis for Revision 02 Drift Degradation Results 
The improved drift degradation analysis, as reported in Revision 02 of the Model Report Drift 
Degradation Analysis (BSC 2003 [162711]), also considers different levels of seismic events, 
thermal stresses, and time-dependent reduction in rock strength. The seismic events were defined 
by their probabilistic seismic hazard level, giving the annual probability that certain levels of 
ground motion would be exceeded. The three annual hazard levels considered are a 5  10-4 
annual probability of exceedance, a 1  10-6 annual probability of exceedance, and a 1  10-7 
annual probability of exceedance (BSC 2003 [162711], Table 9). These levels correspond to a 
2,000-year event with a peak ground acceleration between 0.16 and 0.19g, to a 1,000,000-year 
event with a peak ground acceleration between 6.86 and 10.46g, and to a 10,000,000-year event 
with a peak ground acceleration between 13.15 and 16.28g, respectively. The latter two seismic 
levels are significantly larger than the ones analyzed in Revision 01 of the drift degradation 
analysis (see Section 6.4.2.4.1). In fact, as pointed out in Section 8.2 of BSC (2003 [162711]), 
some of the ground motions used are larger than the largest ground motions observed and may 
not be realistic. Therefore, the drift degradation predictions for the 1  10-6 and the 1  10-7 
hazard levels can be considered worst-case scenarios. 
The impact of rock strength reduction was analyzed by selected cases using different levels of 
joint-cohesion reduction and tensile-strength reduction. Because no information on the most 
probable values of strength reduction was available, the analysis covered the entire range of 
possible joint cohesion loss, with the two extreme scenarios being the 0% reduction case (i.e., 
present-day rock strength) and the 100% reduction case (complete loss of cohesion strength). 
The other cases assume 20, 40, 60, and 80% reduction in joint cohesion. Since information on 
the cohesion reduction as a function of time was not available either, the cohesion parameters 
were stepwise adjusted and the model was run to an equilibrium state, without considering the 
time scale for cohesion reduction. Therefore, the temporal evolution is not known; only the 
ultimate extent of drift degradation for the different reduction levels is provided by this analysis. 
Results from the drift degradation analysis demonstrate fundamental differences between the 
nonlithophysal and the lithophysal rocks. Drift degradation in the hard, strong, jointed rock of 
the nonlithophysal units is limited to local gravitational drop of rock blocks at the drift ceiling, 
even for the extreme seismic cases and complete loss of joint cohesion (BSC 2003 [162711], 
Figures 101 through 105). The drifts remain intact openings with the horizontal extent essentially 
unchanged, similar to the results obtained in the earlier Revision 01 of the Model Report Drift 
Degradation Analysis (BSC 2001 [156304], compare with profiles in Figures 39 and 40). Thus 
the seepage prediction studies discussed in the previous section are still applicable to the 
nonlithophysal rocks. Thus, in the Tptpmn and the Tptpln, the impact of drift degradation on the 
predicted mean seepage is almost negligible. 
In contrast, significant drift degradation is predicted for the relatively deformable lithophysal 
rock (BSC 2003 [162711], Section 6.4). In this geologic unit, most of the extreme simulation 
cases, among them the 1  10-6 and the 1  10-7 seismic hazard levels and the 80% through 100% 
strength reduction cases, result in complete drift collapse, as discussed in BSC (2003 [162711], 
Sections 6.4.1.1 and 6.4.2.4). How this may affect seepage is discussed in the paragraphs below.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 75 August 2003 
No significant rock fall is predicted for the thermal stress case, the 5  10-4 seismic hazard case, 
and most of the 0% through 60% strength reduction cases (BSC 2003 [162711], Sections 6.4.1.2, 
6.4.1.1, and 6.4.2.4). In these cases, the impact of drift degradation on seepage is expected to be 
small, similar to the nonlithophysal unit. 
During collapse, either sudden or gradual, the rock mass above an underground opening 
disintegrates into a number of fragments that fall down and begin to fill the open space. Because 
there are large voids between the rock fragments, the bulk porosity of the fragmented rubble is 
much larger than the intact rock. As a result, the open space of the original excavation plus the 
collapsed portion of rock above completely fill with rubble at a certain stage. When this occurs, 
the broken rock provides backpressure, which prevents further collapse of the rock mass (BSC 
2003 [162711], Section 6.4.2.1). Therefore, the final situation after drift collapse can be 
categorized as follows: The original opening has increased in size, but is filled with fragmented 
rubble and large voids. The solid wall rock surrounding the rubble-filled opening is intact, but 
may have increased permeability because of the dynamic motion and the stress redistribution 
(see Section 6.4.4.1). For convenience, we refer to the rubble-filled opening as a collapsed 
drift, although technically there is no drift after collapse. The size and the shape of a collapsed 
drift mainly depend on the porosity of the rubble material and on the type of caving mechanism 
as collapse occurs. This explains why the collapsed drift profiles provided in DTN: 
MO0306MWDDPPDR.000 [164736] are all similar, independent of the event leading to collapse 
(i.e., seismic stress or rock fatigue). (Note that these profiles are also depicted in Attachment 
XVIII of BSC (2003 [162711]).) Collapsed drifts are shown for Scenarios 2 through 5, 11, 12, 
17, 18, 23, 24, 28, 29, and 30. All drifts remain approximately circular after collapse. However, 
the diameter of collapsed drifts approximately doubles to about 11 m. 
It was already discussed in Section 6.3.1 that complete drift collapse may lead to significantly 
different seepage behavior. However, even though the collapsed drifts are filled with rubble 
material, capillary barrier effects still give rise to considerable flow diversion at the interface 
between the solid rock and the rubble-filled drift opening. This is because of the large scattered 
voids between the fragmented rock particles (particle sizes on the order of centimeters and 
decimeters (BSC 2003 [162711], Section 8.1)), suggesting that the capillary strength parameter 
in the rubble filled drift is very small, most likely close to the zero capillarity of an air-filled 
opening. Also, an open space can be expected between the solid rock at the ceiling and the 
collapsed rubble material. Therefore, capillary-driven flow diversion remains an important 
mechanism reducing seepage in collapsed drifts, which should be included in the seepage 
abstraction. Additional simulation cases were conducted with the SMPA to study seepage into 
collapsed drifts. A worst-case drift profile was selected representative of the drift collapse 
scenarios depicted in MO0306MWDDPPDR.000 [164736] (see also Attachment XVIII of BSC 
(2003 [162711])). The chosen profile has a circular shape with a diameter of 11 m. A capillary 
strength parameter of 100 Pa was used for the fragmented rock material within the collapsed drift 
(see Section 5). This value is considered a conservative choice for seepage calculations, because 
the capillarity of the rubble material is most likely smaller. Otherwise, the conceptual model of 
the seepage simulations remains identical to the SMPA analysis for non-degraded drifts (see 
Section 6.4.2.1).

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 76 August 2003 
Systematic seepage simulations for the collapsed drift case were conducted for the full set of 
parameter combinations, with capillary strength values ranging from 100 Pa to 1,000 Pa, mean 
permeability values ranging from 14 to 10 (in log10), and percolation flux values ranging from 
1 mm/yr to 1,000 mm/yr. (These are the same parameter cases as simulated for the non-degraded 
drift in Section 6.4.2.3). The resulting seepage values are provided in a seepage look-up table for 
the collapsed drift scenario (DTN: LB0307SEEPDRCL.002 [164337]). The format of this lookup 
table is identical to the non-degraded drift case in Section 6.4.2.3. Thus, to account for 
collapsed drifts, seepage abstraction would simply sample from this second look-up table, 
without changing the basic abstraction methodology (see Section 6.5.1.5). The collapsed drift 
look-up table in DTN: LB0307SEEPDRCL.002 [164337] is based on results from 10 
realizations. 
Example results for the collapsed drift scenario are illustrated in Figures 6.4-8 through 6.4-11, 
showing contours of simulated seepage percentage. Comparison with results from the nondegraded 
drift scenario (Figures 6.4-3 through 6.4-6) indicates a moderate increase in seepage 
percentage, caused by the larger size of the collapsed drift (reducing the effectiveness of flow 
diversion around the drift) and by the non-zero capillary strength in the drift (reducing the 
effectiveness of the capillary barrier). The simulation results demonstrate that most of the 
percolation flux is still diverted around the collapsed drift for most of the considered parameter 
range. Note, however, that the related seepage rates for the collapsed drift scenario are much 
larger than for nondegraded drifts. This is because the footprint of the drifts has doubled in size, 
thereby doubling the amount of percolation flux arriving at the collapsed drift. (As defined in 
Section 6.1.2, seepage denotes the flow of liquid water into a drift. Whether the seeping water 
can actually contact waste packages is not considered in this definition. Obviously, the larger the 
horizontal extent of a drift, the higher the possibility that seeped water does not hit a waste 
package.)

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 77 August 2003 
1 
1 
5 
5 
5 
10 
10 
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20 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0307SEEPDRCL.002 [164337] 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-8. Mean Seepage Percentage for the Collapsed Drift Scenario as a Function of Capillary- 
Strength Parameter and Mean Permeability for a Percolation Flux of 5 mm/yr 
1 
1 
5 
5 
10 
10 
20 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0307SEEPDRCL.002 [164337] 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-9. Mean Seepage Percentage for the Collapsed Drift Scenario as a Function of Capillary- 
Strength Parameter and Mean Permeability for a Percolation Flux of 50 mm/yr

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 78 August 2003 
1 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0307SEEPDRCL.002 [164337] 
NOTE: Horizontal and vertical lines indicate simulated parameter cases. 
Figure 6.4-10. Mean Seepage Percentage for the Collapsed Drift Scenario as a Function of Capillary- 
Strength Parameter and Mean Permeability for a Percolation Flux of 200 mm/yr 
1 
1 
5 
10 
20 
20 
30 
30 
40 
40 
50 
50 
60 
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k [in log10] 
1/a [Pa] 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 100.0 
200.0 
300.0 
400.0 
500.0 
600.0 
700.0 
800.0 
900.0 
1000.0 
1 5 10 20 30 40 50 60 70 80 90 95 99 
Mean Seepage [in %] 
Source: LB0307SEEPDRCL.002 [164337] 
Figure 6.4-11. Mean Seepage Percentage as a Function of Capillary-Strength Parameter and Mean 
Permeability for a Percolation Flux of 500 mm/yr

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 79 August 2003 
6.4.2.5 Model Results: Impact of Rock Bolts 
To evaluate the potential impact of rock-bolt ground support on seepage, a refined seepage 
model including rock bolts was developed (BSC 2003 [163226], Section 6.5). The model 
features a 3 m long rock-bolt borehole extending vertically upward from the crown of a drift. A 
fine discretization was chosen at the interface between the rock and the borehole, using grid 
elements as small as 0.1 mm. The simulated radius of the borehole was 1 inch. This is 
comparable to the probable rock bolt design, which is given in BSC (2003 [164101]). The small 
differences are not relevant for the model results. The model was applied to several selected 
simulation cases, using a representative range of formation properties and percolation fluxes. 
As pointed out in Section 6.3.1, the current repository design uses rock bolts without grout. In 
contrast, the base-case simulation model considered grouted boreholes, which is consistent with 
previous repository designs. A wide range of grout properties was simulated to account for the 
fact that the grout would most likely not retain its designed hydraulic properties over many 
thousands of years. In particular, one sensitivity case, designated to represent completely 
disintegrated grout in the borehole, used a large grout permeability of 10-10 m2 and a small 
capillary strength of 10 Pa, which corresponds essentially to an open rock-bolt borehole (BSC 
2003 [163226], Figure 6-3, Case G2). This is the simulation case relevant for seepage 
abstraction. 
Results of the SMPA simulations with explicit consideration of rock bolts are described in BSC 
(2003 [163226], Section 6.6.4). Essentially, no seepage enhancement was found for the 
simulation case representing an open rock-bolt borehole without grout (BSC 2003 [163226], 
Table 6-4, Case G2). This result is understandable, considering that the open borehole acts as a 
capillary barrier to flow in the fractured rock, similar to the barrier that exists at the rock-drift 
interface. Also, the cross-sectional area between the borehole and the rock is rather small. Thus 
the presence of open rock-bolt boreholes is not considered a major factor for seepage into drifts. 
6.4.3 TH Seepage Model 
The TH Seepage Model is employed to evaluate the coupled TH processesand their impact on 
seepage processesin the vicinity of waste emplacement drifts during the heating phase of the 
repository (BSC 2003 [161530]). This drift-scale process model is designed to analyze the 
combined effect of the two barriers that may prevent seepage into drifts at elevated temperatures: 
(1) the capillary barrier, which is independent of the thermal conditions, and (2) the vaporization 
barrier, which is in effect only if boiling temperatures prevail. While incorporating the 
conceptual framework for ambient seepage from the SCM, the TH Seepage Model accounts for 
all important flow and energy transport processes in response to the heat emplacement. Transient 
simulations were performed to explicitly calculate fluid flow down to the drift during the heating 
phase of the repository, and to directly calculate transient seepage rates into the drift. Results of 
this model are used in the seepage abstraction to develop an appropriate methodology of 
adjusting the SMPA results to account for thermally perturbed conditions. The thermal analyses 
with the TH Seepage Model is conducted for nondegraded drifts. Modeling analyses on the 
impact of drift collapse on the TH behavior is ongoing (Assumption 3 in Section 5 [164618]).

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6.4.3.1 Model Description 
Simulation of the coupled TH processes in fractured rock requires a modeling framework of 
considerable complexity. The processes described by the TH Seepage Model include the 
movement of both gaseous and liquid phases, transport of latent and sensible heat, phase 
transition between liquid and vapor, and vapor pressure lowering (BSC 2003 [161530], Section 
6.2.1.1). While fluid flow is described with a multiphase extension of Darcys law, heat flow 
occurs by conduction (with heat conductivity a function of saturation) and convection. The 
thermodynamic conditions are based on a local equilibrium model of the three phases (liquid, 
gas, and solid rock). In contrast to the SCM and the SMPA, where only the fracture continuum is 
represented, the contribution of the rock matrix cannot be neglected in TH simulations. The 
fractured rock is therefore treated as a dual-permeability domain, accounting for the fractures and 
the rock matrix as two separate, overlapping continua (Doughty 1999 [135997]). The active 
fracture model (AFM) is employed to account for the fact that unsaturated flow may be restricted 
to a limited number of (active) fractures and that flow within a fracture is likely to be 
channelized (Liu et al. 1998 [105729], p. 2636). Both effects may effectively reduce fracturematrix 
interaction, and thus have to be considered in TH simulations where strong transfer of 
vapor and condensate is expected between the fractures and the matrix. For further details on the 
conceptual framework of the TH Seepage Model, see BSC (2003 [161530], Section 6.2.1). Note 
that this conceptual framework is consistent with other models for unsaturated flow and transport 
at Yucca Mountain (CRWMS M&O 2000 [141187], Section 6.4.5). Rock-property changes as a 
result of THM and THC effects are not considered in the TH Seepage Model. These are 
evaluated with separate models as discussed in Section 6.4.4. 
Based on the recommendations listed in Section 6.4.1.2 of this report, the conceptual framework 
for seepage in the TH Seepage Model is similar to the SCM and SMPA conceptualization. A 
stochastic continuum model is implemented for fractures near the drift, that considers the smallscale 
variability of permeability to account for flow channeling, and the capillary-strength 
parameter close to the drift wall is derived from the properties provided by the SCM calibration 
(BSC 2003 [161530], Section 6.2.1.1.2). Also, the specific seepage boundary condition used in 
the SCM is implemented for all fracture continuum gridblocks immediately at the rock-drift 
interface. Seepage from the rock matrix into the drift is unlikely because of the strong capillarity 
and low permeability of the matrix; thus, seepage from the matrix into the drift is neglected in 
the TH Seepage Model. Note that predictions of thermal seepage have been previously 
conducted with the Multiscale Thermohydrologic Model, as presented in BSC (2001 [158204], 
Section 6.14). However, in this model, the conceptual framework for the capillary barrier at the 
drift wall was not based on the recent experimental and modeling analyses of seepage into 
niches, as described in BSC (2003 [162267]). 
In contrast to ambient seepage, the thermal seepage behavior of the fractured rock is simulated 
with a 2-D model, in a vertical model domain perpendicular to the drift axis. Considering that 
several simulation cases must be studied to account for the variability in rock properties and 
boundary conditions important for thermal seepage, a full 3-D simulation of the coupled 
processes is not feasible because of computational limitations. Similar to the SCM and the 
SMPA, the TH Seepage Model needs to focus on near-drift conditions, using a refined 
discretization in the drift vicinity. However, at the same time, the TH simulation requires a large 
vertical model domain because the thermally disturbed zone extends far into the overlying and

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 81 August 2003 
underlying geological units. The main deviations between a 3-D and a 2-D model occur at the 
end of each emplacement drift and at the edges of the repository. Such effects are accounted for 
in BSC (2003 [161530], Sections 6.2.2.1.3 and 6.2.4.2) by considering several sensitivity cases 
for the thermal load. Also note that, with respect to the effectiveness of the capillary barrier for 
seepage into drifts, a 2-D representation gives a conservative prediction of seepage for most 
cases of heterogeneous fracture permeability fields because the potential diversion of flow in the 
third dimension is neglected. 
To account for the two main host-rock units of the repository, two submodels with slightly 
different numerical gridding and different stratigraphy were studied with the TH Seepage Model. 
The first one, the Tptpmn Submodel, considers a drift located in the Topopah Spring tuff middle 
nonlithophysal unit (Tptpmn unit). The second one is the Tptpll Submodel, considers a drift 
located in the Topopah Spring tuff lower lithophysal unit (Tptpll unit). In both submodels, the 
discretization in close vicinity to the drift is identical; the differences occur only at some distance 
from the drifts where geological contacts to other rock units are encountered. As an example, 
Figure 6.4-12 shows the discretization chosen for the Tptpmn Submodel, illustrating the entire 
vertical mesh and a close-up view of the drift vicinity. The model extends from the ground 
surface at the topwith an open atmosphere boundary conditionto the water table at the 
bottomrepresented as a flat, stable surface. Symmetry considerations were applied to reduce 
the model domain in the lateral direction, perpendicular to the drift axis, to increase the 
computational efficiency of the simulation runs. The current repository design of parallel drifts 
spaced at 81 m can be represented as a series of symmetrical, identical half-drift models with 
vertical no-flow boundaries between them. Accordingly, the numerical mesh was reduced to a 
half-drift model with a width of 40.5 m, extending from the drift center to the midpoint between 
drifts. 
Note that the grid design of the TH Seepage Model is different from the ambient seepage studies, 
in that it uses radial symmetry and small gridblocks in the drift vicinity with gradual conversion 
into larger cartesian gridblocks at increasing distance from the drift. This is important for TH 
models because sufficient resolution is provided at key locations where steep gradients of TH 
properties occur, while maintaining computational efficiency. At the drift wall, gridblocks are 
about 20 cm in the radial direction, which is twice the size of the uniform gridblocks used in 
ambient seepage models. To account for spatial variability in the drift vicinity, stochastic 
permeability values with random correlation structure and a standard deviation of to ss 
= 0.84 (in 
log10 space) are mapped to the gridblocks. This standard deviation is slightly smaller than the 
value used in the current revisions of SMPA modeling analysis (BSC 2003 [163226], Section 
6.3.3). It was taken from BSC (2003 [162267], Table 10), representing the maximum standard 
deviation from small-scale air permeability tests conducted in Tptpmn niches. The differences in 
the grid design and the stochastic parameter representation will bring out differences in the 
model results between the ambient and thermal seepage models, which are analyzed in Section 
6.2.2.2.2 of BSC (2003 [161530]). The relevance of these differences for seepage abstraction is 
discussed in Section 6.5.2 of this Model Report.

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x (m) 
z (m) 0.0 10.0 20.0 30.0 40.0 
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-30.0 
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-10.0 
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20.0 
30.0 
40.0 
50.0 
Tptpmn 
Tptpul 
Tptpll 
Ground Surface 
Water Table 
Source: BSC (2003 [161530], Figure 6.2.1.2-1) 
NOTE: In this example, the emplacement drift is located in the Tptpmn unit (Tptpmn Submodel). 
Figure 6.4-12. Example of Numerical Grid for the TH Seepage Model

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Predictive simulations with the TH Seepage Model were performed for two main suites of 
simulation cases. The first suite of cases addresses the relevant TH conditions in the drift 
vicinity, mainly for informative purposes. The second suite of cases focuses specifically on the 
potential of thermal seepage for further use in seepage abstraction and TSPA, applying the 
specific modeling framework for seepage that was outlined in Section 6.4.1. Because modeling 
of coupled processes is so computationally intensive, it was not feasible to conduct a systematic 
study with thousands of parameter combinations as done with the SMPA. Thus it was not 
possible to arrive at similar look-up tables that would provide the rate of thermal seepage as a 
function of various parameters. Instead, sensitivity analyses were performed with selected 
simulation cases, varying a small number of parameters that are important for thermal seepage. 
The scope of this study was to demonstrate that thermal seepage can be described by a simple 
abstraction method that uses the ambient seepage rates as a base estimate. Thus, modeling results 
from the TH Seepage Model are not expected to provide the exact quantitative amount of 
seepage for all possible parameter combinations. Rather, the model is expected to qualitatively 
describe the evolution of seepage in comparison to the ambient seepage rates. Enough sensitivity 
cases must be considered to demonstrate that the proposed simple abstraction method for thermal 
seepage holds for the relevant ranges of parameters (see Section 6.4.3.3). 
The relevant parameters varied in the evaluation of thermal seepage are the thermal operating 
mode, the local percolation flux, and selected rock properties. The parameter ranges studied were 
chosen to cover the expected variability and uncertainty in these relevant factors. The 
temperature conditions, for example, will vary considerably in the repository, arising from heatoutput 
variation among individual waste packages, emplacement-time differences among 
repository sections, and three-dimensional (3-D) edge effects (e.g., BSC 2001 [158204], Sections 
6.12 and 6.14). Therefore, four different thermal operating modes were analyzed with the TH 
Seepage Model (BSC 2003 [161530], Table 6.2.1.3-1). The reference mode denotes a thermal 
load representative of the average thermal conditions for the current repository design, resulting 
in maximum rock temperatures above the boiling point of water for several hundred years close 
to the emplacement drifts. The other thermal-operating modes are studied as sensitivity cases, 
resulting in rock temperature conditions that can be as high as 143oC ( high-temp mode), that 
will barely exceed boiling temperature ( additional heat mode), and that will never even reach 
boiling conditions (low-temp mode). In each case, the thermal load is reduced by a large 
percentage during the preclosure periodas the forced ventilation effectively removes heat from 
the emplacement driftsand decreases with time as a result of the radioactive decay (BSC 2003 
[161530], Section 6.2.1.3.3). 
As explained in Section 6.6.4.1, the local percolation fluxes arriving at emplacement drifts can 
vary considerably in space, and will be affected by future climate changes. The TH Seepage 
Model accounts for this spatial and temporal variation by using appropriate flux boundary 
conditions at the top of the model domain. Consistent with the future climate analyses for the 
Yucca Mountain (USGS 2001 [158378]; USGS 2001 [160355]), the model considers three longterm 
climate periods with constant net infiltration: the present-day climate (up to 600 years from 
now), the monsoon climate (6002,000 years from now), and the glacial transition climate (more 
than 2,000 years from now). The base-case simulation has assigned percolation fluxes of 6, 16, 
and 25 mm/yr, respectively, for these three periods (BSC 2003 [161530], Table 6.2.1.4-1), 
slightly larger than the average fluxes over the repository area for the mean climate scenario (see 
Table 6.6-11). These average fluxes may vary because of spatial variability in surface

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infiltration, heterogeneity in rock properties, flow diversion at stratigraphic contacts, and flow 
focusing, giving maximum fluxes much larger than the average values (see Section 6.6.4). 
Additional flux scenarios have been studied with the TH Seepage Model to cover the expected 
range of percolation fluxes within the repository units. These scenarios are defined by 
multiplying the boundary fluxes of the base case using factors of 5, 10, and 20. For the three 
climate periods, the resulting fluxes are: (1) 30, 80, and 125 mm/yr for a multiplication factor of 
5; (2) 60, 160, 250 mm/yr for a multiplication factor of 10; and (3) 120, 320, and 500 mm/yr for 
a multiplication factor of 20. Together, these cases cover the major fraction of the parameter 
distributions for percolation flux developed in Section 6.6.4. As discussed in Section 6.6.4.3, the 
probability of local percolation fluxes larger than the range defined by these cases is very small. 
As mentioned above, the percolation flux boundary condition is applied at the top of the model 
domain, which represents the ground surface. The ground surface was selected as the top 
boundary because appropriate boundary conditions for temperature, pressure, and saturation can 
be easily defined. It is important to note, however, that the definition of boundary fluxes at this 
location faces a conceptual difficulty for a drift-scale model such as the TH Seepage Model. This 
is because the percolation flux distribution below the Paintbrush nonwelded hydrogeological unit 
(PTn), which defines the TH conditions in the repository units, is considerably different from the 
distribution of net infiltration at the ground surface, mainly a result of lateral diversion in the 
PTn. Since the TH Seepage Model is essentially a vertical column model, it cannot account for 
lateral flow diversion in the PTn. Therefore, instead of using the net infiltration rates at the top 
boundary, the TH Seepage Model needs to use boundary fluxes representative of the fluxes 
within the repository units. Thus, the flux boundary conditions at the top of the model domain 
are designated to represent the range of percolation fluxes below the PTn rather than the range of 
net infiltration at the ground surface. (Note that this approach is appropriate because the PTn 
fluxes are hardly affected by TH processes.) As discussed in Section 6.6.4.1, the distribution of 
percolation fluxes below the PTn is provided by simulation results from the three-dimensional 
UZ Flow and Transport Model (BSC 2003 [163045]). 
The rock properties assigned to the various stratigraphic units in the TH Seepage Model have 
been mainly derived from site-scale calibration runs (most hydrological properties) and 
supplemental data analyses (thermal properties). Since these properties are different for the 
Tptpmn and the Tptpll host-rock units, some effect of parameter variation is already accounted 
for by analyzing the Tptpmn and the Tptpll submodels. The rock properties that have the 
strongest impact on the TH conditions in the fractured tuff are the thermal properties, most 
importantly the bulk thermal conductivity (important for conductive heat transport) and the 
fracture permeability (important for moisture redistribution). In the TH Seepage Model, the bulk 
thermal conductivity varies by about 10% between the Tptpmn and the Tptpll, while fracture 
permeability varies by about one-half order of magnitude (BSC 2003 [161530], Table 4.1-2). 
These parameter ranges are smaller than the estimated variability of these properties over the 
repository area. For comparison, the standard deviation describing the variability of bulk thermal 
conductivity was reported on the order of 0.25 W/m/K in the Tptpmn and the Tptpll units (Table 
7-10 of BSC (2002 [160319]), while the standard deviation of mean permeability is 0.34 and 
0.47 (in log10) for the two units, respectively (see Section 6.7.1.1). Effects of variation of these 
parameters beyond the range explicitly accounted for by the thermal seepage simulation were 
qualitatively discussed in Section 6.2.4.3 of BSC (2003 [161530]), based on sensitivity analyses 
reported in BSC (2001 [155950], Sections 5.3.1.4.7 and 5.3.1.4.8). These analyses demonstrate

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that the potential variability in thermal conductivity and fracture permeability is expected to 
result in considerable variability in the TH conditions. However, it was concluded that the 
validity of the simple abstraction method for thermal seepage should not be affected. This 
assessment was based on the fact that the range of TH conditions expected from the variability of 
these properties is smaller than the range of TH conditions explicitly addressed in the modeling 
analysis by varying the thermal load and using vastly different percolation rates. 
For consistency with the ambient seepage models, the capillary-strength parameters close to the 
drift wall must be based on the effective properties provided by the SCM calibration. In the TH 
Seepage Model, these calibrated parameters were applied to the entire unit hosting the 
emplacement drifts, for reasons explained in BSC (2003 [161530], Section 6.2.2.1.4). A basecase 
value of 1/a = 589 Pa was assigned to both the Tptpmn and the Tptpll unit, similar to the 
mean value of the respective probability distribution developed in Section 6.4.1 of this report 
(591 Pa). As a sensitivity case, the capillary-strength parameter was set to a smaller value of 400 
Pa (i.e., a parameter choice promoting seepage), similar to the lower bound of the respective 
probability distribution that describes the spatial variability of this parameter (402 Pa). 
Additional sensitivity analyses were conducted to evaluate the impact of different conceptual 
models for fracture-matrix interaction. The AFM, used for the base-case simulations, was 
compared to a standard dual-permeability method in which all fractures are considered actively 
flowing. It was demonstrated that the seepage rates calculated with the AFM are slightly higher 
than the DKM results. Note that the AFM is not needed for the drift-scale seepage models 
considering ambient conditions, such as the SCM and the SMPA. This is because (1) fracturematrix 
interaction is not relevant for the steady-state simulations employed in these ambient 
seepage predictions, and (2) the effect of flow channeling on ambient seepage is already 
accounted for through explicit modeling of small-scale heterogeneity in the SCM and the SMPA. 
Also, the potential impact of all AFM effects on ambient seepage are automatically reflected in 
the observed seepage-rate data from liquid release tests and thus represented in the effective 
capillary-strength parameters calibrated by the SCM (BSC 2003 [162267], Section 6.3.2). 
6.4.3.2 Model Validation 
The TH Seepage Model was validated in comparison with three in situ heater tests conducted at 
Yucca Mountain (BSC 2003 [161530], Section 7). Particular focus was on the Drift Scale Test 
(DST), which is best suited for validation of the local TH processes because of its geometric 
setup. The model validation included quantitative evaluation of continuously measured 
temperature datawith a detailed analysis of subtle temperature signals indicative of TH 
couplingas well as qualitative evaluation of periodic measurements that monitored moisture 
redistribution processes, using geophysical methods, air-injection data, and withdrawal of liquid 
water in packed-off boreholes. It was concluded from the good overall agreement between model 
and data that the uncertainty of predicted temperature, saturation, and water flux data was within 
acceptable ranges, implying that the model is essentially validated. 
As pointed out in BSC (2003 [161530], Section 8.2), there are limitations related to the 
validation of the TH Seepage Model. While the DST resultsas well as results from the other in 
situ testsallow for a unique model validation with respect to the strongly perturbed near-field 
TH conditions in the rock mass, they offer no seepage data (observed seepage rates) that could 
be used directly for thermal seepage validation purposes. Direct validation of thermal seepage

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would require a heater test operated at artificially enhanced percolation fluxes, to observe the 
seepage potential for extreme percolation conditions. Also, there was concern that the setup of 
the DST was allowing vapor to escape from the heated drift through the bulkhead (BSC 2003 
[161530], Section 7.3.4). The issue of heat and mass losses through the bulkhead was discussed 
and evaluated in several thermal workshops. It was concluded that the objectives of the DST 
acquiring a more in-depth understanding of the coupled TH processes and validating the 
conceptual models in comparison with datawere being met despite these heat and mass losses. 
However, it was also understood that the measurements in the DST should not be directly used to 
evaluate the potential of seepage into drifts during the thermal period, because the potential of 
seepage in the DST might be reduced as a result of the vapor losses. As a result of these 
limitations, validation of the seepage part of the TH Seepage Model is an indirect one, based on a 
separate assessment of the two relevant barriers. Validation of the coupled TH processes (using 
the DST data and data from other in situ tests) provides confidence regarding the predicted 
effectiveness of the vaporization barrier, while validation of the ambient-seepage conceptual 
model (using liquid-release data) provides confidence regarding the predicted effectiveness of 
the capillary barrier. (The conceptual framework for the capillary barrier treatment in the TH 
Seepage Model can already be considered validated, because the conceptual model is identical to 
the one validated and successfully applied in the SCM [see Section 6.4.1.1].) However, some 
uncertainty remains, since no direct test data on thermal seepage at extreme flux conditions are 
available. Another limitation stems from the fact that all three thermal tests have been conducted 
in the Tptpmn unit at Yucca Mountain; so far, there has been no testing in the lower lithophysal 
unit. Thus, validation of the TH Seepage Model does not include direct comparison with 
measured data from the Tptpll. While application of the validated model to the Tptpll unit is 
appropriate since similar TH processes need to be described, some uncertainty remains about the 
rock properties in this unit and the influence of lithophysal cavities. 
The TH Seepage Model predictions regarding the effectiveness of the vaporization barrier were 
also tested in comparison with an alternative conceptual model of water flow in the superheated 
rock environment (BSC 2003 [161530], Section 6.3). In this model, the thermally perturbed 
downward flux from the condensation zone towards the superheated rock zone is conceptualized 
to form in episodic preferential-flow patterns. The effectiveness of the vaporization barrier was 
then tested for these extreme conditions where downward flux is fast and large in magnitude 
compared to average flow. A semi-analytical solution (Birkholzer 2003 [163686]) was employed 
to simulate the complex flow processes of episodic finger flow in a superheated fracture. With 
this solution, the maximum penetration distance into the superheated rock was determined for 
specific episodic flow events and thermal conditions, and the amount of water arriving at the 
drift crown was calculated. 
It was demonstrated in BSC (2003 [161530], Section 6.3) that results of the alternative 
conceptual model are fairly consistent with the process-model results obtained with the TH 
Seepage Model. Most importantly, it was shown that finger flow is not likely to penetrate 
through the superheated rock during the first several hundred years of heating, when rock 
temperature is high and boiling conditions exist in a sufficiently large region above the drifts. 
These are the conditions when the largest thermal perturbation occurs, or, in other words, when 
the potential for episodic finger flow is highest. Only later, when the boiling zone is small and 
the impact of vaporization is limited, can finger flow arrive at the drift crown. The fact that water 
can reach the drift during the period of above-boiling temperatures makes the alternative

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conceptual model distinct from the TH Seepage Model. However, the strong thermal 
perturbation observed at early heating stages has already diminished during this time period, and 
the net result of water arrival at the driftconsidering the combined impact of water buildup in 
the condensation zone and vaporization in the superheated zoneis similar to ambient 
percolation. It was pointed out (BSC 2003 [161530], Section 6.3) that seepage of water into the 
drift is not expected from this limited water arrival, because the flow should be effectively 
diverted around the drift by the capillary barrier capability of the cavity. Note that these findings 
were consistent over a wide range of finger flow characteristics studied in a sensitivity analysis, 
covering the potential uncertainty in finger flow patterns. Thus the alternative conceptual model 
results clearly supports the main findings of the TH Seepage Model, adding confidence into the 
model and reducing the conceptual model uncertainty. 
6.4.3.3 Model Results 
The TH Seepage Model was applied to simulate the TH coupled processes for a period of 4,000 
years after waste emplacement. This is the period when the main flow perturbations are expected 
to occur as a result of heating. A series of selected simulation cases was conducted for both the 
Tptpmn and Tptpll Submodels, comprising different thermal loads, various percolation flux 
scenarios, different capillary-strength parameters, and different conceptual models for fracturematrix 
interaction (see overview of simulation cases in BSC 2003 [161530], Section 6.2.1.6). 
The resulting simulation data sets are provided in DTN: LB0303DSCPTHSM.001 [163688]. 
Transient seepage rates that were developed from these simulation data sets are given in DTN: 
LB0301DSCPTHSM.002 [163689]. The simulation results relevant for seepage abstraction are 
briefly discussed below. 
For a given rock-property set, the predicted TH conditions are strongly driven by the thermal 
load placed into the drifts and by the local percolation flux. Example results are provided in 
Figure 6.4-13 in the form of rock temperature evolution along the perimeter of the drift. For the 
reference thermal mode, the heat generated from the waste canisters results in maximum rock 
temperatures at the drift wall between 120oC and 130oC, depending on the amount of percolation 
considered (see Figure 6.4-13a). The period of above-boiling rock temperature is about 1,000 
years for the base-case flux scenario (i.e., 6, 16, and 25 mm/yr for the three climate states), and 
rock temperature at the end of the simulation period is still as high as 65oC. Increasing the 
percolation flux has considerable impact on the temperature evolution. Elevated percolation 
leads to cooler temperatures and a shorter boiling period. Strong heat-pipe signals become 
particularly evident for the simulation case with a flux multiplication factor of 20, where the 
percolation fluxes imposed at the top model boundary are 120, 320, and 500 mm/yr. Note that 
the intensity of heat pipes varies locally as a result of heterogeneity, giving rise to considerable 
differences along the drift wall in the duration of the boiling period. Three of the four thermal 
modes depicted in Figure 6.4-13b result in boiling conditions in the drift vicinity, with the 
maximum temperature and the duration of the boiling period depending on the respective heat 
load. Only one thermal mode, the low-temp mode, results in rock temperatures that never reach 
boiling conditions. Thermal effects on flow and seepage are negligible in this case, so that the 
potential for thermal seepage can be estimated from ambient seepage results. 
As a first assessment of the potential for thermal seepage, the moisture redistribution processes 
in response to boiling of rock water have been analyzed in BSC (2003 [161530], Section 6.2) for

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all simulation cases. In general, thermal seepage is possible only when (a) water arrives at the 
drift wall, depending on the vaporization impact, and (b) the saturation at the drift wall exceeds a 
given threshold value, defined by the capillary barrier effect at the rock-drift interface. The 
modeling results consistently demonstrate that the thermal perturbation of the flow field 
causing increased downward flux from the condensation zone towards the driftsis strongest 
during the first few hundred years after closure, corresponding to the time period when rock 
temperature is highest and the vaporization barrier is most effective. Even for high percolation 
fluxes into the model domain, and strong flow channeling as a result of fracture heterogeneity, 
water cannot penetrate far into the superheated rock during the time that rock temperature is 
above boiling. Thus, the potential for seepage is small. The majority of the vaporized (and 
subsequently condensed) matrix water is diverted around the dryout zone and drains away from 
the drift. At the time when temperature has returned to below-boiling conditions, fractures start 
rewetting at the drift wall. However, while the vaporization barrier has become ineffective, the 
capillary barrier at the drift wall may continue to reduce (or prevent) water seepage into the drift, 
as long as the seepage threshold saturation at the drift wall has not been exceeded. These 
transient processes are illustrated in Figure 6.4-14, giving the evolution of fracture saturation at 
all gridblocks adjacent to the drift wall. A representative simulation case has been selected with 
large percolation flux so that seepage is eventually observed (BSC 2003 [161530], Simulation 
Case MN-HET-03). The saturation curves show that no water arrives at the drift during the 
boiling period of approximately 500 years duration. As rock temperature decreases to and below 
the boiling point, and the first stepwise change in the percolation boundary condition occurs at 
600 years, the saturation values build up strongly, while significant variability in saturation 
becomes evident. Water starts seeping into the drift at about 1,400 years after emplacement when 
the seepage threshold saturation is exceeded. 
In a second step, transient seepage rates were explicitly calculated by the TH Seepage Model to 
directly quantify the potential for seepage during the thermally perturbed time period. These 
transient seepage rates were compared with results from ambient (steady-state) simulations that 
were conducted to provide reference values for evaluating the vaporization barrier. This allows 
for comparison of seepage results considering the combined effectiveness of the vaporization and 
the capillary barrier with seepage results considering only the capillary barrier contribution. 
Ambient seepage rates were derived by running the thermal seepage model without thermal load 
until a steady state was achieved (BSC 2003 [161530], Sections 6.2.2.2.2 and 6.2.3.2.2). This is 
done separately for each climate period using the respective percolation-flux boundary condition. 
Example results illustrating the evolution of thermal seepage are given in Figure 6.4-15, for the 
same simulation case as selected in Figure 6.4-14. The magnitude of seepage is provided in 
percent, relative to the total liquid flux percolating with constant boundary flux through an area 
corresponding to the footprint of the drift. There is no seepage until about 1,400 years after waste 
emplacement. Seepage starts to occur several hundred years after the rock temperatures have 
dropped below boiling conditions, the delay caused by the retarded saturation buildup in the 
fractures. Initially, thermal seepage is considerably smaller than the respective ambient seepage 
value. With the stepwise increase of percolation flux at 2,000 years, the thermal seepage 
percentage increases considerably, but still remains smaller than ambient seepage. There is no 
enhanced seepage as a result of reflux of water (because most of the condensate has long before 
drained down away from the drift). At the end of the simulation period, the thermal seepage 
percentage is at 17%, slightly less than the ambient value of 20%.

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Note that the ambient seepage percentage for the present-day infiltration rate with multiplication 
factor 10i.e., 60 mm/yris zero in this case. In other words, even without heating of the 
repository, the capillary barrier at the drift wall is predicted to be fully effective during the first 
600 years after waste emplacement. This provides additional confidence, as two barriers prevent 
seepage simultaneously and independently. It also suggests that incorporating the effect of 
vaporization into seepage abstraction may be less important than expected. This is because the 
period when vaporization processes are most effective always coincides with the period of 
present-day climate, where percolation flux is comparably small and ambient seepage is much 
less likely than during the monsoon and the glacial transition climate.

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(a) Time (years) 
Temperature (oC) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 4000. 20.0 
30.0 
40.0 
50.0 
60.0 
70.0 
80.0 
90.0 
100.0 
110.0 
120.0 
130.0 
140.0 
Multiplication Factor: 20 
Base Case Fluxes of 6, 16, and 25 mm/yr 
Multiplication Factor: 10 
Multiplication Factor: 5 
(b) Time (years) 
Temperature (oC) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 4000. 20.0 
30.0 
40.0 
50.0 
60.0 
70.0 
80.0 
90.0 
100.0 
110.0 
120.0 
130.0 
140.0 
Additional Heat Mode 
Reference Mode 
High-Temp Mode 
Low-Temp Mode 
Source: DTN LB0303DSCPTHSM.001 [163688]1 
Figure 6.4-13. Rock Temperature Evolution at the Drift Wall for Tptpmn Submodel Showing (a) Different 
Percolation Flux Scenarios for Reference Thermal Mode, and (b) Different Thermal 
Modes for Percolation Flux Scenario with a Multiplication Factor of 10. For each scenario, 
the temperature histories in all gridblocks along the drift perimeter are depicted in the 
same color. 
1 The Readme document in DTN LB0303DSCPTHSM.001 [163688] explains where the various simulation cases 
can be found in the DTN data set. Figure 6.4-13a uses results from simulation cases MN-HET-01, MN-HET-02, 
MN-HET-03, and MN-HET-04. Figure 6.4-13b uses results from simulation cases MN-HET-03, MN-HET-05, 
and MN-HET-06. The simulation case for the Low-Temp Mode in Figure 6.4-13b is not named in the Readme 
document. Here, the results are in subdirectory /tptpmn_hetero_focus10_real1.dir/heat_lowtemp.dir. All results 
are for Realization 1 of the random permeability field.

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Time (years) 
Fracture Saturation 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 4000. 0.000 
0.100 
0.200 
0.300 
0.400 
0.500 
0.600 
0.700 
Water starts seeping 
160 mm/yr 250 mm/yr 60 mm/yr 
Source: BSC (2003 [161530], Figure 6.2.2.2-7a), underlying simulation results available in 
DTN LB0303DSCPTHSM.001 [163688], Simulation Case MN-HET-03, Realization 1 
Figure 6.4-14. Fracture Saturation in Different Grid blocks along Drift Perimeter for Tptpmn Submodel 
and Reference Thermal Mode Using Percolation Flux Scenario with Multiplication 
Factor 10 
Time (years) 
Seepage Percentage (%) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 4000. 
0.0 
5.0 
10.0 
15.0 
20.0 
25.0 
30.0 
Thermal Seepage 
160 mm/yr 250 mm/yr 60 mm/yr 
Ambient Seepage 
for 250 mm/yr 
Ambient Seepage 
for 160 mm/yr 
Ambient Seepage 
for 60 mm/yr 
Source: BSC (2003 [161530], Figure 6.2.2.2-7b), underlying simulation results available in 
DTN LB0301DSCPTHSM.002 [163689], Simulation Case MN-HET-03, Realization 1 
Figure 6.4-15. Seepage Percentage for Tptpmn Submodel and Reference Thermal Mode Using 
Percolation Flux Scenario with Multiplication Factor 10 
As mentioned above, the different simulation cases studied with the TH Seepage Model show 
considerable variability with respect to the TH conditions in the rock. Despite this variability, 
there were several important observations with respect to thermal seepage that are common to all 
cases (BSC 2003 [161530], Sections 6.2.4 and 8.1):

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(1) Thermal seepage was never observed in simulation runs where the respective ambient 
seepage was zero. 
(2) Thermal seepage never occurred during the period of above-boiling temperatures in the 
rock close to the emplacement drifts. 
(3) In simulation cases where ambient seepage was obtained, thermal seepage was initiated 
at several hundred to a few thousand years after rock temperature has returned below 
boiling. 
(4) Thermal-seepage rates were always smaller than the respective ambient reference values. 
The ambient seepage values provide an asymptotic upper limit for thermal seepage. 
While these main conclusions hold for all simulation cases, considerable variability exists among 
simulation runs with respect to the thermal-seepage initiation time, the evolution of seepage with 
time, and the long-term rate of thermal seepage. From the quantitative and qualitative results 
presented in BSC (2003 [161530], Sections 6.2.2.2.4, 6.2.3.2.3, 6.2.4.2, 6.2.4.3), the key 
parameters affecting thermal seepage can be categorized as follows: (1) parameters mainly 
affecting the TH conditions, (2) parameters mainly affecting the capillary barrier behavior, and 
(3) parameters with impact on both the TH conditions and the capillary barrier behavior. 
The thermal load and thermal conductivity, for example, belong to the first category. Varying 
these parameters results in considerable differences in the duration of the boiling period and the 
predicted maximum temperature in the rock. These conditions are important for the initiation 
time and the evolution of thermal seepage, but do not change the ambient seepage rate (which 
defines the asymptotic upper limit for thermal seepage at later stages). Results showing the 
sensitivity of thermal seepage to the thermal load are given in Figures 6.2.4.2-3 and 6.2.4.2-4 of 
BSC (2003 [161530]). Fracture capillary strength belongs to the second category. This parameter 
has minor impact on the TH behavior in the fractured rock, as shown in Section 6.2.2.1.4 of BSC 
(2003 [161530]), but significantly affects the asymptotic upper limit for thermal seepage at later 
stages. As a result of the different seepage threshold saturation, the initiation time and evolution 
of thermal seepage can also be affected. Results for a small capillary-strength parameter of 1/a = 
400 Pa are presented in BSC (2003 [161530], Figure 6.2.4.2-5), to be compared with the 
respective simulation case using 1/a = 589 Pa (BSC 2003 [161530], Figure 6.2.2.2-7b, which is 
also Figure 6.4.11 in the present report). 
The third category comprises parameters that are important for ambient seepage and also affect 
the intensity of TH coupling. Large percolation fluxes, for example, are typically related to large 
ambient seepage rates (see Section 6.4.1.2). At the same time, increased percolation flux gives 
rise to a reduction of temperature and a shorter duration of the boiling period. Thus, for large 
percolation fluxes, thermal seepage may start earlier and approach larger asymptotic values at 
later stages of heating. Example results illustrating the impact of percolation flux changes are 
given in Figures 6.2.4.2-3 and 6.2.4.2-4 of BSC (2003 [161530]). 
Changes in fracture permeability can also affect both the vaporization and the capillary barrier, 
but are expected to have counteracting effects on these barriers. Large permeabilities are 
generally beneficial for the performance of the capillary barrier, because they allow for more 
flow diversion around the drifts. The vaporization barrier, on the other hand, may be less 
effective because large permeabilities may cause strong heat-pipe processes that would result in

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considerably lower rock temperatures. This discussion indicates that the relationship between the 
relevant parameters and the seepage results can become very complicated when coupled TH 
processes are considered. 
6.4.4 Supporting THM and THC Models 
This section briefly describes the conceptual framework and modeling results from the coupled 
THM and THC simulation models. The information provided by these models is utilized in the 
seepage abstraction to assess the magnitude and impact of mechanical and chemical parameter 
alterations of relevant rock properties during the heating phase of the repository. Note that these 
models do not directly calculate seepage rates. Having coupled THM or THC models directly 
provide seepage rates is desirable, but not feasible because of the computational burden involved 
in such simulationsin particular because seepage calculations have to be available for a large 
number of parameter sets to cover spatial variability and uncertainty of relevant properties. 
6.4.4.1 Drift-Scale THM Model 
The Drift-Scale THM Model is applied to assess the magnitude and distribution of stress-induced 
changes in hydrological properties and to analyze the impact of such changes on the percolation 
flux in the rock mass around a repository drift (BSC 2003 [162318]). Heating of the rock will 
cause thermal expansion of the rock, which in turn will change the stress field around 
emplacement drifts. Thermally induced changes in the stress field will act upon pre-existing 
fractures, with the result of changing the hydrological properties of the rock mass. Note that the 
model analysis focuses on these thermal expansion effects; the impact of drift collapse on the 
hydrological properties of the remaining rock above the rubble-filled cavea potential result of 
joint cohesion loss or seismic eventshas not been considered in this THM analysis. 
The Drift-Scale THM Model uses the simulation tool TOUGH2-FLAC for the thermalhydrological-
mechanical processes, based on joining together the multiphase flow and transport 
simulator TOUGH2 V1.6 (LBNL 2003 [161491]) with the rock and soil mechanics industrystandard 
FLAC3D V2.0 code (LBNL 2001 [154783]; BSC 2003 [162318], Section 6.2). The 
modeling framework for the TH processesboundary conditions and rock propertiesis similar 
to the TH Seepage Model as described in Section 6.4.3. However, while the TH Seepage Model 
focuses on the TH conditions to evaluate seepage rates for various seepage-relevant parameter 
cases, the THM simulations concentrate on the heat-induced stress changes and resulting impact 
on the flow field. Predictive simulations were conducted with the Drift-Scale THM Model for 
10,000 years after waste emplacement. Careful model validation was performed in comparison 
with rock-mass displacement data (for TM processes) and air-permeability data (for HM 
processes) measured during the heating phase of the DST (BSC 2003 [162318], Section 7). 
Generally, the model captured the THM behavior in the heated DST rock mass reasonably well. 
In particular, the THM Model was capable of representing the transient changes in airpermeability 
data, stemming from two simultaneous processes: fracture aperture changes in 
response to stress changes and relative permeability changes in response to water saturation 
changes (BSC 2003 [162318], Section 7.4.3). Note that the air-injection results from the DST 
were also used to calibrate the stress-permeability relationship that is needed for coupled THM 
simulations. For the predictive model runs, a conservative estimate of this relationship was 
adopted. This relationship is conservative in the sense that it is calibrated to capture the strongest

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observed changes in air permeability in the DST (BSC 2003 [162318], Section 7.4.3). The 
predictive model results are thus likely to overestimate the impact of stress on the flow processes 
compared to a more moderate relationship. 
Results from the THM predictive simulations are given in Sections 6.5 (for a drift located in the 
Tptpmn unit) and 6.6 (for a drift located in the Tptpll unit) of BSC (2003 [162318]). Both 
simulation cases assume average thermal loads and percolation boundary conditions. At the drift 
ceiling (i.e., the region important for seepage), the calculations show generally a decrease in 
vertical permeability as a result of temperature-induced stresses, while the horizontal 
permeabilities remain essentially unchanged from the initial post-excavation values (BSC 2003 
[162318], Figures 6.5.4-3, 6.5.4-4, 6.6.1-4 and 6.6.1-5). The vertical permeability changes are 
much more pronounced in the Tptpmn unit compared to the Tptpll. In both units, the transient 
permeability changes are strongest at around 100 to 500 years after emplacement. At later stages, 
the declining temperatures allow the stresses and the vertical permeability values to recover 
somewhat, but not to their initial values because the rock temperatures are still higher than 
ambient. For example, in the Tptpmn, the vertical permeability immediately above the drift 
crown at 10,000 years after emplacement still remains one order of magnitude below its initial 
value. 
The impact of these permeability changes on the flow field was investigated in BSC (2003 
[162318], Sections 6.5.5 and 6.6.2) by comparison of the fully coupled THM simulations with 
TH simulations where the stress-induced property changes were neglected. This analysis 
indicated that the flow field differences are small to moderate, but that the reduction in vertical 
permeability combined with the basically unchanged horizontal permeability appeared to give 
rise to less water reaching the drift crown (Figure 6.4-16). It was suggested in BSC (2003 
[163226], Section 6.7) that these anisotropic THM property changes would increase the 
likelihood of flow being diverted around the drift and thus decrease the potential for seepage. 
x (m) 
z (m) 
0 5 10 15 20 
-5
0
5 
10 
0 
-10 
-20 
-30 
-40 
Qz (mm/year) 
x (m) 
z (m) 
0 5 10 15 20 
-5
0
5 
10 
0 
-10 
-20 
-30 
-40 
Qz (mm/year) 
(a) THM (b) TH 
Source: BSC (2003 [162318], Figure 6.5.5-5) 
Figure 6.4-16. Example Result Illustrating the Difference in Vertical Percolation Flux in the Fractures at 
10,000 Years for a (a) Fully Coupled THM Simulation and (b) TH Simulation (Tptpmn 
Model Domain)

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To confirm this point, ambient seepage calculations were conducted with the THM model using 
the initial post-excavation permeability field without THM changes (BSC 2003 [162318], Figure 
6.5.1-1) and the permeability field at 10,000 years after emplacement including THM changes 
(BSC 2003 [162318], Figures 6.5.4-3(d) and 6.5.4-4 (d)). The conceptual framework applied for 
these seepage calculations was similar to that of the SMPA, except that the small-scale 
heterogeneity in the permeability field was neglected. Results from these simulations are shown 
in Figure 6.4-17. The calculated seepage rates for the THM permeability field are reduced by 
about 10% from the values calculated for the initial permeability field, over the entire range of 
percolation fluxes analyzed (0 mm/yr to 3,000 mm/yr). It was concluded in BSC (2003 [163226], 
Section 6.7), that an ambient seepage model without consideration of anisotropic THM property 
changes is capable of predicting seepage rates with sufficient accuracy. Thus, the SMPA results 
are representative over most of the 10,000-year compliance period, with the possible exception 
of the first 1,000 to 2,000 years where the TH processes are strongly perturbed from boiling. 
Percolation FLux (mm/Year) 
Seepage Percentage (%) 
0 1000 2000 3000 0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9
1 
HM Excavation Effects 
THM 10,000 Years 
Source: DTN: LB0304SMDCREV2.004 [163691] (File Fig6-22.wmf) 
Figure 6.4-17. Seepage Percentage as a Function of Percolation Flux Simulated Using the Initial Post- 
Excavation Permeability Field without THM Changes (HM Excavation Effects) and the 
Permeability Field at 10,000 years after Emplacement Including THM Changes (THM 
10,000 Years) 
6.4.4.2 THC Seepage Model 
The THC Seepage Model is a drift-scale process model for predicting (1) the composition (not 
the rate) of gas and water that could enter waste emplacement drifts and (2) the effects of mineral 
alteration on flow in rocks surrounding drifts (BSC 2003 [162050]). The latter effect can be 
important for seepage abstraction: Mineral precipitation is predicted to form precipitation caps 
of calcite, silica, and other minerals above emplacement drifts, leading to changes in fracture 
porosity, permeability, and local percolation.

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The THC Seepage Model is based on the thermal-hydrological model introduced in Section 
6.4.3. As a result, the modeling framework for the thermal-hydrological simulationsincluding 
grid design, boundary conditions, and rock propertiesis similar in these models. However, 
whereas the TH Seepage Model focuses on the TH conditions to evaluate seepage rates for 
various seepage-relevant parameter cases, the THC simulations concentrate on the chemical 
processes and their related sensitivities. Predictive simulations are conducted with the THC 
Seepage Model for a time period of 100,000 years after waste emplacement. The model includes 
a wide range of major and minor aqueous species and minerals. Sensitivity studies were 
performed to evaluate the impact of, for example, alternative geochemical systems, initial water 
compositions, and reaction rates. Careful model validation was conducted in comparison with 
measured gas compositions, water chemistry, and analyses of mineral composition in the DST 
(BSC 2003 [162050], Section 7). Model results were also compared with measured water 
compositions from a laboratory plug-flow dissolution experiment. In addition, a fracture-sealing 
laboratory experiment was simulated to compare precipitation data. In general, the model 
captured the trends in gas composition, water chemistry, and mineral precipitation reasonably 
well. 
The effects of mineral precipitation and dissolution of flow processes in the drift vicinity are 
discussed in Section 6.8.5.4 of BSC (2003 [162050]). The simulation results suggest that a thin 
region of significantly decreased fracture permeability will form several meters above the drift 
crown, created by mineral deposition at the boiling front (mainly silica, to a lesser extent calcite). 
Mineral precipitation is particularly strong in this region because the boiling front remains at this 
location for several hundred years. Note that there is no indication that significant precipitation 
may occur immediately at the drift wall. This means that the local permeability and porosity in 
the boundary layer above the drift wall, important for the capillary barrier behavior, are not 
affected by THC alterations. Figure 6.4-18 illustrates the spatial distributions of permeability 
changes and demonstrates its impact on the flow conditions at 2,400 years after emplacement. 
While the permeability values directly at the drift wall remain unchanged, there is a permeability 
decrease by a factor of 10 in an area 7-8 m above the drift. As a result, percolating water is 
partially deflected sideways at this low-permeability zone, so that less water arrives at the drift 
crown (BSC 2003 [162050], Figure 6.8-42). Since the amount of seepage is correlated to the 
local percolation flux, this kind of umbrella effect would give rise to less seepage compared to 
a simulation without permeability changes. Note that the permeabilities shown at 2,400 years 
remain essentially unchanged for the rest of the simulation period of 100,000 years, because the 
silica solubility decreases with declining temperature.

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(a) (b) 
0 5 10 15 20 
X (m) 
-20 
-15 
-10 
-5
0
5 
10 
15 
20 
Z (m) 
1.00 
0.90 
0.80 
0.70 
0.60 
0.50 
0.40 
0.30 
0.20 
0.10 
K/Kinitial 
Tptpll Fractures - 2,400 Years (W0) 
0 5 10 15 20 
X (m) 
-20 
-15 
-10 
-5
0
5 
10 
15 
20 
Z (m) 
0.050 
0.045 
0.040 
0.035 
0.030 
0.025 
0.020 
0.015 
0.010 
Liquid Saturation 
84 
82 
86 
Tptpll Fractures - 2,400 Years (W0) 
Source: BSC (2003 [162050], Figure 6.8-40) 
Figure 6.4-18. Example of Effects of Mineral Alteration as Predicted by the THC Seepage Model: 
Contour Plot of Modeled (a) Permeability Change and (b) Liquid Saturation and 
Temperature Contours (oC) at 2,400 Years 
BSC (2003 [162050], Section 6.8.5.4) presents several sensitivity cases for mineral alteration 
results using different initial water compositions (Water W0 through W5), showing significantly 
different permeability changes using these waters. Fracture porosity changes also depend 
strongly on the initial porosity estimate for the fracture continuum, which is hard to quantify 
(BSC 2003 [162050], Section 8.2). It is also expected that variability in the TH conditions (e.g., 
stemming from thermal-load differences, percolation-flux variability) will bring out strong 
differences in the precipitation patterns. This variability was not addressed in the THC 
simulation runs.

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6.5 SEEPAGE ABSTRACTION METHODOLOGY 
The purpose of the seepage component in TSPA-LA is to calculate the seepage rate (amount of 
seepage per time) and the seepage fraction (the fraction of waste packages affected by seepage) 
as a function of time and location in the repository (CRWMS M&O 2000 [148384], Section 
6.3.1.2). The calculation is performed using a probabilistic approach that accounts for the spatial 
and temporal variability and inherent uncertainty of seepage-relevant properties and processes. 
The resulting information takes the form of probability distributions for seepage events. These 
distributions are used for subsequent TSPA calculations that may handle, for example, waste 
form degradation or radionuclide transport. Depending on the downstream modules, the resulting 
distributions may be directly used or propagated in simplified form (histograms, sorting in bins). 
The purpose of this seepage abstraction is to provide the necessary methodology, tools, 
parameter distributions, look-up tables, and simplifications to the TSPA-LA, so that the seepage 
calculations can be performed by the respective TSPA module. The abstraction does not provide 
TSPA-LA with the resulting distributions of seepage rate and seepage fraction over the 
repository area. However, Section 6.8 of this Model Report does include a stochastic evaluation 
of seepage, where the probabilistic seepage calculation of the TSPA module is adopted in a 
simplified manner. The purpose of this stochastic evaluation is (1) to demonstrate the barrier 
capability of the UZ above the repository, and (2) to derive the sensitivity of seepage results to 
various parameters. The latter helps to justify some of the choices made in the abstraction 
process (e.g., the choice of particular shapes of probability distribution functions). While the 
results of Section 6.8 are not directly utilized in the TSPA-LA, they may be useful as 
corroborative information for comparison with results from the TSPA seepage module. 
Seepage is variable in space because of variability in percolation flux and heterogeneity in key 
hydrological properties. In addition, seepage may be affected by heat output from the decaying 
radioactive waste, from changes in hydrological properties as a result of mechanical and 
chemical effects, from changes in the drift shape due to drift degradation, and from the presence 
of rock bolts used for ground support. Several of these factors are also time-dependent, such as 
percolation flux and thermal effects. The methodology of incorporating each of these factors in 
seepage abstraction is directly based on the process-model results as described in Section 6.4. 
The general procedure has two main steps, as follows: 
(1) The ambient seepage results derived from the SMPA provide the basis for the 
quantitative evaluation of seepage as a function of key hydrological properties. The key 
hydrological parameters defining ambient seepagecapillary-strength 1/ a, 
permeability k, and local percolation flux qperc,ffare described by appropriate 
probability distributions, as defined in Section 6.6. For a particular set of these key 
parameters, sampled from the respective distributions, the ambient seepage rate and its 
inherent estimation uncertainty are interpolated from the seepage look-up tables 
provided by the SMPA. The sampling and interpolation procedure is further explained 
in Section 6.5.1. Depending on the considered TSPA event, the sampling will be either 
conducted from the look-up table for non-degraded drifts (presented in Section 6.4.2.3) 
or from the look-up table for collapsed drifts (presented in Section 6.4.2.4.2). Specifics 
to the abstraction of drift degradation effects are provided in Section 6.5.1.5.

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(2) The ambient seepage rates are then adjusted to account for potentially important factors 
such as thermal effects on seepage, drift degradation, and rock bolts, if necessary. 
Thermal effects on seepage include potential changes in key properties (Section 
6.5.1.4) as well as changes in the resulting seepage rates (Section 6.5.2). These 
adjustments involve simplifications of complex model results. The simplification 
approaches and their scientific bases are explained in detail in the subsections below. 
The TSPA procedure of calculating seepage is schematically illustrated in Figure 6.5-1. The 
TSPA calculations run over several time steps to account for the temporal variability of relevant 
processes (e.g., CRWMS M&O 2000 [148384], Section 6.3.1.2). Within each time step, random 
sampling of uncertainty distributions is conducted for a sufficiently large number of realizations 
R. Within each realization R, the seepage rate is evaluated at a sufficiently larger number of 
spatial locations r in the repository area. Over all locations, the resulting number of locations 
with seepage, relative to the total number of locations, defines the seepage fraction fseep for the 
realization and the time considered. Details are provided in the following subsections. 
Note that abstraction as outlined above attempts to extract the salient features of the expected 
seepage behavior by compiling and reviewing field data and by simplifying the results 
previously obtained with complex process models (such as the SMPA and Thermal Seepage 
Model). No new mathematical model was developed for seepage abstraction, and consequently 
there are no related equations, algorithms, numerical methods, or other software/computational 
methods that need to be discussed in this Model Report. Statistical concepts and methods are 
used to develop parameter distributions; the related computations are fully documented in this 
Model Report, specifically in Attachments I through V. 
6.5.1 Abstraction of Ambient Seepage 
The seepage component in TSPA directly uses the seepage look-up tables provided by the SMPA 
model results to calculate ambient seepage rates. There is no simplification or other processing 
of these results involved that would need to be developed within the abstraction process. Thus, 
the relevant small-scale processes simulated with the ambient-seepage process models are 
inherently included in TSPA without loss of information. One important role of seepage 
abstraction is to derive the appropriate probability distributions for seepage-relevant parameters 
that feed into the look-up tables for seepage. The probability distributions developed within the 
abstraction need to account for the spatial variability and the uncertainty of these seepagerelevant 
parameters. The impact of additional factors, such as THC and THM parameter changes, 
drift degradation, and rock bolts, needs to be assessed and appropriately accounted for. 
6.5.1.1 Random Sampling Methodology 
The key hydrological parameters for ambient seepage are the capillary strength 1/ a, local 
permeability k and percolation flux qperc,ff. According to the conceptual model of the seepage 
process models (SCM and SMPA, see sections 6.4.1 and 6.4.2), the following guidelines apply 
for defining the respective parameter distributions:

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Figure 6.5-1. Probabilistic TSPA Procedure for Calculating Seepage at Selected Time Steps

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 101 August 2003 
Since small-scale heterogeneity (on the order of 1 foot or less) is explicitly accounted for in the 
SMPA, the spatial variability to be described by probability distributions is the variability over 
the repository area that occurs on the spatial resolution of a few drift diameters or more 
(intermediate-scale heterogeneity). 
(1) The capillary-strength parameter distribution must be representative of the effective 
parameter values calibrated from the SCM. 
(2) The permeability parameter distribution provided to the TSPA must represent the mean 
values of the small-scale permeability fields used in the SMPA. The standard deviation 
and the correlation structure of these small-scale fields are not varied in the TSPA 
because the best parameter estimates produced seepage rates that were either 
comparable to or larger than seepage rates calculated from selected sensitivity cases 
(Section 6.4.2). 
(3) The permeability distribution must be representative of the excavationdisturbed zone 
in the vicinity of the drifts. 
Following this guidance and based on evaluation of available measurements and model data, 
appropriate probability distributions are developed in Section 6.6 of this report. For permeability 
and capillary strength, the resulting distributions are representative of the ambient conditions in 
the drift vicinity prior to heating of the rock. The abstraction methodology for incorporating 
time-dependent changes in these properties, e.g., stemming from THM or THC effects, will be 
discussed in Section 6.5.1.4. It will be demonstrated that these changes can be neglected in 
seepage abstraction, so that the derived parameter distributions are valid for the entire 20,000- 
year TSPA period. The percolation flux, on the other hand, is time-dependent, as a result of 
future climate changes. Three different spatial distributions representative of three future climate 
stages are used to account for the temporal evolution of percolation flux (Section 6.6.4). 
The probabilities assigned to the relevant parameters distinguish explicitly between spatial 
variability and uncertainty, using separate distributions. Distinguishing between aleatory and 
epistemic uncertainty is not important for estimates of mean risk, but helps us to better 
understand the respective contributions of variability and uncertainty (BSC 2002 [158794], 
Section 4.1.2). As explained in Section 6.6, spatial variability of permeability is described by a 
log-normal probability distribution, whereas spatial variability of the capillary-strength 
parameter is expressed by a uniform distribution. The uncertainty of both parameters is 
represented by triangular distributions, with a mean of zero and a range value defining the 
uncertainty of the parameter (Mishra 2002 [163603], Section 2.3). Potential sources of 
uncertainty included in this triangular distribution are (1) measurement uncertainty, (2) spatial 
variability uncertainty, (3) conceptual model uncertainty, and (4) estimation uncertainty (Section 
6.6). A schematic illustration of the random sampling procedure is given in Figure 6.5-2. In the 
outer calculation loop over realizations R, the spatial variability distributions for permeability 
and capillary strength are adjusted to account for uncertainty, using random samples of the 
triangular uncertainty distribution. The inner calculation loop of the TSPA seepage component 
conducts random sampling of the adjusted spatial variability distributions at each of the several 
thousand locations r in the repository area, to derive values of permeability and capillary 
strength. Separate distributions of r are used for each realization R, accounting for the 
uncertainty in the generated random fields. Note that the two main host-rock units, the Tptpll and

Abstraction of Drift Seepage U0120 
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the Tptpmn, may have separate distributions to account for differences in the hydrological 
properties. 
Resulting Value: 
1/a = 1/am(R,r) + .1/an(R) 
Step 2: Spatial Variability 
Spatial Variability of 1/a [Pa] 
Cumulative Probability 
0
1 
Mean 
1/am 
Lower Bound 
Upper Bound 
Random 
Number m 
Step 1: Uncertainty 
Uncertainty Adjustment for 1/a [Pa] 
Cumulative Probability 
0
1 
Mean + Range 
. 1/an 
Random 
Number n 
Mean 
Mean - Range 
Figure 6.5-2. Schematic Illustration of Random Sampling Procedure for Capillary-Strength Parameter 
1/a, Using Cumulative Probability Distributions for Spatial Variability (Uniform) and 
Uncertainty (Triangular) 
The procedure for sampling of local percolation fluxes qperc,ff is slightly different from sampling 
of the other parameters. As explained in Section 6.6.4, the flux variability is provided by model 
results from the UZ Flow and Transport Model (BSC 2003 [163045], Section 6.6). These fluxes 
are provided for three different climate stages (present-day climate, monsoon climate, and glacial 
transition climate), during which the UZ model flow fields are considered steady state. Which 
one of these flow fields is to be used for sampling depends on the time step considered in the 
TSPA calculation. For each time step, the local fluxes at the several thousand locations r in the 
repository area are interpolated from the simulated flux distributions. Uncertainty inherent in 
these flux distributions is expressed by using several alternative scenarios of spatial flux 
distributions, each of them associated with a certain occurrence probability. These scenarios are 
the mean, the upper-bound and the lower-bound scenario, available for two different 
conceptualizations of flow in the PTn unit. Because each scenario covers three climate periods, 
there are 18 different flux distributions altogether. (However, only nine of them are relevant for

Abstraction of Drift Seepage U0120 
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TSPA, as shown in Sections 6.6.4.1 and 6.8.2.) For reasons discussed in Section 6.6.4, the local 
flux values need to be multiplied with flow focusing factors fff that lead to increased fluxes in 
some areas, while reducing them in others. These factors, also spatially variable and thus 
randomly sampled in r, account for intermediate-scale heterogeneity not represented in the above 
mentioned flux distributions. Multiplication of the local fluxes qperc from the site-scale model 
with the flow focusing factors fff gives the local percolation flux qperc,ff to be used in the TSPA 
calculation. 
Note that the respective probability distributions for capillary strength, permeability, and 
percolation flux are not correlated. This means that the random variables used to sample from the 
respective distributions should be generated independently in the TSPA. There are theoretical 
reasons to expect that the permeability and the capillary strength in a single fracture should be 
negatively correlated, since both are related to the fracture aperture. Overall, such negative 
correlation would give rise to smaller seepage estimates compared to a no-correlation 
assumption. This is because, for each sampled parameter set, a parameter value promoting 
seepage (e.g., large capillary strength 1/ a) would coincide with a parameter value reducing 
seepage (e.g., small permeability k), and vice versa. These opposite effects would partially cancel 
out so that there would be fewer extreme seepage cases, leading to an overall reduction in 
seepage. However, the calibrated capillary-strength parameter derived from the SCM is not just 
related to fracture aperture; it is an effective continuum process parameter that implicitly 
accounts for many additional factors affecting seepage (see Section 6.4.1.1). Thus, no predictable 
correlation exists between permeability and the effective capillary-strength parameter, and the 
no-correlation model should be used in TSPA (Section 6.6.1.1). One might also expect that local 
permeability and flux were positively correlated (which is the case in saturated flow). Though 
this would again give rise to less seepage, TSPA should not assume such a correlation. The flow 
patterns in the heterogeneous, unsaturated rock at Yucca Mountain are determined by various 
factors (e.g., boundary conditions, geological structure) in addition to local properties. 
6.5.1.2 Seepage Interpolation 
Seepage rates are calculated for each set of seepage-relevant parameters derived in the random 
sampling procedure over R realizations and r locations, using the seepage look-up tables 
provided by the SMPA. The sampling and interpolation procedure is identical for the two lookup 
tables provided for non-degraded and collapsed drifts. Seepage results should be derived from 
a linear interpolation between the seepage values in the SMPA look-up table. The tabulated 
value resolution provided by the systematic SMPA simulation runs is fine enough to justify 
linear interpolation, even though the functional relation between seepage results and input 
parameters may be nonlinear overall. The look-up tables (DTN: LB0304SMDCREV02.002 
[163687]) generated by the SMPA contain the mean seepage value Qseep and the standard 
deviation sseep over 20 realizations, given either as seepage rate per waste package or as seepage 
percentage. 
The finite probability distributions chosen for the capillary-strength parameter are sufficiently 
bracketed by the parameter range covered in the SMPA results (i.e., 100 Pa to 1,000 Pa, see 
Section 6.4.2.3). In contrast, the unbounded distribution for permeability can possibly exceed the 
range simulated with the SMPA (i.e., 14 to 10 in log10, see section 6.4.2.3). However, the

Abstraction of Drift Seepage U0120 
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probability that sampled permeability values fall outside of the SMPA range is very small, as 
demonstrated in Section 6.8.2. (For comparison, the 99% confidence interval for the spatial 
variability distributions developed in Section 6.6.2 ranges approximately from 13.2 to 11.2 for 
the Tptpmn and from 12.9 to 10.1 for the Tptpll). In the unlikely case that a sampled logpermeability 
value is smaller than 14, it should be set to 14. Similarly, log-permeability values 
exceeding the upper bound of 10 should be set to 10. The impact of this truncation on seepage 
results is insignificant. 
A similar methodology is recommended for truncating the local percolation-flux distributions 
qperc,ff. Flux values smaller than 1 mm/yr should be set to 1 mm/yr, the smallest percolation flux 
simulated with the SMPA. At such small fluxes, seepage is only expected for extreme parameter 
combinations, so that this flux adjustment has almost no effect on the seepage results. 
Percolation fluxes larger than 1,000 mm/yr should be set to 1,000 mm/yr. As pointed out in 
Section 6.6.4.3, local percolation fluxes larger than 1,000 mm/yr are theoretically possible (as a 
result of climate changes, spatial variability, and flow focusing), but extremely unlikely. 
Therefore the impact on the seepage results is negligible. 
6.5.1.3 Ambient Seepage Uncertainty 
The uncertainty inherent in the ambient seepage results is a result of uncertainty in the key input 
parameters to the model, as well as uncertainty that arises from the modeling methodology 
independent of the model input. As mentioned above, uncertainty in the input parameters is 
accounted for in the TSPA by feeding appropriate probability distributions into the seepage lookup 
tables derived from the SMPA. These distributions are developed in Section 6.6. Uncertainty 
inherent in the modeling methodology can stem from uncertainty in the conceptual model used 
for the seepage simulations (conceptual model uncertainty) and from uncertainty about the local 
heterogeneity considered in the SMPA (estimation uncertainty). These are accounted for in the 
abstraction as follows: 
(1) The conceptual model used in the SMPA is adopted from the conceptual framework of 
the seepage calibration analyses, conducted with the SCM. As pointed out in Section 
6.4.1, the SMPA and the SCM are in fact similar models that are used for different 
purposes. Both are sophisticated seepage-process models considering the scale and the 
conditions of interest. The modeling framework is consistent with the conceptual and 
numerical models used for calculating flow and transport in the UZ at Yucca Mountain. 
The calibrated SCM with the appropriate effective parameters is capable of reproducing 
and predicting observed seepage data from liquid-release tests conducted above and 
below the seepage threshold. The SMPA predictions are thus likely to yield reasonable 
estimates of seepage into waste emplacement drifts. Alternative conceptual models that 
corroborate the findings of the SCM have been qualitatively discussed in BSC (2003 
[162267], Section 6.4). Altogether, the conceptual model uncertainty should be small 
compared to other sources of uncertainty that are explicitly accounted for using 
cautiously realistic uncertainty estimates. Therefore, the contribution of conceptual 
model uncertainty is neglected in the abstraction of ambient seepage. 
(2) Because the exact structure of local heterogeneity in the drift vicinity is unknown, 
multiple realizations of stochastic permeability fields were studied with the SMPA. The

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spread of results stemming from these realizations defines the estimation uncertainty of 
seepage. This uncertainty contribution, as described by the standard deviation values 
given above, must be accounted for in the TSPA calculations. As recommended in 
Section 6.4.2, a uniform probability distribution should be used to describe the 
estimation uncertainty. This uniform distribution, which is different for each parameter 
set, has a mean of zero and a range defined by the interpolated value of the seepage 
standard deviation. It can be easily shown that the upper bound of a uniform 
distribution with mean zero and standard deviation sseep is given as +1.732 sseep, the 
lower bound is 1.732 sseep (derived from Mishra 2002 [163603], Section 2.3; see 
Scientific Notebook Birkholzer 2003 [164526], p. 123). Seepage uncertainty values are 
randomly sampled from the uniform uncertainty distribution and then used to adjust the 
mean seepage values. After adjusting the seepage rates, the results must be checked for 
consistency. Seepage rates smaller than zero are set to zero. Seepage rates that 
correspond to a seepage percentage of more than 100% (i.e., the resulting seepage is 
larger than the percolation flux over the drift segment) are set to a seepage rate 
corresponding to a seepage percentage of 100%. 
6.5.1.4 Abstraction of THM and THC Parameter Alterations 
The section below explains why the expected time-dependent alterations of seepage-relevant 
properties, stemming from THM and THC effects in response to the elevated temperatures in the 
repository, can be (or should be) neglected in the seepage simulations conducted in the TSPA. In 
other words, the ambient SMPA results can be directly applied for most of the 20,000-year 
TSPA period (except for the time period of strongly perturbed thermal conditions). Also, the 
parameter distributions for capillary strength and permeability can be considered constant in the 
seepage abstraction. 
THM Parameter Alterations 
The THM simulations discussed in Section 6.4.4.1 suggest that temperature-induced stress 
changes give rise to changes in the vertical fracture permeability in the vicinity of waste 
emplacement drifts, in particular in the Tptpmn unit. It was demonstrated, however, that these 
permeability changes do not result in significant changes in the flow fields. In particular, the 
seepage rates calculated for a permeability field including THM permeability changes were 
similar to, but slightly smaller than those calculated for a permeability field representative of the 
initial post-excavation conditions. The SMPA simulation results provide reasonably accurate 
(slightly conservative) estimates of the expected seepage rates at long-term conditions with 
coupled THM property changes. Therefore, the impact of THM property changes is neglected in 
the seepage abstraction. The seepage abstraction model uses the ambient seepage rates without 
accounting for the transient THM changes in seepage-relevant properties. The rationale for 
neglecting THM effects is listed below: 
(1) Including the impact of THM property changes would result in slightly smaller seepage 
rates in the TSPA analyses. However, the limited benefit of including THM effects 
does not justify the complexity of implementing these processes in the TSPA analyses.

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(2) The THM simulation results were conducted for average TH conditions using best 
THM property estimates. Because of computational limitations, the potential variability 
in these conditions and properties could not be fully addressed in the THM simulation. 
(3) The stress-permeability relationship used in the predictive THM simulations is 
relatively conservative; i.e., the permeability changes predicted by the model are likely 
to be overestimated. 
(4) While the DST displacement results indicate predominantly elastic reversible 
mechanical behavior during the heating phase of the test, cooling-phase data had not 
been available at the time of conducting the THM analysis to support this assessment. 
Also, some uncertainty remains regarding the predicted THM behavior on the Tptpll, 
because of the lack of in situ heater tests in this unit (BSC 2003 [162318], Section 8.2). 
These uncertainties are not relevant for the suggested simple THM abstraction model, 
but would need to be considered for a less conservative abstraction using a timedependent 
representation of the THM property changes. 
Note that the THM model analysis is restricted to thermal expansion effects on seepage-relevant 
properties. Analysis of the impact of drift collapse on the hydrological properties of the 
remaining rock above the rubble-filled cavea potential result of joint cohesion loss or seismic 
eventsis ongoing. It is assumed that these properties are similar to the properties of the initial 
excavation-disturbed zone around nondegraded drifts (Assumption 2 in Section 5 [164617]). 
THC Parameter Alterations 
The THC simulations discussed in Section 6.4.4.2 suggest formation of a precipitation cap about 
78 m above the drift crown. The zone of decreased fracture permeability acts as an umbrella 
that partially deflects percolating water sideways, limits the amount of flux at the drift crown, 
and reduces seepage. Seepage abstraction does not incorporate this effect, considering the 
considerable uncertainty and potential variability in these simulated results. As pointed out in 
BSC (2003 [162050], Section 8.1), both natural variability and process uncertainties exist in 
modeling the coupled THC processes because of the large amount of input data needed and the 
complexity of the natural system. Studies conducted with different initial water compositions 
have demonstrated significant sensitivity of the predicted permeability changes. Other relevant 
sources of uncertainty are the initial fracture porosity and the relation between porosity and 
permeability changes. In addition, the THC simulations were conducted using average thermalhydrological 
conditions. The location and magnitude of the precipitation umbrella can change 
considerably if the boiling front or the duration of boiling is different from these average 
conditions. A final note on the DST measurements used for the model validation: While the 
predicted locations and relative abundances of secondary minerals were consistent with in situ 
sidewall core samples retrieved from zones that had undergone boiling in the DST, the total 
amount of mineral precipitation was small and did not create measurable permeability changes. 
This is because the DST heating phase of 4 years was too short to allow for mineral alteration 
strong enough to affect permeability. 
Because of these uncertainties inherent in the THC results, the seepage abstraction model uses 
the ambient seepage rates from the SMPA without accounting for the umbrella effect. It should 
be recognized, however, that the simulated trend of a precipitation cap forming at some distance 
above the drift crown appears to be reliable in a qualitative sense. This adds confidence in the

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seepage abstraction results, in that the amount of seepage is likely to be smaller than the 
abstracted seepage because of THC effects. Note that the general conceptual model for seepage 
simulations of the SMPA is still valid independent of THC alterations, as pointed out in BSC 
(2003 [163226], Section 6.7). This is because the seepage-relevant flow diversion occurs in a 
boundary layer of less than 1 m from the drift wall; they are not affected by these THC porosity 
and permeability changes. 
6.5.1.5 Abstraction of Drift Degradation 
As pointed out in Section 6.4.2.4, drift degradation can occur as a result of thermal stresses, 
seismic ground motion, and rock strength decrease. The degree to which drift degradation occurs 
is fundamentally different between drifts located in nonlithophysal and lithophysal rocks. 
Moderate drift degradation, limited to local rock fall at the drift ceiling, is predicted for 
nonlithophysal rocks, regardless of the considered event leading to degradation. The SMPA 
seepage simulations discussed in Section 6.4.2.4.1 suggest that local breakout at the drift ceiling 
is not likely to increase seepage, as long as the drifts stay essentially intact and the horizontal 
extent remains mostly unchanged. In lithophysal units, the degree of drift degradation depends 
on the considered stress scenario. Moderate drift degradation similar to the nonlithophysal results 
is predicted for the thermal stress cases, the 5  10-4 seismic hazard case, and most of the 0% 
through 60% strength reduction cases. Other events, among them the 1  10-6 and the 1  10-7 
seismic hazard levels and the 80% through 100% strength reduction cases, result in complete 
drift collapse, leading to enlarged openings filled with fragmented rock material. Systematic 
seepage simulations have been conducted for a selected (worst-case) drift collapse scenario, 
modeling seepage into a rubble-filled drift of 11 m diameter. Results from these simulations are 
available in a seepage look-up table similar in structure to the one developed for non-degraded 
drifts (DTN: LB0307SEEPDRCL.002 [164337]). 
For seepage abstraction, the different degrees of drift degradation simulated in BSC (2003 
[162711]) are categorized as follows: The first category comprises degraded drifts that may show 
local rock breakout but stay essentially intact. In this category, seepage is interpolated from the 
look-up table for nondegraded drifts in DTN: LB0304SMDCREV2.002 [163687]. All drifts 
located in nonlithophysal rock are automatically included in Category 1. For drifts located in 
lithophysal rock, the thermal stress case, the 5  10-4 seismic hazard case and the 0% through 
40% strength reduction cases are included in Category 1. The second category comprises the 
cases with complete drift collapse. These are the 1  10-6 and the 1  10-7 seismic hazard levels 
and the 80% through 100% strength reduction cases for lithophysal rock units. Note that this 
assessment is based on a visual analysis of the drift profiles given in MO0306MWDDPPDR.000 
[164736] (see also Figures XVIII-1 through XVIII-30 in Attachment XVIII of BSC (2003 
[162711])). While most scenarios in these figures can be clearly identified as belonging to 
Category 1 or 2, the 60% strength reduction case has ambiguous results, depending on which 
random generation seed is used in the degradation analysis. To be conservative, the 60% strength 
reduction case should be included in Category 2, representing collapsed drifts. Based on this 
categorization, the TSPA seepage simulation will sample seepage rates and percentages from 
either the non-degraded drift or the collapsed drift look-up tables, depending on the considered 
geologic unit, the selected nominal or disruptive scenarios, and the assumed rock strength 
reduction case. Note that information on the time-dependence of rock fatigue is not available at 
this time. Therefore, the collapsed drift look-up table should be used for the entire postclosure

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period when one of the strength-induced collapsed drift scenarios is considered in TSPA. On the 
other hand, given that the time of a seismic event leading to drift collapse is considered in TSPA, 
the collapsed drift scenario should be used starting with the assumed time of the seismic event. 
Quantitative assessment of uncertainties involved in the assessment of seepage for degraded 
drifts is not easy. There may be uncertainty in the drift degradation analysis (degradation 
profiles) as well as uncertainty in the seepage simulation results for these scenarios. However, it 
should be recognized that most cases leading to complete drift collapse are based on very 
conservative assumptions and represent worst-case scenarios. The ground motions associated 
with the 10-6 and the 10-7 seismic hazard scenarios, for example, or the cohesion losses of 80% or 
100% may not be realistic and may overestimate the importance of time-dependent rock strength 
degradation in the lithophysal rocks. Therefore, the following procedure is suggested for 
covering uncertainty in the seepage predictions for degraded drifts. For all cases in Category 2, 
(uses the seepage look-up table for collapsed drifts), uncertainty is fully accounted for by the 
conservatism involved in the worst-case analysis. For all cases in Category 1 (uses the seepage 
look-up table for nondegraded drifts), the interpolated seepage rates are increased by 20%, to 
account for uncertainty associated with the seepage evaluation for these cases. This uncertainty 
stems in part from the limited number of simulation cases studied for moderately degraded drifts, 
but is mainly related to the large estimation differences between the stochastic realizations 
conducted for these cases (see Section 6.4.2.4.1). The maximum standard deviation of seepage 
percentage for degraded drifts was found to be above 30%, compared to about 16% for nondegraded 
cases. The proposed increase of seepage by 20% accounts for the impact of large 
estimation differences between realizations. 
It was already pointed out in Section 6.3.1 that drift collapse may have an impact on the 
hydrological properties of the remaining rock above the rubble-filled cave. It is assumed that 
these propertieslocal fracture permeability and capillary strengthare similar to the properties 
of the initial excavation-disturbed zone around nondegraded drifts (Assumption 2 in Section 5 
[164617]). This means that the respective parameter distributions for these properties, developed 
in Section 6.6.1 and 6.6.2, are applicable to both the nondegraded and the collapsed drift cases. 
6.5.1.6 Abstraction of Rock-Bolt Effects 
The simulated rock-bolt cases in Section 6.4.2.5 indicated that there is essentially no seepage 
enhancement for nongrouted boreholes housing rock bolts. The impact of rock bolts is therefore 
neglected in the seepage abstraction. 
6.5.1.7 Abstraction for Igneous Events 
Igneous intrusions (BSC 2003 [161839]) are likely to introduce large thermal, mechanical, and 
chemical perturbations both within the intersected emplacement drifts and in the surrounding 
rock. These perturbations may greatly affect the integrity of the natural and engineered barriers 
in the vicinity of and within the waste emplacement drifts. 
Several different configurations are possible after an igneous intrusion event, when magma has 
filled intersected emplacement drifts and eventually cooled off. One possible scenario is that 
thermal contraction gives rise to numerous fractures or joints in the cooling magma, such that the

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drift would be filled with fractured magma of relatively high permeability and small capillarity. 
In case this capillarity is much smaller than the capillarity of the surrounding rock (or if a small 
gap opens at the magma-rock interface as a result of cooling), the capillary barrier and flow 
diversion potential at the interface between the magma and the rock would be maintained. The 
magma may also drain out of the drift interior, leaving an air space that would also maintain the 
capillary barrier capability. Such processes are evident, for example, in the formation of lava 
tubes present in basaltic lavas (Williams and McBirney 1979 [164334], pp. 106108). To 
determine the water inflow into magma-filled drifts with the capillary barrier potential still in 
place, the use of the seepage table for a nondegraded drift would provide a reasonable seepage 
estimate for the abstraction (DTN: LB0304SMDCREV2.002 [163687]). However, in view of the 
considerable uncertainty about the in-drift conditions after an igneous event, it may be 
reasonable to use a second abstraction method providing more conservative seepage estimates. 
This abstraction method would use the look-up table for collapsed rubble-filled drifts (DTN: 
LB0307SEEPDRCL.002 [164337]). Both look-up tables account for the effects of site-scale flow 
focusing and small-scale flow channeling, as caused by drift-scale heterogeneity. This 
abstraction recommendation should be an acceptable simplification for implementation of these 
low-probability and localized disruptive events into TSPA. 
Another possible in-drift configuration after an igneous event is that the waste may be 
encapsulated by solidified magma with few cooling joints. In this case, there will be no capillary 
barrier at the interface between the magma and the fractured tuff. However, water contact with 
the waste would be limited by the small permeability of the solidified magma. A conservative 
abstraction method for such cases is to set the seepage percentage in intersected drifts to 100%; 
i.e., the seepage flux potentially contacting the waste is equal to the local percolation flux 
arriving at the drifts. This is equivalent to assuming that the cooled magma and the surrounding 
tuff have the same hydrological properties, leading to an undistorted flow field in the vicinity and 
through the drifts. This third method is easily implemented into TSPA by setting seepage flux 
equal to percolation flux. 
Information on which one of the different in-drift conditions after an igneous event is to be 
expected at Yucca Mountain is not available. It is therefore recommended that TSPA conduct 
sensitivity analyses with the three abstraction methods described above. The more conservative 
seepage estimates should be chosen and propagated to the downstream TSPA modules. If the 
time of an igneous intrusion event is considered in TSPA, the selected abstraction method for 
igneous intrusion should be used, starting with the assumed time of the event. 
6.5.2 Abstraction of Thermal Seepage 
Thermal seepage is accounted for in TSPA using results from the TH Seepage Model as 
introduced in Section 6.4.3. This seepage process model simulates the coupled TH processes 
occurring as a result of the heat generated by the radioactive waste and explicitly calculates 
seepage rates during the time period of significant flux perturbation. Having a sophisticated 
thermal-seepage-process model available for seepage abstraction is a significant improvement to 
previous TSPA approaches for seepage, such as the Total System Performance Assessment 
(TSPA) for the Site Recommendation (CRWMS M&O 2000 [153246], Section 3.3.3.2.3) and the 
Fiscal Year (FY)01 Supplemental Science and Performance Analyses, Volume 2: Performance 
Analyses (BSC 2001 [154659], Section 3.2.2.6).

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As pointed out in Section 6.4.3.1, the TH Seepage Model was applied to selected simulation 
cases by varying parameters that are important for thermal seepage (e.g., thermal-operating 
mode, local percolation flux, and seepage-relevant rock properties). Because of computational 
limitations, the number of thermal-seepage-simulation cases was much smaller than in the 
systematic SMPA analysis of ambient seepage. It was not practical to derive thermal seepage 
look-up tables that would allow direct interpolation of thermal seepage for any given 
combination of key properties. Therefore, the seepage abstraction approach developed in this 
Model Report uses the thermal seepage results to qualitatively describe the evolution of seepage 
in comparison to the ambient seepage rates. The first step of this approach is to interpolate the 
ambient seepage rates for the respective parameter case, as described in Section 6.5.1. 
Depending on the time step considered in the TSPA calculation, the ambient rate is then adjusted 
to account for the transient impact of thermal seepage, based on the qualitative results of the TH 
Seepage Model. For time steps that fall into the period of above-boiling rock temperatures, the 
ambient seepage rates may be set to zero as seepage is effectively suppressed. For late periods, 
on the other hand, there is no need to distinguish between thermal and ambient seepage, because 
the thermal perturbation has become insignificant. The abstraction approach for thermal seepage 
implicitly defines (and justifies) the time period when seepage can be treated with ambient 
seepage estimates. 
Another advantage of this approach is that results from the TH Seepage Model are not required 
to provide the exact quantitative amount of seepage. It was already pointed out in Section 6.4.3.1 
that the TH Seepage Model and the SMPA are not expected to arrive at identical simulation 
results of ambient seepage because of unavoidable differences in the model setups. From the two 
models, the SMPA results are considered quantitatively more reliable than the results from the 
TH Seepage Model. This is because (1) the SMPA is a 3-D model similar to the SCM, (2) the 
grid orientation and resolution of the SMPA, as well as the stochastic parameter representation, 
are identical to the SCM, and (3) the SMPA considers a much larger number of stochastic 
realizations. The TH Seepage Model, on the other hand, has a radially oriented 2-D and slightly 
different grid resolution in the drift vicinity. Also, the standard deviation of the stochastic 
permeability field is slightly smaller than the one used in the SMPA. Because the scope of the 
thermal seepage abstraction is to derive qualitative seepage rates, these model-setup differences 
between the SMPA and the TH Seepage Model are not relevant for the abstraction results. Using 
the SMPA results as the quantitative basis for ambient and thermal seepage ensures consistency 
in the time-dependent seepage rate. 
The abstraction methodology for thermal seepage, relative to the ambient seepage results, is 
based on the consistent trends that were observed in the thermal seepage results (see Section 
6.4.3.3). Despite different thermal loads, percolation conditions, rock properties, and host rock 
units studied in various simulation cases, the modeling results from the TH Seepage Model 
demonstrated that thermal seepage did not occur at above-boiling temperatures and that the 
ambient seepage values provide an asymptotic upper limit for thermal seepage. It was concluded 
in BSC (2003 [161530], Sections 6.2.4 and 8.1) that these qualitative trends hold for all relevant 
TSPA parameter cases. This assessment was based on the wide range of simulation cases 
explicitly addressed with the TH Seepage Model, corroborated by additional analysis of 
sensitivity studies conducted in BSC (2001 [155950], Section 5.3.1.4.8).

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While these consistent trends are helpful in developing a simplified abstraction methodology 
using ambient seepage rates as base estimates, the complex transient nature of the TH coupled 
processes makes a detailed time-dependent seepage abstraction very tedious. The modeling 
results presented in BSC (2003 [161530], Section 6.2) demonstrate considerable variability 
among simulation runs with respect to the duration of the boiling period, the transient rewetting 
processes, the initiation time of thermal seepage, and the evolution of thermal seepage in 
comparison with the ambient seepage rates. As pointed out in Section 6.4.3.3, the key parameters 
affecting these processes are not only those important for ambient seepagepercolation flux, 
capillary strength, and permeabilitybut also the thermal load generated by the waste and the 
thermal conductivity of the rock. In addition, the TH coupling occurring in the superheated rock 
results in highly nonlinear relationships between these key parameters and the observed TH 
conditions in the rock. For example, a large percolation flux may not only promote ambient 
seepage, but also suppress the temperature build-up in the rock, giving rise to a less extended 
boiling period and faster rewetting at the drift wall. Thus, implementation of a detailed timedependent 
seepage abstraction in TSPA involve prediction of the transient local TH conditions 
throughout the repository, dependent on a number of spatially varying key parameters. 
6.5.2.1 Alternative Thermal-Seepage-Abstraction Approaches 
The benefit of a detailed time-dependent abstraction of thermal seepage may not merit the 
difficulty of implementing it into the TSPA. Therefore, two alternative abstraction approaches 
are proposed for the seepage calculation component in TSPA, based on the recommendations 
made in Section 6.2.4.1 of BSC (2003 [161530]). Both approaches define thermal seepage 
relative to the ambient seepage rates for the respective climate stagesone using a very simple 
model, one with considerably more complexity. They are defined as follows: 
Abstraction Model 1: 
This simplified abstraction model sets thermal seepage equal to the respective ambient 
seepage throughout the entire TSPA period of 20,000 years. The abstraction is based on 
the model finding that ambient seepage provides an asymptotic upper limit for thermal 
seepage (i.e., there is no enhanced seepage as a result of thermal perturbation). The 
approach does not incorporate the vaporization barrier that prevents seepage during the 
period of above-boiling temperatures. Implementation in TSPA is straightforward, since 
the time-dependent evolution of thermal seepage is not accounted for in this abstraction 
model. 
Abstraction Model 2: 
This abstraction model sets thermal seepage to zero for the period of above-boiling 
temperatures in the drift vicinity. For the remaining time period, thermal seepage is set 
equal to the respective ambient seepage. The abstraction is based on model findings that 
thermal seepage never occurs at above-boiling temperatures and that the ambient seepage 
values provide an asymptotic upper limit for thermal seepage. The transient seepage 
result obtained from this abstraction model is more realistic, since the model does not 
incorporate the vaporization barrier limiting water flux towards the drifts. For 
implementation of this model, detailed information is required about the duration of the 
boiling period for a large number of parameter cases. These cases need to cover the

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spatial variability and uncertainty in the above-mentioned relevant parameters for thermal 
seepage. 
Figures 6.5-3 and 6.5-4 give examples of the proposed abstraction models for two simulation 
cases where seepage occurs. The first figure shows the simulation case that was already 
presented in Section 6.4.3.3 of this Model Report (see Figures 6.4-9, 6.4-10 and 6.4-11). The 
second figure gives the same simulation case, except that a smaller capillary-strength parameter 
was used. The assumed percolation fluxes are relatively large compared to the average flux 
conditions expected for present-day and future climate stages, totaling 60 mm/yr during the 
present-day climate, 160 mm/yr during the monsoon climate, and 250 mm/yr during the glacial 
transition climate. In both cases, the period of above-boiling rock temperature covers only the 
first 500 years after emplacement. This means that the boiling period is shorter than the presentday 
climate period (up to 600 years from emplacement), where percolation fluxes (and seepage 
rates) are typically smaller than at later times. In Figure 6.5-3, for example, ambient seepage 
does not occur during the present-day climate, because the capillary barrier is fully effective at a 
percolation flux of 60 mm/yr. This explains why both abstraction models arrive at the same 
abstraction result in this simulation case. 
On the other hand, in Figure 6.5-4, the capillary-strength parameter is small enough to allow for 
ambient seepage even during the present-day climate period. In this case, there are distinct 
differences between the two approaches. While Abstraction Model 1 results in seepage at all 
times, Abstraction Model 2 does not allow seepage during the above-boiling period. Note that 
the 100oC-isotherm of the fracture continuum is used as the threshold temperature to define the 
duration of the boiling period for abstraction (to be measured at the drift wall). This temperature 
is a few degrees centigrade higher than the nominal boiling point of water at prevailing 
pressures. This guarantees that no heat pipe conditions occur in the fractures close to the drift 
(because temperature would be at boiling) and also accounts for some uncertainty in the 
modeling results (see Section 6.5.2.2). Furthermore, because of potential variability in the TH 
conditions, the boiling period should be evaluated locally for all the grid elements along the drift 
wall, and the shortest period should be used for abstraction. In the selected simulation case, the 
resulting time period associated with fracture temperature above 100oC is about 420 years. 
Note that the simulated thermal seepage rates in Figure 6.5-4 are equal to the ambient ones at the 
onset of the simulation runs. However, they drop to zero shortly after the 50-year ventilation 
period, as soon as the rock temperatures approach boiling conditions. This early seepage is 
merely an artifact of the TH Seepage Model that neglects the impact of reduced relative humidity 
as a result of forced ventilation with dry air during the 50-year preclosure period (BSC 2003 
[161530], Section 6.2.1.3.3). The reduced relative humidity in the emplacement drifts leads to 
evaporation of water at the drift surfaces and the development of a dryout zone in the drift 
vicinity. For the range of present-day percolation fluxes expected at Yucca Mountain, seepage in 
ventilated drifts is highly unlikely. As stated in Wang et al. (1996 [101309], Section 5), forced 
ventilation in drifts is expected to evaporate the equivalent of 100 mm/yr to more than 
200 mm/yr percolation flux from the rock surfaces of the drifts. Since the predicted percolation 
fluxes during this time period are much smaller (see Section 6.6.4.1), seepage during the 
preclosure period can be neglected in both abstraction models.

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Time (years) 
Temperature (oC) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 20.0 
40.0 
60.0 
80.0 
100.0 
120.0 
140.0 Present-day 
Climate 
Glacial Transition 
Climate 
(a) 
Monsoon 
Climate 
T > 100oC 
Time (years) 
Seepage Percentage (%) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 0.0 
5.0 
10.0 
15.0 
20.0 
25.0 
30.0 
35.0 
(b) 
Simulated Thermal Seepage 
Both Abstraction Models 
T > 100oC 
Source: Temperature Evolution from DTN: LB0303DSCPTHSM.001 [163688], Simulation Case MN-HET-03, 
Realization 1. Seepage Percentage from DTN: LB0301DSCPTHSM.002 [163689], Simulation Case 
MN-HET-03, Realization 1. 
Figure 6.5-3. Illustration of Recommended Seepage Abstraction Models 1 and 2 for Simulation Case 
with Tptpmn Submodel, Reference Thermal Mode, Percolation Flux Multiplication Factor 
10, and Capillary-Strength Parameter 1/a = 589 Pa: (a) Temperature Evolution of 
Fracture Continuum at the Drift Wall; (b) Abstracted Seepage Percentage as a Function 
of Time.

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Time (years) 
Temperature (oC) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 20.0 
40.0 
60.0 
80.0 
100.0 
120.0 
140.0 Monsoon 
Climate 
Present-day 
Climate 
Glacial Transition 
Climate 
(a) 
T > 100oC 
Time (years) 
Seepage Percentage (%) 
0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0 3500.0 0.0 
5.0 
10.0 
15.0 
20.0 
25.0 
30.0 
35.0 
(b) 
Abstraction Model 2 
Abstraction Model 1 
Simulated Thermal Seepage 
Both Abstraction Models 
T > 100oC 
Source: Temperature Evolution from DTN: LB0303DSCPTHSM.001 [163688], Simulation Case MN-HET-09, 
Realization 1. Seepage Percentage from DTN: LB0301DSCPTHSM.002 [163689], Simulation Case 
MN-HET-09, Realization 1. 
Figure 6.5-4. Illustration of Recommended Seepage Abstraction Models 1 and 2 for Simulation Case 
with Tptpmn Submodel, Reference Thermal Mode, Percolation Flux Multiplication Factor 
10, and Capillary-Strength Parameter 1/a = 400 Pa: (a) Temperature Evolution of 
Fracture Continuum at the Drift Wall; (b) Abstracted Seepage Percentage as a Function 
of Time

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MDL-NBS-HS-000019 REV00 115 August 2003 
Neither of the proposed abstraction models for thermal seepage incorporates the time-dependent 
saturation buildup at the drift wall after boiling conditions have ended. As demonstrated in 
Figures 6.5-3 and 6.5-4 (and in additional simulation cases presented in BSC (2003 [161530], 
Section 6.2), thermal seepage is usually initiated at a few hundred to a few thousand years into 
the post-boiling period, the delay caused by the retarded rewetting of the dryout zone. These 
rewetting processes and their temporal evolution depend on many of the key properties for 
thermal seepage that were mentioned in the previous sectionas well as on matrix capillarity, 
permeability, and fracture-matrix interface area. With so many key properties involved, 
prediction of the time-dependent buildup of saturation is a formidable task, and the related 
variabilities and uncertainties are hard to quantify. Seepage abstraction therefore assumes 
immediate increase in saturation to the respective ambient value at the end of the boiling period. 
The decision regarding which one of the two abstraction models should be used will be made 
during the TSPA process, weighing the benefit of less seepage in Abstraction Model 2 against 
the complexity of implementing it into the TSPA. Note that in many parameter cases, the two 
abstraction models are identical. These are the parameter cases giving no seepage at any time, as 
well as most of the cases where seepage is possible during future climate periods with higher net 
infiltration, but does not occur at the present-day climate stage with comparably small net 
infiltration. This is because vaporization processes are most intense during the first several 
hundred years after emplacement, falling into the time period where the capillary barrier is fully 
effective in many parameter cases. An estimate of the potential merit of Abstraction Model 2 is 
provided in Section 6.8.1 of this Model Report. 
Both abstraction models are implicitly valid for repository drifts where the local rock 
temperatures never reach boiling (e.g., as a result of 3-D edge effects or heat-load variability 
among waste packages). As pointed out in Section 6.4.3.3, thermal effects on flow and seepage 
are negligible in such cases, so that the potential for thermal seepage is similar to ambient 
seepage results. For nonboiling temperatures, both abstraction models per definition use the 
ambient seepage rates at all times. 
Note that the thermal simulation analyses with the TH Seepage Model were conducted only for 
nondegraded drifts. As pointed out in Section 6.3.1, the thermal behavior in the fractured rock 
around collapsed drifts can be different from the conditions in the vicinity of an open drift. 
Modeling analyses for the impact of drift collapse on TH behavior is ongoing. Until supporting 
results become available, it is assumed that the two abstraction methods for thermal seepage are 
also applicable to the conditions in collapsed drifts (Assumption 3 in Section 5 [164618]). 
6.5.2.2 Thermal Seepage Uncertainty 
Uncertainty in the abstracted thermal seepage results is a result of (1) uncertainty in the ambient 
seepage estimates used as the quantitative basis of the abstraction, and (2) uncertainty in the 
evolution of thermal seepage compared to the ambient seepage estimates. The first contribution 
to thermal seepage uncertainty is automatically included in the thermal abstraction, because the 
abstracted ambient seepage rates explicitly account for the conceptual model uncertainty and the 
uncertainty in seepage-relevant parameters (see Section 6.5.1.3). The second contribution to 
thermal seepage uncertainty needs further discussion.

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As pointed out in Section 6.4.3.2, conceptual model uncertainty related to the TH Seepage Model 
has been addressed in BSC (2003 [161530], Sections 7 and 8.2) by careful validation of the 
coupled TH processes in comparison with in situ heater tests. This validation provides 
confidence regarding the thermally induced flux processes and the predicted effectiveness of the 
vaporization barrier. In addition, validation of the ambient seepage conceptual model (in 
comparison with liquid-release tests) provides confidence in the predicted effectiveness of the 
capillary barrier. Results of an alternative conceptual model, considering the potential 
penetration of episodic preferential flow into the superheated zone above emplacement drifts, 
corroborate the main findings of the thermal-seepage process model. Based on this discussion, 
the conceptual model uncertainty of the TH Seepage Model is expected to be small. It is 
recognized, however, that some conceptual model uncertainty remains because the in situ heater 
tests used for model validation were operated at natural percolation, which is comparably small, 
and heater tests were conducted only in the Tptpmn unit. Thus, they do not provide seepage data 
for extreme percolation conditions and cannot account for the potential effect of lithophysal 
cavities on the TH conditions (Section 6.4.3.2). The two abstraction models account for 
conceptual model uncertainty as follows: 
Abstraction Model 1: 
This conservative abstraction model does not incorporate the vaporization barrier formed 
as a result of heating. The abstraction merely requires that no enhanced seepage occurs 
during the thermal period compared to ambient seepage estimates. Enhanced seepage 
could only occur when strong reflux of condensate coincides with late heating periods 
when vaporization is not effective. However, model results clearly demonstrate that this 
potential can be neglected in the abstraction, since the thermal perturbation is strongest 
early in the heating period when vaporization is most intense (see Section 6.4.3.3). The 
model validation, corroborated by the alternative conceptual model, provides sufficient 
confidence to support this conservative abstraction model without explicit consideration 
of conceptual model uncertainty. 
Abstraction Model 2: 
This abstraction model assumes that no thermal seepage occurs during the period of 
above-boiling temperatures in the drift vicinity. Here, the remaining uncertainty related to 
the predictive effectiveness of the vaporization barrier needs to be accounted for in the 
abstraction. This is done by using a threshold temperature higher than the nominal boiling 
temperature to define the duration of the boiling period for abstraction. As explained 
above, it is recommended to use the 100oC isotherm of the fracture continuum as the 
threshold temperature. This ensures that the boiling isotherm is at some distance from the 
drift (and there is a small dryout zone around the wall) when the zero seepage is switched 
back to ambient seepage in the abstraction. Additional confidence is provided because the 
abstraction model does not incorporate the delayed seepage initiation caused by the timedependent 
saturation buildup at the drift. Thus, Abstraction Model 2, despite assuming no 
seepage for rock temperature above 100oC, still gives conservative seepage estimates 
compared to the predicted thermal seepage results.

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Another source of uncertainty in the thermal-seepage modeling results is uncertainty in the 
relevant input parameters to the model. The selected sensitivity cases analyzed in BSC (2003 
[161530], Section 6.2) provide enough confidence that the recommended abstraction procedures 
are valid over the required range of conditions and values used in TSPA. In other words, it can 
be stated that the general conclusions about the qualitative magnitude and evolution of thermal 
seepagei.e., no seepage during boiling and thermal seepage bounded by ambient seepage 
estimatesremain consistent for all parameter combinations of capillary strength, permeability, 
percolation flux, thermal load, and/or thermal conductivity studied in the TSPA calculations. 
They also remain consistent for the three different realizations analyzed in BSC (2003 [161530], 
Section 6.2); consequently, there is no estimation uncertainty regarding these conclusions. 
Based on this assessment, Abstraction Model 1 can be immediately implemented into TSPA, 
because the abstraction is independent of the thermal processes. Abstraction Model 2, on the 
other hand, requires detailed knowledge about the duration of the boiling period in the vicinity of 
emplacement drifts. In this case, the spatial variability and uncertainty of additional parameters 
relevant for the thermal conditions in the repository must be described (e.g., thermal load, 
thermal conductivity), and more thermal simulation cases have to be analyzed for various 
combinations and stochastic realizations of these additional parameters. The latter tasks are not 
included in this Model Report.

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6.6 PARAMETER DISTRIBUTIONS FOR SEEPAGE-RELEVANT PROPERTIES 
This section explains the background, methodology, and results of a data analysis intended to 
derive probability distribution functions for the seepage-relevant properties varied within the 
TSPA calculations. Separate probability distributions are developed for spatial variability and for 
uncertainty of these properties. As explained in Section 6.5, the seepage component in TSPA-LA 
will sample from these distributions at spatial locations r and uncertainty realizations R. 
The relevant parameters to be described are the capillary-strength parameter 1/ a, the 
permeability k, and the percolation flux qperc,ff. These parameters are defined according to the 
conceptual framework of the ambient-seepage process models; thus, the parameter distributions 
developed must correspond to the designated use of these parameters within the SMPA 
simulation model (see Section 6.4.2). This means that (1) the capillary strength is the calibrated 
effective parameter as estimated from the SCM, (2) the permeability represents the mean value 
of the small-scale stochastic permeability fields in the SMPA domain, and (3) the percolation 
flux is the local flux arriving at the upper boundary of the SMPA model (Section 6.5.1.1). It also 
means that these parameters are representative of properties or processes derived for a typical 
drift-scale model domain. The spatial variability distributions need to cover the intermediatescale 
distribution of these drift-scale parameters within the repository units. According to the 
definition given in Section 6.1.3, intermediate-scale heterogeneity defines heterogeneity on a 
resolution similar to the typical drift-scale model domain. 
The following Sections 6.6.1 and 6.6.2 explain the development of the parameter distributions 
for the capillary-strength parameter and the permeability, respectively. The discussion focuses on 
the two main repository units, the Tptpll and the Tptpmn. Both these units have been extensively 
characterized by underground testing in the Exploratory Studies Facility (ESF) and the Enhanced 
Characterization of Repository Block (ECRB), to provide sufficient basis for predictive 
modeling and seepage abstraction. How the less important Tptpul and Tptpln units are treated in 
the seepage abstraction is briefly explained in Section 6.6.3. Finally, the magnitude and 
distribution of percolation fluxes is provided in Section 6.6.4. 
Only the key properties for ambient seepage are included in the following sections. Whether 
additional probability distributions need to be developed for parameters affecting the local TH 
conditions depends on the method of choice for the abstraction of thermal seepage (Section 
6.5.2). 
Note that, in addition to active testing of seepage-relevant properties (liquid-release tests, air 
injection tests), the fracture inventory of the repository units has been extensively characterized 
from geological mapping and scanline surveys along the ESF and the ECRB Cross Drift as well 
as from borehole cores and video logs. In general, the distribution of fractures in the units 
exposed is similar in the Cross Drift and the ESF in terms of frequency, character, and 
orientation (Mongano et al. 1999 [149850], p. 44). The information gathered in the scanline 
surveys includes location, orientation, trace length, width, and roughness. The average fracture 
frequencies calculated from these data indicate a high degree of fracturing in both the Tptpll 
(tsw35) and the Tptpmn (tsw34), with about three fractures per meter in the Tptpll versus four 
fractures per meter in the Tptpmn (BSC 2003 [161773], Table 7). The calculated fracture 
porosities, reported in the same table in BSC (2003 [161773]) are 9.6  10-3 in the Tptpll vs. 8.5

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MDL-NBS-HS-000019 REV00 119 August 2003 
 10-3 in the Tptpmn. Small fractures with a trace length of less than one meter are not included 
in this analysis, because the scanline surveys were mostly conducted using a 1 m cutoff threshold 
(CRWMS M&O 2000 [152286], Section 6.2.2). These small fractures, however, may be 
important for seepage, which is governed by small-scale flow processes in the drift vicinity. 
Other fracture characteristics differ between the geological units. As reported in CRWMS M&O 
(2000 [151945], Section 4.6.6.2.3), fractures in the Tptpmn tend to be planar or arcuate, with low 
surface roughness, while fractures within the Tptpll are mainly subplanar and have rougher 
surfaces. CRWMS M&O (2000 [151945], Section 4.6.6.2.3) states that the average fracture 
length in the Tptpmn is larger than in the Tptpll. This, however, is not supported by the 
quantitative data given in Table 13 of CRWMS M&O (2000 [152286]), where the trace lengths 
in the Tptpll are slightly larger than in the Tptpmn. 
6.6.1 Capillary-Strength Parameter 
The local capillary-strength parameter 1/ a of the fractured rock is one of the key parameters 
affecting the capillary barrier behavior at the drift crown. The larger this parameter, the stronger 
the capillary force, which holds water in the fractures and prevents it from seeping into the drift. 
A value of zero is the lower limit for the capillary-strength parameter, corresponding to a 
fractured rock with zero capillary forces. 
6.6.1.1 Supporting Information 
As explained in Section 6.4.1, appropriate estimates of the fracture capillary-strength parameter 
are obtained by inverse modeling; this is the main purpose of the SCM (BSC 2003 [162267]). 
This process model was calibrated and validated against seepage-rate data from multiple liquidrelease 
tests conducted in three niches along the ESF (Niches 3107, 3650, and 4788), one niche 
in the ECRB (Niche 1620), and in three systematic-testing boreholes (SYBT-ECRB-LA#13) 
drilled into the ceiling of the ECRB. The tests were performed by sealing a short section of a 
borehole above the opening using an inflatable packer system, releasing water at a specified rate 
into the isolated test interval, and recording the amount of water dripping into the opening. For 
each interval tested, optimal values of a seepage-relevant capillary-strength parameter were 
calibrated. Seepage-rate data from multiple test events, using different liquid-release rates, were 
calibrated simultaneously in the inverse modeling approach. Inversions for the lower lithophysal 
zone were repeated for multiple realizations of the underlying stochastic permeability field to 
capture their influence on the calibrated results. Inversions for the middle nonlithophysal zone 
were conducted for only one realization of the underlying permeability field, which was justified 
by the smaller importance of this geologic unit for TSPA (12% of the repository is in the 
Tptpmn, compared to 81% in the Tptpll). The resulting capillary strength values for the Tptpmn 
may thus be affected by the specifics of the single realization of the permeability field, making 
them less robust compared to the Tptpll results. This contribution to uncertainty is accounted for 
in Section 6.6.1.3. 
A summary of all calibrated capillary-strength values is provided in Table 6.6-1 (DTN: 
LB0302SCMREV02.002 [162273], also given in BSC 2003 [162267], Table 14). Data from six 
test intervals are available in the lower lithophysal zone: four intervals in boreholes located 
above the ECRB Cross Drift, and two intervals in boreholes above Niche 1620. Four intervals in 
the middle nonlithophysal zone have been analyzed, one interval in a borehole above Niche 3107

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and three intervals in boreholes above Niche 4788. Since multiple inversions with different 
realizations of the underlying heterogeneous permeability field were performed for test locations 
in the lower lithophysal zone, the capillary-strength parameter 1/ a is calculated as the average of 
all inverse modeling results at that location. A standard deviation (SD) representing the related 
uncertainty of each inversion is computed. The standard error (SE) of the mean is calculated as 
SE = SD/(i1/2), where i is the number of inversions performed (between 17 and 30 inversions). 
The estimates for the middle nonlithophysal zone are based on a single inversion, i.e., no 
estimation uncertainty as a result of uncertainty in small-scale heterogeneity can be given. Note 
that the values provided in Table 6.6-1 reflect characterization of 1/ a that does not include the 
potential effects of mechanical and chemical rock alteration in response to heating the drifts. 
Effects of excavation, however, are implicitly accounted for in the calibrated values (Section 
6.4.1). 
Table 6.6-1. Summary Statistics of Estimated Capillary-Strength Parameter for Lower Lithophysal 
Zone and Middle Nonlithophysal Zone 
Lower Lithophysal Zone (Tptpll) 
Estimate 1/ a [Pa] 
Location Interval Number of 
Inversions(1) 
Mean Std. Dev.(2) Std. Error(3) Min. Max. 
SYBT-ECRB-LA#1 zone 2 17 534.3 56.8 13.8 447.7 674.1 
SYBT-ECRB-LA#2 zone 2 21 557.1 56.4 12.3 457.1 676.1 
SYBT-ECRB-LA#2 zone 3 19 534.8 57.8 13.3 443.1 645.7 
SYBT-ECRB-LA#3 zone 1 23 452.0 54.7 11.4 382.8 616.6 
Niche 1620 BH #4 30 671.2 223.2 40.8 356.0 1197.0 
Niche 1620 BH #5 24 740.5 339.0 69.2 231.1 1840.8 
Middle Nonlithophysal Zone (Tptpmn) 
Niche 3107 UM 1 741     
Niche 4788 UL 1 646     
Niche 4788 UM 1 603     
Niche 4788 UR 1 427     
(1) Each inversion is based on a different realization of the heterogeneous permeability field. 
(2) Represents estimation uncertainty on account of small-scale heterogeneity (not available for estimates for the middle 
nonlithophysal zone). 
(3) Standard error of mean. 
Source: DTN: LB0302SCMREV02.002 [162273], also given in BSC (2003 [162267], Table 14) 
The values given in Table 6.6-1 provide the basis for developing appropriate probability 
distributions that cover spatial variability and uncertainty of seepage-relevant fracture capillarity 
for use in TSPA-LA. For that matter, it is important to understand the nature of the calibrated 
parameter 1/a. From capillary theory, the capillary-strength parameter in a single fracture is 
governed by the aperture distribution. Aperture and capillary strength in a single fracture are

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negatively correlated; i.e., large apertures are typically associated with a smaller capillarystrength 
parameter. Since permeability in a single fracture increases with aperture (positive 
correlation), the fracture permeability and capillary-strength parameter are also negatively 
correlated. In a fracture continuum, with 1/ a being a representative continuum parameter (as in 
the seepage process models), a change in continuum permeability can be related to (1) a change 
in fracture aperture or (2) a change in the fracture density (Birkholzer et al. 1999 [105170], 
Section 2). In the first case, 1/ a is approximately negatively correlated to the square root of 
fracture continuum permeability; in the second case, there is no change in 1/ a. 
Most process models for unsaturated flow at Yucca Mountain use values for 1/a that represent 
the physically based capillary-strength parameter of the fracture ensemble in the rock, as 
discussed above (CRWMS M&O 2000 [141187], Section 6.1). Seepage process models like the 
SCM, however, consider 1/a as an effective process parameter for drift seepage that implicitly 
accounts for a number of additional factors affecting seepage, as listed in Section 6.4.1.1. 
Estimating 1/a as an effective process parameter in the inversion makes the explicit inclusion of 
these factors into the seepage calibration model unnecessary (BSC 2003 [162267], Section 
6.3.2.2). The calibrated 1/a value therefore represents a process-related parameter for estimating 
seepage with a specific conceptual model on a given spatial scale. Since some of the above-listed 
additional factors are not affected by intermediate-scale to large-scale rock type changes, the 
effective capillarity may exhibit less significant variation between and within different 
geological units compared to other fracture properties such as, for example, the fracture 
permeability. For similar reasons, the calibrated capillary-strength parameter is not expected to 
be correlated to the mean fracture permeability used in the seepage process models (compare 
with Table 6.6-3). 
Note that this specific seepage-related definition of 1/a requires that downstream models using 
this parameter for seepage prediction must be fully compatible with the SCM. The process 
models used for predicting seepage during ambient and thermally perturbed conditions (the 
SMPA and the TH Seepage Model, respectively) are compatible in this sense (see Section 6.4). 
6.6.1.2 Spatial Variability 
The intermediate-scale variability of 1/ a refers to the variation of this effective process 
parameter, provided on the spatial resolution similar to the SCM and SMPA model domain, 
within the repository rock units. Figure 6.6-1 shows a schematic illustration of the location of 
niches and drift sections where seepage tests have been conducted. The first three niche sites are 
located along the west side of the ESF in the Tptpmn and were selected for seepage testing based 
on their different fracture densities (BSC 2003 [162267], Section 6.5.1). Niche 3107, at 
construction station (CS) 31+07 in the ESF, consists of a 6.3 m long drift located in an area of 
relatively low fracture density. Niche 3650, at CS 36+50, consists of a 9 m long drift located in a 
competent rock mass exhibiting relatively moderate fracture density. Niche 4788, at CS 47+88, 
consists of an 8.2 m long drift located in an area exhibiting relatively high fracture density. Niche 
4788 is located in a 950 m long exposure of an intensely fractured zone. Fractures in this zone 
are not uniformly spaced, but instead occur in clusters of closely spaced fractures. The 15.0 m 
long Niche 1620 is located on the south side of the ECRB Cross Drift in the Tptpll. This unit 
comprises many small fractures (less than 1 m long) interspersed with many lithophysal cavities,

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ranging in size from 15 cm to 100 cm (BSC 2001 [158463], Section 6.11). Additional tests in the 
Tptpll were conducted in three systematic testing boreholes drilled into the ceiling of the ECRB 
Cross Drift. Note that no calibrated capillary-strength values are available for Niche 3650. 
Though 27 liquid-release tests have been conducted (13 of which resulted in seepage), the testing 
methodology was considered less reliable because of the short test duration, making the test 
results very sensitive to storage effects (BSC 2003 [162267], Section 7.3). All boreholes shown 
in Figure 6.6-1 are approximately parallel to the niche (drift) axis. Test intervals in the niches are 
approximately 1 ft long (0.3 m); test intervals in the systematic testing boreholes are 
approximately 1.8 m long. 
Source: BSC (2003 [162267], Figures 4 and 5) 
Figure 6.6-1. Schematic Geological Map Showing Approximate Location and Schematic Setup of 
Niches and Systematic Testing Boreholes SYBT-ECRB-LA#13 (formations are depicted 
at the elevation of the ESF) 
From Table 6.6-1 and Figure 6.6-1, calibrated 1/a values are available from ten test intervals in 
four different niche or drift locations. The four locations provide broad spatial coverage of the 
primary repository units, with a north-south distance of about 1,700 m between Niches 3107 and 
4788, and an east-west distance of about 800 m between the ESF niches and the location of the 
systematic testing boreholes. Considering the geological units separately, the Tptpmn test 
locations (Niche 3107 and 4788) are separated by a distance of about 1,700 m, covering areas in 
the middle nonlithophysal zone with distinct fracture characteristics, while the Tptpll test 
locations (Niche 1620 and systematic testing boreholes) are in relatively close proximity within a 
150 m long section of the ECRB Cross Drift. Where several boreholes were tested in one niche, 
the typical distance between test intervals was on the order of a few meters (boreholes are

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typically a few meters apart). A similar distance is measured between the two tested intervals in 
systematic testing borehole SYBT-ECRB-LA#2 (BSC 2003 [162267], Section 6.5.1). Thus, the 
ten available 1/ a values are not randomly placed over the entire repository area; instead, sample 
points are clustered at four carefully selected test locations within two different rock types. 
To develop appropriate probability distributions from these data, it is important to recall the 
nature of the parameter in question. If 1/ a solely represented capillary behavior of the fractured 
rock, one would expect this parameter to vary considerably between the two geological units, a 
result of potential differences in fracture aperture and fracture wall roughness. Thus, the analysis 
would need to be conducted separately for the two geological units. Another consequence of 1/ a 
solely representing capillary behavior would be that the 1/a values available in one niche 
location (or drift section) could not be used to constrain the intermediate-scale variation because 
of the close test proximity. Therefore, these values would need to be treated as statistically 
dependent; one would have to use the average of the available samples at each distinct niche 
location (or drift section) for further statistical analysis of intermediate-scale variability. This 
would leave a very small sample size at two distinct locations (Niches 3107 and 4788 in the 
Tptpmn; Niche 1620 and systematic testing boreholes in the Tptpll) as the basis for estimating 
intermediate-scale variability within the repository units, making the calculated statistical 
measures more uncertain. 
On the other hand, as pointed out earlier, seepage models derive and apply 1/a as an effective 
process parameter that accounts for a number of additional factors affecting seepage. Some of 
these factorsdrift-wall roughness, drop formation and detachment, artifacts of finite 
discretizationare largely independent of intermediate- and large-scale rock type variation. 
Other factors depend on fracture characteristics that are not significantly different for the two 
units. For example, the possible existence of individual fractures cutting into the opening is 
mainly governed by the fracture frequency. It is therefore possible that the 1/a-variability is not 
significantly dependent on the geological unit. In this case, analysis of statistical measures could 
be conducted without distinguishing between geological units. It is also possible that the 
variation of effective fracture capillarity is not closely tied to the location (or drift section). In 
this case, all ten samples could be considered as independent. 
Table 6.6-2 provides statistical parameters defining spatial variability distributionsmean  and 
standard deviation scalculated from the ten test samples provided in Table 6.6-1. (The mean 
of 1/a over multiple inversions is used for the Tptpll unit.) Also given is the standard error of the 
mean (SE), an estimate for the uncertainty in the mean value caused by a limited number of 
measurements. In light of the above discussion on the possible statistical independence of the ten 
test intervals, these statistical parameters have been derived using four different methods. These 
methods are as follows: 
A. Derive mean and standard deviation from all ten samples in both units. 
B. Calculate average values from multiple tests in one location, then derive mean and 
standard deviation from the resulting four samples in both units. 
C. Derive mean and standard deviation separately for geological units, from six samples 
in the Tptpll and four samples in the Tptpmn.

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D. Calculate average values from multiple tests in one location, then derive mean and 
standard deviation separately for each geological unit. 
Except for the standard error, the statistical values provided in Table 6.6-2 are consistent among 
the four calculation methods. The maximum differences in the mean (about 60 Pa) and the 
standard deviation (about 20 Pa) between methods A through D are significantly smaller than the 
variability in the calibrated 1/a-values (or the range of results from multiple inversions, see 
Table 6.6-1). The two geological units do not show a significant difference in the effective 
capillary-strength parameter. In fact, the difference of 1/a-values within a geological unit is on 
the same order as the difference of 1/a between geological units. However, the standard error of 
the mean varies between the different methods as a result of the varying sample size. The more 
statistically independent samples available, the more reliable the estimate of the mean. 
Table 6.6-2. Intermediate-Scale Variability Statistics of Estimated Capillary-Strength Parameter over 
Repository Rock Block, Using Different Calculation Methods 
Method Number of 
Samples 
Mean  
(Pa) 
Std. Dev. s 
(Pa) 
Std. Error 
of Mean SE 
(Pa) 
A 
All Samples, Both Units 10 591 1091 35 
B 
All Locations, Both Units 4 631 109 54 
C 
All Samples in Tptpmn 
All Samples in Tptpll 
46 
604 
582 
131 
105 
66 
43 
D 
All Locations in Tptpmn 
All Locations in Tptpll 
22 
650 
613 
129 
132 
91 
93 
Output-DTN: LB0308AMRU0120.001 
NOTE: 1 Due to rounding, the standard deviation of Method A was set to 109 Pa in this analysis 
instead of 110 Pa, as suggested by the Excel spreadsheet results referred to in Attachment 
II. This difference of less than 1% in the second moment is not relevant for the resulting 
parameter distributions. 
Note that statistical tests are available to rigorously analyze the statistical independence of 
samples. However, because of the rather small sample size, such statistical tests have not 
provided clear indication about which one of the four methods is most appropriate for defining 
the spatial variability of the 1/ a-parameter. On the other hand, the sensitivity analysis conducted 
in Section 6.8 indicates that the overall seepage results are not significantly affected by the 
statistical evaluation method. It is therefore recommended that all four methods be included in 
TSPA as alternative representations of spatial variability and uncertainty in the 1/ a-parameter. 
The four methods should be uniformly sampled (i.e., equally weighted) in TSPA, incorporating 
the global epistemic uncertainty about these different statistical evaluation methods. The 
significance of this uncertainty in the seepage model can be evaluated as part of the TSPA linear 
regression analyses of individual dose. For further discussion of spatial variability and 
uncertainty treatment of 1/ a in the remainder of this document, we use the resulting mean and 
standard deviation of Method A as an example.

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The question of which parametric probability distribution should be best suited to represent the 
spatial variability of the ten samples deserves further attention. Since capillary strength in a 
single fracture is correlated to the aperture distribution, which is approximately lognormal in 
most fractured rocks, one would expect 1/ a to be lognormally distributed as well. However, the 
histogram of the sample values in Figure 6.6-2 gives no clear indication of the parametric model, 
arguably because the calibrated capillary-strength parameter includes many additional factors 
affecting seepage. In light of this uncertainty about the relative likelihood of the data, the 
distribution of choice for the capillary-strength parameter is a simple uniform distribution, where 
all possible values between the specified minimum and maximum values are equally probable. 
The minimum and maximum values of this distribution can be easily calculated from the given 
mean and the standard deviation of the sample (Mishra 2002 [163603], Section 3.2). The 
minimum value is defined as  - 1.732 s, giving 402 PA using the respective values from 
Method A. The maximum value is defined as  + 1.732 s, which is 780 Pa. The resulting 
probability distribution for spatial variability of 1/ a is also depicted in Figure 6.6-2. (Note that 
the impact of choosing another parametric distribution, such as a normal distribution, is rather 
small, as shown in Section 6.8.2.) The uncertainty associated with capillary-strength parameter 
will be discussed in the next subsection. 
Capillary-Strength Parameter 1/a [Pa] 
Probability 
200 300 400 500 600 700 800 900 1000 0 
0.001 
0.002 
0.003 
0.004 
0.005 
780 Pa 
591 Pa 
402 Pa Source DTN: LB0302SCMREV02.002 [162273] 
NOTE: Vertical lines indicate mean and range of distribution. Blue symbols indicate 
calibrated sample values. 
Figure 6.6-2. Histogram and Related Probability Distribution for Spatial Variability of Capillary-Strength 
Parameter 1/a, Using Statistical Parameters Based on Method A.

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6.6.1.3 Uncertainty 
The different sources of uncertainty related to the capillary-strength parameter are as follows: 
Measurement Uncertainty: 
The capillary-strength parameter is determined by calibrating the SCM against seepagerate 
data from liquid-release tests (Section 6.4.1). As described in BSC (2001 [158463], 
Sections 6.2, 6.11, 7.2.3), these tests have been carefully designed, and potential 
problems observed in early tests (i.e., memory effect, short test duration, ventilation) 
were fixed in subsequent testing phases, with tests conducted over longer periods, at 
various release rates, and under better control of ventilation regime and relative humidity 
conditions. Only these later tests were selected for calibration of the SCM, and 
evaporation effects were incorporated into the model (BSC 2003 [162267], Section 
6.5.3). The remaining uncertainty in the measured seepage rates is mainly caused by the 
possibility of unaccounted water losses, e.g., from evaporation in the seepage capture 
system or from unaccounted storage capacity in the rock. In an attempt to obtain a 
complete water balance from the experimental data (including seepage, storage, 
evaporation, and flow diversion) a horizontal slot was designed near the spring line of 
Niche 1620 (see Figure 6.6-1). However, excavation and instrumentation of the slot 
proved difficult, and capturing of diverted water was only partially successful. While the 
flow diversion capability has been demonstrated, a complete water balance has not been 
achieved (BSC 2003 [162267], Section 6.8). Thus, uncertainty related to the measured 
seepage rates and potential water losses should be included in and propagated through the 
abstraction, using appropriate probability distribution functions. 
Conceptual Model Uncertainty: 
As explained in Section 6.4.1, the SCM is a sophisticated seepage process model for 
calibration of capillary-strength parameters on the scale and for the conditions of interest. 
The calibrated model with the appropriate effective parameters is capable of reproducing 
and predicting observed seepage data from liquid-release tests conducted above and 
below the seepage threshold; they are thus likely to yield reasonable seepage predictions 
into waste emplacement drifts. Alternative conceptual models corroborate the findings of 
the SCM. Altogether, the conceptual model uncertainty should be small compared to 
other sources of uncertainty inherent in the 1/a-values. Therefore, conceptual model 
uncertainty is ignored in abstraction. 
Estimation Uncertainty: 
The estimation uncertainty of 1/ a in the Tptpll is mainly a result of uncertainty in the 
small-scale fracture permeability distribution used in the inversion model. Multiple 
realizations were performed with different realizations of conditioned random 
permeability fields. It was demonstrated in BSC (2003 [162267], Section 6.6.4) that the 
estimation uncertainty on account of small-scale heterogeneity differences is on the order 
of 50 to about 300 Pa. However, it is also pointed out in BSC (2003 [162267], Section 
8.2) that this source of estimation uncertainty should not be incorporated in the parameter 
distribution used for sampling in TSPA-LA. This is because the impact of undetermined 
details pertaining to small-scale heterogeneity is directly evaluated in the predictive

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seepage models, i.e., the SMPA and the TH Seepage Model. Using the range of results 
from these models in the TSPA calculations assures that the estimation uncertainty is 
intrinsically included in and propagated through the abstraction (see Section 6.5.1.3). In 
the Tptpmn, only one realization of permeability fields was analyzed, which makes the 
calibration results less robust than those for the Tptpll. This contribution to uncertainty 
should thus be accounted for in the abstraction. Another uncertainty factor may stem 
from the misfit between the results and the data; i.e., from the goodness of fit between 
calibration model and measurement. It is stated in BSC (2003 [162267], Section 6.6.4) 
that uncertainty related to the misfit between the model and the data was significantly 
smaller than the uncertainty from small-scale heterogeneity. This contribution to the 
estimation uncertainty can be ignored in abstraction. 
Spatial Variability Uncertainty: 
Spatial variability uncertainty needs to account for uncertainty in the probability 
distribution chosen to represent spatial variability. This requirement stems from the fact 
that only a limited number of data points are available to derive the distribution 
parameters. The related uncertainty can be uncertainty both in the mean and in the 
standard deviation of 1/a, defining the potential range of the data. In measurement 
theory, the standard error of the mean is often used to describe the potential error in the 
estimated mean resulting from a limited sample size. For the analysis based on all ten 1/a 
values (Method A), this error is comparably small at 35 Pa. Method D, on the other hand, 
arrives at much higher standard errors of 91 and 93 Pa for the Tptmn and the Tptpll units. 
Thus, spatial variability uncertainty needs to be included in and propagated through the 
abstraction, using appropriate probability distribution functions. 
With most of the above discussion being a qualitative assessment of uncertainty, the assignment 
of quantitative measures for uncertaintyi.e., the definition of appropriate probability 
distributions describing uncertaintyis necessarily subjective and somewhat arbitrary, and must 
be based on scientific judgment. The method chosen here is to account for uncertainty by varying 
the mean of the chosen probability distribution for spatial variability within appropriate ranges; 
i.e., by shifting the uniform distribution for spatial variability to smaller or larger 1/a-values. The 
uncertainty distribution designated to provide this random adjustment of the mean is a symmetric 
triangular distribution, with a mean of zero, a maximum of three standard errors, and a minimum 
of minus three standard errors. For Method A, this would give a range of 105 Pa. For Method 
D, as an example, this range is much higher at 273 Pa for the Tptpmn and 279 Pa for the 
Tptpll. A range of approximately plus/minus two standard errors defines the 95%-confidence 
interval for the estimated mean value of the sample; i.e., this uncertainty range accounts for the 
fact that the estimated mean value of the limited sample may be different from the mean value of 
the entire population. As a consequence, the chosen uncertainty distribution covers sufficient 
uncertainty in the estimated mean values, while leaving room for additional uncertainty sources 
(e.g., measurement uncertainty, estimation uncertainty for the Tptpll stemming from a limited 
number of realizations). The parameter range is believed to cautiously but realistically represent 
the potential total uncertainty in 1/a, comprising the respective contribution of measurement 
errors and spatial variability errors.

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The triangular distribution is an appropriate model for uncertain quantities where a most likely 
value is known in addition to an estimated range of parameters. The triangular distribution 
represents the key features desired, which are that a probability distribution with a mean value of 
591 Pa, a lower bound of 402 Pa, and an upper bound of 780 Pa is the most likely case; while 
distributions with higher or lower mean are possible, but less likely. No uncertainty distribution 
is assigned to the specified minimum and maximum values of the distribution. Uncertainty about 
these values is believed to be small in light of the above discussion about the nature of this 
parameter; it should be fully accounted for in the triangular probability distribution for 
uncertainty of the mean. 
A schematic of the spatial variability and uncertainty model is given in Figure 6.6-3, using the 
values derived from Method A as an example. The heavy blue line shows the triangular-shaped 
uncertainty distribution, assigning a probability to the mean of the uniform-shaped spatial 
variability distribution. The most likely spatial variability distribution is the one defined in 
Section 6.6.1.2, with a probability of 0.0095 per Pa-1 corresponding to the peak value of the 
triangular distribution. This spatial variability distribution has a mean of 591 Pa, a minimum 
value of 402 Pa, and a maximum value of 780 Pa. Least likely are the two bounding cases, where 
the triangular distribution indicates a zero probability. The lower bounding case has a uniform 
spatial variability distribution with a mean of 486 Pa, a minimum value of 297 Pa, and a 
maximum value of 675 Pa. The upper bounding case has a uniform spatial variability distribution 
with a mean of 696 Pa, a minimum value of 507 Pa, and a maximum value of 885 Pa. Together, 
considering the combined effect of spatial variability and uncertainty, the range of 1/a-values to 
be used in TSPA-LA is quite large, extending from 297 Pa to 885 Pa. 
Capillary-Strength Parameter 1/a [Pa] 
Probability 
200 300 400 500 600 700 800 900 1000 0 
0.002 
0.004 
0.006 
0.008 
0.01 
3 SE 3 SE 
696 Pa 
591 Pa 
486 Pa 
Least Likely Upper 
Probability Distribution 
Most Likely 
Probability Distribution 
Least Likely Lower 
Probability Distribution 
NOTE: The black dashed line shows the most likely spatial probability distribution (at the peak of the triangular 
distribution). The green and the red dashed lines show the least likely spatial probability distributions (at the 
minimum and the maximum of the triangular distribution); based on statistical parameters summarized in 
Table 6.6-2. 
Figure 6.6-3. Triangular Probability Distribution (Heavy Blue Line) for Covering Uncertainty of the 
Capillary-Strength Parameter by Varying the Mean of the Spatial Probability Distribution, 
Using Statistical Parameters Based on Method A.

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6.6.2 Fracture Permeability 
The second key parameter affecting the diversion of water around drifts is the tangential fracture 
permeability in the boundary layer near the drift wall. The larger this parameter, the more likely 
is water-flow around the drift and the less likely is seepage (Birkholzer et al. 1999 [105170], 
Sections 3 and 5). Similar to the SCM, the predictive models for seepagethe SMPA and the 
TH Seepage Modelapply a stochastic continuum conceptualization for fracture permeability in 
the drift vicinity. The small-scale variability of the continuum permeability (resolution of about 1 
foot) is implicitly accounted for in these models, using lognormal probability distributions based 
on air-injection measurements that were performed on the same scale. While the standard 
deviation of these small-scale permeability distributions, sS, can be treated as a constant for 
abstraction (see discussion in Section 6.4.2), their mean values, S, may vary significantly over 
the repository rock units. (For simplification, these mean values of small-scale permeability were 
simply referred to as k in the previous sections.) For TSPA, distributions covering the 
intermediate-scale variability and the uncertainty of these mean values of small-scale 
permeability need to be developed (see Figure 6.6-4). The statistical parameters describing the 
distribution of intermediate-scale variability are the mean permeability  
and the standard 
deviation s. 
As discussed in Sections 6.4.1 and 6.4.2, the permeability values provided to the SMPA need to 
account for the effect of excavation. Typically, the excavation-disturbed permeabilities in the 
vicinity of an underground opening are larger than the permeabilities measured in undisturbed 
rock. It is therefore important to use permeability data for the seepage abstraction that reflect the 
impact of excavation (see Section 6.6.2.1 below). Additional effects potentially changing 
fracture permeability, stemming from THM or THC rock alteration during or after the heating 
phase of the repository, do not need to be included in the permeability distributions for TSPA, 
for the reasons presented in Section 6.5.1.4.

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Figure 6.6-4. Schematic Showing the Relation between Statistics of Small-Scale Measurements (Mean 
Permeability S) and the Intermediate-Scale Variability Distribution of the Repository 
Units (Mean  
and standard deviation s)

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6.6.2.1 Supporting Information 
The most appropriate information on fracture permeabilitymeasured on the scale of interest 
stems from the air-injection testing conducted in the boreholes and niches displayed in Figure 
6.6-1. The tests were performed by isolating a short section of the boreholes (1 foot [0.3 m] in 
niches, 6 ft [1.8 m] in systematic testing borehole SYBT-ECRB-LA#2), using an inflatable 
packer system, and then injecting compressed air at a constant rate into the isolated injection 
interval. The pressure buildup in the injection interval and in nearby observation intervals was 
monitored with time until steady-state conditions were reached, which typically occurred within 
a few minutes. Air injection was terminated after reaching steady-state pressures, and the decline 
in air pressure was then monitored as it recovered to its initial pre-test condition. Using the 
pressure response as input, the air-permeability value of the tested interval is calculated based on 
a commonly used analytical solution (BSC 2001 [158463], Section 6.1.2.1; LeCain 1995 
[101700], p. 10, Eq. (15)). With the exception of the systematic testing boreholes, which were 
constructed after excavation of the drift, air-permeability values are available both before and 
after excavation. This is because the boreholes above Niches 3107, 3566, 3650, 4788, and 1620 
had been drilled and tested prior to niche construction. Except for Niche 3566, testing was 
repeated using the same testing methodology and identical packer setup after excavation. 
Analysis of pre-excavation measurements at Niches 3107, 3566, 3650, and 4788 is provided in 
BSC (2001 [158463], Table 6.1.2-3), giving the mean and standard deviation of the small-scale 
permeabilities (DTN: LB990901233124.004 [123273]). Statistical parameters for pre-excavation 
air-permeability data from Niche 1620 were calculated in this Model Report from DTN: 
LB0012AIRKTEST.001 [154586]; details are provided in Attachment III. Post-excavation data 
for Niches 3107, 3650, 4788, and 1620, as well as for systematic testing borehole SYBT-ECRBLA#
2, were analyzed in BSC (2003 [162267], Section 6.5.2 and Table 10). The resulting 
statistics are provided in DTN: LB0302SCMREV02.002 [162273]. Note that Niche 3566, 
located at CS 35+66, offers pre-excavation air-permeability data, but was not tested after 
excavation, as the niche was sealed with a bulkhead to conduct long-term monitoring of in situ 
conditions. No air-permeability data are available from boreholes SYBT-ECRB-LA#1 and 
SYBT-ECRB-LA#3 because of equipment problems during air-injection testing. 
The mean values S and the standard deviations sS of all appropriate small-scale permeability 
data conducted at each of the test locations are summarized in Table 6.6-3, for both undisturbed 
and disturbed conditions, if available. Here, standard deviations reflect spatial variability within 
the test bed, on a 1-foot test interval scale. Statistical analyses are conducted with logtransformed 
values, as the niche permeabilities are approximately log-normally distributed (BSC 
2003 [162267], Section 6.6.2.1; note that log denotes base-10 logarithm in this report).

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Table 6.6-3. Summary Statistics of Air Permeabilities Derived from Small-Scale Air-Injection Tests for 
Undisturbed and Excavation-Disturbed Conditions in the Middle Nonlithophysal Zone and 
the Lower Lithophysal Zone 
Middle Nonlithophysal Zone (Tptpmn) 
Location Mean S (in log k) 
Undisturbed Disturbed Factor 
Dist./Undist.3 
Standard Deviation sS (in log k) 
Undisturbed Disturbed Factor 
Dist./Undist. 
Niche 3107 -13.41 -12.142 18.2 0.701 0.802 1.14 
Niche 3566 -13.01   0.921   
Niche 3650 -13.41 -11.662 55.0 0.811 0.722 0.89 
Niche 4788 -13.01 -11.792 16.2 0.851 0.842 0.99 
Lower Lithophysal Zone (Tptpll) 
Location Mean S (in log k) 
Undisturbed Disturbed Factor 
Dist./Undist. 
Standard Deviation sS (in log k) 
Undisturbed Disturbed Factor 
Dist./Undist. 
Niche 1620 -11.54 -10.952 3.5 1.124 1.312 1.17 
SYBT-ECRB-LA#2  -10.732   0.212  
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
1 Source: DTN: LB990901233124.004 [123273], also given in BSC (2001 [158463], Table 6.1.2-3) 
2 Source: DTN: LB0302SCMREV02.002 [162273], also given in BSC (2003 [162267], Table 10) 
3 Ratio of disturbed and undisturbed permeability values, not in log space. 
4 Source: Statistics were calculated from individual measurements given in DTN: LB0012AIRKTEST.001 
[154586] 
The small-scale mean permeabilities and their spatial variability as calculated for the four (three) 
niches located in the Tptpmn zone are consistent with one another, for both undisturbed and 
disturbed conditions. The Tptpll permeabilities show distinct differences to the Tptpmn values. 
Undisturbed permeability measured at one location in the lower lithophysal zone is 
approximately 1 orders of magnitude larger than the respective Tptpmn value; disturbed zone 
permeabilities differ by approximately one order of magnitude. While the post-excavation 
permeabilities in the Tptpmn are between 16 and 55 times larger than the pre-excavation values, 
the Tptpll permeability at Niche 1620 differs by a factor of only 3.5. Possibly, these differences 
are related to the initially higher permeability in the Tptpll (before excavation), in case this 
higher permeability is a result of initially larger fracture apertures. The effect of excavationrelated 
fracture dilation should be relatively small if the undisturbed fracture apertures are 
already large (Wang and Elsworth 1999 [104366]). 
Note that the standard deviations in all niches are similar for the undisturbed and the disturbed 
conditions. Changing the mechanical stress field does not give rise to substantial changes in the 
small-scale variability. For the lower lithophysal unit, the variability as measured in Niche 1620 
is significantly larger than that obtained in borehole SYBT-ECRB-LA#2. This is mainly a result 
of the injection intervals of borehole SYBT-ECRB-LA#2 being six times longer than those in 
Niche 1620.

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As mentioned earlier, the small-scale variability of permeability sS is explicitly accounted for in 
the predictive seepage models. Most of the SMPA simulations were conducted using a base case 
standard deviation of 1.0 (see Section 6.4.2.1). It was demonstrated that a considerable increase 
in standard deviation ( sS = 2.0) produced seepage rates that were only slightly larger than in the 
base case (BSC 2003 [163226], Section 6.6.2). Therefore, variation of sS can be neglected for 
seepage abstraction. On the other hand, the mean permeability S and its potential intermediatescale 
variation within the repository units is very important for seepage and needs to be provided 
to the TSPA. In the discussion below, these mean values of small-scale permeability will be 
simply referred to as permeability, to avoid confusion with the statistical parameters (mean  and 
standard deviation s) developed for the intermediate-scale variation of this rock property. 
Supporting information on the impact of excavation on permeability distributions in the drift 
vicinity is available from rock-mechanical model simulations. Preliminary analyses of 
excavation-related permeability changes were performed (BSC 2001 [155950], Section 4.3.7.4), 
using a fully coupled thermal-hydrological-mechanical continuum model (TOUGH2-FLAC), 
which was calibrated to available niche and drift air-permeability data conducted at ambient 
conditions. These initial studies are complemented by more recent modeling analyses presented 
in BSC (2003 [162318]). In theory, the excavation-related permeability increase at the crown of 
a circular-shaped drift should be slightly smaller than in niches where the ceiling has a flatter, 
approximately elliptic shape. Thus, the disturbed-zone permeabilities for niches may need 
adjustments to represent permeabilities of the excavation-disturbed zone around circular-shaped 
emplacement drifts. Modeling results also suggest anisotropic behavior; the horizontal 
permeability increase at the crown can be significant while the vertical increase at the crown is 
almost negligible. Considering a circular drift in the Tptpmn, the permeability at the drift crown 
is predicted to increase by a factor of up to 19 in the tangential and by a factor of 1.5 in the radial 
direction (BSC 2003 [162318], Section 6.5.1, Figure 6.5.1-1). The permeability increase in the 
Tptpll is smaller, with a factor of up to 8 tangentially and about 1.3 radially (BSC 2003 
[162318], Section 6.6.1, Figure 6.6.1-1). These values compare reasonably well with the 
measured changes at the crown of the niches, where the permeability increase is between 16 and 
55 in the middle nonlithophysal unit (with a geometric mean of 25 over the three niches) and 3.5 
in the lower lithophysal unit. 
It was pointed out earlier in this Model Report that the diversion of water around drifts mainly 
depends on the tangential fracture permeability in the boundary layer near the drift wall. Thus, 
the increased tangential permeability component at the drift crown needs to be accounted for in 
seepage models. However, the process models developed for drift seepage predictions, like the 
SMPA and the TH Seepage Model, do not consider anisotropy in permeability. Therefore, the 
increased tangential permeability component is used in these models as an isotropic value. That 
the radial permeability component in the excavation-disturbed zone is overestimated as a result 
of such simplification makes the predicted seepage values conservative. As explained in BSC 
(2003 [163226], Section 6.3.2), a higher tangential permeability in conjunction with a smaller 
vertical permeability will facilitate the flow of water laterally around the drift and hence reduce 
seepage probability. Note that the tangential permeability, as predicted by the TOUGH2-FLAC 
model, not only increases at the crown of the drift, but also at the springline (BSC 2003 
[162318], Sections 6.5.4 and 6.6.1).

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Since measured and predicted permeability differences between undisturbed and disturbed 
conditions show reasonable agreement, there are two possible methods of defining the parameter 
distributions of the disturbed-zone permeability for TSPA-LA: (1) directly use the measured 
post-excavation air-permeability data, or (2) use pre-excavation data and adjust them according 
to the predicted permeability changes. In this Model Report, the more conservative of these 
methods is used in order to account for uncertainties in both the measured and the simulated 
results. For the Tptpmn, where the measured permeability change is larger than the predicted, the 
THM model results are applied. Based on BSC (2003 [163226], Section 6.3.2), a factor of 10 is 
chosen as a representative average for the tangential permeability changes in the seepagerelevant 
boundary layer along the crown, which is smaller than the maximum predicted increase 
of 19 directly at the drift crown. (As explained earlier, the impact of excavation alterations 
decreases with distance from the drift wall.) For the Tptpll, where the measured change is 
smaller than the predicted, the measured factor of 3.5 is chosen as appropriate. 
6.6.2.2 Spatial Variability 
The intermediate-scale variability of permeability refers to the variation of this parameter 
provided as the mean S of small-scale permeability measurementswithin the repository rock 
units. From Table 6.6-3, the two main host rock units have significantly different permeability 
ranges, as measured in Niches 3107, 3650, and 4788 for the Tptpmn, and in Niche 1620 and 
borehole SYBT-ECRB-LA#2 for the Tptpll. Thus, analyses of intermediate-scale variability 
need to be conducted separately for the two units, which makes the sample size for developing 
appropriate probability distributions rather small. For the Tptpmn, pre-excavation permeability 
values are available at four niche locations, covering a stretch of about 1,700 m length along the 
ESF (see Figure 6.6-1). Post-excavation data are available at three niches. Less information is 
available in the lower lithophysal unit. Pre-excavation data are available at one test location only 
(Niche 1620); post-excavation measurements are available at two locations that are relatively 
close (Niche 1620 and borehole SYBT-ECRB-LA#2). Since this sample size is not sufficient to 
derive defensible probability distributions, information from other sources is needed to better 
constrain the parameter distributions. The following paragraphs describe the treatment of spatial 
variability separately for the Tptpmn and the Tptpll. 
6.6.2.2.1 Middle Nonlithophysal Unit 
Table 6.6-4 summarizes the intermediate-scale statistical parameters (mean  and standard 
deviation s) calculated from the four niches located in the Tptpmn (see Table 6.6-3). Though 
covering a large distance along the ESF, and despite the fact that the niche locations have been 
carefully selected to represent rock zones with different fracture intensity (Section 6.6.1.2), the 
variability s of mean permeability observed among the four (three1) test locations is very small. 
This is consistent for both undisturbed- and disturbed-zone data, with standard deviations of 0.23 
and 0.25 (in log10), respectively. Because standard deviation is small, the standard error of the 
mean is also small, although the number of samples is limited. This would lead to relatively 
narrow probability distributions for spatial variability and uncertainty. 
1 Numbers in parenthesis indicate excavation-disturbed measurement locations.

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The question arises whether these statistical values accurately represent the permeability 
distribution over the entire host rock unit. Additional air-permeability data need to be evaluated 
to confirm or, if needed, to adjust the parameter distributions given in Table 6.6-3. 
Measurements of air permeability in the Tptpmn have been conducted in four vertical surfacebased 
boreholes (NRG-7a, NRG-6, SD-12, and UZ#16) and in numerous boreholes drilled into 
the SHT and DST rock block (Alcove 5). Note that the full name of borehole UZ#16 is UE-25 
UZ#16. Out of convenience, the short name UZ#16 is used throughout this Model Report. 
The locations of these air permeability measurements are illustrated in Figure 6.6-5. Boreholes 
NRG-6 and UZ#16 are located some distance from the ESF; thus, they cover rock areas that have 
not been represented by niche measurements. The packer lengths in the vertical boreholes were 
fairly consistent between 3.5 m and 4.6 m (more than one order of magnitude larger than in the 
niches), while the packer length in the heater test areas varied widely from approximately 2 to 11 
m for the SHT, and 3 to 39 m in the DST. 
Table 6.6-4. Intermediate-Scale Variability Statistics (Mean  and Standard Deviation s) of 
Permeability over Repository Rock Block, for Pre- and Post-Excavation Data in the 
Tptpmn 
Parameter Number of 
Samples 
Mean  
(in log k) Std. Dev. s Std. Error 
of Mean SE 
Pre-excavation Permeability 
(undisturbed) 
4 -13.2 0.23 0.12 
Post-excavation 
Permeability 
(disturbed) 
3 -11.9 0.25 0.14 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
Figure 6.6-6 compares the mean values (in log space) of data from the pre-excavation niche 
measurements (from Table 6.6-1), the surface-based boreholes, and the SHT/DST area, 
separately for each location (BSC 2003 [161773], Section 6.1.1.1). The ranges of data, calculated 
as mean plus/minus one standard deviation, overlap, but generally the means of the small-scale 
niche data are lower than the other values. This is expected because the mean effective 
permeability increases as the scale of measurements increases (Neuman 1994 [105731]). (Also, 
the observed range of data is larger for the niche measurements. This again is expected from 
scaling effects, because a larger measurement scale tends to neglect the impact of small-scale 
variability.) Thus direct comparison of the mean permeability data observed at the niches with 
data from surface-based boreholes (or from the heater test block) is not appropriate; statistical 
analyses have to be conducted over locations with similar measurement scale. However, it can be 
beneficial to apply adequate scaling laws to adjust the mean permeability values obtained with 
different interval lengths. Also, the variability of mean permeability between surface boreholes 
should be similar to the variability of mean permeability between the niche locations.

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Northing (m) 
Easting (m) 
SD-12 
NRG-6 
NRG-7a 
UZ#16 
LEGEND 
UZ2002 Model Boundary 
Repository Boundary 
ESF and ECRB 
Boreholes 
Contingency 
Area 
Alcove 5 
Source: Revised from BSC (2003 [160109], Figure 1b) using 800-IED-WIS0-00101-000-00A (BSC 2003 [162289]) 
NOTE: The boundary of the current repository design is also given. 
Figure 6.6-5. Map Showing Approximate Location of Surface-Based Boreholes NRG-7a, NRG-6, SD- 
12, and UZ#16 and SHT/DST Heater Test Area (Alcove 5)

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MDL-NBS-HS-000019 REV00 137 August 2003 
-14.50 
-14.00 
-13.50 
-13.00 
-12.50 
-12.00 
-11.50 
-11.00 
log (k) 
NRG6 NRG7a SD-12 UZ#16 SHT&DST Niche3566 Niche3650 Niche3107 Niche4788 
Source: BSC (2003 [161773], Figure 2) 
NOTE: X are the geometric means. Bars indicate mean plus/minus one standard deviation. 
Figure 6.6-6. Mean Fracture Permeabilities for Different Locations in the Tptpmn Unit 
A more detailed analysis of intermediate-scale variation of permeability among the four vertical 
boreholes is presented in Table 6.6-5. Between 7 and 19 vertical intervals have been tested in the 
Tptpmn in each of these boreholes. The resulting air-permeability data for each test interval are 
provided in DTN: GS960908312232.013 [105574]. From these data, the mean permeability sur 
was calculated for each location. In a second step, the statistics of the variation of this parameter 
over the repository unit were derived. Data from the SHT/DST heater test block have not been 
considered in this analysis, mainly because of the large, inconsistent measurement scale. Also, 
some of the tested boreholes are in close proximity to the alcoves or drifts, and thus they may not 
represent undisturbed rock. As shown in Table 6.6-5, the average of the mean log-permeability 
values at the four vertical boreholes is 12.2, which is one order of magnitude higher than the 
intermediate-scale mean  estimated from the niche data. The standard deviation is 0.34, about 
50% larger than from the undisturbed niche data. It appears that the variability inferred from the 
niche experiments is on the low side when compared with additional information from vertical 
boreholes. To define a cautiously realistic variability for TSPA-LA, the larger s of 0.34 should 
be used. It will be discussed below whether the one-order-of-magnitude difference in the mean 
values can be fully explained by scale effects. (Note that there are minor differences in the mean 
permeabilities of boreholes NRG7a and UZ#16 between Figure 6.6-6 and Table 6.6-5. These 
differences stem from a slightly different averaging procedure. For the analysis in Table 6.6-6, 
air-injection intervals that intersect two geological units have been assigned to both units.)

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Table 6.6-5. Mean Permeabilities of Undisturbed Rock from Tptpmn Unit Measured in Surface-Based 
Boreholes and Intermediate-Scale Variability Statistics over the Repository Rock Block 
Location Number of 
Intervals 
Mean sur 
(in log k) 
Interval 
Length (m) 
NRG-6 7 -12.2 4.31 
NRG-7a 8 -12.5 3.5 
SD-12 7 -11.8 4.6 
UZ#16 19 -12.5 4 
Statistics over All Four 
Locations 
Number of 
Locations 
Mean  
(in log k) 
Std. Dev. s Std. 
Error of 
Mean SE 
Tptpmn 4 -12.2 0.34 0.17 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
1 One interval length is 11.3 m in NRG-6. All others are 4.3 m. 
While many theoretical upscaling approaches are available in the literature, an upscaling method 
for highly heterogeneous porous media is described by the following expression (Paleologos et 
al. 1996 [105736], p. 1336, Eq. (26)): 
)] 2 / 1 ( ) (ln exp[ 2 D k k k S S S eff - = s (Eq. 1) 
where keff is the effective permeability at a larger scale L (about 3.5 to 4.6 meter scale), kS is the 
geometric mean of the small-scale permeability (about 1-foot scale), sS(ln kS) is the standard 
deviation of the natural log-transformed small-scale permeability, and D is a function of the 
spatial dimensions and the correlation scale. The exponential expression in Equation (1) is 
always larger than or equal to 1, indicating that the upscaled effective permeability is always 
larger than or equal to the small-scale geometric mean. Using an approximate value of 1 m for 
the correlation scale . of permeability in the niches, estimated from BSC (2003 [162267], Table 
13), and with L in the range between 3.5 and 4.6 m, the domain integral D can be evaluated from 
Figure 2 in Paleologos et al. (1996 [105736]) as approximately 0.3. (Note: 2 . = 2L/ . as defined 
in Paleogolos et al. (1996 [105736], p. 1335) becomes 7 for L = 3.5 and . = 1). In this case, 
Equation (1) becomes 
( ) ( ) 2 46 . 0 log log S S eff k k s + = (Eq. 2) 
where sS is the standard deviation of the log-transformed small-scale permeability, as given in 
Table 6.6-3. Note that the logarithm of the geometric mean of a sample is equal to the arithmetic 
mean of the log-transformed data. This relationship can be used to derive the expected value of 
log-transformed permeability measurements on a larger scale, eff, from the expected value of the 
small-scale measurements, S, given in Table 6.6-3. The resulting equation is given as: 
2 46 . 0 S S eff s   + = (Eq. 3)

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If the differences between the niche measurements and the data from surface-based boreholes are 
merely a result of scale effects, these values for eff should be consistent with the permeability 
values sur from the surface-based boreholes. 
Another approach for upscaling is to directly use the 1-foot permeability measurements of the 
niches. Estimates of effective permeabilities on a larger scale can be derived by calculating the 
arithmetic mean of these 1-foot values (not log-transformed) over appropriately long sections of 
the boreholes. It is presumed in this approach that the 1-foot data represent the exact spatial 
variability along the borehole, and that this exact variability is being measured using packed-off 
intervals of a larger interval length. It can be shown that the measured permeability over this 
larger interval would be the arithmetic mean of the 1-foot values within the interval. Since the 
arithmetic mean of heterogeneous data gives more weight to large values, the resulting effective 
permeability is higher than the geometric mean of the 1-foot data. The proposed upscaling 
approach was conducted for the four niches 3107, 3566, 3650, and 4788, using the permeability 
data given in the following DTNs: LB980901233124.101 [136593] and LB0011AIRKTEST.001 
[153155]. A 3.6-m interval length was chosen, comprising twelve 1-foot intervals. The available 
sample of undisturbed small-scale permeability at each niche location was divided into groups of 
twelve subsequent 1-foot measurements; i.e., one group represents the length covered by a 3.6 m 
measurement interval. The arithmetic mean of permeability was calculated for each group. Then 
the means were log-transformed, and the mean of all groups belonging to one niche location was 
derived. The final result is the mean effective permeability eff measured at a 3.6 m interval 
length, which can be compared with the mean of the small-scale measurements. 
Table 6.6-6 summarizes results for both upscaling methods outlined above, giving the predicted 
increase of permeability as a result of scale effects, and listing the adjusted upscaled permeability 
values for measurements conducted on a 3.6 m scale. While both methods are consistent in their 
trends, there are differences in magnitude. The average permeability increase as predicted by 
Equation (2) is 0.3 (in log10), while the average increase estimated from the arithmetic mean 
data analysis is 0.6 (in log10). Much of this larger increase, however, is provided by data from 
Niche 3566, where the fewest small-scale measurements are available. If data from Niche 3566 
were disregarded, the respective average increases would be approximately 0.3 and 0.4 for the 
two approaches (in log10).

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Table 6.6-6. Upscaling Factors for Air Permeabilities in the Tptpmn Derived Using Two Different 
Upscaling Approaches 
Small-Scale Measurements Upscaling Factor and Adjusted Mean eff 
Location Mean S 
(in log k) 
Std. Dev. sS Using Eq. (3) 
(in log k) 
Using Data Analysis 
(in log k) 
Niche 3107 -13.41 0.701 0.2 -13.2 0.4 -13.0 
Niche 3566 -13.01 0.921 0.4 -12.6 1.0 -12.0 
Niche 3650 -13.41 0.811 0.3 -13.1 0.4 -13.0 
Niche 4788 -13.01 0.851 0.3 -12.7 0.5 -12.5 
Mean All Niches -13.22  0.3 -12.9 0.6 -12.6 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
1 Data from Table 6.6-3 (undisturbed) 
2 Data from Table 6.6-4 (undisturbed) 
The estimated scaling factors are significantly smaller than the one-order-of-magnitude 
difference between the mean permeability of the four niches (-13.2) and the mean permeability 
from the four surface-based boreholes (-12.2). Or, in other words, the effective permeability 
values eff derived from upscaling the small-scale measurements are much smaller than the 
permeability values measured in surface-based boreholes. Thus, even though scale effects are 
considered to make both measurement scales comparable, the remaining difference is still half an 
order of magnitude. This may suggest that the four niche locations have relatively low 
permeability, compared to the average permeability the Tptpmn unit, and that the scale-corrected 
data from surface-based boreholes could be used to adjust the mean of the niches to higher 
values for TSPA-LA, which would generally reduce seepage. On the other hand, one should 
keep in mind in this abstraction process that the four niche measurements represent the more 
reliable data source for the purpose of seepage modeling: they represent the scale of interest. The 
surface-based data, on the other hand, should be handled with care because of uncertainties 
related to the upscaling analysis. Therefore, seepage abstraction does not incorporate this 
possible adjustment of the niche permeabilities to larger values. The additional information 
available from the surface-based boreholes is used as corroborative evidence demonstrating that 
the niche data provide conservative parameter estimates for mean permeability in the Tptpmn. 
Based on the discussion above, the parameter distributions for permeability variation within the 
middle nonlithophysal unit in TSPA-LA are defined as follows: The intermediate-scale mean 
log-permeability of the undisturbed measurements is 13.2, derived from the mean over four 
niche locations (Table 6.6-4). Cautiously realistic, the intermediate-scale variability is described 
by a standard deviation of 0.34, derived from the four surface-based borehole locations (Table 
6.6-5). This standard deviation is larger than the one calculated from the niches. The 
permeability increase as a result of excavation is conservatively accounted for by increasing the 
mean permeability by one order of magnitude. This is consistent with modeling predictions, but 
smaller than the measured effect of excavation. The final probability distribution is given by a 
mean of  
= 12.2 and a standard deviation of s 
= 0.34 (log-normal distribution); the 95%- 
confidence interval of this distribution ranges approximately from 12.9 to 11.5. Uncertainty 
related to this distribution is discussed in Section 6.6.2.3.

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6.6.2.2.2 Lower Lithophysal Unit 
Table 6.6-7 summarizes statistical parameters for intermediate-scale variability within the Tptpll, 
based on the small-scale measurements conducted in Niche 1620 and borehole SYBT-ECRBLA#
2 (see Table 6.6-3). It is recognized that the supporting sample size is very small: There is 
only one location where pre-excavation data have been measured (Niche 1620). Two locations 
are available for post-excavation data (Niche 1620 and borehole SYBT-ECRB-LA#2, see Table 
6.6-3), though the relatively close proximity limits the value of these data. Hence, the small 
standard deviation of 0.16 measured for the disturbed-zone permeabilities, relating to a small 
95% confidence interval that covers about half an order of magnitude, is arguably not 
representative for the variability of Tptpll permeability values over the repository block. 
Additional air-permeability data are therefore evaluated. 
Table 6.6-7. Intermediate-Scale Variability Statistics (Mean  and Standard Deviation s) of Permeability 
over the Repository Rock Block, for Pre- and Post-Excavation Data in the Tptpll 
Parameter Number of 
Samples 
Mean  
(in log k) 
Std. Dev. s Std. Error 
of Mean SE 
Pre-excavation Permeability 
(undisturbed) 
1 -11.5   
Post-excavation 
Permeability 
(disturbed) 
2 -10.8 0.16 0.11 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
Surface-based boreholes NRG-7a and UZ#16 offer undisturbed air-permeability values for the 
Tptpll measured over interval lengths of 3.5 and 4 m, respectively (see Figure 6.6-5 for the 
location of these boreholes). The measured data are given in DTN: GS960908312232.013 
[105574]. No Tptpll measurements are available at boreholes NRG-6 and SD-12, but the 
hydrogeologically comparable upper lithophysal unit (Tptpul) has been tested at four locations, 
i.e., at NRG-7a, NRG-6, SD-12, and UZ#16. Table 6.6-8 below provides the calculated mean 
permeability sur at each of these borehole locations in the Tptpll and the Tptpul, and gives 
summary statistics of the variation of this parameter within the repository. 
Both units have consistent permeability values; the values at available locations are identical 
(NRG-7a) or differ by only 0.1 in log space (UZ#16). Over all locations, the intermediate-scale 
mean  is 12.1 in the Tptpll versus 11.8 in the Tptpul. Standard deviations are 0.58 versus 
0.47, respectively. The Tptpul values are considered the more reliable estimates for the 
lithophysal units (because the statistical analysis is based on a sample size of four) and shall be 
used below. Data analysis suggests that the differences in the intermediate-scale means and 
standard deviations can mainly be attributed to the one permeability value of 12.5 at NRG-7a, 
which is significantly smaller than those at all other measurement locations. 
Comparison between the summary statistics over the surface-based boreholes, given in Table 
6.6-8, and the Niche 1620/borehole SYBT-ECRB-LA#2 data, given in Table 6.6-7, indicates

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significant differences. It appears that the standard deviation estimated from the small-scale 
measurements ( s 
= 0.16) is not representative of the intermediate-scale spatial variability within 
the Tptpll. Therefore, the larger value of 0.47 derived from surface-based boreholes (while using 
the Tptpul value as explained above) is recommended for use in the TSPA-LA. 
Table 6.6-8. Mean Permeabilities of Undisturbed Rock from Tptpll and Tptpul Units Measured in Vertical 
Boreholes and Intermediate-Scale Variability Statistics over Repository Rock Block 
Lower Lithophysal Unit (Tptpll) Upper Lithophysal Unit (Tptpul) 
Location Number of 
Intervals 
Mean sur 
(in log k) 
Interval 
Length (m) 
Number of 
Intervals 
Mean sur 
(in log k) 
Interval 
Length (m) 
NRG-6    5 -11.7 4.31 
NRG-7a 16 -12.5 3.5 10 -12.5 3.5 
SD-12    6 -11.4 4.6 
UZ#16 18 -11.7 4 5 -11.8 4 
Statistics over all 
Locations 
Number of 
Locations 
Mean  
(in log k) 
Std. Dev. 
s 
Std. Error 
of Mean 
SE 
Tptpll 2 -12.1 0.58 0.41 
Tptpul 4 -11.8 0.47 0.23 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
1 Two interval lengths are 11.3 m in NRG-6. All others are 4.3 m. 
As for the mean permeability, scaling effects need to be accounted for to make the different 
estimates from surface-based boreholes and from small-scale niche experiments comparable. 
Using the same two upscaling approaches as for the middle nonlithophysal unit, the upscaling 
factors for Niche 1620 are 0.6 from Equation (2) and 0.7 from the arithmetic-mean data analysis 
(Table 6.6-9). One can use these upscaling factors to adjust the mean permeability from the 
surface-based borehole data, making them representative of 1-foot-interval measurements. With 
an upscaling factor of 0.7 from the arithmetic data analysis and using the more reliable Tptpul 
permeability value of sur = 11.8 (Table 6.6-8), the resulting scale-adjusted mean permeability 
for the surface-based borehole data is 12.5. This permeability value is one order of magnitude 
smaller than the mean pre-excavation permeability of  = 11.5 from the small-scale niche 
measurements (Table 6.6-7). Quite possibly, Niche 1620 is located in a fairly permeable section 
of the lower lithophysal unit and may not be representative for all other areas in the repository. 
For the purpose of seepage abstraction, the permeability value measured in Niche 1620 should be 
adjusted to smaller values to account for the possible existence of less permeable regions in the 
repository. It is therefore proposed to use adjusted permeability distributions for the Tptpll with a 
decreased mean permeability of 12.0, which is derived from simply averaging the respective 
values from the niche analysis (-11.5) and the scale-adjusted surface-based data (-12.5). This 
shift in the parameter distribution is conservative, because smaller permeabilities generally lead 
to more seepage. One should keep in mind in this abstraction process that the measurements in 
Niche 1620 represent the more reliable data source for the purpose of seepage modeling: they 
represent the scale of interest. The surface-based data, on the other hand, should be handled with

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care because of uncertainties related to the upscaling analysis. In light of this discussion, a 
permeability decrease by half an order of magnitude is a cautious, yet realistic parameter choice 
for the Tptpll unit. 
Based on the above discussion, the parameter distributions for permeability variation within the 
lower lithophysal unit in TSPA-LA are defined as follows: The intermediate-scale mean logpermeability 
of the undisturbed measurements is 12.0, derived from the averaging the scaleadjusted 
mean of surface-based boreholes with the mean permeability from the niche 
measurements. Cautiously realistic, the intermediate-scale variability can be described by a 
standard deviation of 0.47, derived from the four surface-based borehole locations. This standard 
deviation is significantly larger than the one calculated from the niche/systematic testing data. 
The permeability increase as a result of excavation is conservatively accounted for by adjusting 
the mean permeability by a factor of 3.5 (Table 6.6-3). This is consistent with the measured 
effect of excavation, but less than predicted from the THM modeling studies (see Section 
6.6.2.1). The final probability distribution for the Tptpll is given by a mean of  
= 11.5 and a 
standard deviation of s 
= 0.47 (log-normal distribution); the 95% confidence interval of this 
distribution ranges approximately from 12.4 to 10.6. Compared to the middle nonlithophysal 
unit, the resulting distribution of disturbed-zone permeability in the Tptpll has a larger mean and 
a larger standard deviation. Uncertainty related to this distribution is discussed in Section 6.6.2.3. 
Table 6.6-9. Upscaling Factors for Air-Permeability Measurements in the Tptpll Derived Using Two 
Different Upscaling Approaches 
Small-Scale Measurements Upscaling Factor and Adjusted Mean eff 
Location Mean Sf 
(in log k) 
Std. Dev. sS Using Eq. (2) 
(in log k) 
Using Data Analysis 
(in log k) 
Niche 1620 -11.51 1.121 0.6 -10.9 0.7 -10.8 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
1 Data from Table 6.6-3 
6.6.2.3 Uncertainty 
The different sources of uncertainty related to the intermediate-scale, disturbed-zone fracture 
permeability distribution are as follows: 
Measurement Uncertainty: 
Air-injection testing is the main method of estimating fracture permeability in the Yucca 
Mountain UZ. The measurement methodology for the niche test dataisolating borehole 
sections using an inflatable packer system, injecting compressed air, and monitoring the 
pressure responseis described in detail in Attachment I of BSC (2001 [158463]). The 
flow rate of air is controlled by four different sizes of mass flow controllers from 1 to 500 
standard liters per minute, ensuring that the anticipated flow rates can be prescribed with 
sufficient accuracy (BSC 2001 [158463], Attachment I). Instrumentation error of the 
pressure sensors is about 0.3 kilopascals and thus negligible (BSC 2001 [158463], 
Attachment I). Short circuiting of gas flow between adjacent boreholes (or borehole

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intervals), a potential error source of measurements conducted in the SHT (BSC 2002 
[160771], Section 6.2.2.4.2) is of no concern in the niche tests because all boreholes in 
one niche location are packed-off with multiple packer strings at one time. Therefore, no 
measurement uncertainty is included in and propagated through seepage abstraction. 
Conceptual Model Uncertainty: 
The measured pressure response from the injection tests is converted into airpermeability 
values using the modified Hvorslevs formula (LeCain 1995 [101700], p. 
10, Eq. (15)), derived for a steady-state ellipsoidal flow field around a finite line source. 
For post-excavation tests, where the niche opening acts as a constant pressure boundary, 
a cylindrical solution is adopted with an ambient constant pressure boundary at the 
external radius (BSC 2001 [158463], Section 6.1.2.1). While the formulas used are based 
on a simplified geometric configuration, the derived permeability values are expected to 
adequately represent the fracture continuum permeability of the medium. Also, due to the 
consistency of the conceptual models for seepage calibration (SCM) and seepage 
prediction (SMPA and TH Seepage Model), a possible bias in estimated air 
permeabilities would be removed from the predictive results because the calibrated 
capillary-strength parameter implicitly accounts for the impact of this bias. A final 
contribution to conceptual model uncertainty of fracture permeability may stem from the 
THM results used to constrain the choice of post-excavation parameters. However, the 
model results are compared (validated) with pre- and post-excavation measurements in 
the niches that give direct evidence of excavation effects. For abstraction, the more 
conservative of the resulting disturbed-zone permeability values is used to acknowledge 
the remaining uncertainty. 
Estimation Uncertainty: 
There is no estimation uncertainty for permeability because the proposed parameter 
distributions covering intermediate-scale permeability differences, described by a lognormal 
distribution with mean  
and standard deviation s, are directly based on the 
permeability data from air-injection testing. Estimation uncertainty would only arise if 
the parameter of interest was estimated from a random process (e.g., from a Monte-Carlo 
analysis). Note that the impact of random variations of small-scale heterogeneity is 
directly evaluated in the seepage models by using several realizations of random fields. 
Spatial Variability Uncertainty: 
The main uncertainty source for permeability is related to the spatial variability assumed 
for this parameter, stemming from the limited number of data points available to derive 
the distribution parameters. Four (three1) niche locations are available in Tptpmn unit, 
compared to only one (two1) locations in the Tptpll. Therefore, additional information 
was used to better define appropriate probability distributions. For this purpose, data from 
surface-based boreholes were analyzed, adjusted to account for scale effects stemming 
from the longer measurement interval, and then compared to the niche data. In case of the 
Tptpll unit, where the surface-based data indicated that the niche measurements might 
represent a rather permeable section, the mean permeability was adjusted to smaller 
1 Numbers in parenthesis indicate excavation-disturbed measurement locations.

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values (which gives rise to more seepage). For standard deviation s, 
the larger value was 
chosen, to make sure that the potential variability of permeability is adequately covered. 
While the use of additional permeability data and the choice of conservative parameters 
have provided confidence in the developed parameter distributions, the spatial variability 
uncertainty is still considered significant because of the limited number of data points. 
This uncertainty is included in and propagated through the abstraction, using appropriate 
probability distribution functions. 
Similar to Section 6.6.1, uncertainty in the parameter of interest is quantitatively accounted for 
by varying the mean of the chosen probability distribution for spatial variability within 
appropriate ranges; i.e., by shifting the log-normal distribution for spatial variability to smaller or 
larger permeability values. Definition of these appropriate ranges is necessarily subjective, 
since it is based on scientific judgment. As pointed out above, the main contribution to 
uncertainty stems from the limited sample size constraining the spatial variability distribution. 
The method of choice in this abstraction is to apply a triangular uncertainty distribution with 
upper/lower bounds defined by plus or minus four standard errors. This adds a significant 
amount of additional parameter variability, believed to cover the uncertainty of the permeability 
estimates. As pointed out before, the standard error describes the potential uncertainty in the 
estimated mean of a sample of given size. A range of plus or minus two standard errors covers 
roughly the 95% confidence interval for the estimated mean. Thus, the defined range of the 
triangular distribution comprises sufficient uncertainty on account of the mean permeability and 
includes another two SE covering other sources of uncertainty. In light of the supporting 
information used to corroborate niche data, and considering the conservative choices made in 
defining the spatial variability distributions, this uncertainty range can be considered cautious, 
yet realistic. Note the difference between the uncertainty range chosen for permeability (4 SE) 
and the uncertainty range chosen for the capillary-strength parameter in Section 6.6.1.3 (3 SE). 
The difference accounts for our assessment that the spatial variability distributions for 
permeability are more uncertain than the ones for the capillary-strength, because of (a) the 
smaller sample size for permeability values, and (b) the fact that 1/a is an effective calibrated 
parameter that may be less dependent on rock type variability. 
A schematic of the spatial variability and uncertainty model for permeability in the middle 
nonlithophysal unit is given in Figure 6.6-7. The heavy blue line shows the triangular-shaped 
uncertainty distribution, assigning a probability to the mean of the normal-shaped spatial 
variability distribution. The most likely spatial variability distribution is the one defined in 
Section 6.6.2.2, with a probability of 1.47 corresponding to the peak value of the triangular 
distribution. This spatial variability distribution has a mean of 12.2 in log space. The triangular 
distribution for shifting this mean has a range of about 0.68 (using the standard error of 0.17 as 
given in Table 6.6-5). Thus, least likely are the two bounding cases with a mean of 12.9 
(minimum) and 11.5 (maximum), respectively, where the triangular distribution indicates a zero 
probability. Together, in consideration of spatial variability and uncertainty, the range of Tptpmn 
permeabilities to be used in TSPA-LA is quite large; using the approximate 95% confidence 
intervals of the minimum and the maximum spatial variability distribution as an estimate, this 
range extends from 13.6 to 10.8 (almost three orders of magnitude, from about 4.0  10-13 m2 
to about 1.6  10-11 m2).

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The respective probability distributions for the lower lithophysal unit are illustrated in Figure 
6.6-8. As a result of the larger standard deviation, both the spatial variability distribution and the 
uncertainty distribution are wider compared to the Tptpmn. The triangular distribution of 
uncertainty covers a range of about 0.92 (using the standard error of 0.23 as given in Table 
6.6-8). This shifts the mean of the spatial variability distribution (most likely value at 11.5 in 
log space) to bounding values of 12.4 (minimum) and 10.6 (maximum). The approximate 95% 
confidence interval of all possible spatial variability distributions ranges from 13.3 to 9.7 
(almost four orders of magnitude; from about 5.0  10-14 m2 to about 2.0  10-10 m2). 
Permeability [log k] 
Probability 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 0.0 
0.2 
0.4 
0.6 
0.8 
1.0 
1.2 
1.4 
1.6 
1.8 
4 SE 4 SE 
-11.5 
-12.2 
-12.9 
Probability Distribution 
for Maximum Mean 
Probability Distribution 
for Most Likely Mean 
Probability Distribution 
for Minimum Mean 
NOTE: The black dashed line shows the most likely spatial probability distribution (at the peak of the triangular 
distribution). The green and the red dashed lines show the least likely spatial probability distributions (at the 
minimum and the maximum of the triangular distribution). Based on Tables 6.6-3, 6.6-4, 6.6-5 and 6.6-6 as 
well as discussion in Sections 6.6.2.2 and 6.6.2.3. 
Figure 6.6-7. Triangular Probability Distribution (Heavy Blue Line) for Covering Uncertainty of 
Permeability in the Tptpmn by Varying the Mean of the Spatial Probability Distribution

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Permeability [log k] 
Probability 
-14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 0.0 
0.2 
0.4 
0.6 
0.8 
1.0 
1.2 
1.4 
1.6 
1.8 
4 SE 4 SE 
-10.6 
-11.5 
-12.4 
Probability Distribution 
for Maximum Mean 
Probability Distribution 
for Most Likely Mean 
Probability Distribution 
for Minimum Mean 
NOTE: The black dashed line shows the most likely spatial probability distribution (at the peak of the triangular 
distribution). The green and the red dashed lines show the least likely spatial probability distributions (at the 
minimum and the maximum of the triangular distribution). Based on Tables 6.6-3, 6.6-7, 6.6-8, and 6.6-9 as 
well as discussion in Sections 6.6.2.2 and 6.6.2.3. 
Figure 6.6-8. Triangular Probability Distribution (Heavy Blue Line) for Covering Uncertainty of 
Permeability in the Tptpll by Varying the Mean of the Spatial Probability Distribution

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6.6.3 Capillary Strength and Permeability Distributions for the Tptpul and Tptpln Units 
According to the current repository design, a small fraction of the emplacement drifts will be 
located in the Tptpul and the Tptpln units at Yucca Mountain. Thus, additional distributions are 
needed that cover the spatial variability and uncertainty of the seepage-relevant parameters 
(capillary strength and permeability) in these units. Due to their limited importance for TSPA, 
results from seepage experiments and small-scale air-injection tests are not available for these 
units. It should be noted, however, that the Tptpul and the Tptpln are lithologically similar to the 
main repository units Tptpll and Tptpmn; all are welded units with moderate to intense 
fracturing. From such considerations, the two nonlithophysal units, the Tptpmn and the Tptpln, 
should have similar hydrogeological characteristics. For example, the fracture frequency in the 
Tptpln is four fractures per meter (BSC 2003 [161773], Table 6), which is similar to the Tptpmn. 
The same is expected for the units with lithophysal cavities embedded in the rock, the Tptpll and 
the Tptpul. However, fracturing in the Tptpul appears to be less intense than in the Tptpll. The 
fracture frequency in the Tptpul is about one fracture per meter compared to about three fractures 
per meter in the Tptpll (BSC 2003 [161773], Table 6). Note that these average fracture 
frequencies do not include fractures with trace lengths smaller than 1 m. 
From the above discussion, the parameter distribution for the Tptpmn unit is expected to provide 
a reasonable estimate for the distributions of capillary-strength parameters in the Tptpln. 
Differences in the fracture characteristics between the Tptpll and the Tptpul may result in 
differences in the effective capillary strength in these units. However, as was pointed out in 
Section 6.6.1.2, the calibrated capillary-strength values are effective parameters that may not be 
strongly affected by the rock type. It may be concluded from the same logic that the distribution 
derived for the Tptpll should be reasonably representative of seepage behavior in the Tptpul. 
Therefore, and in view of the limited importance of these additional units, the capillary-strength 
distributions in the Tptpul and the Tptpln are based on the respective Tptpll and Tptpmn results 
discussed in Section 6.6.1. 
While there are no small-scale permeability data in the Tptpul or the Tptpln, information on the 
permeability in these units can be derived from air injection tests conducted in surface-based 
boreholes. As pointed out in Section 6.6.2.1, permeability values from surface-based boreholes 
cannot be directly used for seepage evaluation purposes because (a) the measurement scale is 
different from the required 1-foot scale and (b) the measurements are conducted in undisturbed 
fractured rock. However, they are useful in comparison with available surface-based 
measurements conducted in the main repository units, in order to assess similarities between the 
respective lithophysal and nonlithophysal units. Such comparison was already conducted for the 
Tptpul and the Tptpll, demonstrating that both units have consistent permeability values (Table 
6.6-8). Based on this assessment, the Tptpul data were utilized in Section 6.6.2.2 to support the 
development of permeability distributions for the Tptpll. Consequently, these permeability 
distributions for the Tptpll also represent the Tptpul unit. 
Permeability data for the Tptpln unit are available at surface-based boreholes SD-12 and UZ#16 
(DTN: GS960908312232.013 [105574]). Table 6.6-10 below provides the mean permeability of 
the several test intervals at each of these locations, and also gives summary statistics of the 
variation of this parameter. The mean permeability value over both locations is 11.9 (in log10); 
the standard deviation is 0.04. These values need to be compared with those given for the

Abstraction of Drift Seepage U0120 
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Tptpmn unit (Table 6.6-5). The mean permeability of the Tptpln is slightly larger than the one 
measured for the Tptpmn, the spatial variability as indicated by the standard deviation is much 
smaller. These results would indicate that less seepage should be expected in the Tptpln unit, 
because both a larger mean and a smaller standard deviation would tend to reduce the overall 
seepage (see Section 6.8.2). However, the sample size of two surface-based boreholes is rather 
small, and the derived statistics may not be fully representative of the entire unit. Therefore, for 
seepage abstraction, the Tptpln unit has been assigned the same spatial variability and 
uncertainty distributions as the Tptpmn, which is a conservative parameter choice with respect to 
seepage. 
Table 6.6-10. Mean Permeabilities of Undisturbed Rock from Tptpln Unit Measured in Surface-Based 
Boreholes and Intermediate-Scale Variability Statistics over the Repository Rock Block 
Location Number of 
Intervals 
Mean sur 
(in log k) 
Interval 
Length (m) 
SD-12 6 -11.9 4.6 
UZ#16 14 -11.9 4 
Statistics over all two 
Locations 
Number of 
Locations 
Mean  
(in log k) 
Std. Dev. s Std. Error of 
Mean SE 
Tptpln 2 -11.9 0.04 0.03 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment III 
Thus, in summary, the parameter distributions developed for the Tptpll unit are suitable for the 
Tptpul unit. Similarly, the parameter distributions for the Tptpmn can be used for the Tptpln. 
This simplified treatment is considered cautiously realistic. Additional testing planned to be 
conducted in the Tptpul and the Tptpln would provide a better data basis, but is not considered 
necessary due to the limited importance of these units. 
6.6.4 Percolation Flux and Flow Focusing 
The magnitude and spatial distribution of local percolation fluxes at the repository horizon are 
other key parameters affecting seepage into drifts. The larger these parameters, the higher the 
potential for seepage to occur and the larger the amount of water that can seep into drifts. In the 
ambient seepage abstraction, the spatial and temporal distribution of percolation fluxes in the UZ 
is provided by the site-scale UZ Flow and Transport Model (BSC 2003 [163045]). This model 
derives relevant information on the overall flow and transport fields at the Yucca Mountain, 
accounting for climate changes and related uncertainties, variability in net infiltration, and the 
effects of different stratigraphic units and faults. However, because of the large model area, the 
spatial resolution of the model is much larger than the extent of drift-scale seepage models, and 
layer-averaged properties are used within stratigraphic units. Thus, intermediate-scale 
heterogeneity is not represented in the UZ Flow and Transport Model. This heterogeneity may 
lead to focusing of flow on a scale smaller than the resolution of the site-scale model; i.e., it may 
increase the site-scale fluxes in some areas, while reducing them in other areas. The additional 
variability and uncertainty of percolation flux stemming from this effect is accounted for in the

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 150 August 2003 
seepage abstraction by appropriate flow focusing factors, to be multiplied with the percolation 
flux distribution from the site-scale model (see Section 6.6.4.2). The resulting flux distribution is 
expected to represent the local percolation flux distribution needed as input to the predictive 
drift-scale seepage models (e.g., the SMPA or the TH Seepage Model). 
6.6.4.1 Percolation Flux from the Site-Scale Model 
For ambient flow conditions, the 3-D spatial flux distributions in the UZ are provided by the sitescale 
UZ Flow and Transport Model, as documented in BSC (2003 [163045]). The site-scale 
model incorporates the entire Yucca Mountain UZ; it accounts for the main stratigraphic units 
using layer-averaged rock properties and represents the major faults. Relevant rock properties of 
each hydrogeologic unit (for fractures, matrix, and fault zones) have been calibrated against 
saturation data, water-potential data, pneumatic-pressure data, perched-water data, temperature 
data, and geochemical data (BSC 2003 [160240], Section 6.2 and BSC 2003 [163045], Sections 
6.2 through 6.5). The calibrated model is validated by comparison of model results with 
additional data that have not been used for calibration, as discussed in BSC (2003 [163045], 
Section 7). Model validation includes comparison with saturation, water-potential, pneumaticpressure, 
perched-water, temperature, and geochemical data (carbon-14, chloride, and strontium 
in the pore water, calcite mineral abundance) as well as results from Alcove 8/Niche 3 seepage 
tests. Model predictions are conducted for three different climate conditions that are expected to 
occur during the 20,000-year time period considered in TSPA (USGS 2001 [158378]). The first 
climate stage is a continuation of the current modern-day climate conditions from present day to 
600 years into the future (present-day climate). The second climate stage begins at 600 years 
from present day and is characterized as a monsoon climate with wetter summers than the 
modern climate. The third climate stage begins at approximately 2,000 years from present day 
and is characterized as a glacial transition climate with (on average) higher infiltration. The 
glacial transition climate is predicted to last the remainder of the 20,000 years. 
Uncertainty in climate predictions is accounted for by defining three alternative climate scenarios 
referred to as the mean, the upper-bound and the lower-bound scenarios (USGS 2001 [158378]). 
Note that the occurrence probability of each climate scenario is provided in the Analysis Report 
Analysis of Infiltration Uncertainty (BSC 2003 [163740]). This can be used in TSPA to assign 
the appropriate weight to the considered site-scale flow field. The mean climate scenario is the 
scenario that gives the best fit between the UZ model results and the available data (BSC 2003 
[163045], Sections 6.3 through 6.5). Based on the precipitation rates and temperature predicted 
for the future climates, distributions of net infiltration have been simulated as documented in 
Simulation of Net Infiltration for Modern and Potential Future Climates (USGS 2001 [160355]). 
Relatively high net infiltration rates occur generally in the northern portion of the site at high 
elevations and along the ridge where fractured bedrock is exposed. 
The infiltration distributions, available for the three climate stages and the associated lowerbound, 
mean, and upper-bound scenarios, are used as direct inputs at the upper boundary of the 
site-scale UZ Flow and Transport Model (BSC 2003 [163045], Section 6.1.3). Steady-state 
simulation runs are conducted with this model for each climate stage and climate scenario, 
resulting in a total of nine 3-D flow fields that give the spatial distribution of percolation flux 
(BSC 2003 [163045], Section 6.1.4). TSPA uses the steady-state flow fields as being 
representative over the entire time period of the respective climate stage. Therefore, stepwise

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 151 August 2003 
changes in percolation flux occur at 600 years (transition from present-day to monsoon climate) 
and at 2,000 years (transition from monsoon to glacial transition climate). Consequently, the 
ambient seepage ratescalculated as a function of percolation fluxalso have a stepwise 
change at 600 years and at 2,000 years, corresponding to climate changes. The times required for 
the flow conditions in the UZ to adjust to the stepwise changes in net infiltration are short 
compared to the duration of the different climatic stages. Therefore the steady-state conditions 
are representative over most of the TSPA time period, except for short durations following the 
stepwise changes at 600 and 2,000 years when the flow field equilibrates. Since the climate 
changes both lead to an increase in average percolation, the steady-state representation leads to 
an overestimation of seepage rates during these equilibration periods. 
In general, percolation flux through the Tiva Canyon welded tuff unit, the first fractured bedrock 
unit below alluvial deposits, is governed by the imposed distribution of net infiltration. Flow in 
this unit occurs mostly in the fractures before entering the underlying Paintbrush nonwelded 
hydrogeological unit (PTn). With its characteristics of high matrix porosity and low fracture 
frequency, and the existence of tilted layers of nonwelded vitric and bedded tuff, the PTn can 
divert a fraction of the percolating water to intercepting faults and fault zones (CRWMS M&O 
2000 [141187], Section 6.1.2; Wu et al. 2002 [161058]; BSC 2003 [163045], Section 6.2.2). 
Also, the PTn unit dampens and homogenizes downward-moving transient pulses from surface 
infiltration events. Therefore, the percolation distribution below the PTn unit is considerably 
different from the distribution of net infiltration, both spatially and temporally. Note that this 
difference is substantiated by geochemical data obtained at Yucca Mountain, as discussed in 
BSC (2003 [163045], Section 6.5). The geological unit below the PTn is the Topopah Spring 
welded tuff (TSw), a thick, densely fractured unit that hosts the repository. Results from the UZ 
Flow and Transport Model indicate that the flux in the TSw is mainly vertical without significant 
lateral diversion; as a result, the flux distribution at the PTn/TSw-interface should be similar to 
the flux distribution at the repository horizon. 
Seepage abstraction uses the percolation flux distributions across the PTn/TSw-boundary to 
provide input to the seepage look-up table. These fluxes incorporate the important effects of flow 
dampening and lateral flow diversion in the PTn, and they are fairly representative of the fluxes 
at the repository horizon. The rationale for using the PTn/TSw fluxes, instead of using the flux 
distribution directly at the repository horizon, is mainly based on consistency considerations. The 
effect of flow focusing is estimated with a submodel that has the bottom of the PTn as its upperboundary 
(see discussion in the following Section 6.6.4.2). Also the Multiscale 
Thermohydrologic Model, used for the simulation of in-drift TH conditions for feed into TSPA, 
has its upper boundary condition at the bottom of the PTn (to be presented in the upcoming 
revision to the Model Report, Multiscale Thermohydrologic Model (BSC 2001 [158204], which 
will have Document Identifier ANL-EBS-MD-000049 REV 01). 
The spatial percolation flux distributions across the PTn/TSw interface are given in DTN: 
LB0302PTNTSW9I.001 [162277], for all three climate stages and scenarios. Flux values have 
been extracted from the 3-D model results for each vertical column of the model grid. Contours 
of these distributions are presented in Figure 6.6-9, using the glacial transition period of the 
mean climate scenario as an example. Note that the model domain is intersected by several major 
fault zones. The percolation fluxes in these fault zones are typically much larger than fluxes in

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 152 August 2003 
nonfault zones. In fact, the extreme values of percolation occur in these zones. Relatively high 
fluxes are also found in the north of the model domain and at the mountain ridge. 
Nevada Coordinate E-W (m) 
Nevada Coordinate N-S (m) 
168000 170000 172000 
230000 
231000 
232000 
233000 
234000 
235000 
236000 
237000 
238000 
239000 
40.0 
38.0 
36.0 
34.0 
32.0 
30.0 
28.0 
26.0 
24.0 
22.0 
20.0 
18.0 
16.0 
14.0 
12.0 
10.0 
8.0 
6.0 
4.0 
2.0 
0.0 
mm/yr 
> 
Source: LB0302PTNTSW9I.001 [162277] 
NOTE: Black symbols denote center nodes of UZ model grid. 
Figure 6.6-9. Contour Map of Vertical Fluxes at the PTn/TSw Interface for the Glacial Transition Climate 
(Mean Climate Scenario) 
For use in TSPA, only those fluxes are needed that are representative of the repository area, 
because only these fluxes are relevant for seepage. Extracting the repository fluxes gives the 
distribution of percolation fluxes to be used in TSPA. Figure 6.6-10 shows the distribution of 
extracted PTn/TSw-fluxes for the repository columns of the UZ model grid. (Note that the socalled 
contingency area at the southern tip of the repository is not included.) Table 6.6-11 
provides statistical measuresaverage percolation and maximum percolationcalculated for the 
flux distributions of the (a) entire UZ domain, and (b) the repository domain. Note that the 
statistical calculation is conducted without accounting for differences in the cross-sectional area 
of each vertical column. The impact of this simplification is small, however, and not relevant for 
the estimation of seepage. This is because the horizontal area of vertical columns is fairly 
uniform over the repository area.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 153 August 2003 
Nevada Coordinate E-W (m) 
Nevada Coordinate N-S (m) 
168000 170000 172000 
230000 
231000 
232000 
233000 
234000 
235000 
236000 
237000 
238000 
239000 
40.0 
38.0 
36.0 
34.0 
32.0 
30.0 
28.0 
26.0 
24.0 
22.0 
20.0 
18.0 
16.0 
14.0 
12.0 
10.0 
8.0 
6.0 
4.0 
2.0 
0.0 
mm/yr 
> 
Source: LB0302PTNTSW9I.001 [162277] 
NOTE: Symbols denote center nodes of UZ model grid over the repository area. 
Figure 6.6-10. Extracted Vertical Fluxes at the PTn/TSw Interface for the Glacial Transition Climate 
(Mean Climate Scenario) 
For both the mean and the upper-bound scenario in Table 6.6-11, the percolation fluxes increase 
significantly as a result of the imposed climate changes at 600 and at 2,000 years. This is 
different for the lower-bound scenario. Here, at overall small percolation rates, the glacial 
transition climate has less percolation than the monsoon climate. The observed trends in 
percolation flux over the UZ model domain are consistent with the trends in net infiltration as 
reported in USGS (2001 [160355], Tables 6.9, 6.13, and 6.18). In general, the fluxes extracted 
for the repository area are smaller than the fluxes over the entire UZ model domain, as indicated 
by the slightly smaller average values and the considerable differences in the maximum values. 
For comparison, Table 6.6-11 also gives statistics for repository fluxes without considering fault 
zones. While the average fluxes are hardly affected, the maximum percolation fluxes are 
significantly smaller without consideration of fault zones. In seepage abstraction, however, the 
large percolation fluxes in fault zone fluxes are included. This is because it is not clear at this 
point if the emplacement of waste canisters in fault zones can be entirely avoided. 
Figure 6.6-11 shows histograms of the distribution of percolation flux over the repository area, 
for the present-day, monsoon, and glacial transition climate stages of the mean climate scenario.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 154 August 2003 
The histograms demonstrate that the maximum values observed in Table 6.6-11 are in fact 
extreme cases that are very sparsely distributed and not representative of the majority of 
locations in the repository area. Most of these extreme cases are associated with fault zones. 
As already mentioned, the diversion capacity of the PTn unit is very important for the spatial 
distribution of percolation fluxes in the TSw. However, the characterization of groundwater flow 
within the PTn is critically dependent on detailed knowledge of the rock properties and the 
heterogeneity within the PTn unit. Related uncertainties have been studied in BSC (2003 
[163045], Section 6.6) by adjusting the PTn properties, allowing for considerably less lateral 
diversion. Based on this alternative property set, nine alternative flow fields have been simulated, 
and the related PTn/TSw fluxes have been provided in DTN: LB0305PTNTSW9I.001 [163690]. 
Table 6.6-11 gives the average and the maximum percolation value for the alternative flow 
model, using the mean climate scenario as an example. While the average fluxes over the 
repository area are not affected by the different PTn flow conceptualization, the maximum flux 
values are considerably smaller compared to the normal scenario. Apparently, less water is 
diverted towards fault zones in the alternative conceptual model. (Note that the mean fluxes over 
the entire UZ are slightly different between the two PTn flow concepts. Because of mass 
conservation, they should be identical for the respective climate stages. The differences occur as 
a result of neglecting the respective cross-sectional area of each vertical column in the statistical 
evaluation, as explained above.) Figure 6.6-12 shows histograms of the percolation flux 
distribution over the repository area for the alternative flow model, using the same interval size 
as in Figure 6.6-11. In addition to the different maximum flux values, the histograms in Figures 
6.6-11 and 6.6-12 reveal some qualitative differences in the distribution of the values. The 
impact of these differences is examined in Section 6.8.2, where seepage rates are calculated in a 
Monte Carlo analysis. It will be demonstrated that the resulting seepage rates are hardly affected, 
so that the alternative flow scenario does not need to be analyzed within the TSPA simulations.

Abstraction of Drift Seepage U0120 
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Table 6.6-11. Statistics of Percolation Flux Distributions at the PTn/TSw Interface 
Mean Climate Scenario: Flux in mm/year 
Climate Period Entire UZ Repository Area 
(used in TSPA) 
Repository Area without 
Fault Zones 
Present Day Average 
Present Day Maximum 
4.8 
111.1 
3.8 
39.9 
3.8 
20.6 
Monsoon Average 
Monsoon Maximum 
13.2 
211.6 
11.7 
127.9 
11.5 
61.3 
Glacial Transition Average 
Glacial Transition Maximum 
18.8 
276.5 
17.9 
192.4 
17.8 
90.9 
Lower-Bound Climate Scenario: Flux in mm/year 
Climate Period Entire UZ Repository Area 
(used in TSPA) 
Repository Area without 
Fault Zones 
Present Day Average 
Present Day Maximum 
1.1 
83.5 
0.4 
3.2 
0.4 
3.2 
Monsoon Average 
Monsoon Maximum 
4.8 
103.3 
4.3 
22.8 
4.4 
16.3 
Glacial Transition Average 
Glacial Transition Maximum 
2.5 
77.5 
1.9 
11.6 
2.0 
10.5 
Upper-Bound Climate Scenario: Flux in mm/year 
Climate Period Entire UZ Repository Area 
(used in TSPA) 
Repository Area without 
Fault Zones 
Present Day Average 
Present Day Maximum 
12.0 
197.5 
11.1 
80.3 
11.2 
44.0 
Monsoon Average 
Monsoon Maximum 
21.7 
358.7 
20.3 
161.1 
20.1 
97.9 
Glacial Transition Average 
Glacial Transition Maximum 
35.6 
530.2 
35.1 
282.2 
35.3 
164.1 
Alternative Flow Model for PTn Unit 
Mean Climate Scenario: Flux in mm/year 
Climate Period Entire UZ Repository Area 
(used in TSPA) 
Repository Area without 
Fault Zones 
Present Day Average 
Present Day Maximum 
4.4 
105.0 
3.8 
26.0 
3.9 
21.0 
Monsoon Average 
Monsoon Maximum 
12.6 
183.6 
11.8 
80.8 
11.7 
61.8 
Glacial Transition Average 
Glacial Transition Maximum 
18.2 
221.3 
17.9 
129.5 
18.0 
98.9 
Sources: DTNs: LB0305PTNTSW9I.001 [163690]; LB03033DSSFF9I.001 [163047] 
Output-DTN: LB0308AMRU0120.001 
NOTE: Computations documented in Attachment IV

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 156 August 2003 
(Present-day) 
0 10 20 30 40 50 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Monsoon) 
0 50 100 150 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Glacial Transition) 
0 50 100 150 200 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
Sources: DTN: LB0302PTNTSW9I.001 [162277] and DTN: LB03033DSSFF9I.001[163047] 
NOTE: The symbols in the histograms (i.e., fluxhist<0> and fluxhist<1>) denote the variable names given in the 
Mathcad 11 spreadsheet used for the calculation, see Attachment IV. Fluxes are extracted for the 
repository area. 
Figure 6.6-11. Histograms of Vertical Fluxes at the PTn/TSw Interface for the Mean Climate Scenario

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 157 August 2003 
(Present-day) 
0 10 20 30 40 50 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Monsoon) 
0 50 100 150 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Glacial Transition) 
0 50 100 150 200 
0 
50 
100 
Percolation Flux in mm/yr 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
Sources: LB0305PTNTSW9I.001 [163690] and LB03033DSSFF9I.001 [163047] 
NOTE: The symbols in the histograms (i.e., fluxhist<0> and fluxhist<1>) denote the variable names given in the 
Mathcad 11 spreadsheet used for the calculation, see Attachment IV. Fluxes are extracted for the 
repository area. 
Figure 6.6-12. Histograms of Vertical fluxes at the PTn/TSw Interface for the Mean Climate Scenario 
Using the Alternative Flow Concept in the PTn

Abstraction of Drift Seepage U0120 
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6.6.4.2 Flow Focusing 
In the framework of seepage abstraction, flow focusing denotes the potential concentration of 
downward flow in the UZ onto a particular drift segment. This flow concentration could increase 
the local percolation flux in some locations, which would then increase the amount of seepage in 
those locations. The potential for flow focusing stems from the scale difference between the UZ 
Flow and Transport Model, which provides the 3-D distribution of percolation fluxes in the UZ, 
and the drift-scale seepage models, which use these percolation fluxes as inflow at the top model 
boundary. While the site-scale model accounts for variability in net infiltration and explicitly 
models the different stratigraphic units and faults, it cannot represent the intermediate-scale 
heterogeneity within geological units. This is because of the layer-averaged rock properties and 
the relatively coarse gridding (on the order of about 100 m). Drift-scale seepage models, on the 
other hand, have a lateral model extent on the order of a few drift diameters; the model domain 
typically includes the vicinity of one particular drift segment. Consequently, the distribution of 
percolation fluxes in seepage abstraction needs to describe the variability of this parameter on the 
spatial resolution of a few drift diameters. Since the site-scale model does not explicitly describe 
this spatial detail, the percolation flux distributions derived from this model need to be adjusted 
by multiplication with appropriately distributed flow focusing factors. 
Note that flow focusing factors should not incorporate heterogeneity below the spatial resolution 
of a few drift diameters, since small-scale variability (on a scale of less than a meter) is explicitly 
accounted for in the drift-scale seepage models (the SMPA and the TH Seepage Model). It is 
shown in these models that small-scale heterogeneity is a key factor for seepage to occur; it gives 
rise to preferential-flow processes and increases the probability of local breaching of the 
capillary barrier at the rock-drift interface. (These small-scale flow processes are referred to as 
flow channeling hereafter.) In the framework of seepage abstraction, it is important to clearly 
distinguish between flow focusing and flow channeling. Flow focusing occurs on an intermediate 
scale and needs to be accounted for by appropriate factors. Flow channeling, on the other hand, 
occurs on a much smaller scale and is automatically included by using the seepage look-up tables 
of the SMPA. 
Flow focusing cannot be directly measured in the field. Therefore, flow focusing phenomena are 
addressed through models that are able to describe the intermediate-scale heterogeneity. Indirect 
field evidence can be used to support the models. As described in BSC (2001 [155950], Section 
4.3.2.), BSC (2001 [156609], Section 6.4.2) and further discussed in Bodvarsson et al. (2003 
[163443]), an intermediate-scale flow model was developed to specifically address the issue of 
spatial flow focusing, bridging the gap between the site scale and the drift scale. Most of the 
studies with this model were conducted in a two-dimensional vertical cross section of the 
unsaturated zone 100 m in horizontal extent and 150 m in vertical extent. The top boundary was 
chosen at the bottom of the PTn unit, and the bottom boundary at the repository horizon. The 
150 m vertical extent of the model corresponds to an average distance between the PTn/TSwinterface 
and the repository. For comparison purposes, a few additional model simulations were 
conducted with a 3-D model, using the same upper and lower boundaries (Bodvarsson et al. 2003 
[163443]). 
In contrast to the site-scale model, in which the rock properties within geological units are 
considered uniform, the intermediate-scale model represents the heterogeneity of the fractured

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 159 August 2003 
rock within the five stratigraphic layers residing in the model domain1. Similar to the SCM (BSC 
2003 [162267]) and the SMPA (BSC 2003 [163226]), the fracture network is treated as a 
stochastic heterogeneous continuum with variable permeability, while flow through the matrix is 
neglected. Modeling simulations were conducted for the 2-D and the 3-D model where uniform 
and non-uniform percolation fluxes were introduced at the top boundary of the heterogeneous 
domain. Flow focusing phenomena were then studied by comparison of the flux distribution 
measured at the repository horizon (bottom boundary) with the original flux distribution 
introduced at the top boundary. The additional variability stemming from the downward flow in 
the heterogeneous model domain is the variability that cannot be described by the site-scale 
results, and that needs to be accounted for by appropriate distributions of flow focusing factors. 
The spatial distribution of fracture permeability was based on air-permeability data stemming 
from surface-based boreholes and, where available, from heater test locations (BSC 2001 
[156609], Section 6.4.2.2). Most of these measurements were conducted with injection intervals 
of a few meters length; thus, the resulting geostatistical parameters appropriately represent 
intermediate-scale heterogeneity (BSC 2003 [161773], Section 6.1.1.1). These geostatistical 
parameters (mean and standard deviation of fracture permeability), and other properties of the 
fracture continuum (layer-averaged porosity, capillary pressure, and relative permeability 
functions), were based on those reported in a previous version (Revision 00) of the Calibrated 
Properties Model (CRWMS M&O 2000 [144426], Section 6). While there are differences 
between these properties and the most recent model calibrations in BSC (2003 [160240], Section 
6), these differences are rather small and not relevant for the scope of the flow focusing study. In 
particular, the variability of fracture permeability in the flow focusing study (Bodvarsson et al. 
2003 [163443], Table 1), as expressed by the standard deviation, is on the order of 0.5 to 0.7 in 
log space, similar to the range of standard deviations for the TSw given in BSC (2003 [160240], 
Table 4). Two correlation lengths of 1 m and 3 m were studied, representing fairly weak spatial 
correlation of fracture continuum permeability. This is consistent with findings reported in BSC 
(2003 [162267], Section 6.6.2.1). Based on the geostatistical data, multiple realizations of 2-D 
and 3-D spatially distributed fracture permeability values were generated and mapped to each 
gridblock. Note that the grid resolution in both 2-D and 3-D cases was on the order of less than a 
meter. Thus grid resolution of flow focusing model was considerably finer than the typical model 
extent of drift-scale seepage models (which is on the order of a few drift diameters). Since flow 
variability is more pronounced in a fine-resolution grid, the maximum flow focusing factors 
derived in this study are arguably on the high end of possible values, providing cautiously 
realistic flux estimates. This means that application of these flow focusing factors may 
overestimate seepage rates (see abstraction sensitivity studies in Section 6.8.2). 
Simulation runs were conducted for several flow scenarios with varying infiltration rates 
imposed at the top boundary (1, 5, 25, 100, and 500 mm/yr), different infiltration patterns 
(uniform versus localized), different correlation lengths, and 2-D versus 3-D representation 
(Bodvarsson et al. 2003 [163443]). As an example, Figure 6.6-13 shows the vertical liquid flux 
across the bottom of the model area for a 2-D simulation case with 5 mm/yr uniform percolation 
1 These stratigraphic layers are the tsw31, tsw31, tsw33, tsw34, and tsw35, using the nomenclature of the UZ model 
reports. Note that the tsw34 corresponds to the Tptpmn unit when using the nomenclature of the Geological 
Framework Model (BSC 2002 [159124]). Similarly, the tsw35 corresponds to the Tptpll unit. The relationship 
between these different unit names are given in several model reports, e.g., in BSC (2003 [160109], Table 11).

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imposed at the top and a spatial correlation of 1 m. The flux profile demonstrates a significant 
variability, with flux values ranging from about 0 to almost 30 mm/yr. Additional cross sections 
not presented here show statistically similar behavior, suggesting that the basic flow focusing 
characteristics develop within tens of meters from the top of the model (or a unit contact), but 
remain similar within a unit over extended vertical distances. 
Infiltration= 5mm/year, at bottom 
Base case, correlation length=1m, realization #1 
0
5 
10 
15 
20 
25 
30 
0 20 40 60 80 100 
X (m) 
Liquid Flux (mm/year) 
Source: BSC (2001 [156609], Figure 6.4-22b) 
Figure 6.6-13. Distribution of Vertical Fluxes at the Bottom of the Model Domain, for the Simulation 
Case with a 5 mm/yr Uniform Infiltration and a 1 m Correlation Length 
A distribution of flow focusing factors can be easily calculated from the cross-sectional flux data 
by normalizing the flux values to the average infiltration rate imposed at the top boundary. 
Factors larger than one correspond to increased percolation fluxes, and factors smaller than one 
correspond to decreased percolation fluxes compared to the average percolation. Figure 6.6-14 
shows the frequency distributions of normalized vertical flux (flow focusing factors) across the 
bottom boundary for the five infiltration cases, as presented in BSC (2001 [156609], Figure 
6.4-24). All infiltration cases, covering a range from 1 mm/yr to 500 mm/yr, are statistically 
similar. This means that flow focusing is independent of the percolation flux; i.e., the distribution 
of flow focusing factors is not correlated to the distribution of percolation fluxes. The majority of 
all normalized fluxes (focusing factors) ranges from 0 to about 2. The maximum flux that occurs 
in the model domain is generally about five to six times higher than the infiltration flux 
prescribed at the upper boundary. The minimum flux is almost zero.

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Base case, correlation length=1m, realization #1 
at bottom 
0
2
4
6
8 
10 
12 
14 
16 
18 
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 
Normalized Flux 
Frequency (%) 
1mm/yr 
5mm/yr 
25mm/yr 
100mm/yr 
500mm/yr 
Source: BSC (2001 [156609], Figure 6.4-24) 
Figure 6.6-14. Frequency Distribution of Vertical Fluxes at the Bottom of the Model Domain, for Different 
Uniform Infiltration Rates and a 1 m Correlation Length 
As pointed out in BSC (2001 [155950], Section 4.3.2.6), BSC (2001 [156609], p. 118), and 
Bodvarsson et al. (2003 [163443]), all other simulation cases mentioned above also featured 
statistical similarity in the normalized flux-frequency distributions. It was concluded that a single 
frequency distribution of flow focusing factors could be developed for use in seepage 
abstraction. A regression analysis was conducted to derive a probability distribution function of 
normalized fluxes at the bottom boundary from all 2-D simulation runs. This function, provided 
in DTN: LB0104AMRU0185.012 [163906], is depicted in Figure 6.6-15, along with the 99% 
confidence band for all simulation results. The function defines the cumulative probability 
distribution of flow focusing factors to be used in seepage abstraction and TSPA. Note that this 
distribution is mass conservative, as required, so that the total amount of downward water flow 
remains unchanged when using flow focusing factors as multipliers to the site-scale percolation 
fluxes (i.e., the arithmetic mean of all flow focusing factors of a large enough ensemble of 
random values is 100%). Also note that the regression curve is zero at a flow focusing factor of 
0.116 and approaches 100% at a flow focusing factor of 5.016. Thus, sampling from the 
cumulative probability distribution will give a distribution of flow focusing factors ranging from 
0.116 to 5.016. The regression curve is not defined outside of this range.

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Bottom flux distribution (with 99% confidence range ) 
y = -0.3137x4 + 5.4998x3 - 35.66x2 + 102.3x - 11.434 
0 
20 
40 
60 
80 
100
0 1 2 3 4 5 
Normalized Flux 
Accumulative Frequency (%) 
Source: DTN: LB0104AMRU0185.012 [163906]) 
Figure 6.6-15. Regression Curve (and 99% Confidence Band) for Cumulative Distribution of Percolation 
Flux at the Bottom of the Model Domain, Averaged Over All Simulations 
Qualitative evidence of preferential flowpaths occurring at Yucca Mountain can be inferred from 
observations of secondary minerals in the UZ. These secondary minerals form coatings on 
fracture foot walls and cavity floors as calcite or silica deposits precipitate by percolating water. 
Whelan et al. (2002 [160442], p. 738) report heterogeneous distribution of these minerals within 
the UZ, with fewer than 6% of fractures (longer than 1 m) mineralized. This suggests that the 
majority of all fractures do not contribute to downward flow, and that the downward flux in the 
small fraction of actively flowing fractures should be about 15 to 20 times higher than the 
average percolation. However, these factors cannot be directly compared to the flow focusing 
factors derived above. The abundance of mineral precipitation in fractures reveals flow 
heterogeneity on a small scale. Thus, the spatial variability as suggested from the interpretation 
of fracture coatings includes components of both flow focusing (intermediate-scale 
heterogeneity) and flow channeling (small-scale heterogeneity). As mentioned above, flow 
channeling is explicitly accounted for in seepage models by a stochastic representation of the 
small-scale variation in the drift vicinity, giving rise to considerable flux variability on a scale 
less than one meter. Thus, the resulting flux variability included in seepage abstraction, 
stemming from large-scale variability as modeled by the UZ Flow and Transport Model, 
intermediate-scale variability as described by flow focusing factors, and small-scale variability, 
as simulated by the seepage process models, is significant. Qualitatively, it appears that this flux 
variability is consistent with, if not cautiously overestimating, the observed heterogeneity in 
mineral deposits on fracture walls as reported in Whelan et al. (2002 [160442]). Note that there is 
additional evidence from measurements that the effect of flow focusing should not be larger than 
estimated above. For example, distributions of water potential in the TSw are nearly uniform, 
indicating that there are many small flow paths instead of a just few large ones.

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Note that the flow focusing factors derived in previous seepage abstractions (CRWMS M&O 
2001 [154291], Section 6.4.3.2) were in general more widely distributed than the ones estimated 
above. These factors were derived using a method developed to estimate the spacing of actively 
flowing fractures on a scale below the resolution of the UZ site-scale model. Based on this, 
maximum flow focusing factors ranged between 9.7 and 47, depending on which climate 
scenario was applied. However, these values are believed to be overly conservative and 
unrealistic. This is mainly because the evaluation of active fracture spacing addresses small-scale 
heterogeneity. In light of the above discussion on the different scales of variability, this smallscale 
heterogeneity should not be included in the flow focusing factors provided for the TSPA 
seepage analysis. The probability distributions function defined in Figure 6.6-15 provides a 
cautiously realistic representation of the intermediate-scale variability of percolation flux in the 
TSw. 
6.6.4.3 Resulting Distribution of Percolation Fluxes 
The resulting spatial distributions of percolation flux qperc,ffto be used in TSPA as input for 
seepage calculationare generated as follows: 
(1) The local PTn/TSw flux qperc is sampled for a large number of locations within the 
repository, using one of the nine site-scale flow fields, depending on the considered 
climate scenario and time period. 
(2) Flow focusing factors fff are randomly sampled for each location, using the cumulative 
flux distribution given in Figure 6.6-15. The flow focusing factors are not correlated to 
the local percolation flux. 
(3) The local flux values are multiplied with the local flow focusing factor to give the 
resulting local percolation flux qperc,ff for input into the seepage look-up table. 
This procedure yields spatial flux distributions much wider and more heterogeneous than the 
ones displayed in Figures 6.6-11 and 6.6-12. In theory, the maximum fluxes of each climate 
scenario/period can be derived by multiplication of the values given in Table 6.6-11 with the 
maximum flow focusing factor of about five. (The mean values remain unchanged for a large 
enough sample, because of mass conservation.) This yields maximum values of about 200 mm/yr 
for the present-day climate, about 640 mm/yr for the monsoon climate, and about 960 mm/yr for 
the glacial transition climate (using the mean climate scenario). For the upper-bound scenario, 
the theoretical maximum flux during the glacial transition climate is over 1,400 mm/yr, which is 
beyond the flux range studied with the SMPA. One must note, however, that these maximum 
fluxes are extremely unlikely, caused by the extremely small probability that two independent 
events have extreme parameter values at the same time. 
There are several sources of uncertainty related to the percolation flux estimates. Uncertainty 
related to the future climate and net infiltration at Yucca Mountain is covered using three 
alternative climate scenarios. These scenarios, used as input to the UZ Flow and Transport 
Model, lead to alternative rock property calibrations and alternative percolation flux distributions 
that are accounted for in TSPA with their respective occurrence probability. Uncertainty related 
to simulation of flow processes in the UZ has been addressed by careful calibration and 
validation of the model to a wide variety and large amount of data from different sources (BSC 
2003 [163045], Sections 6 and 7). For the scope of evaluating seepage, the most important

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sources of uncertainty are the flow diversion capacity of the PTn and the impact of spatial 
variability within stratigraphic units. The impact of the PTn flow diversion is addressed in 
Section 6.8.2, where seepage rates are estimated using results of an alternative flow model for 
the PTn, one that does not allow for significant lateral flow. It is shown that the alternative flow 
model does not significantly impact the seepage estimates. The effect of intermediate-scale 
spatial variability, not accounted for in the UZ model results, is explicitly incorporated in 
seepage abstraction using the flow focusing concept. It is recognized that the flow focusing 
factors developed in BSC (2001 [156609], Section 6.4.2) may be overestimating the variability 
of percolation flux, because a fine grid resolution was used for the numerical study. Therefore, 
the resulting flux distributions used for seepage evaluation are expected to cautiously cover the 
spatial variability of this parameter and all related uncertainties.

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6.7 SUMMARY OF SEEPAGE ABSTRACTION 
6.7.1 TSPA Seepage Calculation Methodology and Relevant Abstraction Results 
This section provides a roadmap of the proposed methodology for the TSPA seepage 
calculations (Output-DTN: LB0308AMRU0120.001). The relevant calculation steps are briefly 
summarized together with the relevant parameter distributions and simplifications. The reader is 
referred to Sections 6.4, 6.5, and 6.6 of this Model Report for the rationale behind the abstraction 
methodology. 
Seepage is treated as a probabilistic process in the TSPA-LA simulations (Figure 6.5-1). Within 
each time step, the TSPA seepage component conducts a stochastic evaluation of seepage over a 
large number of realizations R, covering seepage uncertainty, and locations r, covering seepage 
variability. The seepage evaluation has two main steps: (1) deriving ambient seepage rates from 
seepage look-up tables provided by the SMPA (for both non-degraded and collapsed drifts), and 
(2) adjusting the ambient seepage rates for other important factors. Both steps are explained in 
detail in the previous sections. According to the definitions in Section 6.1.2, the seepage rates are 
given for a reference drift section of 5.1 m length; as a result, they correspond to the amount of 
water that potentially drips on one waste package. 
6.7.1.1 Step 1: Ambient Seepage 
Ambient seepage is a function of three key parameters: capillary strength 1/ a, permeability k, 
and percolation flux qperc,ff. Probability distributions have been developed within the abstraction 
process to represent the spatial variability and uncertainty inherent in these parameters. These 
distributions distinguish explicitly between spatial variability and uncertainty. 
Four different methods have been identified in Section 6.6.1 to derive statistical parameters for 
describing the spatial variability and uncertainty in 1/ a. The four statistical methods provide four 
different probability distributions for spatial variability and uncertainty defined below. Methods 
A and B arrive at similar distributions for all the geological units, Methods C and D have 
separate distributions for the nonlithophysal and lithophysal units. These four methods are to be 
used as four equally probable alternative representations of spatial variability and uncertainty in 
the capillary-strength parameter. 
. Parameter Space for Capillary-Strength Parameter 1/ a using Methods A and B: 
(Method B values in parentheses) 
Spatial Variability Distribution: 
Uniform Distribution with Mean 591 Pa (631 Pa). Lower Bound is 402 Pa (442 Pa). Upper Bound is 780 Pa 
(820 Pa). 
Uncertainty Distribution: 
Triangular Distribution with Mean 0. Lower Bound is 105 Pa (162 Pa). Upper Bound is +105 Pa 
(+162 Pa). 
These distributions are identical for all units (Tptpll, Tptpul, Tptpmn, Tptpln).

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. Parameter Space for Capillary-Strength Parameter 1/ a using Methods C and D: 
(Method D values in parentheses) 
Tptpmn Unit: 
Spatial Variability Distribution: 
Uniform Distribution with Mean 604 Pa (650 Pa). Lower Bound is 377 Pa (427 Pa). Upper Bound is 831 Pa 
(873 Pa). 
Uncertainty Distribution: 
Triangular Distribution with Mean 0. Lower Bound is 198 Pa (273 Pa). Upper Bound is +198 Pa 
(+273 Pa). 
These distributions for the Tptpmn are also used for the Tptpln unit. 
Tptpll Unit: 
Spatial Variability Distribution: 
Uniform Distribution with Mean 582 Pa (613 Pa). Lower Bound is 400 Pa (384 Pa). Upper Bound is 764 Pa 
(841 Pa). 

Uncertainty Distribution: 
Triangular Distribution with Mean 0. Lower Bound is 129 Pa (279 Pa). Upper Bound is +129 Pa 
(+279 Pa). 
These distributions for the Tptpll are also used for the Tptpul unit. 
The spatial variability and uncertainty distributions for permeability are defined as follows: 
. Parameter Space for Permeability k (in log10) 
Tptpmn Unit: 
Spatial Variability Distribution: 
Lognormal Distribution with Mean 12.2 and Standard Deviation 0.34. 
Uncertainty Distribution: 
Triangular Distribution with Mean 0. Lower Bound is 0.68. Upper Bound is +0.68. 
The permeability distributions for the Tptpmn are also used for the Tptpln unit. 
Tptpll Unit: 
Spatial Variability Distribution: 
Lognormal Distribution with Mean 11.5 and Standard Deviation 0.47. 
Uncertainty Distribution: 
Triangular Distribution with Mean 0. Lower Bound is 0.92. Upper Bound is +0.92. 
The permeability distributions for the Tptpll are also used for the Tptpul unit. 
Values for 1/ a and k sampled from the spatial variability distributions are adjusted using values 
for .1/ a and .k sampled from the uncertainty distributions, to arrive at the final parameter 
distribution covering both spatial variability and uncertainty. 
The procedure for sampling percolation fluxes is slightly different. The percolation flux 
distributions are provided by model results from the UZ Flow and Transport Model (DTN: 
LB0302PTNTSW9I.001 [162277]). These spatial distributions are time-dependent; they are 
given separately for three climate stages (present-day, monsoon, and glacial transition), during 
which the UZ flow fields are considered steady state. Uncertainty is expressed by three different

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scenarios of spatial flux distributions (mean, upper-bound, and lower-bound scenario), each of 
them associated with a certain occurrence probability (provided to TSPA in a separate Model 
Report). (Note that the nine flux distributions simulated using an alternative conceptual model 
for flow diversion in the PTn do not need to be considered in the TSPA.) The TSPA-LA seepage 
component samples from the spatial flux distribution at given locations r within the repository 
area, using the present-day, monsoon, or glacial transition flow field, depending on the 
considered time step. Over the R uncertainty realizations, the three flux scenarios are weighted 
according to their respective occurrence probability. 
These sampled percolation fluxes qperc need to be adjusted for intermediate-scale heterogeneity, 
which is not represented in the flux distributions from the UZ Flow and Transport Model. This is 
done using a spatial distribution of flow focusing factors fff. Multiplication of the sampled fluxes 
qperc from the site-scale model with the flow focusing factors fff gives the local percolation flux 
qperc,ff to be used in the TSPA calculation. The spatial variability distribution for the flow 
focusing factor is defined as follows (DTN: LB0104AMRU0185.012 [163906]): 
. Flow Focusing Factor fff 
Spatial Variability Distribution: 
Cumulative Probability Distribution given as 
Y = -0.3137 x4 +5.4998 x3 35.66 x2 +102.3 x 11.434 (in %) 
Uncertainty Distribution: 
No Uncertainty. 
Note that the respective probability distributions for capillary strength, permeability, percolation 
flux, and flow focusing factor are not correlated. This means that the random variables used to 
sample from the respective distributions should be generated independently in the TSPA. 
For each set of seepage-relevant parameters 1/a, k, and qperc,ff derived in the random sampling 
procedure over R realizations and r locations, seepage rates are calculated using the seepage 
look-up tables provided by the SMPA simulation results. These look-up tables are available for 
non-degraded drifts (DTN: LB0304SMDCREV2.002 [163687]) as well as for collapsed drifts 
(DTN: LB0307SEEPDRCL.002 [164337]). For both look-up tables, the SMPA simulation cases 
cover the parameter values given below. All possible combinations of these values were 
simulated in the systematic SMPA analysis, and a complete suite of results is provided in the 
seepage look-up tables. 
. SMPA Simulation Cases 
Capillary-Strength Parameter 1/ a: 100 to 1000 Pa (intervals of 100 Pa) 
Permeability k: -14.0 to 10.0 (intervals of 0.25) 
Local Percolation Flux qperc,ff: 1, 5, 10, 20, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 mm/year 
TSPA will select the appropriate look-up table, depending on the considered geologic unit, the 
selected nominal or disruptive scenario, and the assumed rock strength reduction case. This 
selection is based on categories of drift degradation that have been introduced in Section 6.5.1.5, 
based on results from BSC (2003 [162711]). Category 1 comprises degraded drifts that may 
show local rock breakout but stay essentially intact. In this category, seepage is interpolated from

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the look-up table for non-degraded drifts. Category 1 includes all drifts located in nonlithophysal 
rock, and, for drifts located in lithophysal rock, the thermal stress case, the 5  10-4 seismic 
hazard case, and the 0% through 40% strength reduction cases. Category 2 comprises the cases 
with complete drift collapse. These are the 1  10-6 and the 1  10-7 seismic hazard levels and the 
60% through 100% strength reduction cases for lithophysal rock units. In this category, seepage 
is interpolated from the look-up table for collapsed drifts. 
Information on the time-dependence of rock strength reduction is not available at this time. 
Therefore, the collapsed drift look-up table should be used for the entire postclosure period when 
one of the strength-induced collapsed drift scenarios is considered in TSPA. On the other hand, if 
the time of a seismic event leading to drift collapse is considered in TSPA, the collapsed drift 
scenario should be used starting with the assumed time of the seismic event. 
The seepage results for each sampled set of seepage-relevant parameters 1/a, k, and qperc,ff 
derived in the random sampling procedure are calculated from a linear interpolation between the 
three independent seepage input parameters in the look-up tables. It is possible (but unlikely) that 
the parameter range covered in the SMPA is exceeded for parameter values sampled from the 
unbounded distributions of permeability and percolation flux. The following recommendations 
are made in this case: 
. Truncation of Parameter Distributions 
a.) If local percolation flux is less than 1 mm/yr, set to 1 mm/yr. 
b.) If local percolation flux is more than 1,000 mm/yr, set to 1,000 mm/yr. 
c.) If permeability is less than 14, set to 14 (in log10). 
d.) If permeability is larger than 10, set to 10 (in log10). 
The interpolated results from the seepage look-up tables are the mean seepage values Qseep and 
the standard deviations sseep. The standard deviations represent the estimation uncertainty in the 
seepage results, which is different for each sampled set of parameters. Since this uncertainty 
must be included in the TSPA simulation, the interpolated mean seepage values Qseep are 
adjusted using values for .Qseep sampled from appropriate uncertainty distributions. These 
distributions are defined as follows: 
. Uncertainty Distribution for Seepage Results 
Uniform distribution with Mean 0. 
Lower bound is -1.7321  sseep. Upper bound is +1.7321  sseep. 
After adjusting the seepage values to account for uncertainty, the results must be checked for 
consistency. If the resulting seepage rates are smaller than 0, they are set to 0. If the resulting 
seepage rates correspond to a seepage percentage larger than 100%, they are set to a rate 
corresponding to a seepage percentage of 100%. The final result of Step 1 of the TSPA seepage 
calculation is a probability distribution of ambient seepage rates (or seepage percentages) over R 
realizations and r locations, given for each time step studied in the TSPA simulation. 
Three alternative abstraction methods are proposed for igneous events (see Section 6.5.1.7). The 
first method is to apply the seepage results obtained for nondegraded drifts, using the look-up

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table in DTN: LB0304SMDCREV2.002 [163687]; the second method is to apply the seepage 
results for collapsed drifts, using the look-up table in DTN: LB0307SEEPDRCL.002 [164337]; 
and the third method is to set the seepage percentage in intersected drifts to 100% (i.e., the 
seepage flux potentially contacting the waste is equal to the local percolation flux arriving at the 
drifts). In view of the significant uncertainty about the in-drift conditions after an igneous event, 
it is recommended that TSPA conduct sensitivity analyses with the three abstraction methods. 
The more conservative seepage estimates should be chosen and propagated to the downstream 
TSPA modules. If the time of an igneous intrusion event is considered in TSPA, the selected 
abstraction method for igneous intrusion should be used starting with the assumed time of the 
event. 
6.7.1.2 Step 2: Adjustments for Other Relevant Factors 
The ambient seepage distributions do not account for (1) alterations of seepage-relevant 
parameters as a result of heat-induced THM and THC effects, (2) uncertainty related to drift 
degradation, and (3) thermal perturbations during the first several hundred years after 
emplacement when boiling occurs in the rock. The following adjustments are necessary to 
incorporate these additional factors into the seepage evaluation: 
. THM and THC Alteration of 1/ a and k 
The time-dependent alterations of these seepage-relevant parameters can (and should) be neglected in the 
seepage abstraction. 
. Effect of Drift Degradation 
For all collapsed drift cases, uncertainty is already accounted for in the interpolated seepage rates. For noncollapsed 
cases, the ambient seepage rates are increased by 20% to account for additional uncertainty. In 
this case, for large ambient seepage, the increased seepage rates may correspond to a seepage percentage 
larger than 100%. These are set to a rate corresponding to a seepage percentage of 100%. The reference 
area used to relate seepage rates and seepage percentages is 5.1 m  5.5 m in this case, i.e., the footprint of 
a 5.1-m long section of a non-degraded drift. 
. Thermal Seepage 
Two alternative abstraction approaches are proposed for the treatment of thermal seepage in the TSPA 
seepage calculation. The decision which one of the two abstraction approaches should be used will be made 
during the TSPA process, weighting the benefit of less seepage in Abstraction Model 2 against the 
complexity of implementing it into the TSPA. The two abstraction approaches are defined as follows: 
Abstraction Model 1 sets thermal seepage equal to the adjusted ambient seepage. In other words, these 
seepage rates are not adjusted for thermal perturbation effects. The abstraction is based on the model 
finding that ambient seepage provides an asymptotic upper limit for thermal seepage (i.e., there is no 
enhanced seepage as a result of thermal perturbation). 
Abstraction Model 2 sets thermal seepage to zero for the period of above-boiling temperatures in the drift 
vicinity. This approach takes credit for the vaporization barrier that prevents seepage during the period of 
above-boiling temperatures. The threshold temperature that defines the duration of the boiling period is set 
to 100oC. For the remaining time period, thermal seepage is set equal to the adjusted ambient seepage rates. 
The abstraction is based on the model findings that thermal seepage never occurs at above-boiling 
temperatures and that the ambient seepage values provide an asymptotic upper limit for thermal seepage. 
For implementation of this model in the TSPA, detailed information is required about the duration of the 
boiling period for a large number of parameter cases.

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Seepage in ventilated drifts is highly unlikely. Therefore, in both abstraction models, seepage during the 
50-year preclosure period can be neglected. 
6.7.1.3 Step 3: Seepage Probability Distributions and Seepage Fraction 
The final results of the TSPA seepage calculation are probability distributions of seepage rates 
appropriately incorporating all relevant factors, given for each time step. Statistical analyses can 
be conducted for a detailed evaluation of the seepage results in each time step. Such analyses can 
be conducted over all realizations and locations (uncertainty and spatial variability), or 
alternatively for one location over all realizations (uncertainty at one location) and one 
realization over all locations (spatial variability). Histograms reveal the shape of the respective 
distributions. Relating the mean seepage rate to the overall percolation flux demonstrates the 
barrier capability of the UZ, limiting the seepage of water into emplacement drifts. The total 
number of samples with a nonzero seepage rate defines the seepage fraction fseep. This parameter 
is important because it reveals the fraction of waste packages potentially in contact with water. 
Other data processing of the seepage results (sorting, averaging, binning) may be needed for 
further use in downstream TSPA model components. 
6.7.2 Propagation of Uncertainty through the Abstraction 
All sources of uncertainty related to seepage-relevant parameters and seepage simulation results 
have been characterized in and propagated through the seepage abstraction for TSPA-LA. 
Uncertainty in the key parameters for ambient seepage has been explicitly represented through 
appropriate probability distributions (Section 6.6). The probabilities assigned to these key 
parameters distinguish between spatial variability (aleatory uncertainty) and uncertainty 
(epistemic uncertainty), using separate distributions. Spatial variability distributions for the 
capillary strength parameter and the local permeability have been derived in this Model Report 
by detailed statistical analysis of the sparsely distributed data (Sections 6.6.1 and 6.6.2). Spatial 
variability distributions for the local percolation flux are provided from site-scale simulations 
with the UZ Flow and Transport Model (Section 6.6.3). These fluxes are then adjusted to account 
for intermediate-scale heterogeneity, using a spatial distribution of flow focusing factors. 
Uncertainty has been characterized by evaluation of all potential sources for uncertaintyi.e., 
uncertainty in the measurements, the conceptual model, the estimation process, and the spatial 
variability. Information on uncertainty provided in upstream analyses or modeling has been 
included in this evaluation. Uncertainty inherent in the capillary strength parameter and the local 
permeability is described by triangular probability distributions (Sections 6.6.1 and 6.6.2). 
Alternative methods have been employed to derive statistical parameters describing the 
probability distributions for the capillary-strength parameter. It is recommended that TSPA 
conduct a sensitivity analysis and choose the most conservative method with respect to the 
overall seepage. Uncertainty in the percolation flux distributions is incorporated using three 
different flow scenarios (Section 6.6.4). In addition, an alternative UZ flow scenario is evaluated 
in Section 6.8.2, but can be neglected in TSPA because of its limited impact. 
Another contribution to uncertainty in the TSPA seepage calculations stems from the simulation 
results of drift-scale models that describe seepage-relevant processes. Drift-scale models are 
introduced in Section 6.4, including a detailed assessment of the respective model validation and

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corroboration with alternative conceptual models. As discussed in Section 6.5, the treatment of 
uncertainty in simulation results is based on this assessment and considers the respective use of 
the model in the abstraction. The estimation uncertainty of SMPA simulation results, used 
directly in TSPA-LA as a quantitative measure of seepage, is explicitly incorporated in the 
seepage abstraction by uniform uncertainty distributions (Section 6.5.1.4). Other drift-scale 
models provide quantitative and qualitative information used to adjust the SMPA seepage results 
for additional factors (THM and THC parameter alterations, drift degradation, rock bolts, and 
thermal seepage). These adjustments are generally based on simplifications of the more complex 
process model results. To incorporate uncertainty, these simplifications are chosen to be 
conservative. This means that the simplified abstractions tend to overestimate the seepage 
compared to the predicted process model results. THC parameter alterations, for example, were 
found to decrease the potential of seepage because of a precipitation umbrella forming a few 
meters above drifts. This process, however, is neglected in the abstraction because of 
considerable uncertainties in modeling the coupled THC processes (Section 6.5.1.4). 
Two different approaches are chosen for seepage estimates in degraded drifts. For moderately 
degraded drifts, the seepage rates are increased by 20% to account for uncertainty in the 
prediction (Section 6.5.1.5). For collapsed drifts, this increase is not necessary because this 
extreme degree of damage is related to a worst-case scenario that includes sufficient 
conservatism. Two alternative abstractions have been proposed for thermal seepage. The first 
approach is simple and conservative; no incorporation of the vaporization barrier in superheated 
fractured rock takes place so that the thermal seepage is not different from ambient seepage. The 
second approach is more realistic; it considers that there is no seepage during the period of 
above-boiling temperatures. To account for uncertainty, the threshold temperature used to define 
above-boiling conditions is chosen to be higher than the nominal boiling temperature of water 
(Section 6.5.2.2).

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6.8 SEEPAGE CALCULATION AND SENSITIVITIES 
In this section, a probabilistic calculation of seepage is conducted following the seepage 
abstraction method described in Sections 6.5 through 6.7. This calculation serves two purposes: 
(1) to demonstrate the barrier capabilities of the UZ and (2) to evaluate sensitivities in the 
abstraction process. As mentioned before, results from this calculation are not utilized in the 
performance assessment; they merely provide information on the expected seepage behavior for 
different test cases. However, they may be useful as corroborating information for validation of 
the seepage calculation procedure in the TSPA-LA. The TSPA-LA seepage component will 
perform a more comprehensive probabilistic seepage calculation within its Monte Carlo 
simulation procedure to provide the final seepage results used in the performance assessment. 
The probabilistic analysis in this Model Report is conducted in a random procedure with sample 
size 10,000. For simplification, spatial variability and uncertainty distributions are 
simultaneously sampled in one calculational loop. In each random seepage case, noncorrelated 
random numbers are generated to sample from spatial variability distributions (for capillary 
strength, permeability, percolation flux, and flow focusing factor) and from uncertainty 
distributions (for capillary strength, permeability, and seepage uncertainty). In contrast, in the 
TSPA-LA calculation, uncertainty is sampled in an outer calculation loop over R realizations, 
while spatial variability is sampled in an inner calculation loop over r spatial locations. These 
spatial locations will be selected in the TSPA-LA so that they are consistent with other 
abstractions (e.g., for the in-drift environment). In this way, the PTn/TSw flux qperc will be 
derived from exact spatial interpolation of the respective flux distribution in the TSPA-LA. Since 
the selection of these locations has not been finalized at the time of writing this Model Report, a 
simplified sampling procedure is utilized in the calculations below. The percolation flux values 
are randomly sampled using the probability distribution functions from the respective percolation 
flux fields (see histograms in Figures 6.6-11 and 6.6-12), without explicit consideration of the 
location in the repository area. Note that these differences may lead to minor differences in the 
predicted seepage results between the TSPA-LA calculation and the calculation conducted in this 
report. However, the main trends are not affected by these differences. 
The probabilistic seepage calculations are carried out using Mathcad 11, a standard technical 
calculation tool for solving mathematical problems of various kinds. The different Mathcad 11 
spreadsheets developed for the several seepage calculation cases are provided in Attachment V. 
The mathematical calculation follows the seepage abstraction steps summarized in Section 6.7. 
Note that only the non-collapsed drift cases are analyzed. In Step 1 of the calculation, random 
numbers are generated to sample probabilistic parameter values from spatial variability 
distributions and to adjust them using respective uncertainty distributions. This is done for the 
three key parameters of ambient seepage, capillary strength, permeability, and percolation flux 
(the latter using site-scale fluxes and flow focusing factors). For each random parameter set, 
mean seepage rates and related standard deviations are interpolated from the appropriate look-up 
table, and the mean seepage rates are adjusted for seepage uncertainty. (The interpolation is 
conducted for the k-variable first, then for 1/ a, and finally for qperc,ff.) Note that all seepage 
calculation cases use the same seed value for the random procedure. This ensures that the 
comparison of different sensitivity cases is not biased by artifacts of the random number 
procedure. Using a fixed random seed also ensures reproducibility of the results. In Step 2, the 
ambient seepage rates are corrected for the effect of drift degradation, increasing them by 20%.

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Finally, the impact of thermal perturbation is accounted for. In this Model Report, we mainly 
apply Abstraction Model 1 for thermal seepage, using the above ambient results as an 
approximation of the thermal period. Since detailed results on the duration of the boiling period 
for all repository drifts including spatial variability and uncertainty cases are not available at the 
time of writing this report, Abstraction Model 2 is applied only as a sensitivity study, assuming 
that the duration of the boiling period in all emplacement drifts corresponds to the duration of the 
present-day climate period. 
The following two subsections give seepage calculation results for the base-case seepage 
evaluation (Section 6.8.1) and for selected sensitivity cases (Section 6.8.2). Histograms of the 
calculated seepage rates (in kg/year per waste package) and seepage percentages (relative to the 
percolation flux) are given for selected cases to demonstrate the variability of seepage over the 
10,000 random cases. Summary results comprise the mean seepage rate and the mean seepage 
percentage, both of which are calculated over all 10,000 locations with and without seepage, and 
the seepage fraction (fraction of locations with seepage). The mean seepage percentage is 
derived as the mean seepage rate (over all 10,000 samples) related to the mean percolation flux 
(over the repository area). (This is different from simply averaging the 10,000 seepage 
percentage values). The seepage fraction is calculated using a threshold seepage rate of 
0.1 kg/year per waste package. Locations with less than this threshold rate are considered no 
seepage, because such small values are mainly a result of the interpolation procedure 
(simulation cases with a seepage rate of less than 0.1 kg/year per waste package are extremely 
rare in the SMPA look-up table). 
6.8.1 Base-Case Seepage Evaluation 
First, the probabilistic seepage analysis is presented for the Tptpll unit, the main repository unit 
according to the current repository design. Results of the seepage calculation are given separately 
for the three different climate scenarios (the mean, upper-bound, and lower-bound scenarios). In 
the TSPA-LA calculations, these separate results will be converted into one final distribution 
according to the respective occurrence probability of each scenario. These probabilities are 
developed in the Analyses Report Analysis of Infiltration Uncertainty (BSC 2003 [163740]), 
which has not been finalized yet. Therefore, this final step is not conducted here. Thermal 
seepage is first accounted for using Abstraction Model 1. The seepage calculations in this section 
are conducted using the parameter distribution for capillary strength as derived from the 
statistical Method A (see Table 6.6-2 and Section 6.1.1.1). The impact of applying the other 
distributions (from Methods B, C, and D) is evaluated in the sensitivity analysis in Section 6.8.2. 
For illustration, Figures 6.8-1 and 6.8-2 present histograms of the calculated seepage rates and 
percentages for the mean climate scenario, showing only the random samples with nonzero 
seepage. (The great majority of the samples have zero seepage.) The seepage rates vary strongly, 
from small values below 0.1 kg/year per waste package up to almost 10,000 kg/year per waste 
package. (For comparison: A percolation flux of 500 mm/year that completely seeps into a 5.1 m 
long drift section would give a seepage rate of more than 14,000 kg/year per waste package.) The 
seepage percentages also show considerable variability covering the entire range from 0% up to 
100%. Most probable, however, are the small seepage percentages; only a few samples reach 
80% seepage and more. In both figures, there is a clear trend of increasing seepage probability as

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a result of the climate changes from present-day to monsoon climate and from monsoon to 
glacial transition climate. 
Table 6.8-1 gives summary results of the probabilistic analysis for the Tptpll, providing the mean 
seepage rate, the mean seepage percentage, and the seepage fraction, during the present-day, the 
monsoon, and the glacial transition climate. For the mean climate scenario, seepage is expected 
to occur at about 8% of all waste packages during the first 600 years after emplacement. This 
percentage rises to about 18% during the monsoon climate, and about 24% during the glacial 
transition climate. On average over all waste packages, the amount of seeping water is 1.7, 17.5, 
and 37.9 kg/year for the three climate stages, respectively. This translates to mean seepage 
percentages of 1.5%, 5.3%, and 7.5%. In other words, during the present-day climate, more than 
98% of the percolation flux is diverted around drifts in the Tptpll unit. For the wetter climate 
stages of the monsoon and the glacial transition period, the percentage of diverted flux is smaller, 
but still at about 95% and 92%, respectively. This illustrates the barrier capability of the 
unsaturated flow processes in the fractured rock at and above the repository horizon. 
As expected, the lower-bound climate scenario results in considerably less seepage. Here, the 
seepage fraction varies from about 1% for the present-day climate to over 9% during the 
monsoon period to 4% during the glacial transition climate. The respective mean seepage 
percentages are as low as 0.3%, 1.5%, and 0.9%. The opposite trend is seen for the upper-bound 
climate scenario, with the seepage fraction as high as 36% during the glacial transition climate. 
The mean seepage percentage during this climate stage is 11.8%. Thus, even for the upper-bound 
climate scenario with comparably strong downward percolation, the diversion capacity of the 
unsaturated rock is at least 88%. 
The tabular values for mean seepage rate, mean seepage percentage and seepage fraction are 
visualized in Figures 6.8-3 through 6.8-5, showing the evolution of seepage over time. Note that 
the time axis starts at 50 years after emplacement, i.e., of the beginning of the postclosure period. 
As a result of the forced ventilation with relatively dry air, seepage is not expected to occur 
during the 50-year preclosure period. For the mean and the upper-bound scenarios, the trend of 
seepage increase with changes in climate is clearly evident. The stepwise increases at 600 years 
and at 2,000 years are particularly strong for the mean seepage rates, which give the absolute 
amount of water seeping into drifts (Figure 6.8-3). Compared to the other two climate scenarios, 
the lower-bound climate scenario has much less seepage during all climate stages. 
The seepage results displayed in Figures 6.8-3 through 6.8-5 can be used as a basis to discuss the 
benefit of using Abstraction Model 2 instead of Abstraction Model 1 for thermal seepage. 
Abstraction Model 2 takes credit for a fully effective vaporization barrier during the time period 
that local rock temperatures in the drift vicinity are above boiling. For average percolation 
fluxes, this time period is expected to last for about 1,000 years (Tptpmn) to about 1,400 years 
(Tptpll) after waste emplacement (see Figure 6.2.3.1-1 in BSC (2003 [161530])), but can be 
significantly shorter for larger percolation fluxes (Section 6.4.3.3). Other relevant factors 
affecting the duration of the boiling period in individual drift sections are the thermal load, the 
location in the repository, and the TH properties of the fractured rock. Since spatial variability in 
these factors will give rise to considerable spatial variability of the boiling period within different 
drift sections, the implementation of Abstraction Model 2 in TSPA-LA requires systematic TH 
simulations for a large number of parameter cases. In view of these systematic results, an

Abstraction of Drift Seepage U0120 
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average boiling period of 600 years is assumed in all emplacement drifts to illustrate the 
differences between the thermal seepage abstraction results. (While most drift sections are 
expected to remain at boiling conditions for more than 600 years, there will be parameter cases 
with shorter boiling periods, for example the extreme percolation flux cases shown in Section 
6.4.3.3.) Then, the benefit of Abstraction Model 2 can be easily derived by setting the seepage 
flux, percentage, and fraction to zero during the present-day climate period, in Figures 6.8-1 
through 6.8-5 and in Table 6.8-1. It is obvious that the time period for a fully effective 
vaporization barrier coincides with the time period of relatively small ambient seepage compared 
to later climate stages. More benefit can be expected from Abstraction Model 2, when an average 
boiling period of 1,000 to 1,400 years is assumed. In this case, the no-seepage period according 
to Abstraction Model 2 would extend several hundred years into the monsoon climate stage, 
when the predicted ambient seepage is higher.

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(Present-day) 
4 2 0 2 4 
0 
100 
200 
Seepage Rate in kg/year/WP (in log10) 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Monsoon) 
4 2 0 2 4 
0 
100 
200 
Seepage Rate in kg/year/WP (in log10) 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Glacial Transition) 
4 2 0 2 4 
0 
100 
200 
Seepage Rate in kg/year/WP (in log10) 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
Output-DTN: LB0308AMRU0120.002 
NOTE: The symbols in the histograms (i.e., fluxhist<0> and fluxhist<1>) denote the variable names given in the 
Mathcad 11 spreadsheet used for the calculation, see Attachment V. Only the samples with non-zero 
seepage are depicted. 
Figure 6.8-1. Histograms of Seepage Rates for Tptpll Unit

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MDL-NBS-HS-000019 REV00 177 August 2003 
(Present-day) 
20 40 60 80 100 
0 
100 
200 
300 
Seepage Percentage 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Monsoon) 
20 40 60 80 100 
0 
100 
200 
300 
Seepage Percentage 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
(Glacial Transition) 
20 40 60 80 100 
0 
100 
200 
300 
Seepage Percentage 
Number of Values 
fluxhist 1 . . 
fluxhist 0 . . 
Output-DTN: LB0308AMRU0120.002 
NOTE: The symbols in the histograms (i.e., fluxhist<0> and fluxhist<1>) denote the variable names given in the 
Mathcad 11 spreadsheet used for the calculation, see Attachment V. Only the samples with non-zero 
seepage are depicted. 
Figure 6.8-2. Histograms of Seepage Percentages for Tptpll Unit

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Table 6.8-1. Summary Statistics for Probabilistic Seepage Evaluation (Tptpll Unit) 
Mean Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 1.7 1.5 7.9 
Monsoon 17.5 5.3 18.3 
Glacial Transition 37.9 7.5 24.2 
Lower-Bound Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.0 0.3 0.8 
Monsoon 1.8 1.5 8.6 
Glacial Transition 0.5 0.9 3.9 
Upper-Bound Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 13.1 4.2 18.3 
Monsoon 44.7 7.8 26.6 
Glacial Transition 117.5 11.8 36.0 
Output-DTN: LB0308AMRU0120.002 
NOTE: Computation documented in Attachment V. 
Upper-Bound 
Climate Scenario 
Mean Climate 
Scenario 
Lower-Bound 
Climate Scenario 
Time after Emplacement (years) 
Mean Seepage Rate (kg/year/WP) 
102 103 104 0.0 
50.0 
100.0 
150.0 Monsoon 
(600 - 2000 years) 
Glacial Transition 
(> 2000 years) 
Present-Day 
(0 - 600 years) 
Source: Table 6.8-1; Output-DTN: LB0308AMRU0120.002 
Figure 6.8-3. Mean Seepage Rate as a Function of Time after Emplacement for Tptpll Unit and 
Different Climate Scenarios

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Upper-Bound 
Climate Scenario 
Mean Climate 
Scenario 
Lower-Bound 
Climate Scenario 
Time after Emplacement (years) 
Mean Seepage Percentage (%) 
102 103 104 0.0 
5.0 
10.0 
15.0 Monsoon 
(600 - 2000 years) 
Glacial Transition 
(> 2000 years) 
Present-Day 
(0 - 600 years) 
Source: Table 6.8-1; Output-DTN: LB0308AMRU0120.002 
Figure 6.8-4. Mean Seepage Percentage as a Function of Time after Emplacement for Tptpll Unit and 
Different Climate Scenarios 
Upper-Bound 
Climate Scenario 
Mean Climate 
Scenario 
Lower-Bound 
Climate Scenario 
Time after Emplacement (years) 
Seepage Fraction (%) 
102 103 104 0.0 
10.0 
20.0 
30.0 
40.0 
Monsoon 
(600 - 2000 years) 
Glacial Transition 
(> 2000 years) 
Present-Day 
(0 - 600 years) 
Source: Table 6.8-1; Output-DTN: LB0308AMRU0120.002 
Figure 6.8-5. Seepage Fraction as a Function of Time after Emplacement for Tptpll Unit and Different 
Climate Scenarios

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Summary statistics for seepage in the Tptpmn unit are listed in Table 6.8-2, for the three climate 
stages and climate scenarios. Comparison with Table 6.8-1 indicates that considerably more 
seepage occurs at a larger number of locations in the Tptpmn compared to the Tptpll (i.e., higher 
mean seepage rate and percentage, higher seepage fraction). This is mainly a result of the smaller 
mean permeability assigned to the spatial variability distribution of the Tptpmn unit. Thus, with 
respect to seepage, the Tptpll unit is the better host rock unit compared to the Tptpmn. The 
trends in the seepage resultsbetween the different climate stages and scenariosare similar to 
the Tptpll.
Table 6.8-2. Summary Statistics for Probabilistic Seepage Evaluation (Tptpmn Unit) 
Mean Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 5.9 5.5 22.6 
Monsoon 50.6 15.3 41.6 
Glacial Transition 103.1 18.8 50.1 
Lower-Bound Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.1 1.2 3.0 
Monsoon 6.6 5.5 24.6 
Glacial Transition 1.8 3.3 13.3 
Upper-Bound Climate Scenario 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 41.0 13.0 42.3 
Monsoon 122.3 21.3 54.0 
Glacial Transition 294.6 29.6 65.4 
Output-DTN: LB0308AMRU0120.002 
NOTE: Computation documented in Attachment V. 
As discussed in Section 6.5.1.2, sampled parameter values for permeability and percolation flux 
are truncated if they fall outside of the parameter space covered by the SMPA look-up table. It is 
important for the validity of the seepage evaluation that the SMPA ranges are not exceeded in 
too many instances. Therefore, during the Mathcad calculation, the number of sampled values 
falling outside of the SMPA parameter space is counted and checked. The results are as follows: 
In the Tptpll unit, none of the 10,000 permeability values is smaller than 14 (in log10); about 72 
values are higher than 10. (Truncating at the upper permeability value, however, is 
conservative, because reducing permeability tends to increase the probability of seepage. Also, at 
this upper bound, seepage is only expected in extreme parameter cases.) In the Tptpmn unit, with 
a narrower distribution for spatial variability and uncertainty, none of the sample values exceeds 
the parameter space. Considering the sampled percolation flux distributions, the number of flux 
values larger than 1,000 mm/yr is zero for all climate stages and scenarios. (Depending on the 
climate scenario, several sample values are smaller than 1 mm/yr, the smallest percolation flux 
simulated with the SMPA. Truncating at this lower bound, however, is not significant because

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seepage hardly occurs at such small fluxes.) A percolation flux of 500 mm/yr, the upper flux 
value of the glacial transition climate analyzed with the Thermal Seepage Model, is exceeded in 
only one instance using the mean climate scenario (seven instances in the upper-bound scenario, 
none in the lower-bound scenario). These small numbers indicate that the supporting seepage 
process models cover parameter spaces that safely include the vast majority of the sample cases 
in the random seepage evaluation. This ensures that the seepage evaluation results are not biased 
by sample truncation. 
6.8.2 Sensitivity Analysis 
Several sensitivity cases have been selected to identify how the overall amount of seepage and 
the seepage fraction in the repository may be affected by some choices made in the abstraction 
process. The sensitivity analysis is conducted only for the Tptpll unit using the mean climate 
scenario; the trends observed should be similar for the Tptpmn unit and should also be 
representative of other climate scenarios. Abstraction Model 1 is used for thermal seepage. Table 
6.8-3 provides summary statistics for the respective sensitivity cases, giving the mean seepage 
rate, the mean seepage percentage, and the seepage fraction during the present-day, the monsoon, 
and the glacial transition climate. For comparison, the table not only provides the absolute 
seepage results for the considered case, but also the differences of these results from the base 
case (Table 6.8-1). A negative value indicates that the sensitivity case result is smaller than that 
of the base case (number in blue). A positive value indicates that the sensitivity case result is 
larger than that of the base case (number in red). Below, the sensitivity cases are discussed item 
by item. 
1. Normal Distribution for Spatial Variability of Capillary Strength Parameter 
As discussed in Section 6.6.1.2, the shape of the spatial variability distribution for the 
calibrated capillary strength parameter is not clearly indicated by the histogram of the 
data sample (Figure 6.6-2). Therefore, the distribution of choice for the TSPA-LA is a 
uniform distribution. For comparison, Sensitivity Case 1 utilizes a normal distribution 
instead of a uniform distribution. The seepage results using a normal distribution are 
almost identical to the base case, indicating a very small sensitivity to the shape of the 
distribution. 
2. Normal Distribution for Uncertainty of Seepage Rate Predictions 
Analysis of the SMPA results in Section 6.4.2.3 does not reveal a consistent trend for the 
uncertainty distribution describing the variability in seepage predictions from different 
realizations. Thus, a simple uniform distribution is selected in the abstraction for the 
TSPA-LA. In Sensitivity Case 2, a normal distribution is assumed as an alternative case. 
Again, the impact of choosing a different shape of the distribution is rather marginal, 
indicating a small sensitivity. 
3. No Spatial Variability in Permeability and Capillary Strength 
Significant effort was devoted in Section 6.6 to define appropriate distributions 
describing the intermediate-scale variability of permeability and capillary strength over 
the repository area. Sensitivity Case 3 ignores the spatial variability of these seepagerelevant 
parameters. The seepage results in Table 6.8-3 indicates the considerable impact

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of using spatially uniform (but still uncertain) parameters. The overall amount of seepage 
(expressed in the mean seepage rate and the mean seepage percentage), as well as the 
seepage fraction, is considerably smaller than in the base case. 
4. No Uncertainty in Permeability and Capillary Strength 
Sensitivity Case 4 assumes that the uncertainty in the parameter distribution for the 
seepage-relevant parameterspermeability and capillary strengthcan be neglected. 
(Spatial variability, however, is still considered, expressed by appropriate probability 
distributions.) Without uncertainty, the overall amount of seepage and the seepage 
fraction are smaller than in the base case. Comparison with Sensitivity Case 3 indicates 
that the impact of neglecting uncertainty is less significant than the impact of neglecting 
spatial variability. 
5. Adjusted Mean Value of Spatial Variability Distribution for Permeability 
In Sensitivity Case 5, the spatial variability distribution for fracture permeability is 
shifted to a larger mean value, from initially 11.5 to 11.0 (in log10). As discussed in 
Section 6.6.2.2, the small-scale permeability measurements conducted in Niche 1620 and 
in the ECRB suggest a mean permeability of about 11.0 in the excavation-disturbed 
zone around drifts. However, additional analysis of surface-based boreholes indicated 
that these permeability measurements might be on the high side, potentially representing 
a rather permeable region in the Tptpll. Therefore, the mean permeability value proposed 
for TSPA-LA seepage calculations was set half an order of magnitude smaller, to a value 
of 11.5. Sensitivity Case 5 uses the initial value of 11.0 to demonstrate the impact of 
this shift in the mean permeability. Having a higher permeability brings down the 
expected seepage in the Tptpll by a significant amount. During the glacial transition 
climate, for example, the mean seepage percentage decreases from 7.5% (base case) to 
2.6%. Similarly, the seepage fraction changes from 24.2% (base case) to 11.3% during 
this climate stage. Thus, an increase in permeability reduces the overall amount of 
seepage as well as the number of waste packages contacted by water. The calculated 
numbers indicate that the proposed mean permeability of 11.5 is clearly a conservative 
parameter choice. 
6. Adjusted Flow Focusing Factors 
Flow focusing is described in the seepage abstraction by a cumulative probability 
distribution giving maximum factors between 5 and 6. Sensitivity Cases 6a and 6b 
demonstrate the impact of flow focusing. Case 6a assumes that there is no flow focusing; 
i.e., the percolation flux distributions derived from the UZ Flow and Transport Model are 
directly applied for TSPA sampling (Section 6.6.4.1). In contrast, Case 6b uses more 
widely distributed flow focusing factors, with a maximum factor of about 33. Note that 
the considered distributions for flow focusing are all mass conservative; i.e., the mean of 
the distributions is always one. Thus, focusing of flow in one location (which tends to 
increase seepage) will reduce flow in other places (which tends to decrease seepage). The 
effect of this shift can be seen in the seepage calculation results. Having no flow focusing 
leads to a small increase in the seepage fraction, while the overall amount of seepage 
decreases slightly. Hence, more waste packages will have contact with water, but the 
average amount of water at these locations will be small. In contrast, a wider distribution

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of flow focusing factors leads to a strong increase in overall seepage, combined with a 
significant decrease in the seepage fraction. As a result, many fewer waste packages will 
have contact with water, but the amount of seepage at these locations will be large. 
7. Percolation Flux Distribution From Alternative PTn Flow Conceptualization 
Alternative percolation flux distributions were introduced in Section 6.6.4.1, based on 
adjusted hydrological properties in the PTn unit. The impact of using these alternative 
distributions is analyzed in Sensitivity Case 7. The resulting differences in both the 
overall amount of seepage and the seepage fraction are small. Using the alternative flow 
fields leads to a minor increase in the mean seepage percentage combined with a small 
decrease in the seepage fraction. Because they have limited impact, alternative flow fields 
can be neglected in the TSPA-LA seepage evaluation. 
8. Alternative Methods for Definition of Capillary Strength Parameter Distribution 
As discussed in Section 6.6.1.2, four different methods have been employed to derive the 
mean and the standard deviation of sampled values of the capillary strength parameter 
1/a. The methods consider alternative statistical relationships between sample values 
measured in different geological units and at different locations, and they arrive at four 
alternative probability distributions for spatial variability and uncertainty of this 
parameter (see Section 6.7.1.1 for the definition of the four parameter distributions). 
Statistical tests have not provided a clear indication about the most appropriate method. It 
was recommended in Sections 6.6.1.2 and Section 6.7.1 that all four methods be included 
in TSPA as equally probable alternative representations of spatial variability and 
uncertainty in the 1/a-parameter. The base-case seepage results in Section 6.8.1 have 
been calculated using Method A. For comparison, Sensitivity Cases 8a, 8b, and 8c use 
parameter distributions for 1/a based on the statistical values derived using Methods B, 
C, and D, respectively. The resulting values of seepage percentage and fraction are 
slightly lower for Method B compared to Method A, whereas Methods C and D lead to 
slightly higher seepage estimates compared to Method A. Overall, the sensitivity of the 
seepage results to the evaluation method is rather small, providing confidence that the 
seepage calculation is not greatly affected by uncertainty about the statistical 
independence of the sample values.

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Table 6.8-3. Summary Statistics for Seepage Sensitivity Cases (Tptpll Unit) 
1. Normal Distribution for 1/a Instead of Uniform Distribution 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 1.6 -0.1 1.5 0.0 7.8 -0.1 
Monsoon 17.2 -0.3 5.2 -0.1 18.2 -0.1 
Glacial Transition 37.5 -0.4 7.4 -0.1 24.0 -0.2 
2. Normal Distribution for Seepage Uncertainty Instead of Uniform Distribution 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 1.4 -0.3 1.3 -0.2 7.6 -0.3 
Monsoon 16.6 -0.9 5.0 -0.3 18.7 +0.4 
Glacial Transition 36.6 -1.3 7.3 -0.2 24.8 +0.6 
3. No Spatial Variability in Permeability and Capillary-Strength Parameter 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.1 -1.6 0.1 -1.4 1.7 -6.2 
Monsoon 5.6 -11.9 1.7 -3.6 10.3 -8.0 
Glacial Transition 14.9 -23.0 3.0 -4.5 16.4 -7.8 
4. No Uncertainty in Permeability and Capillary-Strength Parameter 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.9 -0.8 0.8 -0.7 4.8 -3.1 
Monsoon 12.5 -5.0 3.8 -1.5 15.4 -2.9 
Glacial Transition 28.6 -9.3 5.7 -1.8 21.0 -3.2 
5. Adjusted Mean Permeability for Tptpll: k = -11.0 instead of k = -11.5 (in log10) 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.4 -1.3 0.4 -1.1 2.4 -5.5 
Monsoon 5.7 -11.8 1.7 -3.6 8.0 -10.3 
Glacial Transition 13.3 -24.6 2.6 -4.9 11.3 -12.9 
6a. No Flow Focusing 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 0.9 -0.8 0.9 -0.6 8.1 +0.2 
Monsoon 10.4 -7.1 3.2 -2.1 19.8 +1.5 
Glacial Transition 23.6 -14.3 4.7 -2.8 26.6 +2.4 
6b. Increased Flow Focusing (Maximum Flow Focusing Factor fff = 33) 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 10.6 +8.9 9.5 +8.0 5.3 -2.6 
Monsoon 68.2 +50.7 19.8 +14.5 11.2 -7.1 
Glacial Transition 127.2 +89.3 24.1 +16.6 14.1 -10.1 
7. Percolation Flux Distribution From Alternative PTn Flow Conceptualization 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 2.1 +0.4 2.0 +0.5 7.7 -0.2 
Monsoon 20.1 +2.6 6.0 +0.7 17.2 -1.1 
Glacial Transition 42.9 +5.0 8.4 +0.9 22.6 -1.6

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Table 6.8-3. Summary Statistics for Seepage Sensitivity Cases (Tptpll Unit) (continued) 
8a. Spatial Variability and Uncertainty Distributions for 1/a Defined From Method B 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 1.2 -0.5 1.1 -0.4 6.0 -1.9 
Monsoon 14.0 -3.5 4.2 -1.1 15.0 -3.3 
Glacial Transition 30.7 -7.2 6.1 -1.4 20.5 -3.7 
8b. Spatial Variability and Uncertainty Distributions for 1/a Defined From Method C 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 1.8 +0.1 1.7 +0.2 8.6 +0.7 
Monsoon 18.8 +1.3 5.7 +0.4 19.4 +1.1 
Glacial Transition 40.1 +2.2 8.0 +0.5 25.4 +1.2 
8c. Spatial Variability and Uncertainty Distributions for 1/a Defined From Method D 
Climate Period Mean Seepage Rate 
(kg/year/WP) 
Mean Seepage 
Percentage (%) 
Seepage Fraction (%) 
Present Day 3.2 +1.5 3.0 +1.5 10.9 +3.0 
Monsoon 24.1 +6.6 7.3 +2.0 20.4 +2.1 
Glacial Transition 48.3 +10.4 9.6 +2.1 25.8 +1.6 
Output-DTN: LB0308AMRU0120.002 
NOTE: Left values in each column given seepage results for sensitivity case, right values give changes to base 
case. Computation documented in Attachment V. 
The main results from the sensitivity analysis can be briefly summarized. The first two 
sensitivity cases prove that the chosen shape of the probability distributions for spatial variability 
of capillary strength and the uncertainty in seepage rates does not affect the seepage evaluation. 
As a consequence, the abstraction choice to use uniform distributions is supported. Sensitivity 
Cases 3 and 4 demonstrate that the consideration of spatial variability and uncertainty in 
seepage-relevant parameters 1/ a and k is important for seepage evaluation in TSPA-LA. 
Sensitivity Case 5 indicates the strong impact of the conservative parameter choice for 
permeability in the Tptpll unit. Additional air injection testing could be beneficial for this unit, to 
provide evidence that the mean permeability in the Tptpll should be higher than the one chosen 
in the abstraction. As shown in Sensitivity Case 6, the distribution of flow focusing factors is 
also important for the seepage evaluation. In contrast to Sensitivity Cases 3, 4, and 5, where a 
consistent trend is seen in both the seepage percentage and the seepage fraction, a change in flow 
focusing factors results in opposite trends for these seepage parameters. An increase in the 
overall amount of seepage is associated with a decrease in the number of waste packages 
affected by seepage, and vice versa. Since both the amount of seepage and the seepage fraction 
are important for TSPA-LA, the overall impact of using different distributions for flow focusing 
factors should be evaluated in further sensitivity testing using the full TSPA model. Sensitivity 
Case 7 shows that the alternative flow conceptualization in the PTn unit is not important for the 
overall seepage evaluation. The final sensitivity cases demonstrate that the seepage estimates are 
not strongly influenced by the evaluation method employed to define the spatial variability and 
uncertainty distribution for the capillary strength parameter.

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7. VALIDATION 
The seepage abstraction developed in this Model Report is based on seepage predictions from 
detailed process models that have been validated in previous analyses, to ensure appropriate 
representation of the physical processes and relevant parameters (Section 6.4). As explained in 
Section 6.5, these results are either propagated to the TSPA-LA without changes (i.e., seepage 
look-up table) or have been simplified within the abstraction process (i.e., thermal seepage, THC 
and THM alterations, drift degradation, rock bolts). From the seepage look-up tables and the 
respective simplifications for additional factors, probabilistic seepage rates and uncertainties can 
be calculated as a function of seepage-relevant parameters. In the abstraction, appropriate spatial 
variability and uncertainty distributions have been developed for these parameters, based on 
either process model results (i.e., capillary strength, percolation flux) or in situ measurements 
(permeability). 
Application of the developed abstraction model gives probabilistic seepage estimates for the 
future conditions in the entire repository, e.g., the expected probability distribution of seepage 
rates and the number of locations in the repository where seepage may occur. The validity of 
these final results depends on (1) the validity of the parameter distributions that feed into the 
seepage look-up table, and (2) the validity of the seepage predictions that provide the look-up 
tables or serve as bases for certain simplifications. Thus, validation of the seepage abstraction 
must demonstrate that (1) the input parameters for the seepage abstraction are justified, (2) the 
seepage abstraction captures the results provided by the process models in a qualitative and 
quantitative manner, and (3) uncertainties in the input parameters and the model results are 
appropriately incorporated. 
The validation activities conducted for the seepage abstraction are consistent with the TWP for 
this Model Report (BSC 2002 [160819], Attachment I, Section I-4-3-1). These include activities 
that are conducted during the model development process (discussion and justification of input 
parameters, discussion and evaluation of uncertainties) and activities for testing the model after 
development (corroboration of abstraction with process models). Note that no initial conditions 
are established in the seepage abstraction model, i.e., a discussion and justification of the 
approach for the establishment of initial conditions do not apply to this Model Report. Results 
obtained from validation are presented and discussed in the remainder of this section. 
Confidence Building during Model Development: Justification of Input Parameters 
The parameters used in the abstraction process have been carefully selected from 
appropriate seepage process models and from in situ testing; they are reasonable and 
consistent with the data. Process models that provide parameter input to the abstraction 
have been discussed and evaluated in Section 6.4. These process models have all been 
validated, typically in comparison with experimental data and through corroboration with 
alternative conceptual models. The seepage process models all have a consistent 
conceptual modeling framework similar to the SCM, an important requirement for 
predictive modeling of ambient and thermal seepage. The boundary conditions and 
parameter ranges applied to these process models are appropriate; they sufficiently cover 
the expected conditions and ranges at Yucca Mountain, including temporal changes and

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spatial variability. This ensures that the seepage abstraction does not extrapolate process 
model results to conditions beyond the model simulation ranges. 
Confidence Building during Model Development: Evaluation of Uncertainties 
A significant effort was spent in the abstraction process to define appropriate probability 
distributions for the spatial variability and uncertainty of seepage-relevant parameters 
(Section 6.6). The spatial variability distributions for the capillary-strength parameter and 
the permeability were developed from statistical calculations in Excel spreadsheets, using 
input from the SCM and from in situ air-injection tests (spreadsheets are given in 
Attachments II and III). Both parameters have been derived on the appropriate scale 
representing the location of interest, i.e., the near-drift excavation-disturbed zone. 
Uncertainty in these spatial variability distributions, stemming from the limited number 
of intermediate-scale measurement locations, was identified and incorporated in the 
abstraction by use of triangular probability distributions. The spatial variability of 
percolation fluxes was provided by simulation results from the UZ Flow and Transport 
Model. To account for heterogeneity occurring below the resolution of this site-scale 
model, additional flux variability was incorporated using a spatial distribution of flow 
focusing factors. Furthermore, measurement and model uncertainty identified in the 
upstream sources feeding into this abstraction was accounted for. For example, seepage 
prediction uncertainty is explicitly included in the seepage abstraction by random 
sampling from a uniform uncertainty distribution, while uncertainty in the future climates 
and percolation fluxes is accounted for by three different climate scenarios. Other 
uncertainties are incorporated in the abstraction by means of conservative upper-bound 
seepage estimates. A sensitivity study was conducted in Section 6.8.2 to evaluate the 
sensitivity of the overall seepage in the repository to certain abstraction choices with 
regard to the treatment of spatial variability and uncertainty. 
Post-Development Validation Activities: Corroboration with Process Models 
The main validation method for an abstraction model is to show that the abstraction 
results are sufficiently close to the predictions of the supporting models. Since the 
supporting models in turn are validated, the abstraction results can be considered 
validated ensuring an appropriate representation of the relevant processes in the TSPALA. 
The validation criterion suggested in the TWP (BSC 2002 [160819], Attachment I, 
Section I-4-3-1) calls for an agreement within 20% between the abstracted results and the 
process model results. It should be added that this 20% threshold may be exceeded if the 
abstracted results lead to more seepage; i.e., if the abstraction method uses conservative 
bounds for seepage. This ensures that the simplifications required in the seepage 
abstraction do not lead to an underestimation of seepage. 
In seepage abstraction, the supporting models providing simulated seepage rates are the 
SMPA for ambient seepage and the Thermal Seepage Model for thermal seepage. In 
addition, the impact of THM and THC alterations is evaluated with the Drift-Scale THM 
Model and the THC Seepage Model, respectively. The agreement between these models 
and the abstracted results can be summarized as follows: 
1. SMPA Results

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The seepage look-up tables for ambient seepage provided by the SMPA are 
propagated through the seepage abstraction without further simplification (Section 
6.5.1). For given random parameter cases, the seepage results are directly interpolated 
from the predicted values in the look-up table. Thus, there is very good agreement 
between the process model results and the abstracted results. Specific simulation 
cases were devoted to analyze the impact of degraded drift shapes on ambient 
seepage (Sections 6.4.2.4 and 6.4.2.5). In order to incorporate uncertainties in the 
results for moderately degraded drifts, the ambient seepage rates were increased by 
20% in the abstraction (Section 6.5.1.5). While there is a difference of about 20% 
between the process model and the abstraction, the abstraction method is on the 
conservative side. 
2. TH Seepage Model Results 
The temporal evolution of seepage at thermally perturbed conditions is abstracted 
using simplified time-dependent seepage estimates that provide an asymptotic upper 
limit to the process model results (Figures 6.5-3 and 6.5-4). During early heating 
stages, these asymptotic estimates are more than 20% off from the process model 
results. However, the abstracted results are always conservative. 
3. Drift-Scale THM Model Results 
The impact of THM parameter alterations is neglected in the seepage abstraction, 
based on model results that predict slightly smaller seepage for simulation runs 
including THM effects (Section 6.4.4.1). The differences between the abstracted 
results and the THM process model results are small, as demonstrated in Figure 
6.4-17, with the abstraction results on the conservative side. 
4. THC Seepage Model Results 
The impact of THC parameter alterations on seepage has been qualitatively evaluated 
with the THC Seepage Model (Section 6.4.4.2). The precipitation cap that may 
potentially form 7-8 m above waste emplacement drifts after several hundred years of 
heating is expected to decrease seepage into drifts, since the percolating water is 
diverted sideways by the low-permeability cap before reaching the drift crown 
(Figure 6.4-18). This effect, however, is not accounted for in the seepage abstraction 
because of significant uncertainties related to these estimates. While the resulting 
disagreement between the process model results and the seepage abstraction cannot 
be exactly quantified because of the qualitative nature of the THC predictions with 
respect to seepage, the abstraction method is conservative. 
Items 1 through 4 above demonstrate that the seepage abstraction is reasonably consistent 
with the respective process model results and conservative where they differ. 
The validation activities conducted during and after development of the seepage abstraction 
provide confidence into the suggested abstraction methodology. The validation criteria defined in 
the TWP (BSC 2002 [160819], Attachment I, Section I-4-3-1) have been met. No further 
activities are needed to complete the validation of the seepage abstraction model for its intended 
use.

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8. CONCLUSIONS 
Seepage into waste emplacement drifts affects the performance of the high-level nuclear waste 
repository at Yucca Mountain, Nevada. Theoretical analyses, numerical modeling studies, and 
field experiments have shown that seepage into underground openings excavated in unsaturated 
formations is smaller than the percolation flux at the given location. This is mainly a result of 
capillary pressures holding the water in the formation, diverting it around the cavity, and 
preventing it from entering the drifts. During the first several hundred to thousands of years after 
waste emplacement at Yucca Mountain, when above-boiling temperatures will develop in the 
formation as a result of heat generated by the decaying waste, seepage can also be prevented by 
the vaporization of percolating water. 
In this Model Report, an abstraction model was developed for evaluating the future amount and 
distribution of seepage into the waste emplacement drifts at Yucca Mountain. The purpose of 
this abstraction is to provide the necessary methodology, parameters, and simplifications to the 
TSPA-LA, so that probabilistic seepage calculations can be conducted within the TSPA 
simulation loops. These probabilistic calculations provide estimates of seepage rates and the 
seepage fraction averaged over drifts segments for given TSPA simulation time steps; they are 
not expected to predict individual seepage events or the precise spatial seepage distribution along 
the drifts and within the repository. 
The seepage abstraction is based on several input sources such as drift-scale and site-scale 
process models as well as in situ testing in the unsaturated zone at Yucca Mountain. This Model 
Report evaluates the respective input sources, analyzes relevant results, and discusses the related 
uncertainties (Section 6.4). On this basis, the abstraction input is then synthesized, integrated, 
and simplified into a form that can be used in the TSPA-LA. All relevant uncertainties are 
characterized and propagated through the abstraction. A short summary of the treatment of 
natural variability and uncertainty in the abstraction is given in Section 6.7.2. All the NRC 
acceptance criteria listed in Section 4.2 are adequately addressed in Section 6. 
The proposed abstraction methodology is explained in detail in Section 6.5 of this Model Report. 
It is recognized that the amount of seepage is sensitive to key hydrological properties that are 
both spatially variable and uncertain. For ambient seepage, these key hydrological properties are 
the capillary-strength parameter, the permeability, and the local percolation flux. One of the main 
tasks of this seepage abstraction model is to define appropriate probability distributions that 
represent the spatial variability and uncertainty inherent in these parameters in a cautiously 
realistic manner. Section 6.6 explains the data evaluation methodology and provides the resulting 
parameter distributions separately for spatial variability and uncertainty. All relevant sources of 
data uncertainty have been identified, described, and accounted for in this process, potentially 
stemming from measurement uncertainty, conceptual model uncertainty, estimation uncertainty, 
and spatial variability uncertainty. 
Based on these parameter distributions, ambient seepage can be derived in a random process 
using predictive seepage simulations from the SMPA (Section 6.5.1). The SMPA results are 
given in the form of seepage look-up tables (DTN: LB0304SMDCREV2.002 [163687]) that 
provide seepage results as a function of the seepage-relevant parameters. For a particular set of

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these parameters, randomly sampled from the respective distributions, the mean seepage rate and 
its inherent estimation uncertainty, expressed by the standard deviation over the 20 realizations 
conducted, are linearly interpolated between the table values. The standard deviation is used to 
define a uniform uncertainty distribution. A seepage uncertainty value is then randomly sampled 
from this distribution and used to adjust the mean seepage values, giving the final ambient 
seepage rate for the considered parameter set. Conducting this procedure over a large number of 
random parameter sets results in the final distribution of ambient seepage. 
It is possible that the initially circular-shaped drifts degrade with time as a result of rock fatigue 
or seismic events. Based on the SMPA modeling results, the impact of drift degradation is 
accounted for by using separate look-up tables for non-degraded and collapsed drifts. TSPA will 
select the appropriate look-up table depending on the considered geologic unit, the selected 
nominal or disruptive scenario, and the assumed rock strength reduction case. This selection is 
based on categories of drift degradation that have been introduced in Section 6.5.1.5, based on 
results from BSC (2003 [162711]). Category 1 comprises degraded drifts that may show local 
rock breakout but stay essentially intact. In this category, seepage is interpolated from the lookup 
table for non-degraded drifts. Category 1 includes all drifts located in nonlithophysal rock, 
and, for drifts located in lithophysal rock, the nominal scenario, the 5  10-4 seismic hazard case, 
and the 0% through 40% strength reduction cases. Category 2 comprises the cases with complete 
drift collapse. These are the 1  10-6 and the 1  10-7 seismic hazard levels and the 60% through 
100% strength reduction cases for lithophysal rock units. In this category, seepage is interpolated 
from the look-up table for collapsed drifts. Note that three alternative abstraction methods are 
proposed for igneous events (see Section 6.5.1.7). In view of the significant uncertainty about the 
in-drift conditions after an igneous event, it is recommended that TSPA conduct sensitivity 
analyses with the three abstraction methods. The more conservative seepage estimates should be 
chosen and propagated to the downstream TSPA modules. 
Since the SMPA look-up tables account for seepage at ambient and somewhat idealized 
conditions, the impact of additional factors affecting seepage needs to be evaluated in a second 
step (Section 6.5.2). These factors include the ground support with rock bolts, the expected 
transient changes in hydrological properties as a result of THM and THC effects, and the thermal 
perturbation of the flow field as a result of boiling in the rock. The method proposed in this 
abstraction is to account for these factors in a simplified form, using the ambient seepage results 
as a basis and adjusting them as suggested by the relative importance of each factor. To 
incorporate uncertainty, the simplifications made in this process are usually conservative, yet 
strive to be as realistic as possible. Conservatism means that the simplified abstractions tend to 
overestimate seepage compared to the predicted process model results. 
The impact of rock bolts as well as THM and THC parameter alterations can be neglected in the 
abstraction, as demonstrated by the drift-scale process models simulating these processes. Two 
alternative abstraction models are proposed for seepage during the period of strong thermal 
perturbation at Yucca Mountain. The first model is very simple; it does not take credit for the 
vaporization barrier that prevents seepage at above-boiling rock temperatures. The second 
abstraction model is more realistic (less conservative), but requires more complex 
implementation into the TSPA-LA. In this model, thermal seepage is set to zero for the period of 
above-boiling rock temperatures in the drift vicinity. The decision on which one of the two 
models should be used will be made during the TSPA process.

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For illustration of the expected seepage behavior in the repository, a probabilistic seepage 
calculation was conducted within this Model Report following the proposed abstraction method 
(Section 6.8). Summary results indicate the importance of the natural barrier formed by the 
unsaturated rock at and above the repository horizon. For drifts located in the main geological 
unit in the repository (Tptpll) and assuming the most probable climate scenario, the mean 
seepage percentage is about 8% during the wet glacial transition climate, when most seepage is 
expected. This means that on average, more than about 92% of the percolating water is diverted 
around the emplacement drifts. The calculation procedure was also applied to understand the 
impact of certain abstraction choices by analysis of various sensitivity cases. Note that the 
calculation results are not directly utilized in the performance assessment, because the seepage 
component in the TSPA simulations will conduct a similar seepage calculation embedded in the 
Monte Carlo simulation procedure. 
The use of the results presented in this Model Report is restricted to a probabilistic TSPA 
simulation that follows the methodology outlined in Section 6.6, summarized in Section 6.7, and 
demonstrated in Section 6.8. Specifically, the distributions of seepage-relevant parameters 
developed in this Model Report can only be used for seepage evaluations if combined with lookup 
tables that were generated based on a consistent conceptual model (such as the SMPA). 
Furthermore, the use of the seepage abstraction model is limited to the conditions considered and 
described in the upstream process models (see Figure 1-1). 
A short roadmap of the proposed abstraction methodology is given in Section 6.7.1. This 
roadmap can be directly followed by the TSPA-LA seepage calculations. The relevant content of 
Section 6.7.1 was submitted to the TDMS under DTN: LB0308AMRU0120.001. This DTN also 
comprises several statistical calculations needed for evaluating parameter distributions. Another 
DTN: LB0308AMRU0120.002 includes the probabilistic seepage calculations conducted in 
Section 6.8. For the purposes of traceability and reproducibility, all files submitted to the TDMS 
are described in detail in Attachments I to V. All file dates and file sizes can be obtained from 
the TDMS.

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9. INPUTS AND REFERENCES 
The following is a list of the references cited in this document. Column 1 represents the unique 
six digit numerical identifier (the Document Input Reference System [DIRS] number), which is 
placed in the text following the reference callout (e.g., BSC 2002 [160819]). The purpose of 
these numbers is to assist the reader in locating a specific reference. Within the reference list, 
multiple sources by the same author (e.g., BSC 2002) are sorted alphabetically by title. 
9.1 DOCUMENTS CITED 
163686 Birkholzer, J. 2003. Penetration of Liquid Fingers into Superheated Fractured 
Rock. Water Resources Research, 39, (4), SBH 9-1 to SBH 9-21. [Washington, 
D.C.]: American Geophysical Union. TIC: 254362. 
105170 Birkholzer, J.; Li, G.; Tsang, C-F.; and Tsang, Y. 1999. Modeling Studies and 
Analysis of Seepage into Drifts at Yucca Mountain. Journal of Contaminant 
Hydrology, 38, (1-3), 349-384. New York, New York: Elsevier. TIC: 244160. 
164526 Birkholzer, J.T. 2003. YMP-LBNL-JTB-2 Unsaturated Zone Synthesis & Modeling - 
Abstraction of Drift Seepage. Scientific Notebook SN-LBNL-SCI-231-V1. ACC: 
MOL.20030728.0385. 
164525 Birkholzer, J.T. 2003. YMP-LBNL-JTB-3 Unsaturated Zone Synthesis & Modeling - 
Abstraction of Drift Seepage. Scientific Notebook SN-LBNL-SCI-231-V2. ACC: 
MOL.20030728.0383. 
154608 Birkholzer, J.T. and Tsang, Y.W. 2000. Modeling the Thermal-Hydrologic 
Processes in a Large-Scale Underground Heater Test in Partially Saturated Fractured 
Tuff. Water Resources Research, 36, (6), 1431-1447. Washington, D.C.: American 
Geophysical Union. TIC: 248278. 
120055 Bodvarsson, G.S.; Boyle, W.; Patterson, R.; and Williams, D. 1999. Overview of 
Scientific Investigations at Yucca MountainThe Potential Repository for High- 
Level Nuclear Waste. Journal of Contaminant Hydrology, 38, (1-3), 3-24. New 
York, New York: Elsevier. TIC: 244160. 
163443 Bodvarsson, G.S.; Wu, Y-S.; and Zhang, K. 2003. Development of Discrete Flow 
Paths in Unsaturated Fractures at Yucca Mountain. Journal of Contaminant 
Hydrology, 62-63, 23-42. New York, New York: Elsevier. TIC: 254205. 
156304 BSC (Bechtel SAIC Company) 2001. Drift Degradation Analysis. ANL-EBS-MD- 
000027 REV 01 ICN 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20011029.0311. 
155950 BSC (Bechtel SAIC Company) 2001. FY 01 Supplemental Science and Performance 
Analyses, Volume 1: Scientific Bases and Analyses. TDR-MGR-MD-000007 REV

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00 ICN 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20010801.0404; MOL.20010712.0062; MOL.20010815.0001. 
154659 BSC (Bechtel SAIC Company) 2001. FY01 Supplemental Science and Performance 
Analyses, Volume 2: Performance Analyses. TDR-MGR-PA-000001 REV 00. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20010724.0110. 
155187 BSC (Bechtel SAIC Company) 2001. Ground Control for Emplacement Drifts for 
SR. ANL-EBS-GE-000002 REV 00 ICN 01. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: MOL.20010627.0028. 
158463 BSC (Bechtel SAIC Company) 2001. In Situ Field Testing of Processes. ANL-NBSHS-
000005 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20020108.0351. 
158204 BSC (Bechtel SAIC Company) 2001. Multiscale Thermohydrologic Model. ANLEBS-
MD-000049 REV 00 ICN 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20020123.0279. 
156609 BSC (Bechtel SAIC Company) 2001. Unsaturated Zone Flow Patterns and Analysis. 
MDL-NBS-HS-000012 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20011029.0315. 
159124 BSC (Bechtel SAIC Company) 2002. Geologic Framework Model (GFM2000). 
MDL-NBS-GS-000002 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20020530.0078. 
158794 BSC (Bechtel SAIC Company) 2002. Guidelines for Developing and Documenting 
Alternative Conceptual Models, Model Abstractions, and Parameter Uncertainty in 
the Total System Performance Assessment for the License Application. TDR-WISPA-
000008 REV 00, ICN 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20020904.0002. 
160405 BSC (Bechtel SAIC Company) 2002. Natural Analogue Synthesis Report. TDRNBS-
GS-000027 REV 00 ICN 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: MOL.20020520.0288. 
160780 BSC (Bechtel SAIC Company) 2002. Risk Information to Support Prioritization of 
Performance Assessment Models. TDR-WIS-PA-000009 REV 01 ICN 01. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: MOL.20021017.0045. 
160819 BSC (Bechtel SAIC Company) 2002. Technical Work Plan for: Performance 
Assessment Unsaturated Zone. TWP-NBS-HS-000003 REV 02. Las Vegas, Nevada: 
Bechtel SAIC Company. ACC: MOL.20030102.0108.

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160319 BSC (Bechtel SAIC Company) 2002. Thermal Conductivity of the Potential 
Repository Horizon Model Report. MDL-NBS-GS-000005 REV 00. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20020923.0167. 
160771 BSC (Bechtel SAIC Company) 2002. Thermal Testing Measurements Report. ANLNBS-
HS-000041 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20021004.0314. 
160146 BSC (Bechtel SAIC Company) 2002. Total System Performance Assessment-License 
Application Methods and Approach. TDR-WIS-PA-000006 REV 00. Las Vegas, 
Nevada: Bechtel SAIC Company. ACC: MOL.20020923.0175. 
161773 BSC (Bechtel SAIC Company) 2003. Analysis of Hydrologic Properties Data. 
MDL-NBS-HS-000014 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20030404.0004. 
163740 BSC (Bechtel SAIC Company) 2003. Analysis of Infiltration Uncertainty. ANLNBS-
HS-000027 REV 01D. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20030807.0339. 
160240 BSC (Bechtel SAIC Company) 2003. Calibrated Properties Model. MDL-NBS-HS- 
000003 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20030219.0001. 
160109 BSC (Bechtel SAIC Company) 2003. Development of Numerical Grids for UZ Flow 
and Transport Modeling. ANL-NBS-HS-000015 REV 01. Las Vegas, Nevada: 
Bechtel SAIC Company. ACC: DOC.20030404.0005. 
161839 BSC (Bechtel SAIC Company) 2003. Dike/Drift Interactions. MDL-MGR-GS- 
000005 REV 00A. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20030711.0105. 
162711 BSC (Bechtel SAIC Company) 2003. Drift Degradation Analysis. ANL-EBS-MD- 
000027 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20030709.0003. 
162318 BSC (Bechtel SAIC Company) 2003. Drift Scale THM Model. MDL-NBS-HS- 
000017 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20030818.0003. 
161530 BSC (Bechtel SAIC Company) 2003. Drift-Scale Coupled Processes (DST and TH 
Seepage) Models. MDL-NBS-HS-000015 REV 00. Las Vegas, Nevada: Bechtel 
SAIC Company. URN-1087

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MDL-NBS-HS-000019 REV00 198 August 2003 
162050 BSC (Bechtel SAIC Company) 2003. Drift-Scale Coupled Processes (DST and THC 
Seepage) Models. MDL-NBS-HS-000001 REV 02. Las Vegas, Nevada: Bechtel 
SAIC Company. ACC: DOC.20030804.0004. 
163938 BSC (Bechtel SAIC Company) 2003. Drift-Scale Radionuclide Transport. MDLNBS-
HS-000016 REV 00C. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20030804.0205. 
164603 BSC (Bechtel SAIC Company) 2003. Repository Design Project, RDP/PA IED 
Typical Waste Package Components Assembly 1 of 9. 800-IED-WIS0-00201-000- 
00B. Las Vegas, Nevada: Bechtel SAIC Company. ACC: ENG.20030808.0004. 
164101 BSC (Bechtel SAIC Company) 2003. Repository Design Project, Repository/PA IED 
Emplacement Drift Committed Materials (2). 800-IED-WIS0-00302-000-00A. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: ENG.20030627.0004. 
164069 BSC (Bechtel SAIC Company) 2003. Repository Design Project, Repository/PA IED 
Emplacement Drift Configuration 1 of 2. 800-IED-EBS0-00201-000-00A. Las 
Vegas, Nevada: Bechtel SAIC Company. ACC: ENG.20030630.0002. 
162289 BSC (Bechtel SAIC Company) 2003. Repository Design, Repository/PA IED 
Subsurface Facilities. 800-IED-EBS0-00401-000-00C. Las Vegas, Nevada: Bechtel 
SAIC Company. ACC: ENG.20030303.0002. 
162267 BSC (Bechtel SAIC Company) 2003. Seepage Calibration Model and Seepage 
Testing Data. MDL-NBS-HS-000004 REV 02. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: DOC.20030408.0004. 
163226 BSC (Bechtel SAIC Company) 2003. Seepage Model for PA Including Drift 
Collapse. MDL-NBS-HS-000002 REV 02. Las Vegas, Nevada: Bechtel SAIC 
Company. ACC: DOC.20030709.0001. 
164325 BSC (Bechtel SAIC Company) 2003. Underground Layout Configuration. 800- 
P0C-MGR0-00100-000-00D. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
ENG.20030716.0002. 
163045 BSC (Bechtel SAIC Company) 2003. UZ Flow Models and Submodels. MDL-NBSHS-
000006 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20030818.0002. 
161770 Canori, G.F. and Leitner, M.M. 2003. Project Requirements Document. TER-MGRMD-
000001 REV 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
DOC.20030404.0003. 
124052 CRWMS M&O (Civilian Radioactive Waste Management System Management and 
Operating Contractor) 1997. The Site-Scale Unsaturated Zone Transport Model of

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Yucca Mountain. Milestone SP25BM3, Rev. 1. Las Vegas, Nevada: CRWMS 
M&O. ACC: MOL.19980224.0314. 
143244 CRWMS M&O 2000. Analysis of Infiltration Uncertainty. ANL-NBS-HS-000027 
REV 00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20000525.0377. 
100356 CRWMS M&O 1998. Unsaturated Zone Hydrology Model. Chapter 2 of Total 
System Performance Assessment-Viability Assessment (TSPA-VA) Analyses Technical 
Basis Document. B00000000-01717-4301-00002 REV 01. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.19981008.0002. 
144426 CRWMS M&O 2000. Calibrated Properties Model. MDL-NBS-HS-000003 REV 
00. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.19990721.0520. 
141187 CRWMS M&O 2000. Conceptual and Numerical Models for UZ Flow and 
Transport. MDL-NBS-HS-000005 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19990721.0526. 
152286 CRWMS M&O 2000. Fracture Geometry Analysis for the Stratigraphic Units of the 
Repository Host Horizon. ANL-EBS-GE-000006 REV 00. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.20000918.0286. 
148384 CRWMS M&O 2000. Total System Performance Assessment (TSPA) Model for Site 
Recommendation. MDL-WIS-PA-000002 REV 00. Las Vegas, Nevada: CRWMS 
M&O. ACC: MOL.20001226.0003. 
153246 CRWMS M&O 2000. Total System Performance Assessment for the Site 
Recommendation. TDR-WIS-PA-000001 REV 00 ICN 01. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.20001220.0045. 
151940 CRWMS M&O 2000. Unsaturated Zone Flow and Transport Model Process Model 
Report. TDR-NBS-HS-000002 REV 00 ICN 02. Las Vegas, Nevada: CRWMS 
M&O. ACC: MOL.20000831.0280. 
151945 CRWMS M&O 2000. Yucca Mountain Site Description. TDR-CRW-GS-000001 
REV 01 ICN 01. Las Vegas, Nevada: CRWMS M&O. ACC: 
MOL.20001003.0111. 
154291 CRWMS M&O 2001. Abstraction of Drift Seepage. ANL-NBS-MD-000005 REV 
01. Las Vegas, Nevada: CRWMS M&O. ACC: MOL.20010309.0019. 
135997 Doughty, C. 1999. Investigation of Conceptual and Numerical Approaches for 
Evaluating Moisture, Gas, Chemical, and Heat Transport in Fractured Unsaturated 
Rock. Journal of Contaminant Hydrology, 38, (1-3), 69-106. New York, New 
York: Elsevier. TIC: 244160.

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MDL-NBS-HS-000019 REV00 200 August 2003 
100145 Fabryka-Martin, J.T.; Wolfsberg, A.V.; Dixon, P.R.; Levy, S.S.; Musgrave, J.A.; and 
Turin, H.J. 1997. Summary Report of Chlorine-36 Studies: Sampling, Analysis, and 
Simulation of Chlorine-36 in the Exploratory Studies Facility. LA-13352-MS. Los 
Alamos, New Mexico: Los Alamos National Laboratory. ACC: 
MOL.19980812.0254. 
151875 Finsterle, S. 2000. Using the Continuum Approach to Model Unsaturated Flow in 
Fractured Rock. Water Resources Research, 36, (8), 2055-2066. [Washington, 
D.C.]: American Geophysical Union. TIC: 248769. 
154365 Freeze, G.A.; Brodsky, N.S.; and Swift, P.N. 2001. The Development of Information 
Catalogued in REV00 of the YMP FEP Database. TDR-WIS-MD-000003 REV 00 
ICN 01. Las Vegas, Nevada: Bechtel SAIC Company. ACC: 
MOL.20010301.0237. 
101700 LeCain, G.D. 1995. Pneumatic Testing in 45-Degree-Inclined Boreholes in Ash-Flow 
Tuff Near Superior, Arizona. Water-Resources Investigations Report 95-4073. 
Denver, Colorado: U.S. Geological Survey. ACC: MOL.19960715.0083. 
105729 Liu, H.H.; Doughty, C.; and Bodvarsson, G.S. 1998. An Active Fracture Model for 
Unsaturated Flow and Transport in Fractured Rocks. Water Resources Research, 
34, (10), 2633-2646. Washington, D.C.: American Geophysical Union. TIC: 
243012. 
163603 Mishra, S. 2002. Assigning Probability Distributions to Input Parameters of 
Performance Assessment Models. SKB TR-02-11. Stockholm, Sweden: Svensk 
Krnbrnslefrsrjning A.B. TIC: 252794. 
149850 Mongano, G.S.; Singleton, W.L.; Moyer, T.C.; Beason, S.C.; Eatman, G.L.W.; Albin, 
A.L.; and Lung, R.C. 1999. Geology of the ECRB Cross Drift - Exploratory Studies 
Facility, Yucca Mountain Project, Yucca Mountain, Nevada. [Deliverable 
SPG42GM3]. Denver, Colorado: U.S. Geological Survey. ACC: 
MOL.20000324.0614. 
105731 Neuman, S.P. 1994. Generalized Scaling of Permeabilities: Validation and Effect 
of Support Scale. Geophysical Research Letters, 21, (5), 349-352. Washington, 
D.C.: American Geophysical Union. TIC: 240142. 
163274 NRC (U.S. Nuclear Regulatory Commission) 2003. Yucca Mountain Review Plan, 
Final Report. NUREG-1804, Rev. 2. Washington, D.C.: U.S. Nuclear Regulatory 
Commission, Office of Nuclear Material Safety and Safeguards. TIC: 254568. 
144773 Or, D. and Ghezzehei, T.A. 2000. Dripping into Subterranean Cavities from 
Unsaturated Fractures under Evaporative Conditions. Water Resources Research, 
36, (2), 381-393. Washington, D.C.: American Geophysical Union. TIC: 246982.

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105736 Paleologos, E.K.; Neuman, S.P.; and Tartakovsky, D. 1996. Effective Hydraulic 
Conductivity of Bounded, Strongly Heterogeneous Porous Media. Water Resources 
Research, 32, (5), 1333-1341. Washington, D.C.: American Geophysical Union. 
TIC: 245760. 
105743 Philip, J.R.; Knight, J.H.; and Waechter, R.T. 1989. Unsaturated Seepage and 
Subterranean Holes: Conspectus, and Exclusion Problem for Circular Cylindrical 
Cavities. Water Resources Research, 25, (1), 16-28. Washington, D.C.: American 
Geophysical Union. TIC: 239117. 
100819 Pruess, K.; Wang, J.S.Y.; and Tsang, Y.W. 1990. On Thermohydrologic Conditions 
Near High-Level Nuclear Wastes Emplaced in Partially Saturated Fractured Tuff, 2. 
Effective Continuum Approximation. Water Resources Research, 26, (6), 1249- 
1261. [Washington, D.C.]: American Geophysical Union. TIC: 224854. 
158378 USGS (U.S. Geological Survey) 2001. Future Climate Analysis. ANL-NBS-GS- 
000008 REV 00 ICN 01. Denver, Colorado: U.S. Geological Survey. ACC: 
MOL.20011107.0004. 
160355 USGS (U.S. Geological Survey) 2001. Simulation of Net Infiltration for Modern and 
Potential Future Climates. ANL-NBS-HS-000032 REV 00 ICN 02. Denver, 
Colorado: U.S. Geological Survey. ACC: MOL.20011119.0334. 
163702 Wang, J.S. 2003. Scientific Notebooks Referenced in Model Report U0120, 
Abstraction of Drift Seepage, MDL-NBS-HS-000019 REV 00. Correspondence 
from J.S. Wang (BSC) to File, August 18, 2003, with attachments. ACC: 
MOL.20030818.0371. 
104366 Wang, J.S.Y. and Elsworth, D. 1999. Permeability Changes Induced by Excavation 
in Fractured Tuff. Rock Mechanics for Industry, Proceedings of the 37th U.S. Rock 
Mechanics Symposium, Vail, Colorado, USA, 6-9 June 1999. Amadei, B.; Kranz, 
R.L.; Scott, G.A.; and Smeallie, P.H., eds. 2, 751-757. Brookfield, Vermont: A.A. 
Balkema. TIC: 245246. 
101309 Wang, J.S.Y.; Flint, A.L.; Nitao, J.J.; Chesnut, D.A.; Cook, P.; Cook, N.G.W.; 
Birkholzer, J.; Freifeld, B.; Flint, L.E.; Ellet, K.; Mitchell, A.J.; Homuth, E.F.; 
Griego, G.J.; Cerny, J.A.; and Johnson, C.L. 1996. Evaluation of Moisture Evolution 
in the Exploratory Studies Facility. Milestone TR31K6M. Berkeley, California: 
Lawrence Berkeley National Laboratory. ACC: MOL.19961231.0089. 
160442 Whelan, J.F.; Paces, J.B.; and Peterman, Z.E. 2002. Physical and Stable-Isotope 
Evidence for Formation of Secondary Calcite and Silica in the Unsaturated Zone, 
Yucca Mountain, Nevada. Applied Geochemistry, 17, ([6]), 735-750. [New York, 
New York]: Elsevier. TIC: 253462.

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164334 Williams, H. and McBirney, A.R. 1979. Volcanology. San Francisco, California: 
Freeman, Cooper, and Company. TIC: 254575. 
161058 Wu, Y-S.; Zhang, W.; Pan, L.; Hinds, J.; and Bodvarsson, G.S. 2002. Modeling 
Capillary Barriers in Unsaturated Fractured Rock. Water Resources Research, 38, 
(11), 35-1 through 35-12. [Washington, D.C.]: American Geophysical Union. TIC: 
253854. 
Software Cited 
154783 LBNL (Lawrence Berkeley National Laboratory) 2001. Software Code: FLAC3D. 
V2.0. PC. STN: 10502-2.0-00. 
161491 LBNL (Lawrence Berkeley National Laboratory) 2003. Software Code: TOUGH2. 
V1.6. PC/MS-DOS under Windows 98, Sun UltraSparc OS 5.5.1, DEC-Alpha OSF1 
V4.0. 10007-1.6-01. 
9.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
156605 10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic 
Repository at Yucca Mountain, Nevada. Readily available. 
AP-2.22Q, Rev. 1. Analayses and Maintenance of the Q-List. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
DOC.20030807.0002. 
AP-SI.1Q, Rev. 05, ICN 01. Software Management. Washington, D.C.: U.S. Department of 
Energy, Office of Civilian Radioactive Waste Management. ACC: DOC.20030708.0001. 
AP-SIII.10Q, Rev. 1, ICN 2. Models. Washington, D.C.: U.S. Department of Energy, Office of 
Civilian Radioactive Waste Management. ACC: DOC.20030627.0003. 
AP-SV.1Q, Rev. 0, ICN 3. Control of the Electronic Management of Information. Washington, 
D.C.: U.S. Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
MOL.20020917.0133. 
YMP-LBNL-QIP-SV.0, Rev. 2, Mod. 1. Management of YMP-LBNL Electronic Data. 
Berkeley, California: Lawrence Berkeley National Laboratory. ACC: MOL.20020717.0319. 
9.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
105574 GS960908312232.013. Air-Injection Testing in Vertical Boreholes in Welded and 
Non-Welded Tuff, Yucca Mountain, Nevada. Submittal date: 09/26/1996. 
153155 LB0011AIRKTEST.001. Air Permeability Testing in Niches 3566 and 3650. 
Submittal date: 11/08/2000.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 203 August 2003 
154586 LB0012AIRKTEST.001. Niche 5 Air K Testing 3/23/00-4/3/00. Submittal date: 
12/21/2000. 
163906 LB0104AMRU0185.012. Section 6.4.2 Focusing and Discrete Flow Paths in the 
TSW - Data Summary. Submittal date: 05/15/2001. 
163689 LB0301DSCPTHSM.002. Drift-Scale Coupled Process Model for Thermohydrologic 
Seepage: Data Summary. Submittal date: 01/29/2003. 
162277 LB0302PTNTSW9I.001. PTN/TSW Interface Percolation Flux Maps for 9 
Infiltration Scenarios. Submittal date: 02/28/2003. 
162273 LB0302SCMREV02.002. Seepage-Related Model Parameters K and 1/A: Data 
Summary. Submittal date: 02/28/2003. 
163047 LB03033DSSFF9I.001. 3-D Site Scale UZ FLow Fields for 9 Infiltration Scenarios: 
Simulations Using Alternative Hydraulic Properties. Submittal date: 03/28/2003. 
163688 LB0303DSCPTHSM.001. Drift-Scale Coupled Process Model for Thermohydrologic 
Seepage: Simulation Files. Submittal date: 03/20/2003. 
163687 LB0304SMDCREV2.002. Seepage Modeling for Performance Assessment, 
Including Drift Collapse: Summary Plot Files and Tables. Submittal date: 
04/11/2003. 
163691 LB0304SMDCREV2.004. Impact of Thermal-Hydrologic-Mechanical Effects on 
Seepage: Summary Plot Files and Tables. Submittal date: 04/23/2003. 
163690 LB0305PTNTSW9I.001. PTN/TSW Interface Percolation Flux Maps for 9 
Alternative Infiltration Scenarios. Submittal date: 05/12/2003. 
164337 LB0307SEEPDRCL.002. Seepage Into Collapsed Drift: Data Summary. Submittal 
date: 07/21/2003. 
136593 LB980901233124.101. Pneumatic Pressure and Air Permeability Data from Niche 
3107 and Niche 4788 in the ESF from Chapter 2 of Report SP33PBM4: Fracture 
Flow and Seepage Testing in the ESF, FY98. Submittal date: 11/23/1999. 
123273 LB990901233124.004. Air Permeability Cross-Hole Connectivity in Alcove 6, 
Alcove 4, and Niche 4 of the ESF for AMR U0015, In Situ Testing of Field 
Processes. Submittal date: 11/01/1999. 
161496 MO0301SEPFEPS1.000. LA FEP List. Submittal date: 01/21/2003. 
164736 MO0306MWDDPPDR.000. Drift Profile Prediction and Degraded Rock Mass 
Characteristics. Submittal date: 06/18/2003.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 204 August 2003 
9.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
LB0308AMRU0120.001. Supporting Calculations and Analysis for Seepage Abstraction and 
Summary of Abstraction Results. Submittal date: 07/17/2003. 
LB0308AMRU0120.002. Mathcad 11 Spreadsheets for Probabilistic Seepage Evaluation. 
Submittal date: 07/17/2003.

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment I-1 August 2003 
ATTACHMENT IHISTOGRAMS OF SMPA REALIZATION RESULTS 
Histograms of SMPA realization results were analyzed in Section 6.4.2.3 (Figure 6.4-7). A 
Mathcad 11 spreadsheet was used to read the SMPA look-up table results for the non-degraded 
drift and calculate the seepage histograms for selected cases. The following procedure was 
followed to conduct the analysis (see also Scientific Notebook, Birkholzer 2003 [164525], pp. 
2025): 
1. Copy file Fig6-3toFig6-8.xls from DTN: LB0304SMDCREV2.002 [163687] into appropriate 
working directory 
2. Open a new Excel file named ResponseSurfaceSMPA_all_realizations_selected.xls, with 
three different Worksheets -12 and 500,-12 and 100,-12 and 1000 
3. Choose three parameter cases in k and 1/a, representing SMPA results with average, large 
and small seepage (Case 1: k = -12, 1/a = 500 Pa, Case 2: k = -12, 1/a = 1,00 Pa, Case 3: 
k = -12, 1/a = 1,000 Pa) 
4. Copy SMPA results for each selected case from Fig6-3toFig6-8.xls into 
ResponseSurfaceSMPA_all_realizations_selected.xls. For each case, copy the full line of all 
percolation rates included in the look-up table. Copy all lines for Case1 into Worksheet -12 
and 500, all lines for Case 2 into Worksheet -12 and 100, and all lines for Case 3 into 
Worksheet -12 and 1000. 
5. Delete the first seven columns in each Worksheet, so that only the 20 columns for each 
realization are left in the Excel file. 
6. Use Mathcad 11 spreadsheet histogram_seepage_uncertainty.mcd to calculate and display 
the seepage histograms over the 20 realizations. The calculation reads the Excel file 
ResponseSurfaceSMPA_all_realizations_selected.xls. It then calculates and displays 
histograms for each case, choosing four percolation fluxes (50 mm/yr, 200 mm/yr, 
500 mm/yr, and 1000 mm/yr). The resulting histograms can be evaluated. 
ResponseSurfaceSMPA_all_realizations_selected.xls and histogram_seepage_uncertainty.mcd 
are in Output-DTN: LB0308AMRU0120.001 (Directory: seepage_uncertainty_evaluation).

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment I-2 August 2003 
INTENTIONALLY LEFT BLANK

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment II-1 August 2003 
ATTACHMENT IISTATISTICAL ANALYSIS OF CAPILLARY-STRENGTH 
PARAMETER VALUES 
The calibrated capillary-strength parameters from the SCM were statistically analyzed in Section 
6.6.1.2 (see also Scientific Notebook, Birkholzer 2003 [164526], pp. 5965, 118127). The 
analysis was conducted with a Microsoft Excel 97 SR-2 calculation. The capillary-strength 
parameters are provided in DTN: LB0302SCMREV02.002 [162273]. The DTN gives six 
calibrated capillary-strength parameter values in the Tptpll unit, available at two locations (Niche 
1620 and systematic testing borehole in the ECRB), and four parameter values in the Tptpmn 
unit, available at two locations (Niche 3107 and Niche 4788). The statistical parameters 
calculated are the mean , the standard deviation s and the mean error SEan estimate for the 
uncertainty in the mean value caused by a limited number of measurements. Four different 
methods of deriving these statistical parameters are chosen to support the above approach. These 
methods are as follows: 
A. Derive mean and standard deviation from all ten samples in both units 
B. Calculate average values from multiple tests in one location, then derive mean and 
standard deviation from the resulting four samples in both units 
C. Derive mean and standard deviation separately for geological units, from six samples 
in the Tptpll and four samples in the Tptpmn 
D. Calculate average values from multiple tests in one location, then derive mean and 
standard deviation separately for each geological unit 
Excel spreadsheet capillary_strength_variability_analysis.xls conducts the calculation. Methods 
A and B are included in Worksheet both units. Methods C and D are included in Worksheets 
tptpm and tptpl, separately for the two units. The Excel spreadsheet is provided in Output- 
DTN: LB0308AMRU0120.001 (Directory: capillary_strength_analysis). The results support 
Table 6.6-2 of this Model Report.

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ATTACHMENT IIISTATISTICAL ANALYSIS OF PERMEABILITY VALUES 
Permeability values measured in air-injection testing were statistically analyzed in Section 
6.6.2.2 (see also Scientific Notebooks, Birkholzer 2003 [164526], pp. 4258, 128132; 
Birkholzer 2003 [164525], pp. 6970). The analyses were conducted with Microsoft Excel 97 
SR-2 calculations. The Excel spreadsheets used for the different calculations are provided in 
Output-DTN: LB0308AMRU0120.001 (Directory: permeability_analysis). 
In a first step, permeability values from small-scale injection testing in close vicinity to niches or 
the ECRB tunnel were analyzed. Except for the systematic testing in the ECRB tunnel, 
permeability values are available for the conditions prior to and after niche construction 
(undisturbed versus disturbed). In most cases, summary statistics (mean and standard deviation) 
of these small-scale measurements had been conducted prior to this seepage abstraction and were 
available in the TDMS, in DTN: LB0302SCMREV02.002 [162273] (disturbed measurements 
statistics) and DTN: LB990901233124.004 [123273] (undisturbed measurements statistics). In 
the case of Niche 1620, summary statistics of the undisturbed small-scale measurements were 
not available. These were therefore calculated directly from DTN: LB0012AIRKTEST.001 
[154586], containing permeability values for all small-scale test intervals. The statistical 
calculation was conducted in Excel spreadsheet niche_1620_preexcavation.xls. The procedure is 
as follows: 
1. Tables 01048_001 from DTN: LB0012AIRKTEST.001 [154586] was extracted from the 
TDMS. File zz_sep_278799.zip was copied to an appropriate working directory and 
unzipped. A text file named zz_sep_278799.txt opens. 
2. A new Excel file named niche_1620_preexcavation.xls was generated. The text file 
zz_sep_278799.txt was opened into Excel, using space and tab delimited options. The last 
data column contains the permeability values in log10 space. This column was analyzed. 
From the 208 measurements in this column, stemming from different boreholes, the mean 
and standard deviation was calculated, also minimum and maximum values were derived. 
3. Finally, editorial changes were conducted in Excel file niche_1620_preexcavation.xls. These 
include adding/changing headers and deleting columns that are not needed. 
The statistics derived from niche_1620_preexcavation.xls support Table 6.6-3 of this Model 
Report. The mean permeability value derived in niche_1620_preexcavation.xls, as well as the 
other mean values from small-scale permeability measurements as provided in DTN: 
LB0302SCMREV02.002 [162273] and DTN: LB990901233124.004 [123273], are used then to 
calculate the intermediate-scale variability over the Yucca Mountain. This calculation is done in 
Excel spreadsheet permeability_variability_analysis_small_scale.xls. Worksheet undisturbed 
comprises analysis of pre-excavation measurements, for four niches in the Tptpmn and one niche 
in the Tptpll. Worksheet disturbed comprises analyses of post-excavation measurements, from 
three niches in the Tptpmn and one niche plus and one systematic testing borehole. The 
statistical parameters calculated are the mean , the standard deviation s and the mean error SE. 
Worksheet comparison analyzes undisturbed versus disturbed permeability values, looking at 
the changes in the mean values and changes in the standard deviation. The Excel spreadsheet is

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MDL-NBS-HS-000019 REV00 Attachment III-2 August 2003 
provided in Output-DTN: LB0308AMRU0120.001 (Directory: Permeability_Analysis). The 
results of this support Tables 6.6-4 and 6.6-7 of this Model Report. 
In a second step, permeability values from injection testing conducted in surface-based boreholes 
were analyzed. Measurements from surface-based boreholes, performed at four borehole 
locations in various units at the Yucca Mountain, are available in DTN: GS960908312232.013 
[105574]. The boreholes included in this DTN: are NRG-6, NRG-7a, SD-12, and UZ#16. The 
relevant units for this Model report are the Tptpll, the Tptpmn, the Tptpul, and the Tptpln. A 
varying number of injection tests had been conducted in each unit and at each location, 
depending on the local thickness of the unit and the chosen injection interval length. For further 
analysis, the mean permeability value of all measurements at each location and each relevant unit 
was calculated in Excel spreadsheet vertical_boreholes.xls. The procedure was as follows: 
1. Table s01163_001 from DTN: GS960908312232.013 [105574] was extracted from the 
TDMS. File zz_sep_208683.zip was copied to an appropriate working directory and 
unzipped. A text file named zz_sep_208683.txt opens. 
2. A new Excel file named vertical_boreholes.xls was generated. The text file 
zz_sep_208683.txt was opened into Excel, using space and tab delimited options. The last 
data column contains the permeability values in log10 space. This column was analyzed. 
Information on locations and units for each measurement is given in the second column 
(borehole) and the tenth column (unit). All data lines that do not represent the Tptpll, 
Tptpmn, Tptpul, and Tptpln units were deleted. This left two units for borehole NRG-6 
(Tptpul and Tptpmn), three units at NRG-7a (Tptpul, Tptpmn, and Tptpll), three units at 
SD-12 (Tptpul, Tptpmn, and Tptpln), and four units at UZ#16 (Tptpul, Tptpmn, Tptpll, and 
Tptpln). Some measurement intervals intersect two units. In such cases, the measured 
permeability value was applied to the statistical calculation of both units (i.e. the respective 
data line was copied and assigned to both units). Then, for each borehole and each unit, the 
mean and standard deviation was calculated, also minimum and maximum values were 
derived. 
3. Finally, several editorial changes were conducted in Excel file vertical_boreholes.xls. 
The mean permeability values derived from vertical_boreholes.xls support Tables 6.6-5, 6.6-8, 
and 6.6-10 of this Model report. These values are then used to calculate the intermediate-scale 
variability over the Yucca Mountain. This calculation is done in Excel spreadsheet 
permeability_variability_analysis_vertical_boreholes.xls. The statistical parameters calculated 
are the mean , the standard deviation s and the mean error SE, separately for the different units. 
The results of this calculation support Tables 6.6-5, 6.6-8, and 6.6-10 of this Model Report. 
A final calculation analysis was performed in Section 6.6.2.2 in order to evaluate the effect of 
measurement interval differences between the niche air injection tests (1-foot-intervals) and 
those conducted in surface-based boreholes (interval lengths between 3.5 and 4.6 m). The mean 
effective permeability increases with the interval length, so that upscaling laws have to be 
applied in order to make the permeability values comparable. One of the methods used in Section 
6.6.2.2 was to use the 1-foot measurements in the niches and presume that these represent the 
exact spatial variability along the borehole. The individual permeability values were then divided

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MDL-NBS-HS-000019 REV00 Attachment III-3 August 2003 
into several groups of twelve consecutive 1-foot measurements; i.e., one group represents the 
length of a 3.6-m interval. The arithmetic mean of the twelve 1-foot values in each group gives 
the permeability value that would have been measured in a 3.6-m-injection interval. The 
arithmetic mean values of all groups in one niche location can then be statistically analyzed to 
evaluate the scaling effects directly from the data. 
The upscaling analysis was conducted using the pre-excavation measurements in Niches 1620, 
3107, 3566, 3650, and 3566. These data are given in DTN: LB0012AIRKTEST.001 [154586] 
(Niche 1620), DTN: LB980901233124.101 [136593] (Niches 3107 and 4788), and DTN: 
LB0011AIRKTEST.001 [153155] (Niches 3650 and 3566), containing permeability values for 
all small-scale test intervals. The statistical calculation was conducted with different Excel 
spreadsheets. The procedure was as follows: 
Niche 1620: 
1. Table s01048_001 from DTN: LB0012AIRKTEST.001 [154586] was extracted from the 
TDMS. File zz_sep_278799.zip was copied to an appropriate working directory and 
unzipped. A text file named zz_sep_278799.txt opens. 
2. A new Excel file named niche_1620_preexcavation_upscaling.xls was generated. The text 
file zz_sep_278799.txt was opened into Excel, using space and tab delimited options. The 
second last data column contains the permeability values in non-logarithmic space. Starting 
with the first measurement interval, the arithmetic mean of groups of twelve consecutive 
values was calculated. Four measurement intervals at the end of the data set were disregarded 
in this analysis since they do not comprise a full group of twelve. The arithmetic mean values 
were transformed into log10 values. Finally, the arithmetic mean over all log10 values was 
calculated, giving the upscaled mean permeability value for the Niche 1620 measurements. 
3. Finally, editorial changes were conducted in niche_1620_preexcavation_upscaling.xls. These 
include adding/changing headers and deleting columns that are not needed. 
Niche 3107: 
1. Table s99469_001 from DTN: LB980901233124.101 [136593] was extracted from the 
TDMS. File zz_sep_208706.zip was copied to an appropriate working directory and 
unzipped. A text file named zz_sep_208706.txt opens. 
2. A new Excel file named niche_3107_preexcavation_upscaling.xls was generated. The text 
file zz_sep_208706.txt was opened into Excel, using space and tab delimited options. The 
last data column contains the permeability values in non-logarithmic space. Starting with the 
first measurement interval, the arithmetic mean of groups of twelve consecutive values was 
calculated. Three measurement intervals at the end of the data set were disregarded in this 
analysis since they do not comprise a full group of twelve. The arithmetic mean values were 
transformed into log10 values. Finally, the arithmetic mean over all log10 values was 
calculated, giving the upscaled mean permeability value for the Niche 3107 measurements.

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MDL-NBS-HS-000019 REV00 Attachment III-4 August 2003 
3. Finally, editorial changes were conducted in niche_3107_preexcavation_upscaling.xls. These 
include adding/changing headers and deleting columns that are not needed. 
Niche 4788: 
1. Table s99469_002 from DTN: LB980901233124.101 [136593] was extracted from the 
TDMS. File zz_sep_208707.zip was copied to an appropriate working directory and 
unzipped. A text file named zz_sep_208707.txt opens. 
2. A new Excel file named niche_4788_preexcavation_upscaling.xls was generated. The text 
file zz_sep_208707.txt was opened into Excel, using space and tab delimited options. The 
last data column contains the permeability values in non-logarithmic space. Starting with the 
first measurement interval, the arithmetic mean of groups of twelve consecutive values was 
calculated. The last group contains only eleven permeability values. The arithmetic mean 
values were transformed into log10 values. Finally, the arithmetic mean over all log10 values 
was calculated, giving the upscaled mean permeability value for the Niche 4788 
measurements. 
3. Finally, editorial changes were conducted in niche_4788_preexcavation_upscaling.xls. These 
include adding/changing headers and deleting columns that are not needed. 
Niche 3650: 
1. Tables s00434_006 through s00434_009, S00434_011, S00434_013, and S00434_015 from 
DTN: LB980901233124.101 [136593] were extracted from the TDMS. Each table relates to 
a separate zip file for the boreholes tested in prior to construction of Niche 3107. The zip 
files were copied to an appropriate working directory and unzipped. Several text files 
containing the measurements of the different boreholes open. 
2. A new Excel file named niche_3650_preexcavation_upscaling.xls was generated. All text 
files for the different boreholes were opened into Excel, using space and tab delimited 
options, and were then copied, one boreholes after the other, into one spreadsheet. The 
second last data column contains the permeability values in non-logarithmic space. Starting 
with the first measurement interval, the arithmetic mean of groups of twelve consecutive 
values was calculated. Nine measurement intervals at the end of the data set were disregarded 
in this analysis since they do not comprise a full group of twelve. The arithmetic mean values 
were transformed into log10 values. Finally, the arithmetic mean over all log10 values was 
calculated, giving the upscaled mean permeability value for the Niche 3650 measurements. 
3. Finally, editorial changes were conducted in niche_3650_preexcavation_upscaling.xls. These 
include adding/changing headers and deleting columns that are not needed. 
Niche 3566: 
1. Tables s00434_001, S00434_002, and S00434_003 from DTN: LB980901233124.101 
[136593] were extracted from the TDMS. Each table relates to a separate zip file for the 
boreholes tested in prior to construction of Niche 3566. The zip files were copied to an

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment III-5 August 2003 
appropriate working directory and unzipped. Several text files containing the measurements 
of the different boreholes open. 
2. A new Excel file named niche_3566_preexcavation_upscaling.xls was generated. All text 
files for the different boreholes were opened into Excel, using space and tab delimited 
options, and were then copied, one borehole after the other, into one spreadsheet. The second 
last data column contains the permeability values in non-logarithmic space. Starting with the 
first measurement interval, the arithmetic mean of groups of twelve consecutive values was 
calculated. Eight measurement intervals at the end of the data set were disregarded in this 
analysis since they do not comprise a full group of twelve. The arithmetic mean values were 
transformed into log10 values. Finally, the arithmetic mean over all log10 values was 
calculated, giving the upscaled mean permeability value for the Niche 3566 measurements. 
3. Finally, editorial changes were conducted in niche_3566_preexcavation_upscaling.xls. These 
include adding/changing headers and deleting columns that are not needed. 
The statistics derived from the upscaling analysis for the five niches support Tables 6.6-6 and 
6.6-9 of this Model report.

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INTENTIONALLY LEFT BLANK

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MDL-NBS-HS-000019 REV00 Attachment IV-1 August 2003 
ATTACHMENT IVANALYSIS OF PERCOLATION FLUX FIELDS 
The percolation flux distributions predicted by the UZ Flow and Transport Model (BSC 2003 
[163045]) were statistically analyzed in Section 6.6.4.1 of this Model Report (see also Scientific 
Notebook, Birkholzer 2003 [164526], pp. 97117, 140146). These fluxes were provided in 
DTN: LB0302PTNTSW9I.001 [162277] (base case) and DTN: LB0305PTNTSW9I.001 
[163690] (alternative conceptual model for flow in the PTn). Several Mathcad 11 spreadsheets 
were used to calculate the mean, minimum, and maximum fluxes in these flow fields, and to 
analyze the distribution of fluxes using histograms. As pointed out in Section 6.6.4.1, the model 
domain of the UZ Flow and Transport Model is much larger than the repository area. Since only 
fluxes over the repository area are relevant for the seepage evaluation, the Mathcad calculation 
must extract the repository fluxes from the overall flux distribution for the entire UZ model 
domain. In addition, it is interesting to evaluate the potential impact of major fault zones that 
intersect the model domain. Therefore, fluxes that represent fault zones need to be identified in 
the Mathcad spreadsheets, and statistical parameters need to be calculated for flux distributions 
with and without fault zones. 
The Mathcad calculation uses file REPO_ZONE.cell from DTN: LB03033DSSFF9I.001 
[163047] to identify the repository fluxes. This file lists the 469 repository elements in the UZ 
model grid by their 7-digit names. The last three digits denote the columns of the numerical grid; 
the first one of these three digits is a letter followed by a two-digit number. An upper-case letter 
indicates that the column represents a fault element, lower case letters stand for non-fault 
(fractured rock) elements. Based on the given list of repository element names, an Excel file 
REPO_ZONE_for_mathcad.xls was generated containing two worksheets. The first worksheet 
Repository Columns includes a list of the 469 repository elements with only the last three 
digits; the first four digits have been eliminated because they are not needed for identification of 
repository elements. The second worksheet No Fault Repo Columns includes a list of all nonfault 
433 repository elements, again only giving the last three digits. This list was generated by 
eliminating all repository elements from Worksheet Repository Columns that have an upper 
case letter. 
The nine flow fields from DTN: LB0302PTNTSW9I.001 [162277] are given in separate data 
files; these are named as follows in the DTN: 
preq_ma_ptn.q mean climate scenario, present-day climate 
monq_ma_ptn.q mean climate scenario, monsoon climate 
glaq_ma_ptn.q mean climate scenario, glacial transition climate 
preq_la_ptn.q lower-bound climate scenario, present-day climate 
monq_la_ptn.q lower-bound climate scenario, monsoon climate 
glaq_la_ptn.q lower-bound climate scenario, glacial transition climate 
preq_ma_ptn.q upper-bound climate scenario, present-day climate 
monq_ma_ptn.q upper-bound climate scenario, monsoon climate 
glaq_ma_ptn.q upper-bound climate scenario, glacial transition climate 
The nine flow fields from DTN: LB0305PTNTSW9I.001 [163690], for the alternative flow 
model in the PTn, are given in separate data files; these are named as follows in the DTN: 
preq_mb_ptn.q mean climate scenario, present-day climate

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MDL-NBS-HS-000019 REV00 Attachment IV-2 August 2003 
monq_mb_ptn.q mean climate scenario, monsoon climate 
glaq_mb_ptn.q mean climate scenario, glacial transition climate 
preq_lb_ptn.q lower-bound climate scenario, present-day climate 
monq_lb_ptn.q lower-bound climate scenario, monsoon climate 
glaq_lb_ptn.q lower-bound climate scenario, glacial transition climate 
preq_mb_ptn.q upper-bound climate scenario, present-day climate 
monq_mb_ptn.q upper-bound climate scenario, monsoon climate 
glaq_mb_ptn.q upper-bound climate scenario, glacial transition climate 
Each of these files contains the PTn/TSw-fluxes for all element columns of the UZ model grid. 
The first two variables in each line give the element coordinates, the third variable gives the 
vertical percolation flux at the PTn/TSw-interface in mm/yr, and the fourth variable gives the 
element name (7-digits). For the Mathcad calculation, the list of element names was copied into 
an Excel file ptntsw_elements_for_mathcad.xls. The first four digits of each name were deleted, 
so that only the 3-digit column name remains in the Excel file. 
The statistical analysis of these percolation flux fields is conducted with various Mathcad 
spreadsheets. Basically, the calculation procedure in these spreadsheets is identical; only the 
input and output file names are different. The Mathcad spreadsheets (1) read Excel files 
REPO_ZONE_for_mathcad.xls and ptntsw_elements_for_mathcad.xls, (2) read one of the 18 
files for the percolation flux fields, (3) calculate over all fluxes (entire UZ domain), (4) identify 
and extract the repository fluxes, (4) write the extracted fluxes into an Excel file for further use 
in seepage evaluation, (5) calculate statistics for the extracted fluxes, and (6) plot a histogram of 
the distribution of extracted fluxes. The statistical analysis is conducted for all repository 
elements and for all non-fault repository elements, in separate spreadsheets. For the base case 
flow concept in the PTN, all nine flow fields are analyzed. For the alternative flow concept in the 
PTn, only the three climate stages of the mean climate scenario are analyzed. 
Table IV-1 provides a list of the Mathcad spreadsheets for the percolation flux analysis using the 
base case flow fields, conducted for all repository elements. Table IV-2 provides a list of the 
Mathcad spreadsheets for the same flow fields, but conducted for no-fault repository elements. 
Table IV-3 gives a list of the Mathcad spreadsheets for the alternative flow fields analysis, 
conducted for the mean climate scenario for all repository elements. Table IV-4 provides a list of 
the Mathcad spreadsheets for the same flow fields, but conducted for no-fault repository 
elements. 
Table IV-1. Mathcad Spreadsheets for Percolation Flux Analysis Using the Base Case Flow Fields from 
DTN: LB0302PTNTSW9I.001 [162277]. Calculation is Conducted for All Repository 
Elements 
Spreadsheet: Repo_Flux_preq_ma.mcd 
Input File: preq_ma_ptn.q Output-File: Extracted_preq_ma_from_Mathcad.xls 
Spreadsheet: Repo_Flux_monq_ma.mcd 
Input File: monq_ma_ptn.q Output-File: Extracted_monq_ma_from_Mathcad.xls 
Spreadsheet: Repo_Flux_glaq_ma.mcd 
Input File: glaq_ma_ptn.q Output-File: Extracted_glaq_ma_from_Mathcad.xls 
Spreadsheet: Repo_Flux_preq_la.mcd 
Input File: preq_la_ptn.q Output-File: Extracted_preq_la_from_Mathcad.xls

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MDL-NBS-HS-000019 REV00 Attachment IV-3 August 2003 
Spreadsheet: Repo_Flux_monq_la.mcd 
Input File: monq_la_ptn.q Output-File: Extracted_monq_la_from_Mathcad.xls 
Spreadsheet: Repo_Flux_glaq_la.mcd 
Input File: glaq_la_ptn.q Output-File: Extracted_glaq_la_from_Mathcad.xls 
Spreadsheet: Repo_Flux_preq_ua.mcd 
Input File: preq_ua_ptn.q Output-File: Extracted_preq_ua_from_Mathcad.xls 
Spreadsheet: Repo_Flux_monq_ua.mcd 
Input File: monq_ua_ptn.q Output-File: Extracted_monq_ua_from_Mathcad.xls 
Spreadsheet: Repo_Flux_glaq_ua.mcd 
Input File: glaq_ua_ptn.q Output-File: Extracted_glaq_ua_from_Mathcad.xls 
Table IV-2. Mathcad Spreadsheets for Percolation Flux Analysis Using the Base Case Flow Fields from 
DTN: LB0302PTNTSW9I.001 [162277]. Calculation is Conducted for No-Fault Repository 
Elements 
Spreadsheet: Repo_Flux_preq_ma_no_fault.mcd 
Input File: preq_ma_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_monq_ma_no_fault.mcd 
Input File: monq_ma_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_glaq_ma_no_fault.mcd 
Input File: glaq_ma_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_preq_la_no_fault.mcd 
Input File: preq_la_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_monq_la_no_fault.mcd 
Input File: monq_la_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_glaq_la_no_fault.mcd 
Input File: glaq_la_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_preq_ua_no_fault.mcd 
Input File: preq_ua_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_monq_ua_no_fault.mcd 
Input File: monq_ua_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_glaq_ua_no_fault.mcd 
Input File: glaq_ua_ptn.q Output-File: NA

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MDL-NBS-HS-000019 REV00 Attachment IV-4 August 2003 
Table IV-3. Mathcad Spreadsheets for Percolation Flux Analysis Using the Alternative Flow Fields from 
DTN: LB0305PTNTSW9I.001 [163690]. Calculation is Conducted for All Repository 
Elements 
Spreadsheet: Repo_Flux_preq_mb.mcd 
Input File: preq_mb_ptn.q Output-File: Extracted_preq_mb_from_Mathcad.xls 
Spreadsheet: Repo_Flux_monq_mb.mcd 
Input File: monq_mb_ptn.q Output-File: Extracted_monq_mb_from_Mathcad.xls 
Spreadsheet: Repo_Flux_glaq_mb.mcd 
Input File: glaq_mb_ptn.q Output-File: Extracted_glaq_mb_from_Mathcad.xls 
Table IV-4. Mathcad Spreadsheets for Percolation Flux Analysis Using the Alternative Flow Fields from 
DTN: LB0305PTNTSW9I.001 [163690]. Calculation is Conducted for No-Fault Repository 
Elements 
Spreadsheet: Repo_Flux_preq_mb_no_fault.mcd 
Input File: preq_mb_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_monq_mb_no_fault.mcd 
Input File: monq_mb_ptn.q Output-File: NA 
Spreadsheet: Repo_Flux_glaq_mb_no_fault.mcd 
Input File: glaq_mb_ptn.q Output-File: NA 
The Mathcad spreadsheets and all input/output files are provided in Output-DTN: 
LB0308AMRU0120.001. The files listed in Tables IV-1 and IV-2 are provided in directory: 
Norm_Flow_Field_Analysis. Results from these files support Table 6.6-11 and Figure 6.6-11 of 
this Model Report. The files listed in Tables IV-3 and IV-4 are provided in directory: 
Alternative_Concept_Flow_Field_Analysis. Results from these files support Table 6.6-11 and 
Figure 6.6-12 of this Model Report.

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MDL-NBS-HS-000019 REV00 Attachment V-1 August 2003 
ATTACHMENT VPROBABILISTIC SEEPAGE CALCULATION 
The results from probabilistic seepage calculations were described and discussed in Section 
6.8.3. Several Mathcad 11 spreadsheets were used to conduct these calculations for various 
evaluation cases. The Mathcad spreadsheets read the SMPA look-up table and the extracted 
repository percolation fluxes (see Attachment IV), perform a random seepage calculation over 
10,000 random samples, and derive seepage histograms as well as seepage summary statistics. 
The following procedure was followed to conduct the analysis (see also Scientific Notebook, 
Birkholzer 2003 [164525], pp. 96142): 
1. Copy file Fig6-3toFig6-8.dat from DTN: LB0304SMDCREV2.002 [163687] into appropriate 
working directory for Mathcad calculation, subdirectory SMPA Input. 
2. Rename file Fig6-3toFig6-8.dat to ResponseSurfaceSMPA_for_Mathcad.dat. Delete first two 
lines. 
3. Copy the Excel files containing the extracted repository fluxes (see Attachment IV, Table 
IV-1 and IV-3) into subdirectories UZ Flow Fields Norm (for base case flow fields) and UZ 
Flow Fields Alternative (for alternative flow fields) 
4. Conduct Mathcad calculations using the various Mathcad spreadsheets listed in Tables V-1, 
V-2 and V-3. Tables V-1 and V-2 give the base case calculations described in Section 6.8.1, 
for the Tptpll and the Tptpmn units, respectively. Table V-2 lists the sensitivity case 
calculations described in Section 6.8.2. 
Table V-1. Mathcad Spreadsheets for Probabilistic Seepage Calculation for the Tptpll Unit (Base Case 
Seepage Evaluation) 
tptpll_preq_ma.mcd seepage calculation for present-day climate, mean climate scenario 
tptpll_monq_ma.mcd seepage calculation for monsoon climate, mean climate scenario 
tptpll_glaq_ma.mcd seepage calculation for glacial transition climate, mean climate scenario 
tptpll_preq_la.mcd seepage calculation for present-day climate, lower-bound climate scenario 
tptpll_monq_la.mcd seepage calculation for monsoon climate, lower-bound climate scenario 
tptpll_glaq_la.mcd seepage calculation for glacial transition climate, lower-bound climate 
scenario 
tptpll_preq_ua.mcd seepage calculation for present-day climate, upper-bound climate scenario 
tptpll_monq_ua.mcd seepage calculation for monsoon climate, upper-bound climate scenario 
tptpll_glaq_ua.mcd seepage calculation for glacial transition climate, upper-bound climate 
scenario

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment V-2 August 2003 
Table V-2. Mathcad Spreadsheets for Probabilistic Seepage Calculation for the Tptpmn Unit (Base 
Case Seepage Evaluation) 
tptpmn_preq_ma.mcd seepage calculation for present-day climate, mean climate scenario 
tptpmn_monq_ma.mcd seepage calculation for monsoon climate, mean climate scenario 
tptpmn_glaq_ma.mcd seepage calculation for glacial transition climate, mean climate scenario 
tptpmn_preq_la.mcd seepage calculation for present-day climate, lower-bound climate scenario 
tptpmn_monq_la.mcd seepage calculation for monsoon climate, lower-bound climate scenario 
tptpmn_glaq_la.mcd seepage calculation for glacial transition climate, lower-bound climate 
scenario 
tptpmn_preq_ua.mcd seepage calculation for present-day climate, upper-bound climate scenario 
tptpmn_monq_ua.mcd seepage calculation for monsoon climate, upper-bound climate scenario 
tptpmn_glaq_ua.mcd seepage calculation for glacial transition climate, upper-bound climate 
scenario 
Table V-3. Mathcad Spreadsheets for Probabilistic Seepage Calculation for the Tptpll Unit (Sensitivity 
Cases) 
tptpll_preq_ma_normal_dist_alpha.mcd 
tptpll_monq_ma_normal_dist_alpha.mcd 
tptpll_glaq_ma_normal_dist_alpha.mcd 
sensitivity case 1: normal distribution for 
spatial variability of capillary strength, three 
climate stages, mean climate scenario 
tptpll_preq_ma_normal_dist_uncertainty_seep.mcd 
tptpll_monq_ma_normal_dist__uncertainty_seep.mcd 
tptpll_glaq_ma_normal_dist_uncertainty_seep.mcd 
sensitivity case 2: normal distribution for 
uncertainty of seepage rate predictions, three 
climate stages, mean climate scenario 
tptpll_preq_ma_mean_k_alpha.mcd 
tptpll_monq_ma_mean_k_alpha.mcd 
tptpll_glaq_ma_mean_k_alpha.mcd 
sensitivity case 3: no spatial variability in 
permeability and capillary strength, three 
climate stages, mean climate scenario 
tptpll_preq_ma_no_uncertainty_k_alpha.mcd 
tptpll_monq_ma_no_uncertainty_k_alpha.mcd 
tptpll_glaq_ma_no_uncertainty_k_alpha.mcd 
sensitivity case 4: no uncertainty in 
permeability and capillary strength, three 
climate stages, mean climate scenario 
tptpll_preq_ma_niche_1620.mcd 
tptpll_monq_ma_niche_1620.mcd 
tptpll_glaq_ma_niche_1620.mcd 
sensitivity case 5: adjusted mean value for 
permeability distribution, three climate stages, 
mean climate scenario 
tptpll_preq_ma_no_focus.mcd 
tptpll_monq_ma_no_focus.mcd 
tptpll_glaq_ma_no_focus.mcd 
tptpll_preq_ma_large_focus.mcd 
tptpll_monq_ma_large_focus.mcd 
tptpll_glaq_ma_large_focus.mcd 
sensitivity cases 6a and 6b: adjusted flow 
focusing factors (no flow focusing and 
increased flow focusing), three climate stages, 
mean climate scenario 
tptpll_preq_mb_alternative_Ptn.mcd 
tptpll_monq_mb_alternative_Ptn.mcd 
tptpll_glaq_ mb_alternative_Ptn.mcd 
sensitivity case 7: percolation flux distribution 
from alternative Ptn flow concept, three 
climate stages, mean climate scenario 
tptpll_preq_mb_alphamethodB.mcd 
tptpll_monq_mb_alphamethodB.mcd 
tptpll_glaq_ mb_alphamethodB.mcd 
tptpll_preq_mb_alphamethodC.mcd 
tptpll_monq_mb_alphamethodC.mcd 
tptpll_glaq_ mb_alphamethodC.mcd 
sensitivity cases 8a, 8b, 8c: percolation flux 
distribution for alternative methods B, C, and 
D for deriving statistical parameters for 
capillary strength, three climate stages, mean 
climate scenario

Abstraction of Drift Seepage U0120 
MDL-NBS-HS-000019 REV00 Attachment V-3 August 2003 
tptpll_preq_mb_alphamethodC.mcd 
tptpll_monq_mb_alphamethodC.mcd 
tptpll_glaq_ mb_alphamethodC.mcd 
All Mathcad spreadsheets listed above are provided in Output-DTN: LB0308AMRU0120.002. 
Results from these files support Figures 6.8-1 through 6.8-3 and Tables 6.8-1 through 6.8-3.

Abstraction of Drift Seepage U0120 
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