UZ Flow Models and Submodels Rev 01, ICN 00 Errata 01

MDL-NBS-HS-000006

August 2003

1. PURPOSE 
The purpose of this Model Report is to document the unsaturated zone (UZ) fluid flow and tracer 
transport models and submodels as well as the flow fields generated utilizing the UZ Flow and 
Transport Model of Yucca Mountain (UZ Model), Nevada. This work was planned in Technical 
Work Plan (TWP) for: Performance Assessment Unsaturated Zone (BSC 2002 [160819], 
Section 1.10, Work Package AUZM06). The UZ Model has revised, updated, and enhanced the 
previous UZ Flow Model REV 00 ICN 01 (BSC 2001 [158726]) by incorporation of the 
conceptual repository design with new grids, recalibration of property sets, and more 
comprehensive validation effort. The flow fields describe fracture-fracture, matrix-matrix, and 
fracture-matrix liquid flow rates and their spatial distributions as well as moisture conditions in 
the UZ system. These 3-D UZ flow fields are used directly by Performance Assessment (PA). 
The model and submodels evaluate important hydrogeologic processes in the UZ as well as 
geochemistry and geothermal conditions. These provide the necessary framework to test 
conceptual hypotheses of flow and transport at different scales and predict flow and transport 
behavior under a variety of climatic conditions. In addition, this Model Report supports several 
PA activities, including abstractions, particle-tracking transport simulations, and the UZ 
Radionuclide Transport Model. 
The base-case 3-D flow fields are generated using the UZ Model, with input parameters based on 
the calibrated property sets documented in Calibrated Properties Model (BSC 2003 [160240]) 
and in this Model Report. The flow fields are developed for spatially varying maps representing 
the mean, lower, and upper bounds of estimated net infiltration for the current climate and two 
projected future climates (monsoon and glacial transition), resulting in a total of 9 base-case flow 
fields. Nine alternative flow fields were also generated to assess the importance of lateral 
diversion in the PTn. These flow fields have been submitted to the Technical Data Management 
System (TDMS) for use by PA and for Total System Performance Assessment (TSPA) activities. 
The UZ Flow Models and submodels documented in this Model Report include the UZ Flow 
Model, Ambient Thermal Submodel, Chloride Submodel, Calcite Submodel, and Strontium 
Submodel. The Flow Model is used for generating 3-D UZ flow fields, estimating current and 
future UZ conditions, and studying tracer-transport behavior. The ambient thermal or 
temperature submodels characterize ambient geothermal conditions with temperature data for 
use in the UZ Model. The pneumatic data are used for additional calibration of the 3-D UZ Flow 
Model. The chloride submodel represents the conceptual model for the spatial and temporal 
variations in chloride chemistry and is compared with pore-water concentrations measured in 
samples from boreholes, the Exploratory Studies Facility (ESF), and the Enhanced 
Characterization of Repository Block (ECRB) Cross Drift. Modeling calcite deposition can be 
used to constrain hydrological parameters such as the infiltration-percolation flux. The calcite 
modeling also provides additional evidence for validation of the UZ Model. In addition, the 
Strontium Submodel incorporates the effects of rate-limited dissolution and precipitation on the 
concentration of a solute, in addition to dispersion, radioactive decay, and linear equilibrium 
adsorption. 
The primary objectives of developing the UZ Flow Model and its submodels are: 
August 2003 17 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
 To integrate all the available field data and conceptual knowledge of the UZ system into 
a single, comprehensive, and calibrated 3-D model for simulating the ambient 
hydrological, thermal, and geochemical conditions, and for predicting system responses 
to future climate conditions. 
 To quantify the flow of moisture, heat, and gas through the UZ, under present-day and 
predicted future climate scenarios. 
 To perform detailed studies of perched water, percolation through the Paintbrush non- 
welded (PTn) unit, flow patterns through Calico Hills nonwelded (CHn) zeolitic units, 
and pore-water chemical and calcite analyses. 
 To predict the migration of potential radionuclide releases after waste emplacement. 
 To contribute model parameters and boundary conditions for drift seepage and other 
modeling studies. 
 To provide Performance Assessment and Repository Design with a scientifically 
defensible and credible model of all relevant UZ flow processes. 
August 2003 18 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
2. QUALITY ASSURANCE 
Development of this Model Report and the supporting modeling activities have been determined 
to be subject to the Yucca Mountain Projects quality assurance (QA) program as indicated in 
Technical Work Plan for: Performance Assessment Unsaturated Zone (BSC 2002 [160819], 
Section 8.2, Work Package (WP) AUZM06). 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. The TWP also identifies the methods used to control the 
electronic management of data (BSC 2002 [160819], Section 8.4, WP AUZM06) without 
variations during the modeling and documentation activities. 
This Model Report provides information pertaining to unsaturated zone (UZ) flow and transport 
through natural barriers (hydrogeologic units of the UZ) important to the demonstration of 
compliance with the postclosure performance objectives prescribed in 10 CFR 63.113 [156605]. 
Therefore, it is classified as Quality Level1 with regard to importance to waste isolation, as 
defined in AP2.22Q, 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 AP2.22Q. 
August 2003 19 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
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20 
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August 2003 UZ Flow Models and Submodels 
3. USE OF SOFTWARE 
The software and routines used in this study are listed in Table 3-1. These are appropriate for the 
intended application and were used only within the range of validation. These codes were 
obtained from software configuration management in accordance with AP-SI.1Q, Software 
Management. All qualified software used in this model report have been run on the OS Platform 
Version Numbers consistent with those listed in the Software Baseline Report readily available 
through the Software Configuration Management. 
Table 3-1. Qualified Software Used in This Report 
DIRS Reference 
Number 
161256 
154785 
147030 
147027 
147023 
Version Software Tracking 
Number (STN) 
TOUGH2 1.4 10007-1.4-01 146496 
T2R3D 1.4 10006-1.4-00 146654 
TOUGHREACT 3.0 10396-3.0-00 
TOUGH2 1.6 10007-1.6-01 161491 
infil2grid 1.7 10077-1.7-00 154793 
2kgrid8.for 1.0 10503-1.0-00 154787 
bot_sum.f 1.0 10349-1.0-00 153471 
vf_con.for 1.0 10466-1.0-00 154345 
WINGRIDDER 2.0 10024-2.0-00 
TOPTEMP_V0.f 1.0 10224-1.0-00 
GET_TEMP_V0.f 1.0 10222-1.0-00 
1.0 10220-1.0-00 
TBgas3D 2.0 10882-2.0-00 160107 
iTOUGH2 4.0 10003-4.0-00 139918 
Bkread.f 1.0 10894-1.0-00 162143 
Smesh.f 1.0 10896-1.0-00 162142 
flow-con 1.0 10993-1.0-00 163162 
T2FEHM 4.0 10997-4.0-00 163161 
The use of the codes listed in Table 3-1 is documented in Section 6 and in the supporting 
scientific notebooks identified in Table 6-1. These codes and routines were qualified under APSI.
1Q, Software Management. The software code TOUGH2 V1.4 (LBNL 2000 [146496]) was 
used to generate flow fields (Section 6) and to conduct model calibrations (Sections 6.2 and 6.3). 
Also, TOUGH2 V1.4 (LBNL 2000 [146496]) was also used to simulate 3-D gas flow (Section 6) 
and for geothermal calibrations. T2R3D VI.4 (LBNL 1999 [146654]) was used for tracer 
transport simulations, tracer transport travel-time estimates (Section 6.8), and modeling porewater 
chemistry (Section 6.5). The infil2grid V1.7 software (LBNL 2002 [154793]) was used to 
apply infiltration maps onto the grids used for simulating flow and transport (Section 6). 
21 
Software Name, 
Codes 
GEN-INCON-V0.f 
MDL-NBS-HS-000006 REV01 
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August 2003 U0050 UZ Flow Models and Submodels 
Standard spreadsheet (Microsoft Excel 97 and Microsoft Excel 2002) and plotting programs 
(Tecplot v 9.0) were also used but are exempt from software qualification requirements of 
AP-SI.1Q. Details and procedures for calculations using Excel and its standard functions in 
post-processing and flow field extraction are provided in Attachment III.. The infil2grid V1.7 
software (LBNL 2002 [154793]) was used, not V1.6 (LBNL 1999 [134754]) as planned in the 
TWP (BSC [160819], Table II-2). This is because infil2grid V1.7 (LBNL 2002 [154793]) can 
handle eight-character grid element names, while infil2grid V1.6 (LBNL 1999 [134754]) cannot. 
For the same reason, 2kgrid8.for V1.0 (LBNL 2002 [154787]) also was used although not 
planned in the TWP (BSC 2002 [160819], Table II-2). 
The 3-D unsaturated zone flow fields are generated using a dual-permeability model with extra 
global fracture-matrix connections, which cannot be directly used by the FEHM code in TSPA 
calculations. Therefore, the TOUGH2 flow fields are converted using the routine: flow-con V1.0 
(LBNL 2003 [163162]) into flow fields on the dual-permeability mesh that does not have the 
extra fracture-matrix connections. These output flow field files of flow-con v1.0 are then used as 
input files to the routine: T2FEHM V4.0 (LBNL 2003 [163161]), which converts TOUGH2 files 
in format of "flow9.dat" into files readable to FEHM. 
The software WTRISE V2.0 (LBNL 2003 [163453] is mentioned in Section 6.6.3, but is not 
used in this report. 
. Attachments I, II, IV, and V are referred to elsewhere in this Model Report. 
August 2003 22 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
4. INPUTS 
This section provides documentation for data (Table 4.1-1) used as inputs to this Model Report. 
The Q-status of all input and a description of the data are shown in the Document Input 
Reference System (DIRS) database. The inputs to the modeling activities described in this Model 
Report are obtained from the Technical Data Management Systems (TDMS) and include the 
following: 
 Stratigraphy data from borehole logs 
 Infiltration maps 
 Calibrated fracture and matrix properties 
 Geochemistry data from the ESF, the ECRB, and boreholes 
 Unsaturated Zone Flow and Transport Model (UZ Model) grids 
 Temperature data for boreholes 
 Pneumatic-pressure data 
 Locations and elevations of perched water in boreholes 
 Uncalibrated fracture properties 
 Water-potential data 
 Matrix liquid-saturation data. 
4.1 DATA AND PARAMETERS 
The key input data used in the UZ Model and its submodel development include the following 
(See Table 4.1-1): 
 Fracture properties (frequency, permeability, van Genuchten  and m parameters, 
porosity, and interface area per unit volume rock) for each UZ Model layer 
 Matrix properties (porosity, permeability, and the van Genuchten  and m parameters) 
for each UZ Model layer 
 Thermal and transport properties (grain density, wet and dry thermal conductivity, grain 
specific heat, and tortuosity coefficients) for each UZ Model layer 
 Fault properties (matrix and fracture parameters) for each major hydrogeologic unit as 
defined by Table 6.1-1. 
August 2003 23 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
The calibrated parameter sets also include an estimate for each model layer of the active-fracture 
parameter,  (Liu et al. 1998 [105729]), that accounts for the reduction in interaction between 
matrix and fracture flow resulting from flow fingering and channelization. Uncertainty in the 
input data and parameters are addressed in Section 6. Specific input data sets and associated Data 
Tracking Numbers (DTNs) are listed in Table 4.1-1. 
Input Data Source and Data Tracking Numbers* Table 4.1-1. 
Location in this report Current DTN 
Text Figure Table 
LB03023DKMGRID.001 [162354] 
GS000308311221.005 [147613] 6.1.4, 6.5, 
6.5.1.2, 
6.8.1, 7.2 
LB02091DSSCP3I.002 [161433] 6.1.5, 6.2, 
6.4.1 
LB02092DSSCFPR.002 [162128] 6.1.5, 6.2, 
6.3.4, 
6.4.1, 7.2 
LB0205REVUZPRP.001 [159525] 6.2.3 Fracture properties 
6.2 LB991121233129.001 [147328] 
6.2 LB991121233129.003 [147335] 
6.2 LB991121233129.005 [147346] 
GS950208312232.003 [105572] 6.3.2, Surface temperature 
6.3.3 
6.3.3 Figure 6.3-2 GS970808312232.005 [105978] 
GS971108312232.007 [105980] 
6.3.3 Figure 6.3-2 GS960808312232.004 [105974] 
GS970108312232.002 [105975] 
GS980408312232.001 [105982] 
Description/Remarks 
Figure 6.1-1 3-D TSPA-LA model 
grid 
Net infiltration maps Figures 6.1- 
2, 6.1-3, 6.1- 
Tables 6.1-2, 
6.2-9, 6.5-2, 
6.6-1, 6.7-2, 4 
6.7-3 
1-D Site scale 
calibrated properties 
2-D site scale 
calibrated fault 
properties 
Tables 6.2-2 PTn fracture-matrix 
parameters (for 
present-day, mean 
infiltration) 
Tables 6.2-3 PTn fracture-matrix 
parameters (for 
present-day, upperbound 
infiltration) 
Tables 6.2-4 PTn fracture-matrix 
parameters (for 
present-day, lowerbound 
infiltration) 
for boreholes NRG-6 
and NRG-7a 
Temperature data in 
boreholes NRG-7a, 
SD-12 UZ#4, UZ#5 
and UZ-7a 
Temperature data in 
boreholes NRG-6, 
NRG-7a, SD-12 UZ#4, 
UZ#5 and UZ-7a 
MDL-NBS-HS-000006 REV01 
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August 2003 24 UZ Flow Models and Submodels 
Input Data Source and Data Tracking Numbers (Continued) 
Location in this report Description/Remarks 
Table 4.1-1. 
Current DTN 
Text Figure Table 
LB0210THRMLPRP.001 [160799] 6.3.4, 
6.4.1, 
6.7.1 
LB0302AMRU0035.001 [162378] 6.4.1 
LB991091233129.001 [125868] 
6.4.2 
LB02092DSSCFPR.001 [162422] 6.4.1, Figure 7.4-1 
6.4.2 
LA0003JC831362.001 [149557] 6.7.1, 
6.8.2.1 
6.7.1 
4.2 CRITERIA 
MDL-NBS-HS-000006 REV01 
Thermal properties 
Measured atmospheric 
barometric pressure 
Pneumatic pressure data 
used for calibration 
Figures 6.2- 
4, 6.4-1, 6.4- 
2, 7.4-2 
Table 6.4-1 
Table 7.4-1 
Table 6.7-1 
25 
LA0010JC831341.001 [162476] 
LA0010JC831341.002 [153321] 
LA0010JC831341.003 [153322] 
LA0010JC831341.004 [153323] 
LA0010JC831341.005 [153320] 
LA0010JC831341.006 [153318] 
LA0010JC831341.007 [153319] 
Note: *DTN LB03013DSSCP3I.001 and DTN LB0303THERMESH.001 are the output DTNs. They are source DTNs 
for DTN LB0303THERMSIM.001 and DTN LB0303GASFLW3D.001 as well. 
This Model Report documents the flow models and submodels in the UZ Flow and Transport 
Model. It utilizes properties from the Calibrated Properties Model (BSC 2003 [160240]). The 
input and output files for the model runs presented in this Model Report are listed in Tables 6.2- 
9, 6.6-1, 6.7-2, and 6.7-3, and some of the model input fracture and matrix parameters are given 
in Attachment I. The data used as direct input for the UZ flow model and UZ flow fields are all 
qualified in accordance with the requirements of QARD. The model input data are available, 
qualified data except some borehole temperature data used as corroborative for thermal model 
validation (Section 7.7) and sorption coefficient (Kd) data used for Sr model validation (Section 
7.10). The qualified data are appropriate for this study because they represent fracture and matrix 
properties calibrated for the UZ at Yucca Mountain,. The appropriateness of the data is also 
discussed in Sections 6 and 7 when they are used for modeling and validation efforts. 
Technical requirements to be satisfied by performance assessment (PA) are based on 
10 CFR 63.114 [156605] (Requirements for Performance Assessment) and 10 CFR 63.115 
[156605] (Requirements for Multiple Barriers). They are 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]). The pertinent requirements and acceptance criteria for 
this Model Report are summarized in Table 4.2-1. 
Pneumatic pressure data 
used for calibration 
Diffusion coefficients for 
conservative and active 
components 
Sorption coefficients 
August 2003 
U0050 UZ Flow Models and Submodels 
Project Requirements and YMRP Acceptance Criteria Applicable to This Model Report 
Requirement Titlea 
Requirements for 
Performance Assessment 
Requirements for Multiple 
Barriers 
10 CFR 63 Link 
26 
10 CFR 63.114(a-c) 
10 CFR 63.115(a-c) 
YMRP Acceptance Criteria 
Criteria 1 to 4 for Flow Paths in the 
Unsaturated Zone b 
Criteria 1 to 3 for Demonstration of 
Multiple Barriers c 
August 2003 
Table 4.2-1. 
Requirement 
Numbera 
PRD-002/T-016 
PRD-002/T-015 
NOTES: a from Canori and Leitner 2003 [161770]) 
b from NRC (2003 [163274], Section 2.2.1.3.6.3) 
c from NRC (2003 [163274], Section 2.2.1.1.3) 
The acceptance criteria applicable to this Model Report identified in Section 2.2.1.3.6.3 of the 
YMRP (NRC 2003 [163274]) relating to flow paths in the UZ are given below, followed by a 
short description of their applicability to this Model Report: 
 Acceptance Criterion 1, System Description and Model Integration Are Adequate: 
The aspects of geology, hydrology, geochemistry, and physical phenomena that may 
affect flow paths in the UZ are adequately considered. Conditions and assumptions in the 
abstraction of flow paths in the UZ are readily identified and consistent with the body of 
data presented in the description. The system and model are described in Section 6.1. 
The process-level model of flow in the UZ uses assumptions, technical bases, data, and 
models that are appropriate and consistent with other related U.S. Department of Energy 
abstractions. The descriptions and technical bases are transparent and traceable to site 
and design data. 
Sufficient data and technical bases to assess the degree to which features, events, and 
processes have been included in this process-level model are provided. 
Adequate spatial variability of model parameters and boundary conditions are employed 
in process-level models to estimate flow paths in the UZ, percolation flux, and seepage 
flux. The infiltration boundary condition is output from an approved model that considers 
future changes in climate. Average parameter estimates used in process-level models are 
representative of the temporal and spatial discretizations considered in the model. 
 Acceptance Criterion 2, Data Are Sufficient for Model Justification: 
Hydrological values used in the safety case are adequately justified. Adequate 
descriptions of how data were used, interpreted, and appropriately synthesized into the 
parameters are provided. 
Estimates of deep-percolation flux rates are based on a technically defensible UZ flow 
model that reasonably represents the physical system. The flow model is calibrated using 
site-specific hydrological, geological, and geochemical data. Deep-percolation flux is 
estimated, using the appropriate spatial and temporal variability of model parameters, and 
MDL-NBS-HS-000006 REV01 
U0050 U0050 UZ Flow Models and Submodels 
boundary conditions that consider climate-induced change in infiltration. Flow fields are 
computed for all relevant future climate states. 
Sensitivity or uncertainty analyses are performed to assess data sufficiency and determine 
the possible need for additional data. 
Accepted and well-documented procedures are used to construct and calibrate numerical 
models. 
Process-level conceptual and mathematical models are used in the analyses. 
Mathematical models are provided that are consistent with conceptual models and site 
characteristics, and the robustness of results from different mathematical models is 
compared. Input data used to develop this model are summarized in Section 4. 
 Acceptance Criterion 3, Data Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
Models use parameter values, assumed ranges, probability distributions, and/or bounding 
assumptions that are technically defensible, and reasonably account for uncertainties and 
variabilities. The multiple flow fields generated by the UZ Model capture the uncertainty 
in the parameter values and boundary conditions, as summarized in Section 8.11. 
The initial conditions, boundary conditions, and computational domain used in sensitivity 
analyses and/or similar analyses are consistent with available data and output from 
approved models. Parameter values are consistent with the initial and boundary 
conditions and the assumptions of the conceptual models for the Yucca Mountain site. 
Uncertainties in the characteristics of the natural system are considered. 
 Acceptance Criterion 4, Model Uncertainty Is Characterized and Propagated through the 
Model Abstraction: 
Alternative modeling approaches of features, events, and processes consistent with 
available data and current scientific understanding are investigated. The results and 
limitations are appropriately considered in the abstraction. Model uncertainty is captured 
by alternative parameter sets as discussed in Section 6.9. 
This includes all the criteria identified in the TWP (BSC 2002 [160819], Table 3.1) except 
Acceptance Criterion 5 for UZ flow paths. This one has been omitted because the flow fields are 
used directly in the TSPA, rather than being abstracted for use in TSPA. Therefore, there is no 
need to show that the abstractions match the results of process-level models. 
The acceptance criteria identified in Section 2.2.1.1.3 of the YMRP (NRC 2003 [163274]) are 
given below, followed by a short description of their applicability to this Model Report: 
 Acceptance Criterion 1, Identification of Barriers Is Adequate: 
The surficial soils and topography, unsaturated rock layers above the repository (and 
below surficial soils), and the unsaturated rock layers below the repository (and above the 
August 2003 27 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
water table) are natural barriers important to waste isolation. These barriers function by 
diverting and substantially reducing the movement of percolating groundwater. The 
capabilities of these barriers are determined by their hydrological properties and the 
hydrological environment as implemented in this and other models for infiltration and 
flow in the UZ. The barriers are identified and analyzed in Section 6. 
 Acceptance Criterion 2, Description of Barrier Capability to Isolate Waste Is Acceptable: 
The capability of the identified barriers to prevent or substantially delay the movement of 
water is adequately identified and described. The uncertainty associated with barrier 
capabilities is adequately described. Section 8.10 describes the barrier capability. 
 Acceptance Criterion 3, Technical Basis for Barrier Capability Is Adequately Presented: 
The technical bases are consistent with the technical basis for the performance 
assessment. The technical basis for assertions of barrier capability is commensurate with 
the importance of each barriers capability and the associated uncertainties. The technical 
basis for the barrier capability is presented throughout Section 6. 
4.3 CODES AND STANDARDS 
No specific formally established standards have been identified as applying to this analysis and 
modeling activity. 
28 MDL-NBS-HS-000006 REV01 
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August 2003 U0050 UZ Flow Models and Submodels 
5. ASSUMPTIONS 
There is no need to assume numerical values for lack of information in the development of the 
UZ flow model and its submodels. Several approximations and idealizations were used for 
model development, such as selection of hydrogeological conceptual models, use of numerical 
modeling approaches, and specification of model boundary conditions. These are discussed and 
justified as appropriate in Section 6. The methodological premises used for specific modeling 
studies are more appropriately discussed in the context of the modeling methodologies in Section 
6. 
August 2003 29 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
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August 2003 U0050 UZ Flow Models and Submodels 
6. MODEL DISCUSSION 
As outlined in Section 1, this Model Report documents the development and results of the 
Unsaturated Zone Flow and Transport Model (UZ Model) and the temperature and geochemistry 
submodels. This section consists of the following: 
 Model description (Section 6.1) 
 3-D (three-dimensional) UZ flow model calibrations (Section 6.2) 
 Ambient geothermal model (Section 6.3) 
 Gas flow analysis and pneumatic calibration (Section 6.4) 
 Geochemical submodel for chloride (Section 6.5) 
 Flow patterns and analysis of 3-D flow fields (Section 6.6) 
 Tracer transport (Section 6.7) 
 Sensitivity analysis of active-fracture-model parameters (Section 6.8) 
 Uncertainty and alternative models (Section 6.9) 
The UZ Flow Model, temperature model, and geochemistry submodels have all been developed 
to simulate past, present, and future hydrological, geothermal, and geochemical conditions in the 
UZ of Yucca Mountain. In the last decade, Yucca Mountain has been studied extensively, and 
many types of data have been collected. These data have been used in developing conceptual and 
numerical models for investigating the hydrological, geothermal, and geochemical behavior of 
the site. These models simulate ambient conditions and perform predictive studies of changes in 
the mountain caused by climatic, thermal, and geochemical perturbations. The comprehensive 
model that integrates all pertinent data from the UZ at Yucca Mountain is the 3-D site-scale UZ 
Model, developed over the past decade (as documented e.g., in Bodvarsson et al. 1999 [120055]; 
Wu et al. 1999 [117161]; 2002 [160195]). Model development described in this Model Report 
results from the continued modeling investigations and field studies of flow and transport 
behavior in the UZ system of Yucca Mountain. 
The UZ Model is a process model developed according to the Technical Work Plan for: 
Performance Assessment Unsaturated Zone (BSC 2002 [160819]) and for support of the License 
Application (LA). The Total System Performance Assessment for License Application (TSPALA) 
will use the UZ flow simulations to provide input to other models such as ambient and 
thermal drift-scale models, the mountain-scale thermal-hydrological model, and the radionuclide 
transport model. The UZ Model and its submodels evaluate features and processes that are 
important to the performance of the repository, all of which contribute to the TSPA-LA, such as: 
 The spatially distributed values of the percolation flux at the repository horizon 
 The components of fracture and matrix flow at and below the repository horizon 
August 2003 31 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
 The flow diversion in the PTn unit 
 The perched-water zones and associated flow barriers 
 The probable flow paths from the repository to the water table 
 Tracer transport times and paths from the repository to the water table, and breakthrough 
curves and areas at the water table for tracers. 
In developing the UZ Model, the main objective has been placed on preparing a defensible and 
credible UZ Model for Yucca Mountain to evaluate it as an underground radioactive waste 
repository. Major activities, as reported in this Model Report, include updated model calibration 
studies of 3-D UZ flow, PTn and perched water, geochemistry, geothermal conditions, estimates 
of tracer transport times and radionuclide transport, and intensive model validation efforts. 
Other activities involved generating 18 3-D flow fields (Sections 6.2 and 6.7) to evaluate the 
uncertainties and sensitivity of the UZ Model relative to fracture-matrix parameters and 
infiltration rates over the mountain, by using six sets of model parameters and nine infiltration 
scenarios. A total of 18 flow fields (nine base cases and nine alternatives) have been submitted to 
the TDMS as output DTNs. The nine base case flow fields are provided for use in TSPA 
calculations of radionuclide transport through the UZ system, and for other activities such as 
drift seepage abstraction. The other nine alternatives show the results of using an alternative 
conceptual model (Sections 6.5, 6.6, and 6.7). 
The scientific notebooks (with relevant page numbers) used for modeling and validation 
activities described in this Model Report are listed in Table 6-1. 
Scientific Notebooks Used in Model-Development Documentation 
Relevant Pages M&O Scientific 
Notebook ID 
32 
Citation 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
Wang 2003 [162417] 
August 2003 
Table 6-1. 
LBNL Scientific 
Notebook ID 
YMP-LBNL-YSW-3 SN-LBNL-SCI-199-V1 pp. 6570, 81278 
YMP-LBNL-YSW-KZ-1 SN-LBNL-SCI-202-V1 pp. 6897, 105111 
YMP-LBNL-ELS-GL-1 SN-LBNL-SCI-219-V1 pp. 7483, 120150 
YMP-LBNL-HHL-GZ-1 SN-LBNL-SCI-227-V1 pp. 87, 89119 
YMP-LBNL-GSB-TX-1 SN-LBNL-SCI-160-V1 pp. 8992 
YMP-LBNL-GSB-LHH-3 SN-LBNL-SCI-215-V1 pp. 107119 
YMP-LBNL-UZ-ELS-1 SN-LBNL-SCI-170-V1 pp. 4244 
YMP-LBNL-YSW-3.1 SN-LBNL-SCI-199-V2 pp. 7-26 
MDL-NBS-HS-000006 REV01 
U0050 U0050 UZ Flow Models and Submodels 
6.1 MODEL DESCRIPTION 
The conceptual and numerical models used for the modeling studies are documented in this 
Model Report as well as in the AMR, Conceptual and Numerical Models for Flow and 
Transport (CRWMS M&O 2000 [141187]). The conceptual and numerical models are presented 
in this section so that a complete discussion of the model can be made. 
6.1.1 Geological Model and Numerical Grids 
The geological model used for developing the UZ Model and its submodels is the Geological 
Framework Model (GFM2000; MO0012MWDGFM02.002 [153777]). The development and 
features of the 3-D model grids are documented in the report entitled Development of Numerical 
Grids for UZ Flow and Transport Modeling (BSC 2003 [160109]). Table 6.1-1 lists the 
geological units/layers for different hydrogeologic units and the associated UZ Model numerical 
grid-layer information. These geologic formations have been organized into layered 
hydrogeologic units based primarily on the degree of welding (Montazer and Wilson 1984 
[100161]). These are the Tiva Canyon welded (TCw) hydrogeologic unit, the Paintbrush 
nonwelded unit (PTn), the Topopah Spring welded (TSw) unit, the Calico Hills nonwelded 
(CHn), and the Crater Flat undifferentiated (CFu) units. 
August 2003 33 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table 6.1-1. GFM2000 Lithostratigraphy, UZ Model Layer, and Hydrogeologic Unit Correlation Used 
in the UZ Flow Model and Submodels 
Major Unit UZ Model Grid Hydrogeologic Unit 
Layer* 
Lithostratigraphic 
Nomenclature (Flint 1998 [100033]) (Modified from Montazer 
and Wilson 1984 [100161]) (BSC 2002 [159124]) (BSC 2003 
[160109]) 
Tpcr tcw11 CCR, CUC Tiva Canyon welded 
(TCw) 
tcw12 CUL, CW Tpcp 
TpcLD 
Tpcpv3 tcw13 CMW 
Tpcpv2 
Paintbrush nonwelded 
(PTn) 
Tpcpv1 ptn21 CNW 
Tpbt4 ptn22 BT4 
Tpy (Yucca) 
ptn23 TPY 
ptn24 BT3 
Tpbt3 
Tpp (Pah) ptn25 TPP 
Tpbt2 ptn26 BT2 
Tptrv3 
Tptrv2 
Topopah Spring welded 
(TSw) 
Tptrv1 tsw31 TC 
Tptrn 
tsw32 TR 
tsw36 
Tptrl, Tptf tsw33 TUL 
Tptpul, RHHtop 
Tptpmn tsw34 TMN 
Tptpll tsw35 TLL 
Tptpln TM2 (upper 2/3 of 
Tptpln) 
tsw37 TM1 (lower 1/3 of 
Tptpln) 
PV2 
Tptpv3 tsw38 PV3 
Tptpv2 tsw39 (vit, zeo) 
August 2003 34 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table 6.1-1. GFM2000 Lithostratigraphy, UZ Model Layer, and Hydrogeologic Unit Correlation Used 
in the UZ Flow Model and Submodels (Continued) 
Major Unit Lithostratigraphic 
Nomenclature 
UZ Model Grid Hydrogeologic Unit 
Layer* (Flint 1998 [100033] 
(BSC 2003 
[160109]) 
ch1 (vit, zeo) BT1 or 
BT1a (altered) 
CHV (vitric) 
or 
CHZ (zeolitic) 
BT 
PP4 (zeolitic) 
PP3 (devitrified) 
ch2 (vit, zeo) 
ch3 (vit, zeo) 
ch4 (vit, zeo) 
ch5 (vit, zeo) 
ch6 (vit, zeo) 
pp4 
pp3 
pp2 PP2 (devitrified) 
pp1 PP1 (zeolitic) 
bf3 BF3 (welded) 
bf2 BF2 (nonwelded) 
tr3 Not Available 
tr2 Not Available 
August 2003 
(Modified from Montazer 
and Wilson 1984 [100161]) 
Calico Hills nonwelded 
(CHn) 
Crater Flat undifferentiated 
(CFu) 
MDL-NBS-HS-000006 REV01 
NOTE: * Defined as a rock material type, represented by the code name, for grid blocks belonging to the 
rock unit. 
The 3-D UZ Model domain, as well as the numerical grid for this study, is shown in plan view in 
Figure 6.1-1, encompassing approximately 40 km2 of the area over the mountain. The UZ Model 
grid, shown in Figure 6.1-1, is referred to as the TSPA-LA grid. It is primarily designed for 
model calibration and simulations of 3-D flow fields delivered for use in TSPA-LA calculations. 
This 3-D model grid uses a refined mesh in the vicinity of the repository, located near the center 
of the model domain, covering the region from the Solitario Canyon fault to Ghost Dance fault in 
the west-east direction, and from borehole G-3 in the south to beyond Sever Wash fault in the 
north. Also shown in Figure 6.1-1 are the locations of several boreholes used in model 
calibrations and analyses. The model domain is selected to focus on the study area of the 
repository area and to investigate the effects of different infiltration scenarios and major faults on 
moisture flow around and below the repository. In the model grid, faults are represented in the 
model by vertical or inclined 30-m-wide zones. 
(BSC 2002 [159124]) 
Tptpv1 
Tpbt1 
Tac 
(Calico) 
Tacbt (Calicobt) 
Tcpuv (Prowuv) 
Tcpuc (Prowuc) 
Tcpmd (Prowmd) 
Tcplc (Prowlc) 
Tcplv (Prowlv) 
Tcpbt (Prowbt) 
Tcbuv (Bullfroguv) 
Tcbuc (Bullfroguc) 
Tcbmd (Bullfrogmd) 
Tcblc (Bullfroglc) 
Tcblv (Bullfroglv) 
Tcbbt (Bullfrogbt) 
Tctuv (Tramuv) 
Tctuc (Tramuc) 
Tctmd (Trammd) 
Tctlc (Tramlc) 
Tctlv (Tramlv) 
Tctbt (Trambt) and below 
35 
U0050 UZ Flow Models and Submodels 
Figure 6.1-1. Plan View of the 3-D UZ TSPA-LA Model Grid, Showing the Model Domain, Faults 
Incorporated, Repository Layout, and Several Borehole Locations 
Borehole 
DTN: LB03023DKMGRID.001 [162354] 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
G-2
WT-24 
236000 
UZ-14 
PaganyWash 
Fault 
Drillhole 
Wash 
Faul 
t 
234000 SD-9 
H-5 
S 
olitario 
Cany 
on 
Fault 
232000 
Ghost 
Dance 
Fault 
230000 
ECRB 
SD-6 SD-12 
SD-7 
G-3 
Nevada Coordinate E-W(m)
174000 168000
LEGEND 
170000 
Repository Blocks 
Tunnel 
36 
Sev 
e 
r 
Wash 
Fault 
ESF 
172000 
Faults
Bow Ridge Fault 
NRG-7A 
UZ#16
Imbricate 
Fault 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
The model grid, as shown in Figure 6.1-1, has 2,042 mesh columns of both fracture and matrix 
continua and an average of 59 computational grid layers in the vertical direction, resulting in 
245,506 gridblocks and 989,375 connections in a dual-permeability grid. 
6.1.2 Numerical Codes and Modeling Approach 
The model calibration and simulation results presented in this Model Report were carried out 
mainly using TOUGH2 V1.4 (LBNL 2000 [146496]), and T2R3D V1.4 (LBNL 1999 [146654]), 
as summarized in Section 3. The single active liquid-phase flow module (EOS9) of the TOUGH2 
code was used to calibrate the UZ Flow Model and several submodels and to generate 3-D 
TSPA-LA flow fields. For gas flow simulation and temperature calibration, the TOUGH2 V1.4 
(LBNL 2000 [146496]) EOS3 module was used. Tracer transport and chloride studies were 
performed using the decoupled module of T2R3D V1.4 (LBNL 1999 [146654]) with flow fields 
generated by the EOS9 module. TOUGH2 V1.4 (LBNL 2000 [146496]) and T2R3D V1.4 
(LBNL 1999 [146654]) were selected because they have been baselined through YMP QA 
procedure AP-SI.1Q for modeling flow and transport in heterogeneous fractured rock (e.g., BSC 
2001 [158726]; Wu et al. 2002 [160195]). No other numerical codes could be used for this work, 
either because they were not qualified and baselined for use at the time of preparing this report, 
or because they did not have the generalized capability of handling global fracture-matrix 
interaction, which was needed in modeling studies of this report. 
To model flow and transport processes in the UZ system at Yucca Mountain, mathematical 
models or governing equations are needed to describe the physical processes quantitatively. The 
physical processes associated with flow and transport in porous media are governed by the 
fundamental conservation laws (i.e., conservation of mass, momentum, and energy), which 
govern the behavior of fluid flow, chemical migration, and heat transfer through fractured porous 
media. The macroscopic continuum approach has been most commonly used in practical 
applications (Bear 1972 [156269]). In this approach, the physical laws governing flow of several 
fluids, transport of multicomponents, and heat transfer in porous media are represented 
mathematically on the macroscopic level by a set of partial differential or integral equations. 
Fluid and heat flow and chemical-transport processes in fracture and matrix systems in the UZ 
are described using a macroscopic, dual-permeability continuum approach. 
In addition to the conservation or continuity equations of mass and thermal energy in fracture 
and matrix systems, specific relationships or mechanisms are needed that describe how fluid 
flow, solute/tracer transport, and heat transfer occur in porous and fractured media. The 
following specific constitutive laws act as such mechanisms by governing local fluid flow, 
component transport, and heat-transfer processes in porous and fractured media: 
1. Under unsaturated conditions, liquid pressure is negative from the capillary suction. In 
the UZ Flow Model we also call capillary pressure as water potential (by changing the 
negative sign to +). The governing equation for isothermal, unsaturated liquid flow is 
the Richards' equation (Richards 1931 [104252]; Pruess et al. 1999 [160778], Equation 
A-17, p. 146), based on the conservation of mass (volumetric water content) and 
Darcy's law (Bear 1972 [156269]) with flux driven by gravity and capillary pressure 
gradient. The unsaturated flux is equal to unsaturated hydraulic conductivity times the 
driving gradient. The hydraulic conductivity is proportional to permeability and fluid 
density, and inversely proportional to fluid viscosity. The fluid properties are treated as 
constants under isothermal conditions. The unsaturated permeability (relative 
August 2003 37 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
permeability times formation permeability or saturated permeability) is related to both 
water content (saturation times porosity) and capillary pressure, as described by the 
model of van Genuchten (1980 [100610]). The governing equations for unsaturated 
flow under isothermal conditions are given in Attachment V. 
The UZ Flow Model adopts the dual-continuum approach for flows through both the 
fractures and matrix. Richards' equation is applied to both fracture continuum and the 
matrix continuum. The fluid exchange between fracture continuum and matrix 
continuum is the fracture-matrix interaction, which is simulated by active fracture 
model in the UZ Flow Model (Liu et al. 1998 [105729]). 
The active fracture model was developed within the context of the dual-continuum 
approach (Liu et al. 1998 [105729]). It is based on the reasoning that, because of the 
fingering flow, only a portion of fractures in a connected, unsaturated fracture network 
contribute to liquid water flow, while other fractures are simply bypassed. The portion 
of the connected fractures that actively conduct water are called active fractures. In 
other words, the active fracture model uses a combination of the continuum approach 
and a simple filtering concept to model fracture flow. Inactive fractures are filtered out 
in modeling fracture-matrix interaction and flow in the fracture continuum. 
The Richards' equation, Darcy's law, and van Genuchten's model can be generalized for 
multi-phase flows under nonisothermal conditions. The governing equations for both 
gas-and liquid-flows and temperature are based on conservation of mass of fluid 
phases, and on conservation of energy for conductive and convective heat transfer 
processes, respectively. The full set of equations of nonisothermal, two-phase flows of 
gas and water in both fractures and matrix are presented in Pruess et al. (1999 
[160778], Appendix A). 
In solving the above equations, a number of variables are known and given as input to 
the UZ Flow Model. Some of those variables are treated as constants, for example, 
fluid density and viscosity under isothermal conditions. Others are provided as known 
parameters measured either in the laboratory or in field tests, and/or further calibrated. 
Examples of known parameters are rock density, porosity, and permeability. The input 
parameters are discussed Section 6.1.5. In addition, boundary conditions are needed to 
solve the equation (Section 6.1.3). The top boundary for the UZ Flow Model is net 
infiltration from the land surface (Section 6.1.4). With these input parameters and 
boundary conditions, the solving of the full set of equations (Pruess et al. 1999 
[160778]) in the UZ Flow Model provides outputs for variables such as saturation, 
capillary pressure, and flux, in addition to temperature in the thermal model. 
2. The migration of dissolved mass components or chemical species within a fluid in the 
two-phase, fractured porous media system is governed by advective, diffusive, and 
dispersive processes. It may also be subject to other processes such as radioactive 
decay, adsorption, mass exchange or partition between phases, and other chemical 
reactions under local thermodynamic equilibration or kinetic reactions. 
3. The generalized Ficks law (Wu and Pruess 2000 [153972], Equations 3.1.53.1.7, pp. 
705), including hydrodynamic dispersion effects in a multiphase system, is used to 
evaluate diffusive and dispersive flux of chemical transport. 
August 2003 38 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
In both research and application, the multiphase extension of Darcys law (Pruess et al. 1999 
[160778], Equation A-5, pp. 145), Richards equation (Richards 1931 [104252]), and the 
generalized Ficks law (Wu and Pruess 2000 [153972], pp. 704707) have been used as 
fundamental laws that govern multiphase flow and transport processes within porous-medium 
and fractured rocks. These fundamental laws or correlations, based on theory, experiment, and 
field studies, reflect our current understanding of porous-medium physics. Note that Richards' 
equation is extended for use in both fracture and matrix flow, as well as interflow. 
A key issue for simulating fluid and heat flow and chemical transport in the fractured porous 
rock of Yucca Mountain is how to handle fracture and matrix flow and interaction under 
multiphase, multicomponent, and isothermal or nonisothermal conditions. The available methods 
for treating fluid flow in fractures and the rock matrix using a numerical approach include: (1) 
an explicit discrete-fracture and matrix representation; (2) the dual-continuum method, including 
double- and multi-porosity, dual-permeability, or the more general multiple interacting 
continua (MINC) method (Pruess and Narasimhan 1985 [101707]); and (3) the generalized 
effective continuum method (ECM). For the work documented in this Model Report, the dualpermeability 
conceptual model is applied to evaluate fluid flow and transport in the fracturematrix 
system of the UZ system of Yucca Mountain, in which the active fracture model is 
adopted to modify fracture-matrix interface areas for flow and transport between fracture and 
matrix systems. 
The dual-continuum conceptualization provides an appropriate representation of flow and 
transport processes within the UZ at Yucca Mountain (Doughty 1999 [135997]; CRWMS M&O 
2000 [141187]). It is much less demanding in computational effort or in data requirements than 
the discrete-fracture-modeling approach. Therefore, the dual-continuum method has become the 
main approach used in the modeling studies of the Yucca Mountain Project (Wu et al. 1999 
[117161]; 2002 [160195]). The dual-permeability methodology for handling fluid flow, tracer 
transport, and heat transfer through fractured rocks treats fracture and rock matrix flow and 
interaction with a multicontinuum numerical approach. It considers global flow occurring not 
only between fractures but also between matrix gridblocks. In this approach, each gridblock of 
the primary mesh is divided into two gridblocks, one for fracture and the other for matrix, 
connected to each other. Because of the one-block representation of fracture or matrix, the 
interflow between fractures and matrix has to be handled using a quasi-steady-state flow 
approximation, and this may limit its application in estimating the gradients of pressures, 
temperatures, and concentrations within the matrix. Note that the UZ Flow Model of this Model 
Report has been developed to simulate steady-state UZ flow conditions at Yucca Mountain. 
Under steady-state flow conditions, however, such gradients near the matrix surfaces become 
minimal, and the one-block matrix-fracture model is expected to produce accurate solutions 
(Doughty 1999 [135997]). 
As applied in this Model Report, the traditional dual-permeability concept is first modified using 
an active fracture model (Liu et al. 1998 [105729]) to represent fingering effects of liquid flow 
through fractures and to limit flow into the matrix system. The active fracture concept has been 
evaluated in the Analysis of Hydrologic Properties Data Report (BSC 2003 [161773]) and 
further sensitivity analyses are provided in Section 6.8 of this Model Report. The dualpermeability 
model is also modified by adding additional global fracture-matrix connections at 
interfaces of TCw-PTn, PTn-TSw, and vitric-nonvitric units to better simulate fracture-matrix 
flow at these transitions. These additional global fracture-matrix connections do not exist in the 
August 2003 39 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
original dual-permeability model. Specifically, TCw fractures are vertically connected to the 
PTn matrix, PTn matrix to TSw fractures, and vitric matrix to nonvitric fractures, respectively, 
along these interfaces. Note that vitric units in the CHn are handled as single-porosity matrix 
only (i.e., the effect of fractures on flow and transport within Calico Hills vitric zones is 
neglected). This conceptual model is supported by observation from the tracer tests (BSC 2001 
[160828]; BSC 2001 [156609], p. 136). 
As an alternative modeling approach, the discrete fracture or weeps type model face extremely 
high uncertainties in fracture distribution data within the mountain and an extensive 
computational burden that cannot be solved in the near future. On the other hand, the ECM 
approach, although the most computationally efficient, may not capture important, nonequilibrium 
interaction in flow and transport between fractures and matrix. 
In model calibration of moisture flow and tracer transport, ambient variably saturated flow in the 
UZ underlying Yucca Mountain is approximated as an isothermal, steady-state flow system. This 
is considered to be a good approximation within the UZ below the PTn unit, because the 
relatively unfractured nonwelded PTn unit is expected to damp and homogenize downwardmoving 
transient pulses arising from episodic surface infiltration events, and geothermal 
temperature or gradients have little effect on ambient percolation (Wu et al. 2000 [154918]; Wu 
et al. 2002 [161058]; Flint et al. 2003 [163967]). Therefore, estimated surface net infiltration 
rates are described as steady-state water recharge (Section 6.1.4). 
In the development of the UZ flow model and its submodels over the past decade, the steadystate 
nature of the flow fields and the damping of transient pulses were evaluated in different 
studies. Wu et al. (1999 [117161], p. 186) referred to the early work of Wang and Narasimhan 
(1985 [108835]; 1993 [106793], Figure 7.4.7) which suggested that effects of infiltration pulses 
at the surface are damped by the underlying tuff units, especially the PTn. The welded tuff of the 
potential repository horizon exhibited only small changes in saturations, pressures, and potentials 
from steady-state values in response to the transient pulses. Pan et al. (1997 [164181]) 
investigated transient flow behavior for downward water flow through sloping layers in the 
vadose zone, with upslope flow developed during heavy rain likely enhancing the downward 
flow. Wu et al. (2002 [161058], p. 35-11) analyzed the capillary barrier capacities in unsaturated 
units and indicated that, on average, it took several thousands years for water to travel through 
the PTn. Both Wu et al. (2000 [154918]; 2002 [161058]) and Flint et al. (2003 [163967]) 
analyzed the implications of capillary barrier development in subunits of the PTn for lateral 
diversion of flow in the PTn. Along sloping layers, strong capillary barrier capacities promote 
lateral diversions. The degree of lateral diversion can be further evaluated by comparative 
sensitivity studies, by detailed analysis of field data including geochemical evidences, and by 
long-term controlled field tests. 
6.1.3 Model Boundary Conditions 
The ground surface of the mountain (or the tuff-alluvium contact in areas of significant alluvial 
cover) is taken as the top model boundary; the water table is treated as the bottom model 
boundary. Both the top and bottom boundaries of the model are treated as Dirichlet-type 
conditions with specified constant but spatially varying temperature and gas pressure. A constant 
liquid saturation value of 0.99 was set for the bottom boundary. For flow simulations using the 
EOS9 module, only pressure or saturation values are needed along the top and bottom model 
boundaries. Surface infiltration, as discussed below in Section 6.1.4, is applied using a source 
August 2003 40 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
term in the fracture gridblocks within the second grid layer from the top. This method was 
adopted because the first layer is treated as a Dirichlet-type boundary with constant pressure, 
saturation, and temperature to represent average atmospheric conditions at the mountain. 
The water table is used as the bottom model boundary, a surface where the water pressure is a 
fixed, single value. Within the numerical models, only one single set of model primary variables 
for solving Richards equations is specified for the bottom boundary, and this is equivalent to 
specifying a constant saturation. For gas and heat flow simulations, the bottom model boundary 
representing the water table is subject to fixed gas pressure, equal to the atmospheric pressure at 
the elevation (Sections 6.3.3 and 6.4.1). All lateral boundaries, as shown in Figure 6.1-1, are 
treated as no-flow (closed) boundaries, which allow flow only along the vertical plane. This 
treatment is reasonable for the eastern boundary, which is along or near the Bow Ridge fault, 
because high vertical permeability and lower capillary forces are expected within the faults (see 
fault properties estimated in BSC (2003 [160240])). For the western and northern lateral 
boundaries, no-lateral-flow boundaries would have little effect on moisture flow within and near 
the repository areas because these boundaries are separated from the repository by faults. For the 
southern lateral boundary, this is also true because it is far from the repository (Figure 6.1-1). 
The spatially distributed values of temperatures along the top and bottom boundaries are based 
on field observation. This treatment is corroborated by data reported by Sass et al. (1988 
[100644]) and the calibrated temperature distribution along the water table (BSC 2001 
[158726]), and further confirmed by matching qualified temperature profiles from a number of 
boreholes as described in Section 6.3. 
Pressure conditions at the bottom boundary of the model are based on observed gas-pressure 
values. The water table, which is the bottom boundary of the UZ Model, is shown to be a 
relatively flat, stable surface in most of the model domain, increasing its elevation only in the 
north (BSC 2003 [160109]). The rise in the north has little effect on flow simulation results 
within the vertical model domain, because the flow is essentially determined by upstream, not 
downstream (water table) conditions in the UZ. In the eastern part of the site to the Solitario 
Canyon fault, the water table elevation of the flat portion is about 730 m above sea level (masl) 
(BSC 2003 [160109]). In specifying water boundary conditions at the water table, capillary rise 
is not included. This is because borehole measurements of matrix saturation and water potential 
show small capillary fringes (e.g., Figure 6.2-3) at the water table, which can be ignored by the 
large-scale UZ model. The gas pressures are estimated using a pressure value of 92 kPa at an 
elevation of 730 m. Surface gas pressures are determined by running the TOUGH2 code, EOS3 
module to steady-state under given temperature, bottom pressure, and surface-infiltration 
conditions. This is necessary to generate a steady state, equilibrated gas-pressure boundary to 
avoid artificial airflow or circulation, which may occur if nonequilibrated pressures are imposed 
on the ground surface boundaries. 
6.1.4 Infiltration Scenarios 
Water entering the UZ as net infiltration from precipitation at land surface is the major control 
on overall hydrological and thermal-hydrological conditions within the UZ at Yucca Mountain. 
Net infiltration is the ultimate source of percolation through the UZ. Water percolating 
downward through the UZ will be the principal means by which radionuclides may be 
transported from the repository to the water table. 
August 2003 41 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
The UZ Model uses net infiltration rates as surface water recharge boundary conditions. The net 
infiltration rates consist of present-day and future scenarios, determined by studies of modern 
and future climates (USGS 2001 [158378] and USGS 2001 [160355]). A total of nine net 
infiltration maps (DTN: GS000308311221.005 [147613]) are implemented with the UZ Model 
and its submodels. These infiltration maps are documented in two AMRs: Future Climate 
Analysis (USGS 2001 [158378]); Simulation of Net Infiltration for Modern and Potential Future 
Climates (USGS 2001 [160355]) for infiltration and climate models. They include present-day 
(modern), monsoon, and glacial transitionthree climatic scenarios, each of which consists of 
lower-bound, mean, and upper-bound rates. The nine infiltration rates are summarized in Table 
6.1-2 for average values over the model domain. Note that the UZ Model is concerned primarily 
with steady-state flow under each infiltration scenario, while in the climate models reference to 
future climates means climates are expected to act sequentially over the modeled period: presentday, 
monsoon, and then glacial transition for specific periods. 
Table 6.1-2. 
Scenario 
Glacial Transition 
Infiltration Rates (mm/year) Averaged over the UZ Model Domain 
Mean Infiltration 
4.43 
11.83 
17.02 
Upper-Bound Infiltration 
10.74 
19.23 
31.69 
As shown in Table 6.1-2, the average rate over the model domain for the present-day mean 
infiltration with the UZ Model grid is 4.43 mm/yr (Wang 2003 [162417], SN-LBNL-SCI-199- 
V1, pp. 99101), which is considered as a base-case infiltration scenario. The use of the lower- 
and upper-bound infiltration values is intended to cover the uncertainties associated with the 
infiltration for each climate. The two future climatic scenarios, the monsoon and glacial 
transition periods, are used to account for possible climate-induced changes in precipitation and 
net infiltration. Note that the glacial transition has higher infiltration rates except for the lowerbound 
case. The average values in Table 6.1-2 are estimated using the TSPA-LA grid, shown in 
Figure 6.1-1 for infiltration maps (DTN: GS000308311221.005 [147613]), and the software 
routine infil2grid V1.7 (LBNL 2002 [154793]). Net infiltration is handled in a consistent manner 
in this report, i.e., mapping the USGS infiltration maps to model grids. 
A plan view of the spatial distribution in the three mean infiltration maps, as interpolated onto 
the TSPA-LA grid, is shown in Figures 6.1-2, 6.1-3 and 6.1-4 respectively, for the present-day, 
monsoon, and glacial transition mean infiltration scenarios. The figures show similar patterns of 
flux distributions with the three infiltration rates, with higher infiltration rates in the northern 
part of the model domain and along the mountain ridge east of the Solitario Canyon fault. 
August 2003 
Lower-Bound Infiltration 
Present-Day/Modern 1.25 
Monsoon 4.43 
2.35 
Values averaged from DTN: GS000308311221.005 [147613] 
42 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Figure 6.1-2. Plan View of Net Infiltration Distributed over the 3-D UZ TSPA-LA Model Grid for the 
Present-Day (Base-Case) Mean Infiltration Scenario 
Nevada Coordinate E-W (m)
DTN: GS000308311221.005 [147613] 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
Present Day Infiltration (Mean) 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
So 
litario 
Canyon 
Fault 
Drillhole 
Wash 
Fault 
Imbricate 
Fault 
Ghost 
Dance 
Fault 
230000 
172000 170000 168000 
43 
174000 
Bow Ridge Fault 
(mm/year) 
15 
14.25 
13.5 
12.75 
12 
11.25 
10.5 
9. 
9 
75 
8.25 
7.5 
6. 
6 
75 
5.25 
4.5 
3. 
3 
75 
2.25 
1.5 
0. 
0 
75 
U0050 
August 2003 UZ Flow Models and Submodels 
Figure 6.1-3. Plan View of Net Infiltration Distributed over the 3-D UZ TSPA-LA Model Grid for the 
Monsoon Mean Infiltration Scenario 
Nevada Coordinate E-W(m) 
DTN: GS000308311221.005 [147613] 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
Monsoon Infiltration (Mean) 
Se 
v 
er 
Wash 
Fault 
Pagany 
Wash 
Fault 
Solitario 
Canyon 
Fault 
Drillhol 
e 
Wash 
Fault 
Imbricate 
Fault 
Ghost 
D 
ance 
Fault 
230000 
172000 170000 168000 
44 
174000 
Bow Ridge Fault 
(mm/year) 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
U0050 
August 2003 UZ Flow Models and Submodels 
Figure 6.1-4. Plan View of Net Infiltration Distributed over the 3-D UZ TSPA-LA Model Grid for the 
Glacial Transition Mean Infiltration Scenario 
Nevada Coordinate E-W (m)
DTN: GS000308311221.005 [147613] 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
Glacial Transition Infiltration (Mean) 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Solitario 
Canyon 
Fau 
lt 
D 
rillhole 
Wash 
Fault 
Imbricate 
Fault 
Ghost 
Dance 
Fault 
230000 
174000 172000 170000 168000 
45 
(mm/year) 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
6.1.5 Model Parameters and Rock Properties 
The key input rock and fluid-flow parameters used in UZ Model development are summarized in 
Section 4. They include (1) fracture properties (frequency, permeability, van Genuchten  and m 
parameters, porosity, fracture-matrix interface area, and residual and satiated saturations) for 
each UZ Model layer; (2) matrix properties (porosity, permeability, the van Genuchten  and m 
parameters, and residual and satiated saturations) for each UZ Model layer; (3) thermal and 
transport properties (grain density, wet and dry thermal conductivity, grain specific heat, and 
tortuosity coefficients) for each UZ Model layer; and (4) fault properties (fracture parameters, 
DTN: LB02092DSSCFPR.002 [162128]) for each of the major hydrogeologic units (Table 6.1- 
1). The development and estimation of these parameters are presented in the report, Calibrated 
Properties Model (BSC 2003 [160240]) (DTN: LB02091DSSCP3I.002 [161433]), as well as the 
calibration results of this Model Report. 
The rock-parameter specification in the 3-D UZ Model and its submodels is, in general, layerwise 
uniform (BSC 2003 [160109]). However, certain portions of grid layers representing the 
CHn unit are partly altered from vitric to zeolitic. In these altered layers, different rock 
properties are specified for vitric or zeolitic zones. The UZ model treats all of the geological 
units, including those representing fault zones, as fracture-matrix systems using a dualpermeability 
approach, except the CHn vitric zones, which are treated as single-porosity matrix. 
Global fracture-matrix connections are added to those across interfaces between TCw-PTn, PTn- 
TSw, and vitric-nonvitric units to model transition between fracture-and matrix-dominated flow 
at these interfaces. In addition, the van Genuchten relative permeability and capillary pressure 
functions (van Genuchten 1980 [100610]) are used to describe flow in both fractures and matrix. 
6.2 3-D UZ FLOW MODEL CALIBRATION 
A critical step in developing the 3-D UZ flow model was to use field-measured liquid saturation, 
water potential, perched-water, pneumatic, and isotopic tracer data for calibrations of the 3-D 
model. This is part of the important iterative processes of model calibration and verification, 
which increase confidence in model predictions for the site conditions. A detailed modeling 
investigation is reported in BSC (2003 [160240]) using one-dimensional (1-D) models for 
estimating model parameters with water potential, saturation, and other types of data. However, 
these 1-D models cannot predict whether lateral flow or perched water occurs in several 
hydrogeological units below the repository level. This section documents a further model 
calibration effort, focusing on the 3-D flow patterns: perched-water calibrations using the 3-D 
model grid (Figure 6.1-1). 
The 3-D flow model calibration is conducted using the three sets of parameters of 1-D site-scale 
calibrated properties (BSC 2003 [160240]; DTN: LB02091DSSCP3I.002 [161433]), 2-D sitescale 
calibrated fault properties (DTN: LB02092DSSCFPR.002 [162128]), three present-day 
infiltration rates (See Table 6.1-2), and the geological model and numerical grid for calibration 
(BSC 2003 [160109]). In addition, previously developed 3-D properties for the PTn unit (BSC 
2001 [158726]; DTNs: LB991121233129.001 [147328]; LB991121233129.003 
[147335]; LB991121233129.005 [147346]) are adopted for the PTn properties in this Model 
Report. A permeability-barrier water-perching model is developed. In this model, rock properties 
are locally adjusted in several grid layers of the lower basal vitrophyre in the TSw unit and upper 
zeolites in the CHn unit. The objective of perched-water calibrations is (1) to match perched- 
August 2003 46 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
water occurrences as observed at the site and (2) to investigate the effects of flow-through and 
bypassing of perched bodies on tracer transport. 
6.2.1 Calibration Data 
The field data used in the 3-D UZ flow model calibration are matrix liquid saturations, matrix 
water potentials, and perched-water elevations, as observed from boreholes. Table 6.2-1 shows 
the types of data from boreholes used in the calibration, and Figure 6.1-1 shows the locations of 
the boreholes and the tunnel at Yucca Mountain. 
Borehole 
USW SD-6 
USW SD-7 
MO0109HYMXPROP.001 
[155989] 
MO0109HYMXPROP.001 
[155989] 
USW NRG-7a MO0109HYMXPROP.001 
[155989] 
GS980808312242.014 [106748] 
MO0109HYMXPROP.001 
[155989] 
USW SD-9 
USW SD-12 
USW UZ-14 MO0109HYMXPROP.001 
[155989] 
UE-25 UZ#16 MO0109HYMXPROP.001 
[155989] 
USW WT-24 
USW G-2 
6.2.2 Conceptual Models of UZ Flow 
MDL-NBS-HS-000006 REV01 
GS980508312313.001 
Subsurface flow and transport processes in the UZ occur in a heterogeneous system of layered, 
anisotropic, fractured volcanic rocks. Greater understanding of such processes has been provided 
by a continuous effort of data collection and analysis as well as modeling studies (BSC 2001 
[158726]). The AMR entitled Conceptual and Numerical Model for the Unsaturated Zone Flow 
and Transport (CRWMS M&O 2000 [141187]) presents a discussion of these conceptual models 
used in the study of UZ flow processes within this Model Report. Figure 6.2-1 illustrates a 
typical geological profile along a vertical east-west transect as well as the conceptual model that 
characterizes potential lateral flow in the PTn unit, and the effects of faults and perched water on 
the UZ system. 
GS980708312242.010 [106752] GS980508312313.001 
[109746] 
[109746] 
August 2003 47 
U0050 
Table 6.2-1. Borehole Data Used for 3-D Flow Model Calibration 
Matrix Liquid Saturation (core) Matrix Liquid Water Potential Perched Water Elevation 
(masl) 
Rousseau et al. 1997 
[100178] 
GS980808312242.014 [106748] 
SNT02110894001.002 
[105067] 
Rousseau et al. 1999 
[102097] 
Rousseau et al. 1997 
[100178] 
GS980408312232.001 [105982] 
(in situ measurement) 
(organized in 
LB0208UZDSCPMI.001 
[161285] and 
MO0109HYMXPROP.001 
[155989]) 
GS960308312312.005 
[107230] 
... 

..!. 
 .	
...&.. 
'.
!.."." 
%
	. 
 .
. ....... ,..	
. !.."." &
	. 
.,!. 
.....	... 
.	.... 
.	
.. 
.......	. ....
..... .......	. ....
..... ....... ....... 
 ....." ..... 
.... 
.... 
.	
. 
.	
.. 
... 
...... 

..... 
.	......... 
 ..!  ... 
....	.	.. 
...	.. .	... 

....	.	.. 
 ..! 
 ... 

.."....... 
.. 
".
.... 
!.."."# 
"..	..	$	.". 
(&..$... .&$$ 
..... ..... 
..... ..... .... 
....	
. 
....... 
%)..*... 
Figure 6.2-1. Schematic Showing the Conceptualized Flow Processes and Effects of Capillary Barriers, 
Major Faults, and Perched-Water Zones within a Typical Cross Section of the UZ Flow 
August 2003 
Model Domain in the East-West Direction 
The PTn unit, as described by the current geological model, consists primarily of non- to 
partially welded tuffs. The dip of these layers is generally less than 10o to the east or southeast. 
The combined thickness of the PTn layers ranges from 150 m in the north end of the model area 
to 30 m in the south end. The PTn unit as a whole exhibits very different hydrogeologic 
properties from the TCw and TSw units that bound it above and below. Both the TCw and the 
TSw have low porosity and intense fracturing typical of the densely welded tuffs at Yucca 
Mountain. In contrast, the PTn has high porosity and low fracture intensity, and its matrix system 
has a large capacity for storing groundwater. It has been shown to effectively damp spatial and 
temporal variations in percolation flux (Wu et al. 2000 [154918], pp. 3032, 3941). Therefore, 
water flow through the UZ is modeled to occur under steady-state conditions. Transient fastpathway 
flow is considered to contribute insignificantly to the total flow below the PTn through 
the UZ (BSC 2001 [158726], pp. 137139). 
6.2.2.1 Capillary Barriers 
Capillary barriers were speculated to exist within the PTn unit, because of the large contrast in 
rock properties across the interfaces of the unit (Montazer and Wilson 1984 [100161], pp. 26 
30). In addition, rock-property contrasts between sublayers within the PTn unit may potentially 
produce capillary barriers. Characterization of groundwater flow behavior within the PTn is 
critically dependent on detailed knowledge of rock properties and the heterogeneity within the 
PTn unit. Considerable amounts of field data, obtained from tens of boreholes and hundreds of 
outcrop samples at the site, constrain the distribution of rock properties within the PTn unit. In 
48 MDL-NBS-HS-000006 REV01 
......	.
 
...... ..... ...
 ... ...... 
U0050 UZ Flow Models and Submodels 
...	..... (...	.. 
%)*+ 
.	.. ..
..
 
..."." %
	.
U0050 UZ Flow Models and Submodels 
general, field data indicate that the Yucca Mountain formation is more heterogeneous vertically 
than horizontally, so that layer-wise representations provide reasonable approximation of the 
complex geologic system. Calibration using this conceptual model matches different types of 
observation data, as further demonstrated in the following sections. However, characterizing 
general flow behavior within the UZ system is complicated by the presence of faults, which 
interrupt the lateral continuity of rock matrix properties. 
The key conceptualizations made in the conceptual model concerning lateral flow above the 
repository horizon are as follows: (1) the hydrogeological units/layers are internally 
homogeneous, and the material properties of each unit are continuous throughout each layer 
(Table 6.1-1) unless interrupted by faults; (2) ambient water flow in the system is at a steadystate 
condition; and (3) faults are represented by vertical or inclined columns of gridblocks 
having finite or small width. The flow patterns associated with capillary barriers within the PTn 
are studied in the following sections using this conceptual model and alternative parameters. 
6.2.2.2 Perched Water 
Conceptual models of perched water occurrence are of particular interest in assessing the system 
performance of the repository and UZ flow patterns below the repository. Waste-isolation 
strategies and UZ natural barrier capability depend in part on sorption within the zeolitic 
portions of the CHn and on tracer transport times between the repository horizon and the water 
table. Several conceptual models have been proposed for the genesis of perched water at Yucca 
Mountain (e.g., Wu et al. 1999 [117167]). 
Perched water may occur where percolation flux exceeds the capacity of the geologic media to 
transmit vertical flux in the UZ. Perched water has been encountered in a number of boreholes at 
Yucca Mountain, including UZ-14, SD-7, SD-9, SD-12, NRG-7a, G-2, and WT-24. These 
perched-water occurrences are found to be associated with low-permeability zeolites in the CHn 
or the densely welded basal vitrophyre (Tptpv3, Table 6.1-1) of the TSw unit. Possible 
mechanisms of water-perching in the UZ of Yucca Mountain may be permeability or capillary 
barrier effects at faults, or a combination of both. 
A permeability-barrier conceptual model for perched water occurrence has been used in UZ flow 
modeling studies since 1996, as summarized in Wu et al. (1999 [117167]). In this model, 
perched-water bodies in the vicinity of the ESF North Ramp (near boreholes UZ- 14, SD-9, 
NRG-7a, G-2 and WT-24) are observed to occur above the base of the TSw, underlain by a zone 
of low-permeability zeolitized rock. The perched-water bodies in this northern area of the 
repository may be interconnected. However, the perched-water zones at boreholes SD-7 and SD- 
12 are considered here as local, isolated bodies. In this conceptual model, both vertical and 
lateral water movement in the vicinity of the perched zones is considered to be controlled mainly 
by the fracture and matrix permeability distribution in these areas. The major aspects of the 
permeability-barrier conceptual model are: (1) no large-scale vertically connected potentially 
fluid-conducting fractures transect the underlying low-permeability units, (2) both vertical and 
horizontal permeabilities within and below the perched-water zone are small compared with 
permeabilities outside perching zones, and (3) sufficient percolation flux (>1 mm/yr) exists 
locally. 
August 2003 49 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Perched-water occurrence caused by permeability barrier effects is consistent with the 
conceptual model that ambient conditions reflect long-term, steady-state or transient flow 
through the UZ, and that perched water under steady-state flow conditions may only result from 
a permeability barrier. Previous modeling studies (BSC 2001 [158726]) concluded that this 
conceptual water-perching model is able to match the observation data of perched water in the 
Yucca Mountain UZ. In the present numerical studies, the occurrence of perched water is 
assumed to follow the conceptual models of a permeability barrier. In other words, perchedwater 
bodies are formed as a result of permeability barrier effects. 
6.2.3 Parameter Adjustment 
The Model Report entitled Calibrated Properties Model (BSC 2003 [160240]) provides basic 
input parameter sets of fractures and matrix rocks for modeling efforts in this Model Report. 
However, these properties were estimated through a series of 1-D model inversions, in which 
lateral flow, perched water, and capillary barrier effects cannot be simulated by the 1-D model. 
Use of a 3-D model allows further parameter adjustment to match field observation data. This 
section presents calibrated parameters after adjustment through a series of 3-D model 
calibrations. The adjusted parameters include fracture-matrix properties of the top TSw layer, 
PTn unit, and perched water zones, and fracture permeabilities in the upper TSw layers. 
In addition to estimating model parameters, these calibration studies can also be used to examine 
the adequacy of discretization for the 3-D model grid. The 3-D UZ TSPA-LA grid (Figure 6.1-1 
for its plan view) uses finer vertical discretization than those used in the TSPA-SR model by 
BSC (2001 [158726]), particularly, for the PTn unit and the potential perched-water layers. For 
example, the PTn unit consists in general of six hydrogeological units vertically (namely, ptn21, 
ptn22, ptn23, ptn24, ptn25, and ptn26; Table 6.1-1), each of which is discretized into one or 
several vertical grid layers with maximum thickness of 2 or 5 m, respectively. The details of 
discretization are described in the Development of Numerical Grids for UZ Flow and Transport 
Modeling (BSC 2003 [160109], Section 6.6). 
The 3-D model calibration efforts were performed by starting with the three sets of calibrated 
parameters from 1-D inversions of the Model Report (BSC 2003 [160240] in forward 3-D 
simulations). Then, model results were compared with the field-observed data of matrix liquid, 
along with water-potential data, perched-water elevations, and gas pressures. In general, some 
model parameters from 1-D calibrations are found to need adjustment in order to capture 3-D 
flow behavior or match observations at the mountain. The following modifications made to the 
1-D rock properties were found necessary: modifying the fracture  of the tsw31 unit, using 
fracture-matrix properties from the previous 3-D calibration, locally adjusting fracture-matrix 
properties for the model layers associated with perched-water occurrence, and adjusting fracture 
permeability for the TSw units. 
When the 1-D calibrated fracture-matrix properties (BSC 2003 [160240]) were used directly 
without any modifications as input to the 3-D model, significant lateral flow was predicted to 
occur along the top layer (tsw31) of the TSw unit under the present-day, mean infiltration 
scenario. This results from the limitation of a 1-D model; there is no evidence to support lateral 
flow within this layer. The 3-D simulation results indicated that a strong capillary barrier is 
formed between this tsw31 layer and the layer below. Examination of the calibrated fracture 
parameters for this layer showed that such large lateral flow was artificially created by the small 
August 2003 50 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
value of fracture  in tsw31, estimated by the 1-D inversion. The 1-D inverted fracture  = 1.597 
~ 10-5 Pa-1, which was even smaller than matrix  (= 8.702 ~ 10-5 Pa-1) for the same layer. This 
was an artifact of the 1-D inversion model, because the top unit of the TSw was used as a 
transitional layer for flow from matrix-dominated flow in the PTn to fracture-dominated flow in 
the TSw, for which a strong capillary suction is needed within the fractures. Physically, there are 
many larger fractures along the top layer of the TSw unit (DTN: LB0205REVUZPRP.001 
[159525]); therefore, a larger fracture  (= 1.000 ~ 10-4 Pa-1) is used instead, leading to a good 
match between observed data (as discussed in Section 6.2.5) and 3-D models and indicating little 
lateral flow in the tsw31 layer. 
The second modification to the three 1-D inverted property sets is to replace the PTn fracturematrix 
properties by those presented in Tables 6.2-2, 6.2-3, and 6.2-4, which are the 
corresponding parameter sets developed in previous UZ flow modeling studies (BSC 2001 
[158726], Tables II-1, II-3, and II-5). The PTn properties of Tables 6.2-2, 6.2-3, and 6.2-4 are 
used because these parameters provide a better match of not only liquid saturation and waterpotential 
data, but also of observed chloride data, as discussed in the following sections. 
ptn21 9.90E-13 1.01E-5 0.176 1.86E-12 1.68E-3 0.580 0.09 
ptn22 2.65E-12 1.60E-4 0.326 2.00E-11 7.68E-4 0.580 0.09 
ptn23 1.23E-13 5.58E-6 0.397 2.60E-13 9.23E-4 0.610 0.09 
ptn24 7.86E-14 1.53E-4 0.225 4.67E-13 3.37E-3 0.623 0.09 
ptn25 7.00E-14 5.27E-5 0.323 7.03E-13 6.33E-4 0.644 0.09 
ptn26 2.21E-13 2.49E-4 0.285 4.44E-13 2.79E-4 0.552 0.09 
DTN: LB991121233129.001 [147328] 
Table 6.2-2. Calibrated PTn Fracture-Matrix Parameters for the Present-Day, Mean Infiltration Scenario 
M M Model Layer mM 
(-) 
K 
(m2) (1/Pa) 
mF 
(-) 
KF 
(m2) 
 
(1/Pa) 
Table 6.2-3. Calibrated PTn Fracture-Matrix Parameters for the Present-Day, Upper-Bound Mean 
M M mM 
(-) 
51 
ptn21 1.26E-13 1.84E-4 0.199 1.00E-11 2.38E-3 0.611 0.08 
ptn22 5.98E-12 2.42E-5 0.473 1.00E-11 1.26E-3 0.665 0.08 
ptn23 3.43E-13 4.06E-6 0.407 1.96E-13 1.25E-3 0.627 0.08 
ptn24 3.93E-13 5.27E-5 0.271 4.38E-13 2.25E-3 0.631 0.08 
ptn25 1.85E-13 2.95E-5 0.378 6.14E-13 1.00E-3 0.637 0.08 
ptn26 6.39E-13 3.54E-4 0.265 3.48E-13 3.98E-4 0.367 0.08 
 
(-) 
August 2003 
mF 
(-) 
KF 
(m2) 
F 
(1/Pa) 
Infiltration Scenario 
K 
(m2) Model Layer 
DTN: LB991121233129.003 [147335] 
MDL-NBS-HS-000006 REV01 
F 
(1/Pa) 
 
U0050 
 
(-) U0050 UZ Flow Models and Submodels 
  
Table 6.2-4. Calibrated PTn Fracture-Matrix Parameters for the Present-Day, Lower-Bound Mean 
M M mF 
(-) 
MM 
(-) 
KF 
(m2) 
F 
(1/Pa) 
K 
(m2) 
 
(-) 
ptn21 1.86E-13 6.13E-5 0.165 1.00E-11 1.66E-3 0.503 0.01 
ptn22 3.27E-12 1.51E-5 0.390 1.00E-11 9.39E-4 0.651 0.01 
ptn23 4.20E-13 2.04E-6 0.387* 1.84E-13 1.28E-3 0.518 0.01 
ptn24 3.94E-13 2.32E-5 0.210 4.31E-13 2.02E-3 0.594 0.01 
ptn25 2.22E-13 2.04E-5 0.296 7.12E-13 7.42E-4 0.555 0.01 
ptn26 5.43E-13 1.82E-4 0.264 3.08E-13 2.00E-4 0.401 0.01 
For perched-water calibration, calibrated parameters of fracture and matrix permeabilities within 
perched zones are determined from many modeling studies with 3-D simulations. The perchedwater 
conceptual model with respect to water-perching scenarios is realized and carried out by 
modifying the 3-D UZ model grid file as follows: 
 The grid-layer properties of tsw38 (tswF8/tswM8), tsw39 (tswF9/tswM9), ch1z 
(ch1Fz/ch1Mz) and ch2z (ch2Fz/ch2Mz) are replaced by (pcF38/pcM38), (pcF39/ 
pcM39), (pcF1z/pcM1z), and (pcF2z/pcM2z), respectively, where the basal vitrophyre 
 Near borehole SD-7, properties for the gridblocks in grid columns q45, i80, i81, i84, i87, 
o92, and o95, over grid layers of ch5z (ch5Fz/ch5Mz), ch6z (ch6Fz/ch6Mz) and pp4 
(pp4Fz/pp4Mz) are replaced by (pcF5z/pcM5z), (pcF6z/pcM6z), and (pcF4p/pcM4p), 
 Near borehole SD-12, properties for the gridblocks in grid columns q47, b93, b99, k61, 
k62 and k67, over grid layers of tsw38 (tswF8/tswM8), tsw39 (tswF9/ tswM9), and ch1v 
(ch1Fv/ch1Mv) are replaced by (pcF38/pcM38), (pcF39/pcM39), and (pcF1z/pcM1z), 
Fracture and matrix permeabilities of potential perched layers/zones, as identified above, are 
calibrated based on the 3-D model calibrated values (BSC 2001 [158726]), and shown in Tables 
6.2-5, 6.2-6, and 6.2-7. All properties except intrinsic permeabilities, van Genuchtens  and m 
parameters, and residual saturations for matrix blocks within perched zones are identical to 
parameters estimated from the current 1-D inversions of the report by BSC (2003 [160240]). The 
active-fracture parameter, , is set to zero for all the perched zones, causing the fracture-matrix 
interface-area factor to be equivalent to liquid saturation (Liu et al. 1998 [105729]). Tables 6.2-5, 
6.2-6, and 6.2-7 present the final three sets of calibrated rock properties at zones with perched 
water, with base-case (mean), upper-bound, and lower-bound present-day infiltration scenarios, 
respectively. The modified fracture properties in the following three tables are close to those 
of the matrix, so that fractures in water perching layers are effectively removed. 
August 2003 
Infiltration Scenario 
Model Layer 
DTN: LB991121233129.005 [147346] 
respectively. 
respectively. 
MDL-NBS-HS-000006 REV01 
(1/Pa) 
NOTE: * In output DTN, this value is 0.388 due to round off. 
of the TSw is underlain by zeolitic units. 
52 M 
Table 6.2-6. Calibrated Parameters of Perched-Water Conceptual Model for the Present-Day, 
Upper-Bound Infiltration Scenario 
 
(-) 
6.23E-6 0.286 3.00E-18 6.23E-6 0.286 0.00 
4.61E-6 0.059 6.20E-17 4.61E-6 0.059 0.00 
2.12E-7 0.349 9.30E-19 2.12E-7 0.349 0.00 
2.25E-6 0.257 2.40E-17 2.25E-6 0.257 0.00 
2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
4.49E-7 0.474 7.70E-19 4.49E-7 0.474 0.00 
M 
Calibrated Parameters of Perched-Water Conceptual Model for the Present-Day, 
Lower-Bound Infiltration Scenario 
 
(-) 
5.58E-6 0.286 3.00E-18 5.58E-6 0.286 0.00 
4.61E-6 0.059 6.20E-17 4.61E-6 0.059 0.00 
2.12E-7 0.349 9.30E-19 2.12E-7 0.349 0.00 
2.25E-6 0.257 2.40E-17 2.25E-6 0.257 0.00 
2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
4.57E-7 0.474 7.70E-19 4.57E-7 0.474 0.00 
M 
UZ Flow Models and Submodels 
Table 6.2-5. Calibrated Parameters of Perched-Water Conceptual Model for the Present-Day, 
Base-Case Infiltration Scenario 
Model Layer K 
(m2) 
3.00E-19 pcM38/ pcF38 
6.20E-18 pcM39/ pcF39 
9.30E-20 pcM1z/ pcF1z 
2.40E-18 pcM2z/ pcF2z 
2.40E-18 pcM5z/ pcF5z 
1.10E-19 pcM6z/ pcF6z 
7.70E-19 pcM4p/ pcF4p 
Output DTN: LB03013DSSCP3I.001 
Model Layer K 
(m2) 
3.00E-19 pcM38/ pcF38 
6.20E-18 pcM39/ pcF39 
9.30E-20 pcM1z/ pcF1z 
2.40E-18 pcM2z/ pcF2z 
2.40E-18 pcM5z/ pcF5z 
1.10E-19 pcM6z/ pcF6z 
7.70E-19 pcM4p/ pcF4p 
Output DTN: LB03013DSSCP3I.001 
Table 6.2-7. 
Model Layer K 
(m2) 
3.00E-19 pcM38/ pcF38 
6.20E-18 pcM39/ pcF39 
9.30E-20 pcM1z/ pcF1z 
2.40E-18 pcM2z/ pcF2z 
2.40E-18 pcM5z/ pcF5z 
1.10E-19 pcM6z/ pcF6z 
7.70E-19 pcM4p/ pcF4p 
Output DTN: LB03013DSSCP3I.001 
MDL-NBS-HS-000006 REV01 
U0050 
M mF 
(-) 
mM 
(-) 
KF 
(m2) 
F 
(1/Pa) (1/Pa) 
M 
 
F 
(1/Pa) 
mF 
(-) 
mM 
(-) 
KF 
(m2) (1/Pa) 
 M 

F 
(1/Pa) 
mF 
(-) 
mM 
(-) 
KF 
(m2) (1/Pa) 
 
(-) 
1.43E-6 0.286 3.00E-19 1.43E-6 0.286 0.00 
4.61E-6 0.059 6.20E-18 4.61E-6 0.059 0.00 
2.12E-7 0.349 9.30E-20 2.12E-7 0.349 0.00 
2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
2.88E-7 0.474 7.70E-19 2.88E-7 0.474 0.00 
August 2003 53 U0050 UZ Flow Models and Submodels 
The fourth and last parameter adjustment is the fracture permeability in the TSw unit under the 
present-day, mean infiltration scenario (see Section 6.4). The present-day, mean infiltration rate 
is used for gas flow calibration, because the pneumatic tests were conducted in a small time scale 
of days to years at present day conditions. This calibration of Section 6.4 was made from 3-D gas 
flow analysis with the calibrated fracture permeability results summarized in Table 6.2-8. In this 
table, the fracture permeabilities of several TSw units from the 1-D inversion are reduced by a 
factor of 15. 
Table 6.2-8. Calibrated TSw Fracture Permeability for the Present-Day, Mean Infiltration Scenario 
Model Layer KF 
(m2) 
tsw31 5.42E-12 
tsw32 4.72E-12 
tsw33 5.18E-12 
tsw34 2.21E-12 
tsw35 6.06E-12 
tsw36 8.99E-12 
tsw37 8.99E-12 
Output DTN: LB03013DSSCP3I.001 
The final results and calibrated parameter sets of the 3-D model studies are given in Tables I-1 to 
I-6 of Attachment I. Tables I-1, I-2, and I-3 are considered as base-case scenario parameter sets, 
and Tables I-4, I-5, and I-6 are regarded as alternative property sets for use in generating the 3-D 
flow fields and estimating tracer transport times. 
6.2.4 Numerical Treatment and Solution Convergence 
Numerical modeling of large-scale 3-D flow and transport in the UZ beneath Yucca Mountain is 
mathematically challenging. The principal difficulty stems from the highly nonlinear coupling of 
the flow system. First, the hydrogeological system is distinctly heterogeneous on all model 
scales, and there are orders-of-magnitude contrasts in permeabilities across geological layers or 
between fracture and matrix rock. Secondly, the two-phase flow functions of relative 
permeability and capillary pressure for Yucca Mountain tuffs are extremely nonlinear for both 
fractures and matrix systems. The mathematical difficulties become even more severe when 
using the dual-permeability modeling approach for handling fracture-matrix interaction. In this 
case, flows through fractures and matrix are on very different time scales, with fracture flow 
being orders of magnitude faster than matrix flow. Furthermore, fracture elements have a much 
smaller storage space than matrix elements. In general, it takes simulation times of thousands to 
millions of years for the system to equilibrate. Rapid flow through fractures, plus the slow 
response in the matrix, makes it very difficult to obtain steady-state solutions numerically (BSC 
2001 [158726], p. 51). 
For all flow simulations (this section and Section 6.7), the EOS9 module of TOUGH2 V1.4 
(LBNL 2000 [146496]) was used to solve Richards equation in the unsaturated flow 
calculations. In this method, air/gas flow dynamics are ignored by using a constant gas-phase 
pressure in an isothermal system. This simplified two-phase flow solution for the 3-D model 
calibrations and TSPA flow field simulations is the most computationally efficient approach, 
August 2003 54 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
while giving accurate results for isothermal two-phase flow. Two-phase flow problems are 
solved with one equation per gridblock instead of solving two or three equations, as required by 
the EOS3 module. Numerical tests allow one to conclude that for moisture flow and distributions 
at steady state, the EOS9 solutions are in general identical to EOS3, true two-phase flow 
solutions (BSC 2001 [158726], pp. 5253). 
Model calibrations and flow-field simulations are both based on steady-state solutions using the 
EOS9 module. In each simulation, fracture, fault, and zeolitic element volumes are increased by 
a factor of 10,000 in the grid to overcome convergence difficulties associated with these nodes, 
while keeping all other mesh geometric information unchanged. This approach does not affect 
the final solution as long as a true steady-state solution is obtained for a given run. The initial 
condition for a new scenario run is estimated using a default (uniform) initial condition or results 
of a previous run with a similar modeling condition. Each simulation is usually subdivided into 
stages. For the first-stage runs, a large convergence tolerance on the order of 10,000 or more is 
used to keep simulation progressing with a large time step. It has been found based on those 
studies that using large residual tolerance in the first stage has no effects on final, steady-state 
solutions as long as no oscillations or unphysical solutions occur. After running the solution to 
109 years or more with a large tolerance, the convergence tolerance is reduced to 102  103, and 
the model is run until a steady-state solution is reached. The final steady-state solutions are 
confirmed using a global mass-balance check, as discussed in Sections 6.2.5 and 6.6.2. 
6.2.5 Simulation Scenarios, Results, and Analyses 
This section summarizes the 3-D flow model calibration scenarios performed for this Model 
Report, including simulation results and analyses. The model calibrations are performed using 
(1) the 3-D TSPA-LA grid (Figure 6.1-1) and nine net infiltration maps, as discussed in Section 
6.1.4; (2) the three parameter sets (Table I-1, I-2 and I-3 in Attachment I of this Model Report); 
and (3) the UZ flow conceptual models of Section 6.2.2 above. Simulation results are called 
TSPA-LA flow simulations or base-case flow fields in this report. Simulation results with the 
alternative model will be discussed in Section 6.6. 
Simulation Scenarios: Table 6.2-9 summarizes these nine simulation scenarios, associated 
parameter sets, and infiltration rates used. 
August 2003 55 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table 6.2-9. Nine UZ Flow Simulation Scenarios: Data Files, Parameter Sets, and Infiltration Maps for 
the UZ Model Calibrations 
Designation/ 
Simulation 
preq_lA 
preq_mA 
preq_uA 
monq_lA 
monq_mA 
monq_uA 
glaq_lA 
glaq_mA 
glaq_uA 
Mass Balance and Solution Convergence: Table 6.2-10 shows the mass-balance results for the 
nine simulation scenarios. In Table 6.2-10, inflow is the total infiltration rate over the entire 
model top boundary, representing a net water recharge rate into the system for the infiltration 
scenario simulated. Outflow is the cumulative total-flow rate out of the model and into the 
lower boundary representing the water table. Global mass-balance errors between inflow and 
outflow from the system, as shown in Table 6.2-10, are all less than 0.06% for the nine 
simulations, leading to the conclusion that steady-state solutions are obtained for all the 
simulations. 
Simulation 
Scenarios 
preq_lA 1.5828143 1.5828216 0.0005 
preq_mA 5.5922355 5.5908953 0.0240 
preq_uA 13.564390 13.556891 0.0553 
monq_lA 5.5922355 5.5922194 0.0003 
monq_mA 14.939317 14.945912 0.0473 
monq_uA 24.286298 24.286685 0.0016 
glaq_lA 2.9648877 2.9648851 0.0001 
glaq_mA 21.494950 21.495135 0.0009 
glaq_uA 40.024949 40.024915 0.0001 
Model Results - DTN: LB03023DSSCP9I.001 
Table 6.2-10. Mass-Balance Results for Nine Flow Simulations 
(kg/s) 
Inflow from infiltration Outflow to water table 
(kg/s) 
Relative error 
(%) 
MDL-NBS-HS-000006 REV01 
Parameter Set/ 
Calibration 
(Output-DTN: LB03013DSSCP3I.001) 
Parameter set from Table I-3, 
lower-bound infiltration 
Parameter set from Table I-1, 
present day/modern, mean infiltration 
Parameter set from Table I-2, 
upper- bound infiltration 
Parameter set from Table I-3, 
lower-bound/present-day infiltration 
Parameter set from Table I-1, 
present day/modern, mean infiltration 
Parameter set from Table I-2, 
upper-bound infiltration 
Parameter set from Table I-3, 
lower-bound/present-day infiltration 
Parameter set from Table I-1, 
present day/modern, mean infiltration 
Parameter set from Table I-2, 
upper-bound infiltration 
56 
U0050 
Infiltration Map 
DTN: GS000308311221.005 [147613] 
Present-day, lower-bound infiltration 
Present-day, mean infiltration 
Present-day, upper-bound infiltration 
Monsoon, lower- bound infiltration 
Monsoon, mean infiltration 
Monsoon, upper-bound infiltration 
Glacial transition, lower-bound 
infiltration 
Glacial transition, mean infiltration 
Glacial transition, upper-bound 
infiltration 
August 2003 U0050 UZ Flow Models and Submodels 
Model Calibrations and Results: As listed in Table 6.2-9, there are nine scenarios for model 
calibrations, sensitivity analyses as well as flow fields, covering nine infiltration rates for three 
climates. The nine simulations have been calibrated against the field-observed data of perched 
water. In addition, the observed matrix liquid saturations and water potentials (when available) 
are also used to examine these modeling results. A perched-water body is defined as fully liquid 
saturated gridblocks with zero capillary pressure for calibration. The data source used in the 
calibrations are listed in Section 4-1. Note that only in situ measurement water potentials among 
the water-potential data are used. In this section, the simulation results are presented and 
discussed in terms of (1) comparisons with matrix liquid saturation, water potential, and 
perched-water data, (2) examination of simulated perched-water bodies, and (3) examination of 
simulated percolation flux and fracture-matrix flow components. 
All nine simulations are checked against observed saturation, water potential, and perched-water 
data. (See Wang 2003 [162417], SN-LBNL-SCI-199-V1, pp. 131157, 242243, for detailed 
comparisons of the saturation and potential profiles of all boreholes evaluated by the model and 
for mass balance results of the simulations.) However, only a few of these comparisons are 
shown in the report, and boreholes UZ-14 and SD-12 are selected to show the match between 
observed and modeled vertical-saturation profiles and perched-water locations for six 
simulations with perched-water occurrence. Matches to other borehole data are similar. Note that 
most borehole observation data used in this section and the following sections are given relative 
to depth. In plots of this Model Report, we use elevations to show model results and 
comparisons. Attachment II lists the surface elevations and coordinates of selected boreholes for 
conversion from depth to elevation. 
Here and in Section 6.6, higher future infiltration scenarios of monsoon and glacial transition, 
used in calibration and sensitivity analysis, are not intended to represent future climates. Instead, 
they are used to assess uncertainties with possible historical high infiltration and its impact on 
the current UZ condition. In addition, such simulation results quantify the range of variation in 
UZ conditions that spans the range of future climates, and are not expected to match measured 
data. In particular, the calibrated mean, lower and upper bound model properties have not been 
adjusted to match conditions generated by future climate infiltration rates. 
Comparisons with Liquid Saturation, Water Potential and Perched-Water Data: Measured 
matrix liquid saturation, water-saturation data and perched-water elevations are compared 
against 3-D model results from the nine simulations. Matrix liquid saturation, water potential, 
and perched-water data used for comparisons are taken from nine boreholes (NRG-7a, SD-6, 
SD-7, SD-9, SD-12, UZ-14, UZ#16, WT-24 and G-2). The locations of these boreholes are 
shown in Figure 6.1-1. 
The comparisons of simulated and observed matrix liquid saturations along the vertical column 
representing boreholes UZ-14 and SD-12 are shown, as examples, in Figures 6.2-2 and 6.2-3 
from the UZ flow models with nine infiltration scenarios. Figure 6.2-4 shows comparison with 
water potentials for SD-12. In general, the modeled results from the nine simulations with the 
UZ flow conceptual model are in reasonable agreement with the measured saturation and waterpotential 
profiles, as shown in Figures 6.2-2, 6.2-3, and 6.2-4. It should be mentioned that the 
modeling results from the three lower infiltration scenarios show some differences from or 
compare more poorly than the rest of the model predictions. The differences between simulated 
August 2003 57 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
TCw 
PTn 
TSw 
CHn 
UZ-14 
preq_mA 
monq_mA 
glaq_mA 
Field Data 
Hydro. Unit 
Perched Water
0.5 
Saturation 
Data - DTN: GS960308312312.005 [107230]; MO0109HYMXPROP.001 [155989]; MO0004QGFMPICK.000 [152554]; 
Model Results - DTN: LB03023DSSCP9I.001 
Figure 6.2-2. Comparison to the Simulated and Observed Matrix Liquid Saturations and Perched-Water 
Elevations for Borehole UZ-14, Using the Results of the Simulations with Three Mean 
and observed saturation data, as shown in Figures 6.2-2 and 6.2-3, are primarily caused by 
heterogeneity and grid coarseness. 
1300 
1200 
1100 
1000 
900 
800 
1 0.75 0.25 
700
0 
Infiltration Rates 
Elevation (m) 
August 2003 58 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Elevation (m) 
1300 
1200 
1100 
1000 
900 
800 
DTN: Rousseau et al. 1997 [100178]; MO0109HYMXPROP.001 [155989]; MO0004QGFMPICK.000 [152554]; 
Model Results - DTN: LB03023DSSCP9I.001: 
Figure 6.2-3. Comparison to the Simulated and Observed Matrix Liquid Saturations and Perched-Water 
Elevations for Borehole SD-12, Using the Results of the Simulations with Three Mean 
Saturation 
SD-12 
preq_mA 
monq_mA 
glaq_mA 
Field Data 
Hydro. Unit 
Perched Water 
700
0.0 0.2 0.4 0.6 0.8 1.0 
Infiltration Rates 
CHn 
MDL-NBS-HS-000006 REV01 
U0050 
TCw 
PTn 
TSw 
August 2003 59 UZ Flow Models and Submodels 
1300 
1200 
1100 
1000 
900 
800 
700
0.0 
Logarithm of Water Potential (Pa) 
NOTE: Field data shown in figure are from DTN: LB991091233129.001 [125868], based on measured water 
potentials in DTNs: GS970808312232.005 [105978] and GS980408312232.001 [105982]. 
Figure 6.2-4. Comparison to the Simulated and Averaged Observed Water Potentials and Perched-Water 
Elevations for Borehole SD-12, Using the Results of the Simulations with Three Mean 
August 2003 60 
SD-12
6.0 4.0 
Infiltration Rates 
Also shown in Figures 6.2-2, 6.2-3, and 6.2-4 are the perched-water elevations at the two 
boreholes, indicating a good agreement between observed and simulated data. In addition, each 
of the nine simulations has been compared to perched-water data as observed from the seven 
perched-water boreholes of Table 6.2-1 (See Wang 2003 [162417], SN-LBNL-SCI-199-V1, pp. 
131157, 242243, for detailed comparisons). The results are as follows: 
 Under the present-day, monsoon, or glacial transitional mean infiltration scenarios of the 
three climates (preq_mA, monq_mA, and glaq_mA, Table 6.2-9), the 3-D flowsimulation-
results model generally matches water-perching conditions in the UZ Model 
domain. 
 Under the present-day, monsoon, or glacial transitional upper-bound infiltration 
scenarios (preq_uA, monq_uA, and glaq_uA, Table 6.2-9), the 3-D flow-simulationresults 
model generally reproduces water-perching conditions in the UZ Model domain. 
MDL-NBS-HS-000006 REV01 
Elevation (m) 
U0050 
TCw 
PTn 
preq_uA 
preq_mA 
preq_lA 
monq_uA 
monq_mA 
monq_lA 
glaq_uA 
glaq_mA 
glaq_lA 
Field Data 
Hydro. Unit 
Perched Water 
TSw 
CHn
10.0 8.0 2.0 
DTN: MO0004QGFMPICK.000 [152554]; 
Model Results - DTN: LB03023DSSCP9I.001: U0050 UZ Flow Models and Submodels 
 Under the present-day, lower-bound infiltration scenarios (preq_lA, Table 6.2-9), the 
models in general do not match the perched-water data very well, except UZ-12, 
because of the low percolation fluxes at these borehole locations. While under the 
lower-bound monsoon or glacial transitional infiltration scenarios (monq_lA and 
glaq_lA, Table 6.2-9), the flow model results match well with observed perched-water 
data. 
6.2.6 Features, Events, and Processes 
The following table of features, events, and processes (FEPs) was taken from the LA FEP List 
(DTN: MO0301SEPFEPS1.000 [161496]). This table is somewhat different from the list of 
included FEPs assigned to this AMR in the Technical Work Plan for: Performance Assessment 
Unsaturated Zone (BSC 2002 [160819], Table 2-6). The changes include revision of the FEPs 
organization and description to address The Enhanced Plan for Features Events and Processes 
(FEPs) at Yucca Mountain TDR-WIS-PA-000005 (BSC 2002 [158966], Section 3.2) and the 
KTI Letter Report, Response to Additional Information Needs on TSPAI 2.05 and TSPAI 2.06 
REG-WIS-PA-000003 REV 00 ICN 02 (Freeze 2003 [164178]). 
The results of this model are part of the basis for the treatment of FEPs as discussed in the Total 
System Performance Assessment-License Application Methods and Approach (BSC 2002 
[160146], Section 3.2.2). The cross-reference for each FEP to the relevant sections of this report 
is also given in Table 6.2-11. 
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 6.2-11 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 FEP report as described in Section 1.12.10 of the TWP (BSC 2002 
[160819]). Excluded FEPs are to be documented in the FEP report as described in Section 
1.12.10 of the TWP (BSC 2002 [160819]). Complete or partial treatment of FEPs is provided 
herein. 
August 2003 61 MDL-NBS-HS-000006 REV01 Treatment of FEP in This Model Report Summary 
Description 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in This Model Report 
FEP Name LA FEP 
Number 
Section(s) 
Where FEP 
is 
Addressed 
1.2.02.01.0A Fractures 6.1.5, 6.2.3, Groundwater flow in 
6.2.5, 6.4, 
6.6.3, 6.7.3, 
6.8, 7.4 
1.2.02.02.0A Faults 6.1.5, 6.2.2, Numerous faults of 
6.2.5, 6.6.2, 
6.6.3, 6.7.3 
U0050 
Included. 
the Yucca Mountain This FEP on Fractures is included in UZ process models, 
region and transport and for this model report, is relevant for mountain-scale 
of any released unsaturated zone flow and transport. The UZ Flow Model is 
radionuclides may based on dual-permeability concept with the fractures 
take place along represented by a continuum. The fracture continuum 
fractures. The rate of represents the spatially averaged flow through discrete 
flow and the extent of fractures. The fracture continuum interacts with the matrix 
transport in fractures continuum which represents matrix blocks separated by 
are influenced by fractures. 
characteristics such 
as orientation, 
aperture, asperity, 
fracture length, 
connectivity, and the 
nature of any linings 
or infills. 
Fracture continuum properties include permeability, porosity, 
interface area per unit volume, van Genuchten  and m 
parameters for the saturation-capillary pressure and relative 
permeability functions, and active fracture parameter. These 
parameters and associated range of values are presented in 
Section 4.1 of this report for each UZ Model layer (DTN 
LB0205REVUZPRP.001 [159525] listed in Table 4.1-1). 
Fracture permeability is based on field measurements, which 
integrate the discrete fracture characteristics such as 
orientation, aperture, asperity, fracture length, connectivity, 
and the nature of any linings or infills. Permeabilities and 
other properties are further calibrated as described in the 
Model Reports Calibrated Properties Model (BSC 2003 
[160240]) and Analysis of Hydrologic Properties Data (BSC 
2003 [161773]). 
The fracture continuum properties are used as inputs to the 
UZ Flow Model and their effects are incorporated into the 
output flow fields developed for use in TSPA (output flow 
fields are in DTN LB0305TSPA18FF.001). 
Included. 
The faults are explicitly discretized in the mountain-scale flow 
and transport models described in this Model Report for the 
unsaturated zone. The major faults are represented in the 
UZ Model Grid as vertical or inclined discrete zones 30 
meters wide. Specific hydrogeological properties are 
assigned to the fault zones. 
Fault properties (matrix and fracture parameters) are in DTN: 
LB02092DSSCFPR.002 [162128] as listed in Table 4.1-1 of 
this AMR. These properties have been calibrated as 
described in the Model Reports Calibrated Properties Model 
(BSC 2003 [160240]) and Analysis of Hydrologic Properties 
Data (BSC 2003 [161773]). 
The fault properties are used as inputs to the UZ Flow Model 
and their effects are incorporated into the output flow fields 
developed for use in TSPA (output flow fields are in DTN 
LB0305TSPA18FF.001). 
various sizes have 
been noted in the 
Yucca Mountain 
Region and in the 
repository area 
specifically. Faults 
may represent (1) an 
alteration of the rock 
permeability and 
continuity of the rock 
mass, (2) alteration or 
short-circuiting of the 
flow paths and flow 
distributions close to 
the repository, and (3) 
represent unexpected 
pathways through the 
repository. 
August 2003 62 MDL-NBS-HS-000006 REV01 Treatment of FEP in this Model Report Summary 
Description 
local climate. Climate 
is typically 
characterized by 
temporal variations in 
precipitation and 
temperature. 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
1.3.01.00.0A Climate 
change, 
global 
6.1.4, 6.2.5, 
6.5.1, 6.6, 
6.7.2, 6.7.3 
1.3.07.02.0B Water table 6.6.3 
rise affects 
UZ 
Climate change could Included. 
produce increased 
infiltration, leading to 
a rise in the regional 
water table, possibly 
affecting the release 
and exposure from 
the potential 
repository by altering 
flow and transport 
pathways in the UZ. 
A regionally higher 
water table and 
change in UZ flow 
patterns might flood 
the potential 
repository. 
U0050 
Climate change may Included. 
affect the long-term 
performance of the Global climate change is addressed in TSPA using a climate 
repository. This model based on paleoclimate information in the Model Report 
includes the effects of Future Climate Analysis (USGS 2001 [158378]). That is, the 
long-term change in 
global climate (e.g., 
glacial/interglacial 
cycles) and shorterterm 
change in 
regional and 
record of climate changes in the past is used to predict the 
expected changes in climate for the future. Future climates 
are described in terms of discrete climate states that are used 
to approximate continuous variations in climate. The effects 
of seasonality are included in the climate model through the 
use of climate analogs with specific seasonal meteorological 
records. More specific information about the methods used to 
predict future climate change and the findings for the climate 
model are given in USGS (2001 [158378], Section 6). Climate 
modeling is incorporated into TSPA through the unsaturated 
zone flow fields that have different surface water infiltration as 
a result of different climates. A description of the modeling 
methods used for infiltration and how infiltration is affected by 
climate is given in USGS (2001 [160355], Section 6). The 
unsaturated zone flow model, which uses the infiltration 
results as upper boundary conditions for unsaturated zone 
flow calculations, is described in the UZ Flow Model of this 
AMR. The incorporation of unsaturated zone flow fields of this 
Model Report (DTN LB0305TSPA18FF.001) into the TSPA is 
done by FEHM software (BSC 2002 [160146], p.13). 
The potential for water table rise due to climate change is 
included in TSPA calculations using a water table rise model 
(Forester et al. 1999 [109425], pp. 46, 56) based on 
paleoclimate data. The paleoclimate data indicates that the 
historical water table has never risen to the level of the 
potential repository (Forester et al. 1999 [109425], pp. 46, 
56). 
Water table changes are implemented in the TSPA by 
allowing the water table to change elevation upon change in 
climate (implemented by the post-processor software 
WTRISE (LBNL 2003 [163453]) for radionuclide transport). 
WTRISE allows the user to specify a water table location and 
removes all the particles in the gridblocks below the specified 
water table instantaneously by setting full saturation to the 
submerged gridblocks. A part of the TSPA-LA model, the 
implementation of WTRISE is conditional on climate change, 
water table level is assumed. 
August 2003 63 MDL-NBS-HS-000006 REV01 Treatment of FEP in this Model Report Summary 
Description 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
6.1.4, 6.2.5, 
6.6.2, 6.6.3, 
6.7.2, 6.7.3 
1.4.01.01.0A Climate 
modification 
increases 
recharge 
6.7.3 
2.1.08.01.0A Water influx 6.2.5, 6.6.3, 
at the 
repository 
U0050 
Climate modification Included. 
(natural or artificial) The effects of climate changes (BSC 2002 [160146], Section 
causes an increase in 4.1) on unsaturated zone flux through the potential repository 
recharge in the Yucca are incorporated through the explicit simulations of UZ flow 
Mountain region. fields corresponding to the upper-bound, mean, and lower- 
Increased recharge bound infiltrations of three distinct different climates: presentmight 
lead to day, monsoon, and glacial-transition. The 9 base-case flow 
increased flux through fields and 9 alternative flow fields are presented in Section 
the repository, 
perched water, or 
water table rise. 
6.6 of this Model Report. The output flow fields are in DTN: 
LB0305TSPA18FF.001, developed for use in Performance 
Assessment (BSC 2002 [160146]; BSC 2001 [158726], 
Section 6.6). 
Above the repository, no perched water bodies were 
observed in the fields and predicted by the UZ Flow Model. 
The potential effect of perched water above the repository is 
indirectly related to lateral diversion of percolation flux in the 
PTn above the repository. PTn effects on the flow field are 
discussed in Section 6.6 of this Model Report. 
The potential for water table rise due to climate change is 
included in TSPA calculations, using the water table rise 
model (implemented by software WTRISE (LBNL 2003 
[163453], also see FEP 1.3.07.02.0B of this table) based on 
paleoclimate data (USGS 2001 [158378], Section 6.2). 
An increase in the Included. 
unsaturated water flux This FEP is considered to be included implicitly in the TSPAat 
the repository LA. Changes in unsaturated zone flow in response to climate 
affects thermal, changes are incorporated in the output flow fields developed 
hydrological, for use in the TSPA (output flow fields are in DTN 
chemical, and LB0305TSPA18FF.001). Furthermore, the outputs from this 
mechanical behavior model AMR are also used by other models and evaluations 
of the system. that are intermediate between this model and the TSPA-LA 
Increases in flux model. 
could result from 
climate change, but 
the cause of the 
increase is not an 
essential part of the 
FEP. 
The thermal model output from this AMR is used for setting 
initial conditions for the downstream mountain-scale coupled 
process evaluation. The effects of changes in unsaturated 
zone flow due to climate change are also included in the 
calculations for the thermal-hydrological behavior of the 
potential repository system (BSC 2001 [158204], Section 
6).The effects of transient flow driven by thermo-hydrological 
processes are also included in TSPA calculations for drift 
seepage in the Model Report Abstraction of Drift Seepage, 
MDL-NBS-HS-000019 (BSC 2003 [162268]). The effects of 
THC and THM on seepage are also addressed in the 
seepage abstraction report. 
Also, the fluxes from the output flow fields of this AMR serve 
as input to flow focusing estimation at drift scale as 
addressed in the Model Report Abstraction of Drift Seepage, 
MDL-NBS-HS-000019 (BSC 2003 [162268]) (Note: The flow 
focusing effects occurs at a scale less than the gridblock 
scale in the UZ Flow Model grid and are, therefore, not 
distinguishable in the results of this Model Report). In the 
seepage abstraction, a probabilistic approach is used to 
account for the spatial and temporal variability and inherent 
uncertainty of seepage-relevant properties and processes 
(BSC 2003 [162268], Section 6.5). In Abstraction of Drift 
Seepage, Section 6.5 provides the steps needed to relate 
site-scale percolation results (based on tuff layer 
representation in the UZ Flow Model) to drift-scale seepage 
calculations, with spatial heterogeneity explicitly taken into 
account) (also see FEP 2.2.07.04.0A of this table). 
August 2003 64 MDL-NBS-HS-000006 REV01 Summary 
Description 
Included. 
information is This FEP on Stratigraphy is included in the UZ Flow 
necessary Model of this AMR by use of the grids developed with the 
information for the information contained in the Geological Framework Model 
Performance (GFM2000; MO0012MWDGFM02.002 [153777]). The 
Assessment. This stratigraphic unit and layers are developed into a model 
information should grid in the Model Report Development of Numerical Grids 
include identification for UZ Flow and Transport Modeling (BSC 2003 [160109]). 
of the relevant rock 
units, soils and In as much as the assignment of hydrologic properties is 
alluvium, and their associated with the grid used for the UZ flow model, the 
thickness, lateral stratigraphy information is implicitly embedded in the TSPA 
extent, and through the output flow fields. Aspects that affect 
relationship to each hydrogeologic properties for flow are further discussed in 
other. Major BSC (2003 [160109], Section 6 and BSC 2003 [160240], 
discontinuities Section 6). See also the TSPA Disposition for FEP 
should be identified. 2.2.03.02.0A in this table. 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
2.2.03.01.0A Stratigraphy 6.1.1, 6.1.2 Stratigraphic 
2.2.03.02.0A Rock 6.1.5, 6.2.3, Physical properties 
properties of 6.4.2 
host rock 
and other 
units 
Included. 
such as porosity and This FEP is similar to FEP 2.2.03.01.00 on stratigraphy. 
permeability of the Rock properties are used define for each of the 
relevant rock units, stratigraphic units/layers classified in the Geological 
soils, and alluvium Framework Model (GFM2000; MO0012MWDGFM02.002 
are necessary for [153777]), which is further developed into model grid in the 
the performance Model Report Development of Numerical Grids for UZ Flow 
assessment. and Transport Modeling (BSC 2003 [160109]). 
Possible 
heterogeneities in For the UZ Flow Model described in this AMR, 
these properties heterogeneity is modeled in terms of the sequence of 
should be hydrogeologic units and discrete faults (BSC 2001 
considered. [158726], Section 6). Therefore, rock properties are 
Questions implicitly embedded in the TSPA through the output flow 
concerning events fields, with site-scale layering and faults explicitly taken 
and processes that into account. 
may cause these On the drift scale, the effects of rock heterogeneity on 
physical properties seepage is discussed in the Model Report Abstraction of 
to change over time Drift Seepage, MDL-NBS-HS-000019 (BSC 2003 
are considered in [162268]). 
other FEPs. 
65 MDL-NBS-HS-000006 REV01 
U0050 
Treatment of FEP in this Model Report 
August 2003 U0050 
Treatment of FEP in this Model Report Summary Description 
Groundwater flow Included. 
occurs in unsaturated This FEP is included in the unsaturated zone process 
rocks in most model for mountain-scale flow of this AMR and for drift 
locations above the seepage in the Model Report Abstraction of Drift Seepage, 
water table at Yucca MDL-NBS-HS-000019 (BSC 2003 [162268]). The UZ 
Mountain, including Flow Model in this AMR is for three-dimensional, steady 
the location of the flow in a heterogeneous dual-permeability system 
repository. See other including discrete fault zones. The flow fields (DTN 
FEPs for discussions LB0305TSPA18FF.001) generated by the UZ Flow Model 
of specific issues of this AMR are used directly by the TSPA and are also 
related to unsaturated implicitly included in the TSPA via the abstractions for drift 
flow. See related seepage and radionuclide transport simulations. These 
FEPs 2.2.07.03.0A models and abstractions use a quasi-steady flow-field 
(capillary rise), approximation for climate change (BSC 2002 [160146]). 
2.2.07.04.0A The effects of soil depth on unsaturated zone flow at 
(focusing of Yucca Mountain are included in the infiltration model 
unsaturated flow), (USGS 2001 [160355], Section 6). 
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 (twophase 
flow/heat 
pipes). 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
6.7 
2.2.07.02.0A Unsaturated 6.2, 6.6, 
groundwater 
flow in 
the 
geosphere 
6.1.3 2.2.07.03.0A Capillary 
rise in the 
UZ 
Capillary rise involves Included. 
the drawing up of Capillary forces are included in the UZ Flow Model of this 
water, above the AMR. These forces affect the distribution of water in the 
water table or above unsaturated zone. Parameters used for capillarity 
locally saturated modeling are incorporated within the matrix properties 
zones, in continuous (DTN LB02091DSSCP3I.002 [161433]) and fracture 
pores of the properties (DTN LB0205REVUZPRP.001 [159525) as 
unsaturated zone, described in Section 4.1 and Table 4.1-1 of this report. 
until the suction These parameters are used as direct input to the UZ Flow 
gradient is balanced Model and are incorporated into the output flow fields used 
by the gravitational in the TSPA. 
pull downward. 
Capillary rise may 
provide a mechanism 
for radionuclides to 
reach the surface 
environment in 
locations where the 
water table is shallow. 
August 2003 66 MDL-NBS-HS-000006 REV01 Treatment of FEP in this Model Report Summary Description 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
2.2.07.04.0A Focusing of 6.1.2, 6.2.5, Unsaturated flow can 
unsaturated 
flow (fingers, 
weeps) 
6.2.2 2.2.07.05.0A Flow in the 
UZ from 
episodic 
infiltration 
U0050 
Included. 
6.6.3, 6.7.3 differentiate into zones The UZ flow fields resulting from this Model Report represent 
of greater and lower the redistribution of infiltration through UZ layers, with faults 
saturation (fingers) that explicitly taken into account. The flux redistribution based on 
may persist as tuff layer properties including fracture and matrix interaction as 
preferential flow paths. represented in the UZ Flow Model is discussed in detail in 
Heterogeneities in rock Section 6.6 of this Model Report. Faults are included in the UZ 
properties, including Flow Model in this AMR as discrete features; therefore, flow in 
fractures and faults, faults is also included in the UZ Flow Model (this Model Report 
may contribute to and CRWMS M&O 2000 [123913], Section 6). 
focusing. Focused flow 
may become locally 
saturated. 
Also, the fluxes from the output flow fields of this AMR serve 
as input to flow focusing estimation at drift scale as 
addressed in the Model Report Abstraction of Drift Seepage, 
MDL-NBS-HS-000019 (BSC 2003 [162268]) (Note: The flow 
focusing effects occurs at a scale less than the gridblock 
scale in the UZ Flow Model grid and are, therefore, not 
distinguishable in the results of this Model Report). In the 
seepage abstraction, a probabilistic approach is used to 
account for the spatial and temporal variability and inherent 
uncertainty of seepage-relevant properties and processes 
(BSC 2003 [162268], Section 6.5). In Abstraction of Drift 
Seepage, Section 6.5 provides the steps needed to relate 
site-scale percolation results (based on tuff layer 
representation in the UZ Flow Model) to drift-scale seepage 
calculations, with spatial heterogeneity explicitly taken into 
account) (also see FEP 2.1.08.01.0A of this table). 
Episodic flow occurs in Screening Decision and Regulatory Basis: Excluded. 
the UZ as a result of 
episodic infiltration. Screening Argument: The process that drives infiltration in 
See also FEP the unsaturated zone is precipitation, which is episodic in 
2.2.07.02.0A nature. Studies of episodic infiltration and percolation have 
(unsaturated found, however, that matrix-dominated flow in the PTn damps 
groundwater flow), out the transient nature of the percolation such that 
2.3.11.03.0A unsaturated zone flow below the PTn is essentially steady 
(infiltration), (CRWMS M&O 1998 [100356], Section 2.4.2.8). Furthermore, 
2.2.07.04.0A (focusing the PTn is found over the entire repository block (in 
of UZ flow), and Underground Layout Configuration BSC 2003 [164325]). This 
1.3.01.00.0A (climate damping of transient flow is due to capillary forces and high 
change). Episodic flow matrix permeability in the PTn that lead to matrix imbibition of 
may affect transport; water from fractures. Therefore, this FEP is excluded, because 
for example, colloidal the unsaturated zone flow is steady at the repository and along 
transport may be radionuclide transport pathways. 
enhanced by episodic 
flow (FEP 
2.2.08.10.0A). 
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). 
August 2003 67 MDL-NBS-HS-000006 REV01 Summary 
Description 
Episodic or pulse 
release of 
radionuclides from the 
repository and 
radionuclide transport 
in the UZ may occur, 
both because of 
episodic flow into the 
repository (see FEP 
2.2.07.05.0A), and 
because of pulse 
releases from failed 
waste packages. 
Zones of perched 
water may develop 
above the water table. 
If these zones occur 
above the repository, 
they may affect UZ 
flow between the 
surface and the waste 
packages. If they 
develop below the 
repository, for 
example at the base 
of the Topopah 
Spring welded unit, 
they may affect flow 
pathways and 
radionuclide transport 
between the waste 
packages and the 
saturated zone. 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
2.2.07.06.0A Episodic / 
pulse 
release from 
repository 
2.2.07.07.0A Perched 
water 
develops 
6.2.2, 6.2.3, 
6.2.5, 6.6.2, 
6.6.3 
6.6.3 
2.2.07.08.0A Fracture flow 6.2.5, 6.6.2, 
in the UZ 
Fractures or other Included. 
analogous channels 
act as conduits for This FEP on Fracture Flow is included in UZ process 
fluids to move into the models for mountain-scale unsaturated zone flow and 
subsurface to interact transport. The UZ Flow Model is based on dual-permeability 
with the repository concept with the fractures represented by a continuum. The 
and as conduits for fracture continuum represents the spatially averaged flow 
fluids to leave the through discrete fractures. The fracture continuum interacts 
vicinity of the with the matrix continuum which represents matrix blocks 
repository and be separated by fractures. 
conducted to the Fracture continuum properties include permeability, porosity, 
saturated zone. Water interface area per unit volume, van Genuchten  and m 
may flow through only 
a portion of the 
fracture network, 
including flow through 
a restricted portion of 
a given fracture 
plane. 
U0050 
The effects of intermittent waste package failure are included 
in the source term model for TSPA (BSC 2001 [155638], 
Section 6 and BSC 2002 [160146], Sec. 5.1, p. 69). This is 
done by modeling the environmental conditions of the waste 
packages in different parts of the repository and by modeling 
corrosion processes under the environmental conditions that 
lead to waste package failure (BSC 2002 [160146]). The 
effects of episodic flow at the mountain scale are excluded on 
the basis of low consequence, as discussed in FEP 
2.2.07.05.0A of this Table. 
The seepage abstraction model contains a wide range of 
seepage possibilities, including flow focusing and variability 
(CRWMS M&O 2001 [154291], Section 6). Therefore, the 
potential for effects of perched water above the repository are 
indirectly captured in the seepage abstraction model through 
cases with high percolation flux (DTN 
LB0305TSPA18FF.001), as described in the Model Report 
Abstraction of Drift Seepage, MDL-NBS-HS-000019 (BSC 
2003 [162268]). However, above the repository, no perched 
water bodies were observed in the fields predicted by the UZ 
Flow Model. The effects of existing perched water zones 
below the repository are also included, and potential changes 
in these perched-water zones due to climate changes are 
also included in the mountain-scale unsaturated zone flow 
model of this AMR (BSC 2001 [158726], Section 6). The 
potential for this effect is capture in the output flow fields 
developed for use in TSPA (output flow fields are in DTN 
LB0305TSPA18FF.001). 
parameters for the saturation-capillary pressure and relative 
permeability functions, and active fracture parameter are 
presented in Section 4.1 of this report for each UZ Model 
layer (DTN: LB0205REVUZPRP.001 [159525] listed in Table 
4.1-1). Permeabilities and other properties are further 
calibrated as described in the Model Reports Calibrated 
Properties Model (BSC 2003 [160240]) and Analysis of 
Hydrologic Properties Data (BSC 2003 [161773]). 
The fracture continuum properties are used as inputs to the 
UZ Flow Model and their effects are incorporated into the 
output flow fields developed for use in TSPA (output flow 
fields are in DTN LB0305TSPA18FF.001). See also FEPs 
1.2.02.01.0A and 2.2.07.02.0A of this table). 
68 MDL-NBS-HS-000006 REV01 
Treatment of FEP in this Model Report 
Included. 
Included. 
August 2003 Summary 
Description 
channels in the 
unsaturated zone 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. 
down Solitario 
Canyon Fault could 
enter waste 
emplacement drifts 
through lateral flow 
mechanisms in the 
Topopah Spring 
welded hydrogeologic 
unit. This percolation 
pathway is more likely 
to transmit episodic 
transient flow to 
waste emplacement 
locations due to the 
major fault pathway 
through the overlying 
units. 
UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP 
is 
Addressed 
6.2.5, 6.6.2, Water flowing in 
6.6.3, 7.6.3.2 fractures or other 
2.2.07.09.0A Matrix 
imbibition in 
the UZ 
2.2.07.19.0A Lateral flow 6.6.3 Water movement Included. 
from Solitario 
Canyon fault 
enters drift 
2.2.08.05.0A Diffusion in 6.5, 6.7.2, 
the UZ 6.7.3, 6.8.2, 
7.5 
Molecular diffusion 
processes may affect 
radionuclide transport 
in the UZ. This 
includes osmotic 
processes in 
response to chemical 
gradients. Discussion 
of diffusion in the drift 
shadow is addressed 
in FEP 2.2.07.21.0A. 
69 MDL-NBS-HS-000006 REV01 
Treatment of FEP in this Model Report 
Included. 
Included. 
U0050 
Matrix imbibition is included in the process model for 
unsaturated zone flow at the mountain scale (CRWMS M&O 
2000 [141187], Section 6). Matrix imbibition refers to the 
movement of water into the matrix due to capillary forces. 
This process affects the distribution of flow between fractures 
and matrix in a dual-permeability flow model for fractured 
rock. The influence of matrix imbibition on episodic flow is 
discussed in Section 6.3.4 (FEP 2.2.07.05.0A of this table). 
Imbibition is captured in the UZ Flow Model through capillarity 
modeling, which again uses matrix and fracture properties as 
model input. Therefore, the effect of imbibition is implicitly 
incorporated in the output flow fields (DTN 
LB0305TSPA18FF.001) used in the TSPA. 
The UZ Flow Model in this AMR contains potential 
hydrogeological connections between Solitario Canyon Fault 
and the waste emplacement horizon. The potential 
connection is captured using a property set of the PTn unit 
(Sections 6.2.2.1, 6.2.3 and 6.6 of this AMR) with calibrated 
fracture-matrix properties that favor later flow. Therefore, flow 
from this fault to waste emplacement locations is addressed. 
This water may seep into waste emplacement drifts if the flux 
is sufficient to overcome the capillary barrier represented in 
the Seepage Calibration Model and Seepage Testing Data 
(BSC 2003 [162267]). The lateral flow effect is implicitly 
incorporated in the output flow fields (DTN 
LB0305TSPA18FF.001) used in the TSPA. 
Diffusion of radionuclides between the fracture and matrix 
continua and partitioning through diffusion for radionuclides 
(released from drifts) between fractures and matrix are 
processes that are represented in the unsaturated zone 
radionuclide transport model (in process) and (BSC 2003 
[163938], 6.4.1). The diffusion model used is based on a 
dual-porosity formulation in which the matrix water is 
stagnant. The abstraction for matrix diffusion includes the 
effects of partial saturation of the matrix, radionuclide sorption 
in the matrix, and finite spacing of fractures. Osmosis would 
tend to cause water from fractures to flow into the matrix, if 
the matrix presents a suitable barrier to the migration of 
dissolved salts. The UZ Flow Model in this AMR does not 
directly address the diffusion issue. However, the model flow 
fields and parameters (porosity) are used in the downstream 
transport Model Report for diffusion modeling (in process). 
Therefore, diffusion is indirectly and implicitly included in the 
flow fields (DTN LB0305TSPA18FF.001) used in the TSPA. 
More discussion on diffusion is referred to FEPs. Matrix 
Diffusion in the UZ (2.2.08.08.0B) and Diffusion in the UZ 
(2.2.08.05.0A). 
August 2003 UZ Flow Models and Submodels 
Table 6.2-11. FEPs Addressed in this Model Report (Continued) 
FEP Name LA FEP 
Number 
Section 
Where FEP is 
Addressed 
2.2.10.03.0B Natural 
geothermal 
effects on 
flow in the 
UZ 
6.3.4 The existing Included. 
geothermal gradient, 
and spatial or 
temporal variability in 
that gradient, may 
affect groundwater 
flow in the UZ. 
2.3.11.01.0A Precipitation 6.5 
6.1.4, 6.5, 
6.6.1, 6.7.2 
2.3.11.03.0A Infiltration 
and 
recharge 
MDL-NBS-HS-000006 REV01 
Summary 
Description 
Precipitation is an 
important control on 
the amount of 
recharge. It transports 
solutes with it as it 
flows downward 
through the 
subsurface or 
escapes as runoff. 
The amount of 
precipitation depends 
on climate. 
Infiltration into the 
subsurface provides a 
boundary condition 
for groundwater flow. 
The amount and 
location of the 
infiltration influences 
the hydraulic gradient 
and the height of the 
water table. Different 
sources of recharge 
water could change 
the chemistry of 
groundwater passing 
through the 
repository. Mixing of 
these waters with 
other ground waters 
could result in 
precipitation, 
dissolution, and 
altered chemical 
gradients. 
70 
Treatment of FEP in this Model Report 
Included. 
Included. 
U0050 
Natural geothermal effects are included in the models of 
thermo-hydrological processes used to describe the effects 
of waste heat in the potential repository (BSC 2001 
[158204], Sections 6.3.3, 6.4.3, 6.5.3) The thermalhydrologic 
models contain the natural geothermal gradient 
in its initialization. This gradient is determined by the ground 
surface temperature, the water table temperature, and the 
thermal conductivity from layer to layer. The results of these 
models are used in the TSPA through abstraction of drift 
thermodynamic environment and percolation flux (CRWMS 
M&O 2001 [154594]). They are also used as boundary 
conditions for the mountain-scale coupled process Model 
Report (in process). 
Precipitation affects the net infiltration, as discussed in 
Simulation of Net Infiltration for Modern and Potential Future 
Climates (USGS 2001 [160355]). The net infiltration map 
outputs (DTN: GS000308311221.005 [147613]) are used as 
a boundary condition for the UZ Flow Model of this AMR 
(Sections 6.1.3 and 6.1.4). Flow fields developed for use in 
TSPA (DTN LB0305TSPA18FF.001) using the UZ Flow 
Model therefore include the effects of precipitation and 
changes of precipitation under future climate conditions, 
including low, mean, and upper bounds of infiltrations in 
glacial, monsoon, and present (or modern) climatic 
scenarios. 
The hydrological effects of infiltration and recharge are 
included in the infiltration model. This model includes the 
effects of seasonal and climate variations, climate change, 
surface-water runoff, and site topology such as hillslopes 
and washes (USGS 2001 [160355], Sections 6.4 and 6.5). 
This is incorporated into the TSPA through the unsaturated 
zone flow fields that use the infiltration model results (DTN 
GS000308311221.005 [147613]) as upper boundary 
conditions (BSC 2001 [158726], Section 6). (Flow fields of 
this AMR for TSPA-LA are in DTN LB0305TSPA18FF.001). 
The effects of chemistry of present-day water infiltrating 
from the ground surface are accounted for in the analysis of 
seepage water chemistry by using the measured pore-water 
chemistry in the unsaturated zone (BSC 2002 [158375], 
Section 6.1.2). 
August 2003 U0050 UZ Flow Models and Submodels 
6.3 TEMPERATURE CALIBRATION 
Prior to performing ambient, thermal-hydrological (TH) studies of the UZ system as well as 
repository performance studies under thermal-loading conditions, the ambient temperature, 
percolation and moisture distributions are first needed. The ambient geothermal and moisture 
conditions serve as the initial and boundary conditions of a thermal model. This section 
describes a 3-D ambient geothermal submodel of the UZ Model developed to evaluate steadystate, 
ambient thermal and moisture conditions of the UZ system for use in various scale TH 
modeling studies. Subsequent temperature calibration then provides an independent examination 
of percolation fluxes simulated by the UZ Flow Model. This is because the ambient temperature 
distribution within the UZ is related to percolation fluxes or infiltration rates (Bodvarsson et al. 
2003 [162477]). By matching borehole temperature measurements, the TH model helps to 
constrain infiltration rate ranges as well as fracture-matrix parameter values. 
6.3.1 3-D Thermal Model Grid 
For thermal calibration as well as the gas flow calibration described in the next section, a new 3- 
D grid (Figure 6.3-1) smaller than the TSPA-LA grid (Figure 6.1-1) is developed. This grid is 
designed to relax intensive computational burdens needed in thermal modeling studies using a 3- 
D dual-permeability grid. The thermal model domain is selected to focus on geothermal 
conditions and thermal-loading effects at and near the repository area. The model domain is 
considered to provide sufficient accuracy for such studies because of the small thermal impact 
expected in the lateral directions. 
This 3-D grid, featuring a smaller model domain than that of the UZ Flow Model (Figure 6.1-1), 
is referred to as the 3-D thermal model grid. As shown in the plan view of Figure 6.3-1, the 
thermal model grid domain covers approximately 20 km2 of the area. Similar to the TSPA-LA 
grid of Figure 6.1-1, the thermal model grid (Figure 6.3-1) also uses a refined mesh in the 
vicinity of the repository and includes the locations of several boreholes used in temperature 
calibrations and analyses. In particular, the thermal grid explicitly incorporates every repository 
drift by taking into account orientations, lengths, elevations, and spacings of the drifts. A grid 
spacing of 81 m is used in the direction perpendicular to drifts, such that each individual drift 
segment can be inserted into the 3-D thermal grid for thermal-loading studies in a different 
report as documented in the TWP (BSC 2002 [160819], Section 1.12.7). In the model, faults are 
also represented in the model by vertical or inclined 30 m wide zones. 
August 2003 71 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
236000 
235000 
234000 
233000 
232000 
231000 
230000 
Figure 6.3-1. Plan View of the 3-D Thermal Model Grid, Showing the Model Domain, Faults 
Incorporated, Several Borehole Locations, and TH Model Boundaries 
The thermal model grid of Figure 6.3-1 consists of 980 mesh columns of both fracture and 
matrix continua, 86,440 gridblocks, and 343,520 connections in a dual-permeability grid. 
Vertically, the thermal grid has an average of 45 computational grid layers. 
6.3.2 Top Boundary Temperature 
To account for variations in atmospheric temperature with surface elevations in the mountain, 
measured mean surface temperatures and a linear equation that correlates surface temperature 
with elevation are used. The annual-average temperature was measured for near-surface sensors 
in boreholes NRG-6 and NRG-7a (DTN: GS960308312232.001 [105573], 
GS951108312232.008 [106756], and GS950208312232.003 [105572]), with several years of 
continuous temperature monitoring data. The surface temperatures T 
MDL-NBS-HS-000006 REV01 
s at any elevation Z are then 
72 
Nevada Coordinate E-W (m) 
Output-DTN: LB0303THERMESH.001 
Nevada Coordinate N-S (m) 
Drillhole 
Wash 
Fault 
S 
olitario 
Canyon 
Fault 
H-5 
170000 
Repository Block 
Pagany 
Wash 
Fault 
Sever 
Wash 
Fault 
WT-18 
NRG-7A UZ#4 
UZ#5 
NRG-6 
Ghost 
Dance 
Fault 
SD-12 H-4 
Imbricate 
Fault 
UZ-7 
SD-7 
172000 
U0050 
174000 
August 2003 U0050 UZ Flow Models and Submodels 
computed using the routine toptemp_v0.f V1.0 (LBNL 2000 [147030]) and are treated as 
constants according to the following equation (Wu et al. 1999 [117161], Equation 4): 
[Z . Z ] (Eq. 6.3-1) T T = ref ref s 
6.3.3 Bottom Boundary Temperature 
 . 
where Tref is mean surface temperature at reference elevation Zref and  is the dry adiabatic 
atmospheric lapse rate in oC/m. This lapse is 0.01oC/m (Driscoll 1986 [116801], p. 50). In this 
formulation, the surface reference temperature used is 18.23oC at an elevation of 1,231.0 m, 
averaged using measured data from borehole NRG-6. The averaged temperature measurement of 
NRG-7a at an elevation of 1,282.2 m is 17.78oC. The calculated mean lapse rate, based on these 
field measurements, is 0.009oC/m. 
The initial estimates of temperature distributions at the bottom boundary of the TH were taken 
from BSC (2001 [158726]). For that report, an effort was made to obtain accurate bottomtemperature 
boundary conditions for use in thermal-hydrological simulations. Following that 
work, the software routine of get_temp_v0.f V1.0 (LBNL 2000 [147027]) was used to estimate 
temperatures at a flat surface of an elevation of 730 m. Because the water table is no longer flat 
with the current UZ and TH models, the actual estimates of the water table or bottom-modelboundary 
temperatures were interpolated between the values at 730 m elevation and the model 
surface boundary. Several non-Q measured temperature profiles (Sass et al. 1988 [100644]) were 
used as corroborative data (BSC 2001 [158726]) for an initial guess of the water-table-boundary 
temperature contours. In this Model Report, initially estimated water table temperatures are 
examined against the qualified temperature data in boreholes NRG-6, NRG-7a, SD-12 UZ#4, 
UZ#5 and UZ-7a (DTN: GS950208312232.003 [105572] and DTNs: GS970808312232.005 
[105978], GS971108312232.007 [105980], GS960808312232.004 [105974], 
GS970108312232.002 [105975], GS980408312232.001 [105982]). 
6.3.4 Calibration of Ambient Temperatures 
The temperature profiles or geothermal gradients with the UZ system are controlled by several 
factors, such as formation thermal conductivity and net infiltration rates, in addition to the 
regional weather condition. Because of the small impact of uncertainties in measured thermal 
conductivities on simulated heat flow, the temperature calibration may be conducted using either 
ambient infiltration, or model boundary temperatures, or both. In this report, the ambient net 
infiltration rate is fixed as the present-day, mean infiltration rate with a value of 3.6 mm/yr 
within the grid domain (Figure 6.3-1). Temperatures are slightly adjusted from the estimated 
values along the top boundary only, and this results in a better match of observed borehole data. 
The reason behind the adjustment is, first, that insufficient temperature data was collected along 
these boundaries for accurate description of temperature distributions. Second, under steady-state 
moisture and heat flow conditions, both top and bottom boundary temperatures are spatially 
varying constants, which leaves room for adjusting to fit measured steady-state temperature 
profiles from boreholes. 
The ambient temperature condition was calibrated using the 3-D thermal model grid of Figure 
6.3-1 (Output-DTN: LB0303THERMESH.001), a dual-permeability mesh. The simulations 
were performed using TOUGH2 V1.4 (LBNL 2000 [146496]) with the EOS3 module. In 
73 August 2003 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
addition to the prescribed temperature conditions on top and bottom boundaries, the infiltration 
was described using the base-case, present-day, mean infiltration scenario. The model 
incorporated the parameter set of Table I-1 (Output-DTN: LB03013DSSCP3I.001), the thermal 
properties (DTN: LB0210THRMLPRP.001 [160799]), and the calibrated fault properties (DTN: 
LB02092DSSCFPR.002 [162128]). The simulations were all run to steady state for comparisons 
with measured borehole temperatures. 
Table 6.3-1 lists the boreholes with qualified temperature measurements and the corresponding 
column element names used in the 3-D calibration of model ambient temperature. Note that in 
both the 3-D thermal model grid and the TSPA-LA grid, each element name is 8 characters long, 
consisting of numbers, alphabets, or symbols. The last three characters of 8-character names are 
assigned to stand for a vertical column, which are determined uniquely for each vertical grid 
column. As shown in Table 6.3-1, boreholes UZ#4 and UZ#5 are so close to each other that they 
fall into the same grid column. Therefore, we use only UZ#5 for calibrations (i.e., using 
temperature data from 5 of 6 boreholes). The comparison between UZ#4 and UZ#5 is 
documented in Wang (2003 [162417], SN-LBNL-SCI-199-VI, pp. 219, 223). During calibration, 
the corresponding simulated temperature profiles for the boreholes were extracted from the 
TOUGH2 output and then plotted against the measurements along each borehole. 
Figure 6.3-2 shows the final model calibrated results and measured temperature profiles in the 
five temperature boreholes. The figure shows a good match between measured and simulated 
temperatures using the specified boundary conditions and the present-day, mean infiltration rate. 
Near the ground surface in five of the boreholes, observed temperatures show significant 
seasonal variations. However, these seasonal changes in surface temperature have little impact 
on steady-state heat flow or temperature profiles in the deeper (more than 20 m) UZ. 
Table 6.3-1. Temperature Boreholes and Corresponding Element Columns of the Thermal Model Grid 
MDL-NBS-HS-000006 REV01 
Borehole Element Column 
NRG-6 h39 
NRG-7A h40 
SD-12 h44 
UZ#5 h45 
UZ-7a h74 
UZ#4 h45 
Output-DTN: LB0303THERMESH.001 
74 August 2003 
U0050 UZ Flow Models and Submodels 
Figure 6.3-2. Comparisons between Measured and Modeled Ambient Temperature Profiles for the 
Five Boreholes under the Present-Day Mean Infiltration Rate 
Figure 6.3-3 shows the contour plot of calibrated temperature distributions at the water table or 
the model bottom boundary. This temperature distribution is used for thermal simulations in 
which the model boundary temperature is fixed at the water table. Figure 6.3-3 indicates that the 
average temperature at the water table ranges from 27oC to 33oC, and lower temperatures are 
located in the north of the model domain where elevations and percolation fluxes are both 
higher. For the top model boundary, the estimated temperature distributions are shown in Figure 
6.3-4. Based on calibration results, the ambient temperature distribution in the UZ TH model can 
be described to specify steady-state, mountain-scale temperature conditions. 
The UZ flow fields for ambient conditions are not sensitive to temperature distributions. For 
corroborative purpose and to build confidence in the flow field representations, the temperature 
contours in Figure 6.3-3 were developed over the years from all available data. Wu et al. (1999 
MDL-NBS-HS-000006 REV01 75 August 2003 
Elevation (m) Elevation (m) 
1300 
1200 
1100 
1000 
900 
800 
700 
NRG-6 
Simulated 
1/1/1995 
3/31/1995 
5 15 25 35 
700
15 
Temperature (oC) 
1200 
1100 
1000 
900 
800 
700
10 
UZ#5 
Simulated 
1/1/1997 
6/30/1997 
20 30 
Temperature (oC) 
DTNs: GS970808312232.005 [105978]; GS971108312232.007 [105980];GS960808312232.004 [105974]; 
GS970108312232.002 [105975]; GS980408312232.001 [105982] 
Model Results DTN: LB0303THERMSIM.001 
25 
1300 
1200 
1100 
1000 
900 
40 
Elevation (m) 
1300 
1200 
1100 
1400 
1300 
1200 
1100 
1000 
Elevation (m) 
1000 
900 900 
800 800 
700 
35 30 
NRG-7a 
Simulated 
1/1/1997 
6/30/1997 
20 
Temperature (oC) 
UZ-7a 
Simulated 
1/1/1997 
6/30/1997 
5 15 25 35 
Temperature (oC)
Elevation (m) 
800 
700 
U0050 
SD-12 
Simulated 
1/1/1997 
6/30/1997 
5 15 25 35 
Temperature (oC)
U0050 UZ Flow Models and Submodels 
[117161], Figure 12) identified that the data sources were from 25 boreholes documented mainly 
in Sass et al. (1988 [100644]) and observed that, in general, the measured data matched 
reasonably with early 3D model results (Bodvarsson et al. 1997 [100103]; Ahlers et al. 1995 
[101180]). The majority of the early temperature data in Sass et al. (1988 [100644]) are currently 
not qualified. In this report, six qualified data sets of temperature distributions along boreholes 
are shown to be consistent with the water table distribution developed from the more extensive 
data set, demonstrating the consistency of water temperature distribution with unsaturated 
processes. The same extensive data set is also the basis for saturated zone interpretation of 
Fridrich et al. (1994 [100575], p. 133168). Fridrich et al. (1994 [100575], p. 157) discussed the 
heat flow anomalies, upward and downward flows, and the uncertainty of ignoring unsaturatedzone 
processes. The consistency of different saturated zone and unsaturated zone interpretations 
can be further evaluated. 
August 2003 76 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
237000 
236000 
235000 
234000 
233000 
232000 
231000 
230000 
Figure 6.3-3. Ambient Temperature Distributions at the Water Table for the Present-Day Mean 
Infiltration Scenario 
Nevada Coordinate E-W(m) 
Temperature distribution at water table 
Solitario 
Canyon 
Fault 
170000 
Sever 
Wash 
Faul 
t 
Pagany 
Was 
h 
Fault 
Drillhole 
Wash 
Fault 
NRG-7A UZ#4 
NRG-6 
Ghos 
t 
Dance 
Fault 
SD-12 
Imbricate 
Fault 
UZ-7 
SD-7 
174000 172000 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
Model Results DTN: LB0303THERMSIM.001 
Temp (oC) 
33 
32 
32
.5 
31 
31
.5 
30 
30
.5 
29 
29
.5 
28 
28
.5 
27 
27
.5 
26 
26
.5 
25 
25
.5 
77 
U0050 
August 2003 UZ Flow Models and Submodels 
237000 
236000 
235000 
234000 
233000 
232000 
231000 
230000 
Nevada Coordinate E-W (m)
Model Results DTN: LB0303THERMSIM.001 
Figure 6.3-4. Temperature Distributions at the Mountain Surface, the Top Model Boundary, for the 
Present-Day Mean Infiltration Scenario 
Temperature Distribution at Land surface 
SolitarioCanyon 
Fault 
170000 
S 
e 
ver 
Wash 
Fault 
Pagany 
Wash 
Faul 
t 
Drillhole 
Wash 
Fault 
NRG-7A UZ#4 
NRG-6 
Ghost 
D 
ance 
Fault 
SD-12 
Imbricate 
Fault 
UZ-7 
SD-7 
174000 172000 
Temp (oC) 
20 
19. 
19 
5 
18. 
18 
5 
17. 
17 
5 
16. 
16 
5 
15. 
15 
5 
14. 
14 
5 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
78 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
6.4 PNEUMATIC CALIBRATION 
As part of the UZ model calibration effort, 3-D pneumatic simulations are conducted and 
summarized in this section. Calibration of the UZ model to pneumatic data will aid in estimates 
of large-scale fracture permeability for the UZ system. This is particularly useful for modeling 
studies of thermal loading, gas flow and transport of gaseous phase radionuclides for the site 
(Ahlers et al. 1999 [109715]). The results of these gas flow simulations are compared with field 
measured pneumatic data from several boreholes to re-estimate fracture permeability in several 
TSw layers. This section focuses on the model calibration and analysis using these pneumatic 
simulation results. 
6.4.1 Model Parameters and Boundary Conditions 
The 3-D mesh used in this gas flow simulation is the same 3-D thermal grid mesh (Figure 6.3-1), 
used for the thermal simulation. The mesh is described in Section 6.3. The grid domain covers 
approximately 20 km2 of the area, which is smaller than the TSPA-LA grid (Figure 6.1-1). 
Similar to TSPA-LA grid, this grid also uses a finer mesh in the vicinity of the repository area. 
The rock properties used for current 3-D pneumatic prediction are initially those developed using 
1-D models for the present-day mean infiltration scenario (BSC 2003 [160240]; DTN: 
LB02091DSSCP3I.002 [161433]) and 2-D site-scale calibrated fault properties (DTN: 
LB02092DSSCFPR.002 [162128]). In addition, the model incorporated the parameter set of 
Table I-1 (Output-DTN: LB03013DSSCP3I.001), and also the thermal properties (DTN: 
LB0210THRMLPRP.001 [160799]). 
The present-day, mean net infiltration rate (with a value of 3.6 mm/yr for the TH model grid) is 
used to describe the surface infiltration conditions. However, additional pneumatic boundary 
conditions needed on land surface are time-dependent and are specified using the routine 
TBgas3D V2.0 (LBNL 2002 [160107]), based on measured atmospheric barometric pressure 
data (DTN: LB0302AMRU0035.001 [162378]). The bottom water table boundary is treated as a 
Dirichlet-type boundary. The pressure conditions at the bottom boundary are based on measured 
surface pressures for boreholes USW SD-7 and SD-12 (DTN: LB991091233129.001 [125868]). 
First, the average surface pressure and the corresponding pressure at the horizon of 730 m are 
calculated for each borehole. Then, the average of the two subsurface pressures, 92 kPa, is used 
to determine the pressures at the water table boundary as a function of elevation change from 
730 m. All lateral boundaries are treated as no-flow boundaries. The UZ system is set at an 
isothermal condition of 25C, the average of the surface temperature and water table 
temperature. The steady state solution of flow simulation for the present-day mean infiltration 
scenario is taken as the initial moisture condition of the current model. 
6.4.2 Modeling Approach and Calibration 
The 3-D pneumatic simulation is run using EOS3 module of the TOUGH2 code V1.4 (LBNL 
2000 [146496]) by neglecting the influence of liquid phase flow. The impact of liquid phase flow 
to the gas flow system is small for gas-flow simulation results. This was shown by examining the 
simulation results for the single-phase gas and two-phase water-gas flow, in which single-phase 
and two-phase flow simulations produce almost identical results in calculated gas pressures, as 
documented in the Scientific Notebook of Wang (2003 [162417], SN-LBNL-SCI-202-V1, pp. 
August 2003 79 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
8485). The simulation of gas flow by itself is implemented by using the linear relative 
permeability function and choosing appropriate parameters to force the relative permeability of 
liquid phase to equal 0. 
The pneumatic model was calibrated against the field-measured pneumatic data from two 
boreholes. The model calibration results indicated that some modification of rock properties in 
several TSw layers is necessary to match field observed gas pressures. In particular, it was found 
necessary to reduce the fracture permeability of TSw31-TSw37 subunits by a factor of 15. The 
lower fracture permeability for the 3-D model may be attributed to the original fracture 
permeability being estimated from inversion of 1-D models with 1-D vertical flow paths only. In 
a 3-D model, some high flux channels, such as faults, exist, and 3-D gas flow is able to find these 
high-permeability pathways with the least resistance for 3-D gas flow. As a result, the fracture 
permeability of a 3-D model may be lower than that estimated by 1-D models. The differences 
for the inversion of 1-D, 2-D, and 3-D pneumatic models for this site have been discussed by 
Ahlers et al. (1999 [109715]). 
The two boreholes used for calibration are USW SD-7 and SD-12. Table 6.4-1 shows the sensor 
elevations, files for averaged observed data (DTN: LB991091233129.001 [125868], and 
LB02092DSSCFPR.001 [162422]) and observing-date ranges for comparison of these boreholes. 
The table also lists the corresponding mesh cells in the 3-D thermal model grid. For SD-12, data 
of the first 30 days are used for the calibration, and the second 30 days are compared to the 
prediction for validation, as discussed in Section 7.4. 
Table 6.4-1. 
Borehole USW SD-7 (LB991091233129.001 [125868]) 
1271.6 Sd7_300_zone1.txt 4/5-6/4/96 F0003h42 
1256.4 Sd7_350_zone2.txt 4/5-6/4/96 F0008h42 
1241.4 Sd7_400_zone3.txt 4/5-6/4/96 F0010h42 
1119.2 Sd7_800_zone11.txt 4/5-6/4/96 F013Ah42 
Observation Data and Corresponding Grid Columns of Boreholes SD-7 and SD-12, Used 
in the Pneumatic Calibration 
Date Range Sensor Elevation (m) File for Observation 
Data 
Corresponding 
Observation Cells 
August 2003 
Borehole USW SD-12 (LB991091233129.001 [125868]) 
1258.5 Sd12_214_PT1679.txt 12/1/95-1/29/96 F002Bh44 
1232.0 Sd12_301_PT1667.txt 12/1/95-1/29/96 F0009h44 
1217.1 Sd12_350_PT1661.txt 12/1/95-1/29/96 F0011h44 
1001.3 Sd12_1058_PT1619.txt 12/1/95-1/29/96 F014Bh44 
6.4.3 Analysis of Results 
Comparisons of the model simulation results and the field measurement data for boreholes SD-7 
(60 days) and SD-12 (30 days) are shown in Figures 6.4-1 and 6.4-2, respectively. In general, the 
simulation results demonstrate a good match with measurement data for the two boreholes. 
Except in the TSw unit of SD-7, the 3-D simulation predicts a slightly smaller amplitude signal 
than the observation data. Many comparisons between model simulated pressures with and 
without fracture-permeability modifications against field measurements show that the calibrated 
3-D model has improved consistently in matching observation data. Note that for borehole SD-7, 
80 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
the calibrated-fracture-permeability values of the TSw unit may be even lower for a better match. 
This might be caused by the effect of the nearby fault on pneumatic signal propagation. In 
addition, slightly greater differences between simulated and observed gas pressures in the lower 
TSw unit may be caused by coarse-grid effects and the larger effect of heterogeneity with depth. 
Overall, a reduction by a factor of 15 (Table 6.2-9) for the TSw fracture permeability provides a 
better fit to observed pneumatic data for all locations and time periods. 
89.6 
89.1 
88.6 
88.1 
87.6 
87.1 
86.6 
86.1 
85.6 
NOTE: Field data from DTN: LB991091233129.001 [125868] are extracted from DTN: GS960908312261.004 
[106784]. Both observations and simulations have been vertically offset for clearer display. 
Time (days from 04/05/96) 
Field Data: LB991091233129.001 [125868] 
Model ResultsDTN: LB0303GASFLW3D.001 
SD-7 
0 10 20 30 
Figure 6.4-1. Comparison of Simulated and Observed Gas Pressure at Borehole SD-7 during a 
60-Day Period 
MDL-NBS-HS-000006 REV01 August 2003 81 
Pressure (KPa) 
U0050 
TSw 
TSw 
PTn 
TCw 
60 50 40 
Observation 
Model prediction UZ Flow Models and Submodels 
91 
90.5 
90 
89.5 
89 
88.5 
88 
87.5 
87 
86.5 
86 
0 5 10 15 20 25 30 
SD-12 
Observation 
Model prediction 
NOTE: Field data from DTN: LB991091233129.001 [125868] are extracted from DTN: GS960308312232.001 
[105573]. Both observations and simulations have been vertically offset for clearer display. 
Time (days from 12/01/95)
Field Data: LB991091233129.001 [125868] 
Model ResultsDTN: LB0303GASFLW3D.001 
Figure 6.4-2. Comparison of Simulated and Observed Gas Pressure at Borehole SD-12 during the First 
30-Day Period 
ANALYSIS AND MODELING OF PORE WATER CHEMICAL DATA 6.5 
This study is part of the continuing effort to model and analyze geochemical data in the UZ at 
Yucca Mountain to support the conceptual model of UZ flow and build confidence in the 
predictive capacity of the model. It consists of using geochemical models to evaluate the 
hydrological systems, through assessing spatial distribution of surface net infiltration and the 
impact of variations in its magnitude. 
The UZ system of Yucca Mountain has been the subject of intense geological, hydrological, and 
subsurface engineering study. One of the main issues is the percolation flux at the nuclear waste 
repository. Percolation flux strongly depends on infiltration rates and their spatial distribution. 
Much work has been done to estimate the infiltration flux based on various evaporation models 
(Hevesi et al. 1992 [116809]; Flint and Flint 1994 [103746]). The present-day mean infiltration 
rate across the study area ranges from one millimeter per year to several tens of millimeters per 
year (Table 6.1-2, DTN: GS000308311221.005 [147613]). The climate over the past 100,000 
years has been used to estimate the possible range in infiltration rates over the next 10,000 years 
(Sonnenthal and Bodvarsson 1999 [117127]). 
Geochemical data provide additional information to analyze the UZ system. Pore-water chemical 
concentration data are used in this section to calibrate the UZ model and to bound the infiltration 
flux, flow pathways, and transport time. The distribution of chemical constituents in both liquid 
MDL-NBS-HS-000006 REV01 August 2003 82 
Pressure (KPa) 
U0050 
TSw 
TSw 
PTn 
TCw 
U0050 UZ Flow Models and Submodels 
and solid phases of the UZ system depends on many factors, such as hydrological and 
geochemical processes of surface precipitation, evapotranspiration, and fracture-matrix 
interaction of flow and transport, large-scale mixing via lateral transport, and the history of 
climate changes and recharge. 
The distribution of chloride in the UZ groundwater provides important information for UZ 
Model calibration and validation. In this study, pore-water chloride (Cl) concentration data are 
analyzed and modeled by 3-D chemical transport simulations using the dual-permeability 
modeling approach. In the UZ flow models on which this chloride transport modeling was based, 
the base-case flow models use the property set of the PTn (Section 6.2.3) that favor lateral 
diversion of flow in the PTn unit, as discussed in 6.6.3. Alternative flow models incorporate 
another property set of the PTn (Section 6.2.3) that is not likely to cause lateral flow. 
6.5.1 Available Data 
Pore-Water Chemical Concentration Data 6.5.1.1 
Chloride transport processes were modeled in this model analysis. The chloride concentrations 
used in our modeling were measured from pore waters extracted from field samples. These 
samples were collected from a set of eleven surface-based boreholes, the ESF, and the ECRB. 
The boreholes are SD-6, SD-7, SD-9, SD-12, NRG-6, NGR-7a, UZ-14, UZ#16, UZ-7a, WT-24, 
and G-2. Data for each borehole are listed in Table 6.5-1. 
August 2003 83 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Boreholes/Facilities DTN * 
SD-6 
SD-7 
SD-9 
SD-12 
NRG-6 
NRG-7a 
UZ-14 
UZ#16 
MDL-NBS-HS-000006 REV01 
U0050 
Table 6.5-1. Chloride Data Sources* 
GS981008312272.004 [153677] A* 
LA0002JF12213U.001 [154760] B 
GS000608312271.001 [153407] C 
GS970908312271.003 [111467] D 
GS961108312271.002 [121708] F 
GS981008312272.004 [153677] A 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
GS970908312271.003 [111467] D 
GS961108312271.002 [121708] F 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
GS000608312271.001 [153407] C 
GS970908312271.003 [111467] D 
GS961108312271.002 [121708] F 
GS981008312272.004 [153677] A 
LA0002JF12213U.001 [154760] B 
GS010708312272.002 [156375] I 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
GS961108312271.002 [121708] F 
GS981008312272.004 [153677] A 
GS010708312272.002 [156375] I 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
GS010708312272.002 [156375] I 
GS961108312271.002 [121708] F 
GS990208312272.001 [146134] J 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
GS010708312272.002 [156375] I 
GS990208312272.001 [146134] J 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
August 2003 84 UZ Flow Models and Submodels 
Table 6.5-1 Chloride Data Sources* (Continued) 
Boreholes/Facilities DTN * 
UZ-7a 
WT-24 
G-2 
ECRB 
ESF 
NOTE: * The letters following the DIRS number is not a part of the 
DTN number. Each letter corresponds to the 
identification of the same DTN in the column. 
6.5.1.2 Chloride Flux 
The sources contributing to the chloride in recharge waters are precipitation, runon, and runoff. 
The portion of these waters that forms net infiltration is small. The modern mean infiltration is 
approximately 5 mm/yr, and the glacial maximum infiltration rate at 28,000 years ago was about 
28 mm/yr (Sonnenthal and Bodvarsson 1999 [117127], p. 148, Figure 23; Flint et al. 1996 
[100147]). As an approximation, a glacial infiltration scenario in this section was obtained by 
multiplying the present-day mean infiltration rate by a factor of 5 with the same distribution 
pattern. 
Four case studies corresponding to four climate scenarios were chosen. They represent modern 
(or present) mean, modern low and modern upper bounds, and glacial mean. Their mean fluxes 
are listed in Table 6.5-2, calculated from four infiltration maps (DTN: GS000308311221.005 
[147613]). Also listed in the table are the notations for these infiltration scenarios. The uppercase 
A represents the corresponding 3-D flow model having a property set for the PTn that 
would allow lateral diversion. The upper-case B is an alternative model that does not account 
for lateral flow at the PTn (Section 6.2.3). The lower case u, m, and l in the notations stand for 
flow models of upper, mean, and lower infiltration, on which the chloride models were built. 
These notations are consistent with the ones used in flow models (Section 6.2). The chloride 
transport model uses the same flow model as the notation indicates. Chloride recharge fluxes to 
these transport models are calculated accordingly for these climate scenarios. 
The sources of chloride recharge into the UZ includes dissolved material in rain, particulate in 
snow, and a contribution from windblown dusts (Tyler et al. 1996 [108774]). Precipitation on the 
land surfaces would experience physical processes such as evaporation, which leaves behind Cl- 
in the remaining water. The chloride mass flux to the chloride transport model depends on the 
amount of water flux and its chloride concentration. In our modeling, we consider the fluxes as 
precipitation, runon, and runoff. Thus, the water fluxes contributing to chloride recharge can be 
calculated using the following equation: 
MDL-NBS-HS-000006 REV01 
LA0002JF12213U.001 [154760] B 
GS981008312272.004 [153677] A 
GS981008312272.004 [153677] A 
LA0002JF12213U.001 [154760] B 
LAJF831222AQ98.011 [145402] H 
LAJF831222AQ98.011 [145402] H 
LA9909JF831222.004 [145598] K 
LA0002JF12213U.002 [156281] L 
GS961108312261.006 [107293] M 
LA0002JF12213U.002 [156281] L 
LA9909JF831222.010 [122733] N 
85 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
+ = (Eq. 6.5-1) F F F F runoff runon prec - 
where F is net flux contributing to chloride in the recharging water (defined as net recharge in 
Table 6.5-2, independent of net infiltration of Table 6.1-2), Fprec is precipitation flux, Frunon is 
runon, and Frunoff is runoff flux. These water flux terms are eventually converted to have units of 
kg/(m2  sec) as input. The calculation of each term in F is performed using the routine infil2grid 
V1.7 (LBNL 2002 [154793] and DTN: GS000308311221.005 [147613]). 
171.9 9.1 429.9 
41.1 3.5 227.1 
13.6 1.0 198.5 
292.0 14.4 594.5 
preq_uA Modern upper 267.1 
preq_uB 
preq_mA Modern mean 189.5 
preq_mB 
preq_lA Modern low 185.9 
preq_lB 
glaq_mA* Glacial mean 316.9 
Source: Fluxes calculated as described in Attachment III, Section III.1.1, data from DTN: GS000308311221.005 
[147613] 
NOTE:  The upper-case letter A in the notation denotes the base-case model property set used by the 
corresponding flow model that would favor lateral flow diversion, and upper-case B denotes the 
alternative model, in which the property set of the PTn would not likely cause flow diversion (Section 
6.2.3). 
Table 6.5-2. Present-Day and Glacial Infiltration Fluxes at Different Scenarios (Averaged over Model 
Domain, mm/year) 
Runoff Runon Precipitation Scenarios Scenario 
Notations  
 Net Recharge 
Used in Calculation 
of Chloride Flux 
 Net water flux contributing to the chloride recharge is calculated by Equation 6.5-1. 
* In our modeling of the glacial scenario, a special case of the model was run for simulating 100,000 
years to steady state with glacial chloride flux and then switched to present-day mean chloride 
August 2003 
recharge for 11,000 years. The simulation is given the notation glaq_pmA. 
Surface chloride concentrations are discussed by Sonnenthal and Bodvarsson (1999 [117127], 
pp. 113-114). The range of 0.550.73 mg/L was considered to bound the average value. Similar 
value was obtained by combining a mean annual precipitation of about 170 mm/year with a 
present-day chloride surface flux of 106 mg/(m2-year), yielding a mean chloride concentration of 
about 0.62 mg/L (Fabryka-Martin et al. 1997 [100145]). A value of 0.55 mg/L (Sonnenthal and 
Bodvarsson 1999 [117127]; Triay et al. 1996 [101014]) is used in the present simulations, 
applied to all infiltrating water in the forms of precipitation, runon, and runoff (Sonnenthal and 
Bodvarsson 1999 [117127], p. 148). Then, the chloride flux is calculated using the following 
formula: 
) F = C 10-6 (F + F - F (Eq. 6.5-2) runoff prec runon p Cl, Cl 
where FCl is chloride flux (kg/sec), Fprec is precipitation flux (kg water/sec), Frunon is runon (kg 
water/sec), and Frunoff is runoff flux (kg water/sec). CCl,p is chloride concentration in 
86 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
precipitation (mg/kg water). Actual calculations and procedures using this equation in preparing 
input files for chloride simulations are summarized in Attachment III, Section III.1. 
6.5.2 Three-Dimensional Simulations 
Chloride transport for the UZ hydrological system was simulated under two-phase isothermal 
flow conditions of water and air. A three-dimensional dual-permeability model and the T2R3D 
V1.4 (LBNL 1999 [146654]) of the TOUGH2 code were employed for the simulations. The 
steady-state liquid-flow fields were obtained using the EOS9 module of T2R3D (LBNL 1999 
[146654]). Chemical distributions were then computed from transport equations using the 
decoupled T2R3D module (LBNL 1999 [146654]). Flow boundary conditions, simulation grids, 
and the basic hydrological properties of the rock matrix and fractures are the same as those used 
in the 3-D UZ flow simulation of nonperched-water model. Boundary conditions for chemical 
components were treated similarly to those for flow simulations, with mass flux described at the 
top boundary and no-flow and water table conditions at the lateral and bottom boundaries, 
respectively. The dispersivities for both fracture and matrix continua in the simulation were 
assumed to be zero (Sonnenthal and Bodvarsson 1999 [117127], Section 5.3, p. 129). The same 
diffusion coefficient used for Cl-, 2.032E-9 m2/s, is used for chemical ions at 25C, which is the 
average of the surface temperature and water table temperature, and dilution in water (Lide 2002 
[160832], p. 5-96). The tortuosity was set to 0.7 for fracture and 0.2 for matrix, respectively 
(BSC 2001 [158726]; Grathwohl 2000 [141512]). 
6.5.2.1 Modeling Results 
The modeling results are represented in Figures 6.5-1 and 6.5-2 for boreholes NRG-6 and UZ- 
14. These figures plot the chloride profiles of present infiltration rates of mean infiltration with 
lower and upper bounds. The results demonstrate that the mean infiltration case has the closest 
match between the calculated concentrations and the field-measured chloride data. The upperbound 
case shows a moderate match; the lower-bound case shows the poorest match. 
The glacial scenario yields generally lower chloride concentration than the present case, upper 
bound (Figures 6.5-1 and 6.5-2). A special scenario run (glaq_pmA) with the glacial case was 
conducted (Figure 6.5-2, purple line). The model was run for 100,000 years to steady state with 
glacial recharge, and then was switched to present-day recharge for 11,600 years. The model 
yields a closer match than the glacial recharge case. 
August 2003 87 MDL-NBS-HS-000006 REV01 preq_uA 
preq_mA 
preq_lA 
glaq_mA 
Field Data 
Hydro. Unit 
103 
U0050 
UZ Flow Models and Submodels 
Elevation (m) 
1300 
TCw 1200 PTn 
1100 
TSw 
1000 
900 
800 
700 
101 
USW NRG-6 
102 
Cl- Concentration (mg/l) 
Field Data: GS010708312272.002 [156375]; LA0002JF12213U.001 [154760]; LAJF831222AQ98.011 [145402]. 
Model ResultsDTN LB0303CLINFL3D.001 
Figure 6.5-1. Chloride Concentration (mg/L) Profiles at Borehole USW NRG-6 for Present Recharge with 
Mean, Upper, and Lower Bounds and Glacial Recharge 
August 2003 88 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
1400 
Q 
1300 PTn 
1200 
TSw 
CHn 
1100 
1000 
900 
800 
700 
101 
Field Data: GS010708312272.002 [156375]; GS961108312271.002 [121708]; GS990208312272.001 [146134]; 
LA0002JF12213U.001 [154760]; LAJF831222AQ98.011 [145402]. 
Model ResultsDTN LB0303CLINFL3D.001 
Figure 6.5-2. Chloride Concentration (mg/L) Profiles at Borehole USW UZ-14 for Present Recharge with 
Mean, Upper, and Lower Bounds and Glacial Recharges 
6.5.2.2 Alternative Model 
In the above base case (A), the property set for the PTn would favor lateral diversion of flow. 
The alternative model (B) uses a different property set for the PTn, one that does not favor largescale 
lateral diversion (Table 6.5-2). A comparison between the base-case model and the 
alternative model results are presented for borehole USW SD-9 and the ECRB in Figures 6.5-3 
and 6.5-4). 
Comparative studies of chloride distributions within the UZ, simulated using the base-case and 
alternative flow fields, indicate consistently that the base-case flow field simulation results 
provide an overall better match with the observed chloride. As discussed in Sections 6.2.5 and 
6.6.3, the main difference between the base-case and alternative flow fields is whether there is 
large- or small-scale lateral flow within the PTn unit, with the base-case flow fields predicting 
relatively large lateral diversion in general. The model calibration results with chloride data of 
this section further reveal that large lateral diversion may exist in the PTn Unit. Therefore, pore 
water chloride may provide key evidence for understanding flow through the PTn, which has a 
direct impact on chloride transport and distributions. 
August 2003 89 MDL-NBS-HS-000006 REV01 
USW UZ-14 
102 
Cl- Concentration (mg/l) 
Elevation (m) 
U0050 
preq_uA 
preq_mA 
preq_lA 
glaq_mA 
glaq_pmA 
Field Data 
Hydro. Unit 
Perched Water 
103
UZ Flow Models and Submodels 
1300 
1200 
1100 
1000 
900 
800 
700 
NOTE: The upper-case letter A denotes the base-case model property set. The uppercase letter B denotes the 
alternative model. 
Figure 6.5-3. Chloride Concentration (mg/L) Profiles at Borehole USW SD-9 for Present Recharge with 
Mean, Upper, and Lower Bounds and Glacial Recharge 
Cl- Concentration (mg/l) 
Field Data: GS970908312271.003 [111467]; GS961108312271.002 [121708]; 
LA0002JF12213U.001 [154760]; LAJF831222AQ98.011 [145402]. 
Model ResultsDTN LB0303CLINFL3D.001 
USW SD-9 
102 
MDL-NBS-HS-000006 REV01 
Elevation (m) 
TCw 
PTn 
CHn 
preq_uA 
preq_uB 
preq_mA 
preq_mB 
preq_lA 
preq_lB 
Field Data 
Hydro. Unit 
Perched Water 
TSw 
101 103 
U0050 
August 2003 90 UZ Flow Models and Submodels 
103 
102 
101 
NOTE: The upper-case letter A denotes base-case model property set. The upper-case letter B denotes the 
alternative model. 
Figure 6.5-4. Chloride Concentration (mg/L) Profiles at the ECRB for Present Recharge with Mean, 
Upper, and Lower Bounds 
Distance from the ECRB Entrance (m) 
Field Data: LA9909JF831222.004 [145598]; LA0002JF12213U.002 [156281] 
Model ResultsDTN LB0303CLINFL3D.001 
MDL-NBS-HS-000006 REV01 
Cl- Concentration (mg/l) 
preq_uA 
preq_uB 
preq_mA 
preq_mB 
preq_lA 
preq_lB 
field data 
2500 2000 
U0050 
ECRB 
1500 1000 500 0 
August 2003 91 UZ Flow Models and Submodels 
6.6 FLOW PATTERN ANALYSIS OF TSPA-LA 3-D FLOW FIELDS 
This section analyzes and summarizes the 18 flow simulation scenarios. Nine of the flow fields 
are the base-case flow fields of Section 6.2, as summarized in Table 6.2-9, and have been 
submitted to TSPA-LA for performance analyses. The remaining nine flow fields are considered 
as alternatives. The 18 model simulations are performed using the TSPA-LA grid (Figure 6.1-1) 
and nine infiltration maps, as discussed in Section 6.1, and the six calibrated parameter sets in 
Attachment I of this Model Report, and the UZ Flow Model of Section 6.2.2. 
6.6.1 Simulation Scenarios 
Table 6.6-1 summarizes the nine alternative simulation scenarios, associated with parameter sets 
(Tables I-4, I-5, and I-6), respectively, for the nine infiltration maps. The alternative property set 
"B" uses different rock parameters for the PTn unit such that less lateral diversion in the PTn is 
expected. 
Table 6.6-1. Nine Simulation Scenarios of Alternative UZ Flow: Data Files, Parameter Sets, and 
Infiltration Maps for the UZ Flow Fields 
Designation/ 
Simulation 
preq_lB 
preq_mB 
preq_uB 
monq_lB 
monq_mB 
monq_uB 
glaq_lB 
glaq_mB 
glaq_uB 
Parameter Set/ 
Calibration 
(Output-DTN: LB03013DSSCP3I.001) 
Parameter set from Table I-6, 
lower-bound infiltration 
Parameter set from Table I-4, 
present day/modern, mean infiltration 
Parameter set from Table I-5, 
upper-bound infiltration 
Parameter set from Table I-6, 
lower-bound infiltration 
Parameter set from Table I-4, 
present day/modern, mean infiltration 
Parameter set from Table I-5, 
upper-bound infiltration 
Parameter set from Table I-6, 
lower-bound infiltration 
Parameter set from Table I-4, 
present day/modern, mean infiltration 
Parameter set from Table I-5, 
upper-bound infiltration 
Infiltration Map 
(DTN: GS000308311221.005 
[147613]) 
Present-day, lower-bound 
infiltration 
Present-day, mean infiltration 
Present-day, upper-bound 
infiltration 
Monsoon, lower-bound infiltration 
Monsoon, mean infiltration 
Monsoon, upper- bound infiltration 
Glacial transition, lower-bound 
infiltration 
Glacial transition, mean infiltration 
Glacial transition, upper-bound 
infiltration 
MDL-NBS-HS-000006 REV01 92 
U0050 
August 2003 UZ Flow Models and Submodels 
As shown in Table 6.6-1, simulations with the alternative model are also carried out for the same 
three climatic scenarios (i.e., present-day, monsoon, and glacial transition), and mean, lowerbound, 
and upper-bound infiltration rates as for the base-case simulations. 
6.6.2 UZ Alternative Flow Model Results 
Similar to the calibration simulations of the base-case flow fields, the mass-balance check has 
been conducted for the nine alternative model simulations. Table 6.6-2 lists the global massbalance 
results for the nine alternative model simulations. Global mass-balance errors between 
inflow and outflow of the system for the nine flow fields, as shown in Table 6.6-2, are all smaller 
than 0.06%, indicating that solutions approximate steady state for these cases. 
(kg/s) 
preq_lB 1.5828143 1.5828178 0.0002 
preq_mB 5.5922355 5.5951199 0.0516 
preq_uB 13.564390 13.568112 0.0274 
monq_lB 5.5922355 5.5920365 0.0036 
monq_mB 14.939317 14.936880 0.0163 
monq_uB 24.286298 24.277515 0.0362 
glaq_lB 2.9648877 2.9648838 0.0001 
glaq_mB 21.494950 21.495094 0.0007 
glaq_uB 40.024949 40.024939 0.0000 
Model ResultsDTNs: LB03033DSSFF9I.001. 
Alternative Model Result Examination: In addition to the nine base-case flow fields of Section 
6.2.5, the nine alternative 3-D flow fields have also been compared against the field-observed 
data of matrix liquid saturation, as well as available water-potential and perched-water data. The 
available data used in the model checking are listed in Table 6.2-1. Overall, examination results 
are as follows (see Wang 2003 [162417], SN-LBNL-SCI-199-V1, pp. 158186, 243, for detailed 
comparisons): 
 The simulation results with the nine alternative flow fields are also able to fit the available 
matrix liquid saturation and water potential data from the nine boreholes (Table 6.2-1) well, 
similarly to the base-case flow fields of Section 6.2.5, including PTn units. 
 For calibrations with perched-water data, except for water-potential results for lower-bound 
infiltration (in SD-7 and SD-9), the six simulations with mean, lower-bound, and upperbound 
present-day infiltration rates, in general, match perched-water data. 
Mass-Balance Results for Nine Simulations of Alternative Flow Model 
Inflow from 
Infiltration 
Outflow to Water Table 
(kg/s) 
93 
Relative Error 
(%) 
August 2003 
Table 6.6-2. 
Simulation 
Scenarios 
MDL-NBS-HS-000006 REV01 
U0050 U0050 UZ Flow Models and Submodels 
6.6.3 Flow Fields and Analyses 
Percolation flux through the UZ is one important natural-barrier factor affecting overall 
repository performance in TSPA calculations. The quantity as well as the spatial and temporal 
variations in percolation flux will directly affect: (1) the amount of water flowing into waste 
emplacement drifts; (2) moisture conditions and the corrosion environment of waste packages 
within the drifts; (3) radionuclide release from the repository; and (4) radionuclide migration 
from the UZ to the saturated zone. Percolation fluxes through unsaturated fractured tuffs cannot 
be readily measured in the field, and thus indirect data and model results have to be used to 
estimate these fluxes. 
Model studies (Wu et al. 1999 [117161] and 2002 [160195]) indicate that the accuracy of model 
predictions for percolation fluxes in the UZ at Yucca Mountain depend on many factors 
including (1) net infiltration rates over the surface boundary (Wu et al. 1999 [117161], pp. 208 
210 and Figure 13; 2002 [160195], p. 227, Figure 6); (2) geological and conceptual models; (3) 
distribution of rock-property values for fractures and matrix; and (4) treatment of fracture-matrix 
flow and interaction. In this section, percolation fluxes at the repository horizon are analyzed 
using the 18 simulation results (Tables 6.2-9 and 6.6-1) of the UZ flow models for TSPA-LA. In 
the analysis, the percolation flux is defined as total vertical liquid mass flux through both 
fractures and matrix, and is converted to millimeter per year (mm/yr) per unit area using a 
constant water density. 
Figures 6.6-1 to 6.6-3 show examples of percolation fluxes along the repository layer for the 
three mean infiltration scenarios of the three climates, respectively, with the base-case flow 
fields. (Note: see Attachment IV for relevant data compilation for the PTn/TSw interface flux.) 
Comparisons of the calculated repository percolation fluxes of Figures 6.6-1, 6.6-2, and 6.6-3 
with those of the surface infiltration maps (Figures 6.1-2, 6.1-3 and 6.1-4, respectively), indicate 
that percolation fluxes at the repository are very different from surface infiltration patterns. Note 
that surface infiltration rates and distributions are independent of faults. The major differences in 
percolation flux at the repository level (Figures 6.6-16.6-3) are (1) flow mainly through faults 
in the very northern part of the model domain (with the north coordinate > 237,000 m); (2) flow 
diverted into or near faults located in the model domain; and (3) about a 500 m lateral flow of the 
high infiltration zones from south to north along the crest located to the east. This large-scale 
lateral flow from west to east (in general) is illustrated by Lateral Flow Scale on Figures 6.6-1, 
6.6-2, and 6.6-3, respectively, for the three mean infiltration scenarios. Lateral flow may also 
occur from the Solitario Canyon fault to the east, reaching the repository blocks, since the fault is 
very close to the repository (Figure 6.1-1), included in the 18 flow fields. Note that flow 
redistribution in the very northern part of the model domain (far beyond the repository block) 
results from the repository gridlayer horizon laterally intersecting the CHn zeolitic and perchedwater 
zones, with major flow paths being faults. Overall, percolation results as shown in Figures 
6.6-1, 6.6-2, and 6.6-3 display very different patterns from the surface infiltrations, because of 
the substantial amount of large-scale lateral flow within the PTn unit. This indicates that within 
the PTn unit, lateral flow has a significant impact on percolation flux distribution in the 
repository layer. 
Simulated percolation fluxes in the repository layer, in addition to those shown in Figures 6.6-1, 
6.6.2, and 6.6-3 (for three mean infiltration rates), include three lower bounds and three upper 
bounds of infiltration rates for the nine base-case flow fields, as well as the nine infiltration rates 
August 2003 94 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
of the three climates with the alternative flow fields. Further examination of all nine base-case 
simulation results against their corresponding surface infiltration maps indicates that more 
significant lateral flow occurs in the PTn for lower-bound and mean infiltration scenarios than 
for the upper bound infiltration. A further examination of all flow fields indicates that the lower 
the infiltration rates, the larger the lateral flow scales. (For examples, see Wang (2003 [162417], 
SN-LBNL-SCI-199-V1, pp. 125, 128130; SN-LBNL-SCI-199-V2, pp. 17, 24, 25).) This is 
because the lower infiltration results in drier condition with stronger capillarity (Wu et al. 2002 
[161058]). On the other hand, the simulation results with the nine alternative flow fields show 
small lateral flow occurrence in the PTn in the area above the repository. These results show that 
the flow patterns through PTn have a large impact on percolation flux distribution in the 
repository horizon. 
August 2003 95 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
U0050 
Model ResultsDTN: LB03023DSSCP9I.001: 
Figure 6.6-1. Simulated Percolation Fluxes at the Repository Horizon under the Present-Day, Mean 
Infiltration Scenario Using the Results of Simulation preq_mA 
August 2003 96 MDL-NBS-HS-000006 REV01 U0050 
Model ResultsDTN: LB03023DSSCP9I.001. 
UZ Flow Models and Submodels 
Figure 6.6-2. Simulated Percolation Fluxes at the Repository Horizon under the Monsoon, Mean 
Infiltration Scenario Using the Results of Simulation monq_mA 
August 2003 97 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
U0050 
Model ResultsDTN: LB03023DSSCP9I.001. 
Figure 6.6-3. Simulated Percolation Fluxes at the Repository Horizon under the Glacial Transition Mean 
Infiltration Scenario Using the Results of Simulation glaq_mA 
August 2003 98 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Figures 6.6-4 through 6.6-21 show the simulated percolation fluxes at the water table using the 
nine base-case and nine alternative flow simulations with nine infiltration scenarios of the three 
climates. When comparing the percolation fluxes at the repository (e.g., Figures 6.6-1-, 6.6-2, 
and 6.6-3), as well as with themselves from the two models (e.g., Figure 6.6-4 versus Figure 6.6- 
5), we find the following: 
 There are fewer differences in the calculated percolation fluxes for the two models at the 
water table than those at the repository level for the same infiltration scenarios. 
 In the northern half of the domain, the base-case flow fields are very similar to the 
alternative ones. Because of the impact of perched water and zeolitic units, flow is 
mainly focused into major faults. 
 In the central and southern portions of the model domain, the base-case flow fields at the 
water table show lateral flow of several hundreds of meters to the east in the area 
directly below the southern repository. This is the area where vitric zones are located 
within the CHn unit. 
Note that all 18 flow fields are calculated using a fixed water table. These flow fields can also be 
used for a rising-water-table case in the future. This is because the water table is handled as a 
sink term in the model, and the flow at or above the water table is determined by the upstream or 
upper-layer conditions. Therefore, a water-rise situation can be handled by simply transecting the 
flow fields vertically at a new water table elevation. The software WTRISE V2.0 (LBNL 2003 
[163453]) is available to obtain those results; however, it has not been used in this report. 
August 2003 99 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Figure 6.6-4. Simulated Percolation Fluxes at the Water Table under the Present-Day, Lower-Bound 
Infiltration Scenario Using the Results of Simulating the Base-Case Model: preq_lA 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_lA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillho 
le 
Wash 
Fault 
Solitario 
Canyon 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
100 
U0050 
mm/year 
5
4.75 
4.5 
4. 
4 
25 
3.75 
3.5 
3. 
3 
25 
2.75 
2.5 
2. 
2 
25 
1.75 
1.5 
1. 
1 
25 
0.75 
0.5 
0. 
0 
25 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-5. Simulated Percolation Fluxes at the Water Table under the Present-Day, Lower-Bound 
Infiltration Scenario Using the Results of Simulating the Alternative Model: preq_lB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_lB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillho 
le 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
101 
U0050 
mm/year 
5
4.75 
4.5 
4. 
4 
25 
3.75 
3.5 
3. 
3 
25 
2.75 
2.5 
2. 
2 
25 
1.75 
1.5 
1. 
1 
25 
0.75 
0.5 
0. 
0 
25 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-6. Simulated Percolation Fluxes at the Water Table under the Present-Day, Mean Infiltration 
Scenario Using the Results of Simulating the Base-Case Model: preq_mA 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_mA at bottom boundary 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
230000 
174000 172000 170000 168000 
102 
U0050 
mm/year 
15 
14.25 
13.5 
12.75 
12 
11.25 
10.5 
9. 
9 
75 
8.25 
7.5 
6. 
6 
75 
5.25 
4.5 
3. 
3 
75 
2.25 
1.5 
0. 
0 
75 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-7. Simulated Percolation Fluxes at the Water Table under the Present-Day, Mean Infiltration 
Scenario Using the Results of Simulating the Alternative Model: preq_mB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_mB at bottom boundary 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
230000 
174000 172000 170000 168000 
103 
U0050 
mm/year 
15 
14.25 
13.5 
12.75 
12 
11.25 
10.5 
9. 
9 
75 
8.25 
7.5 
6. 
6 
75 
5.25 
4.5 
3. 
3 
75 
2.25 
1.5 
0. 
0 
75 
August 2003 UZ Flow Models and Submodels 
Nevada Coordinate N-S (m) 
Figure 6.6-8. 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_uA at bottom boundary 
Solitario 
Cany 
on 
Fault 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
230000 
170000 168000
Nevada Coordinate E-W (m) 
Simulated Percolation Fluxes at the Water Table under the Present-Day, Upper-Bound 
Infiltration Scenario Using the Results of Simulating the Base-Case Model: preq_uA 
Model ResultsDTN: LB03023DSSCP9I.001. 
174000 172000 
104 
U0050 
mm/year 
20 
19 
18 
17 
16 
15 
14 
13 
12 
11 
10 
9876543210 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-9. Simulated Percolation Fluxes at the Water Table under the Present-Day, Upper-Bound 
Infiltration Scenario Using the Results of Simulating the Alternative Model: preq_uB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for preq_uB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
105 
U0050 
mm/year 
20 
19 
18 
17 
16 
15 
14 
13 
12 
11 
10 
9876543210 
174000 
August 2003 UZ Flow Models and Submodels 
238000 
236000 
234000 
232000 
230000 
vertical flux for monq_lA at bottom boundary 
168000 
Solitario 
Cany 
on 
Fault 
170000 174000 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillhole 
Wash 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
172000 
Nevada Coordinate E-W (m) 
Figure 6.6-10. Simulated Percolation Fluxes at the Water Table under the Monsoon, Lower-Bound 
Infiltration Scenario Using the Results of Simulating the Base-Case Model: monq_lA 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
U0050 
mm/year 
10 
9. 
9 
5 
8. 
8 
5 
7. 
7 
5 
6. 
6 
5 
5. 
5 
5 
4. 
4 
5 
3. 
3 
5 
2. 
2 
5 
1. 
1 
5 
0. 
0 
5 
August 2003 106 UZ Flow Models and Submodels 
Figure 6.6-11. Simulated Percolation Fluxes at the Water Table under the Monsoon, Lower-Bound 
Infiltration Scenario Using the Results of Simulating the Alternative Model: monq_lB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for monq_lB at bottom boundary 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillhole 
Wash 
Fault 
Solitario 
Canyon 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
230000 
174000 172000 170000 168000 
107 
U0050 
mm/year 
10 
9. 
9 
5 
8. 
8 
5 
7. 
7 
5 
6. 
6 
5 
5. 
5 
5 
4. 
4 
5 
3. 
3 
5 
2. 
2 
5 
1. 
1 
5 
0. 
0 
5 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-12. Simulated Percolation Fluxes at the Water Table under the Monsoon, Mean Infiltration 
Scenario Using the Results of Simulating the Base-Case Model: monq_mA 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for monq_mA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
108 
U0050 
mm/year 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-13. Simulated Percolation Fluxes at the Water Table under the Monsoon, Mean Infiltration 
Scenario Using the Results of Simulating the Alternative Model: monq_mB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
vertical flux for monq_mB at bottom boundary 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
230000 
174000 172000 170000 168000 
109 
U0050 
mm/year 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-14. Simulated Percolation Fluxes at the Water Table under the Monsoon, Upper-Bound 
Infiltration Scenario Using the Results of Simulating the Base-Case Model: monq_uA 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for monq_uA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
110 
U0050 
mm/year 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9630 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-15. Simulated Percolation Fluxes at the Water Table under the Monsoon, Upper-Bound 
Infiltration Scenario Using the Results of Simulating the Alternative Model: monq_uB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for monq_uB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
111 
U0050 
mm/year 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9630 
174000 
August 2003 UZ Flow Models and Submodels 
238000 
236000 
234000 
232000 
230000 
Figure 6.6-16. Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Lower- 
Bound Infiltration Scenario Using the Results of Simulating the Base-Case Model: 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
168000 
glaq_lA 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
vertical flux for glaq_lA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
172000 170000 
Bow Ridge Fault 
112 
U0050 
mm/year 
10 
9. 
9 
5 
8. 
8 
5 
7. 
7 
5 
6. 
6 
5 
5. 
5 
5 
4. 
4 
5 
3. 
3 
5 
2. 
2 
5 
1. 
1 
5 
0. 
0 
5 
174000 
August 2003 UZ Flow Models and Submodels 
238000 
236000 
234000 
232000 
230000 
Figure 6.6-17. Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Lower- 
Bound Infiltration Scenario Using the Results of Simulating the Alternative Model: 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
168000 
glaq_lB 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
vertical flux for glaq_lB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
172000 170000 
Bow Ridge Fault 
113 
U0050 
mm/year 
10 
9. 
9 
5 
8. 
8 
5 
7. 
7 
5 
6. 
6 
5 
5. 
5 
5 
4. 
4 
5 
3. 
3 
5 
2. 
2 
5 
1. 
1 
5 
0. 
0 
5 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-18. Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Mean 
Infiltration Scenario Using the Results of Simulating the Base-Case Model: glaq_mA 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for glaq_mA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
114 
U0050 
mm/year 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
174000 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-19. Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Mean 
Infiltration Scenario Using the Results of Simulating the Alternative Model: glaq_mB 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
vertical flux for glaq_mB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
115 
U0050 
mm/year 
40 
38 
36 
34 
32 
30 
28 
26 
24 
22 
20 
18 
16 
14 
12 
10 
86420 
174000 
August 2003 UZ Flow Models and Submodels 
238000 
236000 
234000 
232000 
vertical flux for glaq_uA at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillhole 
Wash 
Fault 
Solitario 
Canyon 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Upper- 
Bound Infiltration Scenario Using the Results of Simulating the Base-Case Model: 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03023DSSCP9I.001. 
168000 
glaq_uA 
Nevada Coordinate N-S (m) 
Figure 6.6-20. 
MDL-NBS-HS-000006 REV01 
172000 170000 
116 
U0050 
mm/year 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9630 
174000 
August 2003 UZ Flow Models and Submodels 
238000 
236000 
234000 
232000 
230000 
Figure 6.6-21. Simulated Percolation Fluxes at the Water Table under the Glacial Transition, Upper- 
Bound Infiltration Scenario Using the Results of Simulating the Alternative Model: 
Nevada Coordinate E-W (m) 
Model ResultsDTN: LB03033DSSFF9I.001. 
168000 
glaq_uB 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
vertical flux for glaq_uB at bottom boundary 
Sever 
Wash 
Fault 
Pagany 
Was 
h 
Fault 
D 
rillhole 
Wash 
Fault 
Solitario 
Cany 
on 
Fault 
Gho 
st 
Danc 
e 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
172000 170000 
117 
U0050 
mm/year 
60 
57 
54 
51 
48 
45 
42 
39 
36 
33 
30 
27 
24 
21 
18 
15 
12 
9630 
174000 
August 2003 UZ Flow Models and Submodels 
Tables 6.6-3 and 6.6-4 list percentages of fracture-matrix flow components and fault flow at the 
repository horizon and the water table within the model domain. Fracture and matrix percentages 
sum to 100%, while fault flow percentages represent total vertical flux through fault blocks 
(procedures for calculating the percentages are explained in Attachment III, Section III.2). These 
statistics are calculated from vertical flow along each grid column, using the nine base-case and 
nine alternative flow fields. A comparison between the statistical data of the two tables indicates 
that the two models generate similar results in terms of fracture-matrix flow components and 
fault flow percentage at the repository layer. This is because the two models differ only in the 
PTn properties (a unit above) and also because the statistics are taken from averaging an entire 
layer without considering spatial distributions of flow percentage. These statistics indicate that 
fracture flow is dominant both at the repository horizon and at the water table. At the repository 
level, fracture flow consists of more than 9095% of the total percolation fluxes. Fracture flow at 
the water table takes 7080% of the total flow. On the other hand, fault flow percentage 
increases from about 3040% at the repository to about 60% at the water table, except for the 
case of the present-day, lower-bound infiltration. Note that according to the active fracture 
concept (Liu et al., 1998 [105729]), not all fractures are transmitting percolation fluxes. Actual 
active fracture spacings (i.e., fracture flow intervals) in the flow fields are much larger than 
measured fracture spacings in different units. 
Nine Base-Case Flow Fields 
Simulation 
Designation 
Flux at Proposed 
Repository Horizon 
(%) 
Table 6.6-3. Comparison of the Water Flux through Matrix, Fractures, and Faults as a Percentage of the 
Total Flux at Two Different Horizons (1) at the Repository and (2) at the Water Table for the 
Flux at Water Table 
(%) 
Matrix Fault 
preq_lA 91.35 8.65 58.78 78.05 21.95 71.78 
preq_mA 94.29 5.71 28.62 70.29 29.71 53.73 
preq_uA 94.02 5.98 27.41 77.72 22.28 60.68 
monq_lA 93.46 6.54 31.89 71.37 28.63 66.54 
monq_mA 94.57 5.43 26.83 72.33 27.67 61.06 
monq_uA 94.34 5.66 26.04 78.86 21.14 64.25 
glaq_lA 92.11 7.89 36.71 70.37 29.63 65.40 
glaq_mA 94.58 5.42 24.27 70.34 29.66 61.57 
glaq_uA 94.53 5.47 23.81 76.44 23.56 65.37 
Model Results  Output DTNs: LB03023DSSCP9I.001. 
August 2003 
Fracture Matrix Fault Fracture 
118 MDL-NBS-HS-000006 REV01 
U0050 U0050 UZ Flow Models and Submodels 
Table 6.6-4. Comparison of the Water Flux through Matrix, Fractures, and Faults as a Percentage of 
the Total Flux at Two Different Horizons, at the Repository and at the Water Table, for 
Flux at Water Table 
(%) 
the Nine Alternative Flow Fields 
Simulation 
Designation 
Flux at Proposed 
Repository Horizon 
(%) 
Fracture Matrix Fault Fracture Matrix Fault 
Preq_lB 90.78 9.21 65.66 79.70 20.30 73.39 
Preq_mB 93.92 6.08 32.86 70.58 29.42 53.79 
Preq_uB 93.79 6.21 30.49 78.32 21.68 60.84 
Monq_lB 93.34 6.66 37.55 70.03 29.97 66.66 
Monq_mB 93.97 6.03 28.80 72.81 27.19 61.13 
Monq_uB 94.25 5.75 28.39 79.37 20.63 64.25 
Glaq_lB 91.60 8.40 43.69 71.64 28.36 66.36 
Glaq_mB 94.07 5.93 26.97 70.72 29.28 61.55 
Glaq_uB 94.44 5.56 25.74 76.60 23.40 65.36 
Model Results  Output DTNs: LB03033DSSFF9I.001. 
Distributions of Percolation Fluxes within the Repository: Percolation fluxes within the 
repository footprint can be further analyzed using a frequency distribution plot. This plot 
displays the average percentage of the repository area subject to a particular percolation rate. 
Note that the normalized flux rates are determined by normalizing an infiltration value with 
respect to the averaged infiltration rate for the scenario. For example, 1 for the normalized flux 
rate corresponds to 4.43, 11.83, and 17.02 mm/yr (Table 6.1-2), respectively, for the three mean 
infiltration scenarios. The information, as shown in Figure 6.6-22 (See Attachment III.2.5 for 
details of the calculation), is important to drift-scale modeling studies of flow and transport at 
drifts and flow-redistributing phenomena through the TSw. Figure 6.6-22 shows the frequency 
distribution of normalized percolation flux within the repository horizon for the three mean 
infiltration rates of the three climates. 
August 2003 119 MDL-NBS-HS-000006 REV01 2.0 1.0 
present day, mean infiltration 
3.0 6.0 5.0 4.0 
Normalized flux rate 
2.0 1.0 
monsoon, mean infiltration 
3.0 6.0 5.0 4.0 
Normalized flux rate 
UZ Flow Models and Submodels 
18 
16 
14 
12 
10
8
6
4
2
0 
0.0 
16 
14 
12 
10
8
6
4
2
0 
0.0 
20 
18 
16 
14 
12 
10
8
6
4
2
0 
0.0 
percentage of Infiltration area percentage of Infiltration area percentage of Infiltration area 
1.0 
glacial transition, mean infiltration 
3.0 2.0 
Figure 6.6-22. Areal Frequency and Distribution of Simulated Percolation Fluxes within the Repository 
Domain Normalized to the Three Mean Infiltration Rates: (a) Present Day, (b) Monsoon, 
and (c) Glacial Transition 
7.0 6.0 5.0 4.0 
Normalized flux rate 
MDL-NBS-HS-000006 REV01 
8.0 7.0 
8.0 7.0 
8.0 
Model Results  DTN: LB03023DSSCP9I.001 
U0050 
120 
10.0 9.0 
10.0 9.0 
10.0 9.0 
August 2003 UZ Flow Models and Submodels 
Figure 6.6-22 indicates that the highest flux frequencies have a normalized flux of about 0.5 or 
less and occur over about 50% of the repository area. The area with normalized percolation 
fluxes greater than 5 comprises less than 1% of the total repository area. In general, the modeling 
results for all of the 18 flow fields show that the percolation flux value with highest areal 
frequencies is always lower than the average values of the corresponding infiltration rates. 
The results of the 18 flow fields analyses, as shown in Figure 6.6-22, can be used to define a 
cumulative flux-frequency distribution, as shown in Figure 6.6-23 (See Attachment III.2.6 for 
details of the calculation). Figure 6.6-23 also presents a regression curve that incorporates the 18 
flow fields. The cumulative frequency of Figure 6.6-23 can be used, for example, in selecting 
ambient flow boundary conditions for drift-scale modeling. The similarity in flux distribution 
patterns for the 18 flow fields helps to define a flux-distribution factor for seepage estimation in 
the TSPA calculations on the scale of the site-scale UZ Model, but is not recommended for use 
in small-scale models, such as the drift-scale seepage models. The regression curve, with the 
equation given on the figure, may be used to correlate cumulative flux frequency within the 
repository with net infiltration rates for any future climatic scenarios. For example, use of the 
equation with x = 1, 2, and 5 gives results of 60%, 88%, and 99%. This indicates that 60%, 88%, 
and 99% repository blocks are subject to less than normalized fluxes of 1, 2 and 5, respectively. 
This provides data for PA calculations. 
100 
90 
80 
70 
60 
50 
40 
30 
20 
10
0 
NOTE: Equation is valid for 0.05 < x < 10. 
Normalized flux, x 
Figure 6.6-23. Cumulative Flux Distribution and Range as Functions of Normalized Percolation Flux 
within the Repository from the 18 Flow Fields 
MDL-NBS-HS-000006 REV01 August 2003 121 
Flux distribution 
y = 0.0117x5 - 0.3872x4 + 4.9349x3 - 30.329x2 + 89.9x - 3.6325 
0 2 4 6 8 10 
Cumulative percentage, y (%) 
U0050 
glaq_lA 
glaq_mA 
glaq_uA 
monq_lA 
monq_mA 
monq_uA 
preq_mA 
preq_uA 
glaq_lB 
glaq_mB 
glaq_uB 
monq_lB 
monq_mB 
monq_uB 
preq__mB 
preq_uB 
preq_lA 
preq_lB 
Poly. (average) 
Model Results  DTNs: LB03023DSSCP9I.001; LB03033DSSFF9I.001 U0050 UZ Flow Models and Submodels 
6.7 TRACER TRANSPORT TIMES 
This section summarizes simulated tracer transport using the nine base-case flow fields and the 
nine alternative flow fields. The results present an evaluation of tracer transport processes from 
the repository to the water table (saturated zone) and within the mountain, including the effects 
of different infiltration scenarios and adsorption. Studies described in this section provide insight 
into UZ flow patterns and transport processes. 
6.7.1 Methodology and Transport Parameters 
Simulation results and analyses of this section are based on transport studies of conservative and 
reactive tracers using the T2R3D V1.4 code (LBNL 1999 [146654]). The dual-permeability 
modeling approach with the 3-D TSPA-LA grid (Figure 6.1-1), as discussed in Section 6.1.1, is 
used in the transport simulations. The 18 steady-state, 3-D flow fields of Section 6.6 are directly 
input to the T2R3D code for modeling transport from the repository to the water table. 
To assess tracer transport times from the repository to the water table, tracers are treated as 
conservative (nonadsorbing) and reactive (adsorbing) components transported through the UZ. 
In both cases, hydrodynamic/mechanical dispersion through the fracture-matrix system is 
ignored, because sensitivity studies indicated that mechanical dispersion has an insignificant 
effect on the cumulative breakthrough curves of tracers at the water table (BSC 2001 [158726]). 
A constant molecular diffusion coefficient of 3.2  10-11 m2/s is used for matrix diffusion of the 
conservative component and 1.6  10-10 m2/s is used for the reactive component (DTN: 
LA0003JC831362.001 [149557]). The two diffusion coefficients are multiplied by porosity and 
tortuosity in the simulation to account for various units. In the case of a reactive or adsorbing 
tracer, several Kd values are used, as given in Table 6.7-1, for different units. These values were 
selected to approximate those for neptunium (237Np) transport (DTNs: LA0010JC831341.001 
[162476]; LA0010JC831341.002 [153321]; LA0010JC831341.003 [153322]; 
LA0010JC831341.004 [153323]; LA0010JC831341.005 [153320]; LA0010JC831341.006 
[153318]; LA0010JC831341.007 [153319]). For a conservative tracer, Kd is set to zero. These 
molecular diffusion coefficients and Kd values are selected to represent technetium and 
neptunium, respectively. Model parameters such as porosity and rock grain density were taken 
from the thermal properties (DTN: LB0210THRMLPRP.001 [160799]). 
Transport simulations were conducted for 1,000,000 years using nine constant infiltration rates 
of three climates. An initial, constant-source concentration was specified for the fracture 
continuum gridblocks representing the repository, released at the starting time of simulation. In 
addition, tracer transport was also investigated with tracers initially released from the matrix 
continuum of the repository block under the present-day mean infiltration scenario. 
August 2003 122 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table 6.7-1. Kd Values Used for Reactive Tracer Transport in Different Hydrogeologic Units 
MDL-NBS-HS-000006 REV01 
Hydrogeologic Unit 
Zeolitic matrix in CHn 
Vitric matrix in CHn 
Matrix in TSw 
Fault matrix in CHn 
Fractures and the matrix in the rest of units 
NOTE: Kd values selected from ranges in the following DTNs: 
LA0010JC831341.001 [162476], LA0010JC831341.002 [153321], 
LA0010JC831341.003 [153322], LA0010JC831341.004 [153323], 
LA0010JC831341.005 [153320], LA0010JC831341.006 [153318], 
and LA0010JC831341.007 [153319] 
6.7.2 Simulation Scenarios 
For each TSPA-LA flow simulation, as listed in Tables 6.2-9 and 6.6-1, there are two transport 
runs, one for conservative (*_tc) and one for reactive (*_np) tracer transport. For most cases, 
tracer first releases from fracture gridblocks within the repository. Tables 6.7-2 and 6.7-3 
summarize a total of 18  2 tracer-fracture-release simulation scenarios, corresponding to the 18 
TSPA-LA flow fields for the nine infiltration maps of three climates, respectively. There are 
only four tracer-matrix-release simulations, two each for the base-case and alternative flow fields 
(*tcM and *npM for Tc and Np, respectively). 
123 
4.0 
1.0 
1.0 
1.0 
0.0 
August 2003 
U0050 
Kd (cc/g) U0050 UZ Flow Models and Submodels 
Table 6.7-2. Transport Simulation Scenarios: Data Files and Corresponding Nine Base-Case Flow 
Fields with Nine Infiltration Rates 
Designation/ 
Transport Simulation 
Infiltration Map 
(DTN: GS000308311221.005 [147613] 
Designation/ 
Flow Simulation 
(Output-DTN: 
LB03023DSSCP9I.001) 
Present-day, lower-bound infiltration preq_lA 
Present-day, mean infiltration preq_mA 
preq_uA Present-day, upper-bound infiltration 
Monsoon, lower-bound infiltration monq_lA 
Monsoon, mean infiltration monq_mA 
Monsoon, upper-bound infiltration monq_uA 
glaq_lA Glacial transition, lower-bound infiltration 
glaq_mA Glacial transition, mean infiltration 
glaq_uA Glacial transition, upper-bound 
infiltration 
preqlA_tc 
preqlA_np 
preqmA_tc 
preqmA_tcM* 
preqmA_np 
preqmA_npM* 
prequA_tc 
prequA_np 
monqlA_tc 
monqlA_np 
monqmA_tc 
monqmA_np 
monquA_tc 
monquA_np 
glaqlA_tc 
glaqlA_np 
glaqmA_tc 
glaqmA_np 
glaquA_tc 
glaquA_np 
Output-DTN: LB03033DUZTRAN.001 
NOTE: * Tracer release from repository matrix blocks 
August 2003 124 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table 6.7-3. Transport Simulation Scenarios: Data Files and Corresponding Nine Alternative Fields 
with Nine Infiltration Rates 
Designation/ 
Flow Simulation 
Output-DTN: 
LB03033DSSFF9I.001 
preq_lB 
preq_mB 
preq_uB Present-day, upper-bound infiltration 
monq_lB 
monq_mB 
monq_uB 
glaq_lB Glacial transition, lower-bound infiltration 
glaq_mB Glacial transition, mean infiltration 
glaq_uB Glacial transition, upper-bound infiltration 
preqlB_tc 
preqlB_np 
preqmB_tc 
preqmB_tcM* 
preqmB_np 
preqmB_npM* 
prequB_tc 
prequB_np 
monqlB_tc 
monqlB_np 
monqmB_tc 
monqmB_np 
monquB_tc 
monquB_np 
glaqlB_tc 
glaqlB_np 
glaqmB_tc 
glaqmB_np 
glaquB_tc 
glaquB_np 
Output-DTN: LB03033DUZTRAN.001 
NOTE: * Tracer release from repository matrix blocks 
6.7.3 Simulation Results and Analyses 
Tracer transport times (since release from the repository to the water table) may be analyzed 
using a cumulative fractional breakthrough curve, as shown in Figures 6.7-1, 6.7-2, and 6.7-3 for 
1 million years. The fractional mass breakthrough in these figures is defined as the cumulative 
mass of a tracer arriving at the water table over the entire bottom model boundary over time, 
normalized by the total mass of the component initially introduced at the repository. In the 
figures, solid-line and dotted-line curves of the same color represent simulation results of 
conservative/nonadsorbing tracer transport and adsorbing tracer transport respectively. The three 
figures show a wide range of tracer transport times with different infiltration rates and types of 
tracers considered in the 40 simulations, listed in Tables 6.7-2 and 6.7-3. 
As indicated by Figures 6.7-1 and 6.7-2, the predominant factors in controlling tracer transport 
are (1) surface-infiltration rates or net water recharge, (2) adsorption effects, i.e., whether the 
tracer is conservative or reactive, and (3) whether it is initially released from fracture or matrix 
125 
Designation/ 
Transport 
Simulation 
MDL-NBS-HS-000006 REV01 
Infiltration Map 
DTN: GS000308311221.005 [147613] 
Present-day, lower-bound infiltration 
Present-day, mean infiltration 
Monsoon, lower-bound infiltration 
Monsoon, mean infiltration 
Monsoon, upper-bound infiltration 
August 2003 
U0050 U0050 UZ Flow Models and Submodels 
blocks in the repository. The figures also show that the base-case flow fields (Figure 6.7-1) and 
the alternative flow fields (Figure 6.7-2) result in very similar tracer transport times. However, 
the base-case flow scenarios generate slightly shorter travel times or more conservative results in 
general. 
Statistics of tracer transport times of 10% and 50% mass breakthrough at the water table for the 
36 simulation scenarios of tracer-fracture release plus four tracer-matrix-release scenarios, are 
given in Tables 6.7-4 and 6.7-5, respectively. Figure 6.7-4 correlates average infiltration rates 
and tracer transport times at 50% mass breakthrough for the 36 simulation scenarios of tracerfracture 
release. Figures 6.7-1, 6.7-2, 6.7-3 and 6.7-4, and the statistical data of Tables 6.7-4 and 
6.7-5, show the following: 
 Tracer transport times vary inversely to the average surface infiltration (net water 
recharge) rate over the model domain (Figure 6.7-4). When the average infiltration rate 
increases from 5 to 35 (mm/yr), average tracer transport (50% breakthrough) times 
decrease by more than one order of magnitude for both adsorbing and nonadsorbing 
species. 
 Nonadsorbing tracers migrate (from the repository to the water table) one to two orders 
of magnitude faster than an adsorbing tracer under the same infiltration condition 
(Figure 6.7-4). 
 Tracer transport times are significantly longer when tracers are initially released from 
repository matrix blocks instead of fractures. For conservative (i.e., nonadsorbing) 
transport, travel times are increased by one to two orders of magnitude (Figure 6.7-3, 
Tables 6.7-4 and 6.7-5). For reactive (i.e., adsorbing) tracers, the travel times are 
increased by more than one order of magnitude at 10% mass breakthrough and by 
approximately two times at 50% breakthrough. 
 A comparison of travel/transport times obtained from the nine base-case flow fields with 
those from the nine alternative flow fields indicates that the base-case flow fields show 
slightly shorter travel times (or slightly more conservative results in general) under the 
same infiltration scenarios. 
August 2003 126 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
1 
0.9 
0.8 
0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1 
100 0
10-1 
Figure 6.7-1. Simulated Breakthrough Curves of Cumulative Tracer Mass Arriving at the Water Table, 
after Release from Fractures in the Repository, Using the Base-Case Flow Fields with 
the Nine Infiltration Scenarios for Nonadsorbing and Adsorbing Tracers 
Time (years) 
MDL-NBS-HS-000006 REV01 
Fractional Mass Breakthrough atWater Table 
preqlA_tc 
preqlA_np 
preqmA_tc 
preqmA_np 
prequA_tc 
prequA_np 
monqlA_tc 
monqlA_np 
monqmA_tc 
monqmA_np 
monquA_tc 
monquA_np 
glaqlA_tc 
glaqlA_np 
glaqmA_tc 
glaqmA_np 
glaquA_tc 
glaquA_np 
106 105 104 103 102 101 
U0050 
Model Results - DTN: LB03033DUZTRAN.001. 
August 2003 127 UZ Flow Models and Submodels 
1 
0.9 
0.8 
0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1 
100 0
10-1 
Figure 6.7-2. Simulated Breakthrough Curves of Cumulative Tracer Mass Arriving at the Water Table, 
after Release from Fractures in the Repository, Using the Alternative Flow Fields with the 
Nine Infiltration Scenarios for Nonadsorbing and Adsorbing Tracers 
Time (years) 
MDL-NBS-HS-000006 REV01 
Fractional Mass Breakthrough atWater Table 
preqlB_tc 
preqlB_np 
preqmB_tc 
preqmB_np 
prequB_tc 
prequB_np 
monqlB_tc 
monqlB_np 
monqmB_tc 
monqmB_np 
monquB_tc 
monquB_np 
glaqlB_tc 
glaqlB_np 
glaqmB_tc 
glaqmB_np 
glaquB_tc 
glaquB_np 
106 105 104 103 102 101 
U0050 
Model Results - DTN: LB03033DUZTRAN.001. 
August 2003 128 UZ Flow Models and Submodels 
Figure 6.7-3. Comparison of Simulated Breakthrough Curves of Cumulative Tracer Mass Arriving at 
the Water Table, after Release from Fractures and Matrix Blocks in the Repository, Using 
the Base-Case and Alternative Flow Fields under the Present-Day Mean Infiltration Flow 
Fields with the Nine Infiltration Scenarios for Nonadsorbing and Adsorbing Tracers 
Time (years) 
MDL-NBS-HS-000006 REV01 
Fractional Mass Breakthrough atWater Table 
1 
0.9 
0.8 
0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1 
101 0
100 
preqmA_tc 
preqmA_np 
preqmB_tc 
preqmB_np 
preqmA_tcM 
preqmA_npM 
preqmB_tcM 
preqmB_npM 
106 105 104 103 102 
U0050 
Model Results - DTN: LB03033DUZTRAN.001. 
August 2003 129 U0050 
Times (years) 
Types 
of Tracer 
10% Breakthrough 50% Breakthrough 
Times (years) 
157,000 
300,000 
160 
7,400 
590 
38,000 
230 
21,000 
69 
2,800 
3,900 
167,000 
110 
9,200 
34 
1,300 
UZ Flow Models and Submodels 
Table 6.7-4. Tracer Transport Times at 10% and 50% Mass Breakthrough Times for 18 Transport 
Simulation Scenarios, Corresponding to the Nine Base-Case Flow Fields with Nine 
Infiltration Rates 
Designation/ 
Transport 
Simulation 
preqlA_tc Nonadsorbing 3,800 169,000 
preqlA_np Adsorbing 305,000 > 1,000,000 
preqmA_tc Nonadsorbing 50 3,900 
preqmA_tcM* Nonadsorbing 4,600 33,000 
preqmA_np Adsorbing 3,400 
preqmA_npM* Adsorbing 48,000 
prequA_tc Nonadsorbing 5 
prequA_np Adsorbing 190 
monqlA_tc Nonadsorbing 13 
monqlA_np Adsorbing 980 
monqmA_tc Nonadsorbing 7 
monqmA_np Adsorbing 340 
monquA_tc Nonadsorbing 2 
monquA_np Adsorbing 44 
glaqlA_tc Nonadsorbing 43 
glaqlA_np Adsorbing 4,400 
glaqmA_tc Nonadsorbing 4 
glaqmA_np Adsorbing 187 
glaquA_tc Nonadsorbing 1 
glaquA_np Adsorbing 8 
Model Results - DTN: LB03033DUZTRAN.001 
NOTE: * Tracer release from repository matrix blocks 
August 2003 130 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table 6.7-5. Tracer Transport Times at 10% and 50% Mass Breakthrough Times for 14 Transport 
Simulation Scenarios, Corresponding to the Nine Alternative Flow Fields with Nine 
Infiltration Rates 
Times (years) 
Types 
of Tracer 
Designation/ 
Transport Simulation 
10% Breakthrough 50% Breakthrough 
Times (years) 
preqlB_tc Nonadsorbing 34,000 226,000 
preqlB_np Adsorbing 940,000 > 1,000,000 
preqmB_tc Nonadsorbing 51 3,600 
preqmB_tcM* Nonadsorbing 5,900 37,000 
preqmB_np Adsorbing 2,900 157,000 
preqmB_npM* Adsorbing 53,000 345,000 
prequB_tc Nonadsorbing 6 180 
prequB_np Adsorbing 220 8,500 
monqlB_tc Nonadsorbing 26 660 
monqlB_np Adsorbing 1,300 60,000 
monqmB_tc Nonadsorbing 6 210 
monqmB_np Adsorbing 330 21,000 
monquB_tc Nonadsorbing 2 74 
monuB_np Adsorbing 50 3,100 
glaqlB_tc Nonadsorbing 120 6,200 
glaqlB_np Adsorbing 6,200 268,000 
glaqmB_tc Nonadsorbing 4 105 
glaqmB_np Adsorbing 180 9,500 
glaquB_tc Nonadsorbing 1 36 
glaquB_np Adsorbing 10 1,350 
Model Results - DTN: LB03033DUZTRAN.001 
NOTE: * Tracer release from repository matrix blocks 
August 2003 131 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
1.0E+06 
1.0E+05 
tc-A 
np-A 
tc-B 
np-B 
1.0E+04 
1.0E+03 
1.0E+02 
1.0E+01 
Average Infiltration Rate Over the model domain (mm/y) 
Travel times for 50% mass breakthrough 
(years) 
U0050 
1 10 100 
Model Results - DTN: LB03033DUZTRAN.001. 
NOTE: Some pairs of points for A and B simulations are superposed. See Tables 6.7-4 and 6.7-5. 
Figure 6.7-4. Correlation of Average Infiltration Rates and Tracer Transport Times at 50% Mass 
Breakthrough for the 36 Tracer-Fracture-Release Simulation Scenarios 
August 2003 132 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
The tracer transport times, as shown in Figures 6.7-1, 6.7-2, and 6.7-3 and Tables 6.7-4 and 6.7- 
5, are relatively shorter than those estimated using the UZ flow and transport model for TSPASR 
(BSC 2001 [158726]) for the same infiltration rates. This is primarily because the LA 
repository design is close to or at several faults, such as the Drill Hole Wash fault (Figure 6.1-1), 
which provide fast flow pathways for tracer transport. Moreover, the current LA UZ model 
predicts higher fracture flow components at the repository level, e.g., fracture flow percentage is 
94% for the case of preq_mA (Table 6.6-3), compared with 84% of the previous SR case of 
pa_pchm1 (BSC (2001 [158726], Table 6-22) for the same infiltration scenario. 
The tracer-transport-simulation results can also be used to estimate potential locations or areas 
where radionuclides are most likely to break through along high-flux flow paths at the water 
table. Figures 6.7-5, 6.7-6, 6.7-7, and 6.7-8 show cumulative and normalized mass arrival 
contours at the water table at 1,000 and 1,000,000 years. The cumulative mass arrival is defined 
as cumulative mass arrived at each grid column of the water table over time, normalized by the 
total initial mass released at the repository. These figures present examples of breakthrough at 
the water table for conservative and reactive tracer transport with the present-day, mean 
infiltration rate. 
Figures 6.7-5 and 6.7-6 compare percentage mass arrival contours of a conservative and reactive 
tracer, respectively, at the water table after 1,000 years, simulated using the present-day, mean 
infiltration of the base-case flow field (preq_mA). The two figures clearly indicate a significant 
difference from the two tracer modeling results in distributions of tracer mass arrivals along the 
water table. Without adsorption, in 1,000 years the conservative tracer (Tc) has a much larger 
area of breakthrough, covering the entire area directly below the repository footprint, spreading 
to the east in the north. At this time, about 40% of the total initial mass of conservative tracers 
has arrived at the water table (see Figure 6.7-3), whereas only about 2% of the reactive tracer 
(Np) breaks through, and only along and near the major faults, owing to adsorption effects in the 
rock matrix. 
At a later time of 1,000,000 years, Figures 6.7-7 and 6.7-8 show nearly identical mass arrival 
contours for the two tracers below the repository footprint. This is because 90100% of both 
tracers have arrived at the water table at this time, which are transported under the same flow 
field. The 1,000- and 1,000,000-year contours are used to illustrate the predominantly downward 
percolation flow patterns, early influence of faults (especially the in-block faults), and small 
long-term effects of slow diffusion. The flow patterns are for hypothetical nonsorbing and 
sorbing tracers without taking into consideration the radioactive decay which reduce the 
concentrations. The 1,000- and 1,000,000-year flow pattern results can be further verified by 
comparing results at intermediate times between 1,000- and 1,000,000-years from radionuclide 
transport models using the unsaturated zone flow fields (transport models described in BSC 2002 
[160819], Section 1.11). The information depicted in Figures 6.7-7 and 6.7-8 does not provide 
direct feeds to TSPA-LA; radionuclide transport is studied in detail in the downstream transport 
models. 
August 2003 133 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Figure 6.7-5. Simulated Cumulative, Normalized Mass Arrival Contours of a Conservative Tracer at the 
Water Table after 1,000 Years, Indicating Potential Breakthrough Locations at the Time, 
Nevada Coordinate E-W (m) 
Using the Present- Day, Mean Infiltration Scenario 
Model Results - DTN: LB03033DUZTRAN.001. 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
Cumulative mass arrival at the water table 
(1000 years, tc)
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillho 
le 
Wash 
Fault 
S 
o 
litario 
Canyo 
n 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
134 
Normalized mass 
arrival (%) 
6.000E-01 
3.461E-01 
1.997E-01 
1.152E-01 
6.645E-02 
3.834E-02 
2.212E-02 
1.276E-02 
7.360E-03 
4.246E-03 
2.449E-03 
1.413E-03 
8.152E-04 
4.703E-04 
2.713E-04 
1.565E-04 
9.029E-05 
5.209E-05 
3.005E-05 
1.733E-05 
1.000E-05 
174000 
U0050 
August 2003 UZ Flow Models and Submodels 
Figure 6.7-6. Simulated Cumulative, Normalized Mass Arrival Contours of a Reactive Tracer at the 
Water Table after 1,000 Years, Indicating Potential Breakthrough Locations at the Time, 
Nevada Coordinate E-W (m)
Model Results - DTN: LB03033DUZTRAN.001. 
Using the Present-Day, Mean Infiltration Scenario 
Nevada Coordinate N-S (m) 
MDL-NBS-HS-000006 REV01 
238000 
236000 
234000 
232000 
Cumulative mass arrival at the water table 
(1000 years, np) 
Bow Ridge Fault 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillhole 
Wash 
Fault 
S 
o 
litario 
Canyon 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
230000 
172000 170000 168000 
135 
Normalized mass 
arrival (%) 
6.000E-01 
3.461E-01 
1.997E-01 
1.152E-01 
6.645E-02 
3.834E-02 
2.212E-02 
1.276E-02 
7.360E-03 
4.246E-03 
2.449E-03 
1.413E-03 
8.152E-04 
4.703E-04 
2.713E-04 
1.565E-04 
9.029E-05 
5.209E-05 
3.005E-05 
1.733E-05 
1.000E-05 
174000 
U0050 
August 2003 UZ Flow Models and Submodels 
Figure 6.7-7. Simulated Cumulative, Normalized Mass Arrival Contours of a Conservative Tracer at the 
Water Table after 1,000,000 Years, Indicating Potential Breakthrough Locations at the 
Nevada Coordinate E-W (m) 
Time, Using the Present-Day, Mean Infiltration Scenario 
Model Results - DTN: LB03033DUZTRAN.001. 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
Cumulative mass arrival at the water table 
(1,000,000 years, tc) 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillhole 
Wash 
Fault 
S 
o 
litario 
Canyon 
Fault 
Gho 
s 
t 
Dance 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
136 
Normalized mass 
arrival (%) 
6.000E-01 
3.461E-01 
1.997E-01 
1.152E-01 
6.645E-02 
3.834E-02 
2.212E-02 
1.276E-02 
7.360E-03 
4.246E-03 
2.449E-03 
1.413E-03 
8.152E-04 
4.703E-04 
2.713E-04 
1.565E-04 
9.029E-05 
5.209E-05 
3.005E-05 
1.733E-05 
1.000E-05 
174000 
U0050 
August 2003 UZ Flow Models and Submodels 
Figure 6.7-8. Simulated Cumulative, Normalized Mass Arrival Contours of a Reactive Tracer at the 
Water Table after 1,000,000 Years, Indicating Potential Breakthrough Locations at the 
Nevada Coordinate E-W (m) 
Time, Using the Present-Day, Mean Infiltration Scenario 
Model Results - DTN: LB03033DUZTRAN.001. 
MDL-NBS-HS-000006 REV01 
Nevada Coordinate N-S (m) 
238000 
236000 
234000 
232000 
Cumulative mass arrival at the water table 
(1,000,000 years, np) 
Sever 
Wash 
Fault 
Pagany 
Wash 
Fault 
Drillhole 
Wash 
Fault 
Solitario 
Canyon 
Fault 
Ghost 
Dance 
Fault 
Imbricate 
Fault 
Bow Ridge Fault 
230000 
172000 170000 168000 
137 
Normalized mass 
arrival (%) 
6.000E-01 
3.461E-01 
1.997E-01 
1.152E-01 
6.645E-02 
3.834E-02 
2.212E-02 
1.276E-02 
7.360E-03 
4.246E-03 
2.449E-03 
1.413E-03 
8.152E-04 
4.703E-04 
2.713E-04 
1.565E-04 
9.029E-05 
5.209E-05 
3.005E-05 
1.733E-05 
1.000E-05 
174000 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
6.8 SENSITIVITY ANALYSES OF ACTIVE FRACTURE PARAMETER AND 
FRACTURE POROSITY 
In the Active Fracture Model conceptualization (AFM, Liu et al. 1998 [105729], pp. 26382641) 
only a portion of fracture networks are active (hydraulically conductive) under unsaturated 
conditions. Numerically, this active portion is defined as a function of water saturation S and the 
active fracture parameter  (Liu et al. 1998 [105729], pp. 26382641). By definition,  ranges 
between 0 and 1, with =0 or S=1 (corresponding to a saturated condition) signifying that all 
fractures are active, and =1 signifying the smallest active fracture portion for a given saturation. 
Water flux in TSw layers of Yucca Mountain tuff is mainly dominated by fracture flow. 
Uncertainties in  impact the partition of water content and water flux between matrix and 
fractures of model gridblocks and, in turn, affect (possibly insignificantly) the entire flow field 
by determining the effective fracture permeability and the effective interface area between 
fracture and matrix (Liu et al. 1998 [105729], pp. 26382641). For transport, in addition to the 
effects of changing the flow field, the determination of effective fracturematrix interface area 
by  will significantly impact the diffusive solute flux between fractures and matrix, and all 
transport behavior is sensitive to . 
As planned in BSC (2002 [160819], Section 1.10.4), the active fracture parameter, , was 
evaluated in BSC (2003 [161773]), using carbon-14 data. In Section 6.8.1 of the present Model 
Report, additional sensitivity studies are reported to analyze the sensitivities of simulated water 
saturation, water potential, and percolation flux with respect to . 
In addition, the sensitivity of tracer transport with respect to  and fracture porosity are discussed 
in Section 6.8.2.1 and Section 6.8.2.2, respectively. 
6.8.1 Sensitivity Analyses of Flow Field with Respect to the Active Fracture Parameter () 
In these analyses, we consider the  of TSw units, except for TSw31, as a sensitivity analysis 
case and the units below the repository horizon (including the units at where the repository is 
located) as another sensitivity analysis case (Table 6.8-1). According to the evaluation of  using 
carbon-14 data, conducted in BSC (2003 [161773], pp. 7882), the proper  value of TSw32 
TSw38 ranges between 0.20.4. However, the calibrated values in DTN: 
LB0208UZDSCPMI.002 [161243] for these units are 0.6 (TSw32TSw33) and 0.569 (TSw34 
TSw38), which are out of the proper range suggested in BSC (2003 [161773], pp. 7882). To 
test the impact of  uncertainties on flow field, we set up two 3-D flow simulations using  
values different from the calibrated values (Table 6.8-1). The  values used in the sensitivity 
analyses simulations are half of the calibrated values of the respective units. For TSw32TSw38, 
these values are within the suggested range. One simulation was performed using reduced  
values of TSw32TSw39 (Table 6.8-1, simulation ID: TSw). The simulation is focused on 
evaluating the impact of uncertainties in TSw units. Another simulation used the reduced  
values of all units below the repository (Table 6.8-1, simulation ID: UnderRepo). The 
simulations take the input to the base case flow field simulation (Output-DTN: 
LB03023DSSCP9I.001, present-day mean infiltration, DTN: GS000308311221.005 [147613]) 
and change the  values of the corresponding units (Table 6.8-1). 
The  Values Used in Flow Simulations for Sensitivity Analyses 
138 
Table 6.8-1. 
MDL-NBS-HS-000006 REV01 August 2003 U0050 UZ Flow Models and Submodels 
 Values 
TSw 
0.129 
0.300 
0.284 
0.185 
0.125 
0.370 
0.250 
0.199 
6.8.1.1 
Rock units 
Output-DTN: LB0303RDTRNSNS.001 
discussed below. 
MDL-NBS-HS-000006 REV01 
Base Case 
TSw31 0.129 
TSw32, TSw33 0.600 
TSw34TSw38 0.569 
TSw39z 0.370 
TSw39v 0.250 
CH1zCH6z, PP4z, PP1z, BF2z and 0.370 
TR2z 
CH1vCH6v 0.250 
PP3d, PP2d, BF3d and TR3d 0.199 
NOTE: Base-case values are from DTN: LB0208UZDSCPMI.002 [161243] and the same with those used 
in the flow field simulations. 
The results of these sensitivity analyses (Figure 6.8-1 and Figure 6.8-2) show that changing  
results in only small changes in matrix liquid saturations and water potentials. The details are 
Liquid Saturation and Potential 
Figure 6.8-1 shows that implementing a  of one half the calibrated value (used for base case 
present-day, mean infiltration flow field simulation, Table 6.8-1) in TSw layers leads to some 
small changes in the simulated matrix water saturation and water potential. The matrix liquid 
saturation and water potential changes in response to the changes of  in all units below the 
repository are also small (Figure 6.8-2). Thus, the saturation and water potential are not sensitive 
to the uncertainties in . In addition, the sensitivity analysis results also indicate that the change 
of  values has an insignificant effect on fracture liquid saturation and water potential. In fact, a 
comparison at SD-6 using the two  values indicates that average absolute changes in fracture 
saturation and capillarity pressure are only 0.004 and 230 Pa, respectively. 
139 
UnderRepo 
0.129 
0.600 
0.284 
0.185 
0.125 
0.185 
0.125 
0.100 
August 2003 Figure 6.8-1. Comparison of (a) Simulated Matrix Liquid Saturation and (b) Water Potential Using 
Calibrated Hydraulic Properties (Solid Line) with That Obtained Using Smaller (Half)  of 
Figure 6.8-2. Comparison of (a) Simulated Matrix Liquid Saturation and (b) Water Potentials Using 
Calibrated Hydraulic Properties (Solid Line) with That Obtained Using Smaller (Half)  of 
Under Repository Units (Dashed Line) for Borehole USW SD-6 
Model ResultsDTN LB0303RDTRNSNS.001 
Model ResultsDTN LB0303RDTRNSNS.001 
U0050 
TCw 
PTn 
TSw 
Calibrated AFP 
Smaller AFP for TSw units 
Model unit interface 
1 
CHn 
10 
TCw 
PTn 
TSw 
Calibrated AFP 
Smaller AFP for under repository 
Model unit interface 
1 
CHn 
10 
UZ Flow Models and Submodels 
1400 TCw 
PTn 
1300 
1200 
1100 
TSw 
CHn 
1000 
900 
800 
700 
0.25 0 
(a) 
TSw Units (Dashed Line) for Borehole USW SD-6 
TCw 1400 
PTn 
TSw 1300 
1200 
Calibrated AFP 
Smaller AFP for under repository 
Model unit interface 
1100 
CHn 1000 
900 
800 
0.25 
700
0 
Elevation (m) Elevation (m) 
(a) 
Calibrated AFP 
Smaller AFP for TSW units 
Model unit interface 
Saturation 
Saturation 
MDL-NBS-HS-000006 REV01 
0.75 0.5 
0.75 0.5 
Elevation (m 
1100 
TSw 
CHn 1400 
1000 
1300 
900 
1200 
800 
0.25 
1100 
700
0 
1000 
900 
800 
Elevation (m) Elevation (m) Elevation 
1 
700
0 2 
Water potential (bar) 
(b) 
Smaller AFP for under repository 
Model unit interface 
1100 
1400 
TSw 
1000 
CHn 
1300 
900 
1200 
800 
1100 
0.25 
700
0 
1000 
900 
800 
1 700
0 2 
Water potential (bar) 
(b) 
140 
0.75 0.5 
Saturation 
8 6 4 
0.75 0.5 
Saturation 
8 6 4 
August 2003 U0050 UZ Flow Models and Submodels 
Percolation Flux through Repository Layers 6.8.1.2 
As listed in Table 6.8-2, the average fracture flux change within the repository area, caused by 
the  changes in TSw layers, is 0.285%. The fracture fluxes, within the repository area, change 
0.497%, on average, in response to the  changes in units below the repository. Average values 
for the entire repository layer and the water table are also listed in Table 6.8-2 (for calculation 
details, see Attachment III, Section III.3). 
Table 6.8-2. 
Simulation ID 
6.8.2.1 
Relative Changes of Fracture Flux in Response to the Changes in  
Water Table 
-2.32E-2 
August 2003 
Entire Repository Layer 
141 
Within the 
Repository Area 
TSw -2.85E-3 1.51E-2 2.48E-2 
UnderRepo -4.97E-3 -2.32E-2 
Output-DTN: LB0303RDTRNSNS.001 
NOTE: Calculation is shown in Attachment III, Section III.3. 
Therefore, considering the rather small changes in modeled saturation, water potential, and the 
very small change in percolation flux (on average), one can conclude that the water flow field is 
not significantly sensitive to . 
6.8.2 Sensitivity Analyses of Transport 
Transport Sensitivity to  
Solute transport through tuff is mainly carried out by advection and diffusion. Because of the 
small flux rate, hydraulic dispersion plays a very minor role and therefore is generally ignored in 
simulations. Advection is determined by water flux and thus not sensitive to . However, because 
the fracture Darcy velocity is two to three orders of magnitude larger than matrix Darcy velocity, 
the advective solute flux in fractures is much faster than in the matrix. This leads to a short-term 
concentration difference between fractures and matrix, causing significant diffusion between 
them. At steady state, when the concentrations in fractures and matrix must reach equilibrium, 
the diffusion between fractures and the matrix will vanish. Therefore, the steady-state 
concentration is insensitive to . The short-term transport behavior may be sensitive to the 
changes in , but the long-term transport behavior will be relatively insensitive to the changes of 
this parameter. 
Diffusion is driven by the concentration gradient and the effective diffusion coefficient of rocks. 
Numerically, the diffusion between fractures and matrix is calculated upon the concentration 
differences between matrix and fractures, the active (effective) interface area between fractures 
and matrix, and the fracture spacing. The active interface area between matrix and fractures is 
related to . A larger  defines a smaller effective interface area. Therefore, the diffusion between 
the matrix and fractures must be sensitive to this parameter, and the entire transport rate is 
affected. 
In the Yucca Mountain UZ, transport is dominated by the fracture advective flux (BSC 2001 
[161340], pp. 3536). The diffusion from fractures into matrix functions as a buffer for the 
MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
advection through fractures, similar to kinetic sorption, which impedes the transport through 
fractures (BSC 2001 [161340], p. 38). 
To test the sensitivity of tracer transport to the active fracture parameter, , three conservativetracer 
transport simulations were performed. One simulation is the base case (or reference case), 
using the calibrated  and the base-case flow field; and the other two are for sensitivity analysis. 
Of these other two, one has a smaller  (1/2 of the calibrated value) for TSw units except for 
TSw1, the same as the first flow-sensitivity-analysis simulation in Section 6.8.1 (simulation ID: 
TSw in Table 6.8-1). The other has a smaller  (1/2 of the calibrated value) for repository units 
and all units below the repository, similar to the second flow-sensitivity-analysis simulation 
(Simulation ID: UnderRepo in Table 6.8-1, see Section 6.8.1). All simulations were performed 
using T2R3D V1.4 (LBNL 1999 [146654]). The base-case flow field (using calibrated rock 
properties) comes from flow simulation (Output-DTN: LB03023DSSCP9I.001, see Section 
6.2.5). The flow fields for two reference simulations were taken from flow sensitivity analyses 
(Section 6.8.1). 
The transport models simulate the tracer transport processes from the repository to the 
groundwater table. The tracer is represented by technetium, with diffusion coefficients of 3.20 ~ 
10-11m2/s (DTN: LA0003JC831362.001 [149557]). The cumulative relative mass of the tracer 
arriving at the groundwater table (breakthrough curves), obtained from simulations using 
different  values, are presented in Figure 6.8-3. The figure shows that tracer transport is 
relatively sensitive to . The base case is conservative, the differences among the cases are small 
after 7,000 years and become negligible after 20,000 years. With the base-case parameter, the 
tracer arrives at the water table faster than the two reference cases. In the base case, 20% of the 
total input mass arrives at the water table at (approximately) 150 years (Figure 6.8-3), and 50% 
of the total mass arrives at the water table at (approximately) 3,400 years (Figure 6.8-3). The two 
sensitivity analyses cases give similar results: 20% mass arrival at 1,900 years and 50% at 
(approximately) 7,100 years, (Figure 6.8-3). However, the times for 100% mass arrival at the 
groundwater table in all three cases are similar. This indicates that long-term transport behavior 
is not very sensitive to the change in . 
The base-case with the larger  values defines a smaller active interface area between fracture 
and matrix. Consequently, the buffer effect of matrix diffusion is smaller. This causes faster 
breakthrough of the tracer. In the two sensitivity cases, a smaller  defines a larger active 
interface area and hence larger buffer effects from matrix diffusion. Therefore, the overall 
transport is slower. In summary, this study indicates that larger  values give more conservative 
or faster tracer transport times from the repository to the water table. 
August 2003 142 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
1.00 
0.80 
0.60 
0.40 
0.20 
0.00 
Figure 6.8-3. Comparison of a Simulated Breakthrough Curve of Relative Tracer Mass at the 
6.8.2.2 
Groundwater Table Obtained for the Base Case (Using Calibrated Rock Hydraulic 
Properties, Red Solid Line), a Case Using the Smaller  of TSw Units (Blue Dash Line), 
and Another Case Using the Smaller  of All Units below the Repository (Green Dash 
Line) 
Transport Sensitivity to Fracture Porosity 
To test the sensitivity of tracer transport with respect to the uncertainties in fracture porosity, a 
transport simulation, using the same input data as the base-case simulation in Section 6.8.2.1 
(except for fracture porosity), was performed. This simulation uses smaller fracture porosity 
(1/10 of the calibrated values) for all units. However, the steady-state flow field is independent 
of fracture porosity, and only the changes in repository units and units below the repository may 
impact the early travel time of the tracer arriving at the groundwater table. Simulation results are 
plotted in Figure 6.8-4 and compared to the base-case results in the figure. The figure shows that 
the fractional mass breakthrough of the tracer arrived at the water table is larger only during the 
first 50 years if the fracture porosity is one order of magnitude smaller than the calibrated value. 
However, the accumulated breakthrough mass arrival is quite small during this period in both 
cases (less than 15% of the released mass). After 100 years, both simulation cases give similar 
results: almost 50% of the tracer mass arrives at the groundwater bottom at 3,400 years, and the 
two breakthrough curves are not differentiable (Figure 6.8-4). 
This result indicates that radionuclide transport is sensitive to fracture porosity only at the early 
times (less than 50 years), and in long-term evolution, transport behavior is not sensitive to this 
MDL-NBS-HS-000006 REV01 August 2003 143 
Time (year) 
1.00E+0 
Fractional Mass Breakthrough 
U0050 
1.00E+6 
1.00E+5 
Calibrated gamma (base case) 
Changed gamma of TSw units 
1.00E+4 
Changed gamma of units below the repository 
1.00E+3 
1.00E+2 
1.00E+1 
Model ResultsDTN: LB0303RDTRNSNS.001 UZ Flow Models and Submodels 
parameter. Overall, this sensitivity analysis shows that fracture porosity has an insignificant 
effect on tracer transport. 
1.00 
0.80 
0.60 
0.40 
0.20 
0.00 
Figure 6.8-4. Comparison of Simulated Breakthrough Curve of Relative Tracer Mass at the 
Groundwater Table Obtained for the Base Case (Using Calibrated Rock Hydraulic 
Properties, Red Solid Line), and a Case Using a Smaller (1/10) Fracture Porosity (Blue 
Dash Line) 
6.9 UNCERTAINTIES AND ALTERNATIVE MODELS 
The calibrated model parameters of Section 6.2, as well as the 18 3-D flow fields of Section 6.6, 
are currently the best estimates with the UZ Flow Model. The validation efforts of Section 7 will 
further show the validity of the model in describing the UZ hydrological, geochemical, and 
geothermal conditions. It should be recognized, however, that some uncertainties are associated 
with these model parameters and output results, although they are based on qualified 
field-observation data, hydrogeological conceptual understanding, and integrated modeling 
studies. Uncertainties arise from (1) uncertainty in observed parameters and field data; (2) 
uncertainty in estimated present-day and future climates; (3) approximations used in 
hydrogeological conceptual models, such as steady-state flow conditions; (4) scale-dependent 
heterogeneity and model input parameters in the UZ fracture-matrix system; (5) the complexity 
of different UZ coupled processes; and (6) the limitations of current modeling and large 
volume-average approaches. 
MDL-NBS-HS-000006 REV01 August 2003 144 
Fractional Mass Breakthrough 
U0050 
1.00E+6 
Calibrated porosity (por_Ca) 
1.00E+5 
0.1*por_Ca 
1.00E+4
Time (year) 
Model ResultsDTN: LB0303RDTRNSNS.001 
1.00E+3 
1.00E+2 
1.00E+1 
1.00E+0
U0050 UZ Flow Models and Submodels 
Numerical representation of a real-world hydrogeologic system involves discretization of a 
model volume into a large number of elements, with each element assigned the necessary 
attributes or properties. For the model used, the necessary properties are known at only a 
relatively few locations within the model domain. Limitations of the software used to develop 
the UZ models at Yucca Mountain may also create a potential source of uncertainty, which is 
addressed in the Software Qualification per AP-SI.1Q, Software Management. 
Efforts have been made to quantify and reduce uncertainties associated with model parameters 
and output results in this Model Report. A total of six sets of model input parameters are 
developed, with three base cases and three alternatives, which cover the effect of uncertainties in 
lower, mean, and upper bound infiltration rates. Uncertainties with present-day and future 
climates are investigated using three scenariospresent day, monsoon and glacial transition 
climateswhich combine for nine different net infiltration scenarios. In addition, sensitivity and 
uncertainty with the active fracture model parameter, , is evaluated for its impact on flow and 
transport modeling results. The sensitivity analysis shows that  has little effect on simulated 
percolation fluxes, but a large impact on tracer transport. The  value used in the UZ Model 
produces more conservative transport results. 
Uncertainties with UZ conceptual flow models are studied using two conceptual models of the 
PTn unit, i.e., base-case and alternative models, and using a permeability-barrier perched water 
model. The base-case PTn model is selected for its better predictions of chloride data as well as 
moisture data. Each of the conceptual models uses three different parameter sets for lower, mean, 
and upper bound infiltration rates to cover the uncertainties and possible ranges with model 
parameters and infiltration rates. This results in nine base-case and nine alternative 3-D UZ flow 
fields. In general, the nine base-case flow fields show more lateral flow occurring within the PTn 
than those predicted by the alternative model. Furthermore, analyses have been done for the 
impact of behavior on tracer or radionuclide transport for the nine base-case and nine alternative 
flow fields. The results of 40 3-D tracer-transport simulations show a wide range of tracer 
transport times from the repository to the water table, and the base-case model gives slightly 
more conservative estimates in general. 
Uncertainty may exist in the analysis of lateral flow in the PTn unit because of the choice of 
approach. Different approaches may yield different conclusions. Flint et al. (2003 [163967]) 
concluded that lateral flow may occur in the PTn on a smaller scale than indicated in Figures 
6.6-1 to 6.6-3. However, their conclusion is based on a different approach from the one used in 
this Model Report. First, Flint et al. (2003 [163967]) use an analytical solution which is only 
applicable to single-porosity porous-medium flow with two semi-infinite layers, and may not be 
suitable for the multi-layered fracture-matrix system, to estimate lateral flow. Second, the 
analytical solution uses a Gardner's equation for relative permeability, which is different from 
what is used in this Model Report. Third, Flint et al. (2003 [163967]) present a 2-D modeling 
study without including 3-D effects, and also acknowledge that no one approach, analysis, or 
data set provides can establish whether large-scale lateral flow occurs above the repository. 
In addition to different flow fields, the timing for changes of climate states is uncertain. Two 
recent studies estimate the future climate states at Yucca Mountain based on past climate 
records, with USGS (2001 [158378]) focusing on the period from present to 10,000 years and 
Sharpe (2003 [161591]) focusing on the period from 10,000 to 1,000,000 years. The TSPA-LA 
presently plans to use essentially the results of USGS (2001 [158378]) to define the future 
August 2003 145 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
climate changes over the next 20,000 years. The following evaluation supports the TSPA base 
timing sequence with present climate from 0 to 600 years, monsoon climate from 600 to 2,000 
years, and glacial transition climate from 2,000 years to 20,000 years. Sharpe (2003 [161591]) is 
used to evaluate the timing uncertainty. 
Both USGS (2001 [158378], p. 26) and Sharpe (2003 [161591], Table 6-5) confirm the existence 
of a long-duration modern (interglacial) climate state for at least the last 9,000 years (-9,000 
years). USGS (2001 [158378], p. 26) estimates that the modern climate regime started between 
-10,000 and -9,000 years. Sharpe (2003 [161591], Table 6-5) calculates that the interglacial 
climate lasted from -12,000 years to -1,000 years, and the present climate is a monsoon climate. 
Table 6.9-1 compares the estimated timing for the future climate between the two analyses. 
There are three timing differences between the two future estimates during the next ~20,000 
years before glacial climate. These are: 
1. Transition from modern to monsoon, difference is 1,600 years (between 600 and 
-1,000 years). 
2. Transition from monsoon to intermediate, difference is 1,500 years (between 2,000 and 
500 years). 
3. Presence of a second 1,500-year monsoon state occurring between 18,500 and 20,000 
in Sharpe (2003 [161591], Table 6-5). 
Table 6.9-1. Comparison between the Two Timing Estimates of Future Climate States for the 
Next ~50,000 Years 
DRI Analysis (Sharpe 2003 [161591]) 
N/A Interglacial Modern 
-1,000 yr. B.P. to 500 yr. A.P. [1,500] Monsoon Monsoon 
500 to 18,500 yr. A.P. [18,000] Intermediate 
18,500 to 20,000 yr. A.P. [1,500] Monsoon Glacial- 
Transition 
20,000 to 38,000 yr. A.P. [18,000] Intermediate 
Glacial 38,000 to 49,000 yr. A.P. [11,000] Glacial 
146 
USGS Analysis1 
0 to 600 yr. A.P. [600] 
600 to 2,000 yr. A.P. [1,400] 
2,000 to 30,000 [28,000] 
30,000 ending before 
50,000 yr. A.P. [<20,000] 
Source: Source: Sharpe 2003 [161591], Table 6-6 
NOTE: yr. A.P. = years after present; B.P. = years before present; brackets denote duration in years 
1 USGS (2001 [158378], Table 2, p. 67) reported durations of modern climate for 400600 
years, and monsoon climate for 9001,400 years. USGS (2001 [160355], p. 57) selected 
600 years and 1,400 years as the duration for these changes in climate states. 
The differences in timing are considered to be insignificant for two reasons. First, both estimates 
are based on the Devils Hole record chronology. Each Devils Hole sample integrates an average 
time interval representing about 1,800 years (Winograd et al. 1992 [100094], p. 255). Therefore, 
the differences of 1,600 years and 1,500 years for the first two timing differences are less than 
the Devils Hole sample resolution of 1,800 years. The duration of the 1,500-year monsoon 
interval is also less than the Devils Hole sampling resolution. Since the monsoon is between two 
intermediate states of long duration (18,500 years), the uncertainties of timing for both the 
beginning and the end of the monsoon climate are dominated by the uncertainties of the 
MDL-NBS-HS-000006 REV01 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
intermediate states. Second, the DRI analysis reports the timing of climate states (Sharpe 2003 
[161591], Table 6-5) to the nearest 500 years. Consequently, the timing of future climate 
estimated in this report confirms the future climates timing suggested in USGS (2001 [158378], 
p. 76). 
August 2003 147 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
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148 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
7. VALIDATION 
The validation activities for the UZ Flow Model include corroboration with experimental data 
and modeling studies using the following: (1) Enhanced Characterization of Repository Block 
(ECRB) observation data within the Exploratory Studies Facility (ESF); (2) WT-24 perchedwater 
data; (3) gas-flow data from boreholes SD-12 and UZ-7a; (4) measured C-14 borehole 
data; and Alcove 8/Niche 3 flow and seepage test results. In addition, validation efforts are also 
performed for the ambient thermal model, chloride model, calcite model, and strontium model. 
In all these cases, simulation results of the UZ Model and submodels are able to match different 
types of available observation data, such as water potentials, perched-water locations, 
temperatures, tracer concentrations, and pneumatic pressures. These efforts have provided 
validation of the UZ Model and its submodels for their accuracy and reliability in describing 
hydrogeological, thermal, and chemical conditions and predicting flow and transport processes 
in the Yucca Mountain UZ. 
In addition, the key flow and transport processes pertaining to the UZ at Yucca Mountain have 
been investigated through natural analogues (CRWMS M&O 2000 [141407]). These analogue 
studies contained both literature studies and analyses. One of the important case studies was the 
flow experiment and tracer infiltration test in fractured media at the Box Canyon (CRWMS 
M&O 2000 [141407], Section 6.5.1.1). The field tests at the site provided calibrations of 
numerical models using extensive in situ measured data. A consistent set of parameters was 
obtained from calibrating the site-wide model using the dual-permeability approach to both 
pneumatic and infiltration tests. The studies demonstrated that conceptual models and largescale, 
volume-averaged numerical modeling approaches use for the UZ Flow and Transport 
model at Yucca Mountain can be applied with confidence (CRWMS M&O 2000 [141407], 
Section 7). The model validation effort of this section is intended to further build confidence in 
the UZ Flow Model and submodels with regard to their ability to predict flow and transport 
processes in the Yucca Mountain UZ system. 
7.1 THE VALIDATION CRITERIA 
Validation activities for the UZ Flow Model and submodels are carried out based on the 
Technical Work Plan for: Performance Assessment Unsaturated Zone (BSC 2002 [160819], 
Attachment I-1-2). The models will be accepted as valid for their purposes through validation 
efforts of corroboration with experimental data in this section, if all the following criteria are 
met: 
 The water-potential data measured from ECRB are used for validation by comparing 
with simulation results of the UZ Model. The criterion for the validation is that 
simulated water-potential values are within the range of measurements along the ECRB 
tunnel (BSC 2002 [160819], Attachment I-1-2-1). Demonstration that this criterion was 
met is shown in Section 7.2. 
 WT-24 perched-water elevation data is used to validate the UZ Model. The criterion for 
the validation is that simulated perched elevation matches the observed value within 10 
August 2003 149 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
m (BSC 2002 [160819], Attachment I-1-2-1). Demonstration that this criterion was met 
is shown in Section 7.3. 
 For pneumatic data measured in SD-12 (for the second 30 days) and UZ-7a (60 days) 
are used for validation. The criterion for the validation is that simulated gas pressures 
and their patterns of variations consistently compare closely with the observed values. 
That is, the simulations will consistently reproduce increases and decreases resulting 
from changes in barometric pressure at the ground surface (BSC 2002 [160819], 
Attachment I-1-2-1). Demonstration that this criterion was met is shown in Section 7.3. 
 Carbon-14 data from gas samples provide approximate C-14 residence times for pore 
water. The residence times can be interpreted as tracer travel time from the ground 
surface to where the gas samples were collected, based on the current conceptual model 
for UZ flow and transport. These data are used to validate the UZ Model. The criterion 
for the validation is that simulated tracer travel times (i.e., peak of the breakthrough 
curve at the sample-collection locations for a pulse input at the ground surface or the 
time for first moment of concentration) are within the range of times estimated from data 
in the TSw unit (BSC 2002 [160819], Attachment I-1-2-1). Demonstration that this 
criterion was met is shown in Section 7.5. 
 Data from Alcove 8/Niche 3 flow and seepage test results are used to validate the UZ 
Flow Model. The tests involve a fault test and a large-infiltration-plot test. The fault test 
includes water infiltration and liquid tracer experiments. Test data from the latter test are 
not available in time for this report and therefore are not used here for model validation. 
Flow and seepage test results are used for model validation. The criterion for validation 
is that the predicted results for the time to reach a given concentration of a conservative 
tracer are within a factor of 5 of the observed times, or that explanations can be found 
for why the observed and simulated results deviate significantly (BSC 2002 [160819], 
Attachment I-1-2-1). Demonstration that this criterion was met is shown in Section 7.6. 
 Boreholes H-5, H-4, and WT-18 observed temperature data are used to validate the 
ambient thermal model, since no temperature measurements of ECRB specified in the 
TWP are available in the TDMS. The validation criterion is the same as that in TWP, 
i.e., to match the observed values with less than 3oC difference (BSC 2002 [160819], 
Attachment I-1-2-2). Demonstration that this criterion was met is shown in Section 7.7. 
 For the chloride model, simulated pore-water chloride concentrations in the ESF are 
compared with analysis of samples. The criterion for validation is that the range of the 
simulated chloride concentration falls within the range of measured concentrations (BSC 
2002 [160819], Attachment I-1-2-3). Demonstration that this criterion was met is shown 
in Section 7.8. 
 The calcite model is used to validate the UZ Flow Model with the abundance data of 
calcite mineral. The calcite model is validated by comparing 1-D simulation results with 
measurements. The criterion is that the simulated volume fraction of calcite coating for 
each UZ model layer falls within the range of measurements for that layer (BSC 2002 
[160819], Attachment I-1-2-4). Demonstration that this criterion was met is shown in 
Section 7.9. 
August 2003 150 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
 Borehole and ECRB strontium (Sr) concentrations are used to check the UZ Model 
results using the Sr modeling analysis. The criterion for validation is qualitative 
agreement between the simulated strontium concentrations and the average of the 
observations at the same elevation, and agreement with the vertical trends (BSC 2002 
[160819], Attachment I-1-2-5). Demonstration that this criterion was met is shown in 
Section 7.10. 
In addition, journal publications are used to provide corroboration for our model validation. A 
three-dimensional UZ numerical model was developed to simulate flow and distribution of 
moisture, gas, and heat at Yucca Mountain (Wu et al. 1999 [117161]). Flow and transport 
processes within the UZ were characterized under current and future climates (Wu et al. 2002 
[160195]). Studies of capillary barriers in the unsaturated rock of Yucca Mountain have also 
been published (Wu et al. 2002 [161058]). The perched-water phenomena in the UZ at Yucca 
Mountain have been investigated (Wu et al. 1999 [117167]). Subsurface pressure variations have 
been used to determine the pneumatic diffusivity of important geological features (Ahlers et al. 
[109715]). Subsurface borehole temperature data were used to estimate percolation flux 
(Bodvarsson et al. 2003 [162477]). Chloride measurements were used to calculate infiltration 
rates along the ESF (Fabryka-Martin et al. 1998 [146355]). Chloride data, in conjunction with 
hydrostructural and hydrogeological features, were also used to constrain infiltration rates (Liu et 
al. 2003 [162478]). In addition, chloride and strontium geochemistry was investigated using 3-D 
modeling for insights into the hydrology of the UZ (Sonnenthal and Bodvarsson 1999 [117127]). 
7.2 VALIDATION USING ECRB WATER-POTENTIAL MEASUREMENTS 
The 3-D UZ numerical model at Yucca Mountain has been the subject of several journal articles. 
In one of them, flow in the UZ was simulated in conjunction with distribution of moisture, gas, 
and heat (Wu et al. 1999 [117161]). In another study, flow and transport processes within the UZ 
under current and future climates were investigated using a three-dimensional numerical model, 
which incorporates a wide variety of field data in the highly heterogeneous, unsaturated fractured 
porous rock (Wu et al. 2002 [160195]). The capillary barriers in the unsaturated rock of Yucca 
Mountain were also studied (Wu et al. 2002 [161058]). 
An east-west cross drift was constructed in 1997 as part of the ECRB program (see Figure 6.1-1 
for the location of the ECRB tunnel). Water-potential data (DTN: GS980908312242.036 
[119820]) were collected from heat-dissipation probes installed in the tunnel wall (at a depth of 2 
m) along the ECRB tunnel inside the ESF. Water-potential data were collected from heat 
dissipation sensors that have been calibrated for matrix potential. Though the boreholes were dry 
drilled, the sensors were installed with wet cement. Thus, the sensors were fully saturated and 
surrounded with contact media to ensure good contact with rock, and equilibrated with the 
matrix potential of the rock. Following the equilibration, the probe would gradually dry out from 
ventilation effects. Since this was the first group of probes installed in the tunnel wall, no steps 
were taken to reduce the effects of ventilation drying in the tunnel. Extra steps, such as installing 
multiple doors, were taken during subsequent installation and monitoring of probes in the ECRB 
tunnel. 
As part of the 3-D flow and transport modeling validation process, modeled results of water 
potentials were compared to field-observation data collected from the wall of the tunnel to check 
the accuracy of the modeling predictions. The three base-cases (preq_uA, preq_mA and preq_lA 
August 2003 151 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
with Output-DTN: LB03023DSSCP9I.001) were selected for validation of the UZ Model, as 
discussed in Section 6.2.5. Here the l stands for low bounds of infiltration, m mean 
infiltration, u upper bounds, and A the base-case flow field with the infiltration rate. A 
complete list of modeling scenarios is in Table 6.7-1. The infiltration boundary conditions are 
the three present-day infiltration rates (DTN: GS000308311221.005 [147613]). The calibrated 
properties used for the 3-D prediction are those developed and listed in Tables I-1, I-2 and I-3 
(Output-DTN: LB03013DSSCP3I.001) and the calibrated fault properties (DTN: 
LB02092DSSCFPR.002 [162128]). 
Figure 7.2-1 shows a comparison of simulated and measured matrix water potentials along the 
wall of the ECRB drift (note that water potential is defined as the absolute value of capillary 
pressure in this report). As shown in the figure, observation data are available only along part of 
the tunnel. Most of the observed water-potential data are distributed between 0.1 (104 Pa) and 1 
(105 Pa) bars, with a maximum of 3.4 bar. The model predicted approximately 1 bar for the same 
section of tunnel, which is higher than most of the observed data. The predicted water potentials 
along the ECRB from the UZ Model ranged between 0.30 and 1.92 bars (Figure 7.2-1) for the 
present-day, mean infiltration scenario (preq_mA). 
The available data for field measured matrix water potentials at the ECRB were distributed in a 
range between 0.1 (104 Pa) and 1 (105 Pa) bar. Their comparison with simulation results may 
have been affected by scale. This is because the model has a gridblock at the ECRB of about 100 
 100  10 m in x, y, and z directions, respectively. Thus, simulated water potentials are the 
results of large-scale average of tuff matrix, which is intercepted by many fractures. On the other 
hand, measurements were made on a much smaller, local scale on the order of a meter, thus 
controlled by local heterogeneity. 
Even though the data available for comparison at the ECRB drift are limited, results indicated 
that the UZ Model generally predicted the range of the water-potential data from in situ 
measurements. Therefore, the criterion of validation is satisfied. 
August 2003 152 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
101 
100 
10-1 
10-2 
2500 
Figure 7.2-1. Comparison of Predicted and Measured Water Potential along ECRB Using the Present- 
Day, Mean Infiltration Rates (preq_mA) 
7.3 VALIDATION USING PERCHED-WATER DATA AT WT-24 
The perched-water phenomena in the UZ at Yucca Mountain were investigated in a multiphase 
subsurface flow model (Wu et al. 1999 [117167]). In that paper, the simulation results were 
shown to agree with the observed perched water data, including water saturation, potential 
profile, and perched-water elevation. In this validation, we use the field-measured perched-water 
data at borehole WT-24. Borehole WT-24 was drilled in 1997 as part of the ECRB program (see 
Figure 6.1-1 for borehole locations). Observed saturation data were collected from the borehole 
(see Section 4-1 for DTNs), and perched water was detected within the basal vitrophyre of the 
TSw at an elevation of approximately 986.69 m (DTN: GS980508312313.001 [109746]). As 
part of the model validation process, modeled results were compared to the field-observation 
perched-water elevation to check the accuracy of the modeled predictions. 
The model scenario (preq_mA, with Output-DTN: LB03023DSSCP9I.001) of the present-day, 
mean infiltration rate is used for the comparison. Figure 7.3-1 shows a comparison of simulated 
matrix water-potential results with field measurement data at borehole WT-24. The observed 
elevation of perched water is also shown in the figure. As indicated there, the field-measured 
data for potentials are limited to the deeper section of the borehole (mostly in the CHn unit). The 
UZ Model prediction closely matches the field measurements of both water potentials and 
perched water elevations. Note that a simulated perched water zone is indicated by zero water 
potential. It should be mentioned that the field-measured perched-water elevation is only 
MDL-NBS-HS-000006 REV01 August 2003 153 
Water Potential (Bar) 
U0050 
preq_mA 
field data 
2000 1000 500 0 
ECRB 
1500 
Distance (m) 
Data - DTN: GS980908312242.036 [119820]; Model Results - DTN: LB03023DSSCP9I.001 UZ Flow Models and Submodels 
measured at one point, at the onset of a pumping test (DTN: GS980508312313.001 [109746]). 
During pumping, the perched-water table was lowered by 20.66 m (DTN: 
GS980508312313.001 [109746]). The actual perched-water zone thickness may be larger than 
this fluctuation of water levels. The value of 20.66 m is very close to the simulated perched 
water thickness of about 30 m, as shown in Figure 7.3-1. Examination of simulated and observed 
perched water elevations show a difference of 6.55 m. This satisfies the validation criterion of 10 
m. 
1400 
1300 
1200 
1100 
1000 
900 
800 
700
0.0 
Figure 7.3-1. Comparison of Predicted and Measured Matrix Water Potentials and Perched-Water 
Elevations at Borehole WT-24 Using the Present-Day Mean Infiltration Rate (preq_mA) 
MDL-NBS-HS-000006 REV01 August 2003 154 
Logarithm of Water Potential (Pa) 
Field Water Potential Data - DTN: GS980708312242.010 [106752]; 
Perched Water  DTN: GS980508312313.001 [109746]; 
Model Results - DTN: LB03023DSSCP9I.001 
Elevation (m) 
U0050 
TCw 
PTn 
TSw preq_mA 
Field Data 
Hydro. Unit 
Perched Water 
WT-24 
CHn 
6.0 4.0 
CHn 
2.0 10.0 8.0 
U0050 UZ Flow Models and Submodels 
7.4 3-D PNEUMATIC MODEL PREDICTION 
Subsurface gas-pressure variations have been used to determine the pneumatic diffusivity of 
important geological features. One-, two-, and three-dimensional numerical models have been 
used to simulate the observed subsurface pressure variations (Ahlers et al. 1999 [109715]). For 
this model validation, the same 3-D gas flow model is used as discussed in Section 6.4. 
Pneumatic data measured in SD-12 and UZ-7a boreholes are used for the model validation. The 
60-day period of observation data from UZ-7a and the second 30-day period (immediately 
following the first 30 days of data used for calibration) from SD-12 are compared to the 
simulation predictions from the 3-D gas flow simulation results. The criterion for the validation 
is that simulated gas pressures and their pattern variations are consistent with the observed 
values. That is, the simulations will consistently reproduce increases and decreases resulting 
from changes in barometric pressure at the ground surface. 
Table 7.4-1 lists the sensor locations, files for observation data, observation period, and 
corresponding model mesh cells for borehole UZ-7a (the information for borehole SD-12 is 
given in Table 6.4-1). Figures 7.4-1 and 7.4-2 show the comparisons of simulation results and 
field-measured values at these observation locations of the validation periods. UZ-7a is close to a 
fault; therefore it sees the surface signal quickly and with little attenuation (Figure 7.4-1). The 
signal to SD-12 at the TSw may be affected by attenuation through the PTn. Overall, both 
figures show good agreement between the predicted gas pressures and observed data. The good 
match in Figures 7.4-1 and 7.4-2 builds confidence that the base-case infiltration-scenario 
calibrated properties are appropriate for gas flow simulations. The comparisons between 
simulated and observed gas pressures at different locations of the two boreholes, shown in the 
two figures, prove that simulated gas pressures and their patterns of variations are consistent 
with observed values. In particular, the simulations consistently reproduce increases and 
decreases resulting from changes in barometric pressure at the ground surface. This satisfies the 
validation criterion for this case. 
Table 7.4-1 Observation Sensors in Borehole USW UZ-7a 
Date range File for observation 
data 
DTN: LB02092DSSCFPR.001 [162422] 
Sensor elevation (m) 
MDL-NBS-HS-000006 REV01 
Corresponding 
observation 
cells 
1243.0 uz7a1343.prn 12/1/95-1/29/96 F002Bh74 
1232.3 uz7a1337.prn 12/1/95-1/29/96 F0003h74 
1221.6 uz7a1331.prn 12/1/95-1/29/96 F0007h74 
1213.4 uz7a1325.prn 12/1/95-1/29/96 F0009h74 
1177.8 uz7a1319.prn 12/1/95-1/29/96 F011Ah74 
August 2003 
155 NOTE: Field data from DTN: LB02092DSSCFPR.001 [162422] are extracted from DTN: GS960308312232.001 
[105573]. Both observations and simulations have been vertically offset for clearer display. 
Figure 7.4-1. Comparison of 3-D Pneumatic Prediction to Observation Data from Borehole UZ-7a 
Field DataDTN: LB02092DSSCFPR.001 [162422]; Model ResultsDTN: LB0303GASFLW3D.001 
U0050 UZ Flow Models and Submodels 
92 
91 
90 
89 
88 
87 
86 
85
Observation 
Model prediction 
91 
90 
89 
88 
87 
86 
Pressure (KPa) Pressure (KPa) 
0 10 20 30 
Observation 
Model prediction 
NOTE: Field data from DTN: LB991091233129.001 [125868] are extracted from DTN: GS960308312232.001 
[105573]. Both observations and simulations have been vertically offset for clearer display. 
Figure 7.4-2. Comparison of 3-D Pneumatic Prediction to Observation Data from Borehole SD-12 
SD-12 
30 35 40 45 
Time (days from 12/01/95) 
Field DataDTN: LB991091233129.001 [125868]; Model ResultsDTN: LB0303GASFLW3D.001 
MDL-NBS-HS-000006 REV01 
UZ-7a 
Time (days from 12/01/95) 
156 
TSw 
PTn 
PTn 
TCw 
TCw 
40 50 60 
TSw 
TSw 
PTn 
TCw 
50 55 60 
August 2003 U0050 UZ Flow Models and Submodels 
7.5 MODEL VALIDATION WITH CARBON-14 DATA 
The UZ of Yucca Mountain is a quasi-steady-state flow system, with very small infiltration and 
percolation rates. The matrix pore-water age corresponds to the mean time required for the 
groundwater to travel from the ground surface to where it is sampled in the system. The age can 
be considered constant at each location in this quasi-steady-state flow system, but spatially 
variable. The migration of water molecules is governed by advection and diffusion, similar to 
solute transport (Goode 1996 [162573]). Thus, tracer transport times (ages) can be simulated 
using a conservative transport model. The tracer travel time within the matrix (or matrix porewater 
age) was defined as the time corresponding to a tracer concentration peak from an initial 
tracer pulse applied on the ground surface. If the simulated age well matches the available 
measured data (age), the transport model is validated. 
In this validation effort, we estimate the groundwater age in the UZ, using a tracer transport 
model based on the calibrated groundwater flow field (Output-DTN: LB03023DSSCP9I.001). 
The simulated groundwater age is compared to available field-measured water age data (carbon- 
14). 
Carbon-14 data were collected from perched water, pore water, and gas samples from the Yucca 
Mountain UZ (BSC 2002 [160247], Section 6.6.4). Pore-water carbon-14 data from various 
boreholes at Yucca Mountain were not representative of the pore-water residence time, because 
of the contamination by atmospheric 14CO2 during drilling, which may result in apparently 
younger residence times (Yang 2002 [160839], Section 4.1.2; BSC 2002 [160247], Section 
6.6.4.2). Carbon-14 data from gas samples are considered to be most representative of in situ 
conditions (Yang 2002 [160839], Section 4.1.2), because exchange of 14C with the atmosphere 
was prevented since the borehole was closed. Carbon-14 is also considered as the most sensitive 
isotope measuring the groundwater age at the Yucca Mountain UZ, due to its half-life duration, 
which is in the same order of magnitude as the groundwater age in the UZ, and its detectable 
abundance. Gas samples were collected from different kinds of boreholes including open and 
instrumented surface-based boreholes. The data from the latter boreholes (USW SD-12 and 
USW UZ-1) are more reliable indicators of in situ matrix pore-water ages (BSC 2002 [160247], 
Section 6.6.4.3). Carbon-14 ages (BSC 2002 [160247], Table 20) calculated using the data from 
these two boreholes, are used for validating the UZ Model. 
Gas-phase 14C ages (DTN: GS961108312271.002 [121708] for borehole USW SD-12 and 
MO0012CARB1314.000 [153398] for borehole USW UZ-1) are interpreted to be representative 
of ages of the in situ pore water. The rationale for this interpretation is provided by Yang (2002 
[160839], Section 4.1.2). This interpretation is based on the rapid exchange of gas-phase CO2 
(reaching equilibrium in hours to days) with dissolved CO2 and HCO3
- in pore water. 
Furthermore, the amount of carbon in an aqueous-phase reservoir is greater by orders of 
magnitude than carbon in the CO2 gas-phase reservoir. Consequently, the aqueous phase will 
dominate the gaseous phase when exchange occurs, indicating the reasonableness of the 
interpretation (Yang 2002 [160839], Section 4.1.2). The continuous calcite precipitation in the 
unsaturated zone removes carbon from groundwater. Although 14C behaves a little differently 
from total carbon, the effect on the carbon isotopic fraction is minor, and the calcite precipitation 
is considered to have an insignificant impact on the 14C activity in the groundwater and gas 
(Codell and Murphy 1992 [100719]). Therefore, we chose the measured gas phase 14C age to be 
August 2003 157 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
the representative age data for the matrix pore-water age of the Yucca Mountain UZ in the 
following model validation effort. 
A 3-D transport model simulation was performed using T2R3D V1.4 (LBNL 1999 [146654]) 
decoupled version. The 3-D flow field obtained with the base-case water-flow simulation 
(present day mean infiltration, Output-DTN: LB03023DSSCP9I.001) is incorporated to simulate 
the matrix pore-water age in the entire model domain. The numerical grid used in this transport 
simulation is the same as that used in the base-case flow simulation (Figure 6.1-1). As discussed 
in Section 6.8, hydrodynamic dispersion was ignored because of low water percolation fluxes. 
Thus, the transport processes were being carried out by advective and diffusive solute fluxes. An 
effective-diffusion-coefficient value of 1.97  10-10 m2/s was adopted, equal to the average value 
of measured coefficients for tritiated water through Yucca Mountain tuffs (DTN: 
LA000000000034.002 [148603]; BSC 2001 [160828], Table 16). We introduce a tracer source, 
as a pulse, on the ground surface through fractures and observe the tracer concentration in rock 
matrix in the entire domain. The simulated matrix pore-water age, at a specific location, is then 
identified as the time required for the tracer pulse (appeared as concentration peaks in the UZ) to 
travel to that location, and is determined from the concentration breakthrough. Then, we 
compare the simulated ages to the measured matrix pore water carbon-14 age. The simulated 
matrix pore-water age for boreholes UZ-1 and SD-12 were plotted and compared to available 
measured age data (14C) (Figure 7.5-1 [UZ-1] and 7.5-2 [SD-12]). 
Figure 7.5-1 (UZ-1) and Figure 7.5-2 (SD-12) show that the simulated matrix pore-water ages 
(curves identified by  = 0.6 on the figures, with  being the active-fracture-model parameter) 
in the upper portion of the TSw unit are larger than the measured 14C ages. This is caused mainly 
by the underestimated advective and diffusive solute flux between fractures and matrix along 
these subunits. The smaller the flux or the slower the diffusion from fractures to matrix, the older 
the ages for matrix pore water. As analyzed in Section 6.8, advective and diffusive fluxes are 
both proportional to the effective interface area between fractures and matrix and, in turn, related 
to the larger  values. The calibrated  values, 0.6 for TSw32 and TSw33, 0.569 for TSw34C 
TSw38, were determined from inverse models of flow processes (DTN: 
LB0208UZDSCPMI.002 [161243]). When compared to transport analyses, sensitivity studies 
(BSC 2003 [161773], pp. 78C82) indicate that a more appropriate range of  value for TSw32C 
TSw38 is between 0.2 and 0.4. A smaller  corresponds to a larger effective fractureCmatrix 
interface area, leading to larger advective or diffusive flux. Therefore, another simulation was 
performed using  equal to 0.4 for TSw2 through TSw8. The simulated ages are also plotted in 
the same figures. 
Figure 7.5-1 (UZ-1) shows that the simulated matrix pore-water ages for the upper TSw units 
with  = 0.4 match the measured 14C ages well. Simulated ages for lower TSw units are a little 
bit younger than the measurements, but still meet the criterion (the simulated ages for TSw unit 
are also within the range of measured 14C age of TSw units, Section 7.1). Figure 7.5-2 (SD-12) 
shows that the match between the simulated groundwater ages using  = 0.4 and the 
measurements is reasonably close, and the deviations meet the criterion (the simulated ages for 
TSw unit are within the range of the measured 14C age of TSw units, Section 7.1). 
A larger gamma (0.6 for the TSw units) was used in the base-case UZ flow model. As 
demonstrated in Section 6.8 (which describes the sensitivity of UZ flow and transport processes 
August 2003 158 MDL-NBS-HS-000006 REV01 1400 
1200 
TSw 
Measured 
gamma=0.6 
1000 
CHn 
800 
gamma=0.4 
100000 1000000 1000 100 
Field DataDTN: MO0012CARB1314.000 [153398] and converted to ages (Wang 2003 [162417], SN-LBNL-SCI-227-V1, p. 87); 
Model ResultsDTN: LB0303C14INF3D.001. 
10000 
Age(Yrs) 
Figure 7.5-1. Simulated Groundwater Age for UZ-1 Borehole Compared to the Measured 14C Age 
August 2003 159 MDL-NBS-HS-000006 REV01 
Elevation(m) 
U0050 UZ Flow Models and Submodels 
to the gamma factor), the larger gamma gives slightly earlier breakthrough times for solute 
transport from the repository to the water table, and therefore provides more conservative results. 
This is because a larger gamma factor corresponds to a larger fracture pore velocity and a 
smaller effective fracture-matrix interface area (or a smaller degree of fracture-matrix 
interaction). 
3D age simulation UZ-1 
PTn
1300 
1200 
1100 TSw 
1000 gamma=0.4 
3D age simulation SD-12 
Measured 
10000 
Elevation(m) 
U0050 UZ Flow Models and Submodels 
TCw 
PTn 
900 
CHn 
800 
gamma=0.6 
100000 1000000 1000 10 
Age(Yrs) 
Field DataDTN: GS961108312271.002 [121708] and converted to ages (Wang 2003 [162417], SN-LBNL-SCI-227-V1, p. 87); 
Model ResultsDTN: LB0303C14INF3D.001. 
100 
NOTE: Only one sample is plotted for each depth. 
Figure 7.5-2. Simulated Groundwater Age for SD-12 Borehole Compared to the Measured 14C Age 
In summary, the comparison between simulated matrix pore-water age and observed 14C ages 
indicates that with  = 0.4 (for UZ Model layers tsw32tsw38), the simulated tracer transport 
time can reasonably represent the measured 14C ages of boreholes UZ-1 and SD-12. In addition, 
the simulated matrix pore-water age is sensitive to the active fracture parameter, . 
August 2003 160 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
7.6 MODELING ANALYSIS OF ALCOVE 8/NICHE 3 FAULT TESTS 
This section simulates in situ field tests of artificial infiltration along a fault at Alcove 8/Niche 3. 
The fault tests caused localized saturated conditions below the test spot, in an otherwise 
unsaturated zone. Under this field condition, test data are compared to results of simulations 
using the same conceptual model, methodology, and modeling approach as those used in the UZ 
Model. This modeling activity presents a different case of validation for the UZ Model. The 
results will build confidence in the UZ Model from a different perspective (in terms of 
different-scale model results and field conditions). 
This modeling analysis uses both model calibration and prediction. Comparisons between 
simulated and observed data are useful for evaluating the validity of the methodology used in the 
UZ Flow Model for capturing UZ flow and transport processes. The criterion for validation is 
that the predicted results for the time required for a conservative tracer to reach a given 
concentration (e.g., peak concentration) are within a factor of five of the observed time. (BSC 
2002 [160819], Section I-1-2-1). As demonstrated in Section 7.6.3.2 below (the discussion of 
modeling results), the criterion is met. This modeling activity is also documented in a Scientific 
Notebook by Wang (2003 [162417], SN-LBNL-SCI-215-V1, pp. 107119). Note that the 
modeling analysis of Alcove 8/Niche 3 fault tests has been used to validate both UZ flow and UZ 
transport models (BSC 2002 [160819], Sections I-1-2-1 and I-2-1-2). 
7.6.1 Field Observations 
Infiltration rate, seepage rate, and tracer concentration data from the fault test are used to 
corroborate model simulations. The fault test used water and two liquid tracers. The test was 
carried out in the upper lithophysal and middle nonlithophysal subunits in the Yucca Mountain 
UZ. These geological subunits correspond to model layers tsw33 and tsw34, respectively, in the 
UZ Model. The tsw33 has some lithophysal cavities that may intersect fractures. Liquid water, 
first without and then with tracers, was released at the floor of an alcove along the fault (about 5 
m long (DTN: GS020508312242.001 [162129])) within tsw33. Seepage from the fault into a 
niche and tracer concentrations of seeping liquid were monitored as functions of time. The niche 
is located within tsw34, about 20 m below the floor of the alcove; the interface between tsw33 
and tsw34 is about 15 m below the floor of the alcove (DTN: LB0301N3SURDAT.001 
[162130]). 
August 2003 161 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
450 
400 
350 
300 
250 
200 
150 
100 
50
0
0 
Figure 7.6-1. Infiltration Rate as a Function of Time 
A pressure head of 2 cm was maintained at the infiltration plot along the fault at the alcove. The 
plot consists of four trenches that have different infiltration rates as a result of subsurface 
heterogeneity along the fault. Figure 7.6-1 shows the total infiltration rate as a function of time 
(DTNs: GS020508312242.001 [162129] and GS020908312242.002 [162141]; Wang 2003 
[162417], SN-LBNL-SCI-215-V1, p. 107). For simplicity, our model considers the uniformly 
distributed infiltration rate along the infiltration plot to be consistent with the uniform property 
distribution in the UZ Model. One consideration in our modeling study is to evaluate approaches 
used in the site-scale model. Considerable temporal variability in the infiltration rate occurred 
during the test, as a result of infilled materials within the fault just below the infiltration plot 
(Figure 7.6-1). In other words, the effective permeability of the fault just below the plot changed 
with time. It is also expected that most portions of the fault and the surrounding fractures away 
from the plot would still be unsaturated, although pressure head at the plot was positive during 
the test. Based on these observations, total infiltration rate (instead of a pressure head of 2 cm) 
was used as the boundary condition in our model. 
MDL-NBS-HS-000006 REV01 162 
200 
Time (d) 
Output DTN: LB0303A8N3MDLG.001 (File: inf_sum.dat) 
Based on DTNS: GS020508312242.001 [162129] and GS020908312242.002 [162141]. 
Infiltration Rate (L/d) 
U0050 
300 100 
400 
August 2003 UZ Flow Models and Submodels 
30 
25 
20 
15 
10
5
0
0 
Figure 7.6-2. Total Seepage Rate as a Function of Time 
Seepage from the fault into the niche was measured during the test, with a number of trays used 
to cover the areas where seepage might occur. Seepage was found to be highly spatially variable. 
The total seepage rate as a function of time is given in Figure 7.6-2 (DTN: 
LB0303A8N3LIQR.001 [162570]). Several boreholes were installed around the niche. Water 
arrival times at these boreholes were monitored by electrical resistivity (ER) probes. Figure 7.6-3 
shows average water travel velocities determined from the arrival times from two boreholes just 
above the ceiling of the niche (DTN: LB0303A8N3LIQR.001 [162570]). The fault is about 2 m 
from the borehole collars in Figure 7.6-3 (DTN: LB0303A8N3LIQR.001 [162570]). Note that 
relatively uniform water-travel-velocity distribution within and near the fault was observed from 
these two boreholes. 
MDL-NBS-HS-000006 REV01 163 
Time (day) 
Seepage rate (L/day) 
U0050 
300 200 100 
DTN: LB0303A8N3LIQR.001 [162570] (file: All_seepage.xls) 
August 2003 UZ Flow Models and Submodels 
1 
0.9 
0.8 
0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1
0
0 1 2 3 4 5 6 
Distance from Collar (m) 
NOTE: Observation boreholes are approximately perpendicular to the fault. The distance from a 
borehole collar (called distance from collar) to the fault is about 2 m 
Figure 7.6-3. Water Travel Velocity Data for Boreholes 9 and 10 
After 209 days, two tracers with different molecular diffusion coefficients, Br and 
pentafluorobenzoic acid (PFBA) were introduced into infiltrating water at the infiltration plot. 
Tracer concentrations in three of the trays (at the niche) capturing seeping water from the fault 
were measured (DTN: LB0303A8N3LIQR.001 [162570]). Seepage rates corresponding to these 
three trays were not measured during the period of tracer concentration measurement. In this 
study, a flux-averaged breakthrough curve (concentration as a function of time) from these trays 
was used to represent the average breakthrough curve for all trays at the niche where seepage 
was captured (Wang 2003 [162417], SN-LBNL-SCI-215-V1, pp. 108109). A constant flux 
value for each of the three trays was used for calculating the flux-averaged breakthrough curve 
shown in Figure 7.6-4. The constant flux values for the three trays were determined as the 
averaged value over 56 days before tracers were introduced. This flux-averaged breakthrough 
curve was compared with simulation results. 
MDL-NBS-HS-000006 REV01 164 
DTN: LB0303A8N3LIQR.001 [162570] 
August 2003 
Velocity of Wetting Front (m/d) 
U0050 
0.2 
Relative Concentration 
U0050 UZ Flow Models and Submodels 
0.15 
0.1 
0.05 
Br 
PFBA 
100 150 50 
0
0 
Time (day) 
Output DTN: LB0303A8N3MDLG.001 (file: BTC.dat) 
Based on DTN: LB0303A8N3LIQR.001 [162570] 
Source: Wang 2003 [162417], SN-LBNL-SCI-215-V1, pp. 108-109 
Figure 7.6-4. Observed Flux-Average Breakthrough Curve 
7.6.2 Numerical Model 
A numerical model was developed for the fault test site to compare the simulation results with 
the relevant field observations. (The grid was generated with a software routine Smesh.f V1.0. 
(LBNL 2002 [162142]).) While comparison results will be presented below in Section 7.6.4, in 
this section, the focus is on schemes used for developing the numerical model. 
165 MDL-NBS-HS-000006 REV01 
August 2003 UZ Flow Models and Submodels 
0 
-5 
-10 
-15 
-20
0 10 20 30 
Figure 7.6-5. Cross Sectional Schematic of the 3-D Numerical Grid Used for Modeling Studies of 
Alcove 8/Niche 3 
A three-dimensional numerical grid is constructed for simulating the fault test (Figure 7.6-5). 
The fault is represented as a vertical fracture, and surrounding fractured rock is approximated as 
a dual-continuum system (consisting of overlapped, interacting fracture and matrix continua). 
Global water flow and solute transport are allowed to occur in both continua. Figure 7.6-5 shows 
a cross section of the grid within the fault. The thickness of the grid in the direction 
perpendicular to fault walls is 3 m along each side of the fault. The fracture frequency used for 
generating the dual-continuum grid is 1.03 for tsw33 (determined from the fracture map at the 
alcove floor) and 1.72 for tsw34 (determined from the fracture map at the ceiling of the niche) 
(DTN: GS030108314224.001 [162131]; Wang 2003 [162417], SN-LBNL-SCI-215-V1, 
pp. 111112). As shown in Figure 7.6-5, within a cross section of the grid along the fault, the 
grid spacing is 10 cm just above the ceiling of the niche, enabling the seepage process to be 
accurately simulated. The grid spacings in the direction perpendicular to the fault are 0.024 m, 
0.168 m, 0.456 m, 0.756 m, and 1.44 m, respectively. The smallest spacing is adjacent to the 
fault, so that water imbibition and tracer diffusion into the fractured rock from the fault can be 
accurately captured. Cross sections in parallel to the fault walls have identical grid meshes 
MDL-NBS-HS-000006 REV01 August 2003 166 
z (m) 
U0050 
Alcove 8 
small plot 
TSw33 
TSw34 
Niche 3 
x (m)
U0050 UZ Flow Models and Submodels 
(Figure 7.6-5) for different distances from the fault. The niche is represented by an opening at 
the bottom of the grid (Figure 7.6-5), with the geometry of the opening determined from the 
survey data of the niche near the fault. Note that this is only an approximation of the geometry of 
the test site; a three-dimensional geometry of the niche with an underground tunnel connected to 
the niche are difficult to incorporate into the model. However, since our main concern is flow 
and transport processes within the fault, this geometric representation is adequate. 
Temporally variable inflow rates are imposed on the top boundary, corresponding to the 
infiltration plot at the alcove floor (Figure 7.6-1). The side boundary corresponds to zero-flow 
conditions (in the direction perpendicular to the simulation domain). The niche wall boundary is 
modeled by a zero capillary-pressure condition, representing capillary barrier effects (Birkholzer 
et al. 1999 [105170]). The bottom boundary was assigned a constant matrix saturation of 0.85, 
which is consistent with field observations under ambient conditions (Flint 1998 [100033], p. 44, 
Table 7). Also based on field observations of Flint (1998 [100033], p. 44), matrix saturations are 
initially assigned to be 0.72 for tsw33 and 0.85 for tsw34. Other initial conditions for the rock 
mass within the model domain are that it is solute-free and has little water saturation (1.05E-2) in 
both the fractures and the fault. Rock properties used in model simulations are presented in 
Section 7.6.3.1. 
Model calibration is performed using an inverse modeling code iTOUGH2 V4.0 (LBNL 1999 
[139918]). The model calibration is defined herein as the adjustment of rock hydraulic 
parameters to make simulation results match the corresponding data. The goodness of match is 
measured using the standard least squares approach, which minimizes the sum of the squared 
residuals weighted by the inverse of variance of the data. T2R3D V1.4 (LBNL 1999 [146654]) is 
used for modeling tracer transport. 
7.6.3 Model Simulations and Discussions 
The numerical model was first calibrated against only the seepage and water-travel-velocity data 
to obtain the calibrated rock properties and the corresponding water flow field using iTOUGH2. 
Then, forward tracer transport simulations with different chemical transport parameters were 
carried out using T2R3D to evaluate the effects of matrix diffusion and other related processes 
on solute transport in the fault. 
Calibration of Seepage-Rate Data and the Average Water-Travel-Velocity Data 7.6.3.1 
Both fracture and matrix properties were assumed to be homogeneous within each geological 
subunit (tsw33 and tsw34). Fault properties were assumed to be the same for both units. This is 
based mainly on the following three considerations: 
(1) Consideration of the heterogeneity within each subunit would introduce a large 
number of rock properties that need to be determined by more data than was available 
from the test site. 
(2) These treatments have been used by the site-scale model of the Yucca Mountain UZ. It 
is of interest to examine how well this simple representation of subsurface 
heterogeneity can be used to model the fault test. 
August 2003 167 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
(3) A study by Zhou et al. (2003 [162133]) implies that flow and transport in the Yucca 
Mountain UZ are mainly determined by large-scale heterogeneity, characterized by 
property differences between different geological units rather than by property 
variability within a geological unit. 
Rock hydraulic properties needed as inputs into the model include fracture and matrix 
permeabilities, fracture and matrix porosities, fault aperture and permeabilities, van Genuchten 
(1980 [100610]) parameters (for matrix, fractures, and the fault), and the parameter of the active 
fracture model, , for fractures (DTNs: LB997141233129.001 [104055]; LB980901233124.101 
[136593]; LB990861233129.001 [110226]; and LB990501233129.001 [106787]; Wang 2003 
[162417], SN-LBNL-SCI-215-V1, pp. 114115). Because fracture van Genuchten parameters 
for tsw33 and tsw34 are similar (Table 7.6.1), a simple average of these parameters was used as 
the corresponding parameters for the fault (Wang 2003 [162417], SN-LBNL-SCI-215-V1, pp. 
114115). The averaged k/ (where k is fracture permeability and  is the corresponding fracture 
porosity) was calculated as fault permeability. Note that because there is no matrix in the fault in 
our model (or  =1), the weighted k/ (rather than weighted k) is employed for estimating fault 
permeability. The aperture of the fault was estimated as the average of fracture apertures of the 
two subunits. Note that the active fracture model was developed for fracture networks rather than 
for a single fracture. Consequently, the active fracture model does not apply to the fault here. In 
fact, most of the parameter values mentioned above and given in Table 7.6-1 are not site specific 
for the fault test site. These values were used as initial guesses for model calibration against the 
seepage rate and water-travel-velocity data observed from the fault test. To reduce the number of 
variables in model calibration (or inverse modeling), parameters expected to significantly affect 
simulated tracer travel time and seepage rate were varied in the calibration, while other 
parameters were kept unchanged. The varied parameters were fracture and fault permeabilities, 
fracture porosity, fault aperture, and fracture and fault van Genuchten  values. 
August 2003 168 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table 7.6-1. Uncalibrated Rock Properties 
Fault e 
Fracture frequency (m-1) 
1.12E-3 
0.608 
tsw33 tsw34 
August 2003 
Fracture Matrix Fracture Matrix 
Permeability (m2) 4.34E-11 5.5E-13c 3.08E-17a 0.35E-13e 4.07E-18a 
Porosity 1.00 6.6E-3d 0.154a 1.E-2d 0.11a 
1.5e 1.03e 
1.14E-3e 1.49E-3e 
0.41a 0.0 0.41a 
3.86E-6a 1.0E-3 1.46E-3a 2.13E-5a 5.16E-4a 
0.608a 0.291a 0.608a 0.298a 
DTN:LB997141233129.001 [104055], 
DTN: LB980901233124.101 [136593], 
c DTN: LB990861233129.001 [110226] 
DTN: LB990501233129.001 [106787] 
Wang (2003 [162417], SN-LBNL-SCI-215-V1, pp. 114-115) 
Infiltration-seepage processes in the fault and the surrounding fractured rock were determined by 
several mechanisms. Liquid water applied at the alcove floor (Figure 7.6-5) flowed first into the 
fault and then into fractured networks connected to the fault. Matrix imbibition occurred at 
interfaces between fractures and the matrix and between the fault and the matrix. When water 
arrived at the intersection between the fault and the niche, it might not immediately seep into the 
niche until the capillary pressure became zero because of capillary barrier effects (Philip et al. 
1989 [105743]; Birkholzer et al. 1999 [105170]). Such effects can divert flow away from the 
opening, resulting in only a portion of the water arriving at the niche ceiling actually seeping into 
the niche. Tracer travel time was determined by fracture porosity, fault aperture, and the matrix 
imbibition process. 
169 
Rock property 
Fracture aperture (m) 
Active fracture model 
parameter  
van Genuchten  (Pa-1) 
van Genuchten m 
a
b
d
e 
MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
40 
35 
30 
25 
20 
15 
10
5
0
0 
Figure 7.6-6. A Comparison between Simulated Seepage Rates as a Function of Time (Run #1) and 
Field Observations 
Figure 7.6-6 shows a comparison between seepage-rate data and the simulation result from a 
model calibration (Run #1) without considering the water-travel-velocity data. In this calibration 
run, fracture porosity and fault aperture were not varied. A fairly good match to the observed 
seepage data was obtained (Figure 7.6-6); however, water travel velocity is significantly 
overestimated (Figure 7.6-7). Water travel velocities were calculated from water arrival times at 
locations about 1 m above the middle of the opening in Figure 7.6-5 (Wang 2003 [162417], SNLBNL-
SCI-215-V1, pp. 116119). The travel time was defined as the time when fault or fracture 
saturation was increased from the initial value of 1.05E-2 to 1.06E-2. This comparison implies 
that seepage rate as a function of time may be mainly controlled by rock properties near seepage 
locations (influence zone of capillary barrier, Liu et al. 2002 [160230], Section 3.3). On the 
other hand, water travel velocities are determined by rock properties from the infiltration plot to 
the locations where water travel velocities are monitored. Table 7.6-2 gives the calibrated 
properties obtained from Run #1. 
MDL-NBS-HS-000006 REV01 August 2003 170 
Seepage Rate (L/d) 
U0050 
Data 
Simulation 
200 300 100 
Time (day) 
Field DataDTN: LB0303A8N3LIQR.001 [162570] (file: All_seepage.dat); 
Model ResultsDTN: LB0303A8N3MDLG.001 (file: Irun1i.tec) UZ Flow Models and Submodels 
1.75 
1.5 
1.25 
0.75 
0.5 
0.25 
Velocity of Wetting Front (m/d) 
2
1
Table 7.6.2. 
Rock property 
Figure 7.6-7. A Comparison among Calculated Water Travel Velocities from Two Calibration Runs and 
the Velocity Data Observed from the Fault Test 
Source: Output-DTN: LB0303A8N3MDLG.001 (file: Irun1i.par) 
NOTE: All other rock properties are the same as those in Table 7.6.1. Rock names "tswF3", "NetF3" and 
"NetF4" in file Irun1i.par correspond to "Fault", "tsw33" and "tsw34", respectively, in this table. 
Distance from Collar (m) 
Field DataDTN: LB0303A8N3LIQR.001 [162570]; 
Model ResultsDTN: LB0303A8N3MDLG.001 (files: WV_Irun1Ni.tec; WV_Irun4Ni.tec) 
Rock Properties Calibrated from Seepage Rate Data (Run #1) 
tsw33 Fault 
Fracture Permeability (m2) 6.67E-11 8.93E-13 3.16E-14 
1.15E-3 1.67E-3 4.59E-4 Fracture van Genuchten  (Pa-1) 
The overestimation of the water travel velocities may result from the following: (1) some 
cavities in tsw33 are connected to fractures and might contribute to increasing the storage in the 
fracture continuum; (2) in reality, the fault is a zone rather than a single fracture. The effective 
MDL-NBS-HS-000006 REV01 
U0050 
Data 
Run #1 
Run #2 
0
0 1 2 3 4 5 6 
tsw34 
August 2003 171 U0050 UZ Flow Models and Submodels 
aperture from this zone may be much larger than the assumed aperture value for the fault (Table 
7.6-1). Neither of these factors was considered in Run #1 (first calibration). Taking these factors 
into consideration, the new calibration (Run #2) allowed both fault aperture and fracture porosity 
in tsw33 to be varied. The resultant values are 3 cm for fault aperture and 0.066 for fracture 
porosity of tsw33 (Table 7.6-3). While the actual width of the fault zone is unknown, the 
estimated equivalent fault aperture (3 cm) is considered to be acceptable. The estimated fracture 
porosity is consistent with those estimated from water release tests performed in the same 
geological unit (BSC 2001 [158463], Section 6.11.3.2). 
Table 7.6.3. Rock Properties Calibrated from Both Seepage Rate and Water Travel Velocity Data 
(Run #2) 
tsw33 
0.066 
0.03 
tsw34 
Fracture Permeability (m2) 1.12E-10 1.23E-12 5.01E-13 
1.24E-3 2.19E-3 1.09E-3 
Source: Output-DTN: LB0303A8N3MDLG.001 (file Irun4Ni.par) 
NOTE: All other rock properties are the same as those in Table 7.6.1. . Rock names "tswF3", "NetF3" and 
"NetF4" in file Irun1i.par correspond to "Fault", "tsw33" and "tsw34", respectively, in this table. 
Figure 7.6-7 shows a comparison among calculated water travel velocities from two calibration 
runs and the velocity data observed from the fault test. The simulated water travel velocities 
from Run #2 are much closer to the observed data than those from Run #1 (especially near the 
fault). However, the water travel velocities away from the fault are still overestimated. One 
possible explanation is that matrix imbibition from fractures above the niche were 
underestimated because the dual-continuum approach considerably underestimates the pressure 
gradient near a fracture matrix interface during transient flow conditions (Pruess and Narasimhan 
1985 [101707]). While this problem can be resolved with the multiple interacting continua 
model of Pruess and Narasimhan (1985 [101707]), the computational intensity of the inverse 
model problem under consideration would be significantly increased. Note that a model 
calibration involves a great number of forward simulation runs. Considering that (1) the transient 
flow effects would be considerably reduced later in the test and that (2) our focus here is on flow 
and transport within and near faults, simulated flow field and calibrated rock properties from 
Run #2 were used for simulating tracer transport at the test site. Figure 7.6-8 also shows a 
comparison between simulated seepage rates as a function of time (Run #2) and field 
observations. The match is reasonable. Figure 7.6-8 matches both wetting-front velocity and 
seepage-rate, and it is therefore, considered a better calibration than Figure 7.6-6, which only 
matches the seepage rate data. Note that to give a reasonable prediction of solute transport, the 
water flow must be correctly modeled. 
August 2003 
Fault 
172 
Rock property 
Fracture Porosity 
Fracture aperture (m) 
Fracture van Genuchten  (Pa-1) 
MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
40 
35 
30 
25 
20 
15 
10
5
0
0 
Figure 7.6-8. A Comparison between Simulated Seepage Rates as a Function of Time (Run #2) and 
Field Observations 
Effects of Matrix Diffusion 7.6.3.2 
Tracer transport within the fault is controlled by several processes, including advection, 
diffusion into the matrix blocks (matrix diffusion), mass exchange between the fault and the 
surrounding fracture networks, and dispersion. Our special attention in this study is given to 
evaluating the relative importance of matrix diffusion. To do so, we used the flow field obtained 
from calibration Run #2 to simulate tracer transport processes and compare simulation results 
with field observations (Figure 7.6-4). The breakthrough curve is obtained from the output of 
T2R3D V1.4 (LBNL 1999 [146654]) using a software routine Bkread.f V1.0 (LBNL 2002 
[162143]). 
Two conservative tracers with different molecular diffusion coefficients (2.08 E-9 m2/s for Br 
(Lide 2002 [160832]) and 7.60E-10 m2/s for PFBA (Benson and Bowman 1996 [153427])) were 
used in the fault test. Based on analyses of the relevant diffusion experiment results, Moridis et 
al. (2003 [161902], Table 1) reported that the tortuosity factor for the tuff matrix can be 
approximated by the corresponding matrix porosity. Therefore, the average matrix porosity for 
tsw33 and tsw34 (0.13) was used as the tortuosity factor. The effective diffusion coefficient for 
MDL-NBS-HS-000006 REV01 August 2003 173 
Seepage Rate (L/d) 
U0050 
Data 
Simulation 
200 300 100 
Time (day) 
Field DataDTN: LB0303A8N3LIQR.001 [162570] (file: All_seepage.dat); 
Model Results -DTN: LB0303A8N3MDLG.001 (file: Irun4Ni.tec) 0.4 
Relative Concentration 
U0050 UZ Flow Models and Submodels 
the matrix diffusion process is the product of the molecular diffusion coefficient and tortuosity 
factor. 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0.05 
Time (day) 
Model ResultsDTN: LB0303A8N3MDLG.001 (files: BTC.dat; BTC_odis.dat) 
Figure 7.6-9. Comparisons between Simulated Breakthrough Curves at the Niche for Two Different 
Fault-Matrix Interface Areas and the Observed Data 
Figure 7.6-9 shows comparisons between simulated breakthrough curves at the niche for two 
different fault-matrix interface areas and the observed data. One simulation corresponds to an 
interface area defined in the original numerical grid, which considers the fault as a fracture with 
two vertical walls. The criterion for validation is that the predicted results for the time required 
for a conservative tracer to reach a given concentration (e.g., peak concentration) are within a 
factor of five of the observed time (BSC 2002 [160819], Section I-1-2-1). Note that this criterion 
is met (Figure 7.6-9). The other simulation corresponds to an interface area increased by 45 
times over that in the first simulation. In these two simulations, the dispersivity is assumed to be 
zero. (The relative importance of the dispersion will be discussed later.) Since the diffusive flux 
from the fault to the matrix is proportional to the product of the tortuosity factor and the fault- 
174 MDL-NBS-HS-000006 REV01 
data (Br) 
Data (PFBA) 
Original interface area (Br) 
Original interface area (PFBA) 
Increased interface area (Br) 
Increased interface area (PFBA) 
0
0 25 50 75 100
August 2003 U0050 UZ Flow Models and Submodels 
matrix interface area, changes in the interface area for a given tortuosity factor are equivalent to 
changes in tortuosity factor for a given interface area. For simplicity, the tortuosity factor value 
was changed in actual simulations, but the numerical grid (defining the interface area) was kept 
unchanged. Note that changes in the interface area do not significantly alter the flow field during 
the period of the tracer test. Tracers were introduced into infiltrating water at about 200 days 
after infiltration started, resulting in the matrix near the fault being almost saturated during the 
tracer test and the matrix imbibition being insignificant. As shown in Figure 7.6-9, the simulated 
breakthrough curve with the original interface area is very different from the observed data. It 
exhibits much larger concentration peak values and much earlier arrival times for these peaks. 
The observed data are favorably matched by the simulated result with increased interface area, 
indicating that matrix diffusion significantly affects the overall solute transport behavior and is 
underestimated by the simulation using the original interface area. 
The need to increase interface areas between fractures (or faults) and the matrix in matching the 
field observations of tracer transport in fractured rock has been recently reported by several 
researchers. Shapiro (2001 [162132]) interpreted concentration measurements for tritium and 
dichlorodifluoromethane collected from a glacial drift and fractured crystalline rock over 4 km2 
in central New Hampshire. He found that the effective diffusion coefficient at the kilometer scale 
is at least three orders of magnitude greater than laboratory estimates of diffusion in crystalline 
rock. Neretnieks (2002 [162140]) presented comparisons between several analytical solutions 
and tracer test results at the sp site and reported a need for a factor 3050 times larger for the 
fracture-matrix interface area than expected. He also indicated that nine other research groups 
reached a similar conclusion in their interpretation of the same test data set. Our results in this 
study are consistent with these previous findings. 
Several mechanisms regarding the increase in the interface area have been reported in the 
literature. They include (1) advective mass exchanges from high-permeability fractures to lowpermeability 
fractures (Shapiro 2001 [162132], Section 7), (2) diffusion into stagnant water 
zones (Neretnieks 2002 [162140]), and (3) enhanced fracture-matrix interface areas for fractures 
with small-trace lengths that do not contribute to global flow and are not considered in the 
survey data (and therefore not included in the numerical grid). In addition to these potential 
mechanisms, two other factors also contribute to the increase in the interface area. First, in the 
relevant analytical and numerical solutions to tracer transport, fracture walls are generally 
assumed to be flat. However, it is now well known that fracture walls are rough and 
characterized by fractal geometry (National Research Council 1996 [139151], pp. 105111). 
Consequently, the actual interface areas between fractures (and faults) and the matrix are larger 
than what are calculated using flat fracture walls. Second, a fault zone may include a great 
number of crushed matrix blocks that have smaller sizes than the fracture spacing in a nonfault 
zone. These crushed matrix blocks can make a significant contribution to the matrix diffusion 
within the fault, but are not considered in our numerical grid, where the fault is simply treated as 
a vertical fracture. To compensate for the effects of these mechanisms mentioned above, an 
increase in fault-matrix interface areas is obviously needed. 
Although simulation results with the increased interface area reasonably match the observed data 
(Figure 7.6-9), the concentration difference at a given time for the two tracers is generally 
overestimated by the model. One plausible explanation is that the crushed matrix blocks within 
the fault zone have much smaller sizes than the fracture spacing. This, however, is not 
considered in our model, in which the matrix block size is characterized by fracture spacing. The 
August 2003 175 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
smaller sizes correspond to the shorter times needed by the equilibrating tracer concentration at 
the center and outer surface of a matrix block, reducing the difference between the effects of 
matrix diffusion on overall solute transport behavior for different molecular-diffusion 
coefficients. This can be further illustrated by an extreme case: an infinitely small block size 
within the fault and without mass exchange between the fault and nonfault fractured rock. In this 
case, the concentrations of the matrix block within the fault can be equilibrated simultaneously 
with those at the outer surface of the block for two tracers with different molecular diffusion 
coefficients. Consequently, although the existence of this kind of matrix block can still 
significantly retard tracer transport within the fault, identical breakthrough curves may be 
observed at Niche 3 for the two tracers. This issue was not further explored in the current 
modeling study because the matrix block size distribution within the fault cannot be 
independently estimated or observed. 
Figure 7.6-10. Comparisons between Simulated Breakthrough Curves (Considering Dispersion) at the 
Niche for the Increased Fault-Matrix Interface Areas and the Observed Data 
Compared with matrix diffusion, the macrodispersion process is not considered to be significant 
within the fault for this particular test. Field measurements indicate that water travel-velocity 
distribution is quite uniform within and near the fault (Figure 7.6-3), whereas macrodispersion 
results from variability in water velocity. These experimental observations are consistent with 
MDL-NBS-HS-000006 REV01 August 2003 176 
Relative Concentration 
U0050 
0.4 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0.05 
data (Br) 
Data (PFBA) 
Simulation (Br) 
Simulation (PFBA) 
75 50 100 25 
0
0 
Time (day) 
Model ResultsDTN: LB0303A8N3MDLG.001 (files: BTC.dat; BTC_dis.dat) U0050 UZ Flow Models and Submodels 
the findings from our model analyses: the observed data were very difficult to match when a 
considerable degree of dispersion was included in the model. For example, Figure 7.6-10 shows 
simulated breakthrough curves with a longitudinal dispersivity value of 1 m and a transverse 
dispersivity value of 0.1 m (and with the increased fault-matrix interface area), compared to 
results in Figure 7.6-9 (without considering dispersion). Larger dispersivity values generally 
correspond to earlier arrival times of peak concentrations and to a larger difference between 
these peak concentrations for the two different tracers. 
Implication for Radionuclide Transport in the Yucca Mountain UZ 7.6.3.3 
Matrix diffusion has been identified as a key mechanism for retarding radionuclide transport in 
both unsaturated and saturated fractured rock (e.g., Bodvarsson et al. 2001 [160133]; Neretnieks 
2002 [162140]). The enhancement of the fracture (fault)matrix interface area (or effective 
matrix diffusion coefficient) seems to be common for matching field-scale solute transport 
observations, as suggested by this study and previous studies (Shapiro 2001 [162132]; 
Neretnieks 2002 [162140]). The current site-scale model for the Yucca Mountain UZ does not 
include the effects of this enhancement. Consequently, the estimated performance of the 
repository, estimated based on the site-scale model, may be conservative. 
The other related issue is the effects of cavities (existing in several geological layers at the 
Yucca Mountain site) on water flow and radionuclide transport processes. One may intuitively 
expect the cavities connected to fractures to act as capillary barriers under unsaturated 
conditions, because the cavity openings are much larger than fracture apertures. However, both 
this study and analyses of water-release tests performed in the related geological units at the 
Yucca Mountain site suggest that cavities are accessible by water within fracture networks, and 
therefore are retarding the downward water flow and radionuclide transport processes. This is 
also supported by field observation that mineral coatings exist in many cavities (BSC 2002 
[160247], Section 6.9). The coating is a signature for liquid-water flow paths. Although the 
cavity openings are larger than fracture apertures, the roughness of cavity walls may result in 
film flow (along cavity walls) from fractures to the cavities (Tokunaga and Wan 1997 [139195]). 
The effects of cavities are also not considered in the site-scale model for the Yucca Mountain 
UZ. This omission would result in further underestimating the performance of the repository. 
MDL-NBS-HS-000006 REV01 August 2003 177 U0050 UZ Flow Models and Submodels 
7.7 AMBIENT THERMAL MODEL 
A site-scale model of heat flow and geothermal conditions in the Yucca Mountain UZ was 
developed in Wu et al. (1999 [117161]). Percolation flux was estimated from temperature data 
(Bodvarsson et al. 2003 [162477]). This ambient thermal model simulates large-scale UZ 
geothermal and heat flow conditions. In addition, the thermal model provides thermal, gas, and 
moisture boundaries, and initial conditions for the mountain-scale and drift-scale thermalhydrological 
(TH), thermal-hydrological-chemical (THC), and thermal-hydrological-mechanical 
(THM) coupled process models. The 3-D ambient thermal model, as discussed in Section 6.3, 
has been calibrated against qualified temperature data measured from five boreholes. The model 
uses the 3-D UZ Model and the base-case, mean infiltration property set as input parameters and 
simulates advective and conductive heat transfer processes within the UZ at ambient, steadystate 
conditions. 
To validate the thermal model, modelers use borehole-measured temperature as corroborative 
evidence in this section. Based on the validation plan (BSC 2002 [160819], Attachment I-1-2- 
2), ECRB temperature data, if available in the Technical Data Management System (TDMS), 
would be used for the validation. Since no ECRB temperature measurements are currently 
available from the TDMS, the temperature data measured from boreholes H-5, H-4 and WT-18 
(Sass et al. 1988 [100644]; with DTN: GS950408318523.001 [107244]) are used instead in the 
following validation. The criterion is the same as planned for ECRB data: agreement within  3 
oC. These boreholes were not used for calibration in Section 6.3. 
The locations of boreholes H-5, H-4, and WT-18 are plotted Figure 6.3-1. All these boreholes 
penetrate the repository block and the entire UZ. The comparisons of simulated and observed 
temperature profiles along this borehole are shown in Figures 7.7-1, 7.7-2, and 7.7-3, indicating 
a good match between the 3-D model prediction and observed data. Borehole H-5 is close by the 
ECRB, and Figures 7.7-1 and 7.7-2 show that the simulated temperatures differ from observed 
values by less than 1.5oC in all elevations. In borehole WT-18, the simulated results again prove 
to be a reasonable match with field-measured data. All these simulation results are within 2C of 
the measured temperature, which is smaller than the criterion of validation of 3oC in the 
Technical Work Plan. Therefore, the validation criterion is satisfied. 
August 2003 178 MDL-NBS-HS-000006 REV01 1500 
1400 
Simulated+1.5 oC 
1300 
1200 
1100 
1000 
H-5 
25 
Elevation (m) 
U0050 UZ Flow Models and Submodels 
Simulated 
Measured on 03/09/83/ 
900 
800 
35 30 20 
700
15 
Field DataDTN: GS950408318523.001 [107244]; 
Model ResultsDTN: LB0303THERMSIM.001 
Temperature oC 
Figure 7.7-1. Comparison of Simulated and Observed Temperature Profiles for Borehole H-5 
August 2003 179 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
1200 
1150 
1100 
1050 
1000 
950 
900 
850 
800 
750 
700
15.0 20.0 
Figure 7.7-2. Comparison of Simulated and Observed Temperature Profiles for Borehole H-4 
Elevation (m) 
MDL-NBS-HS-000006 REV01 
U0050 
H-4 
simulated 
Measured 0n 
03/09/83 
simulated+1.0 oC
35.0 30.0 25.0 
Temperature oC 
Field DataDTN: GS950408318523.001 [107244]; 
Model ResultsDTN: LB0303THERMSIM.001 
August 2003 180 1200 
Elevation (m) 
UZ Flow Models and Submodels 
1150 
1100 
1050 
1000 
950 
900 
850 
800 
750 
700
15.0 20.0 
Figure 7.7-3. Comparison of Simulated and Observed Temperature Profiles for Borehole WT-18 
7.8 VALIDATION USING CHLORIDE MEASUREMENTS ALONG ESF 
Natural chemical tracers in subsurface systems can be useful tools in examining water flow and 
solute transport and their history. Chloride is hydrologically very mobile and chemically inert, 
and a nearly ideal natural tracer for the study of water movement in the liquid phase. Chloride 
has been used already in some relevant applications to flow and transport modeling of Yucca 
Mountain UZ. It has been used to calculate infiltration rates along the ESF (Fabryka-Martin et al. 
1998 [146355]). Liu et al. (2003 [162478]) calibrated infiltration rates using chloride data, in 
conjunction with hydrostructural and hydrogeological features, and demonstrated that the impact 
of infiltration alteration on percolation fluxes is less than that on chloride concentration. 
Chloride has been used in this Model Report to calibrate UZ Flow Model predictions of 
percolation fluxes and moisture distributions within the UZ system, as discussed in Section 6.5. 
The chloride model simulates large-scale UZ chloride transport processes. It uses the 3-D flow 
fields calculated by the UZ Model and incorporates chloride-in-precipitation data to model 
advective and diffusive chloride transport in the UZ. Its purpose is to validate the UZ Model by 
testing it with data not used in the development or calibration of the UZ Model. The simulated 
pore-water chloride concentration is compared with analysis of samples collected along the ESF. 
MDL-NBS-HS-000006 REV01 181 August 2003 
simulated 
Measured 0n 
06/03/84 
simulated +2.0 oC
35.0 30.0 
WT-18 
25.0 
Temperature oC
Field DataDTN: GS950408318523.001 [107244]; 
Model ResultsDTN: LB0303THERMSIM.001 
U0050 
UZ Flow Models and Submodels 
Chloride concentrations of the ESF at different infiltration scenarios are plotted in Figure 7.8-1. 
The range of the simulated chloride concentration of the base case flow field (preq_mA) in the 
ESF fall in general within the range for measured concentrations, which satisfies the validation 
criterion. The figure also indicates that the trend of chloride concentrations in samples is 
preserved in the calculated chloride concentrations. Note that measured chloride data are 
clustered around three areas with distances of about 1,000, 3,600, and > 6,800 m. For the first 
two locations, at 1,000 and 3,600 m, the simulated (preq_mA) results are either within or at the 
range of measurements. For the last portion, however, the simulations are well within the range 
of measurement for > 7,000 m and are close to (but a little higher than) the measurement. 
103 
102 
101 
1000 0 
Field Data: GS961108312261.006 [107293]; LA0002JF12213U.002 [156281]; LA9909JF831222.010 [122733]; 
Model ResultsDTN: LB0303CLINFL3D.001 
Figure 7.8-1. Comparison of Chloride Concentration (mg/L) Profiles under Present-Day Recharges 
with Mean, Upper, and Lower Bounds and Glacial Transition Recharge at the ESF 
7.9 CALCITE RESULTS 
7.9.1 Introduction 
The percolation flux in the UZ is an important parameter, because it controls seepage into drifts 
that may contact waste packages. As shown in Section 6.6, it depends strongly on the infiltration 
flux, which is a boundary condition of the UZ Model. Observations of precipitated calcite in the 
UZ constrain the infiltration flux. Therefore, comparing observed hydrogenic calcite deposits to 
simulations increased confidence in the models ability to capture this boundary condition. 
Because direct measurements of infiltration flux is not possible, this confirmation of the 
boundary condition generally builds confidence in the UZ model. Hydrogenic calcite deposits in 
fractures and lithophysal cavities at Yucca Mountain have been studied to estimate past 
MDL-NBS-HS-000006 REV01 August 2003 182 
Cl- Concentration (mg/l) 
U0050 
8000 7000 6000 5000 4000 2000 
ESF
preq_uA 
preq_mA 
preq_lA 
glaq_pmA 
field data 
3000 
Distance from the North Portal Entrance (m)
U0050 UZ Flow Models and Submodels 
percolation fluxes (Carlos et al. 1995 [162118]; Marshall et al. 1998 [107415]; BSC 2002 
[160247]). 
One objective of these previous studies was to investigate the relationship between percolation 
flux and measured calcite abundances. The U.S. Geological Survey (USGS) determined calcite 
abundances from a deep surface-based borehole (WT-24) (GS021008315215.007 [162127]). 
Geochronology work performed by the USGS (Neymark et al. 2001 [156889]) indicates that this 
calcite formed over approximately 10 million years. Hydrogenic mineral coatings in the UZ are 
nonuniformly distributed and located almost entirely on fracture footwalls and cavity floorsin 
contrast to saturated environments, in which vein and cavity deposits usually coat all surfaces. 
A column corresponding to the location of a deep borehole (WT-24) was chosen for modeling 
calcite deposition, because measured calcite abundances (GS021008315215.007 [162127]) were 
available for comparison. Here, the results of a reactive transport numerical model for calcite 
deposition under different infiltration conditions are presented. The setup and results of the 
problem are cited from Xu et al. (2003 [162124]). The reactive transport model used here 
considers the following essential factors affecting calcite precipitation: (1) infiltration, (2) the 
ambient geothermal gradient, (3) gaseous CO2 diffusive transport and partitioning in liquid and 
gas phases, (4) fracture-matrix interaction for water flow and chemical constituents (dual 
permeability), and (5) water-rock interaction. Any water-rock interaction effects (e.g., pH 
modification) also affect the calcite solubility hence its abundance in each rock unit. The dualpermeability 
model allows us to address not only the abundances of calcite with depth, but also 
its relative abundance in fractures and in the rock matrix as a function of the 
hydrological/geochemical processes in each medium, as well as the interaction of water flowing 
between fractures and matrix. 
7.9.2 Calcite Precipitation Mechanisms 
Along with wind-blown dust, precipitation carries much of the calcium to the surface (Vaniman 
et al. 2001 [157427]). In the soil zone, strong evapotranspiration, along with some water-rock 
interaction and root-zone biological processes, leads to saturation with respect to calcite. The 
depth to reach calcite equilibrium depends on climate and infiltration variations over time, 
episodic water flow, and near-surface biogeochemical conditions. During more typical smaller 
infiltration events, calcite may reach equilibrium close to the surface. However, large infiltration 
pulses of calcite-undersaturated water can dissolve near-surface calcite and reach equilibrium at 
a greater depth. This model validation activity concerns calcite deposition in a deep geological 
unit, the TSw, where the repository is located. Uncertainty in the infiltrating water composition 
near the surface is thus in significant because calcite reaches saturation well above this unit. In 
addition, the constant infiltration rate and steady-state water flow conditions over geological 
time used in our simulations are also justified by evidence that the rate of calcite growth in the 
UZ has remained approximately constant over at least the past 8 million years (Paces et al. 1998 
[107408]). 
The primary driving force for calcite precipitation from percolating waters in the UZ is its 
decreasing solubility with increasing temperature; calcite precipitates as water flows downward 
because of the geothermal gradient. Therefore, consideration of the ambient geothermal gradient 
is very important for calcite precipitation. The temperature distribution is a function of the 
crustal heat flow and the effect of infiltration. The modeled temperature distributions in borehole 
August 2003 183 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
WT-24 are discussed later in Section 7.9.5.2. Pore waters extracted from deep locations of the 
Yucca Mountain rock matrix are close to equilibrium with respect to calcite (BSC 2002 
[160247]), and no measurements of aqueous concentrations are available from fractures because 
they generally have low liquid saturations. 
The calcium concentration and CO2 partial pressure in percolating water is a major factor 
controlling the abundances of calcite and its stability. This is a result of the decreasing solubility 
of CO2 gas in water with increasing temperature, which in turn causes the following degassing 
process: HCO3
- + H+  CO2 (g) + H2O. Gaseous CO2 is also redistributed by gas-phase 
diffusive transport. Degassing increases the pH, and then contributes to calcite precipitation: 
Ca2+ + HCO3
-  CaCO3 (calcite) + H+. Water and gas flow between fractures and the adjacent 
matrix governs the resulting calcite distribution within each medium. Calcite precipitation is also 
affected by other factors, such as the dissolution and precipitation of aluminosilicate minerals 
(mainly through modifying the pH and the CO2 partial pressure). 
7.9.3 Reactive-Transport Model 
Modeling of calcite deposition in the Yucca Mountain UZ was performed using the reactive 
transport computer code TOUGHREACT (Xu and Pruess 1998 [117170]; 2001 [156280]). (This 
version of the code has not been qualified under AP-SI.1Q, but its use for corroboration is 
appropriate.) The code uses a sequential iteration approach similar to Yeh and Tripathi (1991 
[162125]), Walter et al. (1994 [162122]), and Xu et al. (1999 [162123]), which solve the 
transport and reaction equations separately. Flow and transport are based on space discretization 
by means of integral finite differences. An implicit time-weighting scheme is used for individual 
components of the model: flow, transport, and kinetic geochemical reaction. The chemical 
transport equations are solved independently for each component, whereas the reaction equations 
are solved on a gridblock basis using Newton-Raphson iteration. Full details of the code are 
given in Xu and Pruess (1998 [117170]; 2001 [156280]). 
In the model, advective and diffusive transport of aqueous chemical species is considered in the 
liquid phase. Molecular diffusive transport of gaseous species (CO2) is considered in the gas 
phase. Aqueous chemical complexation and gas dissolution/exsolution are accounted for under 
local equilibrium, whereas mineral dissolution/precipitation can proceed at equilibrium and/or 
can be kinetically controlled. Gas species in the chemical computations are assumed to behave as 
ideal gases (i.e., fugacity equals partial pressure). Temperature effects are considered for 
geochemical reaction calculations, because equilibrium and kinetic data are functions of 
temperature. 
Changes in porosity and permeability from mineral dissolution and precipitation of water flow 
are not considered for the present modeling. This feedback between transport and chemistry can 
be important, but a rather large computational time penalty has to be paid if this is modeled 
explicitly. By neglecting porosity and permeability change, modelers obtain quasi-steady flow 
conditions. This makes it possible to consider geochemistry in great detail for a simulation 
period of 10 million years. 
A dual-permeability approach, in which fractures and matrix are treated as two separate 
continua, was employed for water flow and chemical transport in the unsaturated fractured tuff. 
In this approach, interflow (water and chemicals) is allowed between fractures and the adjacent 
August 2003 184 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
matrix, and global flow occurs within both fracture and matrix continua. The active fracture 
model (AFM), developed by Liu et al. (1998 [105729]), was used to describe fracture-matrix 
interaction and preferential liquid flow in fractures. 
7.9.4 Hydrogeological and Geochemical Conditions 
7.9.4.1 Hydrogeological Conditions 
The Yucca Mountain UZ consists of layers of welded and nonwelded volcanic tuffs. The welded 
and nonwelded tuffs have vastly different hydrological properties. Welded units are 
characterized by relatively low porosity, low matrix permeability, and high fracture density, 
whereas the nonwelded tuffs have higher matrix porosity and permeability, and lower fracture 
density (Liu et al. 1998 [105729]). Montazer and Wilson (1984 [100161]) developed a 
conceptual model for the UZ at Yucca Mountain that identified five main hydrogeological units 
based on the degree of welding and on the associated relationships to fracture intensity. This 
conceptual model has formed the basis for modeling flow in the UZ at Yucca Mountain. Table 
7.9-1 describes each unit, which is further divided into a number of model layers with different 
hydrogeological and geochemical properties (BSC 2003 [160240]; BSC 2002 [158375]). The 
Calico Hills nonwelded (CHn) unit is comprised of zeolitic and vitric nonwelded tuffs 
underlying the basal vitrophyre of the Topopah Spring Tuff. Below the CHn are the Crater Flat 
undifferentiated (CFu) units, consisting of the lower Bullfrog and Tram Tuffs of the Crater Flat 
Group. The hydrogeological units below the TSw were not considered in geochemical transport 
simulations, so details regarding these units are not given in Table 7.9-1. We are primarily 
interested in calcite deposition within the TSw unit, where the repository is located (tsw4 and 
tsw5 model layers in Table 7.9-1). The exclusion of the underlying hydrogeological units does 
not affect the results in the TSw unit because flow is predominantly gravity driven, and upward 
chemical diffusion is subordinate to downward advective transport. 
August 2003 185 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table 7.9-1. Hydrogeologic Units, Model Layers, and Hydrogeological Properties for the Yucca 
Mountain Unsaturated Zone Flow and Transport Model, as Given by the Calibrated 
Property Model 
Hydrogeologic 
Unit 
TCw: Tiva 
Canyon 
Welded unit 
PTn: 
Paintbrush 
Nonwelded unit 
TSw: Topopah 
Spring welded 
unit 
DTN: LB997141233129.001 [104055] 
7.9.4.2 Geochemical Model 
Minerals considered in the simulations are calcite, gypsum, goethite, tridymite, cristobalite-, 
quartz, amorphous silica, hematite, fluorite, albite, K-feldspar, anorthite, Ca-smectite, Mgsmectite, 
Na-smectite, illite, kaolinite, opal-CT, stellerite, heulandite, mordenite, clinoptilolite, 
and glass (Xu et al. 2003 [162124]). This full assemblage of minerals and the corresponding 
aqueous species are hereafter termed the gextended-case geochemical system.h This assemblage 
has complexities and uncertainties in terms of thermodynamics and kinetics of mineral solid 
solutions (clays, zeolites and feldspars), effects on pH, and the partial pressure of CO2. A simpler 
set of minerals and aqueous species (base-case geochemical system) disregards all 
aluminosilicates, as well as Fe- and Mg-bearing minerals. 
Calcite and gypsum dissolution and precipitation were assumed to take place under geochemical 
equilibrium, whereas dissolution and precipitation of the other minerals were treated under 
kinetic constraints. Initial mineral abundances were taken from DTN: LB991200DSTTHC.003 
MDL-NBS-HS-000006 REV01 
Description Model 
Layer 
tcw1 
tcw2 
tcw3 
Moderately to 
densely welded 
portions of the Tiva 
Canyon Tuff of the 
Paintbrush Group 
ptn1 
ptn2 
ptn3 
ptn4 
ptn5 
ptn6 
Variably welded 
Paintbrush Tuff and 
its associated 
bedded tuffs, 
including those 
located at the 
bottom of the Tiva 
Canyon and top of 
the Topopah Spring 
Tuffs 
tsw1 
tsw2 
tsw3 
Moderately to 
densely welded 
portions of the 
Topopah Spring 
Tuff down to and 
including the tsw4 
tsw5 
densely welded 
basal vitrophyre 
tsw6 
tsw7 
tsw8 
tsw9 
U0050 
Fracture Matrix 
Porosity Permeability Porosity 
(m2) 
Permeability 
(m2) 
0.253 2.41~10-12 3.7~10-2 3.86~10-15 
0.082 1.00~10-10 2.6~10-2 2.74~10-19 
0.203 5.42~10-12 1.9~10-2 9.23~10-17 
0.387 1.86~10-12 1.4~10-2 9.90~10-13 
0.439 2.00~10-11 1.5~10-2 2.65~10-12 
0.254 2.60~10-13 3.2~10-3 1.23~10-13 
0.411 4.67~10-13 1.5~10-2 7.86~10-14 
0.499 7.03~10-13 7.9~10-3 7.00~10-14 
0.492 4.44~10-13 4.6~10-3 2.21~10-13 
0.053 3.21~10-11 7.1~10-3 6.32~10-17 
0.157 3.56~10-11 1.2~10-2 5.83~10-16 
0.154 3.86~10-11 8.5~10-3 3.08~10-17 
0.110 1.70~10-11 1.0~10-2 4.07~10-18 
0.131 4.51~10-11 1.5~10-2 3.04~10-17 
0.112 7.01~10-11 2.0~10-2 5.71~10-18 
0.094 7.01~10-11 2.0~10-2 4.49~10-18 
0.037 5.92~10-13 1.6~10-2 4.53~10-18 
0.173 4.57~10-13 5.9~10-3 5.46~10-17 
August 2003 186 U0050 UZ Flow Models and Submodels 
[161276]. Potential secondary minerals (i.e., those allowed to precipitate but which may not 
necessarily form) were determined from field and experimental observations of water-rock 
interaction and from equilibrium geochemical model calculations. Reactive surface areas of 
minerals on fracture walls were calculated from the fracture-matrix interface area/volume ratio, 
the fracture porosity, and the derived mineral volume fractions (DTN: LB0101DSTTHCR1.003 
[161278]). These areas were based on the fracture densities, fracture porosities, and mean 
fracture diameter. Mineral surface areas in the rock matrix were calculated using the geometric 
area of a cubic array of truncated spheres that make up the framework of the rock and reductions 
to those areas, owing to the presence of alteration phases such as clays and zeolites. 
Initial pore water chemical concentrations were based on analyses of ultracentrifuged water and 
chemical speciation calculations presented in BSC (2002 [158375]). Except for perched water 
that lies well below the potential repository horizon, water has not been observed in fractures in 
the UZ. Therefore, the initial composition of water in the fractures was set to be the same as the 
matrix pore water (Table 7.9-2). The same water composition, re-equilibrated at the temperature 
of the top model boundary, was assumed for infiltrating water. Oxidizing conditions were 
considered for this water, because the fracture permeability of the rock is high and the system is 
unsaturated (air phase is present everywhere). The CO2 gas partial pressures used for initial and 
top boundary conditions of the gas transport are in equilibrium with the corresponding aqueous 
chemical composition. An elevated gas partial pressure (relative to an atmospheric value of 
0.344  10-3 bar) at the upper boundary can be attributed to soil-zone CO2 production. 
August 2003 187 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table 7.9-2. Water and Gas Chemistry Used for Initial and Boundary Conditions of the Reaction- 
Transport Simulations 
August 2003 MDL-NBS-HS-000006 REV01 
Component Concentration Unit 
Ca 101 mg/L 
Mg 17 mg/L 
Na 61.3 mg/L 
K 8 mg/L 
SiO2 (aq) 70.5 mg/L 
Al 1.6710-5 mg/L 
HCO3 200 mg/L 
Cl 117 mg/L 
SO4 116 mg/L 
F 0.86 mg/L 
Fe 6.4610-8 mg/L 
pH 
bar PCO2 
8.32 (at 25 oC) 
7.75 (at 17 oC) 
2.72610-3 at 17 oC 
Source: Xu et al. 2003 [162124]) 
7.9.4.3 Simulation Setup 
Simulations were performed using three infiltration rates, a base-case rate of 5.92 mm/yr (BSC 
2003 [160240]), and bounding rates of 2 mm/yr and 20 mm/yr. The corresponding (to infiltration 
rates) steady-state water flow conditions were used for geochemical transport simulations. 
Steady-state water saturation distribution is presented in Figure 7.9-1. Steady-state temperature 
distributions corresponding to the same three infiltration rates are shown in Figure 7.9-2. These 
were obtained using a top temperature of 15.6oC at the land surface and a bottom temperature of 
28oC at the water table. For the three infiltration rates, the same water and gas chemistry was 
used for the top boundary condition. As discussed in Section 7.9.2, the infiltrating water 
composition applied here is considered to be the water chemistry after transformation by soilzone 
processes (evapotranspiration predominantly). Calcite precipitation in the TSw unit is not 
sensitive to uncertainties in the infiltrating water chemistry, because it is well below the region 
where calcite becomes saturated. Moreover, episodic flow likely has been strongly dampened by 
the overlying weakly fractured bedded tuffs in the PTn hydrogeological unit. 
188 UZ Flow Models and Submodels 
1500 
1400 
1300 
1200 
1100 
1000 
900 
0.02 0 
(a) Fracture 
Source: Xu et al. 2003 [162124] 
Figure 7.9-1. Modeled Steady-State Water Saturations for the WT-24 Column Using Infiltration Rates: 
2, 5.92, and 20 mm/yr 
Source: Xu et al. 2003 [162124] 
Figure 7.9-2. Modeled Temperature Profiles in Borehole WT-24 as a Function of Depth for Three 
Infiltration Rates 
Elevation (m) 
Infiltration rate (mm/yr) 
0.04 0.06 
Water saturation 
MDL-NBS-HS-000006 REV01 
TCw 
---------- 
5.92 
2
20 
0.1 0.08 
1500 
1400 
TCw 
---------- 
1300 
PTn 
---------- 
Elevation (m) 
Elevation (m) 
PTn 
---------- 
TSw 
1200 
TSw 
1100 
1000 
Infiltration rate (mm/yr) 
900 2 mm 
5.92 
800 
20 mm 
700 
22 20 16 14 18 
Temperature (oC) 
189 
U0050 
1500 
TCw 
---------- 
1400 
PTn 
1300 
---------- 
TSw 
1200 
Infiltration rate (mm/yr) 
1100 
5.92 
2 
1000 20 
900 
1 0.8 0.4 0.2 0 0.6 
Water saturation 
(b) Matrix 
28 26 24 
August 2003 UZ Flow Models and Submodels 
For the reactive transport simulations of calcite precipitation, a simulation time of 10 million 
years was selected, because this calcite formed over approximately 10 million years (Neymark et 
al. 2001 [156889]). Infiltration rates and temperatures were held constant throughout the time of 
the simulation; therefore, the results reflect the average conditions over this period of time. 
7.9.5 Results and Discussion 
Comparison with Measured Data 7.9.5.1 
The simulated total (fracture plus matrix) calcite abundances in the WT-24 column obtained 
using the three different infiltration rates are presented together with measured data in Figure 
7.9-3a (extended-case geochemical system) and Figure 7.9-3b (base-case geochemical system). 
Generally, the results obtained using the base-case infiltration rate (5.92 mm/yr) agree better 
with the measured WT-24 mineral abundances than those obtained using the other infiltration 
rates, especially for the PTn unit. The extended-case geochemical system gives a better match to 
the total calcite abundances, especially for the TSw unit, indicating that some contribution of Ca 
from the rock is required. 
1500 
TCw 
---------- 
1400 
PTn 
1300 
---------- 
1200 TSw 
1100 
Infiltration (mm/yr) 
5.92 
1000 
20 
2 
Elevation (m) 
900 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
NOTE: 
(a) Extended case 
Diamonds represent bulk rock calcite abundances measured by the U.S. Geological Survey 
(GS021008315215.007 [162127]). Simulated results are from Xu et al. 2003 [162124]. 
Figure 7.9-3. Simulated Total (Fracture plus Matrix) Calcite Abundances (in ppmV or 10-6 Volume 
Fraction) in the WT-24 Column for Different Infiltration Rates after 10 Million Years: (a) 
Extended-Case Geochemical System, (b) Base-Case Geochemical System 
The simulated calcite abundances in the basal PTn layer for the three infiltration simulations are 
higher than that measured in WT-24. This results from an increase in the temperature gradient 
(Figure 7.9-2) resulting in a concomitant decrease in calcite solubility. The relatively larger 
calcite abundances in the bottom layer of the PTn have been observed at other locations such as 
in another deep borehole, USW G-2 (Carey et al. 1998 [109051]). The lower measured calcite 
abundances may also result for lateral flow not captured in the one-dimensional simulations. 
MDL-NBS-HS-000006 REV01 190 August 2003 
Elevation (m) 
U0050 
1500 
TCw 
---------- 
1400 
PTn 
1300 
---------- 
1200 TSw 
1100 
Infiltration (mm/yr) 
1000 5.92 
20 
2 900 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
(b) Base case UZ Flow Models and Submodels 
Results for the welded TSw unit (of the most interest to the YMP) generally fall in the wide 
range of measured calcite data. Calcite deposition values obtained from the highest infiltration 
rate (20 mm/yr) are closer to the high bound of measured values. Those values from the base 
case (5.92 mm/yr) fall in the middle of the TSw measured data range. This may imply that the 20 
mm/yr percolation rate is the high bound for the WT-24 location, whereas the base infiltration 
(5.92 mm/yr) from the flow property calibration (used for the flow model) may be close to the 
long-term mean infiltration rate for this location. The extended-case geochemical system 
provides the closest match to the measured data in the TSw unit, because of the contribution of 
Ca from feldspars. 
Calcite Precipitation in Fractures and Matrix 7.9.5.2 
Modeled calcite abundances in the fracture and matrix continua are very different for the various 
hydrogeological units (Figures 7.9-4 and 7.9-5). Figure 7.9-4 shows calcite abundances in 
fractures and matrix for the three infiltration rates with the extended-case geochemical system. In 
the PTn unit (Figures 7.9-5a and 7.9-5b), the matrix shows a similar or larger proportion of 
calcite than the fractures, except near the contact with the TSw, where the distribution reverses. 
In the uppermost part of the TSw unit (just above the repository horizon), calcite precipitation in 
fractures is dominant, indicating that flow is enhanced in the fractures. Calcite coatings are 
frequently found in fractures and lithophysal cavities (intersected with fractures) in the TSw tuffs 
(Paces et al. 1998 [107408]). 
1500 
TCw 
---------- 1400 
---------- 
TSw 
1200 
1100 Infiltration rate (mm/yr) 
5.92 
Figure 7.9-4. Modeled Calcite Abundances (in ppmV or 10-6 Volume Fraction) in Fractures and in the 
Matrix after 10 Million Years for Differing Infiltration Rates Using the Extended-Case 
Geochemical System 
191 August 2003 
MDL-NBS-HS-000006 REV01 
Elevation (m) 
PTn 
1300 
1000 2
20 
900 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
(a) Fracture 
Source: Xu et al. 2003 [162124] 
Elevation (m) 
U0050 
1500 
TCw 
---------- 1400 
PTn 
1300 
---------- 
TSw 
1200 
1100 Infiltration rate (mm/yr) 
5.92 
1000 2
20 
900 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
(b) Matrix UZ Flow Models and Submodels 
1500 
1400 
1300 
1200 
1100 
1000 
900 
Source: Xu et al. 2003 [162124] 
(a) Extended case 
Figure 7.9-5. Modeled Calcite Abundances (in ppmV or 10-6 Volume Fraction) in Fractures and in the 
Matrix for (a) the Extended-Case and (b) Base-Case Geochemical Systems 
However, the abundances in Figures 7.9-4 and 7.9-5 reflect only the proportion of calcite within 
the fracture and the matrix volumes individually. Figure 7.9-6 shows changes of calcite volume 
fraction versus infiltration rate for layer tsw4 (at an elevation of 1,126 m). In the extended case 
(Figure 7.9-6a), there are about equal total amounts of calcite in the matrix and fractures (the 
total is about twice that in the matrix, which makes up most of the volume of the rock), even 
though the fractures show a much larger proportion of calcite (Figure 7.9-5a). For the base-case 
system (Figure 7.9-6b), calcite in the fractures is about three-fourths of the total calcite in the 
rock, owing to the limited amount of Ca coming from the rock matrix and forming calcite. 
Greater simulated calcite abundances in the fractures result from the fractures carrying higher 
water fluxes in these strongly fractured, densely welded rocks. 
MDL-NBS-HS-000006 REV01 192 August 2003 
Elevation (m) 
TCw 
---------- 
PTn 
---------- 
TSw 
Fracture 
Matrix 
6 mm/yr infiltration 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
1500 
TCw 
---------- 
1400 
PTn 
1300 
---------- 
TSw 
1200 
1100 
Fracture 
1000 Matrix 
6 mm/yr infiltration 
900 
1E-1 1E+0 1E+1 1E+2 1E+3 1E+4 1E+5 
Change of volume fraction (ppmV) 
(b) Base case 
Elevation (m) 
U0050 
UZ Flow Models and Submodels 
1E+5 
1E+4 
1E+3 
1E+2 
1E+1 
1E+0 
5 0 15 
Infiltration rate (mm/yr) 
(a) Extended case 
Source: Xu et al. 2003 [162124] 
Figure 7.9-6. Changes in Calcite Volume Fraction (ppmV) vs. Infiltration Rates for TSw4 Layer (at an 
Elevation of 1,126 m) for (a) the Extended-Case and (b) Base-Case Geochemical 
7.9.5.3 
August 2003 193 
Systems 
The extended-case and base-case simulations also show that from about 2 to 5.92 mm/yr, the 
amount of calcite precipitated in the welded Topopah Spring tuff is sensitive to the infiltration 
rate (Figures 7.9-6a and 7.9-6b). This dependence decreases at higher infiltration rates (5.92 
mm/yr infiltration rate to 20 mm/yr) owing to a modification of the geothermal gradient from the 
increased percolation flux (Figure 7.9-2). The decrease in temperature at this level is about 2C, 
which resulted in less calcite precipitating in the TSw, and more calcite being transported below 
the TSw . 
Spatial Variation in Calcite Deposition 
Observed calcite abundances vary significantly in space and as a function of depth. Studies for 
the WT-24 column can give some general insight into calcite deposition conditions, but cannot 
represent the entire picture at Yucca Mountain. Calcite abundance data for borehole USW SD-6 
were later released by USGS in 2002 (DTN: GS020608315215.002 [162126]), but modeling of 
calcite deposition for SD-6 has not been performed. Measured calcite data have a wide range of 
values (orders from 102 to 104 ppmV). To compare the two columns, which show large 
variability, the geometric means over a specified depth range were compared. The calcite data 
were grouped according to (1) every 10 m in depth (if possible) and (2) within one geologic unit. 
The calculation of calcite geometric means (in Microsoft Excel) are documented in Wang (2003 
[162417], SN-LBNL-SCI-160-V1, pp. 8992). The original measured calcite data and calculated 
geometric means versus depth are presented in Figure 7.9-7. To better compare WT-24 with SD- 
6, we plot both geometric means in Figure 7.9-8. For the PTn, SD-6 has much more calcite 
deposition than WT-24. The thickness of the PTn unit for SD-6 is thinner than that of WT-24. 
For the TSw, calcite abundances in SD-6 fall in a range similar to those in WT-24. In the 3-D UZ 
Flow Model, a mean infiltration of 19.6 mm/yr is used in SD-6, which is higher than the 5.9 
mm/yr in WT-24. This once again indicates that between a 5.92 and 20 mm/yr infiltration rate, 
the amount of calcite is not expected to be significantly different in the TSw. 
MDL-NBS-HS-000006 REV01 
Abundance (ppmV) 
1E+5 
Matrix 
Abundance (ppmV) 
U0050 
1E+4 
Total (F+M) 
Fracture 1E+3 
Total (F+M) 1E+2 
Fracture 
1E+1 Matrix 
1E+0 
20 10 20 10 5 0 15 
Infiltration rate (mm/yr) 
(b) Base case UZ Flow Models and Submodels 
0 
WT-24 50 
100 
150 
200 
250 
300 
350 
400 
450 
500 
550 
1E+2 1E+1 1E+4 
Calcite abundance (ppmV) 
(a) WT-24 
NOTE: Symbols represent USGS-measured calcite abundances. Solid lines represent geometric means. 
Figure 7.9-7. Calcite Abundances (in ppmV or 10-6 Volume Fraction) with Depth in Boreholes WT-24 
and SD-6 
Depth (m) 
SOURCE: Wang 2003 [162417], SN-LBNL-SCI-160-V1, p. 92 
Figure 7.9-8. Geometric Means of Calcite Abundances with Depth for Boreholes WT-24 and SD-6 
7.9.6 Concluding Remarks 
Modeling calcite deposition provides additional evidence for validation of the UZ Model. Over a 
range of 2-20 mm/yr infiltration rates, the simulated calcite abundances generally fall within the 
range of calcite observed in the field, which satisfies the validation criterion. The simulated 
MDL-NBS-HS-000006 REV01 
--------------- 
Depth (m) 
TCw --------------- 
PTn
TSw 
--------------- CHn 
1E+5 1E+3
0 
50 
100 
150 
200 
250 
300 
350 
400 
450 
500 
550
1E+1 1E+2 1E+4 
Calcite abundance (ppmV) 
194 
Depth (m) 
0 
SD-6 50 
100 
150 
200 
250 
300 
350 
400 
450 
500 
550 
1E+2 1E+1 1E+4 
Calcite abundance (ppmV) 
(b) SD-6 
Field DataDTNs: GS021008315215.007 [162127] for WT-24; 
GS020608315215.002 [162126] for SD-6 
SD-6 
WT-24 
1E+5 1E+3 
U0050 
TCw 
PTn --------------- 
--------------- 
TSw 
--------------- 
CHn
1E+5 1E+3 
August 2003 U0050 UZ Flow Models and Submodels 
calcite distributions capture the U.S. Geological Survey-measured data from the WT-24 well 
cuttings (GS021008315215.007 [162127]). The 20 mm/yr infiltration rate may be the upper 
bound for WT-24 location, whereas the base-case infiltration rate (5.92 mm/yr) used for the flow 
model gives the closest match to the data. The observed calcite precipitation for the top of TSw 
occurs mostly in the fractures, which is also captured. The modeling results can provide useful 
insight into process mechanisms such as fracture-matrix interaction, as well as conditions and 
parameters controlling calcite deposition. The modeled calcite abundances generally increase 
with increasing infiltration rate, but become less sensitive to infiltration at higher rates as a result 
of changes to the geothermal gradient. Between a 5.92 and 20 mm/yr infiltration rate, the amount 
of calcite is not expected to be significantly different in the TSw, a conclusion supported by the 
similar abundances in WT-24 and SD-6 in the TSw. 
One-dimensional simulation is appropriate because both flow and geothermal gradient are 
primarily vertical. The current observed calcite is formed cumulatively over about 10 million 
years. A number of uncertainties are involved in the numerical simulation results. The most 
influential of which are variations of geothermal gradient and infiltration over time. Differences 
between 1-D and 3-D flow are much less than the differences in geothermal gradient and 
infiltration over 10 million years. Agreement between simulated and measured calcite abundance 
could works to establish the validity of the flow field and infiltration rates used. 
7.10 MODEL VALIDATION USING STRONTIUM GEOCHEMISTRY AND ISOTOPIC 
RATIOS 
This section describes the use of strontium (Sr) and strontium isotopic ratios (87Sr/86Sr) for 
validation of the UZ Model. Validation methodology consists of work presented in peerreviewed 
journals, as well as comparison of model results to data collected for pore waters. The 
criterion for the validation is a qualitative agreement between simulated Sr concentrations and 
the average of the observations at the same elevation, and an agreement with vertical trends 
(BSC 2002 [160819], Attachment 1, Section I-1-2-5). 
7.10.1 Background 
Strontium concentrations and the 87Sr/86Sr ratio in pore fluids and secondary minerals can 
provide important constraints on infiltration rates, flow paths, residence times, and degrees of 
water-rock and fracture-matrix interaction at Yucca Mountain (Stuckless et al. 1991 [106947]; 
Marshall et al. 1991 [106335]; Peterman et al. 1992 [147110]; Peterman and Stuckless 1993 
[101149]; Johnson and DePaolo 1994 [162560]; Vaniman and Chipera 1996 [100089]; 
Sonnenthal and Bodvarsson 1999 [117127]; Paces et al. 2001 [156507]). Strontium 
concentrations in pore waters are related to the infiltration rate (through evaporation), the 
dissolution of minerals in surface deposits, reaction with minerals or glass in the tuffs, 
precipitation of calcite, and exchange with clays and zeolites (Vaniman et al. 2001 [157427]; 
Vaniman and Chipera 1996 [100089]; Sonnenthal and Bodvarsson 1999 [117127]). The 
similarity of Sr to Ca in charge and ionic radius results in Sr incorporation into Ca-bearing 
minerals such as plagioclase feldspar and secondary minerals such as Ca-zeolites and Ca-rich 
smectite. 
The 87Sr/86Sr ratio in pore waters depends on its initial ratio and is affected along a flow path by 
dissolution of Sr-bearing phases (e.g., calcite, feldspars, volcanic glass), exchange with clays or 
August 2003 195 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
zeolites, and the mixing of waters having differing isotopic ratios (Johnson and DePaolo 1994 
[162560], p. 1571). If such fluids have differing strontium concentrations, mixing will result in 
hyperbolic curves of 87Sr/86Sr versus total Sr (Faure 1986 [105559], p. 143), making them more 
complex chemically, but adding additional constraints to understanding the hydrological system. 
In nature, almost no fractionation of Sr isotopes occurs; however, fractionation of Rb from Sr 
leads to variations in 87Sr/86Sr because of the decay of 87Rb to 87Sr. Because the half-life of 87Rb 
is about 5  1010 years, this decay effect is insignificant over the few hundred thousand years of 
interest for the UZ flow system. 
The effect of the rock on the isotopic composition of water thus depends on the Sr content as 
well as on its isotopic ratio. The amount of rock dissolution and mineral precipitation depends on 
the degree to which the mineral assemblage is in disequilibrium with the water. Unaltered 
volcanic glass is more reactive than minerals, so that the rate of reaction of devitrified tuff with 
water is lower than that with glass. However, several factors can result in reduced apparent rates 
of reaction. The development of amorphous silica saturation in the soil zone has been proposed 
as a strong inhibitor of water-rock reaction in the underlying tuffs at Yucca Mountain (Meijer 
2002 [158813], pp. 803804). Alteration products on the surface of fractures or on mineral 
grains would also lead to rates limited by diffusion through such films rather than by reaction, 
retarding the dissolution rate (Sonnenthal and Ortoleva 1994 [117914], p. 407). Incongruent 
dissolution of volcanic glass has also been proposed as a cause for lower quantities of Sr 
dissolved into pore fluids in the PTn bedded tuffs (Vaniman and Chipera 1996 [100089], p. 
4421). 
7.10.2 Validation of Conceptual and Numerical Models of UZ Transport Based on 
Corroborative Information from Published Works 
Based on Cl/Sr ratios in a relatively few number of pore-water samples that were similar to those 
of an estimated effective precipitation composition, and the lack of a substantial shift in 87Sr/86Sr 
ratio in pore salts and calcite, Sonnenthal and Bodvarsson (1999 [117127], pp. 111, 151) 
suggested that the Sr concentrations in the UZ above the zeolitic units were in large part 
inherited from surface evapotranspiration processes, with only a minor contribution from waterrock 
interaction. Three-dimensional UZ transport modeling, including ion exchange, showed 
relatively high pore-water Sr concentrations in nonzeolitic units and a strong shift to lower 
concentrations (by a few orders of magnitude) in zeolitic units, which was consistent with porewater 
and perched-water compositions in contact with the respective rock units. This comparison 
of modeled Sr pore water concentrations was good supporting evidence for the approximately 5 
mm/yr mean infiltration rate at Yucca Mountain based on Cl concentrations in pore water 
(Sonnenthal and Bodvarsson 1999 [117127], p. 107). The model also produced Sr compositions 
at perched-water locations that were high where the perching was on the basal vitrophyre of the 
Topopah Spring tuff and very low where the perched water contacted zeolitic rocks. The results 
were consistent with several measurements made in the various boreholes that had intersected 
perched water bodies. 
Extensive exchange of Sr with Ca in clinoptilolite, other Ca-rich zeolites, and clays is well 
documented in the Yucca Mountain UZ (Vaniman et al. 2001 [157427]; Vaniman and Chipera 
1996 [100089], p. 4431). An analysis performed by Vaniman et al. (2001 [157427], p. 3409) 
showed that the excess Sr in the zeolitic rocks from the UZ-16 borehole, produced by ion 
exchange, was consistent with 10 million years of infiltration at about 5 mm/year. Therefore, this 
August 2003 196 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
published work provides independent corroboration of the results obtained by Sonnenthal and 
Bodvarsson (1999 [117127], p. 107), based on conceptual and numerical models that form the 
basis for the UZ Model presented in this report. Support for the 5 mm/yr being a long-term 
maximum is the observation that the 87Sr/86Sr ratios in calcite are shifted to slightly higher values 
in calcite precipitated more recently compared to early-formed calcite, indicating some 
contribution of Sr from water-rock interaction (Paces et al. 2001 [156507], pp. 75). Therefore, 
some of the Sr in the zeolitic units must have been derived from dissolution of tuff. An increase 
in the 87Sr/86Sr ratio in pore water at lower infiltration rates is a result of the longer residence 
time that the water has in contact with rock having a much higher 87Sr/86Sr ratio. This evidence, 
and the generally high Sr concentrations through the UZ above zeolitic rocks, suggests that 
although some Sr is lost to precipitating calcite, a comparable amount is gained by tuff 
dissolution. If some of the Sr in the zeolitic rocks is in excess of that produced by infiltration, 
then the estimated infiltration rate, based on Sr concentrations in the zeolitic rocks, would be an 
upper limit. 
Another piece of corroborating evidence comes from the compositions of calcite in fracture 
coatings. An excellent long-term record of the loss of Sr through ion exchange in the zeolites is 
given by the Sr concentrations in coexisting calcite, which are a few hundred ppm through much 
of the UZ, and then drop to a few ppm below zeolitic layers in the Calico Hills unit (Vaniman 
and Chipera 1996 [100089], pp. 44284429, Table 3). This corroborates the model results 
showing flow through the zeolitic units and Sr exchange with Ca in zeolites, resulting in waters 
having very low Sr concentrations. 
7.10.3 Model for 3-D Sr Concentrations 
A first approximation to modeling Sr concentrations in the UZ is to consider Sr as a conservative 
species in the nonzeolitic units and an exchangeable species in the zeolitic units (Sonnenthal and 
Bodvarsson 1999 [117127], p. 143). Simulations were run based on the model for Cl used in 
Section 6.5 employing the base-case present-day infiltration rate (scenario preq_mA in Table 
6.5-2). The only modifications to this model were for the Sr diffusion coefficient (7.94  10-10 
m2/s at 25C; Lasaga 1998 [117091], p. 315) and the Sr distribution coefficient (Kd) in zeolitic 
rocks (1.0 m3/kg; DTN: LA0302AM831341.002 [162575]). The latter value for the Kd is the 
approximate mean based on a range from 0.05 to 2.0 m3/kg provided in the DTN. Because all the 
Kd values in this range would result in a moderate to strong shift in pore water Sr concentrations 
from nonzeolitic to zeolitic rocks, the effect of assuming a uniform Kd only results in uncertainty 
in the degree to which concentrations within and below the zeolitic units are shifted to lower 
values. 
Strontium input at the surface was assumed to be wholly from precipitation, using an 
approximate Cl/Sr concentration ratio in precipitation of approximately 100 (Sonnenthal and 
Bodvarsson 1999 [117127], p. 147). The lower boundary condition (saturated zone) was set to 
zero concentration. Although this is less than the potential Sr concentrations in the saturated 
zone, the effect on the UZ would be limited to gridblocks adjacent to the lower boundary only, 
where few samples have been collected. The simulation was run for 4 million years using 
T2R3D V1.4 (LBNL 1999 [146654]), which resulted in a steady state concentration distribution. 
Simulations run for 1 and 2 m.y. showed some deviation from steady-state in low permeability 
gridblocks near the base of the domain, but not in most of the UZ locations where samples were 
collected. Although the 4 m.y. simulation may still show slight deviation from steady state in the 
August 2003 197 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
bottom gridblocks where concentrations are very low, the differences do not impact the model 
validation. 
Modeled Sr concentrations are compared to measured values for pore salts extracted (by 
leaching) from two surface-based boreholes (SD-9 and SD-12; DTN: GS990308315215.004 
[145711]), perched waters, and pore waters obtained by ultracentrifugation of core samples from 
the ECRB (DTN: GS020408312272.003 [160899]). Comparison of measured and modeled Sr 
concentrations as a function of elevation for the surface-based boreholes is shown in Figure 
7.10-1(a and b). Inputs and outputs for the 3-D Sr Model simulations have been submitted to the 
TDMS under Output-DTN: LB0304UZSRTRAN.001. Measured concentrations in the UZ above 
the perched water show a range of concentrations from about 0.1 to 2 mg/L, with perched-water 
concentrations (and pore-water concentrations at a similar depth) closer to 0.01 mg/L. This sharp 
reduction in Sr concentrations is greater than the equivalent drop in Cl concentrations in the 
perched-water bodies and is consistent with ion exchange in zeolitic rocks. Strong variations 
exist in the measured Sr concentrations as a function of depth in the UZ. However, no distinct 
trends can be discerned. The steady-state modeled concentrations above the perched water are 
very close to the mean values in boreholes SD-9 and SD-12. Where perched water samples were 
collected in SD-9, the model results capture the drop in concentration quite closely. In SD-12, 
the measured and modeled concentrations below 900 meters exhibit a reversal to higher 
concentrations. This reversal is consistent with lateral flow in the vitric units, rather than simple 
vertical flow through the zeolitic units that would result in consistently low concentrations below 
them. The criterion for validation is qualitative agreement between the simulated strontium 
concentrations and the average of the observations at the same elevation, and agreement with the 
vertical trends (BSC 2002 [160819], Attachment I-1-2-5). The comparisons shown for the 
surface-based boreholes meet the validation criteria. 
August 2003 198 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels
1300 
SD-9 
1250 
1200 
UZ 3D Mean Infiltration 
Pore Water (Measured) 
Perched Water (Measured) 
1150 
1100 
1050 
1000 
950 
900 
0.01 
850 
0.001 
SD-12 
UZ 3D Mean Infiltration 
Pore Water (Measured) 
(a)
Elevation [masl] 
1350 
1300 
1250 
1200 
1150 
1100 
1050 
1000 
950 
900 
850 
800 
750 
0.01 0.001 
(b) 
Figure 7.10-1. Comparison of Measured and Modeled Sr Concentrations as a Function of Elevation for 
the Surface-Based Boreholes (a) SD-9 and (b) SD-12 
Measured and modeled Sr concentrations in pore waters extracted from cores taken in the ECRB 
are shown in Figure 7.10-2. Measured concentrations are nearly all between 1 and 2 mg/L, with 
one sample having a concentration of about 3.6 mg/L. These concentrations are generally much 
higher than those measured in the surface-based boreholes. It is not clear if the different 
techniques used (leaching in the surface-based samples and ultracentrifugation in the ECRB) 
have resulted in any systematic bias in the Sr concentrations. Consistent with the lower Cl 
concentrations in most ECRB samples, compared to areas to the east where the infiltration rates 
are expected to be lower, the modeled Sr concentrations are also generally lower. Although the 
comparison of measured and modeled concentrations generally meet the order-of-magnitude 
criterion, the deviations are generally greater than that for the surface-based boreholes. Ratios for 
87Sr/86Sr are not available for these samples, and therefore the degree to which Sr concentrations 
may have been shifted to higher values by water-rock interaction cannot be assessed. Bulk-rock 
compositions for major and trace elements for tuffs in the ECRB are virtually identical to 
samples collected elsewhere (Peterman and Cloke 2002 [162576], pp. 696). Consequently, a 
shift caused by locally greater water-rock interaction (in the welded tuffs) is not likely. 
199 MDL-NBS-HS-000006 REV01 August 2003 
Elevation [masl] 
1 0.1 
Sr [mg/L] 
1 0.1 
Sr [mg/L] 
DTN: GS990308315215.004 [145711] 
Model Input and Outputs DTN: LB0304UZSRTRAN.001 
U0050 
UZ Flow Models and Submodels 
5
2
1 
0.5 
0.2 
0.1 
UZ 3D Mean Infiltration (Modeled) 
Pore Water (Measured) 
1000 500 
0.05
0 
Figure 7.10-2. Measured and Modeled Sr Concentrations in Pore Waters Extracted from Cores Taken 
in the ECRB 
The Sr model has been validated sufficiently for the purpose of LA. Additional confidence will 
be gained by direct incorporation of water-rock interaction and Sr isotopes to constrain the 
extent of Sr addition/loss to the rock, thus reducing uncertainties. 
MDL-NBS-HS-000006 REV01 
August 2003 200 
Distance [m] 
Sr [mg/L] 
U0050 
ECRB 
2000 1500 2500 
DTN: GS020408312272.003 [160899] 
Model Input and Outputs DTN: LB0304UZSRTRAN.001 UZ Flow Models and Submodels 
8. CONCLUSIONS 
This Model Report documents the development, results, and analyses of the UZ Flow Model and 
its submodels in accordance with the requirements of Section 5.4.1(b) of AP-SIII.10Q. These 
models and modeling analyses include the following: 
 3-D UZ calibration and flow field model for generating nine base-case flow fields 
 3-D UZ alternative flow field model for generating nine alternative flow fields 
 Mountain-scale, ambient TH model 
 3-D gas flow model 
 Chloride submodel 
 Calcite analysis 
 Tracer transport analysis 
 Modeling analysis of active fracture model 
 Model validation activities and results. 
The UZ Flow Model and its submodels are developed to simulate past, present, and future 
hydrogeological, geothermal, and geochemical conditions and processes within the Yucca 
Mountain UZ to support various TSPA-LA activities. In particular, as part of the output of this 
Model Report, nine 3-D, base-case steady-state flow fields of the Yucca Mountain UZ system 
have been generated for TSPA-LA calculations. This Model Report has documented the UZ 
Flow Model and its submodels in terms of modeling approaches, hydrogeological conceptual 
models, data source and incorporation, methodology of model calibrations, model parameter 
estimation and modifications, and model results and analysis of the 18 flow fields (9 base-case + 
9 alternative). This report also includes associated analyses on tracer transport with the 18 flow 
fields. The development and calibration of the mountain-scale ambient TH, gas flow, chloride, 
calcite, and strontium models are mainly for building confidence in the UZ Flow Model, with the 
output data and tracking numbers listed in Table 8-1. 
201 MDL-NBS-HS-000006 REV01 
U0050 
August 2003 UZ Flow Models and Submodels 
Table 8-1. 
9.4 
9.4 
DTN 
LB03023DSSCP9I.001* 
LB03033DSSFF9I.001 
LB03033DUZTRAN.001 
LB0303THERMSIM.001 
LB0303THERMESH.001 
LB0303GASFLW3D.001 
LB0303C14INF3D.001 
LB03013DSSCP3I.001 
LB0303A8N3MDLG.001 9.4 
6.8.1, 6.8.2.1, 
7.2, 7.3, 7.5, 
9.4 
6.623 
9.4 Figures: Tables: 
6.7-26.7-5 
9.4 Figures 6.3-24, 
7.7-13 
6.3.4, 
9.4 
9.4 
6.3.4, 6.4.1, 
7.2, 9.4 
LB0303CLINFL3D.001 9.4 
LB0304RDTRNSNS.001 9.4 
LB0304UZSRTRAN.001 7.10.3, 9.4 Figures 7.10-1, 
7.10-2 
LB0305TSPA18FF.001 
LB0305PTNNTSW9I.001 Attachment IV PTN/TSW Interface 
IV.1 
LB0302PTNTSW9I.001 Attachment IV PTN/TSW Interface 
IV.1 
NOTE: *The nine base-case flow fields will be directly used for TSPA-LA. 
Location in this report 
Text Figure Table 
Tables 6.2-10, 
6.6-3, 6.7-2 
Results of nine flow fields 
(base case), input/output 
files, and simulations results 
to be used by TSPA-LA 
Figures 6.2-24, 
6.6-14, 6.6-6, 
6.6-8, 6.6-10, 6.6- 
12, 6.6-14, 6.6-16, 
6.6-18, 6.6-20, 
6.6-22, 6.6-23, 
Tables 
6.6-2, 6.6-4, 
6.7-3 
7.2-1, 7.3-1 
Figures 
6.6-5, 6.6-7, 6.6-9, 
6.6-11, 6.6-13, 
6.6-15, 6.6-17, 
6.6-19, 6.6-21, 
6.7-16.7-8 
Table 6.3-1 Figure 6.3-1 
Results of nine flow fields 
(alternative and supporting 
files for both base case and 
alternative flow fields), 
input/output files, and 
simulation results 
Tc and Np Transport 
simulation scenarios, 
input/output files; using nine 
base-case and nine 
alternative flow fields 
3-D UZ ambient thermal 
model, Input/output, 
supporting files, and 
simulation results 
3-D UZ thermal model grid 
Figures 6.4-1, 6.4- 
2, 7.4-1, 7.4-2 
3-D gas flow model, 
input/out, supporting files, 
and simulation results 
C-14 simulations, 
Attachment I 
Figures 7.5-1, 7.5- 
2 input/output files, and 
simulation results 
Tables 6.2-59, 3-D site scale model 
6.6-1, calibrated property sets: 
Data Summaries 
Tables I-1 to I-6 
Figures 6.5-14, 
7.8-1 
Figures 6.8-14 Tables 6.8-1, 
6.8-2 
Tables 7.6-2, 
7.6-3 
Cl transport simulation 
results, input/output files 
3-D flow and transport 
sensitivity analysis with 
active fracture model 
parameter, input/output, 
supporting files, and 
simulation results 
UZ strontium transport 
model, input/output files, and 
simulation results 
Alcove 8/Niche 3 Seepage 
Modeling: 
Simulation files and results 
Figures 7.6-1, 7.6- 
4, 7.6-6, 7.6-7, 
7.6-8, 7.6-9, 7.6- 
10 
Table 6.2-11 Eighteen 3-D Site Scale UZ 
Flow Fields Converted from 
TOUGH2 to T2FEHM format 
(See Attachment IV.2) 
Percolation Flux Maps for 9 
Alternative Infiltration 
Scenarios 
Percolation Flux Maps for 9 
Alternative Infiltration 
Scenarios 
MDL-NBS-HS-000006 REV01 
U0050 
Output Data and Data Tracking Numbers 
Remarks 
August 2003 202 U0050 UZ Flow Models and Submodels 
8.1 UZ FLOW MODEL CALIBRATION 
As a critical step, field-measured saturation, water potential, and perched-water data have been 
used to calibrate the UZ Flow Model. Such calibrations are part of the important iterative 
processes of model development to increase confidence in model predictions of site conditions. 
This Model Report continues this model calibration effort using the 1-D inversions reported in 
the Model Report (BSC 2002 [160240]) and in the previous 3-D modeling effort (BSC 2001 
[158726]). This work focuses particularly on the PTn unit and potential perched-water layers 
using a 3-D forward calibration approach. 
Calibration was conducted using three sets of rock-property parameters (BSC 2002 [160240]), 
associated with present-day lower, mean, and upper infiltration rates, and the current geological 
model and numerical grid (BSC 2002 [160109]). Two sets of rock properties were examined for 
the PTn units, with one set selected to be included in the base case and the other for the 
alternative set, based on analysis of chloride data. In addition, a permeability-barrier conceptual 
model was adopted for modeling water-perching occurrences. Under the permeability barrier 
concept, rock properties were locally modified to better match data in several grid layers near the 
observed perched zones. 
The model calibration efforts conclude that the UZ Flow Model can reproduce moisture 
conditions in the Yucca Mountain UZ in terms of liquid saturations and water potentials, as 
verified by observations. In general, the modeled results from all nine base-case flow-field 
simulations using the perched-water conceptual model are in good agreement with the measured 
water-perching elevations at seven boreholes for upper-bound and mean present-day infiltration 
scenarios. However, under the lower-bound present-day infiltration rate, the models did not 
match the perched-water data very well in boreholes SD-7, SD-9, NRG-7a, and UZ- 14 because 
of the low percolation fluxes at these locations. This will have little effect on flow fields. 
The UZ Flow Model provides results of steady-state fluid and heat flow as well as transient 
tracer transport. Flow processes for model layers above the TSw and PTn units may be subject to 
episodic infiltration. Since the model results with these layers may not reflect actual conditions, 
which are time- and scale-dependent, therefore the results may be directly applicable to studies 
on a much smaller scale such as the emplacement drift. In this report, the uncertainties in the 
results owing to input-parameter and model-gridding uncertainties are evaluated by generating a 
number of flow fields with various parameter sets, infiltration maps, and conceptual models. 
8.2 GEOTHERMAL MODEL CALIBRATION 
The ambient geothermal model simulates large-scale UZ geothermal and heat flow conditions. 
The 3-D ambient thermal model was calibrated against qualified temperature data measured 
from five boreholes, using the base-case present-day infiltration parameter set with a 3-D dualpermeability 
thermal grid. Simulated temperature results are in good agreement with the 
observed temperature profiles from the boreholes. Such results provide the ambient temperature 
distributions that determine boundary and initial conditions for the mountain-scale and driftscale 
TH, THC, and THM coupled-process models. 
August 2003 203 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
8.3 GAS FLOW MODEL 
A 3-D pneumatic simulation has been performed as a part of the UZ Flow Model calibration 
effort. Results of gas flow simulations are compared to the measured pneumatic data from three 
boreholes, including SD-7, SD-12, and UZ-7a, for the purposes of calibration and validation. As 
a result of calibration, fracture permeability in several TSw layers were reduced by a factor of 
15, leading to an overall good match between the 3-D model prediction and measurement of 
pneumatic data under 3-D flow conditions. The gas flow calibration results add confidence that 
the UZ Flow Model is reliable and appropriate for modeling gas flow in the UZ. 
8.4 CHLORIDE SUBMODEL 
Chloride is a naturally occurring conservative tracer. It enters the groundwater system as a solute 
in the infiltration flux. Thus, chloride data can be used to examine the long-term infiltration rate 
in the Flow Model. The chloride flux to the UZ at Yucca Mountain is calculated based on the 
precipitation, runon, and runoff at the ground surface of the mountain. 
The chloride transport modeling considered four scenarios of infiltration rates, including presentday 
(or modern) mean, upper, and lower bounds, as well as glacial transition mean infiltration. 
Each scenario compares the results of two sets of flow fields with different PTn properties, one 
set denoted as a base-case flow field and the other as an alternative set, in order to study the 
effect of potential lateral flow in the PTn unit. The base case incorporates a property set of the 
PTn that would more likely cause lateral flow diversion, with the alternative less likely. Results 
show that the chloride transport model with the base case in the present-day, mean infiltration 
yields the closest and most consistent match with field data. In other words, the property set of 
the PTn that would favor lateral flow diversion (in the PTn unit) yields more reasonable results 
in matching the chloride concentration in the field samples. It indicates that lateral diversion may 
occur in the PTn unit at Yucca Mountain. 
8.5 CALCITE SUBMODEL 
Calcite precipitation has been modeled in unsaturated fractured rocks, considering several 
essential factors: (1) infiltration rate, (2) ambient geothermal gradient, (3) gaseous CO2 diffusive 
transport and partitioning between liquid and gas phases, and (4) fracture-matrix interaction for 
water flow and chemical constituents, and (5) water-rock interaction. 
Modeling calcite deposition can be used to build some constraints on the infiltration-percolation 
flux. The modeling also provides additional evidence for validation of the UZ Model. Over a 
range of 2-20 mm/yr infiltration rates, the simulated calcite distributions capture the measured 
data from the WT-24 well cuttings. The 20 mm/yr infiltration rate may be the upper bound for 
the WT-24 location, whereas the base-case infiltration rate (5.92 mm/yr) used for the flow model 
gives the closest match to the data. The observed calcite precipitation for the top of TSw occurs 
mostly in the fractures, which is also captured by the modeling. The modeled results can provide 
useful insight into process mechanisms such as fracture-matrix interaction, as well as conditions 
and parameters controlling calcite deposition. The modeled calcite abundances generally 
increase with increasing infiltration rate, but become less sensitive to infiltration at higher rates 
as a result of its impact to the geothermal gradient. Data from borehole SD-6 are roughly 
consistent with the relation between infiltration rate and calcite abundances, although a locally 
higher thermal gradient in the PTn can also be a factor in the calcite distribution. 
August 2003 204 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
8.6 TSPA FLOW FIELDS 
Eighteen 3-D UZ flow fields (nine for the base case and nine for the alternative ones) were 
generated for TSPA-LA calculations. The nine base-case flow fields were produced for use by 
FEHM in the TSPA calculations. These flow fields were based on (1) the TSPA-LA grid (BSC 
2002 [160109]), (2) nine infiltration maps representing three climates; (3) the six parameter sets; 
and (4) the two conceptual models of PTn flow and a perched-water conceptual model with the 
calibrated perched-water parameters. The purpose of studying a large number of flow fields for 
various modeling scenarios was to cover all TSPA-LA scenarios and to account for possible 
current and future site conditions. Alternative parameter sets for the PTn were used to investigate 
conceptual model uncertainty. The main uncertainties currently considered in the UZ Flow 
Model included fracture-matrix properties, present-day and future net infiltration rates over the 
mountain, and conceptual models for perched water occurrence, and the role of PTn for lateral 
flow. 
A detailed analysis of simulated percolation fluxes at the repository level and at the water table 
was conducted for all simulation scenarios of 18 flow fields. These percolation fluxes and their 
distributions at the repository level indicated that there exists a certain amount of large-scale 
lateral flow or diversion by the PTn unit (on the order of several hundreds of meters) for the nine 
base-case simulations. In comparison, the nine alternative flow fields predict smaller lateral 
diversion when flowing through the PTn unit. In both models, however, significant flow 
diversion and redistribution into faults within the PTn unit is predicted. On the other hand, a 
comparison of simulated percolation fluxes at the repository level with those at the water table 
indicated that significant lateral flow occurs at perched or zeolitic layers when traveling through 
the CHn layers. 
Fracture-matrix flow components at the repository horizon and at the water table were also 
analyzed for the 18 simulations. The statistics show that fracture flow is dominant in the welded 
tuffs, both at the repository horizon and at the water table, in all the 18 flow fields. For three 
present-day infiltration scenariosfracture-matrix flow components simulated at the repository 
levelfracture flow contributes more than 90% of total flow, and at the water table7080% of 
the total flow. Furthermore, faults provide major flow pathways for focused percolation fluxes. 
Fault flow percentage increases with depth from 30-40% at the repository level to 60% at the 
water table (Section 6.6.3, Tables 6.6-3 and 6.6-4). 
8.7 TRACER TRANSPORT TIMES 
A total of 40 tracer transport simulations were conducted to obtain insight into the impacts of 
infiltration rates, perched-water and PTn conceptual models, and retardation effects on tracer 
migration from the repository to the water table (Section 6.7). All the 18 TSPA-LA flow fields 
were incorporated into these 40 transport runs. For each flow field, there were two tracer 
transport runs, one for conservative (or nonadsorbing) and the other for reactive (or adsorbing) 
tracer transport, respectively, with tracer release from repository fracture blocks. For the two 
present-day mean infiltration cases, tracer release from repository matrix blocks was also 
investigated, resulting in four additional transport runs. These tracer-transport studies indicate 
that there exist a wide range of tracer transport times associated with different infiltration rates, 
type of tracers, and PTn water conceptual models. The most important factors for tracer-transport 
times are found to be (1) surface infiltration rates; (2) adsorption effects in the CHn unit; and (3) 
release from repository fracture or matrix blocks. 
August 2003 205 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Statistics of tracer transport times at 10% and 50% mass breakthrough at the water table from the 
40 simulations show that tracer-transport times are inversely proportional to average surface 
infiltration. When the average infiltration rate increases from 5 to 35 (mm/yr), average tracer 
transport (50% breakthrough) times decrease by two to three orders of magnitude. Nonadsorbing 
tracers migrate one to two orders of magnitude faster than adsorbing tracers when traveling from 
the repository to the water table under the same infiltration conditions. The base-case flow fields 
predict a little shorter or more conservative tracer transport times than the alternative ones in 
general. The simulation results show that the two different PTn models have an insignificant 
impact on tracer transport at the lower units, i.e., from the repository to the water table. 
8.8 SENSITIVITY ANALYSIS OF ACTIVE-FRACTURE-MODEL PARAMETER 
The impacts of uncertainty in the active fracture parameter () on the simulated flow and 
transport were evaluated by sensitivity analyses of flow and transport simulation results (Section 
6.8). Two additional 3-D flow simulations, using reduced values of active fracture parameter  
for TSw units and all units below the repository (including the units where the repository is 
located), respectively, were performed to carry out the analyses. By comparing liquid saturation, 
water potential, and the percolation flux obtained by these two simulations with those obtained 
by simulations using unmodified (calibrated)  values, we found that the changes in liquid 
saturation, water potential, and percolation flux are rather small. In general, the flow fields are 
not very sensitive to the active fracture parameter . On the other hand, tracer transport times 
were found to be sensitive to the value. 
8.9 MODEL VALIDATION 
Model validation efforts have been documented in this Model Report. Validation activities for 
the UZ Flow Model include corroboration with experimental data and modeling studies, using 
the following corroboration with experimental data: (1) ECRB observation data; (2) WT-24 
perched-water data; (3) gas flow data from boreholes SD-12 and UZ-7a; (4) borehole measured 
14C data; and Alcove 8 flow and seepage test results. In addition, validation efforts are also 
performed for the ambient thermal model, chloride model, calcite model, and strontium model 
using field observed data from surface-based boreholes or from the ESF. 
In all these validation examples, the simulation results of the UZ Flow Model and Submodels are 
shown to be able to match different types of available observation data, such as water potentials, 
perched-water locations, tracer and geochemical concentrations, temperatures and pneumatic 
pressures. The criteria of the model validation of the TWP are in general satisfied. These efforts 
have provided validation of the UZ Flow Model and its submodels for their accuracy and 
reliability in describing hydrological, thermal and chemical conditions and predicting flow and 
transport processes in the UZ system of Yucca Mountain. 
8.10 BARRIER CAPABILITY OF THE UZ 
The 500-700 m thick UZ consists of surficial soils and the TSw, PTn, TSw and CHn units above 
and below the repository. The thick UZ formation itself is a natural barrier to downward water 
percolation and transport. The flow model documented in this Model Report quantitatively 
describes the barrier capabilities of the UZ. The surficial soils function as a barrier by diverting 
(as runoff) some of the water that arrives as precipitation and runon, and by storing the 
remainder so that some of it is evaporated as evapotranspiration. Substantial amount of water 
August 2003 206 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
thus diverted or evaporated cannot percolate as net infiltration (USGS 2001 [160355], p. 23). 
The surficial soils are shown to be an effective natural barrier to water recharge and surface 
infiltration, using the statistics of Tables 6.5-2 and 6.1-2 for the present-day climates. Table 8-2 
lists the estimated percentage of net infiltration (Table 6.1-2) over net recharge 
(=precipitation+runon-runoff, Table 6.5-2), indicating that only 0.63%, 1.95%, and 2.5% of net 
water recharge on the model top boundary penetrates the top soil layer and becomes net 
infiltration, respectively, for the lower-, mean, and upper-bound infiltration scenarios. More than 
97% of net recharge returns to the atmosphere by evapotranspiration. 
Table 8-2. Percentage of Net Infiltration over Net Recharge after Evapotranspiration for Present- 
Day/Modern Climates over the Model Domain) 
Net infiltration2 
(mm/year) 
Precipitation1 
(mm/year) 
Net Infiltration % 
August 2003 
Net recharge1 
(mm/year) 
207 
Present-day/Modern 
Infiltration 
Scenarios 
Low 185.9 198.5 1.25 0.63 
Mean 189.5 227.1 4.43 1.95 
Upper 267.1 429.9 10.74 2.50 
NOTES: 1 from Table 6.5-2 
2 from Table 6.1-2 
Barrier capability of the UZ below the surficial soils is the subject of the present model report. 
We first consider UZ tuffs above and below the repository: water not diverted or evaporated by 
surficial soils and evapotranspiration entering the UZ formation as net infiltration, which is set as 
the surface boundary recharge condition for the UZ flow model. The UZ formation units above 
the repository serve as a barrier by storage and diversion. Percolating water flow through the 
PTn is matrix-dominated flow rather than fracture-dominated flow, which has been shown in 
Section 6.6-3 to subject to strong capillary barrier effects and lateral diversion. Therefore, the 
PTn unit buffers the repository from sudden changes in percolation flux and from episodic 
surface infiltration pulses. Because of predicted climate changes, the Yucca Mountain region as 
well as the UZ is expected in the future to receive more precipitation and more infiltration than 
at present, so that percolation flux at the repository horizon will increase. Storage in the UZ 
above the repository, particularly in the PTn unit, will significantly delay the increase in 
percolation flux below the PTn. However, the modeling approach adopted here ignores that 
delay because the flow field is modeled as changing instantly to the long-term steady state. This 
ignores the lower net infiltration during the transitional period and results in more conservative 
model predictions. 
Lateral flow diversion within the PTn due to the capillary barrier effect is demonstrated in this 
Model Report by two conceptual models. This lateral diversion diverts a large amount of 
percolation flux into faults, which becomes fast and focused flow pathways, which may bypass 
repository drifts. For example, the UZ Model predicts with the present-day mean infiltration that 
more than 25% of percolation is diverted to faults by the units above the repository, mainly by 
the PTn. This will, on average, reduce percolation fluxes at the repository horizon, which is the 
driving force for potential seepage into drifts. Lateral diversion in the PTn thus reduces the 
amount of water seeping into drifts, which may transport radionuclides to the saturated zone. 
Below the repository horizon, the UZ also has a barrier capability of delaying radionuclide 
transport to the saturation zone. The UZ does this by retarding the water flow and by removing 
MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
some of the radionuclides by sorption. The flow in the UZ below the repository horizon is 
partially through perched water bodies and low-permeability zeolitic zones of the CHn. Flow 
through perched water bodies or zeolitic zones is slow because of the permeability in these areas. 
Percolation flux may also be diverted horizontally over perched water bodies caused by 
permeability barriers. This laterally diverted flow through perched water bodies travels slowly, 
because of the longer distance and the smaller driving force (gravity gradient). Overall, lowpermeability 
zeolitic zones and perched-water bodies retard downward percolation flux and 
increase tracer travel times. This increased residence time enhances adsorption and matrix 
diffusion effects while radionuclides are transported through these regions. 
The other portion of the flow below the repository goes through unfractured CHn vitric zones of 
the Calico Hills formation, and is retarded by low matrix permeability and large pore storage. 
This will also promote diffusion of radionuclides from fractures to matrix, and delay overall 
transport times. Furthermore, all UZ tuffs, even in faults, have some adsorptive capacity for 
radionuclides, regardless of zeolitic or vitric units of the CHn. Adsorbing effect of radionuclides 
on unsaturated tuffs not only delays its arrival at the water table, but reduces the transported 
mass by radioactive decay. 
8.11 LIMITATIONS 
The UZ Flow Model and submodels are appropriate tools for characterizing flow and transport 
processes in the UZ of Yucca Mountain. The accuracy and reliability of the UZ Flow Model 
predictions are critically dependent on the accuracy of estimated model properties, other types of 
input data, and hydrogeological conceptual models. These models are limited mainly by the 
current understanding of the mountain system, including the geological and conceptual models, 
the volume-average modeling approach, and the available field and laboratory data. 
Past site investigations have shown that large variabilities exists in the flow and transport 
parameters over the spatial and temporal scales of the mountain. Even though considerable 
progress has been made in this area, uncertainty associated with the UZ Flow Model input 
parameters will continue to be a key issue for future studies. The major uncertainties in the UZ 
Model parameters are: (1) accuracy of estimated current, past, and future net infiltration rates 
over the mountain; (2) quantitative descriptions of the heterogeneity of welded and nonwelded 
tuffs, their flow properties, and detailed spatial distributions within the mountain, especially 
below the repository; (3) fracture properties in zeolitic units and faults from field studies; (4) 
evidence of lateral diversion caused by zeolites in the CHn units and within the PTn units; and 
(5) transport properties (e.g., adsorption or Kd coefficients in different rock types, matrix 
molecular diffusion coefficients in different units for different radionuclides, dispersivities in 
fracture and matrix systems). These uncertainties exist, but they have been addressed with the 
modeling studies in this Model Report. In particular, most uncertainties are captured in the range 
of flow field generated. 
This document and its conclusions may be affected by technical product input information 
However, the results and conclusions of the UZ flow fields will not be affected by the status of 
temperature and geochemistry data used in the calibration or validation studies, because these 
flow fields are based on flow simulations under isothermal and different climate conditions. 
August 2003 208 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
8.12 SATISFACTION OF ACCEPTANCE CRITERIA 
In section 4.2, seven NRC acceptance criteria were identified. How they are satisfied are 
discussed as follows. 
For criteria from Section 2.2.1.1.3 of YMRP (NRC 2003 [163274]) 
 AC 1 System Description and Model Integration are adequate: The UZ system is 
described in Section 6.1. This description is based on data from field and laboratory 
investigations, and is consistent with standard conceptual models of the UZ at Yucca 
Mountain. In addition, spatial variability of model parameters are adequately 
represented by the 3-D model grid, with more than 60 different types of fracture-matrix 
properties, while spatial variability of model parameters are sufficiently covered by six 
sets of parameters calibrated to different infiltration rates. The model calibration and 
validation activities show that the description is adequate for modeling UZ flow and 
transport 
 AC 2 data are sufficient: Data from field and laboratory testing have been synthesized 
and used to calibrate the model. The model validation shows that these data are 
sufficient to justify the model for its intended use. 
 AC3 Data uncertainty is characterized and propagated: Hydrological properties used in 
the UZ Flow Model have been calibrated (BSC 2003 [160240]; [161773]) using mean, 
upper, and lower bound infiltration maps, thus capturing the uncertainty in model 
parameters. Three flow fields were generated for each future climate condition (present 
day, monsoon, and glacial transition). 
 AC4 Model uncertainty is characterized and propagated: Two property sets for the PTn 
were used to study the alternative flow model in which lateral flow occurs in the PTn . 
For criteria from Section 2.2.1.3.6.3 of YMRP (NRC 2003 [163274]): 
 AC1 Identification of barriers is adequate: The model simulates flow and transport in 
the unsaturated rocks above and below the repository horizon, two natural barriers 
identified in BSC (2002 [160146]) TDR-WIS-PA-000006. Barrier capability is 
described in Section 8.10. 
 AC 2 Description of barrier capability to isolate waste is acceptable: Barrier capability 
is determined by the delay in transport of radionuclides to the water table. Simulations 
of tracer transport time have been validated, showing that the description is acceptable. 
 AC3 Technical basis for barrier capability is adequately presented: In Section 6.7, 
radionuclide transport from repository to the water table is presented in more than forty 
simulations, incorporating eighteen 3-D flow fields. 
August 2003 209 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
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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 
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154291 CRWMS M&O 2001. Abstraction of Drift Seepage. ANL-NBS-MD-000005 REV 
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154594 CRWMS M&O 2001. Abstraction of NFE Drift Thermodynamic Environment and 
Percolation Flux. ANL-EBS-HS-000003 REV 00 ICN 02. Las Vegas, Nevada: 
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Evaluating Moisture, Gas, Chemical, and Heat Transport in Fractured Unsaturated 
Rock. Journal of Contaminant Hydrology, 38, (1-3), 69-106. New York, New 
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Software Cited 
139918 LBNL (Lawrence Berkeley National Laboratory) 09/16/1999. Software Code: 
iTOUGH2. V4.0. SUN, DEC. 10003-4.0-00. 
134754 LBNL (Lawrence Berkeley National Laboratory) 1999. Software Code: infil2grid. 
V1.6. PC with Windows/95 or 98. Sun or DEC Workstation with Unix OS. 10077- 
1.6-00. 
146654 LBNL (Lawrence Berkeley National Laboratory) 1999. Software Code: T2R3D. 
V1.4. FORTRAN 77, SUN, DEC / ALPHA. 10006-1.4-00. 
146496 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Code: TOUGH2. 
V1.4. Sun Workstation and DEC/ALPHA. 10007-1.4-01. 
153471 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Routine: 
bot_sum.f. V1.0. SUN AND DEC. 10349-1.0-00. 
147023 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Routine: genincon-
v0.f. V1.0. Sun workstation w/Unix OS. 10220-1.0-00. 
147027 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Routine: 
get_temp_v0.f. V1.0. Sun workstation w/Unix OS. 10222-1.0-00. 
147030 LBNL (Lawrence Berkeley National Laboratory) 2000. Software Routine: 
toptemp_v0.f. V1.0. Sun workstation w/Unix OS. 10224-1.0-00. 
154345 LBNL (Lawrence Berkeley National Laboratory) 2001. Software Routine: 
vf_con.for. V1.0. PC w/Windows. 10466-1.0-00. 
162143 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: Bkread.f. 
V1.0. SunOS 5.5.1. 10894-1.0-00. 
154793 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: infil2grid. 
V1.7. DEC-Alpha, PC. 10077-1.7-00. 
160107 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: TBgas3D. 
V2.0. SUN UltraSparc. 10882-2.0-00. 
August 2003 222 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
161256 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: 
TOUGHREACT. V3.0. DEC-Alpha with Unix OSF1 V5.1 and OSF1 V5.0, Sun 
Solaris 5.5.1, Linux Redhat 7.2. 10396-3.0-00. 
154785 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: 
WINGRIDDER. V2.0. PC. 10024-2.0-00. 
162142 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Code: Smesh.f. 
V1.0. SunOS 5.5.1. 10896-1.0-00. 
154787 LBNL (Lawrence Berkeley National Laboratory) 2002. Software Routine: 
2kgrid8.for. V1.0. DEC-Alpha, PC. 10503-1.0-00. 
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. 
163453 LBNL (Lawrence Berkeley National Laboratory) 2003. Software Code: WTRISE. 
V2.0. PC/WINDOWS 2000/98; DEC ALPHA/OSF1 V5.1. 10537-2.0-00. 
163162 LBNL (Lawrence Berkeley National Laboratory) 2003. Software Code: flow-con. 
V1.0. PC/ WINDOWS 95/98(MS-DOS emulation); DEC ALPHA/OSF1 V5.1. 
10993-1.0-00. 
163161 LBNL (Lawrence Berkeley National Laboratory) 2003. Software Code: T2FEHM. 
V4.0. DEC ALPHA / OSF1 V4.0/ V5.1. 10997-4.0-00. 
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. 0, ICN 1. Classification Criteria and Maintenance of the Monitored Geologic 
Repository Q-List. Washington, D.C.: U.S. Department of Energy, Office of Civilian 
Radioactive Waste Management. ACC: DOC.20030422.0009. 
AP-SI.1Q, Rev. 5, Mod 1. 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. 
9.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
147613 GS000308311221.005. Net Infiltration Modeling Results for 3 Climate Scenarios for 
FY99. Submittal date: 03/01/2000. 
August 2003 223 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
153407 GS000608312271.001. Pore-Water Hydrochemistry and Isotopic Data for Boreholes 
USW NRG-6, USW NRG-7A, USW SD-7, USW SD-9, USW SD-12, USW UZ-14 
and UE-25 UZ#16 from 10/1/96 to 1/31/97. Submittal date: 06/23/2000. 
156375 GS010708312272.002. Chemical Data for Pore Water from Tuff Cores of USW 
NRG-6, USW NRG-7/7A, USW UZ-14, USW UZ-N55 and UE-25 UZ#16. 
Submittal date: 09/05/2001. 
160899 GS020408312272.003. Collection and Analysis of Pore Water Samples for the 
Period from April 2001 to February 2002. Submittal date: 04/24/2002. 
162129 GS020508312242.001. Trench Fault Infiltration in Alcove 8 Using Permeameters 
from March 5, 2001 to June 1, 2001. Submittal date: 05/22/2002. 
162126 GS020608315215.002. Carbon Dioxide Abundances, Carbon Dioxide 
Concentrations, and Normative Calcite Concentrations for Cuttings from Borehole 
USW SD-6, USW WT-24, and ECRB Cross Drift Boreholes, Determined by Carbon 
Dioxide Evolution, May 25, 2000 and September 8, 2000. Submittal date: 
06/26/2002. 
162141 GS020908312242.002. Trenched Fault Infiltration in Alcove 8 Using Permeameters 
from June 1, 2001 to March 26, 2002. Submittal date: 09/17/2002. 
162127 GS021008315215.007. Carbon Dioxide and Normative Calcite Concentrations in 
Powdered Cuttings from Borehole USW WT-24 Determined by CO2 Evolution 
between July 1998 and August 1999. Submittal date: 11/07/2002. 
162131 GS030108314224.001. Geotechnical Data for Alcove 8 (ECRB) and Niche 3 (ESF): 
Full Periphery Geologic Map (Drawing OA-46-356). Submittal date: 02/05/2003. 
105572 GS950208312232.003. Data, Including Water Potential, Pressure and Temperature, 
Collected from Boreholes USW NRG-6 and USW NRG-7A from Instrumentation 
through March 31, 1995. Submittal date: 02/13/1995. 
107244 GS950408318523.001. Temperature, Thermal Conductivity, and Heat Flow Near 
Yucca Mountain, Nevada. Submittal date: 04/21/1995. 
106756 GS951108312232.008. Data, Including Water Potential, Pressure and Temperature, 
Collected from Boreholes UE-25 UZ#4 & UZ#5 from Instrumentation through 
September 30, 1995, and from USW NRG-6 & NRG-7A from April 1 through 
September 30, 1995. Submittal date: 11/21/1995. 
105573 GS960308312232.001. Deep Unsaturated Zone Surface-Based Borehole 
Instrumentation Program Data from Boreholes USW NRG-7A, USW NRG-6, UE-25 
UZ#4, UE-25 UZ#5, USW UZ-7A, and USW SD-12 for the Time Period 10/01/95 
through 3/31/96. Submittal date: 04/04/1996. 
August 2003 224 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
107230 GS960308312312.005. Water-Level, Discharge Rate and Related Data from the 
Pump Tests Conducted at Well USW UZ-14, August 17 through August 30, 1993. 
Submittal date: 03/15/1996. 
105974 GS960808312232.004. Deep Unsaturated Zone Surface-Based Borehole 
Instrumentation Program Data for Boreholes USW NRG-7A, USW NRG-6, UE-25 
UZ#4, UE-25 UZ#5, USW UZ-7A and USW SD-12 for the Time Period 4/1/96 
through 8/15/96. Submittal date: 08/30/1996. 
106784 GS960908312261.004. Shut-in Pressure Test Data from UE-25 NRG#5 and USW 
SD-7 from November, 1995 to July, 1996. Submittal date: 09/24/1996. 
107293 GS961108312261.006. Gas Chemistry, ESF Alcoves 2 and 3, 11/95 - 4/96; Water 
Chemistry, Alcove 2 (Tritium), Alcove 3, and ESF Tunnel; and Pneumatic Pressure 
Response from Boreholes in Exploratory Studies Facility Alcoves 2 and 3, 10/95 - 
5/96. Submittal date: 11/12/1996. 
121708 GS961108312271.002. Chemical and Isotopic Composition of Pore Water and Pore 
Gas, 199496, from Boreholes USW UZ-1, USW UZ-14, UE-25 UZ#16, USW NRG- 
6, USW NRG-7A, USW SD-7, USW SD-9, ESF-AL#3-RBT#1, and ESF-AL#3- 
RBT#4, and ESF Rubble. Submittal date: 12/04/1996. 
105975 GS970108312232.002. Deep Unsaturated Zone, Surface-Based Borehole 
Instrumentation Program - Raw Data Submittal for Boreholes USW NRG-7A, USW 
NRG-6, UE-25 UZ#4, UE-25 UZ#5, USW UZ-7A, and USW SD-12, for the Period 
8/16/96 through 12/31/96. Submittal date: 01/22/1997. 
105978 GS970808312232.005. Deep Unsaturated Zone Surface-Based Borehole 
Instrumentation Program Data from Boreholes USW NRG-7A, UE-25 UZ#4, UE-25 
UZ#5, USW UZ-7A and USW SD-12 for the Time Period 1/1/97 - 6/30/97. 
Submittal date: 08/28/1997. 
111467 GS970908312271.003. Unsaturated Zone Hydrochemistry Data, 2-1-97 to 8-31-97, 
Including Chemical Composition and Carbon, Oxygen, and Hydrogen Isotopic 
Composition: Porewater from USW NRG-7A, SD-7, SD-9, SD-12 and UZ-14; and 
Gas from USW UZ-14. Submittal date: 09/08/1997. 
105980 GS971108312232.007. Deep Unsaturated Zone Surface-Based Borehole 
Instrumentation Program Data from Boreholes USW NRG-7A, UE-25 UZ #4, UE-25 
UZ #5, USW UZ-7A and USW SD-12 for the Time Period 7/1/97 - 9/30/97. 
Submittal date: 11/18/1997. 
105982 GS980408312232.001. Deep Unsaturated Zone Surface-Based Borehole 
Instrumentation Program Data from Boreholes USW NRG-7A, UE-25 UZ #4, USW 
NRG-6, UE-25 UZ #5, USW UZ-7A and USW SD-12 for the Time Period 10/01/97 - 
03/31/98. Submittal date: 04/16/1998. 
August 2003 225 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
109746 GS980508312313.001. Water-Level and Related Data Collected in Support of 
Perched-Water Testing in Borehole USW WT-24, September 10, 1997 through 
February 3, 1998. Submittal date: 05/07/1998. 
106752 GS980708312242.010. Physical Properties of Borehole Core Samples, and Water 
Potential Measurements Using the Filter Paper Technique, for Borehole Samples 
from USW WT-24. Submittal date: 07/27/1998. 
106748 GS980808312242.014. Physical Properties of Borehole Core Samples and Water 
Potential Measurements Using the Filter Paper Technique for Borehole Samples from 
USW SD-6. Submittal date: 08/11/1998. 
119820 GS980908312242.036. Water Potentials Measured with Heat Dissipation Probes in 
ECRB Holes from 4/23/98 to 7/31/98. Submittal date: 09/22/1998. 
153677 GS981008312272.004. Analysis for Chemical Composition of Pore Water from 
Boreholes USW UZ-7A, USW WT-24, USW SD-6, USW SD-7, and USW SD-12 
During FY 1997 and 1998. Submittal date: 10/28/1998. 
146134 GS990208312272.001. Analysis for Chemical Composition of Pore Water from 
Borehole USW UZ-14 and UE-25 UZ#16 and Groundwater from UE-25 UZ#16. 
Submittal date: 02/23/1999. 
145711 GS990308315215.004. Strontium Isotope Ratios and Strontium Concentrations in 
Rock Core Samples and Leachates from USW SD-9 and USW SD-12. Submittal 
date: 03/25/1999. 
148603 LA000000000034.002. Diffusion of Sorbing and Non-Sorbing Radionuclides. 
Submittal date: 06/22/1993. 
154760 LA0002JF12213U.001. Chemistry Data for Porewater Extracted from Drillcore from 
Surface-Based Boreholes USW NRG-6, USW NRG-7A, USW UZ-7A, USW UZ-14, 
UE-25 UZ#16, USW UZ-N55, USW SD-6, USW SD-7, USW SD-9, USW SD-12, 
and USW WT-24. Submittal date: 02/15/2000. 
156281 LA0002JF12213U.002. Chemistry Data for Porewater Extracted from ESF, Cross 
Drift and Busted Butte Drill Core. Submittal date: 02/15/2000. 
149557 LA0003JC831362.001. Preliminary Matrix Diffusion Coefficients for Yucca 
Mountain Tuffs. Submittal date: 4/10/2000. 
162476 LA0010JC831341.001. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Barium. Submittal date: 10/19/2000. 
153321 LA0010JC831341.002. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Cesium. Submittal date: 10/19/2000. 
153322 LA0010JC831341.003. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Strontium. Submittal date: 10/19/2000. 
August 2003 226 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
153323 LA0010JC831341.004. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Selenium. Submittal date: 10/19/2000. 
153320 LA0010JC831341.005. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Uranium. Submittal date: 10/19/2000. 
153318 LA0010JC831341.006. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Plutonium. Submittal date: 10/19/2000. 
153319 LA0010JC831341.007. Radionuclide Retardation Measurements of Sorption 
Distribution Coefficients for Neptunium. Submittal date: 10/19/2000. 
162575 LA0302AM831341.002. Unsaturated Zone Distribution Coefficients (KDS) for U, 
NP, PU, AM, PA, CS, SR, RA, and TH. Submittal date: 02/04/2003. 
145598 LA9909JF831222.004. Chloride, Bromide, and Sulfate Analyses of Busted Butte and 
Cross Drift Tunnel Porewaters in FY99. Submittal date: 09/29/1999. 
122733 LA9909JF831222.010. Chloride, Bromide, Sulfate, and Chlorine-36 Analyses of 
ESF Porewaters. Submittal date: 09/29/1999. 
145402 LAJF831222AQ98.011. Chloride, Bromide, Sulfate and Chlorine-36 Analyses of 
Springs, Groundwater, Porewater, Perched Water and Surface Runoff. Submittal 
date: 09/10/1998. 
161278 LB0101DSTTHCR1.003. Attachment III - Mineral Reactive Surface Areas: 
TPTPMN and DST THC Models for AMR N0120/U0110 REV01, Drift-Scale 
Coupled Processes (Drift-Scale Test and THC Seepage) Models.. Submittal date: 
01/26/2001. 
159525 LB0205REVUZPRP.001. Fracture Properties for UZ Model Layers Developed from 
Field Data. Submittal date: 05/14/2002. 
161285 LB0208UZDSCPMI.001. Drift-Scale Calibrated Property Sets: Mean Infiltration 
Supporting Files. Submittal date: 08/27/2002. 
161243 LB0208UZDSCPMI.002. Drift-Scale Calibrated Property Sets: Mean Infiltration 
Data Summary. Submittal date: 08/26/2002. 
161433 LB02091DSSCP3I.002. 1-D Site Scale Calibrated Properties: Data Summary. 
Submittal date: 09/18/2002. 
162422 LB02092DSSCFPR.001. 2-D Site Scale Calibrated Fault Properties: Supporting 
Files. Submittal date: 09/18/2002. 
162128 LB02092DSSCFPR.002. 2-D Site Scale Calibrated Fault Properties: Data Summary. 
Submittal date: 09/18/2002. 
August 2003 227 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
160799 LB0210THRMLPRP.001. Thermal Properties of UZ Model Layers: Data Summary. 
Submittal date: 10/25/2002. 
162130 LB0301N3SURDAT.001. Niche 3107 Measurements and Elevations Used for Grid 
Generation. Submittal date: 01/29/2003. 
162354 LB03023DKMGRID.001. UZ 3-D Site Scale Model Grids. Submittal date: 
02/26/2003. 
162378 LB0302AMRU0035.001. Model Validation and Parameter Uncertainty: Supporting 
Files. Submittal date: 02/07/2003. 
162570 LB0303A8N3LIQR.001. Alcove 8 - Niche 3 Seepage Data Compilation. Submittal 
date: 03/19/2003. 
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. 
106787 LB990501233129.001. Fracture Properties for the UZ Model Grids and Uncalibrated 
Fracture and Matrix Properties for the UZ Model Layers for AMR U0090, Analysis 
of Hydrologic Properties Data. Submittal date: 08/25/1999. 
110226 LB990861233129.001. Drift Scale Calibrated 1-D Property Set, FY99. Submittal 
date: 08/06/1999. 
125868 LB991091233129.001. One-Dimensional, Mountain-Scale Calibration for AMR 
U0035, Calibrated Properties Model. Submittal date: 10/22/1999. 
147328 LB991121233129.001. Calibrated Parameters for the Present-Day, Mean Infiltration 
Scenario, Used for Simulations With Perched Water Conceptual Model #1 (Flow 
Through) for the Mean Infiltration Scenarios of the Present-Day, Monsoon and 
Glacial Transition Climates. Submittal date: 03/11/2000. 
147335 LB991121233129.003. Calibrated Parameters for the Present-Day, Upper-Bound 
Infiltration Scenario, Used for Simulations with Perched Water Conceptual Model #1 
for the Upper-Bound Infiltration Scenarios of the Present-Day, Monsoon and Glacial 
Transition Climates. Submittal date: 03/11/2000. 
147346 LB991121233129.005. Calibrated Parameters for the Present-Day, Lower-Bound 
Infiltration Scenario, Used for Simulations with Perched Water Conceptual Model #1 
for the Lower-Bound Infiltration Scenarios of the Present-Day, Monsoon and Glacial 
Transition Climates. Submittal date: 03/11/2000. 
161276 LB991200DSTTHC.003. Mineral Initial Volume Fractions: Attachment II of AMR 
N0120/U0110, Drift-Scale Coupled Processes (Drift-Scale Test and THC Seepage) 
Models. Submittal date: 03/11/2000. 
August 2003 228 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
104055 LB997141233129.001. Calibrated Basecase Infiltration 1-D Parameter Set for the 
UZ Flow and Transport Model, FY99. Submittal date: 07/21/1999. 
152554 MO0004QGFMPICK.000. Lithostratigraphic Contacts from 
MO9811MWDGFM03.000 to be Qualified Under the Data Qualification Plan, TDPNBS-
GS-000001. Submittal date: 04/04/2000. 
153398 MO0012CARB1314.000. Water - Carbon 13 and Carbon 14 Abundance. Submittal 
date: 12/01/2000. 
153777 MO0012MWDGFM02.002. Geologic Framework Model (GFM2000). Submittal 
date: 12/18/2000. 
155989 MO0109HYMXPROP.001. Matrix Hydrologic Properties Data. Submittal date: 
09/17/2001. 
161496 MO0301SEPFEPS1.000. LA FEP List. Submittal date: 01/21/2003. 
105067 SNT02110894001.002. Geologic Core Logs for USW SD-7. Submittal date: 
9.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
LB03013DSSCP3I.001. 3-D Site Scale Calibrated Properties: Data Summaries. Submittal date: 
01/27/2003. 
LB03023DSSCP9I.001. 3-D Site Scale UZ Flow Field Simulations for 9 Infiltration Scenarios. 
Submittal date: 02/28/2003. 
LB0302PTNTSW9I.001. PTn/TSw Interface Percolation Flux Maps for 9 Infiltration Scenarios. 
Submittal date: 02/28/2003. 
LB03033DSSFF9I.001. 3-D Site Scale UZ Flow Fields for 9 Infiltration Scenarios: 
Simulations. Submittal date: 03/28/2003. 
LB03033DUZTRAN.001. Tc and Np distributions/transport in UZ flow-fields. Submittal date: 
03/28/2003. 
LB0303A8N3MDLG.001. Alcove 8 - Niche 3 Seepage Modeling: Simulations. Submittal date: 
03/31/2003. 
LB0303C14INF3D.001. Carbon-14 isotope distributions in UZ flow-fields. Submittal date: 
03/28/2003. 
LB0303CLINFL3D.001. Chloride ion distributions in UZ flow-fields. Submittal date: 
03/28/2003. 
LB0303GASFLW3D.001. Time-dependent gas pressure distributions in UZ flow-fields. 
Submittal date: 03/28/2003. 
LB0303THERMESH.001. Thermal Model Mesh. Submittal date: 03/28/2003. 
August 2003 229 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
LB0303THERMSIM.001. UZ Thermal Modeling: Simulations. Submittal date: 03/28/2003. 
LB030432DGRIDS.001. Three 2-D Cross-section Grids. Submittal date: 04/29/2003. 
LB0304GASFLW3D.001. Time-dependent gas pressure distributions in UZ flow-fields (using a 
5-character element thermal mesh). Submittal date: 04/29/2003. 
LB0304RDTRNSNS.001. Supporting Files of 3D Flow and Transport Sensitivity Analyses. 
Submittal date: 04/29/2003. 
LB0304UZSRTRAN.001. UZ strontium transport modeling: Simulations. Submittal date: 
04/29/2003. 
LB0305PTNTSW9I.001. PTn/TSw Interface Percolation Flux Maps for 9 Alternative 
Infiltration Scenarios. Submittal date: 05/12/2003. 
LB0305TSPA18FF.001. Eighteen 3-D Site Scale UZ Flow Fields Converted from TOUGH2 to 
T2FEHM format. Submittal date: 05/09/2003. 
August 2003 230 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Nine Flow Fields, and Tracer Transport Times 
Table I-1. Calibrated Parameters for the Present-Day, Mean Infiltration Scenario, Used for Simulations 
with the Mean Infiltration Scenarios of the Present-Day, Monsoon, and Glacial Transition 
 M M M m
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) (1/Pa) 
ATTACHMENT I 
Calibrated Parameter Sets, Combining One-Dimensional Inversions and Three- 
Dimensional Perched-Water and Chloride-Transport Modeling, Used in Generating the 
Climates 
Model Layer mF 
(-) 
 
(-) 
tcw11 3.74E-15 1.01E-5 0.388 4.24E-11 5.27E-3 0.633 0.587 
tcw12 5.52E-20 3.11E-6 0.280 9.53E-11 1.57E-3 0.633 0.587 
tcw13 5.65E-17 3.26E-6 0.259 1.32E-11 1.24E-3 0.633 0.587 
ptn21 9.90E-13 1.01E-5 0.176 1.86E-12 1.68E-3 0.580 0.09 
ptn22 2.65E-12 1.60E-4 0.326 2.00E-11 7.68E-4 0.580 0.09 
ptn23 1.23E-13 5.58E-6 0.397 2.60E-13 9.23E-4 0.610 0.09 
ptn24 7.86E-14 1.53E-4 0.225 4.67E-13 3.37E-3 0.623 0.09 
ptn25 7.00E-14 5.27E-5 0.323 7.03E-13 6.33E-4 0.644 0.09 
ptn26 2.21E-13 2.49E-4 0.285 4.44E-13 2.79E-4 0.552 0.09 
tsw31 2.95E-17 8.70E-5 0.218 5.42E-12 1.00E-4 0.633 0.129 
tsw32 2.23E-16 1.14E-5 0.290 4.72E-12 1.00E-4 0.633 0.600 
tsw33 6.57E-18 6.17E-6 0.283 5.18E-12 1.59E-3 0.633 0.600 
tsw34 1.77E-19 8.45E-6 0.317 2.21E-12 1.04E-4 0.633 0.569 
tsw35 4.48E-18 1.08E-5 0.216 6.08E-12 1.02E-4 0.633 0.569 
tsw36 2.00E-19 8.32E-6 0.442 8.99E-12 7.44E-4 0.633 0.569 
Tsw37 2.00E-19 8.32E-6 0.442 8.99E-12 7.44E-4 0.633 0.569 
Tsw38 2.00E-18 6.23E-6 0.286 8.10E-13 2.12E-3 0.633 0.569 
Tswz (zeolitic portion of tsw39) 3.50E-17 4.61E-6 0.059 8.10E-13 1.50E-3 0.633 0.370 
Tswv (vitric portion of tsw39) 1.49E-13 4.86E-5 0.293 a a a a 
ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.40E-3 0.633 0.370 
ch1v 6.65E-13 8.73E-5 0.240 a a a a 
ch2v 2.97E-11 2.59E-4 0.158 a a a a 
ch3v 2.97E-11 2.59E-4 0.158 a a a a 
ch4v 2.97E-11 2.59E-4 0.158 a a a a 
ch5v 2.97E-11 2.59E-4 0.158 a a a a 
ch6v 2.35E-13 1.57E-5 0.147 a a a a 
ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.370 
pp4 8.77E-17 4.49E-7 0.474 2.50E-14 1.83E-3 0.633 0.370 
pp3 7.14E-14 8.83E-6 0.407 2.20E-13 2.47E-3 0.633 0.199 
August 2003 Attachment I-1 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table I-1. Calibrated Parameters for the Present-Day, Mean Infiltration Scenario, Used for Simulations 
with the Mean Infiltration Scenarios of the Present-Day, Monsoon, and Glacial Transition 
Climates (continued) 
 Model Layer M M M mF 
(-) 
m
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) (1/Pa) 
7.70E-19 
 
(-) 
pp2 1.68E-15 2.39E-6 0.309 2.20E-13 3.17E-3 0.633 0.199 
pp1 2.35E-15 9.19E-7 0.272 2.50E-14 1.83E-3 0.633 0.370 
bf3 4.34E-13 1.26E-5 0.193 2.20E-13 2.93E-3 0.633 0.199 
bf2 8.10E-17 1.18E-7 0.617 2.50E-14 8.90E-4 0.633 0.370 
pcM38/ pcF38 3.00E-19 6.23E-6 0.286 3.00E-18 6.23E-6 0.286 0.00 
pcM39/ pcF39 6.20E-18 4.61E-6 0.059 6.20E-17 4.61E-6 0.059 0.00 
pcM1z/ pcF1z 9.30E-20 2.12E-7 0.349 9.30E-19 2.12E-7 0.349 0.00 
pcM2z/ pcF2z 2.40E-18 2.25E-6 0.257 2.40E-17 2.25E-6 0.257 0.00 
pcM5z/ pcF5z 2.40E-18 2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
pcM6z/ pcF6z 1.10E-19 1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
pcM4p/ pcF4p 4.49E-7 0.474 7.70E-19 4.49E-7 0.474 0.00 
NOTE: These data have been developed as documented in this Model Report and submitted under Output- 
DTN: LB03013DSSCP3I.001 
a = Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
August 2003 Attachment I-2 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table I-2. Calibrated Parameters for the Present-Day, Upper-Bound Infiltration Scenario, Used for 
Simulations with the Upper-Bound Infiltration Scenarios of the Present-Day, Monsoon, and 
Glacial Transition Climates 
M M M Model Layer  
(1/Pa) 
mF 
(-) 
m
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
 
Tcw11 3.90E-15 1.23E-5 0.388 3.16E-12 5.01E-3 0.633 0.500 
Tcw12 1.16E-19 3.39E-6 0.280 1.00E-10 2.19E-3 0.633 0.500 
Tcw13 4.41E-16 3.25E-6 0.259 9.67E-13 1.86E-3 0.633 0.500 
ptn21 1.26E-13 1.84E-4 0.199 1.00E-11 2.38E-3 0.611 0.08 
ptn22 5.98E-12 2.42E-5 0.473 1.00E-11 1.26E-3 0.665 0.08 
ptn23 3.43E-13 4.06E-6 0.407 1.96E-13 1.25E-3 0.627 0.08 
ptn24 3.93E-13 5.27E-5 0.271 4.38E-13 2.25E-3 0.631 0.08 
ptn25 1.85E-13 2.95E-5 0.378 6.14E-13 1.00E-3 0.637 0.08 
ptn26 6.39E-13 3.54E-4 0.265 3.48E-13 3.98E-4 0.367 0.08 
Tsw31 1.77E-17 4.85E-5 0.218 6.46E-11 1.00E-4 0.633 0.100 
Tsw32 2.13E-16 1.96E-5 0.290 5.62E-11 1.00E-4 0.633 0.561 
Tsw33 2.39E-17 5.22E-6 0.283 6.17E-11 1.58E-3 0.633 0.561 
Tsw34 2.96E-19 1.65E-6 0.317 2.63E-11 1.00E-4 0.633 0.570 
Tsw35 8.55E-18 5.03E-6 0.216 7.24E-11 5.78E-4 0.633 0.570 
Tsw36 7.41E-19 1.08E-6 0.442 1.07E-10 1.10E-3 0.633 0.570 
Tsw37 7.41E-19 1.08E-6 0.442 1.07E-10 1.10E-3 0.633 0.570 
Tsw38 7.40E-18 5.58E-6 0.286 8.10E-13 8.91E-4 0.633 0.570 
Tswz (zeolitic portion of 3.50E-17 4.61E-6 0.059 8.10E-13 1.50E-3 0.633 0.500 
tsw39) 
Tswv (vitric portion of tsw39) 2.24E-13 4.86E-5 0.293 a a a a 
ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.40E-3 0.633 0.500 
ch1v 1.39E-12 8.82E-5 0.240 a a a a 
ch2v 4.90E-11 2.73E-4 0.158 a a a a 
ch3v 4.90E-11 2.73E-4 0.158 a a a a 
ch4v 4.90E-11 2.73E-4 0.158 a a a a 
ch5v 4.90E-11 2.73E-4 0.158 a a a a 
ch6v 2.72E-13 1.67E-5 0.147 a a a a 
ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.500 
pp4 1.02E-15 4.57E-7 0.474 2.50E-12 8.91E-4 0.633 0.500 
pp3 1.26E-13 9.50E-6 0.407 2.20E-12 1.66E-3 0.633 0.500 
pp2 1.70E-15 2.25E-6 0.309 2.20E-13 1.66E-3 0.633 0.500 
pp1 2.57E-15 8.77E-7 0.272 2.50E-14 8.91E-4 0.633 0.500 
bf3 3.55E-14 3.48E-5 0.193 2.20E-13 1.66E-3 0.633 0.500 
August 2003 Attachment I-3 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table I-2. Calibrated Parameters for the Present-Day, Upper-Bound Infiltration Scenario, Used for 
Simulations with the Upper-Bound Infiltration Scenarios of the Present-Day, Monsoon, and 
Glacial Transition Climates (continued) 
Model Layer 
pcM38/ pcF38 
pcM39/ pcF39 
pcM1z/ pcF1z 
pcM2z/ pcF2z 
pcM5z/ pcF5z 
pcM6z/ pcF6z 
pcM4p/ pcF4p 
bf2 8.10E-17 1.18E-7 0.617 2.50E-14 8.90E-4 0.633 0.500 
NOTE: These data have been developed as documented in this Model Report and submitted under Output- 
DTN: LB03013DSSCP3I.001 
a = Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
 
0.286 3.00E-18 5.58E-6 0.286 0.00 
0.059 6.20E-17 4.61E-6 0.059 0.00 
0.349 9.30E-19 2.12E-7 0.349 0.00 
0.257 2.40E-17 2.25E-6 0.257 0.00 
0.257 2.40E-18 2.25E-6 0.257 0.00 
0.499 1.10E-19 1.56E-7 0.499 0.00 
0.474 7.70E-19 4.57E-7 0.474 0.00 
MDL-NBS-HS-000006 REV01 
M K 
(m2) 
3.00E-19 
6.20E-18 
9.30E-20 
2.40E-18 
2.40E-18 
1.10E-19 
7.70E-19 
M  
(1/Pa) 
5.58E-6 
4.61E-6 
2.12E-7 
2.25E-6 
2.25E-6 
1.56E-7 
4.57E-7 
Attachment I-4 
U0050 
M mF 
(-) 
m
(-) 
KF 
(m2) 
F 
(1/Pa) 
 
(-) 
August 2003 U0050 UZ Flow Models and Submodels 
Table I-3. Calibrated Parameters for the Present-Day, Lower-Bound Infiltration Scenario, Used for 
Simulations with the Lower-Bound Infiltration Scenarios of the Present-Day, Monsoon, and 
Glacial Transition Climates 
Model Layer M M  
(1/Pa) 
mF 
(-) 
MM 
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
 
tcw11 3.44E-15 1.16E-5 0.388 3.16E-12 4.68E-3 0.633 0.483 
tcw12 3.00E-20 2.67E-6 0.280 9.73E-11 3.20E-3 0.633 0.483 
tcw13 3.96E-17 1.64E-6 0.259 9.47E-13 2.13E-3 0.633 0.483 
ptn21 1.86E-13 6.13E-5 0.165 1.00E-11 1.66E-3 0.503 0.01 
ptn22 3.27E-12 1.51E-5 0.390 1.00E-11 9.39E-4 0.651 0.01 
ptn23 4.20E-13 2.04E-6 0.388 1.84E-13 1.28E-3 0.518 0.01 
ptn24 3.94E-13 2.32E-5 0.210 4.31E-13 2.02E-3 0.594 0.01 
ptn25 2.22E-13 2.04E-5 0.296 7.12E-13 7.42E-4 0.555 0.01 
ptn26 5.43E-13 1.82E-4 0.264 3.08E-13 2.00E-4 0.401 0.01 
tsw31 1.42E-17 8.02E-5 0.218 5.13E-11 1.00E-4 0.633 0.037 
tsw32 3.96E-16 9.46E-6 0.290 4.47E-11 1.31E-4 0.633 0.528 
tsw33 1.60E-18 4.25E-6 0.283 4.90E-11 1.94E-3 0.633 0.528 
tsw34 1.38E-19 1.19E-6 0.317 2.09E-11 6.55E-4 0.633 0.476 
tsw35 2.33E-18 1.97E-6 0.216 5.75E-11 1.35E-3 0.633 0.476 
tsw36 5.58E-19 4.22E-7 0.442 8.51E-11 1.31E-3 0.633 0.476 
tsw37 5.58E-19 4.22E-7 0.442 8.51E-11 1.31E-3 0.633 0.476 
tsw38 2.93E-18 1.43E-6 0.286 8.10E-13 1.75E-3 0.633 0.476 
tswz (zeolitic portion of tsw39) 3.50E-17 4.61E-6 0.059 8.10E-13 1.50E-3 0.633 0.276 
tswv (vitric portion of tsw39) 3.15E-13 1.86E-5 0.293 a a a a 
ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.40E-3 0.633 0.276 
ch1v 3.15E-14 4.50E-5 0.240 a a a a 
ch2v 1.13E-11 1.22E-4 0.158 a a a a 
ch3v 1.13E-11 1.22E-4 0.158 a a a a 
ch4v 1.13E-11 1.22E-4 0.158 a a a a 
ch5v 1.13E-11 1.22E-4 0.158 a a a a 
ch6v 2.54E-13 9.05E-6 0.147 a a a a 
ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.276 
pp4 2.98E-16 2.88E-7 0.474 2.50E-14 1.88E-3 0.633 0.276 
August 2003 Attachment I-5 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table I-3. Calibrated Parameters for the Present-Day, Lower-Bound Infiltration Scenario, Used for 
Simulations with the Lower-Bound Infiltration Scenarios of the Present-Day, Monsoon, and 
Glacial Transition Climates (continued) 
Model Layer M M  
(1/Pa) 
mF 
(-) 
MM 
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
DTN: LB03013DSSCP3I.001 
 
pp3 5.37E-14 7.97E-6 0.407 2.20E-13 1.32E-3 0.633 0.248 
pp2 4.24E-16 2.41E-6 0.309 2.20E-13 2.80E-3 0.633 0.248 
pp1 7.02E-16 1.36E-6 0.272 2.50E-14 6.39E-4 0.633 0.276 
bf3 2.97E-14 1.32E-5 0.193 2.20E-13 1.92E-3 0.633 0.248 
bf2 8.1E-17 1.18E-7 0.617 2.50E-14 8.9E-4 0.633 0.276 
pcM38/ pcF38 3.00E-19 1.43E-6 0.286 3.00E-19 1.43E-6 0.286 0.00 
pcM39/ pcF39 6.20E-18 4.61E-6 0.059 6.20E-18 4.61E-6 0.059 0.00 
pcM1z/ pcF1z 9.30E-20 2.12E-7 0.349 9.30E-20 2.12E-7 0.349 0.00 
pcM2z/ pcF2z 2.40E-18 2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
pcM5z/ pcF5z 2.40E-18 2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
pcM6z/ pcF6z 1.10E-19 1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
pcM4p/ pcF4p 7.70E-19 2.88E-7 0.474 7.70E-19 2.88E-7 0.474 0.00 
NOTE: These data have been developed as documented in this Model Report and submitted under Outputa 
= Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
(Section 5). 
August 2003 Attachment I-6 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table I-4. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Mean 
Infiltration Scenario, Used for Simulations with the Mean Infiltration Scenarios of the Present- 
Day, Monsoon, and Glacial Transition Climates 
 M M M Model Layer mF 
(-) 
m
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) (1/Pa) 
 
(-) 
tcw11 3.74E-15 1.01E-5 0.388 4.24E-11 5.27E-3 0.633 0.587 
tcw12 5.52E-20 3.11E-6 0.280 9.53E-11 1.57E-3 0.633 0.587 
tcw13 5.65E-17 3.26E-6 0.259 1.32E-11 1.24E-3 0.633 0.587 
ptn21 4.60E-15 1.62E-4 0.245 2.11E-12 8.70E-4 0.633 0.232 
ptn22 4.43E-12 1.46E-4 0.219 9.41E-12 1.57E-3 0.633 0.232 
ptn23 9.20E-15 2.47E-5 0.247 5.35E-13 5.18E-3 0.633 0.232 
ptn24 2.35E-12 7.90E-4 0.182 1.00E-11 1.86E-3 0.633 0.232 
ptn25 2.15E-13 1.04E-4 0.300 1.24E-12 1.33E-3 0.633 0.232 
ptn26 1.00E-11 9.83E-4 0.126 3.17E-13 1.34E-3 0.633 0.232 
tsw31 2.95E-17 8.70E-5 0.218 5.42E-12 1.00E-4 0.633 0.129 
tsw32 2.23E-16 1.14E-5 0.290 4.72E-12 1.00E-4 0.633 0.600 
tsw33 6.57E-18 6.17E-6 0.283 5.18E-12 1.59E-3 0.633 0.600 
tsw34 1.77E-19 8.45E-6 0.317 2.21E-12 1.04E-4 0.633 0.569 
tsw35 4.48E-18 1.08E-5 0.216 6.08E-12 1.02E-4 0.633 0.569 
tsw36 2.00E-19 8.32E-6 0.442 8.99E-12 7.44E-4 0.633 0.569 
Tsw37 2.00E-19 8.32E-6 0.442 8.99E-12 7.44E-4 0.633 0.569 
Tsw38 2.00E-18 6.23E-6 0.286 8.10E-13 2.12E-3 0.633 0.569 
Tswz (zeolitic portion of tsw39) 3.50E-17 4.61E-6 0.059 8.10E-13 1.5E-3 0.633 0.370 
Tswv (vitric portion of tsw39) 1.49E-13 4.86E-5 0.293 a a a a 
Ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.4E-3 0.633 0.370 
Ch1v 6.65E-13 8.73E-5 0.240 a a a a 
Ch2v 2.97E-11 2.59E-4 0.158 a a a a 
Ch3v 2.97E-11 2.59E-4 0.158 a a a a 
Ch4v 2.97E-11 2.59E-4 0.158 a a a a 
Ch5v 2.97E-11 2.59E-4 0.158 a a a a 
Ch6v 2.35E-13 1.57E-5 0.147 a a a a 
Ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
Ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
Ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
Ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.370 
Ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.370 
Pp4 8.77E-17 4.49E-7 0.474 2.50E-14 1.83E-3 0.633 0.370 
Pp3 7.14E-14 8.83E-6 0.407 2.20E-13 2.47E-3 0.633 0.199 
Pp2 1.68E-15 2.39E-6 0.309 2.20E-13 3.17E-3 0.633 0.199 
Pp1 2.35E-15 9.19E-7 0.272 2.50E-14 1.83E-3 0.633 0.370 
Bf3 4.34E-13 1.26E-5 0.193 2.20E-13 2.93E-3 0.633 0.199 
Bf2 8.10E-17 1.18E-7 0.617 2.50E-14 8.90E-4 0.633 0.370 
August 2003 Attachment I-7 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table I-4. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Mean 
Infiltration Scenario, Used for Simulations with the Mean Infiltration Scenarios of the Present- 
Day, Monsoon, and Glacial Transition Climates (continued) 
Model Layer 
PcM38/ pcF38 
pcM39/ pcF39 
pcM1z/ pcF1z 
pcM2z/ pcF2z 
pcM5z/ pcF5z 
pcM6z/ pcF6z 
pcM4p/ pcF4p 
NOTE: These data have been developed as documented in this Model Report and submitted under Output- 
DTN: LB03013DSSCP3I.001 
a = Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
 
(-) 
0.286 3.00E-18 6.23E-6 0.286 0.00 
0.059 6.20E-17 4.61E-6 0.059 0.00 
0.349 9.30E-19 2.12E-7 0.349 0.00 
0.257 2.40E-17 2.25E-6 0.257 0.00 
0.257 2.40E-18 2.25E-6 0.257 0.00 
0.499 1.10E-19 1.56E-7 0.499 0.00 
0.474 7.70E-19 4.49E-7 0.474 0.00 
MDL-NBS-HS-000006 REV01 
M M K 
(m2) (1/Pa) 
6.23E-6 3.00E-19 
4.61E-6 6.20E-18 
2.12E-7 9.30E-20 
2.25E-6 2.40E-18 
2.25E-6 2.40E-18 
1.56E-7 1.10E-19 
4.49E-7 7.70E-19 
Attachment I-8 
U0050 
 M mF 
(-) 
m
(-) 
KF 
(m2) 
F 
(1/Pa) 
August 2003 U0050 UZ Flow Models and Submodels 
Table I-5. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Upper-Bound 
Infiltration Scenario, Used for Simulations with the Upper-Bound Infiltration Scenarios of the 
Present-Day, Monsoon, and Glacial Transition Climates 
M M M Model Layer  
(1/Pa) 
mF 
(-) 
m
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
 
tcw11 3.90E-15 1.23E-5 0.388 3.16E-12 5.01E-3 0.633 0.500 
tcw12 1.16E-19 3.39E-6 0.280 1.00E-10 2.19E-3 0.633 0.500 
tcw13 4.41E-16 3.25E-6 0.259 9.67E-13 1.86E-3 0.633 0.500 
ptn21 2.14E-14 1.56E-4 0.245 1.00E-11 2.69E-3 0.633 0.100 
ptn22 1.29E-11 1.33E-4 0.219 3.85E-13 1.38E-3 0.633 0.100 
ptn23 4.07E-14 2.39E-5 0.247 9.04E-14 1.23E-3 0.633 0.100 
ptn24 4.27E-12 5.62E-4 0.182 3.16E-13 2.95E-3 0.633 0.100 
ptn25 1.01E-12 9.48E-5 0.300 1.59E-13 1.10E-3 0.633 0.100 
ptn26 1.00E-11 5.23E-4 0.126 9.23E-13 9.55E-4 0.633 0.100 
tsw31 1.77E-17 4.85E-5 0.218 6.46E-11 1.00E-4 0.633 0.100 
tsw32 2.13E-16 1.96E-5 0.290 5.62E-11 1.00E-4 0.633 0.561 
tsw33 2.39E-17 5.22E-6 0.283 6.17E-11 1.58E-3 0.633 0.561 
tsw34 2.96E-19 1.65E-6 0.317 2.63E-11 1.00E-4 0.633 0.570 
tsw35 8.55E-18 5.03E-6 0.216 7.24E-11 5.78E-4 0.633 0.570 
tsw36 7.41E-19 1.08E-6 0.442 1.07E-10 1.10E-3 0.633 0.570 
tsw37 7.41E-19 1.08E-6 0.442 1.07E-10 1.10E-3 0.633 0.570 
tsw38 7.40E-18 5.58E-6 0.286 8.10E-13 8.91E-4 0.633 0.570 
tswz (zeolitic portion of 3.50E-17 4.61E-6 0.059 8.10E-13 1.50E-3 0.633 0.500 
tsw39) 
tswv (vitric portion of tsw39) 2.24E-13 4.86E-5 0.293 a a a a 
ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.40E-3 0.633 0.500 
ch1v 1.39E-12 8.82E-5 0.240 a a a a 
ch2v 4.90E-11 2.73E-4 0.158 a a a a 
ch3v 4.90E-11 2.73E-4 0.158 a a a a 
ch4v 4.90E-11 2.73E-4 0.158 a a a a 
ch5v 4.90E-11 2.73E-4 0.158 a a a a 
ch6v 2.72E-13 1.67E-5 0.147 a a a a 
ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.500 
ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.500 
pp4 1.02E-15 4.57E-7 0.474 2.50E-12 8.91E-4 0.633 0.500 
pp3 1.26E-13 9.50E-6 0.407 2.20E-12 1.66E-3 0.633 0.500 
pp2 1.70E-15 2.25E-6 0.309 2.20E-13 1.66E-3 0.633 0.500 
pp1 2.57E-15 8.77E-7 0.272 2.50E-14 8.91E-4 0.633 0.500 
bf3 3.55E-14 3.48E-5 0.193 2.20E-13 1.66E-3 0.633 0.500 
August 2003 Attachment I-9 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
Table I-5. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Upper-Bound 
Infiltration Scenario, Used for Simulations with the Upper-Bound Infiltration Scenarios of the 
Present-Day, Monsoon, and Glacial Transition Climates (continued) 
Model Layer 
pcM38/ pcF38 
pcM39/ pcF39 
pcM1z/ pcF1z 
pcM2z/ pcF2z 
pcM5z/ pcF5z 
pcM6z/ pcF6z 
pcM4p/ pcF4p 
bf2 8.10E-17 1.18E-7 0.617 2.50E-14 8.90E-4 0.633 0.500 
NOTE: These data have been developed as documented in this Model Report and submitted under Output- 
DTN: LB03013DSSCP3I.001 
a = Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
 
0.286 3.00E-18 5.58E-6 0.286 0.00 
0.059 6.20E-17 4.61E-6 0.059 0.00 
0.349 9.30E-19 2.12E-7 0.349 0.00 
0.257 2.40E-17 2.25E-6 0.257 0.00 
0.257 2.40E-18 2.25E-6 0.257 0.00 
0.499 1.10E-19 1.56E-7 0.499 0.00 
0.474 7.70E-19 4.57E-7 0.474 0.00 
MDL-NBS-HS-000006 REV01 
M K 
(m2) 
3.00E-19 
6.20E-18 
9.30E-20 
2.40E-18 
2.40E-18 
1.10E-19 
7.70E-19 
M  
(1/Pa) 
5.58E-6 
4.61E-6 
2.12E-7 
2.25E-6 
2.25E-6 
1.56E-7 
4.57E-7 
Attachment I-10 
U0050 
M mF 
(-) 
m
(-) 
KF 
(m2) 
F 
(1/Pa) 
 
(-) 
August 2003 U0050 UZ Flow Models and Submodels 
Table I-6. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Lower-Bound 
Infiltration Scenario, Used for Simulations with the Lower-Bound Infiltration Scenarios of the 
Present-Day, Monsoon, and Glacial Transition Climates 
Model Layer M M  
(1/Pa) 
mF 
(-) 
mM 
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
 
a 
tcw11 3.44E-15 1.16E-5 0.388 3.16E-12 4.68E-3 0.633 0.483 
tcw12 3.00E-20 2.67E-6 0.280 9.73E-11 3.20E-3 0.633 0.483 
tcw13 3.96E-17 1.64E-6 0.259 9.47E-13 2.13E-3 0.633 0.483 
ptn21 5.55E-15 6.38E-5 0.245 1.00E-11 2.93E-3 0.633 0.065 
ptn22 8.40E-12 1.67E-4 0.219 1.00E-11 6.76E-4 0.633 0.065 
ptn23 1.92E-14 4.51E-5 0.247 1.16E-13 3.96E-3 0.633 0.065 
ptn24 6.66E-13 2.52E-3 0.182 1.00E-11 2.51E-3 0.633 0.065 
ptn25 1.96E-14 1.24E-4 0.300 4.37E-13 1.53E-3 0.633 0.065 
ptn26 1.00E-11 1.63E-3 0.126 8.29E-14 1.52E-3 0.633 0.065 
tsw31 1.42E-17 8.02E-5 0.218 5.13E-11 1.00E-4 0.633 0.037 
tsw32 3.96E-16 9.46E-6 0.290 4.47E-11 1.31E-4 0.633 0.528 
tsw33 1.60E-18 4.25E-6 0.283 4.90E-11 1.94E-3 0.633 0.528 
tsw34 1.38E-19 1.19E-6 0.317 2.09E-11 6.55E-4 0.633 0.476 
tsw35 2.33E-18 1.97E-6 0.216 5.75E-11 1.35E-3 0.633 0.476 
tsw36 5.58E-19 4.22E-7 0.442 8.51E-11 1.31E-3 0.633 0.476 
tsw37 5.58E-19 4.22E-7 0.442 8.51E-11 1.31E-3 0.633 0.476 
tsw38 2.93E-18 1.43E-6 0.286 8.10E-13 1.75E-3 0.633 0.476 
tswz (zeolitic portion of tsw39) 3.50E-17 4.61E-6 0.059 8.10E-13 1.50E-3 0.633 0.276 
tswv (vitric portion of tsw39) 3.15E-13 1.86E-5 0.293 a a a a 
ch1z 3.50E-17 2.12E-7 0.349 2.50E-14 1.40E-3 0.633 0.276 
ch1v 3.15E-14 4.50E-5 0.240 a a a a 
ch2v 1.13E-11 1.22E-4 0.158 a a a a 
ch3v 1.13E-11 1.22E-4 0.158 a a a a 
ch4v 1.13E-11 1.22E-4 0.158 a a a a 
ch5v 1.13E-11 1.22E-4 0.158 a a a 
ch6v 2.54E-13 9.05E-6 0.147 a a a a 
ch2z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch3z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch4z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch5z 5.20E-18 2.25E-6 0.257 2.50E-14 8.90E-4 0.633 0.276 
ch6z 8.20E-19 1.56E-7 0.499 2.50E-14 1.40E-3 0.633 0.276 
pp4 2.98E-16 2.88E-7 0.474 2.50E-14 1.88E-3 0.633 0.276 
pp3 5.37E-14 7.97E-6 0.407 2.20E-13 1.32E-3 0.633 0.248 
pp2 4.24E-16 2.41E-6 0.309 2.20E-13 2.80E-3 0.633 0.248 
pp1 7.02E-16 1.36E-6 0.272 2.50E-14 6.39E-4 0.633 0.276 
August 2003 Attachment I-11 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
Table I-6. Calibrated Parameters for the Alternative Modeling Studies of the Present-Day, Lower-Bound 
Infiltration Scenario, Used for Simulations with the Lower-Bound Infiltration Scenarios of the 
Present-Day, Monsoon, and Glacial Transition Climates (continued) 
Model Layer M M  
(1/Pa) 
mF 
(-) 
mM 
(-) 
KF 
(m2) 
K 
(m2) 
F 
(1/Pa) 
 
(-) 
bf3 
bf2 
DTN: LB03013DSSCP3I.001. 
 
2.97E-14 1.32E-5 0.193 2.20E-13 1.92E-3 0.633 0.248 
8.10E-17 1.18E-7 0.617 2.50E-14 8.90E-4 0.633 0.276 
pcM38/ pcF38 3.00E-19 1.43E-6 0.286 3.00E-19 1.43E-6 0.286 0.00 
pcM39/ pcF39 6.20E-18 4.61E-6 0.059 6.20E-18 4.61E-6 0.059 0.00 
pcM1z/ pcF1z 9.30E-20 2.12E-7 0.349 9.30E-20 2.12E-7 0.349 0.00 
pcM2z/ pcF2z 2.40E-18 2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
pcM5z/ pcF5z 2.40E-18 2.25E-6 0.257 2.40E-18 2.25E-6 0.257 0.00 
pcM6z/ pcF6z 1.10E-19 1.56E-7 0.499 1.10E-19 1.56E-7 0.499 0.00 
pcM4p/ pcF4p 7.70E-19 2.88E-7 0.474 7.70E-19 2.88E-7 0.474 0.00 
NOTE: These data have been developed as documented in this Model Report and submitted under Outputa 
= Conceptual model for Calibrated Properties Model does not include fractures in these model layers 
(Section 5). 
August 2003 Attachment I-12 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
ATTACHMENT IILOCATION COORDINATES AND SURFACE ELEVATIONS OF 
SELECTED BOREHOLES USED IN MODEL CALIBRATION AND STUDIES 
Table II-1. Coordinates and Surface Elevations of Selected Boreholes 
Northing (m) Elevation (m) Easting (m) Northing (ft) Elevation (ft) Easting (ft) Borehole 
NRG-6 564187 766726 4093 171964.198 233698.0848 1247.5464 
NRG-7A 562984 768880 4209 171597.523 234354.624 1282.9032 
SD-6 558608 762421 4906 170263.718 232385.9208 1495.3488 
SD-7 561240 758950 4475 171065.952 231327.96 1363.98 
SD-9 561818 767998 4275 171242.126 234085.7904 1303.02 
SD-12 561606 761957 4343 171177.509 232244.4936 1323.7464 
UZ#4 566140 768716 3941 172559.472 234304.6368 1201.2168 
UZ#5 566136 768593 3954 172558.253 234267.1464 1205.1792 
UZ-7a 562270 760693 4230 171379.896 231859.2264 1289.304 
UZ-14 560142 771310 4427 170731.282 235095.288 1349.3496 
UZ#16 564857 760535 4002 172168.414 231811.068 1219.8096 
H-5 558908 766634 4852 170355.158 233670.0432 1478.8896 
G-2 560504 778826 5098 170841.619 237386.1648 1553.8704 
G-3 558483 752780 4858 170225.618 229447.344 1480.7184 
WT#24 562329 776703 4900 171397.879 236739.0744 1493.52 
Source: From: DTN: MO0012MWDGFM02.002 [153777], file contacts00el.dat 
August 2003 Attachment II-1 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
MDL-NBS-HS-000006 REV01 
INTENTIONALLY LEFT BLANK 
Attachment II-2 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
ATTACHMENT IIICOMPUTATIONS IN INPUT DATA PREPARATIONS AND 
SIMULATION RESULT ANALYSES 
This attachment presents the details of calculations in postprocessing of the simulation results 
and data analyses of Sections 6.5, 6.6, and 6.8. The working files are saved in the attached CD 
labeled as: Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01. 
III.1 Calculating the Chloride Flux of Section 6.5 
III.1.1 Formulation 
The chloride flux is calculated using the following formulation: 
) F = C 10-6 (F + F - F (Eq. III.1.1-1) runoff prec runon p Cl, Cl 
where FCl is chloride flux (kg Cl-/second); Fprec is precipitation flux (kg water/second); Frunon is 
runon (kg water/second); Frunoff is runoff flux (kg water/second). CCl,,p is chloride concentration 
in precipitation, and was assumed to 0.55 mg/l Cl- (Sonnenthal and Bodvarsson 1999 [117127], 
p. 148). These flux terms are generated using software routine infil2grid V1.7 (LBNL 2002 
[154793]), using the net infiltration data listed below: 
Table III.1.1-1 Infiltration Data Files 
modernu.dat modern upper bound 
infiltration 
modernm.dat modern mean 
infiltration 
modernl.dat modern low bound 
infiltration 
glacial mean infiltration glacialm.dat 
DTN: GS000308311221.005 [147613] 
NOTE: Attached CD (Files of Attachments III for Model Report, 
MDL-NBS-HS-000006 REV01): III.1/files in Table III.1.1-1/ 
III.1.2 Input and Output Files Used in the Calculations 
All the input files for the calculations are in the GENER format of the TOUGH2 code. Chloride 
(Cl) fluxes to be calculated are defined as mass recharge rates on the surface with infiltration and 
are represented also in terms of GENER. The input file uses the following format: 
Line 1: NGENER (an integer for the total number of GENER terms) 
Line 2: Notation giving mean flux 
Lines from 3 to NGENER: 
Format (A8, 28x, A5, F10.4, F10.4) 
Attachment III-1 August 2003 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
The input and output files used in the calculation are listed in Tables III.1.2-1  III.1.2-4 for the 
three present-day infiltration scenarios and one glacial transition infiltration scenario, 
Precipitation flux modernugenprec.dat 
Runon flux 
Runoff flux 
Output file (Cl flux) 
Table III.1.2-2. Cl Flux Calculation for Present-Day, Mean Infiltration 
Precipitation flux 
Runon flux 
Runoff flux 
Output file (Cl flux) 
Precipitation flux 
Runon flux 
Runoff flux 
Output file (Cl flux) 
Table III.1.2-4. Cl Flux Calculation for Glacial Infiltration 
Precipitation flux 
Runon flux 
Runoff flux 
Output file (Cl flux) 
modernugenrunon.dat 
modernugenrunoff.dat 
modernu_GENER_Cl 
genmmprec 
genmmrunon 
genmmrunoff 
Gen_mm_Cl 
modernlgenprec 
modernlgenrunon 
modernlgenrunoff 
modernl_GENER_Cl 
glacmgenprec 
glacmgenrunon 
glacmgenrunoff 
glacm_GENER_Cl 
respectively. 
Table III.1.2-1. Cl Flux Calculation for Present-Day, Upper-Bound Infiltration 
Input files 
(GENER 
files) 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 
REV01): III.1/files in Table III.1.2-1/ 
Input files 
(GENER 
files) 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 
REV01): III.1/files in table III.1.2-2/ 
Table III.1.2-3. Cl Flux Calculation for Present-Day Lower-Bound Infiltration 
Input files 
(GENER 
files) 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 
REV01): III.1/files in table III.1.2-3/ 
Input files 
(GENER 
files) 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 
REV01): III.1/files in table III.1.2-4/ 
MDL-NBS-HS-000006 REV01 
ELEMENT, COMPONENT, FLUX, ENTHALPY. 
U0050 
August 2003 Attachment III-2 U0050 UZ Flow Models and Submodels 
III.1.3 Calculation Procedures 
Cl flux is calculated by taking the following steps: 
Step 1: Open three input files using EXCEL. Then copy/paste them into one worksheet in the 
order of precipitation, runon, and runoff. 
Step 2: Identify columns representing precipitation (column C), runon (column G), and runoff 
(column K). 
Step 3: Use Equation (III.1.1-1) to calculate Cl flux by typing: 
 = 0.55*1E-6*(C3+E3-K3) in Column M, Row 3. Then press <ENTER> key to get Cl 
flux for the cell of Column M and Row 3. 
 Calculate Cl fluxes for the rest of the elements by highlighting and dragging down the 
cursor from the lower right-hand corner until Row (NGENER+2) appears. 
Details of the calculation are documented in a Scientific Notebook (see Wang 2003 [162417], 
SN-LBNL-SCI-219-V1, pp. 122, 135140). 
III.2 Calculating Vertical Fluxes, Distribution, and Percentage of Fracture-Matrix Flow 
for Section 6.6 
III.2.1 Extraction of Vertical Flux at the PTn Bottom 
Extracting vertical fluxes at the PTn bottom or the TSw top requires two files: (1) model mesh 
file of the 3-D TSPA-LA model grid (MESH_2KN.V1, Output-DTN:LB03023DSSCP9I.001), 
and an output file of the base-case flow fields or any other cases of interest (e.g. preq_mA.out, 
Output-DTN: LB03023DSSCP9I.001). 
In addition, the following are the four mesh-related files of identifying gridblocks, element 
coordinates and connection areas of the PTn bottom and TSw top layers. They have the names 
PTN_BOT.XY, PTN.BOT, and TSW.TOP, and CONN.area, respectively. 
PTN_BOT.XY contains three columns and 2,042 rows. The three columns are gridblock name, 
x coordinate, and y coordinate. Each row corresponds to one gridblock located at the bottom 
layer of the PTn unit. 
PTN.BOT: this file contains 2042 columns and only one row containing the names of the 
gridblocks located at the bottom layer of the PTn unit. 
TSW.TOP: this file also contains 2042 columns and only one row containing the names of the 
gridblocks located at the top layer of the TSw unit. 
CONN.area: contains connection areas for all PTn bottom/TSw top vertical connections. 
August 2003 Attachment III-3 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
These four files will be used for extracting all vertical fluxes at the PTn bottom for different 
infiltration scenarios and flow fields. The extracting steps are as follows: 
Step 1: Extracting flow components: 
Find vertical fracture-fracture (F-F) flux, matrix-matrix (M-M) flux and fracture-matrix (F-M) 
flux using UNIX commends (with flow field of preq_mA.out as an example): 
fgrepf TSW.TOP preq_mA.out> tsw_top.conn 
fgrepf PTN.BOT tsw_top.conn>ptn-tsw.conn 
grepv ' F0' ptn-tsw.conn>ptn-tsw_M.conn 
The nonvertical connections (data rows 1,9932,377) are then deleted from the file ptntsw_
M.conn. Now, file ptn-tsw_M.conn contains only vertical matrix-matrix flux at the PTn 
bottom. 
The vertical fracture-fracture flux at the PTn bottom is obtained in the following form: 
grep -v ' M0' ptn-tsw.conn>ptn-tsw_F.conn 
Delete data rows 1,9932,377 from ptn-tsw_F.conn to exclude nonvertical connections. 
The vertical fracturematrix flux at the PTn bottom is obtained as: 
grep ' M0' ptn-tsw.conn>tem.conn 
grep ' F0' tem.conn>ptn-tsw_FM.conn 
Step 2: Importing flow components to the spreadsheet 
Use Microsoft Excel to sum the vertical fluxes from the three types of vertical connections as 
follows: 
Open file ptn-tsw_M.conn, ptn-tsw_F.conn, and ptn-tsw_FM.conn. 
Copy the columns for the gridblock name of connected pairs and the fluxes in these files to a 
working spreadsheet (total_ptn_flux.xls). Note that the connections in above three files are 
written in the same sequence. 
Step 3: Handling PTn absence: 
In the UZ Flow Model grid, TCw and PTn units are absent in a number of grid columns. For 
these columns, infiltration rates are directly added to the top layers of the TSw. Therefore, these 
infiltration rates are considered as bottom PTn fluxes. 
Use command fgrep f TSW.TOP GENER>preq_m.inf to extract infiltration data, and then 
edit preq_m.inf using a PC MS DOS editor. File preq_m.inf consists of two columns. 
Column 1 is the name of gridblocks that are directly connected to the model top boundary; the 
second column is its corresponding infiltration rate (in kg/s). Note the GENER file is different 
for different infiltration scenarios. 
August 2003 Attachment III-4 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
There are a total of 110 direct connections to the top boundary from the TSw unit. 
Step 4: Incorporation of additional files and summation 
Import preq_m.inf to the working spreadsheet (total_ptn_flux.xls). 
Import PTN_BOT.XY to the working spreadsheet. 
Import CONN.area to the spreadsheet (Note that all connections in different files should be in 
the same sequence). 
Sum fluxes along fracture-fracture (F-F), matrix-matrix (M-M), and fracture-matrix (F-M) 
connections for the same column and infiltration corrections. 
Transform the unit of total vertical flux from kg/s to mm/y by performing qn = 
qo*31557600.0/area, where qn is the vertical flux in mm/year, qo is the vertical flux in kg/s, and 
area is the corresponding connection area. 
The calculations are all done using the Excel standard formula function. 
Export the columns of x and y coordinates and total fluxes (mm/y) to a text file, 
preq_mA_ptn.q. 
Use Tecplot to plot the vertical flux distribution map at the PTn bottom using the data file 
preq_mA_ptn.q. 
Table III 2.1-1 lists the files used to extract vertical fluxes at the PTn bottom. 
Table III.2.1-1. Files Used in Extracting Vertical Flux at the PTn Bottom 
Mesh file 
Model output 
Input Files 
Processing used files 
MESH_2KN.V1 
preq_mA.out 
PTN_BOT.XY, PTN.BOT,TSW.TOP 
CONN.area, GENER 
tsw_top.conn, ptn_tsw.conn, 
Working Files ptn_tsw_F.conn, ptn_tsw_M.conn, 
ptn_tsw_FM.conn, preq_m.inf, tem.conn 
total_ptn_flux.xls, preq_mA_ptn.q 
Attachment III-5 
Working Spreadsheet and 
Output Files 
Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): 
III.2/files in Table III.2.1-1/ 
III.2.2 Extraction of Vertical Flux at the Repository Layer 
Extracting vertical fluxes at the repository layer requires the 3-D TSPA-LA model grid 
(MESH_2KN.V1, Output-DTN: LB03023DSSCP9I.001) and an output file of the base-case 
MDL-NBS-HS-000006 REV01 August 2003 U0050 UZ Flow Models and Submodels 
flow fields or any other case of interest (e.g., preq_mA.out, Output-DTN: 
LB03023DSSCP9I.001). 
In addition, we need the following four mesh-related files of identifying gridblocks, their 
coordinates and connection areas: REPO.XY, REPO.DAT, REPO+1.DAT, and 
CONN_rep.area, respectively: 
REPO.XY: this file contains three columns: grid block name, x and y. There are a total 
of 2,042 rows for all the grid blocks located at the entire repository horizon. 
REPO.DAT: this file contains 2042 rows and only one column listing the names of the 
gridblocks located at the entire repository horizon. 
REPO+1.DAT: this file contains 2042 rows and only one column listing the names of 
the gridblocks located at the layer just above the repository horizon. 
CONN_rep.area: this file contains connection areas for all vertical connections at the 
repository layer. 
These four files will be used to extract all vertical fluxes through the repository horizon with 
different infiltration scenarios. 
Step 1: Find vertical fracture-fracture flux, matrix-matrix flux and fracture-matrix flux, 
using the following Unix commands: 
 fgrep -f REPO+1.DAT preq_mA.out>repo+1.conn 
 fgrep -f REPO.DAT repo+1.conn>repo.conn 
 grep -v 'F0' repo.conn>repo_M.conn 
 grep -v 'M' repo.conn>repo_F.conn 
 grep 'M' repo.conn>tem.conn 
 grep 'F0' tem.conn>repo_FM.conn 
Step 2: Remove data rows of nonvertical connections 
Using a text editor, remove rows of nonvertical connections from repo_F.conn and 
repo_M.conn. For vertical connections, the last three characters in the block names of two 
connected blocks are the same. Actually, the vertical connections are the first 2,042 connections 
in repo_M.conn and repo_F.conn. 
Step 3: Import flow components to spreadsheet: 
Open three files: repo_M.conn, repo_F.conn, and repo_FM.conn in Excel, and then copy 
the columns for gridblock name of connected cells and flux in these files to a working 
spreadsheet (total_rep_flux.xls). Make sure all connections are in the same sequence. 
Step 4: Incorporate additional files and summation 
August 2003 Attachment III-6 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
 Import CONN_rep.area and REPO.XY to the spreadsheet. 
 Transform unit of flux from kg/s to mm/y by performing 
qn= qo*31557600.0/area 
where qn is vertical flux in mm/year, qo is vertical flux in kg/s, and area is 
corresponding connection area. 
The above calculations are all performed using the Excel formulas function. 
 Export the columns of x and y coordinates and total flux (mm/y) to a text file 
preq_mA_rep.q 
 Use Tecplot to plot the vertical flux distribution map at repository horizon, using the 
data file preq_mA_rep.q. 
Table III 2.2-1 lists the files used to extract vertical fluxes at the repository layer: 
Working Files 
III.2.3 
Table III.2.2-1. Files Used to Extract Vertical Fluxes at the Repository Layer 
Mesh file 
Model output 
Input Files 
Processing used files 
MESH_2KN.V1 
preq_mA.out 
REPO.XY, REPO.DAT, REPO+1.DAT 
CONN_rep.area 
repo+1.conn, repo.conn, repo_M.conn, repo_F.conn, 
repo_FM.conn, tem.conn 
total_rep_flux.xls, preq_mA_rep.q 
Attachment III-7 
Working Spreadsheet and Output 
Files 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): III.2/files in 
Table III.2.2-1/ 
Extraction of Vertical Flux at the Domain Bottom 
In addition to the UZ grid mesh file and a TOUGH2 output file of the flow field of interest, we 
also need two files containing the bottom grid coordinates and vertical connection area: 
BT.XY and CONN_bt.area, respectively. BT.XY contains three columns: grid block 
name, x, and y. There are a total of 2,042 rows for all the gridblocks located at the domain 
bottom. CONN_bt.area contains connection areas along all vertical connections at the domain 
bottom. 
These two files will be used to extract all vertical fluxes through the bottom of different 
infiltration scenarios. 
Step 1: Find vertical fracture-fracture flux and matrix-matrix flux, using the following Unix 
commands: 
 grep BT preq_mA.out>bt.conn 
 grep F0 bt.conn>bt_F.conn 
MDL-NBS-HS-000006 REV01 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
 grep M0 bt.conn>bt_M.conn 
Setp 2: Sum the F-F and M-M vertical fluxes. 
Use Microsoft Excel to open files bt_M.conn and bt_F.conn, and then copy the columns for 
gridblock name of connected cells and flux in the two files to a working spreadsheet 
(total_bt_flux.xls). Note that both "bt_M.conn" contains 2042 M-M connections and 
"bt_F.conn" has 2,042 F-F connections. 
 Import CONN_bt.area and BT.XY to the spreadsheet. 
 Transform unit of flux from kg/s to mm/y by performing 
qn= qo*31557600.0/area, where qn is vertical flux in mm/year, qo is vertical flux in kg/s, 
and area is the corresponding connection area. 
All above calculations are done using the Excel standard formula function. 
 Export the columns of X, Y and total flux (mm/y) to a text file preq_mA_bt.q. 
 Use Tecplot to plot the vertical flux distribution map at domain bottom using the data 
file preq_mA_bt.q. 
Table III 2.3-1 lists the files used in extracting the vertical fluxes at the domain bottom: 
Table III.2.3-1. Files Used to Extract Vertical Fluxes at the Domain Bottom 
Mesh file 
Model output Input Files 
Processing used files 
MESH_2KN.V1 
preq_mA.out 
BT.XY, CONN_bt.area 
bt.conn, bt_F.conn, bt_M.conn Working Files 
Working Spreadsheet and Output Files total_bt_flux.xls, preq_mA_bt.q 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): III.2/files in 
table III.2.3-1/ 
III.2.4 Calculation of Percentage of Flux in Fractures, Matrix, and Faults 
Step 1: Calculate the total flux in fractures at the entire repository horizon as follows: 
 Copy the columns of F-F flux, F-M flux, and total flux and paste them onto a working 
spreadsheet (compu_frac_q_percentage.xls) from total_rep_flux.xls (see Section 
III.2.2 of this attachment for more information about this file). 
 In accounting for F-M flux, note that some connections are F-M and others are M-F 
(matrix-fracture). Delete F-M connections from this column (i.e., F-M flux is not treated 
as fracture flux). 
Attachment III-8 MDL-NBS-HS-000006 REV01 August 2003 U0050 UZ Flow Models and Submodels 
 Sum the cells in columns of F-F flux and M-F flux, and total fracture flux is obtained 
(qf). 
 Sum the cells in the column of total flux, and total vertical flux at the repository horizon 
is obtained (q). 
 Percentage is computed by qf/q*100%. 
Step 2: Calculate total flux in the matrix at the repository layer: 
 Total matrix flux (qm), qm =q-qf. Its percentage is qm/q*100%. 
Step 3: Calculate total flux in faults at the repository layer 
 Export the columns of the connected gridblock names and total flux to a text file 
(fault_rep_q.dat) from total_rep_flux.xls. 
 Edit the fault_rep_q.dat by deleting characters at column 15 and column 78 from 
data column of gridblock names. Those gridblocks with uppercase letter at column 6 
in their names are fault blocks. 
 Import fault_rep_q.dat to a working spreadsheet (compu_fault_q.xls). Use the Excel 
sort function to sort the data by the sixth character of the gridblock name. 
 Delete the lines with lowercase letters at column 6 of the gridblock names. 
 Sum the column of total flux. Total fault flux is obtained as (qfa). 
 Its percentage is calculated by qfa/q*100%. 
Step 4: Calculate the total vertical flux in fractures and the matrix at water table 
 Sum the columns of F-F flux and total flux in working file total_bt_flux.xls (Section 
III.2.3, Step 2). Total F-F flux (qf) and total flux (q) are obtained. 
 Fracture flux percentage is computed by qf/q*100%. 
 Matrix flux percentage is computed by (1-qf/q)*100%. 
Step 5: Calculate the total flux in faults at the water table 
Total flux in faults at the water table is calculated in the same way as for calculation of total flux 
in faults at repository horizon, except the flux data is from total_bt_flux.xls. See the working 
file compu_bt_fault_q.xls and fault_bt_q.dat. 
Table III 2.4-1 lists all the files used in this section (III.2.4) 
Table III.2.4-1. Files Used in Calculation of Percentage of Flux in Fractures, Matrix, and Faults 
total_rep_flux.xls, total_bt_flux.xls Input Files 
compu_frac_q_percentage.xls, fault_rep_q.dat, compu_fault_q.xls, 
compu_bt_falut_q.xls, fault_bt_q.dat 
Working Spreadsheet and 
Output Files 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): 
III.2/files in table III.2.4-1/ 
August 2003 Attachment III-9 MDL-NBS-HS-000006 REV01 U0050 UZ Flow Models and Submodels 
III.2.5 Extraction of Vertical Flux within the Repository for Cumulative Flux Distribution 
The list of gridblock names is needed for extracting the vertical flux through the repository zone 
from file total_rep_q.dat. There are a total of 469 blocks within the repository zone, their 
names are listed in file REPO_Z. 
 Export flux data at repository horizon from the spreadsheet total_rep_flux.xls (III.2.2, 
Step 3) to a text file total_rep_q.dat. 
 Use Unix command: 
fgrep f REPO_Z total_rep_q.dat>rep_zone_q.dat 
to extract fluxes through gridblocks within the repository zone. 
 Import rep_zone_q.dat to a spreadsheet and copy the columns of fluxes to a working 
spreadsheet preq_ma_rep.xls. 
 Calculate flux frequency using the histogram function under the Data Analysis menu of 
Excel, and plot the frequency distribution using the Graph function. 
Table III 2.5-1 lists all the files used in this section (III.2.4) 
Table III.2.5-1. Files Used in Extracting Vertical Flux at the Repository Zone: 
REPO_Z, total_rep_flux.xls Input Files 
total_rep_q.dat, rep_zone_q.dat, preq_ma_rep.xls 
Attachment III-10 
Working Files and Output 
Files 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): III.2/files in 
table III.2.5-1/ 
III.2.6 Cumulative Flux Distribution 
 Copy all cumulative flux distribution frequency data to a working spreadsheet 
(cumu_flux.xls, see Attached CD, Files of Attachments III for Model Report, MDLNBS-
HS-000006 REV01: III.2/files in Section III.2.6) from the flux data of different 
climate scenario (e.g., preq_ma_rep.xls). 
 Plot the cumulative flux distribution curve using the Excel graph function. 
III.3 Postprocessing Flow Fields In Sensitivity Analyses Of Section 6.8 
III.3.1 Formulation 
The equation for calculating the relative changes of percolation flux is 
MDL-NBS-HS-000006 REV01 August 2003 UZ Flow Models and Submodels 
where Rc is the relative change, Fc is the percolation flux calculated in the sensitivity 
simulations, F0 is the basecase percolation flux. 
III.3.2 Input/output Files 
The percolation fluxes through the gridblocks within the repository area and the entire repository 
horizon are extracted from TOUGH 2 output files (Table III.3.2-1): 
Table III.3.2-1 Postprocessing Input Files (Simulation Output Files) 
flow9.dat_preq_mA.dat Base-case flow field output 
File REPO_ZONE.cell and REPO_layer.cell list the names of the gridblocks within the 
repository zone and within the entire repository horizon, respectively. 
Data extraction and calculation produce the following final results and intermediate data files in 
which the matrix and fracture percolation flux through gridblocks within the repository area and 
the entire repository horizon are saved (Table III.3.2-2): 
Description 
Flow-field output for the case with reduced 
gamma for TSw units 
Flow-field output for the case with reduced 
gamma for units below the repository (including 
units at where the repository is located) 
Names of gridblock within the repository area 
Names of gridblock within the entire repository 
horizon. 
Attachment III-11 
File name 
flow9.dat_TSw 
flow9.dat_Urepo 
REPO_ZONE.cell 
REPO_layer.cell 
MDL-NBS-HS-000006 REV01 
DTN 
LB0304RDTRNSNS.001 
LB0304RDTRNSNS.001 
LB0304RDTRNSNS.001 
LB03033DSSFF9I.001 
LB03033DSSFF9I.001 
0 c R = (Eq. III.3.1-1) c 
F - F 
F0 
U0050 
August 2003 UZ Flow Models and Submodels 
Table III 3.2-2. Working Files of Percolation Flux Data Extraction 
File name 
FLOW9_BM.out 
FLOW9_BF.out 
FLOW9BM.out 
FLOW9BF.out 
FLOW9_TM.out 
FLOW9_TF.out 
FLOW9TM.out 
FLOW9TF.out 
FLOW9_UM.out 
FLOW9_UF.out 
FLOW9UM.out 
FLOW9UF.out 
Relative_changes_of_flux_in_repo.dat 
Relative_changes_of_flux_repo_layer.dat 
NOTE: Attached CD (Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01): III.3/files in table 
III.3.2-2/ 
III.3.3 Procedure for Extracting and Calculating the Relative Changes in Percolation Flux 
The matrix and fracture percolation flux data are extracted by the following steps: 
Step 1: Use the following Unix commands to extract the data related to the gridblocks listed in 
REPO_ZONE.cell and REPO_layer.cell, respectively: 
MDL-NBS-HS-000006 REV01 
U0050 
Description 
Repository area matrix percolation flux, basecase. 
Repository area fracture percolation flux, basecase. 
Matrix percolation flux of the entire repository horizon, basecase. 
Fracture percolation flux of the entire repository horizon, base 
case. 
Matrix percolation flux of the repository area, the case with 
reduced gamma for tsw units. 
Fracture percolation flux of the repository area, the case with 
reduced gamma for TSw units. 
Matrix percolation flux of the entire repository horizon, the case 
with reduced gamma for TSw units. 
Fracture percolation flux of the entire repository horizon, the case 
with reduced gamma for TSw units. 
Matrix percolation flux of the repository area, the case reduced 
gamma for units below the repository (including units at where the 
repository is located). 
Fracture percolation flux of the repository area, the case reduced 
gamma for units below the repository (including units at where the 
repository is located). 
Matrix percolation flux of entire repository horizon, the case 
reduced gamma for units below the repository (including units at 
where the repository is located). 
Fracture percolation flux of the entire repository horizon, the case 
reduced gamma for units below the repository (including units at 
where the repository is located). 
Matrix, fracture and total percolation flux of the repository area, for 
base case, the case reduced gamma for TSw units and the case 
reduced gamma for units below the repository (including units at 
where the repository is located) and, the relative changes of the 
flux due to gamma change. 
Matrix, fracture and total percolation flux of the entire repository 
horizon, for base case, the case reduced gamma for TSw units 
and the case reduced gamma for units below the repository 
(including units at where the repository is located) and, the relative 
changes of the flux due to gamma change. 
August 2003 Attachment III-12 UZ Flow Models and Submodels 
fgrep f REPO_ZONE.cell flow9.dat_preq_mA.dat >a1 
fgrep f REPO_layer.cell flow9.dat_preq_mA.dat >a2 
fgrep f REPO_ZONE.cell flow9.dat_TSw.dat >a3 
fgrep f REPO_layer.cell flow9.dat_TSw.dat >a4 
fgrep f REPO_ZONE.cell flow9.dat_Urepo.dat >a5 
fgrep f REPO_layer.cell flow9.dat_Urepo.dat >a6 
Before applying these commands, make sure that the first column (before the block names) in 
REPO_ZONE.cell and REPO_layer.cell is deleted, if it is blank. 
Step 2: Split the matrix data and fracture data as follows: 
grep M0 a1> FLOW9_BM.out 
grep F0 a1> FLOW9_BF.out 
grep M0 a2> FLOW9BM.out 
grep F0 a2> FLOW9BF.out 
grep M0 a3> FLOW9_TM.out 
grep F0 a3> FLOW9_TF.out 
grep M0 a4> FLOW9TM.out 
grep F0 a4> FLOW9TF.out 
grep M0 a5> FLOW9_UM.out 
grep F0 a5> FLOW9_UF.out 
grep M0 a6> FLOW9UM.out 
grep F0 a6> FLOW9UF.out 
Step 3: Extract the connection data by manually deleting the gridblock data rows: 
Manually delete the gridblock data rows (should be the first 469 rows) from FLOW9_BM.out, 
FLOW9_BF.out, FLOW9_TM.out, FLOW9_TF.out, FLOW9_UM.out, FLOW9_UF.out. 
Manually delete the gridblock data rows (should be the first 2,042 rows) from FLOW9BM.out, 
FLOW9BF.out, FLOW9TM.out, FLOW9TF.out, FLOW9UM.out, and FLOW9UF.out. Then 
save the files. 
Step 4: Manually delete data rows related to the fracturematrix connections: 
Manually delete data rows related to fracturematrix and matrixfracture connection data from 
files FLOW9_BM.out, FLOW9_BF.out, FLOW9_TM.out, FLOW9_TF.out, FLOW9_UM.out, 
FLOW9_UF.out (should be data rows 939the end), and from data files: FLOW9BM.out, 
FLOW9BF.out, FLOW9TM.out, FLOW9TF.out, FLOW9UM.out and FLOW9UF.out (should be 
data rows 4095the end). 
Step 5: Manually select one vertical connection related to each gridblock: 
Manually select one vertical connection related to each gridblock by deleting the other 
connections related to the gridblock from files FLOW9_BM.out, FLOW9_BF.out, 
FLOW9BM.out, FLOW9BF.out, FLOW9_TM.out, FLOW9_TF.out, FLOW9TM.out, 
FLOW9TF.out, FLOW9_UM.out, FLOW9_UF.out, FLOW9UM.out and FLOW9UF.out. 
Attachment III-13 MDL-NBS-HS-000006 REV01 
U0050 
August 2003 UZ Flow Models and Submodels 
The selected vertical connections are constantly in the same direction (upward or downward). 
Step 6: Combine the fracture and matrix percolation flux and calculate the relative changes of 
the percolation flux: 
Copy the third data column from FLOW9_BM.out and FLOW9_BF.out, and paste them onto 
column A and column B in an Excel spread sheet. Using the standard function, column 
C=column A + column B, to combine the matrix and fracture percolation flux for gridblocks 
within the repository area (base case), where Column C is the total percolation flux of the 
gridblocks, column A is the fracture flux of the gridblocks, and column B is the matrix flux of 
the gridblocks. 
The same operation is also applied to the following couples of files: 
FLOW9BM.out and FLOW9BF.out 
FLOW9_TM.out and FLOW9_TF.out 
FLOW9TM.out and FLOW9TF.out 
FLOW9_UM.out and FLOW9_UF.out 
FLOW9UM.out and FLOW9UF.out 
Doing this yields the percolation fluxes through the gridblocks within the repository area and the 
gridblocks within the entire repository horizon, in the base case, the case with reduced gamma 
for TSw units and the case with reduced gamma for units below the repository (including the 
repository units). 
The data columns for percolation flux of gridblocks within the repository area are stored in the 
file called: Relative_changes_of_flux_in_repo.dat. Data columns in the file are described in 
Table III.3.3-1. 
Column K: 
Urepo_re_change 
Table III.3.3-1. Definition of Data Columns in the File Called Relative_changes_of_flux_in_repo.dat 
Relative_changes_of_flux_repo_layer.dat 
Column Name 
MDL-NBS-HS-000006 REV01 
Column A: base_F 
Column B: base_M 
Column C: base_total 
Column D: TSw_F 
Column E: TSw_M 
Column F: Tsw_total 
Column G: 
TSw_re_change 
Column H: Urepo_F 
Column I: Urepo_M 
Column J: Urepo_total 
Content 
Total fluxes 
Fracture fluxes 
Matrix fluxes 
Total fluxes 
Relative flux 
changes 
Fracture fluxes 
Matrix fluxes 
Total fluxes 
August 2003 
Simulation 
Base case Fracture fluxes 
Base case Matrix fluxes 
Base case 
Reduced gamma for TSw units 
Reduced gamma for TSw units 
Reduced gamma for TSw units 
Reduced gamma for TSw units 
Reduced gamma for units below the repository 
including the repository units 
Reduced gamma for units below the repository 
including the repository units 
Reduced gamma for units below the repository 
including the repository units 
Reduced gamma for units below the repository 
Attachment III-14 
U0050 
Relative flux 
changes including the repository units U0050 UZ Flow Models and Submodels 
The relative changes of each gridblock within the repository area are calculated as follows: 
column G=(column F C column C)/column C 
where column G is the relative change in the percolation flux caused by the changes of gamma 
value for TSw units, and 
column K=(column J C column C)/column C 
where column K is the relative change in the percolation flux caused by the changes of gamma 
value for units below the repository (including the unit where the repository is located). 
The same operation is also applied to the extracted percolation flux of the gridblocks within the 
entire repository horizon, and the data are saved in file called 
Relative_changes_of_flux_repo_layer.dat 
Step 7: Calculate the average changes as follows: 
Use a standard function to calculate: A= columnG 
where column G, in file 
469 
Relative_changes_of_flux_in_repo.dat, contains the relative changes in the percolation flux of 
every gridbock within the repository area (due to changes of gamma for TSw units), A is the 
average relative change of the percolation flux, and 469 is the total number of repository blocks. 
The same operation is also applied to column K. This yields the average of the relative changes 
in the percolation fluxes of the gridblocks within the repository area (in response to the gamma 
changes of units below the repository, including units where the repository is located). 
The same operation is also applied to column G and column K, in the file called 
Relative_changes_of_flux_repo_layer.dat. Note that the number of the gridblocks within the 
entire repository horizon is 2042. The average of the relative changes in the percolationCfluxes 
through the gridblocks within the entire repository horizon (in response to the gamma changes of 
TSw units and units below the repository, including the units at where the repository is located) 
are then obtained (Table III.3.3-4, file name table_relative_flux_change.doc): 
Table III.3.3-2 (Table 6.8-2). Relative Changes of Percolation Flux in Response to the Changes of  
The whole 
Repository Layer 
August 2003 
Simulation ID 
MDL-NBS-HS-000006 REV01 
Within the 
Repository Area 
TSw -4.478E-3 -1.450E-2 
UnderRepo -5.655E-3 -0.016 
Attachment III-15 UZ Flow Models and Submodels 
III.4. ATTACHED DATA FILES (CD: FILES OF ATTACHMENTS III FOR MODEL 
REPORT, MDL-NBS-HS-000006 REV01) 
This CD has been submitted to the Records Processing Center with the report and can be 
accessed through the Records Processing Center. All files mentioned above except for the files 
from the TDMS (submitted with DTN) are attached in the CD called Files of Attachments III 
for Model Report, MDL-NBS-HS-000006 REV01. The files are organized in the CD in the 
order in which they appeared above and are described below: 
CD label: 
List of the Contents: 
The folder structure, file names, file size, and dates stored in the subfolders of the CD are 
described in the following table (Table III.4-1) and screen captures. 
Table III.4-1. List of the File Contents of the Attached CD, Files of Attachments III for Model Report, 
MDL-NBS-HS-000006 REV01 
III.1 
III.1 
III.2 
Folder Subfolder 
Files in Table III.1.1-1 
Files in Table III.1.2-1 
Files in Table III.1.2-2 
Files In Table III.1.2-3 
Files In Table III.1.2-4 
Files in Table III.2.1-1 
modernu.dat 
modernm.dat 
modernl.dat 
glacialm.dat 
modernugenprec.dat 
modernugenrunon.dat 
modernugenrunoff.dat 
modernu_GENER_Cl 
genmmprec 
genmmrunon 
genmmrunoff 
Gen_mm_Cl 
modernlgenprec 
modernlgenrunon 
modernlgenrunoff 
modernl_GENER_Cl 
glacmgenprec 
glacmgenrunon 
glacmgenrunoff 
glacm_GENER_Cl 
PTN_BOT.XY 
PTN.BOT 
TSW.TOP 
CONN.area 
tsw_top.conn 
ptn_tsw.conn, 
ptn_tsw_F.conn 
ptn_tsw_M.conn 
ptn_tsw_FM.conn 
preq_m.inf 
total_ptn_flux.xls 
preq_mA_ptn.q 
tem.conn 
Files 
MDL-NBS-HS-000006 REV01 
Files of Attachments III for Model Report, MDL-NBS-HS-000006 REV01 
Attachment III-16 
U0050 
August 2003 UZ Flow Models and Submodels 
Table III.4.1. List of the Contents of the Attached CD, Files of Attachments III for Model Report, MDLNBS-
HS-000006 REV01 (Continued) 
III.2 
III.3 
MDL-NBS-HS-000006 REV01 
Folder Subfolder 
Files in Table III.2.2-1 
Files in Table III.2.3-1 
Files in Table III.2.4-1 
Files in Table III.2.5-1 
REPO.XY 
REPO.DAT 
REPO+1.DAT 
CONN_rep.area 
repo+1.conn 
repo.conn 
repo_M.conn 
repo_F.conn 
repo_FM.conn 
tem.conn 
total_rep_flux.xls 
preq_mA_rep.q 
BT.XY 
CONN_bt.area 
bt.conn 
bt_F.conn 
bt_M.conn 
total_bt_flux.xls 
preq_mA_bt.q 
total_rep_flux.xls 
total_bt_flux.xls 
compu_frac_q_percentage.xls 
fault_rep_q.dat 
compu_fault_q.xls 
compu_bt_falut_q.xls 
fault_bt_q.dat 
REPO_Z 
total_rep_flux.xls 
total_rep_q.dat 
rep_zone_q.dat 
preq_ma_rep.xls 
cumu_flux.xls File in Section III.2.6 
Files in Table III.3.2-2 
FLOW9_BM.out 
FLOW9_BF.out 
FLOW9BM.out 
FLOW9BF.out 
FLOW9_TM.out 
FLOW9_TF.out 
FLOW9TM.out 
FLOW9TF.out 
FLOW9_UM.out 
FLOW9_UF.out 
FLOW9UM.out 
FLOW9UF.out 
Relative_changes_of_flux_in_repo.dat 
Relative_changes_of_flux_repo_layer.dat 
Attachment III-17 
U0050 
Files 
August 2003 UZ Flow Models and Submodels 
MDL-NBS-HS-000006 REV01 
Attachment III-18 
U0050 
August 2003 UZ Flow Models and Submodels 
Attachment III-19 MDL-NBS-HS-000006 REV01 
U0050 
August 2003 UZ Flow Models and Submodels 
MDL-NBS-HS-000006 REV01 
Attachment III-20 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
ATTACHMENT IVPTN/TSW FLUX AND FLOW-FIELD CONVERSION 
IV.1 PTN/TSW PERCOLATION FLUX DATA 
To use the simulated 3-D UZ percolation fluxes for other modeling studies, vertical percolation 
fluxes at PTn/TSw interfaces are extracted (procedures given in Attachment III.2.1) for the 18 
flow fields of nine base cases and nine alternatives. The PTn/TSw percolation data for the nine 
base case flow fields are submitted to the TDMS with DTN: LB0302PTNTSW9I.001, while the 
PTn/TSw percolation data for the nine alternative flow fields are submitted with DTN: 
LB0305PTNTSW9I.001. 
IV.2 FLOW FIELD CONVERSION 
The 18 3-D UZ flow fields are generated using a dual-permeability model with extra global 
fracture-matrix connections, which cannot be directly used by the FEHM code in TSPA 
calculations. Therefore, the 18 TOUGH2 flow fields need to be converted, using the routine 
flow-con V1.0 (STN: 10993-1.0-00; LBNL 2003 [163162]), into flow fields on the dualpermeability 
mesh (mesh_2kb.dkm), which does not have the extra fracture-matrix connections. 
The output-flow field files of flow-con V1.0 (LBNL 2003 [163162]) are then used as input files 
to the routine: T2FEHM V4.0 (STN: 10997-4.0-00, LBNL 2003 [163161]), which converts 
TOUGH2 files in the format of "flow9.dat" into files readable to FEHM. 
These converted 18 flow fields are submitted to the TDMS for the use of the TSPA with DTN: 
LB0305TSPA18FF.001. 
August 2003 Attachment IV-1 MDL-NBS-HS-000006 REV01 UZ Flow Models and Submodels 
MDL-NBS-HS-000006 REV01 
INTENTIONALLY LEFT BLANK 
Attachment IV-2 
U0050 
August 2003 U0050 UZ Flow Models and Submodels 
ATTACHMENT V 
Mathematical Equations 
This attachment lists the equations used in this UZ Flow Model for unsaturated liquid flow under 
isothermal conditions. They include Darcyfs law (Bear 1972 [156269]), Richardfs equation 
(Richards 1931 [104252], van Genuchten model (1980 [100610]), and active fracture model (Liu 
et al. 1998 [105729]). 
For fracture continuum or matrix continuum in the dual continuum system, the basic mass 
balance equations solved by TOUGH2 can be written in the following form: 
d 
(Eq.V-1) f n f n f fm dtV V 
n 
n n 
d 
(Eq.V-2) 
  M dV = F .nd +(q + q )dV 
n 
m n m n m fm dtV V 
n 
n n 
(Eq.V-3) 
(Eq.V-4) 
  M dV = F .nd +(q .q )dV 
n 
where subscripts f, m stand for fracture continuum and matrix continuum, respectively. (We 
denote p = f or m in the following discussion). The integration is over an arbitrary subdomain V 
mass per volume with 
n 
of the flow system under study, which is bounded by the closed surface n. F denotes mass flux. 
n is a normal vector on surface element n, pointing inward into Vn. The quantity M represents 
M =  S  (where p is porosity, Sp is saturation, and  is liquid p p p 
density). qp denotes sinks and sources per unit volume. qfm is fracture-matrix exchange flux per 
unit volume, a coupling term for describing fluid flow between the fracture and the matrix 
continuum. The qfm takes positive values if the exchange flux is from matrix to fracture, and 
negative if from fracture to matrix; see Equation V-13. 
Darcyfs law is expressed as: 
krp  
F = g) P k . u p p p p . = 
Pp 
(
pl . = 
where Ppl is water phase pressure and Ppg is the gas pressure. 
MDL-NBS-HS-000006 REV01 
 
where up is the Darcy velocity (volume flux), kp is absolute permeability, krp is relative 
permeability,  is viscosity, g is gravity acceleration constant, and Pp is capillary pressure. In the 
Darcyfs law written in this form, the variation of gas pressure is neglected. The capillary 
pressure is related to the gas pressure by the following equation: 
P + P pg 
Attachment V-1 August 2003 U0050 UZ Flow Models and Submodels 
By combining mass balance equations (Equations V-1.V-2) with Darcys law (Equation V-3), 
we have Richards equation (Richards 1931 [104252]; Pruess et al. 1999 [160778], Equation 
A.17, p. 147) for both the fracture and matrix continuum described as: 
= fm f f f f (Eq.V-5) t 

m (Eq.V-6) 
 

t 
z + k g k P ( g) rp p Kp = p is the total water potential, 

p p 
 
 
is hydraulic conductivity, where 
z is elevation, and  =  S is specific volumetric moisture content for fracture or matrix. p 
m 
= 
em (Eq.V-7) 
(Eq.V-8) 
(Eq.V-9) 
div[ ] K   + q + q 
fm m m m div[ ] K   + q .q 
m ] 1 [ 1 S .1/m 
em 
m 
p S . Srp 
1. Srp 
S 1 { . 
S ) = 
S 2 / 1 
em 
m . / ) 1 ( f 
p = 
The water capillary pressure for the matrix continuum is described by the well-known van 
Genuchten relation (van Genuchten 1980 [100610]), described as: 
/ 1 nm 
/ 1 nf 
. 
] } / 1 m m 2 
em 
] 1 
 
where Pm is matrix capillary pressure, m (Pa.1), and nm and mm=1-1/nm are van Genuchten 
parameters for the matrix continuum, Sem is the effective matrix water saturation, together with 
effective fracture water saturation Sef discussed below: 
Sep = 
where Sp is the water saturation of fracture or matrix and Srp is the residual fracture or matrix 
saturation. 
The relative permeability krm for the matrix continuum is given as: 
1 [ .
S [ 1 
ef f 
. 
Attachment V-2 

P ( 
krm 
P ( f 
= 
The water capillary pressure Pf for the fracture continuum is determined by: 
ef (Eq.V-10) S ) = 
MDL-NBS-HS-000006 REV01 August 2003 UZ Flow Models and Submodels 
where Sef is the effective water saturation of all connected fractures (defined in Equation V-8), f 
(Pa.1), nf, and mf=1-1/nf are van Genuchten parameters for the fracture continuum,  is the active 
fracture parameter (Liu et al. 1998 [105729]). 
The relative permeability krf for the fracture continuum is given as: 
S k ef rf 
pressure: 
= 
In an unsaturated fracture network, the ratio of the interface area contributing to flow and 
transport between fractures and the matrix, to the total interface area determined geometrically 
from the fracture network, is called the fracture-matrix interface area reduction factor. The 
reduction factor R is introduced by Liu et al. (1998 [105729]) with the following expression: 
The interface area between fracture continuum and matrix continuum, used to calculate qfm, is 
multiplied by the factor R in the active fracture model (Liu et al. 1998 [105729]). 
The fracture-matrix exchange flux introduced in Equations V-1.V-2 can be approximated as 
quasi-steady, with rate of matrix-fracture flux proportional to the difference in (local) average 
MDL-NBS-HS-000006 REV01 
where A is the total interface area between fracture-matrix (area/volume), and lf is the fracture 
spacing (BSC 2003 [160109]). 
 .  
f 
Attachment V-3 
U0050 
m . m 1 ( + 1 (  / )  2 / ) f 2 f S 1 [ . 1 { . } ] (Eq.V-11) ef 
1+  R . (Eq.V-12) ef S 
f m q fm  (Eq.V-13) l 
A R 
August 2003 UZ Flow Models and Submodels 
MDL-NBS-HS-000006 REV01 
INTENTIONALLY LEFT BLANK 
Attachment V-4 
U0050 
August 2003