value of information analysis for corrective action …/67531/metadc703479/...doe/nv/13052--079...

DOE/NV/13052--079ITLV/13052--079

Value of Information Analysis for Corrective Action Unit 97: Yucca Flat, Nevada Test Site, Nevada

Prepared for U.S. Department of Energy, Nevada Operations Office under Contract No. DE-AC08-97NV13052.

Approved for public release; further dissemination unlimited.

Revision No.: 0

November 1999

DOE/NV/13052--079ITLV/13052--079

VALUE OF INFORMATION ANALYSIS FOR CORRECTIVE ACTION UNIT 97: YUCCA FLAT, NEVADA TEST SITE, NEVADA

Revision No.: 0

November 1999

IT CORPORATION2621 Losee RoadBuilding B-1, Suite 3050-01North Las Vegas, Nevada 89030

Prepared for U.S. Department of Energy, Nevada Operations Office under Contract No. DE-AC08-97NV13052.

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VALUE OF INFORMATION ANALYSIS FOR CORRECTIVE ACTION UNIT 97: YUCCA FLAT,

NEVADA TEST SITE, NEVADA

Table of Contents

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1

1.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1 Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.1.1 Hydrostratigraphic Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.1.2 Groundwater Flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

1.1.3 Radionuclides of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6

1.1.4 Extent of Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.1.5 Generic Pathline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.1.6 Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-10

1.2 Objectives, Scope, and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-10

1.3 Panel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11

1.4 Key Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-12

1.5 Report Organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-15

2.0 Technical Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Methods for Quantifying Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2 Methods for Estimating and Analyzing Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.3 Methods for Specifying Probability Distributions on Input Variables . . . . . . . . . . . . . . . . . 2-4

2.4 Methods for Estimating the Impact of Information on Uncertainties . . . . . . . . . . . . . . . . . . 2-5

2.5 Relationship Between Bayesian Analysis and Value of Information Analysis . . . . . . . . . . . 2-6

2.6 Analysis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

3.0 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Sensitive Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2 Characterization Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.3 Assessment of Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

3.4 Posterior Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

3.5 Calculation of the Contaminant Boundary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

3.5.1 Preliminary Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13

3.5.2 Calculation of the Uncertainty Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-15

3.6 Measures of Uncertainty Reduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-16

3.6.1 Measure 1: Expected Reduction in Parameter Uncertainty . . . . . . . . . . . . . . . . 3-16

3.6.2 Measure 2: Expected Reduction in Contaminant Boundary Uncertainty. . . . . . 3-17

11/18/99I:\097_Yucca Flat\VOIA\Final\UncontrolmasterTOC.fm i

Table of Contents (Continued)

3.6.3 Measure 3: Expected Change in the Upper Bound (95th Fractile) of the Contaminant Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-18

3.6.4 Measure 4: Expected Reduction in Weighted Average Parameter Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

4.0 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1 Ability of Characterization Options to Reduce Parameter Uncertainty. . . . . . . . . . . . . . . . . 4-1

4.2 Ability of Characterization Options to Reduce Contaminant Boundary Uncertainty and Uncertainty in the Upper Bound Estimate of the Contaminant Boundary . . . . . . . . . . . . . . 4-6

4.3 Ability of Characterization Options to Reduce Weighted Average Parameter Uncertainty - Measure 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4.5 Analysis Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16

5.0 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Suggestions for Conducting Future VOIA Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

6.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

Appendix A - Expert Panel Qualifications

Appendix B - Description of Groundwater Flow and Transport Model

B.1.0 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.1 Groundwater Flow and Particle Tracking Codes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

B.1.2 Contaminant Transport Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2

B.1.2.1 Mobile Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-2

B.1.2.2 Immobile Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-3

B.1.2.3 Transport Parameter Variability and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . B-4

B.1.2.3.1 Groundwater Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5B.1.2.3.2 Generic Pathline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-5

B.1.2.4 Hydrologic Source Term Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-6

B.1.3 Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7

B.2.0 Prior Probability Assessment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-8

B.2.1 Hydrologic Source Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-9

B.2.2 Effective Porosity in the LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-9

B.2.3 Diffusion Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-10

B.2.4 Groundwater Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-10

B.2.5 Slope Multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-11

B.3.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-12

ii


Appendix C - Activity, Subgroup, and Group Descriptions with Cost Estimates

C.1.0 Summaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

C.2.0 Descriptions and Costs of Activities and Subgroups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-5

C.2.1 Subgroup S1: Basin Boundaries - North and West (Total $5,905,486) . . . . . . . . . . . . . . . . C-5

C.2.1.1 Activity A1: Alternate Hydrogeologic Models . . . . . . . . . . . . . . . . . . . . . . . . . . C-5

C.2.1.2 Activity A2: New Wells North of Yucca Flat . . . . . . . . . . . . . . . . . . . . . . . . . . . C-6

C.2.1.3 Activity A3: New Wells on the Western Boundary of Yucca Flat . . . . . . . . . . . C-6

C.2.2 Subgroup S2: Basin Boundaries - General (Total $1,549,531) . . . . . . . . . . . . . . . . . . . . . . C-7

C.2.2.1 Activity A4: Isotope/Geochemistry Mass Balance Studies . . . . . . . . . . . . . . . . . C-7

C.2.2.2 Activity A5: Basin Recharge Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-8

C.2.3 Subgroup S3: Shortcut - Vertical Gradient (Total $8,817,432) . . . . . . . . . . . . . . . . . . . . . . C-9

C.2.3.1 Activity A6: Groundwater Heads in the VCU and LCA . . . . . . . . . . . . . . . . . . C-10

C.2.3.2 Activity A7: Potentiometric Trough in South-Central Yucca Flat. . . . . . . . . . . C-10

C.2.4 Subgroup S4: Shortcut - Faults (Total $10,111,312) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-11

C.2.4.1 Activity A8: Geologic Structure of Major Faults . . . . . . . . . . . . . . . . . . . . . . . . C-11

C.2.4.2 Activity A9: Hydraulic Properties of Fault Zones . . . . . . . . . . . . . . . . . . . . . . . C-12

C.2.5 Subgroup S5: Shortcut - VCU Continuity (Total $1,311,000) . . . . . . . . . . . . . . . . . . . . . . C-13

C.2.5.1 Activity A10: Geophysical Interpretation of Yucca Flat Basin Structure . . . . . C-13

C.2.5.2 Activity A11: Variability of VCU Mineralogy . . . . . . . . . . . . . . . . . . . . . . . . . C-14

C.2.6 Subgroup S6: Shortcut - Near-Field (Total $157,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-14

C.2.6.1 Activity A12: Existing Phenomenologic Data on Test Effects . . . . . . . . . . . . . C-14

C.2.6.2 Activity A13: Mineback Data from Rainier Mesa Tunnel Tests . . . . . . . . . . . . C-15

C.2.7 Subgroup S7: Hydrologic Source Term (Total $9,509,320). . . . . . . . . . . . . . . . . . . . . . . . C-16

C.2.7.1 Activity A14: Near-Field Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-16

C.2.7.2 Activity A15: Test-Cavity Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-17

C.2.7.3 Activity A16: Hydrologic Source-Term Modeling . . . . . . . . . . . . . . . . . . . . . . C-18

C.2.8 Subgroup S8: Transport Parameters - 1 (Total $3,653,270) . . . . . . . . . . . . . . . . . . . . . . . . C-18

C.2.8.1 Activity A17: Multi-Well Tracer Test in the LCA. . . . . . . . . . . . . . . . . . . . . . . C-18

C.2.8.2 Activity A18: Analysis of Two Existing Tracer-Test Datasets . . . . . . . . . . . . . C-19

C.2.8.3 Activity A19: Laboratory Diffusion Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . C-19

C.2.9 Subgroup S9: Transport Parameters - 2 (Total $7,221,777) . . . . . . . . . . . . . . . . . . . . . . . . C-20

C.2.9.1 Activity A20: Single-Well Tracer Tests in the VA and the LCA . . . . . . . . . . . C-20

C.2.9.2 Activity A21: Earth-Tide Analysis of the VA and the LCA (Total $199,153) . C-21

C.2.10 Subgroup S10: Basin Structure (Total $1,550,000) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-21

C.2.10.1 Activity A22: New Seismic Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-22

C.2.10.2 Activity A23: Analysis of Existing Seismic Data . . . . . . . . . . . . . . . . . . . . . . . C-22

iii


. . E-2

. . E-3

C.2.11 Subgroup S11: Basin Anomalies (Total $2,488,400) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-23

C.2.11.1 Activity A24: High-Pressure Zone in Northern Yucca Flat . . . . . . . . . . . . . . . . C-23

C.2.11.2 Activity A25: Elevated Groundwater Temperatures in Eastern Yucca Flat . . . C-24

C.2.12 Subgroup S12: LCA Characterization (Total $241,100) . . . . . . . . . . . . . . . . . . . . . . . . . . C-25

C.2.12.1 Activity A26: Potential for Karst in the LCA. . . . . . . . . . . . . . . . . . . . . . . . . . . C-25

C.2.12.2 Activity A27: Hydraulic Conductivity with Depth . . . . . . . . . . . . . . . . . . . . . . C-26

C.3.0 Descriptions of Groups and Cost Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-26

C.3.1 Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-26

C.3.2 Group Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-26

C.4.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-29

Appendix D - Assessment of Distributions Representing the Accuracy of Characterization Options

Appendix E - Bayesian Analysis

E.1.0 Bayesian Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

E.1.1 Priors, Posteriors, and Likelihoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1

E.1.2 Bayes’ Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-2

E.1.3 Determining the Value of Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

E.2.0 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-3

E.2.1 Approximations to Priors and Likelihoods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

E.2.2 Calculation of the Posteriors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-3

E.2.3 Moment Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-4

E.3.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-4

Appendix F - Transport Simulation Results for Activities and Groups

iv

List of Figures

Number Title Page

1-1 Location of Underground Test Area Corrective Action Units . . . . . . . . . . . . . . . . . 1-2

1-2 Regional Groundwater Flow System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8

1-3 Pathlines for Yucca Flat Sources Based on Regional Flow Model . . . . . . . . . . . . . . 1-9

1-4 Example of Contaminant Boundary Confidence Levels . . . . . . . . . . . . . . . . . . . . . 1-13

1-5 Illustrative Probability Density Function Describing Uncertainty of Contaminant Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-14

2-1 Illustrative Cumulative Distribution Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2-2 Box Plot Representation of a Probability Distribution. . . . . . . . . . . . . . . . . . . . . . . 2-2

2-3 Results of Applying Bayes’ Theroem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7

2-4 Overview of the Analysis Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

3-1 Process for Assessing Probabilities for Calculating Prior andPosterior Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9

3-2 Influence Diagram Showing Factors Influencing the Accuracy of Characterization Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

3-3 Subgroup Accuracy Estimates Indicate Uncertainty, Asymmetry, and Sensitivity of Test Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-12

3-4 Typical Relationship Between Posterior and Prior Distributions . . . . . . . . . . . . . 3-14

3-5 Example of Prior and Posterior Distributions for the Flux . . . . . . . . . . . . . . . . . . 3-14

4-1 Measure 1 - Ranked Expected Reduction in Average Parameter Uncertainty. . . . 4-2

4-2 Measure 1 - Expected Reduction in Average Parameter Uncertainty vs. Cost (Activities, Subgroups) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4-3 Measure 1 - Expected Reduction in Average Parameter Uncertainty vs. Cost (Groups) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4-4 Measure 2 - Ranked Expected Reduction in Average Deviation from the Mean . . 4-7

4-5 Measure 2 - Expected Reduction in Average Deviation from the Mean vs. Cost . . 4-8

4-6 Measure 3 - Ranked Expected Reduction in the 95 Percent Boundary . . . . . . . . . . 4-9

4-7 Measure 3 - Expected Reduction in 95 Percent Boundary vs. Cost . . . . . . . . . . . . 4-10

4-8 Measure 4 - Ranked Expected Reduction in the Weighted Average Parameter Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13

4-9 Measure 4 - Expected Reduction in the Weighted Average Parameter Uncertainty vs. Cost (Activities, Subgroups) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14

4-10 Measure 4 - Expected Reduction in the Weighted Average Parameter Uncertainty vs. Cost (Groups) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

v

List of Figures (Continued)

Number Title Page

B-1 Slope Multiplier Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-7

vi

List of Tables

Number Title Page

3-1 Sensitive Parameters in Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

3-2 Activities and Estimated Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3-3 Subgroups and Estimated Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3-4 Subgroups Included in Each Group and Group Cost Estimates . . . . . . . . . . . . . . . 3-6

3-5 Uncertain Parameters Addressed by Each Activity . . . . . . . . . . . . . . . . . . . . . . . . . 3-7

3-6 Uncertain Parameters Addressed by Each Subgroup . . . . . . . . . . . . . . . . . . . . . . . 3-8

3-7 Uncertain Parameters Addressed by Each Group . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8

3-8 Prior Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

3-9 Example Illustrating the Computation of the Average Expected Reduction in Parameter Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17

B-1 Prior Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-8

B-2 Hydrologic Source Term Constituent Prior Distributions . . . . . . . . . . . . . . . . . . . . B-9

C-1 Uncertain Parameters Addressed by Each Activity . . . . . . . . . . . . . . . . . . . . . . . . . C-2

C-2 Uncertain Parameters Addressed by Each Subgroup . . . . . . . . . . . . . . . . . . . . . . . C-3

C-3 Activities of Each Subgroup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-4

C-4 Activity A1 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-6





C-9 Activity A6 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-10




C-13 Activity A10 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-14









vii

List of Tables (Continued)

Number Title Page










C-31 Group Cost Estimates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-27

C-32 Uncertain Parameters Addressed by Each Group . . . . . . . . . . . . . . . . . . . . . . . . . C-27

D-1 Component Activity Accuracies, Assessed as a Percentage of the Accuracy of the Best Subgroup Addressing Each Parameter . . . . . . . . . . . . . . . . . D-3

D-2 Parameter Estimate Ranges for Subgroups Addressing Slope Multiplier . . . . . . . D-4

D-3 Parameter Estimate Ranges for Subgroup Combinations AddressingSlope Multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-4

D-4 Parameter Estimate Ranges for Subgroups and Subgroup Combinations Addressing Flux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-5

D-5 Parameter Estimate Ranges for Subgroups and Subgroup Combinations Addressing Hydrologic Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-5

D-6 Parameter Estimate Ranges for Subgroups and Subgroup Combinations Addressing Porosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-5

D-7 Parameter Estimate Ranges for Subgroups and Subgroup Combinations Addressing Diffusion (alpha) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-5

F-1 Activity Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-1

F-2 Prior and Subgroup Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-4

F-3 Simulation Results for the Less-Expensive Balanced Group. . . . . . . . . . . . . . . . . . F-6

F-4 Simulation Results for the No Drilling Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-7

F-5 Simulation Results for the Value Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-8

F-6 Simulation Results for the LCA Transport Group . . . . . . . . . . . . . . . . . . . . . . . . . . F-9

F-7 Simulation Results for the Hydrologic Source Term Group. . . . . . . . . . . . . . . . . . F-10

F-8 Simulation Results for the Faults and Gradients Group . . . . . . . . . . . . . . . . . . . . F-10

viii

List of Acronyms and Abbreviations

AA Alluvial aquifer

amsl Above mean sea level

BN Bechtel Nevada

CAI Corrective Action Investigation

CAU Corrective Action Unit

CDF Cumulative distribution function

DOE U.S. Department of Energy

DOE/NV U.S. Department of Energy, Nevada Operations Office

EPA U.S. Environmental Protection Agency

FFACO Federal Facility Agreement and Consent Order

FGE Forced Gradient Experiment

ft Foot (feet)

HSU Hydrostratigraphic unit

in. Inch(es)

km Kilometer(s)

LCA Lower Carbonate Aquifer

LCCU Lower Clastic Confining Unit

LLNL Lawrence Livermore National Laboratory

m Meter(s)

m3/d Cubic meter(s) per day

mrem/yr Millirem(s) per year

NTS Nevada Test Site

pCi/L Picocurie(s) per liter

PDF Probability density function

RREMP Routine Radiological Environmental Monitoring Plan

TTBT Threshold Test Ban Treaty

TWG Technical Working Group

UCA Upper Carbonate Aquifer

UCCU Upper Clastic Confining Unit

UGTA Underground Test Area

ix

List of Acronyms and Abbreviations (Continued)

VA Volcanic Aquifer

VCU Volcanic Confining Unit

VOIA Value of information analysis

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Executive Summary

The objective of the value-of-information analysis (VOIA) was to evaluate data collection options for characterizing groundwater transport of contamination associated with the Yucca Flat and Climax Mine Corrective Action Units (CAUs) of the Nevada Test Site (NTS). These CAUs are two of several areas of the NTS previously used for nuclear underground testing. Underground testing in Yucca Flat started in 1951 and continued until 1992. Testing in the Climax Mine CAU was comprised of three small tests conducted in the early- to mid-1960s. Up to one third of the underground nuclear tests in Yucca Flat were conducted near or below the water table within alluvium or volcanic rocks. A small number of tests were conducted in or very near the underlying regional carbonate aquifer. The tests have resulted in the deposition of radionuclides and other contaminants into the ground and groundwater in the vicinity of the underground test area. The extent of contaminated groundwater is expected to gradually expand downgradient of the testing areas due to transport within the groundwater flow system. Ultimately, the extent of contamination will shrink due to radionuclide decay.

A primary concern is how large the area of contamination will become. Estimates of the maximum extent of the contamination in groundwater (the contaminant boundary) are highly uncertain due to limited understanding of the geologic and hydrologic characteristics of the area and how contaminants move away from the nuclear test locations. This study was intended to help decision makers evaluate cost-effective information-collection characterization options to reduce these uncertainties.

A group of experts knowledgeable about the NTS was selected by the U.S. Department of Energy, Nevada Operations Office (DOE/NV) to define and provide inputs for the evaluation of options for characterizing the Yucca Flat underground test area. A total of 48 characterization options were evaluated. These options include 27 component activities, 12 combinations of activities referred to as “subgroups,” and 9 combinations of subgroups of activities term“groups.” The resulting characterization options range from an individual studusing existing data and intended to address a relatively narrow uncertainty to52-million dollar group of activities designed to collect and analyze new information to broadly address multiple uncertainties. The characterization options were compared and ranked based on their costs and estimates of theeffectiveness at reducing key uncertainties relevant to predicting the maximumcontaminant boundary.

Theory

This VOIA is based on well-established theory for analyzing uncertainty. Thetheory involves quantifying uncertainty using probabilities. A method known as

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Bayes’ Theorem stipulates how probabilities should be changed, or updated, bon new information. In Bayesian analysis, expert opinion is used to quantify current uncertainties and to quantify the accuracy of information collection optioProbabilities based on existing information are called prior probability distributions, while the updated probabilities that account for new information acalled posterior probability distributions.

The posterior distributions that result from a characterization option depend onoutcome of the activities that constitute that option. For example, an activity mproduce results that confirm current suspicions, or it might produce an unanticipated “surprise.” Results that confirm current thinking tend to reduce uncertainty. Surprise results may increase uncertainty. Whether a characterizactivity will increase or decrease uncertainty cannot be known ahead of time because either type of outcome could occur. However, Bayes’ Theorem can bused to compute how the posterior distributions would change under every possibility (i.e., to estimate what the posterior would be if the new information what is “expected” and what it would be under every conceivable “surprise”). Furthermore, based on the prior distributions and estimates of activity accuracyprobability of each possible outcome of the characterization activity can be calculated. Therefore, the possible posterior distributions and their probabilitiecan be computed and combined into expected values that weight the possiblechanges in uncertainty by their probabilities. By this approach, the expected change in the uncertainty between the prior and posterior probabilities as a rescharacterization activities can be compared. The most effective characterizatioptions according to this theory are those that produce the greatest expected reductions in uncertainty. In this analysis, reducing uncertainty increases the certainty of how far the contaminant boundary will extend in the future. The cost-effectiveness of characterization options can be determined by comparingmuch uncertainty reduction is expected with the estimated cost of conducting option.

Approach

A modified version of the contaminant transport component of the regional mowas used to simulate contaminant transport and to estimate the maximum extethe contaminant boundary over a 1,000-year time frame. These simulations sto identify the model parameters most responsible for uncertainty over the contaminant boundary and to determine weights indicating the relative importaof these parameters. The key parameters, weights, and simulations of the contaminant boundary were used to define measures for quantifying the ability of characterization options to reduce uncertainty.

The regional model was developed to determine if any immediate risks to humhealth and the environment exist from radionuclide migration in groundwater downgradient of the underground test area. This model includes a three-dimensional groundwater flow model of the area encompassing the NTSa one-dimensional contaminant transport model. Transport was predicted for representative nuclear tests for the radionuclide tritium. The model is documein the Regional Groundwater Flow and Tritium Transport Modeling and Risk

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Assessment of the Underground Test Area, Nevada Test Site, Nevada (DOE/NV, 1997).

The contaminant transport model was modified for this VOIA to include a methodology to simulate the uncertainty of a specific feature of the flow path. This allowed the use of a generic representative pathline for tests in Yucca Flat. Appropriate groundwater fluxes were derived from analysis of the latest calibration of three-dimensional regional groundwater flow model. Initial evaluation of transport for the nine most important radionuclides (americium-241, carbon-14, cesium-137, iodine-129, neptunium-237, plutonium-239/240, strontium-90, tritium, and uranium-238) found that three radionuclides, tritium, carbon-14, and plutonium 239/240, determined the contaminant boundary within the time frame of concern.

Because a one-dimensional transport model was used, the contaminant boundary is represented as a maximum distance rather than a two-dimensional boundary. The specification of the contaminant boundary for this analysis was defined as that distance beyond which the committed effective dose equivalent from the residual radionuclides in the groundwater will not exceed 4 millirem per year within a 1,000-year time frame.

The key inputs to the contaminant transport model were identified through sensitivity analysis. In a sensitivity analysis, the value of a parameter is varied and the model is run many times to see how much the contaminant boundary will change, or how sensitive it is to changes in that parameter. Five parameters were selected based on a sensitivity analysis of the regional model. These parameters are the flux for flow into the Yucca Flat CAU from the north, hydrologic source term, effective porosity in the lower carbonate aquifer (LCA), the diffusion parameter for the LCA, and the length of the transport path through the volcanic confining unit (VCU) into the LCA.

• Flux for flow into Yucca Flat from the north: Flux, the amount of groundwater moving through a given area of the flow system, is currenan important parameter in the prediction of potential contaminant migration. Uncertainty in the flux into the Yucca Flat CAU from the north results from lack of knowledge about the geology and hydrologynorth of Yucca Flat. This parameter is the primary control for flux in thLCA, the regional aquifer.

• Hydrologic Source Term: As applied to this modeling effort, hydrologic source term is the concentration of radionuclides from the nuclear testis available for groundwater transport. Some of the radionuclides aredirectly into the groundwater environment following a nuclear test. Others are contained within glass formed from melted rock in the cavof the nuclear test following detonation. Others may be sorbed onto tsurfaces of the glass and rock. The rate at which radionuclides are released to the groundwater either by leaching out of the glass or desorbing from the cavity materials controls the concentration of that radionuclide in groundwater. A rapid release rate produces a short-tehigh-concentration source term. A slow release rate produces a long-



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lower concentration contamination source. The concentration of a radionuclide in groundwater as it leaves the location of the nuclear test is a significant factor in determining the downgradient extent and concentration of the contamination. For this modeling, the radionuclides were treated as having been instantaneously dissolved in the groundwater at the time of detonation.

• Effective porosity in the LCA: In fractured rocks, not all fractures may bconnected. Some fractures or fracture networks may be connected fofew inches or feet, and the water contained within these networks is essentially stagnant. Effective porosity is a measure of the interconnenetwork of fractures along the flow path where groundwater predominantly flows and contaminant migration can occur. The effectiporosity is a major factor in determining the speed with which groundwater moves in an aquifer. For a given flux, the smaller the effective porosity, the faster groundwater will move through an aquifer. This parameter was important for flow through the LCA.

• Diffusion Parameter for the LCA: Most groundwater movement in the aquifer occurs in the fractures. However, slower movement occurs in interconnected pores within the rock matrix. Similarly, most of the radionuclide transport will occur within the fractures, particularly if groundwater is moving quickly. However, a fraction of the radionuclidewill diffuse into the rock matrix pore spaces along the fracture surfaces. Radionuclides diffusing into the rock matrix reduce the concentration within the fractures, and slows the rate of migration. The larger the diffusion parameter, the shorter the migration distance.

• Path Length from the VCU to the LCA: Transport distance is time-dependent, and although transport occurs in all units, it occurs quickly in the LCA. The contaminant boundary occurs in that unit. Factors that slow the transport of radionuclides from the tests to the LChave a substantial influence on the maximum extent of transport in the1,000-year time frame. Most tests are separated from the LCA by sominterval of VCU, a confining unit through which transport is slow due tounit properties and low gradients. Thus, transport is a function of the distance that radionuclides are transported through the VCU. The distais uncertain because of geologic uncertainties and because of uncertain the transport paths through the VCU resulting from flow uncertaintieThis parameter was devised to simulate the overall uncertainty and evaluate its relative importance.

A panel composed of members from the Underground Test Area Technical Working Group familiar with the NTS and representing the areas of geology, geophysics, hydrology, geochemistry, and statistical analysis was convened toprovide expert judgment for the analysis. To provide a basis for defining



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characterization options, the panel identified 27 component activities and organized them into 12 subgroups designed to address the key parameters:

• Subgroup S1, Basin Boundaries - North and West, geologic characterization, drilling, and evaluation of alternate models, is designto investigate uncertainties affecting calibration of groundwater flux frothe north and west into Yucca Flat.

• Subgroup S2, Basin Boundaries - General, geochemistry and rechargestudies, is designed to investigate groundwater flow patterns and basrecharge in Yucca Flat.

• Subgroup S3, Shortcut - Vertical Gradient, drilling and recompletions, is designed to characterize the vertical hydraulic gradient to the LCA Yucca Flat.

• Subgroup S4, Shortcut - Faults, drilling and testing, is designed to investigate groundwater flow to the LCA along faults in Yucca Flat.

• Subgroup S5, Shortcut, VCU Continuity, analysis of existing data refines the definition of Yucca Flat hydrostratigraphic units.

• Subgroup S6, Shortcut - Nearfield, evaluation of existing data, is focused on improved definition of the near-field environment of nucleatests.

• Subgroup S7, Hydrologic Source Term, drilling, sampling, analysis, and modeling, is designed to determine the hydrologic source term foYucca Flat transport modeling.

• Subgroup S8, Transport Parameters - 1, a multiple-well tracer test and analyses of two previous tracer tests, was designed to determine valufor effective porosity and the diffusion parameter for LCA.

• Subgroup S9, Transport Parameters - 2, single-well tracer tests and earth-tide data collection and analyses, was designed to evaluate variability of diffusion parameter values and effective porosity for the LCA.

• Subgroup S10, Basin Structure, collecting and interpreting seismic data, is designed to characterize the geologic structure of western YuFlat and refine the structural definition of the basin.

• Subgroup S11, Basin Anomalies, a variety of field data collection tasks,is designed to characterize two basin anomalies: (1) the high-pressuzone and the thermal gradient, and (2) evaluate the importance to flowand transport modeling.



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• Subgroup S12, LCA Characterization, geologic investigation of the LCA, is designed to evaluate the LCA regarding possible karst development and the hydraulic conductivity with depth relationship.

In addition, the panel created nine groups composed of combinations of thesesubgroups based on various themes. Twenty-four of the 27 activities, the first 11 subgroups in the above list, and the 9 groups were considered in the quantitative analysis. Subgroup S12 and its associated Activities A26 and A27, and Activity A3 were not used in the quantitative analysis due to difficulty in quantifying the uncertainties that would be reduced, or in the case of Activity A3, because theparameter addressed by the activity was determined not to be a sensitive paraThey were, however, deemed to merit mention.

Based on existing data and expert judgment, the panel defined the prior probadistributions describing the current uncertainty over each of the key parameterThen the panel estimated the accuracies of each component activity, subgrougroup; that is, the panel provided the estimates needed to compute using BayTheorem the ability of characterization options to reduce uncertainty on the keparameters. By comparing the uncertainties represented by prior and posterioprobability distributions, the uncertainty reducing potential of the characterizatoptions was estimated and compared.

The uncertainty that is represented by a probability distribution can be expressmany ways. For this study, four measures were defined for quantifying uncertareduction:

• Measure 1. Ability to reduce uncertainty in key parameters. Rather thdirectly measure a reduction in the uncertainty in the contaminant boundary, this measure quantifies the ability of an option to reduce uncertainties in parameters important to the calculation of the contaminboundary. Variance is used as the measure of uncertainty, the higher variance, the higher the uncertainty. Specifically, the measure of uncertainty reduction is the average (across the parameters) of the ratithe expected posterior parameter variances to the variances of the pridistributions, expressed as a percent.

• Measure 2. Ability to reduce uncertainty in the contaminant boundary. This is a direct measure of the reduction in uncertainty in the contaminboundary. Unlike Measure 1, Measure 2 uses average mean deviatioquantify uncertainty, a measure that, compared to variance, is less sento errors in the “tails” of the probability distribution. This was regarded a useful characteristic, given the difficulty of accurately estimating the lprobabilities associated with extreme values for the contaminant boundary. Specifically, Measure 2 is the expected average mean deviation of theposterior distribution, expressed in kilometers.

• Measure 3. Ability to reduce the 95 percent confidence level predictionthe contaminant boundary. Another way of describing the change in contaminant boundary uncertainty is to look at a conservative upper boestimate, specifically, a value that has only one chance in 20 of being



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exceeded. The higher that estimate, the higher the uncertainty. Specifically, the measure is the difference between the 95 percent confidence estimates for the contaminant boundary under the prior and posterior distributions. Like Measure 2, Measure 3 is expressed in kilometers.

• Measure 4. Weighted ability to reduce uncertainty in key parameters.This measure is similar to Measure 1 except that computed reductionparameter variance are weighted according to the sensitivity of the contaminant boundary to variations in the parameters. Like MeasureMeasure 4 is expressed as a percent.

The approach consisted of evaluating and ranking characterization options baon each of the above four measures and in terms of cost effectiveness; that isability to achieve uncertainty reductions per dollar of estimated cost.

Results

• Evaluations and rankings of characterization options based on Measuand 4 were successful. The attempt to obtain results for Measures 2 3, which required simulating contaminant transport using the transpormodel, was not successful due to the very large number of required transport simulations. Thus, it was not possible to provide a reliable ranking of options based on their ability to resolve uncertainty over thcontaminant boundary or uncertainty over the upper bound estimate ocontaminant boundary (Measures 2 and 3). The failure to obtain resufor Measures 2 and 3 was the reason for defining Measure 4, which approximately captures some of the characteristics of Measures 2 an

• Characterization options were found to differ significantly in terms of their costs and in terms of the estimated ability to reduce key geohydrologic and source uncertainties. The costs of options ranged less than $45,000 to $52,000,000. The impact of options on key parameter uncertainties varied by an even wider range.

• An option was defined as “optimal” if it was estimated to provide at leaas much uncertainty reduction as any other option that costs the sammore. Six options were identified as optimal on both Measures 1 and

- Activity A12: “Existing phenomenologic data on test effects”

- Subgroup S6: “Shortcuts – Near Field”

- Group ND: “No Drilling,” composed of subgroups S2, S5, S6, and S10

- Group T5: “Top 5,” composed of subgroups S1, S5, S7, S8, and S

- Group B: “Balanced,” composed of subgroups S1, S2, S3, S5, S7,S8



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- Group C: “Comprehensive,” composed of subgroups S1, S2, S3, SS5, S7, S8, S9, and S10

In addition, four other options were optimal on at least one measure ornearly optimal on both measures:

• Activity A19: “Laboratory diffusion studies”

• Group V: “Value,” composed of subgroups S2, S3, S5, S7, and S10

• Group LEB: “Less Expensive Balanced,” composed of subgroups S1,S5, and S7

• Group LCA: “LCA Transport,” composed of subgroups S1, S2, S8, anS9

Conclusions

• For the purpose of improving estimates of contaminant transport from Yucca Flat, subgroups that resolve uncertainty regarding hydrologic source term, groundwater flux through the LCA, and diffusion (alpha) aeffective porosity in the lower carbonate can be particularly effective. Cost-effective ways of resolving these uncertainties involve geochemistry-based investigations and exploratory drilling on the northand west sides of Yucca Flat, as well as tracer tests and diffusion tests

• Some specific, cost-effective activities include evaluating mineback dafrom Rainier Mesa tunnels, conducting isotope/geochemistry mass balastudies and recharge studies, conducting new tracer tests and analyziexisting data from single- and multi-well tracer tests in the LCA, and conducting laboratory diffusion tests. Characterization options that include one or more of these activities appear to be relatively effectivereducing uncertainties important to estimating contaminant transport.

• Although subgroups focused on groundwater flux, hydrologic source tediffusion, and effective porosity are particularly effective, there is declining incremental improvement from including more and more of these types of activities in a characterization effort. In other words, moof the uncertainty reduction benefit appears to accrue from including within the group only a single or a very few subgroups of each type.

• Conducting a few activities that address multiple uncertain parameters igenerally more effective than conducting many activities focused on a single or very few parameters. In other words, characterization approathat address diverse uncertainties are generally more effective than approaches that are narrowly focused.

• Activities that require drilling are less cost effective. It appears possibleobtain nearly half the potentially available uncertainty reduction througactivities that do not involve new drilling.



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• Although drilling increases costs, it provides increased potential for reducing uncertainty regarding flux, diffusion (alpha), and porosity, theparameters to which the contaminant boundary is most sensitive. For this reason, Group “LCA Transport” is estimated to be very effective. Nevertheless, the group is expensive because it includes drilling new wells on the north and west sides of Yucca Flat. It appears likely thatvariations of Group “LCA Transport” that include less drilling would beestimated as nearly as effective at reducing critical uncertainties, but significantly less expensive. The reasons for this conclusion are (1) tinexpensive Subgroup S2 (Basin Boundaries - General) is estimated tnearly as well at resolving uncertainty on flux north as the drilling-intensive Subgroup S1 (Basin Boundaries - North and West), a(2) results generally indicate that jointly conducting two activities that aequally effective at addressing the same parameter is typically estimato be only slightly more effective than conducting either one by itself. is not possible to conclude more generally that drilling could be removfrom any activity or group without significantly reducing its effectivenesbecause in the case of the effective porosity and diffusion parametersonly activities identified for significantly reducing uncertainty involve drilling.

• Although the analysis suggests that most of the uncertainty-reducing potential of characterization can be captured without new drilling, drillimay provide information value not well captured in the analysis. Lookifor surprises can be a valuable approach to learning. If one looks for surprises and does not find them, uncertainty is not reduced by muchHowever, if surprises are found, they often produce dramatic improvements in understanding. Drilling and other activities that provnew data are likely undervalued in this analysis. However, even if thisthe case, the analysis results strongly suggest that most of the value drilling and similar activities that produce new data can be obtained bconducting only a few of the most cost-effective of the available activities of this type.

• Properly designing and conducting extremely large-scale simulations push the boundary of available computation capability is very difficult. Teams charged with conducting future VOIA applications should consider approaches for overcoming the computational limitations thaprevented this study from comparing characterization options directlyterms of their impacts on contaminant boundary uncertainty. These approaches include the use of (1) uncertainty-reduction measures thaless sensitive to sampling error (e.g., measures that more effectively average out sampling “noise”), (2) alternative evaluation measures thanot penalize characterization options that may increase uncertainty (e.g., ranking options based on total expected change in uncertainty rathan based on uncertainty reduction), (3) error filtering methods such Fast Fourier Transforms that could be used to reduce noise in compudistributions), and (4) response surface methods that would significanreduce the need for contaminant transport simulations.



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Analysis Limitations

In theory, the comparative evaluations presented in this report account for:

• The specific uncertainties that the option is likely to impact. The analysis distinguishes between options that address only one or two source, geologic, or hydrologic uncertainties and those that address most or alluncertainties.

• The sensitivity of the distance to the contaminant boundary as it relatethe impacted uncertainty. The analysis accounts for whether contaminantransport is believed to be very sensitive to the impacted uncertaintiesnot very sensitive. However, due to the inability to obtain results for uncertainty reduction Measures 2 and 3, the incorporation of sensitivitinformation had to be performed under an assumption of linear model response (Measure 4).

• The current level of information/understanding regarding the impacteduncertainties. The analysis accounts for whether the option is expectedadd information to an area that is already relatively well understood or information to an area about which very little is known.

• The accuracy/reliability/sensitivities of activities. The analysis accounts for the estimated capabilities of the individual activities that make up thoption; namely, their uncertainties, whether they tend to give biased estimates, and whether their accuracy varies depending on actual siteconditions.

• Informational interdependencies. The analysis accounts for the degree twhich informational synergies may exist among the component activitiof an option and whether the information that is collected is complementary or redundant.

• Cost and cost interdependencies. The analysis accounts for the costs of conducting the activities that make up the subgroup and has the capability of accounting for cost savings or cost increases that may be expectedto economies of scale, synergisms or antagonisms over resource use,other factors. In this case, the cost estimates for groups were judged approximately equal to the sum of the costs of the individual subgroupthat compose the group.

Among the limitations that must be taken into account when interpreting resultsthe following:

• Limited quality of input judgments. The expert panel was required to provide numerous, difficult assessments as input to the analysis. Althopanel members were selected for their expertise and understanding ofspecific issues addressed, the estimates that they were required to pro



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are inherently difficult. Understanding about the component activities and subgroups and their accuracies is limited, and the expression of uncertainty in terms of probabilities is a difficult task. Errors in input judgments may produce errors in results.

• Limitations of the regional transport model. The transport model is an interim model that will be replaced by a more sophisticated model as additional work focuses on the CAU-scale transport. It involves manyapproximations and assumptions which limit its ability to translate geohydrologic and other parameters into reliable estimates of contaminant transport. Approximations in the transport model may produce errors in the rankings, including the rankings produced usingMeasure 1 and 4, which provided the primary basis for conclusions.

• Precision of Monte Carlo results. To ensure that Monte Carlo analyses accurately translate distributions over model input variables into the distributions that they imply over model outputs, it is necessary to run model many times to ensure that the number of input values sampled finput distributions is very large. For practical reasons, the Monte Caranalyses conducted for this evaluation were limited (in most cases) tomodel simulations per analysis. Tests were conducted to determine tmagnitude of errors possibly introduced by limiting the number of MonCarlo simulations. These indicated significant random errors. For thireason, it was not possible to obtain reliable results for Measures 2 an

• Appropriateness of named distributions. The process of generating inpuprobability distributions for the analysis was simplified by allowing theexpert panel to specify two-parameter probability distributions and theth and 95th fractiles of those distributions. However, named distributions,while mathematically convenient, may have characteristics that do noreflect the uncertainties they are intended to represent very well. Thenamed distributions were truncated to eliminate the possibility of infeasible parameter values (e.g., negative values for parameters thacannot be negative). However, the resulting truncated distributions stcontain features (such as the potential for values higher than possible)may distort results. Sensitivity analyses show that rankings are sensito the functional form of the probability distributions assumed.

For these reasons, the results summarized above and in the main body of thireport should be interpreted with caution. The results are best considered as to decision making, to be factored in with other inputs to the decision making process.



1.0 Introduction

Underground nuclear testing was conducted by the U.S. Department of Energy (DOE) and the U.S. Department of Defense from 1951 until 1992 at the Nevada Test Site (NTS), located in southern Nevada (DOE/NV, 1994). These underground tests released radionuclides into the ground and groundwater. To ensure protection of public health and the environment, the U.S. Department of Energy, Nevada Operations Office (DOE/NV) established an environmental restoration program.

The Nevada Division of Environmental Protection regulates the corrective actions implemented by the DOE/NV through the Federal Facility Agreement and Consent Order (FFACO) (1996). The individual sites covered by the agreement are known as Corrective Action Sites which are grouped into Corrective Action Units (CAUs) (Figure 1-1).

Yucca Flat was one of several areas used for underground nuclear testing and has been designated a CAU. This report presents the results of a value-of-information analysis (VOIA) for the Yucca Flat CAU. The VOIA evaluated and compared potential characterization options for the Yucca Flat underground test area.

The results of the regional modeling and of the VOIA will be used by DOE as a management tool to determine the scope for a corrective action investigation (CAI) for each CAU. During the CAI, a CAU-scale flow and transport model will be developed and used to predict the maximum extent of the contamination over a 1,000-year period.

The DOE recently completed a regional evaluation of the underground test areas to determine whether any immediate risks to human health and the environment exist (DOE/NV, 1997). A groundwater flow and transport model was used to simulate contaminant migration from the underground test areas along pathlines identified by the regional flow model, using tritium as the contaminant of concern. Although the groundwater flow model is three-dimensional, the transport model is one-dimensional to allow for stochastic simulations. The regional evaluation was used to identify data gaps and set priorities for the CAIs for the Underground Test Area (UGTA) CAUs.

1.1 Site Description

This VOIA focuses on the Yucca Flat CAU and the adjacent Climax Mine CAU, located in northern Yucca Flat (Figure 1-1). Preliminary analysis indicates that radionuclides of concern transported from the Climax Mine CAU would

1.0 Introduction1-1


Figure 1-1Location of Underground Test Area Corrective Action Units

1.0 Introduction 1-2


immediately enter the Yucca Flat CAU, thus, the Climax Mine CAU was considered along with the Yucca Flat CAU in this analysis.

The area of interest stretches from the Belted Range in the north to the southern boundary of Yucca Flat. This area includes the Climax Stock and an area of steep potentiometric gradient to the north of the Yucca Flat CAU.

Yucca Flat is an alluvial basin typical of the Basin and Range physiographic province, and is located in the northeastern quadrant of the NTS. Outflow sheets of tuffs from the volcanic centers west of the basin were emplaced on the irregular paleotopographic surface of the basin during the Tertiary Period. The youngest sediments of the valley are sand and gravel, derived from the volcanic and sedimentary rocks in the surrounding highlands. Today, most prominent structures are related to basin-and-range extensional faulting that is generally younger than the volcanic rocks. In Yucca Flat, fault strikes are mostly north-south.

The Yucca Flat basin is topographically closed and the basin delineation is approximately equivalent to the hydrographic area. The basin floor rises from an elevation of approximately 1,207 meters (m) above mean sea level (amsl) in the south to about 1,493 m in the north. The alluvium- and tuff-filled valley is rimmed by Precambrian and Paleozoic sedimentary rocks and Cenozoic volcanic rocks. Mesozoic intrusive rocks are located at the north-northeast edge of Yucca Flat. Precambrian and Paleozoic rocks are regionally extensive and occur under the basins as basement rocks. The uppermost 4,600 m of silicic clastics and carbonates are Cambrian through Pennsylvanian dolomite, interbedded limestone, and persistent shale and quartzite layers. The lowermost 3,000 m of the pre-Tertiary section consist of Late Precambrian to Middle Cambrian quartzites and siltstones.

The Climax Mine CAU is comprised of mined shafts in the Climax Stock, a granitic intrusive which intrudes Paleozoic and Precambrian rocks at the northern end of Yucca Flat. The stock is exposed over an area of approximately 2.4 kilometers (km) north-south and 1.6 km east-west. Three underground nuclear tests were conducted within horizontal drifts accessed by two mined vertical shafts. These modern shaft/tunnel complexes are separate from the historical Climax Mine located at the northeast corner of the stock.

1.1.1 Hydrostratigraphic Units

The hydrostratigraphy of the Yucca Flat CAU can be simply characterized as consisting of alluvial and volcanic hydrostratigraphic units within a structural basin, underlain by a regionally extensive carbonate unit and clastic basement rocks. The alluvial fill is referred to as the Alluvial Aquifer (AA). The underlying volcanics are comprised of the Volcanic Aquifer (VA) and the Volcanic Confining Unit (VCU). The regionally extensive Lower Carbonate Aquifer (LCA) extends beneath the basin.

1.0 Introduction1-3


The LCA is a thick assemblage of interbedded dolomite and limestone that was deposited throughout the region between Middle Cambrian and late Middle Devonian time. Following deposition, there may have been a long period of exposure resulting in possible paleokarst features. The aggregate stratigraphic thickness of carbonate rock is about 4,600 m (Cole, 1997), although the effective thickness below the water table may be quite different at any given location due to thrust repetition or tilting, and to subsequent extensional faulting. The carbonate rocks generally dip west-southwest beneath the eastern side of Yucca Flat. Drilling has shown them to be continuous under the main areas of underground testing and south into Frenchman Flat. Thrust faults have repeated sections of the Paleozoic and Precambrian rocks, and low-angle gravity faulting has created isolated blocks of the Paleozoic rocks out of stratigraphic order.

Tertiary volcanic rocks, where saturated, form both aquifers and confining units depending mainly upon alteration and/or degree of welding. The altered (commonly zeolitized) volcanic rocks typically form confining units. The unaltered volcanic rock in Yucca Flat can be divided into two hydrogeologic units: welded-tuff aquifers and vitric-tuff aquifers.

The welded Rainier Mesa Tuff is generally confined to the south-central portion of the basin east of the Topgallant fault where this ash-flow unit is thickest. In the deeper subbasins where the Rainier Mesa Tuff may occur below the water table, it forms a welded tuff aquifer characterized by high fracture permeability. Elsewhere, the unaltered, nonwelded and bedded tuffs form vitric-tuff aquifers characterized by higher matrix porosity and intermediate transmissivities. These two volcanic hydrogeologic units are combined in the UGTA regional model as the VA, but will be considered separately in the current CAU-specific hydrogeologic characterization efforts.

The volcanic strata in Yucca Flat have been organized into two volcanic hydrostratigraphic units (HSUs), the VA and the underlying VCU. In general, the altered volcanic rocks (typically zeolitized tuffs) are the confining units, and the unaltered rocks comprise the aquifers (partially welded to densely welded tuffs). These two units have approximately the same distribution in Yucca Flat and also occur as erosional remnants preserved in the deeper parts of the Tertiary basin.

The valley-fill aquifer coincides with the deepest parts of the post-volcanic fault-bounded depressions in southern Yucca Flat and central Frenchman Flat. The alluvium is variably cemented and consists of moderately sorted deposits of gravel and sand that show high interstitial porosity and permeability and transmit water efficiently. The playa lakebed in the extreme southern portion of Yucca Flat consists of siltstone and claystone deposits that are substantially less permeable than the coarser alluvium, but for the most part occur above the water table.

Granite stocks intruded during Cretaceous time are present in three areas: to the northeast of Yucca Flat (Twinridge), at the north end of Yucca Flat (Climax Stock), and the third just north of Rainier Mesa (Gold Meadow Stock). These rocks form small localized bulbous bodies, and have a low primary porosity and permeability. In the regional model, they are grouped with rocks of the Lower Clastic Confining



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Unit (LCCU). In the Yucca Flat CAU model they comprise the Intrusives Confining Unit. The stocks may be connected at depth.

1.1.2 Groundwater Flow

The Yucca Flat test areas were interpreted as being within the Ash Meadows groundwater subbasin (Waddell et al., 1984; Laczniak et al., 1996). However, more recent regional modeling (DOE/NV, 1997; D’Agnese et al., 1997) suggeusing mass-balance constraints that water flows from Yucca Flat through JacFlats into the Amargosa Desert, and does not discharge at Ash Meadows. EaYucca Flat is characterized by the following hydrostratigraphic sequence: AAVA, VCU, LCA, and LCCU. Western Yucca Flat contains much thinner sequences of the AA, VA, and VCU (in some locations these are nonexistent)in addition contains the Upper Carbonate Aquifer (UCA) and the Upper ClastiConfining Unit (UCCU), which stratigraphically and structurally overlies the LCA. The AA, VA, and UCA are saturated only in the structurally deepest portions of the valley. The spatial distribution of the LCCU, the UCCU, and thregionally continuous LCA allows groundwater flow into Yucca Flat, whereas outflow is postulated to occur primarily through the LCA. From the groundwaflow path perspective, the VA, AA, and UCA HSUs are discontinuous, preventthese units from providing flow paths into or out of the valley. Rather, paths originating in these upper units must somehow enter the underlying LCA to ethe basin.

Several hydrogeologic investigations have inferred a north-south trending potentiometric trough in central Yucca Flat (Winograd and Thordarson, 1975)An area of deep alluvium in Area 3 may be associated with the trough. Howethe supporting data, geologic information, and the configuration of the water tis sparse for this area. This feature would suggest a hydraulic sink where shapotentially contaminated groundwater is moving into the regional carbonate aquifer more readily.

Although geologically and hydrologically well characterized by the UndergrouTest Containment Program, anomalous hydrological behavior in an area knowthe Tuff Pile, which includes portions of Areas 3, 4, and 7, has never been weexplained. Observations in several wells in zones of high fluid pressures, fluilevels much higher than expected (up to several hundred meters above the accepted static water level), and radionuclide contamination of groundwater iexploratory wells, sited within several hundred meters of historic undergroundlocations, document contaminant migration in the upper 650 m of the Tuff Pilestratigraphy.

The composite potentiometric surface ranges from 725 to 1,145 m amsl, withlower heads observed in the carbonate aquifer with respect to all other units. highest heads are observed in the UCCU and UCA. A downward hydraulic gradient is prevalent throughout the VCU (representing perched water). Groundwater within the VCU is semiperched and, as a rule, the unit acts as aconfining boundary for the LCA. In addition, horizontal gradients within the

1.0 Introduction1-5


saturated volcanic units exist and may indicate groundwater flow toward the central areas of Yucca Flat prior to infiltration into the underlying LCA.

Most groundwater flowing beneath the Yucca Flat and Frenchman Flat basins passes through the LCA. This aquifer is the primary subsurface pathway by which groundwater leaves the basin. As generalized from contours constructed from water-level measurements made in wells that penetrate the aquifer, groundwater flows south from Yucca Flat into Frenchman Flat and then southwest toward the major downgradient discharge areas (primarily Ash Meadows, but possibly Alkali Flat or Death Valley). Flow of water into, through, and out of the LCA beneath these basins is controlled, in part, by (1) the occurrence of low permeability rocks both above and laterally adjacent to the aquifer, (2) lateral continuity with upgradient and downgradient sections of the LCA, (3) the occurrence of geologic structure (faults), and (4) secondary rock openings both within rocks of and adjacent to the LCA (fractures, joints, and dissolution openings).

Much of the groundwater flowing to Ash Meadows passes beneath Yucca and Frenchman Flats or beneath the area just south of Frenchman Flat, but originates primarily from outside these areas.

Contributions to Frenchman Flat from Yucca Flat are small because inflows to Yucca Flat are limited by confining units bounding the basin: (1) the LCCU on the northeast, (2) granitic rocks on the north, and (3) the UCCU on the west. The low permeability of the rocks that laterally bound the LCA is evidenced by the steep hydraulic gradient across these units. Another potential source of water to Yucca Flat is inflow from the west through the regional carbonate rock that underlies the UCCU at depth, but this source is likely to be small because rates of inflow are limited from above by the UCCU and from the west by lower permeability volcanic rocks. Conversely, it is also possible that some groundwater from the LCA flows west-southwest into this block of carbonate rock that underlies the UCCU. The actual depth at which carbonate rock occurs below the UCCU and the degree of hydraulic continuity between carbonate rocks of eastern and western Yucca Flat have yet to be determined. The total inflow to the LCA beneath eastern Yucca Flat from lateral sources is estimated to account for about 70 percent of the total inflow to the regional aquifer beneath Yucca Flat.

1.1.3 Radionuclides of Concern

A moratorium on United States nuclear testing has been in place since 1992, and the multilateral Comprehensive Test Ban Treaty was signed in 1996. Between 1957 and 1992, a total of 828 announced underground tests were conducted on the NTS. Of this total, 658 tests were conducted in Yucca Flat. The 658 tests comprise about 79 percent of the underground tests conducted on the NTS, but represent less than one-third of the total nuclear yield of all the tests (DOE/NV, 1994). More than 90 percent of the Yucca Flat underground nuclear tests were conducted in vertical emplacement holes in the eastern part of the basin. Tests were detonated in the alluvium or the volcanic rocks near or below the water table. Two tests were detonated in the carbonate rocks underlying the volcanics beneath northern Yucca Flat during the early years of the testing program. Three small tests were detonated



in the granitic rocks of the Climax Stock in the 1960s. The static water level at Climax is not confidently known. It could be below the deepest test; however, perched water is present in shallow fractures a few hundred feet below ground surface.

Detonation of the nuclear devices released radionuclides to the subsurface. The radionuclides of concern based on production, half-life, and mobility as they contribute to human health risk include americium-241, carbon-14, cesium-137, iodine-129, neptunium-237, plutonium-239/240, strontium-90, tritium, and uranium-238. Due to the density of the testing in Yucca Flat, it is probable that radionuclides transported from multiple tests have merged plumes in larger areas that may have altered physical properties (Laczniak et al., 1996).

1.1.4 Extent of Contamination

The lateral and vertical extent of contamination from tritium has previously been estimated from the regional model (DOE/NV, 1997). In general, the radionuclides of concern are currently located within the test cavities and downgradient of the test locations. The regional modeling produced estimates of the maximum extent of tritium transport at 20,000 picocuries per liter (pCi/L) 20 to 36 km (50th and 95th fractiles) downgradient of the starting point, the KANKAKEE test, in Yucca Flat. For comparison purposes, the U.S. Environmental Protection Agency (EPA) regulatory limit of 20,000 pCi/L of tritium in drinking water would result in a dose that is less than the 4-millirem per year (mrem/yr) dose rate used in the contaminant boundary definition in this analysis.

Based on the regional modeling results, the predicted direction of radionuclide migration is south from Climax Mine and Yucca Flat through Frenchman Flat and then southwest to the Amargosa Desert (Figure 1-2). The direction of contaminant movement varies spatially due to geologic variability and temporally due to changes in recharge. Incomplete knowledge about the geology covered by basin-fill deposits, multiple source locations, and parameter uncertainty lead to a range of possible contaminant movement pathlines (see Figure 1-3).

1.1.5 Generic Pathline

In addition, a new element was added to the transport model for the Yucca Flat VOIA modeling, uncertainty in the pathline. A generic pathline was used to model transport from sources in Yucca Flat. This pathline was constructed to represent flow from a source in the VCU to the LCA, and then downgradient in the LCA. The primary uncertainties determining the maximum distance of transport at the 4-mrem/yr boundary level during the next 1,000 years are the time for contaminants to reach the LCA, in which transport is fastest, and the distance traveled through the VCU. The time to reach the LCA is primarily a function of the vertical gradient from the VCU to the LCA and the vertical separation of the source from the LCA. The distance traveled through the VCU is a function of the vertical separation and the horizontal transport resulting from the horizontal

1.0 Introduction1-7


Figure 1-2Regional Groundwater Flow System



Figure 1-3Pathlines for Yucca Flat Sources Based on Regional Flow Model

1.0 Introduction1-9


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gradient. The total distance can vary from the minimum vertical separation distance to much longer oblique paths. Other uncertainties in the path length to the LCA result from poor definition of the location of the top of the LCA, possible shortcuts to the LCA through permeable fault zones or by local juxtaposition of the LCA upwards along faults, and altered hydraulic characteristics of the VCU around the nuclear tests. All of these uncertainties were combined and treated as a variable path length through the VCU along the pathline, represented as a probability distribution.

1.1.6 Uncertainties

Uncertainties associated with the conceptual model of the Yucca Flat CAU include the following:

• Subsurface characterization for the areas west and north of Yucca Flarelating to inflow

• Subsurface characterization in the nontesting areas of the basin, specifically relating to faulting, the potentiometric trough, the high-pressure zone, and elevated subsurface temperatures

• Subsurface characterization of the LCA beneath Yucca Flat

• Understanding the geochemistry of the radionuclides of concern

• Understanding the role of major faults in groundwater flow

• Contaminant transport processes and associated parameters, particulCAU-specific information

1.2 Objectives, Scope, and Limitations

The objective of the VOIA was to evaluate and compare options for characterigroundwater transport of radionuclides of concern associated with Yucca Flat nuclear testing. More specifically, the task involved using a model to rank characterization options based on their expected ability to reduce uncertaintierelevant to understanding the current and future geographic extent of contamination. As defined in this report, characterization options include (1) individual component activities, (2) subgroups of activities, defined as collections of related activities focused on one or two key uncertainties, and (3) groups of activities, defined as collections of subgroups based on different information-collection strategies or themes (groups may address between onefive key uncertainties). The uncertainty-reduction model includes the regionaltransport model as modified for this analysis (described in Appendix B) along with the model for implementing Bayesian analysis, which is described in Section 2.0. The uncertainty-reduction model uses descriptions of current uncertainties (te“prior probability distributions”) and estimates of the accuracy of characterizati



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options as inputs to produce descriptions of the uncertainties (termed “posterprobability distributions”) that would exist if those characterization options weto be implemented.

The goal of the Yucca Flat VOIA was to provide information to assist decisionmakers in determining what characterization options to conduct, if any. Like amodel, the model developed for this effort involves approximations and assumptions. When making real choices, decision makers should interpret thresults presented in this report in light of the approximations and limitations ofanalysis, and account for factors outside the scope of the analysis. Thus, therankings and other results documented here do not represent the sole, or eveprimary, basis for developing a characterization plan. Rather, the results are intended to serve as an aid to the decision-making process.

Uncertainty reduction is the only benefit produced by characterization that waconsidered formally in this analysis. The analysis did not attempt to account any other benefits of characterization, nor to answer the question of which characterization options produce benefits that justify their costs. However, it attempt to estimate the relative uncertainty-reducing capability of various optiand compared those estimates with cost estimates. In this way, the results aintended to help decision makers identify cost-effective strategies for reducinguncertainty.

1.3 Panel

A panel representing the areas of geology, geophysics, hydrology, geochemisand statistical analysis was convened as a subcommittee (Appendix A) of the UGTA Technical Working Group (TWG) to provide expert judgment for the analysis. The primary tasks assigned to the panel were as follows:

• Assign prior probability distributions describing the current uncertaintyover each of the key model parameters, based on existing data and bprofessional judgment.

• Identify potential characterization activities and groups of characterization activities for Yucca Flat designed to address each of key parameters.

• Estimate the accuracies of each characterization activity and group oactivities.

• Evaluate results of the analysis and make recommendations at majordecision points.

1.0 Introduction1-11


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1.4 Key Assumptions

A critical assumption for the VOIA is the method used to quantify and measure the uncertainty-reducing potential of a characterization option. An important uncertainty for future risk-mitigation decisions is the maximal geographic extent of contamination found to be above regulatory limits; therefore, the VOIA focused on the ability of characterization options to reduce uncertainties relevant to estimating the maximal geographic extent of contamination.

Due to radionuclide transport processes within the groundwater flow system, the area of contamination associated with Yucca Flat is expected to expand for some period of time, and then contract due to radionuclide decay.

The use of a one-dimensional transport model in the VOIA reduced the contaminant boundary to a single point along a pathline originating at a contaminant source that was generated by the regional flow model. For the purposes of this VOIA, the level of contamination for the boundary was defined as the combined doses from each radionuclide concentration that together would produce exposures to individuals at levels of 4 mrem/yr (Figure 1-4). Simulated radionuclide concentrations were converted to dose equivalents using a drinking water ingestion exposure model (Adams, 1996b).

Regulators, policy makers, and scientists would like to say with confidence that the contaminant boundary will remain within some specified distance of the CAU. Based on this reasoning, characterization options were evaluated according to their impact on uncertainties relevant to the calculation of the contaminant boundary. Probability distributions provide a means for quantifying uncertainties. Probability distributions may be characterized by various statistical measures, including the median (50th fractile), mean, variance, and 95th fractile (the value which has only a 5 percent probability of being exceeded) (Figure 1-5).

Statistical measures computed from probability distributions were used to quantify the uncertainty-reducing potential of characterization options. Specifically, four measures of uncertainty-reduction ability were defined:

• Measure 1. Expected average percentage reduction in the variances ouncertain parameters. Rather than directly measure a reduction in theuncertainty in the contaminant boundary, this measure quantifies the ability of an option to reduce uncertainties in parameters important to tcalculation of the contaminant boundary. Variance is used as the meaof uncertainty, the higher the variance, the higher the uncertainty. Specifically, the measure of uncertainty reduction is the average (acrothe parameters) of the ratios of the expected posterior parameter variato the variances of the prior distributions, expressed as a percent.

• Measure 2. Expected average mean deviation in the uncertainty in thecontaminant boundary. This is a direct measure of the reduction in uncertainty in the contaminant boundary. Unlike Measure 1, Measureuses average mean deviation to quantify uncertainty, a measure that compared to variance is less sensitive to errors in the “tails” of the



Figure 1-4Example of Contaminant Boundary Confidence Levels



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probability distribution. This was regarded as a useful characteristic, given the difficulty of accurately estimating the low probabilities associated with extreme values for the contaminant boundary. Measure 2 is expressed in units of kilometers.

• Measure 3. Expected decrease in the upper 95 percent confidence leprediction of the contaminant boundary. Another way of describing thechange in contaminant boundary uncertainty is to look at a conservativupper bound estimate, specifically, a value that has only one chance inof being exceeded. The higher that estimate, the higher the uncertainThe measure is the difference between the 95 percent confidence estimfor the contaminant boundary under the prior and posterior distributionexpressed in kilometers.

• Measure 4. Expected weighted average percentage reduction in the variances of key uncertain parameters. This measure is similar to Measure 1 except that computed reductions in parameter variance areweighted according to the sensitivity of the contaminant boundary to variations in the parameters. Like Measure 1, Measure 4 is expressedpercent.

Results were successfully obtained for Measures 1 and 4, but not for Measureand 3. The calculations for Measures 2 and 3 were not successful due the computational complexity of conducting the required very large number of transport simulations needed to compute probability distributions for the contaminant boundary. All calculations and results are described in Section 3.5.

The analytical techniques employed for the analysis are complex, and a numbsimplifying assumptions were made. Some assumptions were needed to reducomputational complexity and to limit the number of inputs. Two such assumptions are most significant. First, it was assumed that the uncertainties

Figure 1-5Illustrative Probability Density Function Describing Uncertainty of

Contaminant Boundary

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would be impacted by conducting any characterization option can be identified. The panel was asked to identify the parameters whose uncertainties would be most impacted by conducting each component activity and each subgroup of activities. The panel concluded that each component activity and subgroup would individually resolve uncertainty on one or at most two parameters (out of the five parameters identified as most critical to determining the contaminant boundary uncertainty). The analysis disregarded any ability that component activities or subgroups may have to reduce uncertainty in areas unrelated to the parameter(s) identified by the panel. Second, the analysis assumed that the ability of any group of activities to resolve uncertainty on a specified parameter depends on the parameters that are addressed by the activities contained in the group. In other words, any synergisms by which the reduction of uncertainty regarding one parameter might facilitate uncertainty reduction for another parameter were not considered. The implication of these assumptions is that the analysis may have underestimated the uncertainty-reducing potential of some subgroups and groups.

1.5 Report Organization

This report is divided into six sections and five appendices. Brief descriptions of each section and appendix are provided here.

• Section 1.0 is the introduction.

• Section 2.0 describes the technical approach, including the theory behthe VOIA, the contaminant transport model used to simulate the migration of radionuclides in groundwater, and the analysis process uto complete the VOIA.

• Section 3.0 describes how the analysis of the characterization options wdone. This section includes descriptions of sensitive flow and transpoparameters; the costs associated with the activities, subgroups, and groups; the assessment of probability distributions; and the calculation of posterior distributions for the sensitive variables. Descriptions of the measures of uncertainty reduction are provided.

• Section 4.0 describes the results of the analysis of the activities, subgroups, and groups for each measure of uncertainty reduction described in Section 3.0. It provides a report on the associated uncertainties.

• Section 5.0 presents the major conclusions derived from the VOIA.

• Section 6.0 is a list of the references cited.

• Appendix A contains a list of the panel members and their qualification

• Appendix B summarizes the regional groundwater flow and transport model, and alterations made for the Yucca Flat VOIA. It also includesdescriptions of the prior probability distributions of the sensitive



parameters elicited from the panel members, and provides the details on the assessment of prior probability distributions for the key uncertainties.

• Appendix C provides descriptions and estimated costs for the characterization options identified for the Yucca Flat investigation.

• Appendix D describes the assessment of probability distributions describing the accuracy of the characterization options.

• Appendix E provides a description of Bayesian updating using prior andposterior probability distributions.

• Appendix F describes the results of the transport simulations for the characterization options.



ty y

CDF onal les. ht

2.0 Technical Approach

The technical approach used for this project is based on well-established theory (e.g., Clemen, 1990; Fisz, 1963) for analyzing uncertainty. This section provides introductory descriptions of some aspects of the theory. Those familiar with the theory of uncertainty analysis may wish to skip this section.

2.1 Methods for Quantifying Uncertainty

According to probability theory, an uncertain quantity may be described by a probability distribution. The three most common ways of displaying a probability distribution are as a probability density function (PDF), cumulative distribution function (CDF), and box plot.

Figure 1-5 displays a probability distribution in the form of a PDF, the form most commonly used. With the PDF, the height of the curve at any given point is proportional to the relative likelihood of the uncertain quantity having that value. Many standard statistical measures can be conveniently related to the PDF. The 95th fractile is that value on the x-axis such that 95 percent of the area under the curve lies to its left and the remaining 5 percent lies to its right. The median is the point along the horizontal axis at which a vertical line would bisect the area under the PDF. The median of the distribution is the equal probability value (the odds are 50:50) that the uncertainty will fall above or below the median. The expected value or mean (µ) is the integral (sum) of all values (v) weighted by their probabilities. Mathematically, if f(v) is the probability density function for a continuous distribution:

(2-1)

Figure 2-1 illustrates a CDF. The height of a CDF curve denotes the probabilithat the actual value of the uncertainty quantity will be less than or equal to anvalue along the horizontal axis.

The PDF and CDF are related; the PDF is the derivative of the CDF, and the is the integral of the PDF. Thus, the height of the PDF at any point is proportito the slope of the CDF at that point. The CDF displays the distribution's fractiFor example, the 95th fractile is the point along the horizontal axis where the heigof the CDF reaches 0.95 cumulative probability. The median is that point along the horizontal axis where the CDF reaches 0.5 cumulative probability.

µ vf v( ) vd

∞–

∞∫=

2.0 Technical Approach2-1


Box plots are simpler than CDFs or PDFs. Whereas CDFs and PDFs display the complete probability distribution, box plots represent confidence bands constructed from specified fractiles of the distribution. For example, the box plot in Figure 2-2 shows a horizontal line from the 5th to 95th fractile and a vertical line at the median.

There is no single, universal measure of the magnitude of the uncertainty represented by a probability distribution. Common measures used for this purpose include the average deviation from the mean, standard deviation, and variance. The average deviation from the mean is computed by subtracting the mean from each possible value (v), taking the absolute value (i.e., ignoring negative signs), and integrating over the probability distribution.

Figure 2-1Illustrative Cumulative Distribution Function

Figure 2-2Box Plot Representation of a Probability Distribution

95th Fractile

Cum

ulat

ive

Pro

babi

lity

0

1.0

0.5

Median

Median

5th

Fractile95th

Fractile

2.0 Technical Approach 2-2


e,

iation

ed by

d

pdate

ical

, the

If f(v) is the probability density function and µ is its mean, the formula is:

(2-2)

The variance, σ2, is computed by subtracting the mean from each possible valusquaring the result, and integrating:

(2-3)

The standard deviation is the square root of the variance:

(2-4)

For a symmetric, bell-shaped PDF, the mean plus or minus one standard devcontains roughly 66 percent of the area under the PDF.

Another common measure of uncertainty is the difference between the 95th and 5th

fractiles of the distribution. In the case of the type of box plot shown in Figure 2-2, this difference is represented by the length of the line.

Several statistical measures of uncertainty were used for the analysis describthis report, including the average deviation, the variance, and the 95th fractile (seeSection 3.5). In general, the amount of uncertainty reduction is determineby comparing the value of the uncertainty measure before conducting the characterization option with the expected value of the uncertainty measure assuming information provided by the option has been received and used to uthe probability distributions describing the uncertainty.

2.2 Methods for Estimating and Analyzing Uncertainty

While experts generally agree that probability is the preferred means for quantifying uncertainty, there is less agreement on methods for obtaining probabilities and on what sources of uncertainty ought to be quantified. The mostcommon approach for generating probability distributions for uncertainties critfor risk management involves constructing models. The models identify the relationships among variables that affect the uncertainties. In this application

average deviation from the mean ν µ– f ν( ) νd

∞–

∞

∫=

variance σ2 ν µ )2f v( )–( vd

∞–

∞

∫= =

standard deviation σ=



g the e t

ables odel ant ng

ting

put

elief nd

uncertainty of interest is the location of the contaminant boundary, and the model is a contaminant transport model.

Specifically, the model used in this analysis to quantify uncertainty over the contaminant boundary was a modified version of the one-dimensional contaminant transport component of the regional model (Appendix B). The flow path used for the contaminant transport model was constructed to represent a generalized flowpath based on flowpaths extracted from the three-dimensional regional groundwater flow model for the sources in Yucca Flat. The groundwater fluxes used were also derived from the regional flow model.

The transport model simulates the movement of three of the nine radionuclides of potential concern identified in Section 1.0: tritium, carbon-14, and plutonium-239/240. The other radionuclides were tested in the transport model, and were not found to contribute significantly to the predicted contaminant boundary given the 1,000-year time frame for the analysis. This was due to various factors such as low initial concentrations, low dose per concentration, short half-lives, and high distribution coefficients (resulting in substantial sorption) relative to other radionuclides of concern.

The transport model consists of functional relationships between the three radionuclides of concern, the various factors that affect their movement in groundwater, and the location of the boundary over time. Uncertainty over the output of the model (location of the contaminant boundary) was quantified by assessing uncertainty on the model’s various input parameters, and then usinmodel to translate input uncertainties into the uncertainties associated with thmodel output. The problem of characterizing uncertainty over the contaminanboundary then becomes how to characterize uncertainties over the input variand how to propagate these uncertainties through the model. The use of a mto quantify uncertainty assumes that the processes associated with contamintransport are sufficiently well understood so that functional relationships amothe important variables can be presumed, but that the values of some of the variables are not precisely known.

The Monte Carlo method is an efficient approach for integrating and propagaprobability distributions through a model. Monte Carlo analysis was used to compute probability distributions for the contaminant boundary under variousassumptions regarding the probability distributions for the model’s key inputs.Monte Carlo simulation involved computing the output of the contaminant transport model for many sets of input combinations. The combinations of invalues were obtained by random sampling from the probability distributions assigned to the input variables. The resulting distribution of outputs was theninterpreted as an approximation of the probability distribution describing uncertainty over the contaminant boundary.

2.3 Methods for Specifying Probability Distributions on Input Variables

The predominant view of decision making under uncertainty is that probabilities are subjective and represent an individual's state of knowledge or degree of babout a quantity, and that this belief depends on the information, experience, a



se

from

s istort s

the es

t that iable ons. rior” t on to

e

ior

theories of the individuals who assign the probability numbers. This view is known as the subjective, or Bayesian view of probability, as it is associated with the 18th century mathematician Reverend Thomas Bayes (Bayes, 1958). Although they are subjective, Bayesian probabilities are not arbitrary. Subjective probabilities must satisfy the same basic axioms as other concepts of probability. In particular, probability numbers must lie between 0 and 1. If two events are disjoint, the probability that either will occur is the sum of the probability that the first will occur plus the probability that the second will occur. Also, if a scientist says, for example, that he believes the probability of some event is 25 percent, it means that he is as confident in the statement as he is in randomly drawing a red ball from an urn containing three white balls and one red ball. Although Bayesian probabilities are “subjective” in the sense that they describe a state of knowledge, they are “objective” (that is, independent of the observer) in the important senthat two “idealized” individuals faced with the same total background of knowledge would assign the same probabilities.

Under the Bayesian perspective, estimates of uncertain quantities are elicitedexperts. Bayesians believe that individuals with the greatest knowledge and familiarity with the uncertainty in question are the logical sources of judgmentregarding that uncertainty. Research has identified various biases that can dprobabilities provided by experts. Consequently, probability-encoding methodhave been developed for countering such biases and improving the quality ofprobabilities elicited from experts (Spetzler and Stael von Holstein, 1975; Merkhofer, 1987). Probability encoding is typically conducted in a process wherein a trained interviewer elicits probability judgments from an expert following a specified series of steps.

2.4 Methods for Estimating the Impact of Information on Uncertainties

A major advantage of the Bayesian view of probability is the ability to quantify effect of obtaining new information (Apostolakis, 1981). The approach assumthat the unknown “true” value of an uncertain variable can be described by a “prior” probability distribution. The prior probability distribution is elicited fromexperts and is based on the current state of information and knowledge abouvariable, as discussed above. When new information about the unknown varis obtained, it should cause the experts to alter or “update” their prior distributiBayes' theorem provides a method for determining what the updated or “postedistribution should be. Bayes' theorem is used in a variety of applications tharequire evaluating information-collection options, including systems for aidingdecisions about what medical tests to conduct on patients and what informaticollect when prospecting for oil.

One way to express Bayes' theorem is as follows (Fisz, 1963). Let p(H) be thprior probability of some hypothesis H and p(D | H) be the probability that datum D will be observed given that H is true. Bayes' theorem states that the posterprobability that H is true given that D has been observed is:

(2-5)p H D )( p D H )p H )((p D )(

---------------------------------=



tates

s of

ically such

long the

bility

s r h value ed by

prior es

s s

ting ian

a ist

that

d not

The probability p(D | H) is often called the “likelihood,” since it specifies the likelihood of obtaining the data given the hypothesis. Thus, Bayes' theorem sthat the posterior probability is proportional to the prior probability and the likelihood of the data, so estimating the posterior distribution requires estimateboth the prior and the likelihood. If there are several different hypotheses anddifferent sets of data that might result from a test, then the likelihood must bespecified for each combination (the specification is then called the likelihood function).

In the case of the tests associated with characterization activities, there is typa continuum of possible test outcomes. One way to apply Bayes' theorem in circumstances is to approximate the continuum by a finite number of discretepossibilities. For example, suppose a characterization activity provides information relevant to estimating the large-scale, average effective porosity athe flow path, a parameter needed for the contaminant transport model. Theappropriate parameter value is uncertain and a prior probability distribution foruncertainty would indicate the relative likelihood of values within a range of possibilities. If tests are conducted to clarify effective porosity, the tests might report estimated parameter values near the mean of the current (prior) probadistribution, or, they might suggest that values either lower or higher than thecurrent mean are more likely. Figure 2-3 indicates how such situations can be addressed using Bayes' theorem. The continuum of possible test outcomes idiscretized into several possibilities, ranging from results indicating parametevalues much lower than expected to results indicating parameter values muchigher than expected. Each value is interpreted as a hypothesis for the actualof the parameter. The possible test outcomes are the data potentially producthe characterization activities, and their likelihoods depend on hypotheses for the actual value of the parameter. Bayes' theorem can be applied to convert the probabilities into posterior probabilities, which depend on the possible outcomof the test. Although the resulting posterior distributions are discrete, continuouprobability distributions can be selected to fit the discrete posterior probabilitieand thereby make them continuous. Figure 2-3 illustrates how the resulting posterior distributions for the parameter might relate to the prior distribution.

2.5 Relationship Between Bayesian Analysis and Value of Information Analysis

Because Bayesian analysis allows the uncertainty-reducing ability of information-collection options to be quantified, it provides a means for estimathe value of the information to be provided by those options. However, Bayesanalysis by itself does not allow the value of uncertainty reductions to be expressed in dollar terms. Consequently, at best, Bayesian analysis providesrelative, not absolute, measure for the value of information. A theory does exfor developing an absolute, dollar measure of the value of information (Demski, 1972). However, this form of value of information analysis requires modeling the decisions and values that might be impacted by the informationis collected. Decisions that might change depending on the results of characterization include DOE/NV's proposed remediation (risk management)strategy and the decisions of regulators to accept or reject the conclusions anproposals from DOE/NV. The values that are potentially impacted by these decisions include the health and safety of future generations and the desire to



eters” ped s and

g the ch

unreasonably restrict community use of public lands. Such issues were not explicitly addressed in this analysis. To address such issues would have required a more complex approach with a much broader scope and would have required many additional assumptions. Because the approach used here did not explicitly address the decisions that might be impacted by uncertainty reductions, it is not a VOIA in the strict sense of decision analytic value-of-information literature. Nevertheless, the more limited definition of value of information employed here was viewed as sufficient to provide a useful tool for decision makers.

2.6 Analysis Process

Figure 2-4 summarizes the major steps of the analysis. The first step was to identify the parameters of the transport model to which the computed contaminant boundary is most sensitive. These parameters are referred to as “key paramor as “sensitive parameters.” Next, a prior probability distribution was develofor each sensitive parameter. Candidate component characterization activitiepotentially attractive groupings of activities were identified. These activities andcollections of activities are referred to as characterization options. Each characterization option was then evaluated to assess its accuracy for estimatinsensitive parameters. Specifically, likelihood functions were developed for eaactivity and subgroup of activities for use in a Bayesian analysis. Since eachgroup of activities was composed of a combination of subgroups addressing

Figure 2-3Results of Applying Bayes’ Theroem

As expected

Activity Results

Posterior distributioncompared to prior

Lower than expected

Higher than expected

Prior - - - - Posterior


2.0 Technical A

pproach2-8

Valu

e of In

form

ation

An

alysis for C

orrective A

ction

Un

it 97: Yu

cca Flat, N

evada T

est Site, N

evada

ynnt

cted

ntper

yn

s

Results(com parewith costs)

Figure 2-4Overview of the Analysis Approach

Conduct Bayesiananalysis to obtain

posteriordistributions

Determine sensitiveparameters for

contaminantboundary estim ate

Develop priordistributions for

sensitiveparameters

Develop accuracyestimates for

activities/groupings

Identify candidateactivities/groupings

Computecontaminant

boundary

Computeuncertaintreduction icontamina

boundary

Compute expechange in

contaminaboundary up

bound

Com puteuncertaintreduction i

sensitiveparameter


distinct parameters, the likelihood functions for the groups were assumed to be the likelihood functions for the subgroups from which the groups were composed. The prior probability distributions and likelihood functions were combined in the Bayesian analysis to obtain posterior distributions for the sensitive parameters. The contaminant transport model was then used to translate changes in the probability distributions for the sensitive parameters into the changes that they imply over the uncertainty in the contaminant boundary. Finally, the posterior distributions were compared with the prior distributions to compute each of the four measures defined to quantify uncertainty reduction: (1) reduction in uncertainty over key geohydrologic parameters, (2) reduction in uncertainty over the maximal extent of the contaminant boundary, (3) reduction in uncertainty over the upper bound (95th fractile) estimate of the maximal extent of the contaminant boundary, and (4) reduction in uncertainty over key geohydrologic parameters weighted according to their contribution to contaminant boundary uncertainty. The first and fourth measures were successfully calculated, the second and third were not. The details and results of these steps are described in Sections 3.0 and 4.0 of this report.



3.0 Analysis

This chapter describes the application of the technical approach outlined in Section 2.0. The application consisted of four major steps. First, sensitive parameters were identified and prior probability distributions were assessed for those parameters. Second, estimates were obtained of the ability of each characterization option to provide an accurate indication of the values of the sensitive parameters. As described previously, characterization options consisted of individual activities, subgroups of activities that address one or two specific parameters, and groups of activities (combinations of subgroups) that may address multiple parameters. Third, the prior distributions and accuracy estimates were mathematically combined through Bayesian updating to compute posterior distributions. Finally, the prior and posterior distributions were input to the transport model to provide prior and posterior distributions for the contaminant boundary.

3.1 Sensitive Parameters

Sensitive parameters are defined as those that, when varied between their maximum and minimum values, produce the greatest change in the estimates of the contaminant boundary. The parameters that were identified as potentially sensitive are the fluxes into Yucca Flat from the west and the north, the effective thickness of the LCA, the hydrologic source term, the effective porosity of the VA and of the LCA, the diffusion parameter for the VA and the LCA, and the path length from the source to the LCA. The diffusion parameter appears in the model as a function of the matrix porosity, block width, and the diffusion coefficient. The path length uncertainty is represented in the model by a "slope multiplier," which is designed to account for uncertainties in the location of the top of the LCA, the path through the VCU resulting from uncertainty in vertical and horizontal gradients and from variability of hydraulic properties, and possible shortcuts to the LCA along faults. A model to represent all these uncertainties was developed and is discussed in Appendix B.

To reduce computational complexity, only the most sensitive of the identified parameters were subjected to Bayesian updating and addressed in the Monte Carlo simulations involving the transport model. The contaminant transport model was used to identify the most sensitive input parameters. Specifically, the sensitivity analysis showed the estimate of the contaminant boundary to be relatively insensitive to changes in flux from the west. Therefore, the flux from the west parameter was fixed at its expected value for the remainder of the analysis. Similarly, lack of sensitivity to parameters involving the VA resulted in fixing those parameters at their expected values. It was judged that the primary concern about path length from the source to the LCA involved those sources deeper in the

3.0 Analysis3-1


VCU that were closer to the LCA. In general the sources starting in the VA had path lengths throughout the entire VCU that were greater than those starting in the VCU. The VA paths would not have contributed to the part of the transport distance distribution determining the contaminant boundary. The five parameters identified as sensitive parameters are the hydrologic source term, the effective porosity in the LCA, the diffusion parameter for the LCA, the flux from the north, and the path length across the VCU to the LCA. Additional information about the parameters judged to be most sensitive is provided in Table 3-1.

3.2 Characterization Options

The expert panel held a workshop to generate candidate characterization options. Panel members first identified 12 basic characterization options containing 27 activities (Table 3-2). Since each option was composed of two or more activities, it was termed a subgroup of activities (Table 3-3). Each activity was also considered to be a characterization option. Finally, to provide more comprehensive characterization options, the expert panel was asked to combine the subgroups of activities into larger groups. Each group was intended to represent a different strategy for information collection (Table 3-4). This step encouraged participants to identify synergies and other interrelationships among the activities and subgroups. The groups were also considered characterization options (Table 3-5).

Detailed descriptions of the work scope and cost estimates were developed for each activity. This information is provided in Appendix C. Cost estimates for each subgroup and group are the sum of the estimated costs for the included activities. The subgroups and groups span a wide range of costs.

Note that Activity 3 and Subgroup S12 and its associated activities A26 and A27 do not appear in the following quantitative analysis. Activity A3 was not included in the quantitative analysis since it was determined to primarily address one of the insensitive parameters (flux from the west). This subgroup was judged by the expert panel to address primarily uncertainties that relate to a concept termed "effective thickness," which is a function of the depth versus hydraulic conductivity distribution in the LCA of a scale important to transport of radionuclides within the Yucca Flat CAU, and believed to be primarily associated with the possible presence of solution features near the top of the LCA. Due to the inability to represent the impact on the contaminant boundary of reducing uncertainties related to effective thickness, and the difficulty of estimating prior distributions and likelihood functions for the effective thickness parameters given the random nature of solution fractures, this subgroup was not addressed in the analysis.

The expert panel was asked to identify the sensitive parameters that each subgroup would primarily address. To reduce the required number of asssessments and the computational load, it was assumed that each subgroup was most effective at reducing uncertainty on, at the most, two of the sensitive parameters. Tables 3-5, 3-6, and 3-7 show which sensitive parameter(s) were estimated to be addressed by

3.0 Analysis 3-2


Table 3-1Sensitive Parameters in Transport Modeling

Parameter Parameter Description Importance

Diffusion (alpha) in the LCA

The diffusion mass transfer coefficient (alpha) controls, in part, the movement of radionuclides between the fractures and the rock matrix. It combines diffusion, matrix and fracture characteristics.

In saturated fractured rocks, most movement of groundwater and radionuclides occurs in the fractures. However, a fraction of the radionuclides will move into the rock’s pore spaces, or diffuse in the rock matrix, and move back out into the fractures at a later time. This process reduces the concentration within the fractures and slows down the rate of radionuclide migration. Matrix diffusion is quantified by the diffusion parameter. The larger the diffusion parameter, the smaller the migration distance.

Hydrologic source term

As used here, this term refers to the concentration of radionuclides within the nuclear test cavity and chimney region that is available for groundwater transport.

Some of the radionuclides are contained deep within the glass that cooled from the melted rock following the detonation and are not readily available for transport. Others may be sorbed or held onto the surfaces of the glass or rock inside the cavity or chimney. The rate at which a radionuclide enters the groundwater, either by leaching out of the near surface of the glass or desorbing from the cavity materials, controls the concentration of that radionuclide in the groundwater. A rapid release rate produces a short-term but high concentration source term. A slow release rate produces a long-term contamination source at a smaller concentration.

Effective porosity in the LCA

Average large-scale effective porosity along the flow path.

In fractured rocks, not all fractures may be connected such that groundwater flow through the area actually moves through all or all parts of the fractures. Some fractures or parts of fractures, or networks of fractures may only be connected over the space of inches or feet, and the water contained within these “dead” networks is essentially stagnant. Effective porosity is a measure of the interconnected network of fractures along the flow path where groundwater predominantly flows and most contaminant migration occurs. The effective porosity is a major factor determining the speed with which groundwater moves in an aquifer. For a given flux, the smaller the effective porosity, the faster groundwater will move through an aquifer.

Slope multiplier across the VCU to

the LCA

The actual distance that contaminants from any particular source would travel in the VCU before entering into the LCA.

The VCU is the confining unit that generally separates the sources from the LCA, in which transport is much faster. The longer the path length, the longer the time it would take contaminants to reach the LCA. Since the prediction of the maximum extent of the contaminant boundary is time limited due to decay of the contaminants, the longer time spent in the VCU reduces the predicted transport distance. Also, the regional flow model does not contain detail at the scale of the individual test that captures the true distance to the LCA for each individual source. Since the contaminant boundary may be determined by a presently undetermined subset of the sources rather than the average of all the sources, this uncertainty could be important.

Flux from the north The rate of groundwater inflow into the Yucca Flat CAU from the north, simulated in the regional flow model.

The amount of groundwater moving through an area is represented by the groundwater flux. Flux is currently the most important parameter in the prediction of potential contaminant migration rate. Uncertainty in the flux resulting from lack of knowledge about the geology, aquifer parameters, and recharge is accounted for in this parameter. This parameter is relatively hard to calibrate since it represents a small percentage of the flux in the regional model.

3.0 Analysis3-3


Table 3-2Activities and Estimated Costs

Activity Activity Description Estimated Cost

A1 Alternate hydrogeologic models $494,150

A2 New wells north of Yucca Flat $5,411,336

A3 New wells on the western boundary of Yucca Flat $4,741,708

A4 Isotope/geochemistry mass balance studies $1,447,181

A5 Basin recharge studies $102,350

A6 Groundwater heads in VCU and LCA $4,338,924

A7 Potentiometric trough in south-central Yucca Flat $4,478,508

A8 Geologic structure of major faults $6,637,736

A9 Hydraulic properties of fault zones $3,473,576

A10 Geophysical interpretation of Yucca Flat basin structure $666,000

A11 Variability of VCU mineralogy $645,000

A12 Existing phenomenologic data on test effects $45,000

A13 Mineback data from Rainier Mesa tunnel tests $112,000

A14 Near-field sampling $2,672,160

A15 Test-cavity sampling $3,357,160

A16 Hydrologic source term modeling $3,480,000

A17 Multi-well tracer test $3,163,270

A18 Analysis of two existing tracer-test datasets $240,000

A19 Laboratory diffusion studies $250,000

A20 Single-well tracer tests in the VA and the LCA $7,022,624

A21 Earth-tide analysis of the VA and the LCA $199,153

A22 New seismic surveys $1,250,000

A23 Analysis of existing seismic data $300,000

A24 High-pressure zone in northern Yucca Flat $2,366,000

A25 Elevated groundwater temperatures in eastern Yucca Flat $122,400

A26 Potential for karst in the LCA $181,100

A27 Hydraulic conductivity with depth $60,000

3.0 Analysis 3-4


Table 3-3Subgroups and Estimated Costs

Subgroup Subgroup Description Activity Activity Description Estimated

Cost

S1Basin Boundaries-North and West

A1 Alternate hydrogeologic models

$5,905,486A2 New wells north of Yucca Flat

A3a New wells on the western boundary of Yucca Flat

S2Basin Boundaries - General

A4 Isotope/geochemistry mass balance studies$1,549,531

A5 Basin recharge studies

S3Shortcut - Vertical Gradient

A6 Groundwater heads in VCU and LCA$8,817,432

A7 Potentiometric trough in south-central Yucca Flat

S4 Shortcut - FaultsA8 Geologic structure of major faults

$10,111,312A9 Hydraulic properties of fault zones

S5Shortcut - VCU Continuity

A10 Geophysical interpretation of Yucca Flat basin structure$1,311,000

A11 Variability of VCU mineralogy

S6 Shortcut - Near FieldA12 Existing phenomenologic data on test effects

$157,000A13 Mineback data from Rainier Mesa tunnel tests

S7 Hydrologic Source Term

A14 Near-field sampling

$9,509,320A15 Test-cavity sampling

A16 Hydrologic source-term modeling

S8 Transport Parameters - 1

A17 Multi-well tracer test in the LCA

$3,653,270A18 Analysis of two existing tracer test datasets

A19 Laboratory diffusion studies

S9 Transport Parameters - 2A20 Single-well tracer tests in the VA and the LCA

$7,221,777A21 Earth-tide analysis of the VA and the LCA

S10 Basin StructureA22 New seismic surveys

$1,550,000A23 Analysis of existing seismic data

S11 Basin AnomaliesA24 High-pressure zone in northern Yucca Flat

$2,488,400A25 Elevated groundwater temperature in eastern Yucca Flat

S12a LCA CharacterizationA26 Potential for karst in the LCA

$241,100A27 Hydraulic conductivity with depth in the LCA

aActivity A3 and Subgroup S12 were not considered in the computational analysis.

3.0 Analysis3-5


each activity, subgroup, and group. As indicated by the tables, most subgroups were estimated by the panel to primarily address uncertainty related to only one sensitive parameter.

In summary, the characterization options defined for the analysis include (1) 24 activities; (2) 11 subgroups of activities, with each subgroup primarily reducing uncertainty on one or at most two sensitive geohydrologic or source term parameters; and (3) 9 groups of activities, with each group composed of multiple subgroups and thereby generally reducing uncertainty on multiple sensitive parameters.

3.3 Assessment of Probability Distributions

Figure 3-1 summarizes the steps used to obtain the probabilities for calculating prior and posterior distributions for the sensitive parameters. Probability distributions were obtained from the expert panel in probability encoding sessions facilitated by a team of decision analysts trained in probability assessment. Sessions were held to: (1) obtain prior probability estimates for the key sensitive variables and (2) obtain likelihood functions representing the accuracy of individual activities, subgroups of activities, and groups of activities.

Prior to providing probability assessments, panel members participated in a debiasing exercise. The debiasing exercise was intended to counter the well-known tendency of individuals to be overconfident and provide probability distributions that are too narrow (Lichtenstein and Fischhoff, 1977). During the debiasing exercise (Merkhofer, 1987) each participant estimated the fractiles of probability distributions describing their state of knowledge about quantities (e.g., the height of Niagara Falls) selected from the World Almanac. The

Table 3-4Subgroups Included in Each Group and Group Cost Estimates

Group Group DescriptionSubgroup

Estimated Costs

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11

C Comprehensive x x x x x x x x x x $52,117,526

B Balanced x x x x x x $30,746,039

LEBLess-Expensive Balanced

x x x x $25,543,238

ND No Drilling x x x x $4,567,531

V Value x x x x x $22,737,283

LCA LCA Transport x x x x $18,330,064

HST Hydrologic Source Term x x x $10,977,320

T5 Top 5 Choices x x x x x $27,600,853

FG Faults and Gradients x x $18,928,744

3.0 Analysis 3-6


Table 3-5Uncertain Parameters Addressed by Each Activity

Activity Activity DescriptionFlux

NorthSlope

MultiplierHydrologic

Source TermEffective Porosity

Diffusion (alpha)

Effective Thickness

A1 Alternate hydrogeologic models x

A2 New wells north of Yucca Flat x

A3a New wells on the western boundary of Yucca Flat

A4Isotope/geochemistry mass balance studies

x

A5 Basin recharge studies x

A6Groundwater heads in the VCU and LCA

x

A7Potentiometric trough in south-central Yucca Flat

x

A8 Geologic structure of major faults x

A9 Hydraulic properties of fault zones x

A10Geophysical interpretation of the Paleozoic framework

x

A11 Variability of VCU mineralogy x

A12Existing phenomenologic data on test effects

x x

A13Mineback data from Rainier Mesa tunnel tests

x x

A14 Near-field sampling x x

A15 Test-cavity sampling x

A16 Hydrologic source-term modeling x

A17 Multi-well tracer test in the LCA x x

A18Analysis of two existing tracer-test datasets

x

A19 Laboratory diffusion studies x

A20Single-well tracer tests in the VA and the LCA

x

A21Earth-tide analysis of the VA and the LCA

x

A22Seismic surveys of the Yucca Flat Basin

x

A23 Analysis of existing seismic data x

A24High-pressure zone in northern Yucca Flat

x

A25Elevated groundwater temperatures in eastern Yucca Flat

x

A26 Potential for karst in the LCA x

A27 Hydraulic conductivity with depth x

aActivity A3 addresses flux from the west; it was not evaluated separately because it was determined not to be a sensitive parameter.

3.0 Analysis3-7


Table 3-6Uncertain Parameters Addressed by Each Subgroup

Subgroup Subgroup DescriptionFlux

NorthSlope



Diffusion (alpha)

Effective Thickness

S1 Basin Boundaries - North and West x

S2 Basin Boundaries - General x

S3 Shortcut - Vertical Gradient x

S4 Shortcut - Faults x

S5 Shortcut - VCU Continuity x

S6 Shortcut - Near Field x x

S7 Hydrologic Source Term x x

S8 Transport Parameters - 1 x x


S10 Basin Structure x

S11 Basin Anomalies x

S12a LCA Characterization x

aSubgroup 12 was not considered in the computational analysis.

Table 3-7Uncertain Parameters Addressed by Each Group

Group Group DescriptionFlux

NorthSlope



Diffusion (alpha)

C Comprehensive x x x x x

B Balanced x x x x x

LEB Less-Expensive Balanced x x x

ND No Drilling x x x

V Value x x x

LCA LCA Transport x x x

HST Hydrologic Source Term x x

T5 Top 5 Choices x x x x x

FG Faults and Gradients x

3.0 Analysis 3-8


probability estimates were calibrated by comparison with the frequency with which actual values of the quantities fell within the ranges defined by the various fractiles. Participating in such exercises has been shown to improve people’s abilities to provide probabilities that accurately reflect their uncertainties (Tversky and Kahneman, 1974).

Next, background information and data relevant to the estimation of each parameter were presented. Also, factors influencing the uncertainty in the sensitive parameters were discussed. For the assessment of the prior distributions, background information was provided on the values for the key input parameters used in the sensitivity analyses (Appendix B). In the assessments of subgroup and group effectiveness, information on other factors affecting test interpretation was discussed. These factors are summarized in the diagram shown in Figure 3-2. The diagram is known as an influence diagram, and it identifies (through nodes and arrows) the relationships among factors thought to influence the uncertainty of interest (Oliver and Smith, 1989). The influence diagram documents the considerations that participants were asked to consider when providing estimates of existing uncertainty over parameters and when making estimates of the ability of characterization activities to produce accurate estimates of the parameters.

Probability estimates were elicited in a process having two stages or rounds. In Round 1, participants provided individual probability estimates. The individual Round 1 estimates were summarized and displayed and participants discussed points of agreement and disagreement. These discussions were intended to promote the sharing of information. Finally, Round 2 assessments were conducted

Figure 3-1Process for Assessing Probabilities for

Calculating Prior and Posterior Distributions

Debiasing exercise

Presentation of background data

Round 1 individual assessments

Discussion

Round 2 assessments

Subcommittee recommendation

3.0 Analysis3-9


as a group exercise, with participants providing consensus recommendations over the probabilities that should be assumed for the analysis.

Participants generated prior distributions by providing the 5 percent and 95 percent fractiles and specifying the form (e.g., normal, lognormal) of the distribution. The indicated form was then fit to the specified fractiles by identifying the numeric values for parameters (e.g., mean and variance) of the distributions which would ensure the resulting distribution had the specified fractiles. Table 3-8 summarizes the consensus judgments regarding the prior distributions as provided by the expert panel.

Estimates of the group, subgroup, and activity accuracies served as likelihood functions for the Bayesian analysis. The first step was to estimate subgroup accuracies. To accomplish this step, the expert panel provided probability distributions for the parameter estimates that would be generated by the subgroups, assuming various true or actual values for the parameters. The approach was similar to that used for generating prior distributions; 5th and 95th fractiles were specified along with the form of the distribution. The resulting probability distributions were interpreted to represent the estimated accuracy of

Figure 3-2Influence Diagram Showing Factors Influencing the Accuracy of Characterization Activities

Accuracyof parameter

estimate derivedfrom test

Corroboratinginformation

Accuracy of testresults

Accuracy of translationof test resultsto parameter

estimates

Accuracyof testdata

Accuracy of data

interpretation

Repre-sentativenessof test data

Quantityof testdata

Accuracyof model used for data inter-pretation

Scale of thetest Spatial

variabilityparameter

Number of tests

Represent-ativeness

of data used fortranslation

Accuracyof data usedfor translation

Accuracyof translation

model

Amount

Reliability

Represent-ativeness ofdata used forinterpretation

Accuracyof data

used for interpretation

Locationtransfer-

ability

3.0 Analysis 3-10


subgroups because they indicate how closely subgroup results are expected to match actual site conditions.

The following summarizes the logic and types of questions posed to obtain the subgroup accuracy estimates. First, a subgroup was specified (e.g., Subgroup S1). The parameter(s) addressed by the subgroup was noted (e.g., flux from the north), and the subgroup was discussed by the participants to ensure a common understanding of the work that would be conducted. Next, the participants were asked to assume that the actual, true values for the flux (values that if used in the transport model would produce the most accurate simulations) were known, for example 0.5 units (the exercise was repeated using various high, medium, and low values for the parameter selected from the range of values spanned by the prior distribution). The group was then asked to imagine that a team of individuals, who do not know the actual value of parameter, was tasked with conducting the subgroup and, based on the results, reporting back their best estimate of parameter. The group specified the range of possible values that the team might report (i.e., they specified the 5th and 95th fractiles of their range of uncertainty). Appendix D provides more detail. In effect, participants were asked to indicate the accuracies that would be obtained if the model was well understood and subgroups were calibrated by application to a geologic setting of known characteristics.

To summarize, the uncertainty ranges can be displayed as box charts. Figure 3-3 illustrates the possible relationship of the posterior estimates to the prior estimates. When compared with the specified actual values, the assessed ranges indicate subgroup uncertainty, whether uncertainties are symmetric or asymmetric, and the sensitivity of subgroup accuracy to the actual parameter value.

Next, accuracy estimates were generated for groups. To assess the accuracy of a group, the subgroups contained in that group that address each parameter were identified. For each parameter, then, the goal was to assess the ability of the combination of subgroups addressing that parameter to provide an accurate

Table 3-8Prior Distributions

ParameterLow5th

High95th

Distribution Form

Flux (m3/day) 2,100 27,000 Normal

Effective porosity 0.22% 2.20% Normal

Diffusion (alpha) (1/yr) 2 x 10-5 2 x 10-3 Lognormal

Hydrologic source term (pCi/L) 1 1,000 Lognormal

Slope multiplier 0 1 Uniform

m3/day = Cubic meters per daypCi/L = Picocuries per liter

3.0 Analysis3-11


bine in the ion ling. ased

the uacy , the for

nsure imates es. roups

ate n the is

essing

nk and ain a f the d an

estimate of that parameter. To facilitate such assessments, the expert panel was provided with box plots displaying the “composite” acuracy of the subgroup combination, computed under the assumption that sugbroup accuracies comas they do in independent sampling. Because of synergies and redundanciesinformation provided by the various subgroups, the accuracy of the combinatof subgroups may not be the same as that computed as in independent sampTherefore, the panel was asked to modify the computed accuracy estimates bon their understanding of the dependencies amoung the subgroups. As was case for the assessment of the individual subgroups, the panel provided accrassessments corresponding to three specified “true” values for the parameter5th, 50th, and 95th fractiles of the corresponding prior distributions. The process reaching consensus on the accuracy estimtes was similar to that used for thesubgroups.

The final step was to generate the accuracy estimates for the activities. To econsistency and minimize the assessment burden, the subgroup accuracy estwere used as benchmarks in the assessment of the activity accuracy estimatNamely, for each parameter, the expert panel was given a ranking of the subgin terms of their relative accuracy, which was computed from the previously obtained estimates. For example, for hydrologic source term, the most accursubgroup (Subgroup S7) received an accuracy score of 100 percent, and theaccuracy score of Subgroup S6 was computed to be 41percent. (Note that thcomputation was obtained by calculating the ratio of the variances of the likelihood functions corresponding to Subgroups S7 and S6, respectively. Because Subgroup S7 was assessed to be the most accurate subgroup addrhydrologic source term, all other subgroups and activities would have a likelihood variance greater than that of Subgroup S7, and thus the accuracy scores arebounded between 0 and 100 percent). The expert panel was then asked to raprovide accuracy scores for the activities addressing the given parameter. Agtwo-step voting process was used. Each member provided initial estimates oaccuracy scores which were displayed and discussed. Each member then ha

Figure 3-3Subgroup Accuracy Estimates Indicate Uncertainty, Asymmetry, and

Sensitivity of Test Outcomes

◆ Uncertainty

◆ Asymmetry

◆ Sensitivity toactual conditions

Low High

3.0 Analysis 3-12


d

rior

inty ions ults

sible r the The

the ter eter ke

s ed

sport

r the

opportunity to change his or her scores and the group average was used. For example, Activity A13 (evaluate mineback data from Rainier Mesa tunnels) received an accuracy score of 20 percent with respect to its ability to resolve uncertainty related to the hydrologic source term. Finally, these scores were used to scale the probability distributions representing the parameter estimates obtained for the subgroups so as to obtain the distributions that would be generated by the activities.

3.4 Posterior Distributions

Posterior distributions for the sensitive parameters were computed using Bayes’ theorem. Bayes’ theorem combines the prior distributions with the activity ansubgroup accuracy estimates to produce posterior distributions. Appendix E describes the details.

Figure 3-4 illustrates the general nature of the results. As indicated, the posteuncertainty depends on the prior uncertainty plus the estimated ability of the characterization option to accurately predict actual site conditions. If uncertaover characterization results is large, posterior (solid lines) and prior distribut(dashed lines) tend to be very similar. If uncertainty over characterization resis narrow, the posterior distributions tend to be tighter and/or shifted from the prior.

To illustrate, Figure 3-5 shows the prior and posterior distributions for Subgroup S1, which addresses the flux-north parameter. There are three posoutcomes to conducting Subgroup S1; results indicating lower than expectedresults, results confirming what is expected, and results indicating higher thanexpected results. These outcomes correspond to three possible estimates foflux parameter that might be produced as a result of conducting Subgroup S1.posterior distributions differ depending on the test outcomes. As illustrated, outcomes suggesting the flux-north parameter value is near the lower end of range currently considered possible would make the posterior distribution tighand shifted lower. Conversely, test outcomes suggesting the flux-north paramvalue is at the upper end of the range currently considered possible would mathe posterior distribution broader and shifted upward.

3.5 Calculation of the Contaminant Boundary

The preliminary transport simulations performed to determine simulation needare described first. This is followed by a description of the simulations performto predict the contaminant boundary.

3.5.1 Preliminary Simulations

Sensitivity analyses were conducted to determine the parameters for the tranmodeling. The results indicated that tritium, carbon-14, and plutonium-239/240 provided the dominant contribution to the contaminant boundary distance ove

3.0 Analysis3-13


Figure 3-4Typical Relationship Between Posterior and Prior Distributions

Figure 3-5Example of Prior and Posterior Distributions for the Flux

◆ If uncertainty over activity results is large, posterior andprior are very similar

◆ If uncertainty over activity results is narrow, posterior istighter and/or shifted from the prior

Prior - - - - Posterior ---

0 5000 10000 15000 20000 25000 30000 35000 40000 45000Flux from the North

Pro

bab

ility

Den

sity

Measured value is low

Measured value is medium

Measured value is high

Prior

3.0 Analysis 3-14


1,000-year time frame. Therefore, only these three radionuclides were used in VOIA transport modeling (Appendix F).

During the initial simulations of the transport model it became apparent that a number of realizations produced a distance to the 4-mrem/yr boundary that exceeded the 130 km distance from the source location to the discharge at Death Valley. Because such outcomes are not meaningful given the physical realities, realizations where the 4-mrem/yr boundary reaches the end of the flow system at 130 km, the distribution of outcomes should logically be truncated. However, such truncated distributions of outcomes would complicate the Bayesian analysis and reduce the ability of the analysis to discriminate among the uncertainty reducing potential of characterization options. Two options were proposed to minimize the truncation of distributions. The first was the approach used previously in the Pahute Mesa VOIA (DOE/NV, 1998). If followed, the solution would have been to reduce the groundwater flux term in the LCA via a scaling factor. This approach was not deemed desirable because it was not clear how the scaling would have impacted the analysis because the Yucca Flat pathline was more complicated (with multiple HSUs) than had been the case for Pahute Mesa pathline (one HSU). The second option was to extend the flow system out beyond a 130 km. Through experimentation, it was discovered that by extending the flow system to 250 km, the degree of truncation was reduced sufficiently to have minimal impact on the Bayesian analysis.

Another factor is that the flux for Yucca Flat was the sum of two components; west and north. It was not clear how scaling the flux would alter the relationship between the two flux components.

For the purposes of analysis, therefore, the flow system was extended thereby significantly reducing the need to truncate the simulated distribution representing uncertainty over the contaminant boundary. The use of a 250 km flow system within the analysis is in no way meant to suggest that contamination would ever extend beyond the end of the flow system at 130 km. This situation of truncated distributions suggests the range of uncertainty in the model is too large. However, the goal of the VOIA was not to validate the range of uncertainty, but rather to assess the relative reduction in uncertainty of suggested data collection activities. The extention of the flow path to 250 km facilitated the interpretation of uncertainty reduction.

3.5.2 Calculation of the Uncertainty Measures

For each parameter under each possible outcome of each characterization option addressing that parameter, 200 realizations of the parameter were generated using the Latin hypercube sampling method (Iman et al., 1980). Each realization was then input to the transport model and contaminant transport simulated over the 1,000-year time frame. Each of the three radionuclides required separate simulations. Each such model run produced concentration values at 500 equidistant locations along a 250 km path, recorded at 200 times representing every five-year increment from 0 to 1,000 years. This produced a matrix of concentration values for each Monte Carlo realization. For each possible outcome of each characterization option, the concentration matrices for each of the 200

3.0 Analysis3-15


realizations were combined into a single file representing the output of one set of Monte Carlo runs.

Note that groups addressing multiple sensitive parameters required a very large number of simulations. In fact, the three groups with subgroups addressing all five parameters would have required 35 = 243 separate parameter combinations for each radionuclide, each with 200 realizations. It was not possible to analyze these cases because the cumulative computer simulation time required exceeded the available computer resources. Therefore, the impacts of these groups were not analyzed for those measures (Measures 2 and 3) that required contaminant boundary simulations.

The output of each set of Monte Carlo runs was postprocessed through two separate programs, one which calculated the distance to the 4-mrem/yr boundary for different fractiles of the realizations and a second which calculated the contaminant boundary uncertainty. The results for all model runs are presented in Appendix F.

3.6 Measures of Uncertainty Reduction

Four measures of uncertainty reduction were computed.

3.6.1 Measure 1: Expected Reduction in Parameter Uncertainty

This measure is intended to represent the ability of characterization options to reduce uncertainty in key parameters important to the calculation of the location of the contaminant boundary. This measure does not involve the contaminant transport model directly; therefore, it is not affected by any uncertainties or questions regarding the accuracy of that model. It accounts for the levels of existing uncertainty in parameters, the specific parameters that are addressed by the characterization options, and the degree to which the option is anticipated to reduce those uncertainties. However, it does not account for differences in the sensitivity of the contaminant boundary to uncertainty reductions in the various parameters. Rather, it treats percentage reductions in parameter uncertainty equally regardless of which parameter has its uncertainty reduced.

To calculate the value of the measure for a characterization option, the variance of each possible posterior distribution was calculated for each parameter addressed by the option. The variances depend on the outcomes because a different posterior distribution results for each possible outcome of the activity or subgroup within the option that addresses that parameter. Therefore, the expected posterior variance is calculated by weighting the individual posterior variances by the probabilities of the corresponding characterization option outcomes. The resulting expected posterior variance is then divided by the variance of the prior distribution for the parameter and the result is subtracted from one. Thus, for each parameter addressed by the characterization option, parameter uncertainty reduction is calculated as:

(3-1)contaminant boundary uncertainty reduction 1expected posterior variance

prior variance------------------------------------------------------------------–=

3.0 Analysis 3-16


Finally, the results are expressed as percentages, and the percentage reductions are averaged across the four parameters to provide the average parameter uncertainty reduction. If an option does not address one or more parameters, no uncertainty reductions are assumed for those parameters. Table 3-9 provides an example illustrating the calculations.

3.6.2 Measure 2: Expected Reduction in Contaminant Boundary Uncertainty

This measure is intended to indicate the impact of characterization options on the uncertainty in the contaminant boundary, as expressed by the probability distribution for the contaminant boundary. Expected average deviation from the mean was selected as a measure of uncertainty. To calculate this measure, the mean deviations were calculated from the posterior distributions describing the contaminant boundary, which were obtained from Monte Carlo analyses of the transport model using the posterior distributions for the sensitive parameters as input.

As indicated previously, for each of the 200 realizations, the transport model creates a matrix of concentration values every 100 m and at 200 points in time. The concentrations are converted to yearly dose using a drinking water ingestion scenario model (Adams, 1996a) and summed by radionuclide to produce a composite dose matrix in distance and time. The location of the 4-mrem/yr dose as a function of distance and time is then determined from the composite dose values. The maximum distance of the 4-mrem/yr contour can occur at any time from 0 to 1,000 years and may range from the first observation distance at 1 km to the maximum extent of the transport path at 250 km. The maximum distance is recorded for each realization. It is considered a random variable because it is

Table 3-9Example Illustrating the Computation of the Average Expected

Reduction in Parameter Uncertainty

Sensitive ParameterExpected Posterior/Prior

VarianceParameter Uncertainty

Reduction

Diffusion (alpha) (1/yr) 0.8 20%

Hydrologic source term 0.6 40%

Effective porosity 1.0 0%

Flux (m3/day) 0.4 60%

Slope multiplier 1.0 0%

Average uncertainty reduction 24%

m3/day = Cubic meters per day

3.0 Analysis3-17


dependent upon the outcome of simulations with random input parameters. The average deviation is then calculated using the formulation.

(3-2)

where:

d(i) = the maximum distance of the 4-mrem/yr contour for realization i

µd = the average maximum distance of the 4-mrem/yr contour for the 200

realizations

The average deviation was chosen over the standard deviation because it is more robust and less susceptible to outlier values (Press et al., 1986). The average deviation is a measure of the spread of the distribution about its mean value. Therefore, if data are collected for a parameter of interest, and that data collection results in a reduction in the uncertainty in a parameter value, then one would expect that the uncertainty in the contaminant boundary location would also be reduced. Therefore, this measure of the contaminant boundary is focused on the uncertainty in the boundary location and not on the location of the boundary itself.

As with the previous measure, different characterization option outcomes produce different posterior distributions. Thus, for each characterization option, the tree of possible outcome combinations was identified, the mean deviation for each possibility was computed, and expected mean deviation was obtained by adding all possibilities weighted by their respective probabilities.

The expected reduction in contaminant boundary uncertainty was calculated by comparing the expected mean deviation calculated from the posterior distributions with the expected mean deviation of the prior distribution and subtracting the ratio from one. Contaminant boundary uncertainty reductions were expressed as percentages.

3.6.3 Measure 3: Expected Change in the Upper Bound (95 th Fractile) of the Contaminant Boundary

This measure is similar to the previous measure. Both measures quantify changes in the shape of the probability distribution describing uncertainty of the location of the contaminant boundary. However, where the previous measure considers the average reduction in the spread or dispersion of the distribution, this measure focuses on the upper tail of the distribution. Specifically, the measure indicates how characterization options affect the 95th fractile of the distance distribution. As with the previous measure, this measure was calculated for each characterization option by identifying the tree of possible outcome combinations for that option. Then, the 95th fractiles for the contaminant boundary distributions corresponding

average deviationd i( ) µd–

200----------------------

i 1=

200

∑=

3.0 Analysis 3-18


ork bility

ight to

to each path through the tree were identified. The expected 95th fractile was computed by weighting the possible values by the path probabilities.

For each Monte Carlo realization, the concentration at each observation location and at 5-year time intervals was stored in a file. The concentrations were each converted to a dose using the drinking water scenario and summed for the various radionuclides. For each realization, this process produced a matrix of composite dose values in distance and time. The maximum distance to the 4-mrem/yr boundary was determined from the matrix of dose values. The distance for each realization was placed in a file and sorted from smallest to largest. Once sorted, the array was sliced at various fractiles. For example, the slice at the 50th fractile represents the distance such that 50 of the data at that point in distance and time had smaller values. The expected change in the upper bound estimate of the location of the contaminant boundary was calculated by comparing the posterior estimate with the prior estimate.

3.6.4 Measure 4: Expected Reduction in Weighted Average Parameter Uncertainty

This measure is a variation of Measure 1. A major limitation of Measure 1 is that it does not reflect the varying sensitivities of the contaminant boundary to the uncertain parameters. All parameters are treated as being equally important in Measure 1. This measure partially reflects the differences in sensitivity. The transport model simulation results were used to generate weights for a linear approximation of parameter sensitivities. The resulting weights were as follows: flux from the North, 0.28; slope multiplier, 0.04; source term, 0.19; carbonate diffusion (alpha), 0.25; and effective porosity, 0.25. These weights were used in place of the equal weights assumed for Measure 1.

Although this measure might be regarded as superior to Measure 1, it has several limitations. First, it assumes that the contaminant boundary is a linear function of the parameters. Actually, the contaminant boundary is a nonlinear function of all parameters together; that is, particular values of different parameters may “wtogether” to change the contaminant boundary. Secondly, it ignores the possithat the sensitivity of the contaminant boundary to a parameter may depend strongly on the actual value of that parameter. For example, the boundary mbe fairly insensitive to the porosity if the porosity is low, but be very sensitive porosity when it is high. Nevertheless, this measure incorporates some of theinformation about the contaminant boundary from the transport model while avoiding the sampling issues involved with Measures 2 and 3.

3.0 Analysis3-19


ver

the

the

the

t.

nit of

ious a rease n

or

4.0 Results and Discussion

This section summarizes the results of evaluating and comparing the uncertainty-reducing capability of alternative characterization options. As indicated previously, 44 characterization options were evaluated in the quantitative analysis. Twenty-four of the options represent component activities (Table 3-2). Eleven of the options represent individual subgroups (Table 3-3). The remaining nine options represent potentially attractive groups (Table 3-3).

Four alternative measures were used to quantify uncertainty reducing potential:

• Measure 1. Expected reduction in parameter uncertainty: This measure was computed to quantify the ability of options to reduce uncertainty okey geohydrologic and source term parameters.

• Measure 2. Expected change in average deviation from the mean of distribution of the contaminant boundary: This measure was computed toquantify the ability of options to reduce uncertainty in the contaminantboundary.

• Measure 3. Expected change in the 95 fractile of the distribution ofcontaminant boundary: This measure was computed to quantify the ability of options to reduce uncertainty in the upper bound estimate ofcontaminant boundary.

• Measure 4. Expected reduction in weighted average parameter uncertainty: This measure was computed to quantify the ability of options to reduce uncertainty over key geohydrologic and source termparameters weighted by their relative impact on contaminant transpor

Options were ranked with respect to each of these measures. Furthermore, options were compared based on their performance on these measures per ucost.

4.1 Ability of Characterization Options to Reduce Parameter Uncertainty

Figure 4-1 displays the results obtained with Measure 1. All 44 options are included in the comparison. In all cases, the average expected change is a reduction in parameter uncertainty. In other words, none of the options is expected to produce an increase in parameter uncertainty. This is not as obvresult as it might at first seem. Characterization options may increase or decparameter uncertainty, depending on the nature of test results. In particular, aunexpected, unusually low or high outcome from a test can cause the posteri

th

4.0 Results and Discussion4-1

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4-2 4.0 R

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tainty
Figure 4-1
Measure 1 - Ranked Expected Reduction in Average Parameter Uncer

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Activities, Subgroups, and Groups


ese

is a ly er the or

tempt

ion as ore

ain ps

reater

inty ss re be t of cific eto on et s B e, the ost. r less.

distribution to display more uncertainty than the prior distribution. However, the results of Figure 4-1 show that when the possibilities are weighted by the probabilities, in all cases the expectation is that conducting the characterization options will lead to a decrease rather than an increase in parameter uncertainty. The average expected reductions in parameter uncertainty range from a low of about 4 percent (Activity A24) to a high of about 85 percent (Group C: “Comprehensive”).

Appendix D provides the expert panel's inputs for the analysis. Specifically, thinputs indicate the estimated ability of the various characterization options to provide accurate estimates of the sensitive parameters. Accuracy, of course,complex concept. As noted in Section 3.2, the accuracy estimates convey not onthe range of estimates that might be produced by a subgroup, but also whethsubgroup tends to produce biased estimates (estimates that tend to be abovebelow the true value), and whether the uncertainty ranges depend on the magnitude of the actual parameter values that the characterization options atto estimate. Nevertheless, a comparison of Tables D-1 to D-7 in Appendix D with Figure 4-1 confirms that tests that were estimated to be more accurate tend toproduce greater reductions in parameter uncertainty. Also, Figure 4-1 results show that subgroups and groups produce at least as much uncertainty reducttheir component activities or subgroups. Also in general, groups that contain msubgroups are more accurate than individual subgroups and groups that contonly a subset of the subgroups in the larger group. Finally, subgroups or grouthat contain more accurate activities or subgroups are estimated to produce greductions in parameter uncertainty.

Figure 4-2 and Figure 4-9 are plots of the expected average parameter uncertareduction of the characterization options versus cost. From a cost-effectivenepoint of view, the options that produce the greatest uncertainty reduction (scohighest on the y-axis) at any given cost level are optimal. These options can identified by starting at the origin and identifying the option for each incremencost that produces the largest uncertainty reduction (i.e., by choosing the spepoints that lie on upper left-hand edge of the envelope that encloses all of thepoints). The options identified in this way are on the Pareto frontier. The Parfrontier is defined as the set of options that no other option dominates (Andersal., 1981). Option A dominates another option B when A is at least as good aon all dimensions and better than B on at least one dimension. In this instancevaluation involves only two dimensions, uncertainty reducing potential and cThus, an option is defined as “optimal” (on the Pareto frontier) if it provides atleast as much uncertainty reduction as any other option that costs the same oWith this logic, Figure 4-3 and Figure 4-9 identify eight characterization options as “optimal” in terms of parameter uncertainty reduction per unit of cost.

The optimal options are:

• Activity A12: “Existing phenomenologic data on test effects”• Subgroup S6: “Shortcuts – Near Field”• Group ND: “No Drilling”• Group V: “Value”• Group LEB: “Less Expensive Balanced”


Valu

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ities, Subgroups)

7

S4

$10 $12

& Subgroups

Figure 4-2Measure 1 - Expected Reduction in Average Parameter Uncertainty vs. Cost (Activ

S

S9

A20

S3

A8

S1

A2

S8A17

A14

A15A16

A6

A7

S2

S10

A22A4

A10A11

A23

A24A25

S11A9A5

A18

A12

A13A19

S6

S5

A1

A21

0%

5%

10%

15%

20%

25%

30%

35%

$0 $2 $4 $6 $8

Cost in Millions of Dollars

Activities

Valu

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An

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orrective A

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Un

it 97: Yu

cca Flat, N

evada T

est Site, N

evada

4-5 4.0 R


t (Groups)

C

$50 $60

Groups

Activities &Subgroups

Figure 4-3Measure 1 - Expected Reduction in Average Parameter Uncertainty vs. Cos

16 %

12 %

14 %

15%

12 %

8%

14%

12%

11 %

13%

10%

8%

1 6%

14%

12%

16%1 6%

5%

2 0%

6%

2 0%

16%

14 %

1 0%

7%

4 %

4 %

28%

20%

20 %

3 0%

2 5%

26 %26 %

26 %

HST

T5

LCA

VNDLEB

B

FG

0%

10%

20%

30%

40%

50%

60%

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80%

90%

$0 $10 $20 $30 $40


S6

A12


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• Group T5: “Top 5”• Group B: “Balanced”• Group C: “Comprehensive”

Although Group V is on the Pareto frontier, it provides only a very small increain estimated uncertainty reduction compared to the much lower cost Group NDSimilarly, Group C provides very little incremental uncertainty reduction over themuch less expensive Group B.

4.2 Ability of Characterization Options to Reduce Contaminant Boundary Uncertainty and Uncertainty in the Upper Bound Estimate of the Contaminant Boundary

As explained previously, computing results for these two uncertainty-reductionmeasures (Measures 2 and 3) required Monte Carlo simulations involving a contaminant transport model. Figures 4-4 through 4-7 provide the results.

The results for these two uncertainty-reduction measures (Measures 2 and 3) dshow the logical consistencies that were identified in the results for the first uncertainty-reduction measure. Specifically, some groups are estimated to proless uncertainty reduction than one of their component subgroups. For exampGroup LCA is estimated to produce less uncertainty reduction than its componActivity A20, and Group V is estimated to produce less uncertainty reduction tits component Activity A15. Also, some options are expected to increase contaminant boundary uncertainty. For example, Subgroup S2 and the compoactivities of Subgroups S8 and S9 that address porosity are estimated to prodnegative amounts of uncertainty reductions on Measure 2.

As demonstrated by the previously conducted VOIA of Pahute Mesa, it is possfor subgroups to be expected to increase uncertainty in the contaminant bounThus, the fact that some characterization options are estimated to provide neguncertainty reduction is not by itself grounds for ignoring the results for uncertaMeasures 2 and 3. However, it is at least surprising, if not theoretically impossthat the results suggest that some combinations of subgroups are expected to uncertainty less than some of their component activities. Such nonintuitive reswere not obtained in the Pahute Mesa VOIA. In short, the results for these twuncertainty-reduction measures appear questionable. Therefore, additional efwas expended to determine whether there may be problems in the way that Measures 2 and 3 were calculated.

The explanation for the nonintuitive results is random error introduced by the Monte Carlo analyses. In Monte Carlo analyses, values for the inputs to the simulation model are selected randomly from the probability distributions describing the respective uncertainties over those parameters. As explained iAppendix B, in this application of Monte Carlo analysis each computation of a probability distribution over the contaminant boundary was obtained using a MonteCarlo analysis involving 200 realizations (i.e., 200 separate sets of sample valfor the parameters). This is the same number of realizations used in the VOIAPahute Mesa. Ideally, the number of samples is chosen to be sufficiently largethat the random errors associated with the sampling process “average out.” Tassumption was checked for the Pahute Mesa VOIA. Increasing the number o

4.0 Results and Discussion 4-6

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realizations for Pahute Mesa from 200 to 1,000 demonstrated that using 200 realizations for Pahute Mesa resulted in random errors that are small compared to the computed differences in the values of the uncertainty-reduction measures.

To estimate the magnitude of the Monte Carlo sampling errors for the Yucca Flat VOIA, the Monte Carlo simulations were repeated for Subgroups S1, S5, and Activity A18 using different, randomly chosen parameter values. The results indicate that the errors from Monte Carlo sampling are much larger here than they were for Pahute Mesa--as large as plus or minus 0.15 units or more on Measure 2, and as much as 20 km or more on Measure 3. These errors are very large compared to the computed differences in the uncertainty measures, and so large as to severely limit the ability to draw quantitative conclusions from the computed results. Apparently, 200 realizations are insufficient for the accurate computation of Measures 2 and 3.

There are several reasons to explain why 200 realizations might be sufficient for the Pahute Mesa Monte Carlo transport simulations but not sufficient for the Yucca Flat analysis. The transport model used for Yucca Flat is considerably more complicated than that used in the Pahute Mesa VOIA. Also, the Yucca Flat parameter ranges are different than those for the like parameters used in Pahute Mesa. These changes and the fact that large contaminant boundaries are being estimated here compared to Pahute Mesa suggest that the model used here is more sensitive to the uncertainties in input parameters. All these factors argue for the need for more realizations. Note that the errors of concern here relate to limitations of the Monte Carlo simulation, they do not indicate potential problems with the transport model itself.

Although the errors associated with the Measures 2 and 3 results are too large to allow supportable, quantitative conclusions, it may be possible to derive a few qualitative conclusions from the results. The fact that several subgroups were estimated to have negative uncertainty reductions on Measure 2 suggests that it is possible to expect that conducting some characterization activities will increase uncertainty. A similar result was obtained in the VOIA of Pahute Mesa. Drawing conclusions regarding the relative uncertainty reducing performance of the characterization options is more difficult, given the random error from the Monte Carlo simulations. In particular, it probably does not make sense to directly compare the results for subgroups with the results for groups because the averaging across test outcomes is likely to effect random errors differently for subgroups than for groups. Comparing just the subgroup results for Measure 1 with those for Measures 2 and 3 suggests that subgroups that resolve uncertainty on carbonate alpha (e.g., Activities A19 and A20) and source (e.g., Activity A14) may be more important than as indicated by the results for Measure 1. Conversely, subgroups (e.g., Subgroup S3) that resolve uncertainty over slope multiplier (i.e., that resolve uncertainty over the presence of “shortcuts” to thecarbonate) may be less important than indicated by the results for Measure 1



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4.3 Ability of Characterization Options to Reduce Weighted Average Parameter Uncertainty - Measure 4

As discussed in Chapter 3, all parameters are treated as being equally important in Measure 1. Measure 4 partially addresses this limitation by using weights generated by the transport model simulation results for a linear approximation of parameter sensitivities. Again, the resulting weights were as follows: flux from the North, 0.28; slope multiplier, 0.04; source term, 0.19; carbonate diffusion (alpha), 0.25; and effective porosity, 0.25. These weights were used in place of the equal weights assumed for Measure 1. The resulting new measure was termed Measure 4, and the results are provided in Figures 4-8, 4-9, and 4-10. As suggested by the weights, the sensitivity analysis indicated that flux, porosity, and diffusion (alpha) were all approximately equal in importance to the contaminant boundary, while the source was somewhat less important and the slope multiplier was significantly less important. Accordingly, subgroups addressing slope multiplier (S3, S4, S5, S6, S7, S10, and S11) and their component activities do not perform as well on Measure 4 as they do on Measure 1. Conversely, subgroups (such as S8 and S9) and component activities addressing diffusion (alpha) and porosity do relatively better on Measure 4 than on Measure 1. Group LCA also performs better on Measure 4, since it addresses alpha, porosity, and flux.

Recall that there are several limitations to this measure. Namely, Measure 4 assumes that the contaminant boundary is a linear function of the parameters and ignores the possibility that the sensitivity of the contaminant boundary to a parameter may depend strongly on the actual value of that parameter. Nevertheless, this measure incorporates some of the information about the contaminant boundary from the transport model while avoiding the sampling issues that distort the results for Measures 2 and 3.

The optimal options (options on the Pareto frontier) for Measure 4 are:

• Activity A12: “Existing phenomenologic data on test effects”• Subgroup S6: “Shortcuts – Near Field”• Activity A17: “Conduct a multi-well tracer test in the LCA”• Subgroup S8: “Transport Parameters – 1”• Group ND: “No Drilling”• Group LCA: “LCA Transport”• Group T5: “Top 5”• Group B: “Balanced”• Group C: “Comprehensive”

4.4 Results

The results of the Yucca Flat VOIA may be summarized as follows:

• Characterization options were found to differ significantly in terms of thcosts and in terms of the estimated ability to reduce key geohydrologic and source uncertainties. The costs of options ranged from $45K to $57MThe impact of options on key uncertainties varied by an even wider ran


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• It was not possible to provide a reliable ranking of options based on thability to resolve uncertainty over the contaminant boundary or uncertaover the upper bound estimate of the contaminant boundary. Howevesome qualitative conclusions were obtained. Also, the contaminant boundary simulation results were used to obtain another measure of uncertainty reduction that partially accounts for the impacts of parameuncertainty reduction on contaminant boundary uncertainty. Qualitatively, there are suggestions from Measures 2 and 3 that there is less value tsubgroups that explore the potential for “shortcuts” to the carbonates.

• An option was defined as “optimal” if it was estimated to provide at least amuch uncertainty reduction as any other option that costs the same or moSix options were identified as optimal on both Measures 1 and 4:

- Activity A12: “Existing phenomenologic data on test effects”

- Subgroup S6: “Shortcuts – Near Field”

- Group ND: “No Drilling,” composed of Subgroups S2, S5, S6, and S10

- Group T5: “Top 5,” composed of Subgroups S1, S5, S7, S8, and S

- Group B: “Balanced,” composed of Subgroups S1, S2, S3, S5, S7,S8

- Group C: “Comprehensive,” composed of Subgroups S1, S2, S3, SS5, S7, S8, S9, and S10

In addition, four other options were optimal on at least one measure ornearly optimal on both measures:

- Activity A19: “Laboratory diffusion studies”

- Group V: “Value,” composed of Subgroups S2, S3, S5, S7, and S1

- Group LEB: “Less Expensive Balanced,” composed of Subgroups S3, S5, and S7

- Group LCA: “LCA Transport,” composed of Subgroups S1, S2, S8and S9

4.5 Analysis Limitations

In theory, the comparative evaluations presented in this section account for mfactors that should logically affect the choice of characterization options. Howedespite the comprehensive consideration of relevant factors and the well-fountheoretical basis for the analysis, the reliability of the rankings and the uncertainty-reduction estimates produced is necessarily limited.



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Major factors incorporated into the analysis include the following:

• The specific uncertainties that the option is likely to impact. The analysis distinguishes between options that address only one or two uncertainand those that address most or all key uncertainties.

• The sensitivity of the distance to the contaminant boundary as it relatethe impacted uncertainty. The analysis accounts for whether contaminatransport is believed to be very sensitive to the impacted uncertaintiesnot very sensitive. However, the incorporation of sensitivity informatiointo the most precise results had to be performed under an assumptiolinear model response.

• The current level of information/understanding regarding the impacteduncertainties. The analysis accounts for whether the option is expectedadd information to an area that is already relatively well understood oadd information to an area about which very little is known.

• The accuracy/reliability/sensitivities of activities. The analysis accounts for the estimated capabilities of the individual activities that make up toption; namely, their uncertainties, whether they tend to give biased estimates, and whether their accuracy varies depending on actual siteconditions.

• Informational interdependencies. The analysis accounts for the degree which informational synergies may exist among the component activities of an option and whether the information that is collected is complementary or redundant.

• Cost and cost interdependencies. The analysis accounts for the costs ofconducting the activities that make up the option and has the capability accounting for cost savings or cost increases may be expected due toeconomies of scale, synergisms, or antagonisms over resource use oother factors. In this case, the cost estimates for groups were judged approximately equal to the sum of the costs of the individual subgroupwithin the group.

Among the limitations that must be taken into account when interpreting resuare the following:

• Limited quality of input judgments. The expert panel was required to provide numerous, difficult assessments as input to the analysis. Although panel members were selected for their expertise and understanding of the specific issues addressed, the estimates that thewere required to provide are inherently difficult. Understanding about subgroups and their accuracies is limited, and the expression of uncertainty in terms of probabilities is a difficult task. Errors in input judgments may produce errors in results.



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• Limitations of the regional transport model. The transport model is an interim model that will be replaced by a more sophisticated model as additional work focuses on the CAU scale transport. It involves many approximations and assumptions which limit its ability to translate geohydrologic and other parameters into reliable estimates of contamitransport. Approximations in the transport model may produce errors the rankings calculated for the two measures related to contaminant boundary location.

• Precision of Monte Carlo results. To ensure that Monte Carlo analyses accurately translate distributions over model input variables into the distributions that they imply over model outputs, it is necessary to run model many times to ensure that the number of input values sampled input distributions is very large. For practical reasons, the Monte Carloanalyses conducted for this evaluation were limited (in most cases) to model simulations per analysis. Tests were conducted to determine thmagnitude of errors possibly introduced by limiting the number of Monte Carlo simulations. These indicated that random errors of 15 percentagpoints on Measure 2 and 20 km or more on Measure 3 severely limit oability to draw supportable, quantitative conclusions from these two measures.

• Appropriateness of named distributions. The process of generating input probability distributions for the analysis was simplified by allowing the expert panel to specify two-parameter probability distributions and theth and 95th fractiles of those distributions. However, named distributions, while mathematically convenient, may have characteristics that do notreflect the uncertainties they are intended to represent very well. The named distributions were truncated to eliminate the possibility of infeasible parameter values (e.g., negative values for parameters that cannot be negative). However, the resulting truncated distributions sticontain features (such as the potential for values higher than possible)may distort results. Sensitivity analyses show that rankings are sensitivthe functional form of the probability distributions assumed.

• Discrete approximations. The discretizing process used for Bayesian updating introduces errors. Discretizing errors can be made small by ua large number of levels for discrete approximations. However, increasthe number of levels would increase the number of Monte Carlo analyrequired. Thus, for the calculations using the two measures related tocontaminant boundary location, practical considerations required that discrete approximations utilize a smaller number of levels than would otherwise be desirable. Sensitivity studies demonstrated that the magnitude of discretizing errors was less than a few percent and not asignificant source of error.

For these reasons, the results should be interpreted with caution. As stated abeginning of this report, the results are best considered as an aid to decision making, to be factored into the decision making process along with other input



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5.0 Conclusions

This section summarizes the main conclusions from the VOIA.

• For the purpose of improving estimates of contaminant transport fromYucca Flat, activities that help resolve uncertainty regarding hydrologisource term, groundwater flux through the LCA, and diffusion (alpha) and effective porosity in the LCA can be particularly effective. Cost-effective ways of resolving these uncertainties involve geochemistry-based investigations and exploratory drilling on the norand west sides of Yucca Flat, as well as tracer tests and diffusion test

• Some specific, cost-effective activities include evaluating mineback dfrom Rainier Mesa tunnels, conducting isotope/geochemistry mass balance studies and recharge studies, conducting new tracer tests ananalyzing existing data from single- and multi-well tracer tests in the LCA, and conducting laboratory diffusion tests. Characterization optiothat include one or more of these activities appear to be relatively effective at reducing uncertainties important to estimating contaminantransport.

• Although activities focused on groundwater flux, hydrologic source terdiffusion, and effective porosity are particularly effective, there is declining incremental improvement from including more of these typesactivities in a characterization effort. In other words, most of the uncertainty reduction benefit appears to accrue from including within group only a single activity or small group of logically connected activities of each type.

• Conducting a few activities that address multiple uncertain parameters generally more effective than conducting activities focused on a singlevery few parameters. In other words, characterization approaches thaddress diverse uncertainties are generally more cost effective than approaches that are narrowly focused.

• Activities that require drilling are less cost effective. It appears that it possible to obtain nearly half the potentially available uncertainty reduction through activities that do not require new drilling.

• Although drilling increases costs, it provides increased potential for reducing uncertainty regarding flux, diffusion (alpha), and porosity, theparameters to which the contaminant boundary is most sensitive. For this reason, Group “LCA Transport” is estimated to be very effective.

5.0 Conclusions5-1


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Nevertheless, this group is expensive because it includes drilling new wells on the north and west sides of Yucca Flat. It appears likely that variations of Group “LCA Transport” that include less drilling would beestimated as nearly as effective at reducing critical uncertainties, but significantly less expensive. The reasons for this conclusion are (1) tinexpensive Subgroup S2 (Basin Boundaries - General) is estimated tnearly as well at resolving uncertainty on flux north as the drilling-intensive Subgroup S1 (Basin Boundaries - North and West), a(2) results generally indicate that jointly conducting two activities that aequally effective at addressing the same parameter is typically estimato be only slightly more effective than conducting either one by itself. is not possible to conclude more generally that drilling could be removfrom any activity or group without significantly reducing its effectivenebecause in the case of the effective porosity and diffusion parametersonly activities identified for significantly reducing uncertainty involve drilling.

• Although the analysis suggests that most of the uncertainty-reducing potential of characterization can be captured without new drilling, drillimay provide information value not well captured in the analysis. For example, drilling produces new, physical data that tend to be more tangible and persuasive for many people. Also, drilling has a greaterpotential for producing unanticipated surprises. The analysis describby this report evaluated characterization options based on their expecability to reduce uncertainty. Characterization options with the ability produce surprises tend to do less well if the goal is to cost-effectively reduce uncertainty. Nevertheless, looking for surprises can be a valuapproach to learning. If one looks for surprises and does not find theuncertainty is not reduced by much. However, if surprises are found, toften produce dramatic improvements in understanding. Thus, drillinand other activities that provide new data are likely undervalued in this analysis. However, even if this is the case, the analysis results strongsuggest that most of the value of drilling and similar activities that produce new data can be obtained by conducting only a few of the mcost-effective of the available activities of this type.

• Although not evaluated in the computational analysis, Subgroup S12,LCA Characterization, may merit further consideration. It is designedexplore the development and continuity of karstic features in the LCAexamining similar carbonate formations and by viewing core and borehole video logs.

• The fact that the Monte Carlo simulations involving the contaminant transport model failed to generate reliable quantitative measures of uncertainty reduction generated several conclusions. An obvious conclusion is that properly designing and conducting extremely large-scale simulations that push the boundary of available computaticapability is very difficult. A factor that may have contributed to the difficulty in interpreting the results for Measures 2 and 3 are that modsensitivity to the different parameters was not exactly the same as it w

5.0 Conclusions 5-2


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on Pahute Mesa because the parameter ranges were different. Despite having performed an analysis before the computer runs which indicated an acceptably low level of error, the actual error was much larger than predicted. Future simulation studies of this nature should plan on more extensive error analyses before committing to a large simulation program.

5.1 Suggestions for Conducting Future VOIA Studies

There are several possible ways in which future VOIA studies might overcome the problems that prevented this study from measuring uncertainty reduction in terms of measures based on a simulation of contaminant transport. First, provided that sufficient computational power is available, a large number of realizations might be used in Monte Carlo simulations. Tests conducted in this study indicate that using 1,000 realizations rather than 200 would give considerable more accuracy. Another possible solution that does not require increasing the number of samples is to use methods, such as Fast Fourier Transforms, to filter sampling error out of the computed probability distributions.

An alternative approach that deserves consideration is to use uncertainty reduction measures that are more sensitive to the differences between the prior and posterior probability distributions. For example, Measures 2 and 3 were relatively insensitive to the differences between the prior and posterior distributions for contaminant boundary. This is because they involve computing an expected value that balances a decrease in uncertainty under outcomes that either confirm current expectations or yield outcomes indicating less transport with the increase in uncertainty under outcomes that indicate greater propensity for transport. This approach often gives results that approximately net to zero (and are thus very sensitive to “noise” introduced by sampling error). Furthermore, this logic impthat tests that yield surprise results are counterproductive, an assumption thamany might dispute. An alternative measure for valuing characterization optithat would be less sensitive to noise and that would value any change in knowledge would be total expected change in uncertainty (e.g., a measure thquantifies the absolute difference between the prior and expected posterior distribution without counting decreases positively and increases negatively).

Even if uncertainty reduction is retained as the goal of characterization, it is possible to define measures of uncertainty reduction that would be less sensitsampling error. For example, Measure 2 expresses uncertainty in terms of expected average deviation from the mean. The alternative measure expecteaverage percent deviation from the mean would be less sensitive to sampling eTo illustrate, suppose that current (prior) uncertainty over the contaminant boundary is 50 km + 10 km. Suppose a test is conducted and the results indicthat transport is faster than previously expected, so that the revised (posterioprobability distribution is 100 km + 15 km. According to Measure 2, uncertaintyhas increased. However, if uncertainty is expressed in percentage terms theuncertainty, 50 km + 20 percent, becomes the posterior uncertainty 100 km + 15 percent. With this measure, uncertainty has decreased. Measuring uncerreductions in percentage terms would tend not to penalize as much those characterization options that have the potential for producing surprising resulthereby potentially increasing the differences computed in the uncertainty

5.0 Conclusions5-3


alysis it

reduction performances relative to the errors introduced by sampling and other sources.

A final possibility that deserves consideration is suggested by the observation that it was relatively easy to roughly approximate the behavior of the transport model using a linear model (uncertainty Measure 4). Analytic models that are much more accurate than the linear model could be used without significantly increasing the difficulty of the analysis. In fact, replacing a complex model with a simpler version for the purposes of uncertainty analysis is a common practice, often referred to as “response surface analysis” (NRC, 1983). Response surface anshould be considered for any future applications of VOIA to the NTS becauseoffers a potential for allowing for many more realizations within Monte Carlo analyses while significantly reducing the necessary time and effort involved.

5.0 Conclusions 5-4


,

s.”

6.0 References

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Adams, S.R. (IT Corporation). 1996b. Memo to B.J. Deshler regarding UGTA Feasibility and Frenchman Flat Value of Information Studies Support Calculations, 8 October. Las Vegas, NV.

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Bayes, T. 1958. “Essay Toward Solving a Problem in the Doctrine of ChanceIn Biometika.

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D'Agnese, F.A., C.C. Faunt, A.K. Turner, and M.C. Hill. 1997. Hydrogeologic Evaluation and Numerical Simulation of the Death Valley Regional Ground-Water Flow System, Nevada and California, USGS-WRIR-96-4300. Denver, CO: U.S. Geological Survey.

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tics

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Fisz, M. 1963. Probability Theory and Mathematical Statistics, 3rd Edition. New York, NY: John Wiley & Sons.

Iman, R.L., J.M. Davenport, and D.K. Ziegler. 1980. Latin Hypercube Sampling (Program User’s Guide), SAND79-1473. Albuquerque, NM: Sandia National Laboratories.

Laczniak, R.J., J.C. Cole, D.A. Sawyer, and D.A. Trudeau. 1996. Summary of Hydrogeologic Controls on Ground-Water Flow at the Nevada Test Site, Nye County, Nevada, USGS-OFR-96-4109. Carson City, NV: U.S. Geological Survey.

Lichtenstein, S., and B. Fischhoff. 1977. “Do Those Who Know More Also Know More About What They Know?” In Organizational Behavior and Human Performance, 20: 159-183.

Merkhofer, M.W. 1987. “Quantifying Judgmental Uncertainty: Methodology,Experiences, and Insights.” In IEEE Transactions on Systems, Man, and Cybernetics, SMC-17(5): 741-752.

NRC, see U.S. Nuclear Regulatory Commission.

Oliver, R.M., and J.Q. Smith (eds.). 1989. Influence Diagrams, Belief Nets, and Decision Analysis. New York, NY: John Wiley & Sons.

Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. 1986. Numerical Recipes: The Art of Scientific Computing. New York, NY: Cambridge University Press.

Spetzler, C.S., and C.A. Stael von Holstein. 1975. “Probability Encoding in Decision Analysis.” In Management Science, 22: 340-358. Stanford, CA: The Institute of Management Science.

Tversky, A., and D. Kahneman. 1974. “Judgment Under Uncertainty: Heurisand Biases.” In Science, September 26: 1124-1131.

U.S. Department of Energy, Nevada Operations Office. 1994. United States Nuclear Tests July 1945 through September 1992, Rev. 14, DOE/NV--209. Las Vegas, NV.

U.S. Department of Energy, Nevada Operations Office. 1997. Regional Groundwater Flow and Tritium Transport Modeling and Risk Assessment of the Underground Test Area, Nevada Test Site, NV, DOE/NV--477. Las Vegas, NV.

6.0 References 6-2


U.S. Department of Energy, Nevada Operations Office. 1998. Value of Information Analysis for Corrective Action Unit Nos. 101 and 102: Central and West Pahute Mesa, Nevada Test Site, Nevada, DOE/NV/13052--41. Las Vegas, NV.

U.S. Geological Survey. 1987. USGS Digital Elevation Model Data. Compilation of elevations covering the Nevada Test Site and surrounding areas. Sioux Falls, SD.

U.S. Nuclear Regulatory Commission. 1983. PRA Procedures Guide. Washington, DC: Office of Nuclear Regulatory Research.

Waddell, R.K., J.H. Robison, and R.K. Blankennagel. 1984. Hydrology of Yucca Mountain and Vicinity, Nevada-California,--Investigative Results through Mid 1983, USGS WRIR-84-4267. Denver, CO: U.S. Geological Survey and U.S. Department of Energy.

Winograd, I.J., and W. Thordarson. 1975. Hydrogeologic and Hydrochemical Framework, South-Central Great Basin, Nevada-California, with Special Reference to the Nevada Test Site, USGS PP-712-C. Washington, DC.

6.0 References6-3

Appendix A

Expert Panel Qualifications


l ation reas a

and ther

licies

Appendix A: Expert Panel Qualifications

The expert panel consisted of members of the TWG VOIA Subcommittee. The primary role of the panel was to provide expert technical input for the decision model. The expert panel members are listed alphabetically along with a summary of their qualifications.

Bruce Crowe - Los Alamos National Laboratory. Dr. Bruce Crowe is a Science Advisor for the NTS Environmental Management programs with Los Alamos National Laboratory. He received a Ph.D. and an M.A. in Geology from the University of California, Santa Barbara, and a B.A. in Geology from Fresno State University. His areas of expertise include geology, volcanology, probabilistic risk assessment, performance assessment, application of expert judgment, and the geology and hydrology of the NTS. Dr. Crowe has prepared multiple refereed papers, book chapters, and consulting publications in geology, volcanology, probabilistic hazard assessment, tectonic processes, and impact on society and geological aspects of disposal of radioactive waste. He has extensive experience with the geology and tectonics of the NTS, and extensive experience with probabilistic hazard and risk assessment. He has been a participant in national and international committees and panels on performance assessment and probabilistic hazard assessment. Dr. Crowe served as an expert panel member on the National Research Council Panel on Active Tectonics and Impact on Society. He was a selected expert panel member on a VOIA conducted to evaluate exploration options for the Calico Hills hydrostragraphic unit and on a probabilistic assessment for volcanic hazards, both for the Yucca Mountain Project. He participated on the methodology team that conducted an expert-judgment based probabilistic approach to assessing future inadvertent human intrusion into sites on the NTS for disposal of low-level radioactive waste and provided input on geology, hydrology, and probabilistic perspectives to transport modeling as a panel member for the value-of-information study of site characterization options for the Pahute Mesa corrective action unit. Dr. Crowe has acquired management experience as group leader of the geological applications group and isotope and geochemistry group of the Los Alamos National Laboratory. He was the recipient of various awards, including the Los Alamos Distinguished Performance Award and the Los Alamos Director’s Postdoctoral Award.

Barbara J. Deshler - IT Corporation. Barbara Deshler has 12 years of environmental consulting experience as a project manager, principal technicaresource, and line manager, primarily as contractor at the NTS, the Waste IsolPilot Plant in New Mexico, and other DOE sites. She has experience in the aof risk assessment, regulatory compliance, and strategic planning, and she isNevada Certified Environmental Manager and IT Corporation Project Management Associate. She has facilitated negotiations among DOE, EPA, state regulatory authorities on agreements, permits, risk assessments, and ocompliance-related documents. Her expertise includes compliance with the requirements of federal regulations, DOE orders, EPA guidance, and state poand regulations for assessment and remediation of contaminated sites and

Appendix AA-1


on. e

a Flat

te

M.S. s

with the

treatment, storage, and disposal of contaminated media. She has participated in several tasks employing decision analysis techniques, including VOIAs for Frenchman Flat and Pahute Mesa. She has a B.S. in Geological Sciences from the University of Texas at El Paso.

Sig Drellack - Bechtel Nevada. Sig Drellack is a Geology/Hydrology Supervisor with Bechtel Nevada (BN) in Las Vegas, Nevada. He received a B.S. in Geology from Arizona State University and has over 23 years experience applying geology, hydrogeology, and geophysics techniques in the fields of petroleum exploration, mining, construction, groundwater, and environmental remediation. He has worked at the NTS for the last 19 years on a variety of programs including the Los Alamos National Laboratory Underground Nuclear Testing Program, Threshold Test Ban Treaty (TTBT) verification, the DOE Technology Transfer Program, water and monitoring well projects for the U.S. Air Force (on the nearby Nellis Air Force Range), and the NTS Routine Radiological Environmental Monitoring Plan (RREMP). He has worked on the UGTA Project since its inception. He has authored or coauthored numerous publications, technical reports, and presentations related to NTS geology and hydrogeology, including the sections concerning surface and groundwater in the RREMP. Mr. Drellack is currently the Group Leader of the BN Geology/Hydrology Section’s UGTA support group.

William Fryer - HSI GeoTrans, Inc. William Fryer is a senior hydrogeologistwith 20 years experience working in many different aspects of hydrogeology, primarily characterizing hydrogeologic systems and groundwater contaminatiHe has been dedicated to the UGTA Project since its inception. At the NTS hwas initially involved in field data collection/well drilling, and managing hydrologic testing activities for the UGTA program. More recently he has participated in the development of the UGTA technical strategy for the Federal Facility Agreement and Consent Order, the Frenchman Flat and the Pahute MesVOIAs, and is presently working on the monitoring design for the Frenchman CAU. He was primary manager for the BULLION Forced-Gradient Tracer Experiment and analysis. Mr. Fryer has a B.S. in Geology from Michigan StaUniversity.

Annie Kersting - Lawrence Livermore National Laboratory. Dr. Annie B. Kersting is a research geochemist in the Analytical and Nuclear ChemistryGroup at Lawrence Livermore National Laboratory. She has a Ph.D. and an in Geology and Geochemistry from the University of Michigan and an A.B. in Geology and Geophysics from the University of California, Berkeley. She haover eight years research experience using radiogenic isotopes as tracers in volcanic and environmental systems. Her current research focuses on the geochemical mechanisms that control the transport of radionuclides in both saturated and unsaturated environments. Her work over the last three years the UGTA program involves field and laboratory studies to better characterizeradiology source term deposited in the subsurface as a result of undergroundnuclear testing at the NTS.

Appendix A A-2


Merrie Martin - IT Corporation. Merrie Martin is an environmental consultant for IT Corporation. Her involvement at NTS includes the Frenchman Flat VOIA and Corrective Action Investigation Plan, drilling at ER-6-1 and UE-6e, electro-magnetic surveys at the Decon Pond and Area 23 Leachfield, risk assessment modeling, and tunnel sampling. Ms. Martin is currently the Task Manager for sampling the UGTA wells at the NTS. Ms. Martin has conducted magnetic, electromagnetic, and ground penetrating radar surveys for the DOE at the Tonopah Test Range, Kirtland Air Force Base, and the Waste Isolation Pilot Plant. She received a B.S. in Geophysics from the University of Texas at El Paso.

Gayle Pawloski - Lawrence Livermore National Laboratory. Gayle Pawloski is a geologist for Lawrence Livermore National Laboratory (LLNL). She received a B.S. in Earth Sciences from California State University Hayward. She has been working at LLNL for 19 years, supporting a wide variety of programs, including Nuclear Test Containment Program, TTBT treaty verification, Underground Coal Gasification, and the Office of Basic Energy Sciences Continental Scientific Drilling Program. She has been part of the UGTA Project since its inception. Ms. Pawloski has worked at the NTS since 1980, specializing in NTS geology, interpretation of geologic and geophysical data, site characterization, nuclear explosion phenomenology, and information management. She is currently the Task Leader for Site Characterization for the LLNL Containment Program and serves as the geologic representative to the UGTA TWG. Ms. Pawloski was involved in identifying characterization activities, elicitation of distributions, and evaluation of plans, progress, and conclusions for the UGTA VOIA for Pahute Mesa.

Kenneth R. Rehfeldt - HSI GeoTrans, Inc. Dr. Kenneth R. Rehfeldt is a Principal Hydrogeologist with HSI GeoTrans in Las Vegas, Nevada. He has a Ph.D. in Civil Engineering from the Massachusetts Institute of Technology, an M.S. in Hydrology from the New Mexico Institute of Mining and Technology, and B.A. in Geological Sciences from the University of Wisconsin-Milwaukee. Dr. Rehfeldt currently serves as technical lead of hydrogeological data analysis and groundwater modeling for the underground test area project of the environmental restoration program at the NTS. He provides technical oversight of potentiometric, water balance, aquifer test, porosity, dispersivity, and sorption data compilation and analysis as well as performs solute transport modeling to simulate radionuclide migration from the underground testing areas of the NTS. He has authored or coauthored more than 50 publications, reports, presentations related to characterization of heterogeneous porous media, geostatistical analyses of aquifer and compacted soil liner properties, regional characterization of groundwater quality, the fate and transport of solutes in saturated and unsaturated flow, the development of a methodology to define well-head protection areas around municipal wells, and the analysis of modeling of the groundwater flow system at the NTS.

Charles Russell - Desert Research Institute. Charles Russell is an Assistant Research Hydrogeologist with the Desert Research Institute in Las Vegas, Nevada. Mr. Russell has an M.S. in Geosciences with special emphasis on stable isotope geochemistry from the University of Nevada, Las Vegas, and a B.S.

Appendix AA-3


ce. lyses

lished s.

f

ce ugh

l

o

an ch

B.S.

sses. y, es ort on

is

in Geology from Eastern Washington University. Mr. Russell has conducted studies of groundwater hydrology and aqueous geochemistry of the NTS for over 14 years. Specific research areas include studies of the hydrogeology of fractured rocks using stable isotopes, tritium reduction utilizing air-stripping, and the variability of aqueous chemistry associated with bacteria in the deep subsurfaApplied research includes the reduction of aquifer test data, geochemical anaand interpretation, and utilization of the chloride mass-balance approach to determine groundwater recharge. The results of these studies have been pubin numerous Desert Research Institute publications, journals, and monogramMr. Russell has served on many DOE groundwater committees and working groups, providing technical expertise and guidance as required, including contributions to previous VOIAs.

Michael Sully - HSI Geotrans, Inc. Dr. Michael Sully is a hydrologist with HSI Geotrans, Inc. He received a Ph.D. in Soil Science from the University oCalifornia, Davis, an M.S. in Atmospheric Science from the University of California, Davis, and a B.A. in Physics from the University of Montana, Missoula. Dr. Sully has contributed to site characterization efforts, performanassessments, closure cover development, and monitoring system design throactivities including developing hydrogeologic conceptual models for heterogeneous porous media, characterizing transport parameters, numericamodeling of flow and transport and soil-plant atmosphere interactions, and conducting field experiments to verify and calibrate numerical models. He alsserved as an Assistant Professor in the Department of Hydrology and Water Resources in the College of Engineering at the University of Arizona, and as Experimental Scientist at the Commonwealth Scientific and Industrial ResearOrganization Division of Environmental Mechanics in Canberra, Australia.

Richard K. Waddell - HSI GeoTrans, Inc. Dr. Richard K. Waddell is a Principal Hydrogeologist and Vice President of HSI GeoTrans. He received aPh.D. in Geology from the Pennsylvania State University, and an M.A. and a in Geology from the University of Texas at Austin. He has 20 years of post-graduate experience in evaluating groundwater flow and transport proceHe was a member of the National Research Program, U.S. Geological Surveperforming studies of the regional hydrology of the NTS, developing approachfor characterizing fractured volcanic rocks, and evaluating radionuclide transpat Yucca Mountain. During the past 12 years, he has worked as a consultantprojects ranging from water-resource management to characterization, risk assessment, and remediation of contaminated waste sites. The majority of hwork over the past six years has been on characterization and modeling of radionuclide transport processes at the NTS.

Appendix A A-4

Appendix B

Description of Groundwater Flow and Transport Model


Appendix B: Description of Groundwater Flow and Transport Model

The regional groundwater flow and transport model (DOE/NV, 1997) was used as the basis for the predictive transport modeling in the Yucca Flat VOIA. This model was originally developed to determine whether any immediate risks to human health and the environment exist as the result of radionuclide transport by groundwater from the underground test areas. The model consists of a three-dimensional flow model encompassing the NTS, and a one-dimensional transport model that simulates transport from sources on the NTS along flow pathlines derived from the flow model. Since publication of the regional modeling report, the regional flow model has been revised in detail and recalibrated. Figure 1-2 in Section 1.0 shows the extent of the regional flow model, the location of the Yucca Flat CAU, and the pattern of groundwater flow on the NTS and downgradient. Figure 1-3 shows pathlines from all sources below the water table in Yucca Flat derived from the regional flow model. A test with a working point above the water table was included as a possible source if the working point was less than two cavity radii above the water table. The current, recalibrated version of the regional flow model and a modified version of the transport model were used for this VOIA.

B.1.0 Model Description

The regional model was developed using three codes. The first was MODFLOW, a three-dimensional groundwater flow modeling code developed by the U.S. Geological Survey (McDonald and Harbaugh, 1988). The second, to characterize three-dimensional pathlines through the groundwater flow model, used MODPATH (Pollock, 1989). The transport simulations were performed along the pathlines using the third, a one-dimensional transport code, MC_TRANS (IT, 1996), developed specifically for the UGTA Project.

B.1.1 Groundwater Flow and Particle Tracking Codes

MODFLOW (McDonald and Harbaugh, 1988) is a numerical code that uses a finite-difference formulation to approximate the partial differential equation for three-dimensional groundwater flow of constant density fluid in a heterogeneous, anisotropic medium. The code uses the Block Centered Flow package (McDonald and Harbaugh, 1991) to calculate conductances and flow between adjacent cells.

The particle tracking computer code, MODPATH (Pollock, 1989), takes the output of the MODFLOW computer code and calculates the groundwater pathways. In MODPATH, an imaginary particle is followed as it flows through the groundwater system as defined by the numerical groundwater flow model. Using the PATHLINE output option of MODPATH, the location coordinates of

Appendix BB-1


the particle, in model space, are recorded where the particle crosses a grid cell boundary.

B.1.2 Contaminant Transport Code

The one-dimensional code MC_TRANS simulates the concentration of dissolved radionuclides along externally defined pathlines. As used in the regional modeling, these pathlines were obtained from the regional flow model. MC_TRANS was developed to simulate the transport of chains of dissolved chemical constituents contained in fractured or porous media, where the diffusion of solute mass into the matrix or dead-end pore space is an important factor. The code can be used in a deterministic mode or probabilistic mode incorporating uncertainty through the Monte Carlo method. The following discussion of the simulation of the transport process pertains to both the version of the code used for the regional modeling and for the Yucca Flat VOIA. Changes were made to the code concerning the pathline along which transport was simulated. These will be explained later in the text.

The transport code accounts in real time for advection, dispersion, radioactive decay, matrix diffusion, and sorption (IT, 1996) by solving a set of coupled equations that govern the transport of radionuclides in a dual porosity medium with a mobile and an immobile zone. The mobile zone is conceptualized by flow in fractures, and the immobile zone represents the matrix between fractures. The fracture flow portion is conceptualized as a porous medium with a small effective porosity consistent with fracture porosity.

A set of coupled equations governs the radionuclide transport in the fracture zone (mobile domain) and diffusion into and out of the matrix (immobile domain). Generic units are shown upper case and in square brackets, while specific units are lower case and in parentheses.

B.1.2.1 Mobile Domain

The transport in the mobile, or fracture, zone is given by

(B-1)

where

Cm = activity per volume of the radionuclide in fluids in the mobile

zone [Activity/L3];

Cim = activity per volume of the radionuclide in the pore fluid of the

immobile zone [Activity/L3];

2m = effective porosity of the mobile zone [dimensionless];

θmRm t∂∂Cm Dm

x2

2

∂

∂ Cm

x∂∂ qCm( )– α Cm Cim–( )– θmRmλCm–=

Appendix B B-2


Dm = mobile zone hydrodynamic dispersion coefficient [L2/T];

Rm = retardation [dimensionless];

q = groundwater flux, also called Darcy flux [L/T];

" = matrix diffusion zone mass transfer coefficient [1/T];

8 = first-order constituent decay rate [1/T];

t = time [T]; and

x = space coordinate [L]

and

(B-2)

In Equation B-2, αl is the longitudinal dispersivity [L]; v is the average linear velocity of groundwater in the mobile zone [L/T]; τ is the mobile zone tortuosity; and Do the molecular diffusion coefficient in free-standing solution [L2/T]. The average linear velocity is defined as

(B-3)

where θm is the mobile zone porosity, or effective porosity. The first order radioactive decay constant is defined as

(B-4)

where T1/2 is the half-life of the radioactive constituent.

B.1.2.2 Immobile Domain

The governing equation for each constituent in the immobile zone can be written as

(B-5)

where

Dm θmα1v θmτD°+=

v q θm⁄=

λ 2lnT1

2⁄

---------=

θimRim t∂

∂Cim θ– imRim

λCim α Cm Cim–( )+=

Appendix BB-3


2im = effective porosity of the matrix immobile zone [dimensionless]; and

Rim = retardation [dimensionless].

The mass transfer coefficient " is regarded as an empirical constant (Coats and Smith, 1964). There are several definitions for " including those found in reports by van Genuchten (1981) and Sudicky (1990). For the dual porosity approach, the formulation of Sudicky is implemented:

(B-6)

where B is the average length dimension of the immobile domain, and Dm* is the

effective diffusion coefficient in the porous matrix and, for isotropic media, is defined as (Bear, 1972)

(B-7)

where Jim is the tortuosity of the porous matrix and Do is the diffusion coefficient in water. In the remainder of the text, the process of radionuclide movement into the immobile domain will be referred to as matrix diffusion.

B.1.2.3 Transport Parameter Variability and Uncertainty

MC_TRANS was used in the stochastic mode to account for variability as well as uncertainty associated with transport parameter values. In both cases, the parameter values were represented as probability distributions of possible values. For parameters that were to be evaluated for the effect of improved knowledge, the model was rerun with those distributions changed from prior to posterior estimates. The transport parameters that were treated as uncertainties are effective porosity in the LCA, and the matrix diffusion parameter (alpha) in the LCA. The sensitivity analysis of the transport model did not show the predicted transport to be sensitive to the other transport parameters; matrix porosity in the LCA, porosity in the VCU, and distribution coefficient for the VCU. The parameter values for the first of these was specified at a conservative value and the other two were input as distributions representing the variability. Note that the VCU was treated as a porous medium while the LCA was treated as a fractured medium. Therefore, there was no matrix porosity or matrix diffusion parameter for the VCU. These parameters originally also pertained to the VA, but the VA is not represented in the generic transport model pathline because sources in the VA do not appear to affect the contaminant boundary with this transport model at a significant level. These would be significant only for specific sources in the VA where transport could short cut through the VCU to the LCA, and even then may not be sufficiently numerous to affect the contaminant boundary.

α 3θim D∗m B

2⁄=

Dm∗ τimD

o=

Appendix B B-4


B.1.2.3.1 Groundwater Flux

The flux along the generic pathline was specified separately for the two parts of the pathline, the flux in the VCU and the flux in the LCA. Flux in the VCU represents inflow into the Yucca Flat basin from the west and local recharge, while flux in the LCA represents regional flow beneath the basin plus the recharge from the basin into the LCA. The flux from the west from the regional model was equated to inflow into the basin, and was apportioned to the flux along the VCU part of the pathline. The flux from the north was equated to the underflow of the basin in the LCA and was apportioned to the flux along the LCA portion of the pathline. The flux components derived from the regional model were apportioned to the pathline based on the grid size in the flow model and the dimension of the source in the transport model. Since the flux in the LCA is much greater, there is likely to be a dilution effect when contaminants enter the LCA from the VCU. The one dimensional pathline modeling in this report ignores this dilution.

Uncertainty in groundwater flow contributes to uncertainty in simulations of transport, both in location and in concentrations of contaminants. The uncertainties about hydraulic conductivity and recharge in the regional flow model lead to uncertainty in the groundwater flux used in the regional transport model. Pathlines from the regional flow model, which define the hydrostratigraphic units through which flow is occurring, and groundwater flux along each pathline link the flow model to the transport model. The hydrostratigraphic unit definition along each pathline determines the flow and transport properties assigned along the path. In the regional modeling, a flux multiplier distribution was imposed on the flux to evaluate the uncertainty in the flux resulting from uncertainty in recharge and hydraulic conductivity. In the Yucca Flat VOIA modeling, the function of the flux multiplier was expanded to include other uncertainties in the hydrogeologic model that would also affect the actual path as well as the flux along the path. The uncertainty applied through this multiplier was estimated separately for the flux from the north and from the west because each represented a different element of uncertainty in the hydrogeologic model, which would be investigated separately. The flux into the Yucca Flat CAU from the west primarily represents inflow to the basin, while the flux from the north occurs in the LCA underlying the Yucca Flat basin. Note that the flux from the north is approximately an order of magnitude greater than the flux from the west. See Section B.2.4 for more detail regarding the calculation of the flux multiplier from the northern and western flux distributions.

B.1.2.3.2 Generic Pathline

In addition, a new element was added to the transport model for the Yucca Flat VOIA modeling, uncertainty in the pathline. A generic pathline was used to model transport from sources in Yucca Flat. This pathline was constructed to represent flow from a source in the VCU to the LCA, and then downgradient in the LCA. The primary uncertainties determining the maximum distance of transport at the 4-mrem/yr boundary level during the next 1,000 years are the time for contaminants to reach the LCA, in which transport is fastest, and the distance traveled through the VCU. The time to reach the LCA is primarily a function of the vertical gradient from the VCU to the LCA and the vertical separation of the

Appendix BB-5


source from the LCA. The distance traveled through the VCU is a function of the vertical separation and the horizontal transport resulting from the horizontal gradient. The total distance can vary from the minimum vertical separation distance to much longer oblique paths, which are up to 23 km as derived from the regional flow model, although 90 percent are less than 10 km. Other uncertainties in the path length to the LCA result from poor definition of the location of the top of the LCA, possible shortcuts to the LCA through permeable fault zones or by local juxtaposition of the LCA upwards along faults, and altered hydraulic characteristics of the VCU around the nuclear tests.

All of these uncertainties were combined and treated as a variable path length through the VCU along the pathline, represented as a probability distribution. A pathline was specified incorporating initial nodes with VCU properties, and extending downgradient with LCA properties. This simple geometry represents the majority of the pathlines including all those most likely to contribute to the contaminant boundary definition. This was implemented by sampling the distribution to assign the node in the pathline at which the contaminant source was started. The pathline length parameter is termed the slope multiplier (designated β), and it varies from 0 (maximum distance from the LCA) to 1 (starting in the LCA) (Figure B-1). Ten initial source locations were specified corresponding to cumulative percentages of 5, 15, 25, 35, 45, 55, 65, 75, 85, and 95 of the initial path length distribution. These locations were then modified for each realization according to equation B-8 using the slope-multiplier selected from the specified slope-multiplier distribution. Note that the 5th fractile initial (β = 0) location is in the LCA while all the others are in the VCU. For β = 1, the remaining nine locations shift into the LCA. For 0 < β < 1, the nine starting locations for sources progressively shift from the VCU to the LCA. The slope factor is a scaling mechanism to reduce the range of values from the distance predicted by the regional flow model.

(B-8)

B.1.2.4 Hydrologic Source Term Uncertainty

During the simulations, it was assumed that the initial contaminant source is emplaced in the fractures and matrix within two cavity radii of the underground test shortly after detonation. This source term is a simplification, as stated in DOE (1997), but is considered reasonable for this purpose. The source term and all other transport parameters were assigned ranges of values and probability distribution types.

Within any one realization of the pathline and transport parameters, 10 sources were started at the nodes determined by the method specified above. The contaminant concentrations specified for each node were independently specified by sampling the distribution for source term. Transport was simulated for all 10 source locations in each realization, and the predicted contaminant boundary was the composite of all 10 sources. This methodology accommodated the concern for interacting sources, which was especially important for Yucca Flat due to the high

new distance initial distance 1 β–( )=

Appendix B B-6


density of sources. Three radionuclides were run separately for each realization, with the three radionuclide concentrations for each node correlated. The resultant concentrations were converted to dose in time and space, and added to produce the predicted contaminant boundary distribution.

B.1.3 Uncertainties

The transport predictions are a function of a large number of parameters representing both flow and transport processes. Nearly all of the parameters in the equations described above are uncertain and known within a range of values that spans several orders of magnitude in many cases (DOE/NV, 1997). Of the transport parameters, only the bulk density and the radioactive decay constants for radionuclides are treated as constants. The remaining transport parameters, effective porosity, matrix porosity, effective diffusion coefficient, fracture spacing, initial source concentration, and dispersivity, are assigned ranges of values based on the amount of uncertainty in measured values.

The Monte Carlo approach using Latin hypercube sampling (Iman et al., 1980) was used to evaluate the impact of parameter uncertainty on the predicted concentrations. Monte Carlo is a technique for modeling a real-world situation in which one or more of the input parameters are either uncertain or may vary in a mathematically describable way. It employs random sampling from probability

Figure B-1Slope Multiplier Concept

0

10

20

30

40

50

60

70

80

90

100

0 5,000 10,000 15,000 20,000 25,000

Distance (m) Along the Pathline to LCA

Cu

mu

lati

ve P

erce

nt

of

Pat

hlin

es in

Yu

cca

Fla

t an

d C

limax

Min

e

representation of path length to LCAY= 0.008603 X + 11.070111

1 Slope Multiplier 0

maximum distance = 10,337 m

Appendix BB-7


s

distributions to assign values to the uncertain parameters in the model. Use of the Latin hypercube approach reduces the number of Monte Carlo realizations required. The Latin hypercube method also allows the incorporation of parameter correlations.

The Monte Carlo technique is typically used in cases where too many parameters are uncertain or the mathematics are too complex to be solved analytically. In this case, the model that describes contaminant transport in the environment includes several parameters that are best described by probability distributions rather than single values. The probability distributions might include the normal, lognormal, uniform, exponential, or one of a number of other mathematical expressions that meet the requirements of a probability distribution function. The Monte Carlo simulation randomly selects the parameter values from the corresponding probability distributions to obtain a single model result (or realization) that is specific to that set of the parameter values. If parameters are rank-correlated, the value of one parameter will depend on the randomly selected value of another parameter. This process is then repeated over a large number of trials until the probability distributions of the model results can be described.

B.2.0 Prior Probability Assessment

Five groundwater flow and transport parameters were identified as having the greatest impact on the contaminant boundary. Probability distributions describing these parameters were elicited from the expert panel. The expert panel typically provided the 5th and 95th fractiles of the distributions, and then specified the mathematical forms. In all cases, the specified distributions are named distributions characterized by two parameters. Consequently, two fractiles were sufficient to compute numerical values for the distribution parameters.

Table B-1 summarizes the prior distributions calculated from the expert panel’consensus judgments of the form and specified fractiles of each sensitive parameter.

Table B-1Prior Distributions

ParameterLow5th

High95th Distribution Form

Flux (m3/day) 2,100 27,000 Normal

Effective porosity 0.22% 2.20% Normal

Diffusion (alpha) (1/yr) 2 x 10-5 2 x 10-3 Lognormal

Hydrologic source term (pCi/L) 1 1,000 Lognormal

Slope multiplier 0 1 Uniform

m3/day = Cubic meters per day

pCi/L = Picocuries per liter

Appendix B B-8


B.2.1 Hydrologic Source Term

As used here, the hydrologic source term represents the amount of each radionuclide that is available to be transported by groundwater. The nature of the hydrologic source term is very complex because of a variety of factors (e.g., the specific configuration of the device, rock type, cooling history, depth of burial, and geochemistry of the surrounding environment). As a result, there is a great deal of uncertainty regarding the appropriate value to use for source term. The expert panel agreed that the appropriate shape of the distribution for hydrologic source term is lognormal. A three-order-of-magnitude range of values was selected. The expert panel agreed to provide inputs on a generic distribution, with the range to be scaled to each radionuclide selected based on the recommendations of the TWG Source Term Subcommittee members who were participating in the expert panel. The selected list of nine radionuclides is shown in Table B-2. The values for these radionuclides were chosen by considering available analytical data as well as the unclassified source term estimates for non-Pahute Mesa tests, converted to concentration. If the solubility limit for the radionuclide was lower than the concentration, that value was used instead.

B.2.2 Effective Porosity in the LCA

The flux and effective porosity control the velocity of groundwater through the relationship that the relative velocity equals the groundwater flux divided by the effective porosity. Measurements of effective porosity cannot be made directly, and the value for this parameter is derived from several types of analyses, all involving multiple uncertainties. The most controlled methodology may be a tracer migration experiment, but such experiments are expensive and only characterize a relatively small area. The UGTA Project has not conducted a tracer migration test in the LCA. As discussed in Appendix C, two tracer tests have previously been conducted in the LCA for other programs, and the information

Table B-2Hydrologic Source Term Constituent Prior Distributions

RadionuclideHalf-life(years)

Minimum Maximum

pCi/L

Americium-241 458 1 x 101 1 x 104

Carbon-14 5,720 1 x 102 1 x 105

Cesium-137 30 1 x 100 1 x 103

Iodine-129 1.6 x 107 1.48 x 10-2 1.48 x 101

Neptunium-237 2.20 x 106 1 x 100 1 x 103

Plutonium-239/240 2.44 x 104 1 x 102 1 x 105

Strontium-90 29 1 x 101 1 x 104

Tritium 12.3 1 x 107 1 x 1010

Uranium-238 4.51 x 109 1 x 10-1 1 x 102

pCi/L = Picocuries per liter

Appendix BB-9


available has been considered, but the analyses of those experiments were not evaluated in light of current knowledge.

The peer review group evaluated the information on effective porosity in carbonates in the Regional Groundwater Flow and Tritium Transport Modeling and Risk Assessment of Underground Test Area, Nevada Test Site (DOE/NV, 1997). Information on fracture spacing in Wells ER-6-1 and ER-6-2 were also evaluated, and compared to the analysis of effective porosity for fractured volcanics presented in the Value of Information Analysis for Corrective Action Unit Nos. 101 and 102: Central and Western Pahute Mesa, Nevada Test Site, Nevada (DOE/NV, 1998). The range of values used in the regional transport modeling was judged to be somewhat too constrained, and a somewhat broader range for the priors for the Yucca Flat VOIA modeling was specified by the expert panel.

B.2.3 Diffusion Parameter

The basic information on the diffusion parameter was also derived from the Regional Groundwater Flow and Tritium Transport Modeling and Risk Assessment of Underground Test Area, Nevada Test Site (DOE/NV, 1997). The priors for this parameter were evaluated based on the three components of the parameter: fracture spacing, effective porosity, and the diffusion coefficient. The reasoning applied to the effective porosity values used in the regional model as presented above was also applied to evaluating this parameter as well as the fracture spacing information mentioned. The diffusion coefficient is relatively independent of the rock type, and the values presented in Value of Information Analysis for Corrective Action Unit Nos. 101 and 102: Central and Western Pahute Mesa, Nevada Test Site, Nevada (DOE/NV, 1998) were used. The result was a broadening of the distribution from the range used in the regional modeling.

B.2.4 Groundwater Flux

The prior distribution for the flux term was derived from an uncertainty analysis of the regional flow model.

In relation to the flux from the north (Qn), a large head gradient is believed to exist between northern Emigrant Valley and northern Yucca Flat, on the basis of limited water level data in Emigrant Valley. This large gradient is used to infer that a barrier to the movement of water into northern Yucca Flat is present, and further that the rate of movement is low. Calculation of the flux into northern Yucca Flat using the UGTA regional model (DOE/NV, 1997) provided a value of approximately 28,000 m3/d. This value is approximately one-third of the measured discharge at Ash Meadows, and is therefore believed to be a high estimate of the flux. The actual flux is likely to be lower than this value.

Flux from the west (Qw), the other major component of water entering Yucca Flat, is derived from an area west and northwest of Yucca Flat. Water levels are higher west of Yucca Flat, and a groundwater divide is likely present along a line extending from Rainier Mesa to Shoshone Mountain. Recharge is known to occur at Rainier Mesa and very likely to occur at Shoshone Mountain, and may occur at

Appendix B B-10


for ost

n the nel CA. ce

r cted

high enough rates to create a divide in the intervening area. The flux of water entering Yucca Flat from the west in the rocks overlying the UCCU would then be determined by the recharge of water east of this divide. The regional model estimated this flux to be approximately 900 m3/d. In addition, the model simulates flow into western Yucca Flat from rocks underlying the UCCU. The model estimates the combined inflow from the west to be about 1,750 m3/d.

The flux multiplier (FMLCA) was calculated by sampling Qn and Qw from distributions, summing them, and dividing by the sum of Qn and Qw as determined from the regional model. The posterior distributions for the LCA flux multiplier (FMLCA) were calculated from posterior distributions of Qn and Qw, as previously described. Because the distributions were determined to exclude negative values, the distributions that assume that the activity will result in a value lower than the prior expected value are truncated and thus highly skewed. As a result, formulas for calculating the distribution of a sum of normal distributions could not be used to determine the posterior distributions for FMLCA. Excel® and the Monte Carlo sampling software Crystal Ball® (Version 4.0g, Decisioneering Inc.) were used to determine the distribution of FMLCA. Five thousand samples of FMLCA were calculated. Histograms were then determined from these samples and exported for direct use by the simulation program.

Evaluation of the effects of performing investigative activities through Bayesian updating requires that simulations be performed using several sets of posterior distributions. For example, different posterior distributions result from assuming that an activity will result in an improved estimate of a parameter that is lower (L) than, about the same (S) as, or higher (H) than the prior expected value. Because the FMLCA is derived from two separate parameters, a complete evaluation would include all nine combinations (LwLn, SwLn, …, HwHn). To reduce the computational effort, only six combinations were evaluated. The distribution Qw was held to the S value, using the assumption that the S result was the mlikely, and paired with the L, S, and H distributions for Qn, resulting in three combinations. Then the parameters were switched, yielding another three combinations.

B.2.5 Slope Multiplier

A slope multiplier was selected as a parameter to account for the uncertainty icontaminant transport path traveled prior to reaching the LCA. The expert paprovided estimates of the slope of a line representing the path length to the LA nearly vertical line (slope multiplier near one) would indicate that the distanto the LCA was close to zero. The value of zero for the slope multiplier was assigned to the line that approximates the Yucca Flat test data, not taking intoaccount any possible shortcuts, faults that might juxtapose the VA and LCA, oother uncertainties about the depth to the LCA beneath tests that were conduin stratigraphically higher units (Figure B-1).

Appendix BB-11


on

r

t ” .

r

B.3.0 References

Bear, J. 1972. Hydraulics of Groundwater. New York, NY: McGraw-Hill.

Coats, K.H., and D.B. Smith. 1964. “Dead-end pore volume and dispersion porous media.” In Society of Petroleum Engineers Journal, 4: 73-84. Richardson, TX.

DOE/NV, see U.S. Department of Energy, Nevada Operations Office.

Iman, R.L., J.M. Davenport, and D.K. Ziegler. 1980. Latin Hypercube Sampling (Program User’s Guide), SAND79-1473. Albuquerque, NM: Sandia National Laboratories.

IT, see IT Corporation.

IT Corporation. 1996. A Stochastic Advective - Dispersive Transport Code forMultiple Constituents in Dual Porosity or Fractured Geological Media. Prepared for the U.S. Department of Energy. Las Vegas, NV.

McDonald, M.G., and A.W. Harbaugh. 1988. Techniques of Water-Resources Investigations of the United States Geological Survey, A Modular Three-Dimensional Finite-Difference Ground-Water Flow Model, Chapter A1, Book 6. Washington, DC: U.S. Geological Survey.

McDonald, M.G., and A.W. Harbaugh. 1991. A Method of Converting No-Flow Cells to Variable-Head Cells for the U.S. Geological Survey Modular Finite-Difference Ground-Water Flow Model, USGS-OFR-91-356. Denver, CO: U.S. Geological Survey.

Pollock, D.W. 1989. Documentation of Computer Programs to Compute and Display Pathlines Using Results from the U.S. Geological Survey ModulaThree-Dimensional, Finite-Difference Ground-Water Flow Model, USGS-OFR-89-381. Denver, CO: U.S. Geological Survey.

Sudicky, E.A. 1990. “The Laplace Transform Galerkin Technique for EfficienTime-Continuous Solution of Solute Transport in Double-Porosity Media.In Geoderma, 46: 209-232. New York, NY: Elsevier Publishing Company

van Genuchten, M.Th. 1981. Non-Equilibrium Transport Parameters for Miscible Displacement Experiments, Research Report No. 119. Prepared fothe U.S. Department of Agriculture. Riverside, CA: U.S. Salinity Laboratory.

U.S. Department of Energy, Nevada Operations Office. 1997. Regional Groundwater Flow and Tritium Transport Modeling and Risk Assessment of Underground Test Area, Nevada Test Site, NV, DOE/NV--477. Las Vegas, NV.

Appendix B B-12


U.S. Department of Energy, Nevada Operations Office. 1998. Value of Information Analysis for Corrective Action Unit Nos. 101 and 102: Central and Western Pahute Mesa, Nevada Test Site, Nevada, DOE/NV/13052--41. Las Vegas, NV.

Appendix BB-13

Appendix C

Activity, Subgroup, and Group Descriptions with Cost Estimates


Appendix C: Activity, Subgroup, and Group Descriptions with Cost Estimates

Appendix C presents detailed information on the characterization subgroups and groups that were chosen by the VOIA TWG Subcommittee to increase the information for a given parameter to decrease the uncertainty. Activities were organized into characterization subgroups to address model parameters. These activities were grouped according to themes based on cost or implementation. Table C-1 presents the uncertain parameters associated with each activity. Table C-2 presents the uncertain parameters associated with each subgroup. Table C-3 shows the activities of each subgroup.

C.1.0 Summaries

The numbers refer to activities or subgroups as described in the next section. The uncertainties are identified by generalized descriptions as well as the specific aspects of the modeling used to predict transport that are addressed by the category.

Subgroups S1 and S2 address the groundwater flux for the flow model, specifically upgradient of the Yucca Flat basin. Concerns range from data gaps in knowledge of the geologic structure surrounding the basin and sparse information about the groundwater head distribution in these areas to poor knowledge of the recharge upgradient of the basin.

Subgroups S2, S3, S4, S5, S10, and S11 address the path lengths from each source (nuclear tests) to the LCA underlying the Yucca Flat basin. These activities focus on structural features in the basin and properties of the hydrostratigraphic units that could provide shortcuts for transport through the VCU to the LCA. Transport of the radionuclides of concern is slow in the VCU and much faster in the LCA. The current transport model does not incorporate a parameter for shortcuts from the VCU to the LCA. Shortcuts such as these are being considered so that they could be incorporated into the CAU-scale flow and transport modeling. Subgroup S11 is slightly different because it addresses the high pressure zone and the effects of elevated temperatures on groundwater flow. The results of the Subgroup S11 investigation might or might not be incorporated into the flow and transport modeling.

Although Subgroup S6 describes shortcuts, it is assigned to the hydrologic source term parameter because the information is related to the hydrologic source term parameter of the CAU-scale transport modeling. This subgroup addresses where and how the hydrologic source term is included in the CAU-scale transport model. The concern is whether contaminants might penetrate the VCU more readily beneath the test cavity due to physical effects from the test.

Appendix CC-1


A

a

Table C-1Uncertain Parameters Addressed by Each Activity

ctivity Activity DescriptionFlux

NorthSlope

Multiplier

Hydrologic Source Term

Effective Porosity

Diffusion (alpha)

Effective Thickness

A1 Alternate hydrogeologic models x

A2 New wells north of Yucca Flat x

A3a New wells on the western

boundary of Yucca Flat

A4Isotope/geochemistry mass balance studies

x

A5 Basin recharge studies x

A6Groundwater heads in the VCU and LCA

x

A7Potentiometric trough in south-central Yucca Flat

x

A8 Geologic structure of major faults x

A9 Hydraulic properties of fault zones x

A10Geophysical interpretation of the Paleozoic framework

x

A11 Variability of VCU mineralogy x

A12Existing phenomenologic data on test effects

x x

A13Mineback data from Rainier Mesa tunnel tests

x x

A14 Near-field sampling x x

A15 Test-cavity sampling x

A16 Hydrologic source term modeling x

A17 Multi-well tracer test in the LCA x x

A18Analysis of two existing tracer-test datasets

x

A19 Laboratory diffusion studies x

A20Single-well tracer tests in the VA and the LCA

x

A21Earth-tide analysis of the VA and the LCA

x

A22Seismic surveys of the Yucca Flat Basin

x

A23 Analysis of existing seismic data x

A24High-pressure zone in northern Yucca Flat

x

A25Elevated groundwater temperatures in eastern Yucca Flat

x

A26 Potential for karst in the LCA x

A27 Hydraulic conductivity with depth x

Activity A3 addresses flux from the west; it was not evaluated separately because it was determined not to be a sensitive parameter.

Appendix C C-2


Subgroup S7 directly addresses the radionuclides of concern from the hydrologic source term, and focuses on emplacement and near-field processes. This information would be used in the near-field source-term modeling to determine the hydrologic source term used in the CAU-scale transport modeling.

Subgroups S8 and S9 address the effective porosity and diffusion for the LCA that would be incorporated into Yucca Flat transport modeling. The two activities have separate approaches that could be used together in a complementary fashion, if desired.

Subgroup S12 is included in this report because it could have a significant effect on the prediction of transport in the LCA. However, this subgroup was not included in the quantitative analysis because it could not be readily represented in the transport model. Depending on the presence and degree of karst in the LCA, highly transmissive passageways may have developed. If present, they have the potential to significantly impact the flow system. Decreasing hydraulic conductivity with depth controls the distribution of groundwater flux with depth. Fast hydraulic conductivity decreases tend to concentrate the flux in the uppermost part of the aquifer. Karst combined with decreasing hydraulic conductivity with depth has an additive effect on transport predictions.

Table C-2Uncertain Parameters Addressed by Each Subgroup

Subgroup Subgroup DescriptionFlux

NorthSlope

Multiplier


Effective Porosity

Diffusion (alpha)

Effective Thickness

S1 Basin Boundaries - North and West x

S2 Basin Boundaries - General x

S3 Shortcut - Vertical Gradient x

S4 Shortcut - Faults x

S5 Shortcut - VCU Continuity x

S6 Shortcut - Near Field x x

S7 Hydrologic Source Term x x



S10 Basin Structure x

S11 Basin Anomalies x

S12a

LCA Characterization x

aSubgroup S12 was not considered in the computational analysis.

Appendix CC-3


Table C-3Activities of Each Subgroup

Subgroup Subgroup Description Activity Activity Description

S1 Basin Boundaries - North and West

A1 Alternate hydrogeologic models

A2 New wells north of Yucca Flat

A3 New wells on the western boundary of Yucca Flat

S2 Basin Boundaries - GeneralA4 Isotope/geochemistry mass balance studies

A5 Recharge studies

S3 Shortcut - Vertical GradientA6 Groundwater heads in the VCU and LCA

A7 Potentiometric trough in south-central Yucca Flat

S4 Shortcut - FaultsA8 Geologic structure of major faults

A9 Hydraulic properties of fault zones

S5 Shortcut - VCU ContinuityA10 Geophysical interpretation of the Yucca Flat basin structure

A11 Variability of VCU mineralogy

S6 Shortcut - Near FieldA12 Existing phenomenologic data on test effects

A13 Mineback data from Rainier Mesa tunnel tests

S7 Hydrologic Source Term

A14 Near-field sampling

A15 Test-cavity sampling

A16 Hydrologic source term modeling

S8 Transport Parameters - 1

A17 Multi-well tracer test in the LCA

A18 Analysis of two existing tracer-test datasets

A19 Laboratory diffusion studies

S9 Transport Parameters - 2A20 Single-well tracer tests in the VA and the LCA

A21 Earth-tide analysis of the VA and the LCA

S10 Basin StructureA22 Seismic surveys of the Yucca Flat basin

A23 Analysis of existing seismic data

S11 Basin AnomaliesA24 High-pressure zone

A25 Elevated groundwater temperatures

S12a

LCA CharacterizationA26 Potential for karst in the LCA

A27 Hydraulic conductivity with depth

aSubgroup S12 was not considered in the computational analysis.

Appendix C C-4


C.2.0 Descriptions and Costs of Activities and Subgroups

Each characterization option is composed of activities that were grouped according to the CAU modeling parameter they addressed. An overview of each subgroup is described followed by a description of the activity. Table C-2 lists the associated uncertainty and the activity for each subgroup. The cost estimates for the characterization activities are derived from process knowledge. The cost estimates provide a basis for comparison. Note that these cost elements are very generic since, in many cases, the details are not defined and generalized assumptions were used for the scope.

C.2.1 Subgroup S1: Basin Boundaries - North and West (Total $5,905,486)

Investigate uncertainties affecting calibration of groundwater fluxes from the north and west into Yucca Flat. Groundwater flux into Yucca Flat from the north and the west is a major factor for making predictions of groundwater flow rates in the LCA. Uncertainties in the hydrogeologic model to the north and west significantly affect the calibration of the regional model and result in substantial uncertainty in the groundwater fluxes for Yucca Flat. There is uncertainty about the nature of the steep groundwater gradient from the north and west related to geologic structure. Flux cannot be measured directly, but uncertainty in the elements of the modeling affecting calibration can be reduced. The interplay of the modeling variables constrains the possible ranges of these fluxes.

Objective: Develop an improved hydrogeologic model bounding Yucca Flat to the north and west to refine the calibration of groundwater fluxes into the CAU. Evaluate alternate hydrogeologic models for the groundwater flow modeling to determine the best overall model and the sensitivity of groundwater flux to the hydrogeologic model.

C.2.1.1 Activity A1: Alternate Hydrogeologic Models

Reevaluate the hydrogeologic interpretations of the "hydraulic barrier" north of Yucca Flat. Extend the geologic model for Yucca Flat approximately 6,550 m north of the NTS boundary to include the area of steep potentiometric gradient associated with the Paleozoic rock outcrops in the southern portion of the Belted Range or the granitic intrusions in the area. Available hydrogeologic and geologic structure information would be reinterpreted. Structure contour maps that correlate with existing information would be produced for the hydrostratigraphic units in the expanded study area. The data would be incorporated into the three-dimensional Yucca Flat geologic model used in the groundwater modeling. Revise and recalibrate the model to include the areas upgradient of Yucca Flat. Additional data collection activities might be identified for refining the model.

Specifications: Incorporate the results directly into the Yucca Flat CAU modeling. Costs are shown in Table C-4.

Appendix CC-5


d be

y of drill

. ding

the

ling.

C.2.1.2 Activity A2: New Wells North of Yucca Flat

Virtually no subsurface geologic data or water level information exist for areas north of Yucca Flat, and limited data are available for the Climax Stock. The southern end of the Belted Range near the northern NTS border is geologically and hydrologically complex. Strategically placed wells would provide geologic information, water levels, hydrogeologic information, and groundwater chemistry for this area. The wells could be used for long-term monitoring to determine temporal changes in water level and groundwater chemistry.

Specifications: Drill three new wells, one well northwest of Climax Stock 2,000 ft deep, one well northeast of Climax Stock 1,500 feet (ft) deep, and one well between Yucca Flat and Kawich Valley 2,000 ft deep. Design the wells to collect geologic information during drilling, provide good quality water samples for geochemical analysis, and to monitor water levels. Drill, log, and construct the wells with 5½-in. completions, providing for a 30-gallon per minute sampling pump for development and geochemical sampling. Collect water level measurements in each well on a quarterly basis for one year. The wells couladded to the annual monitoring list if desired. Costs are shown in Table C-5.

C.2.1.3 Activity A3: New Wells on the Western Boundary of Yucca Flat

The geology of western Yucca Flat is much different than the volcanic geologthe central and eastern portions of the basin; however, there are relatively fewholes in the western third of the basin. The depth of the existing drill holes isinsufficient to provide structural data. Several new drill holes would greatly enhance the understanding of this complex and hydrologically important areaThe information obtained from these boreholes would enhance the understanof the hydrologic relationship between Yucca Flat, Pahute Mesa, and Rainier Mesa. This investigation would also improve the understanding of two majorgeologic structures in the western Yucca Flat, the CP thrust fault system and Tertiary-age faulting. In addition, the structural relationships of the major hydrostratigraphic units at several locations need to be defined.

Specifications: Drill two new wells on the west margin of Yucca Flat, one approximately 3,000 ft deep and one approximately 4,000 ft deep. Collect geologic information, water quality samples, and water level data from these wells. Drill, log, and construct wells with 5½ -in. completions, providing for a 30-gallon per minute sampling pump for development and geochemical samp

Table C-4Activity A1 Costs

Activity Description Quantity Unit Cost Cost

Evaluate existing information, develop alternate models 1 $181,000 $181,000

Northwest Yucca Flat structural interpretation 1 $98,700 $98,700

Extend Yucca Flat geologic model to north 1 $137,000 $137,000

Develop and evaluate alternative flow models 1 $77,450 $77,450

Total $494,150

Appendix C C-6


Collect water level measurements in each well on a quarterly basis for one year. The wells could be added to the annual monitoring list if desired. Costs are shown in Table C-6.

Although this activity was regarded as reasonable to include with Subgroup S1, the expert panel could not estimate any incremental ability of this activity to reduce uncertainty on any of the sensitive parameters. Consequently, for the purposes of the analysis, this activity was excluded and its costs do not appear in the total for Subgroup S1.

C.2.2 Subgroup S2: Basin Boundaries - General (Total $1,549,531)

Investigate groundwater flow patterns through the Yucca Flat CAU and characterize basin recharge. Calibration of the CAU model could be improved with independent data on groundwater flow patterns and flow rates into Yucca Flat. In addition, better definition of the distribution and rate of recharge in the Yucca Flat basin would improve calibration of vertical gradients and the consequent vertical flow rates from the basin into the LCA.

Objective: Collect geochemistry data for evaluation of the regional flow patterns and flow rates to refine the calibration of groundwater flux and groundwater velocity in the Yucca Flat CAU model. Also, determine the recharge distribution and refine estimates for recharge to the Yucca Flat basin.

C.2.2.1 Activity A4: Isotope/Geochemistry Mass Balance Studies

Groundwater geochemistry is an indicator of past and current reactions between groundwater and the subsurface materials that it has contacted along the flow path.



Criteria document 1 $160,000 $160,000

Drilling plan 3 $15,000 $45,000

Roads, drilling pads, sumps 2 $200,000 $400,000


Drill new hole, borehole logging, install completion 1 $948,000 $948,000

Drill new hole, borehole logging, install completion 2 $1,207,000 $2,414,000

Support well development, sampling, hydraulic testing 3 $40,000 $120,000

Completion report 3 $80,000 $240,000

Drilling support, data reports 1 $182,160 $182,160


Well development, sampling, hydraulic testing 3 $47,094 $141,282

Testing analysis 3 $6,000 $18,000

Water-level measurements 12 $800 $9,600

Geologic model revision 1 $46,000 $46,000

Total $5,411,336

Appendix CC-7


Evaluation of groundwater geochemistry also provides information on the amount of time that has elapsed since the groundwater entered the flow system.

The current conceptual model of Yucca Flat has aquitards in the north limiting groundwater flow into the Yucca Flat basin. Additional environmental isotope and geochemical data from locations in and surrounding Yucca Flat would support geochemical studies to evaluate the origin and flow paths of groundwater. Water quality samples from boreholes and springs that are both upgradient and within the Yucca Flat basin would be analyzed to determine the geochemistry and approximate age of the water.

Specifications: Collect 20 water quality samples from springs and wells north and west of Yucca Flat in addition to locations in Yucca Flat. Analyze these water quality samples for major ion chemistry, trace metals, and environmental isotopes. Analysis of the geochemistry data would provide information on the direction of groundwater movement through the study area. Compare this information to the flow paths generated by the numerical flow models. If the results of the geochemical evaluation support the flow paths derived from the numerical models, calculate the groundwater flux into the basin using mixing models. Compile results into a report. Costs are shown in Table C-7.

C.2.2.2 Activity A5: Basin Recharge Studies

Recharge in the Yucca Flat basin will be a significant factor in the CAU-scale modeling of groundwater flow in the alluvium and volcanic units. The majority of




Drilling plan 2 $15,000 $30,000


Drill new hole, borehole logging, install completion

1 $1,521,000 $1,521,000

Drill new hole, borehole logging, install completion

1 $1,720,000 $1,720,000

Support well development, sampling, hydraulic testing

2 $40,000 $80,000


Drilling support, data reports - 3,000 ft hole

1 $287,160 $287,160

Drilling support, data reports - 4,000 ft hole

1 $344,960 $344,960

Well development, sampling, hydraulic testing

2 $47,094 $94,188




Total $4,741,708

Appendix C C-8


the recharge is from surface drainage inflow from the surrounding highlands. Studies to support the CAU-scale modeling would include field mapping of the drainage system with estimates for recharge. Field tasks would include

precipitation measurements in the uplands and flow monitoring in selected drainages accompanied by neutron probe investigations. In addition, crater studies of inflow and infiltration at U-10i and BILBY would be conducted for relevant information.

Specifications: Recalibrate regional model using the improved recharge estimates and perform sensitivity analyses. Assemble results into a report. Costs are shown in Table C-8.

C.2.3 Subgroup S3: Shortcut - Vertical Gradient (Total $8,817,432)

Characterize the vertical hydraulic gradient to the LCA in Yucca Flat. The vertical hydraulic gradient drives vertical flow of groundwater to the underlying LCA. While the water table in the basin is fairly well known, information on the vertical gradient to the LCA and the head distribution in the LCA are limited. The relationship of the potentiometric trough to the vertical gradient is not well known. The potentiometric trough could indicate more transmissive vertical flow paths. This subgroup would improve the knowledge of vertical gradient distribution



Tritium, carbon isotope, and noble gas analysis of groundwater and carbon isotope analysis of aquifer solids. Interpretations and presentations of resulting data.

1 $400,000 $400,000

Strontium and uranium isotope analysis of groundwater. Reaction path modeling of geochemistry along groundwater flow paths. Documentation and presentation of results

1 $526,306 $526,306

Petrographic/micrographic analysis of aquifer solids. Interpretation and presentation of resulting data. Reaction path modeling of geochemistry along groundwater flow paths.

1 $363,750 $363,750

Stable isotope and general chemical analysis of groundwater. Interpretation and presentation of resulting data.

1 $157,125 $157,125

Total $1,447,181



Surface drainages, precipitation map, model 1 $102,350 $102,350

Appendix CC-9


across the hydrostratigraphic units from the water table head to the LCA, the head distribution in the LCA, and determine the reason for the potentiometric trough.

Objective: Collect data on the vertical gradient of HSUs above the LCA to determine the variability of the gradient across the basin to refine the CAU-scale model. Specifically investigate the potentiometric trough associated with the water table to determine its significance.

C.2.3.1 Activity A6: Groundwater Heads in the VCU and LCA

The vertical hydraulic gradient in Yucca Flat is poorly defined due to the paucity of vertically discrete head data, particularly measurements at different depths in close proximity. Several exploratory drill holes from the weapons testing program penetrating the saturated units have been identified to achieve the objectives of this investigation. These drill holes might provide suitable access to the HSUs of interest, requiring minor modifications or temporary installation of equipment in the boreholes to isolate discrete heads at various depths.

Specifications: Measure groundwater heads at six existing boreholes in the VCU and six existing boreholes in the LCA within Yucca Flat. Monitor heads in each borehole over a period of time or measured multiple times to establish the representativeness of the measurements. Produce a short report showing well locations, detailing the final well configurations, and results of the head measurements. Costs are shown in Table C-9.

C.2.3.2 Activity A7: Potentiometric Trough in South-Central Yucca Flat

Several hydrogeologic investigations have inferred a north-south trending potentiometric trough in central Yucca Flat (Winograd and Thordarson, 1975). An area of deep alluvium in Area 3 might be associated with the trough. However, the data supporting the geologic interpretation, including the configuration of the water table, are sparse for this area. This area might be a hydraulic sink where




Drilling plan 6 $15,000 $90,000


Recomplete existing hole, borehole logging 3 $375,000 $1,125,000

Recomplete existing hole - drill additional 500 ft 3 $520,000 $1,560,000

Support well development 6 $40,000 $240,000



Well development 6 $47,094 $282,564


Total $4,338,924

Appendix C C-10


shallow, potentially contaminated groundwater could be moving into the regional carbonate aquifer more readily. Strategically placed wells in Area 3 would provide new information to define the situation.

Specifications: Drill two wells in south-central Yucca Flat penetrating the upper LCA. Determine the geology, measure the vertical gradient, and collect groundwater samples from different depths. Costs are shown in Table C-10.

C.2.4 Subgroup S4: Shortcut - Faults (Total $10,111,312)

Investigate groundwater flow to the LCA along faults in Yucca Flat. In general, the groundwater flow rate (and the related contaminant transport rate) from shallower HSUs (AA and VA) to the LCA is restricted by the low permeability of the intervening VCU. However, contaminant transport in the LCA is more rapid. It is thought that a fast path from the shallower HSUs to the LCA might occur along faults. Juxtaposition of HSUs along faults might reduce the travel distance between the AA or VA to the LCA. Fault zones might also provide a more permeable path through the VCU as well.

Objective: Determine the geologic structure (fault offsets and juxtaposition of units) along major faults in Yucca Flat, determine the hydraulic properties of fault zones, and investigate vertical flow along faults. Incorporate the results from the investigation into the CAU-scale modeling to enhance the prediction capability of the model for contaminants along such flow paths.

C.2.4.1 Activity A8: Geologic Structure of Major Faults

The Yucca Flat basin contains a number of large-scale, north-south trending normal faults. These faults could provide pathways to the LCA from permeable shallow HSUs (AA and VA) which bypass the intervening VCU by (1) juxtaposition of the LCA with shallower HSUs along the faults, and (2) the




Drilling plan 2 $15,000 $30,000




2 $40,000 $80,000






Total $4,478,508

Appendix CC-11


increased hydraulic conductivity associated with fault zones. This activity consists of drilling in fault zones to determine the vertical offset and characteristics of the fault zone. Faults in the area of the apparent "potentiometric trough" would be of primary interest for this investigation.

Specifications: Drill at three locations, penetrating major faults with offset to the LCA. Cross the fault at an angle using directional downhole drilling methodology. Determine the geology, vertical gradient, measure the head, and collect water quality samples from the HSUs of interest. Equip the wells with the appropriate equipment to achieve the objectives of the investigation. Costs are shown in Table C-11.

C.2.4.2 Activity A9: Hydraulic Properties of Fault Zones

The hydrologic character of the large-scale fault structures in Yucca Flat has not been evaluated. The faulting might provide avenues for faster groundwater flow due to altered hydraulic characteristics of the formations in the fault zones. Two additional wells would be drilled at one of the investigation locations of the above fault investigation. Hydraulic tests would be conducted at the well cluster to characterize the hydraulic properties within and across the fault zone.

Specifications: Drill two wells penetrating the upper LCA at the location chosen for the major fault studies. Determine the geology, vertical gradient, measure the wells with the appropriate equipment to achieve the objectives of the investigation. Conduct a suite of pumping tests to characterize the hydraulic conductivity within and across the fault zone. Costs are shown in Table C-12.




Drilling plan 3 $15,000 $45,000





3 $40,000 $120,000


Drilling support, data reports - 2,000 ft 2 $218,647 $437,294

Drilling support, data reports - 3,000 ft 1 $287,160 $287,160




Total $6,637,736

Appendix C C-12


C.2.5 Subgroup S5: Shortcut - VCU Continuity (Total $1,311,000)

Refine definition of Yucca Flat HSUs based on existing data. The transport prediction of the CAU-scale model might be improved with an updated geologic model of the Yucca Flat geology. Existing data are being used to revise the maps of the upper LCA. Updating the maps would provide a more accurate measurement of the vertical distance from the test cavities to the LCA, the thickness of the intervening VCU, and the continuity of the VCU. The existing data are being evaluated to determine the mineralogic variability of the VCU as it affects retardation properties. These two factors affect the predicted time for radionuclides to reach the LCA.

Objective: Improve the geologic model used in the flow and transport modeling by refining the map of the upper LCA, evaluating the continuity of the VCU and its thickness.

C.2.5.1 Activity A10: Geophysical Interpretation of Yucca Flat Basin Structure

Existing drill hole, borehole log, gravity, aeromagnetic, and seismic reflection data for Yucca Flat would be reevaluated to refine the surface of the Paleozoic rocks for the geologic model. The evaluation should give more resolution in projected depth to the top of the Paleozoics and permit interpretation of juxtaposition of various HSUs at (major) faulted surfaces. Although resolution at depth may be on the order of 30 to 75 m, this would still provide significant information for the CAU-scale model. Work should include a linked gravity-magnetic inversion constrained by drill hole data.

Specification: Incorporate the interpretation of the LCA surface and Paleozoic structure based on geophysical data into the Yucca Flat geologic model. Independently verify the inversion data set using data from existing seismic data. Costs are shown in Table C-13.




Drilling plan 1 $15,000 $15,000



Support well development, sampling, hydraulic testing 2 $40,000 $80,000





Total $3,473,576

Appendix CC-13


C.2.5.2 Activity A11: Variability of VCU Mineralogy

A study is currently in progress to evaluate mineralogic variability of the VCU. The process includes evaluation of existing mineralogic data, identifying data gaps, acquiring additional data from existing holes, and incorporating the information into a map illustrating the mineralogic variability of the VCU in Yucca Flat. Retardation capacity can be assessed from the mineralogic properties of the VCU. Costs are shown in Table C-14.

C.2.6 Subgroup S6: Shortcut - Near-Field (Total $157,000)

Evaluate existing data to better characterize the near-field environment of nuclear tests. Nuclear tests create cavities, modify the properties of surrounding rocks, and introduce radionuclides of concern into the subsurface. The initial distribution of radionuclides in the cavities and rubble from nuclear tests is not well known. Of particular concern is the resultant fracturing from the testing that might provide permeable pathways from the test cavity to the LCA.

Objective: Use existing data from BASEBALL (U7-ba), INGOT (U-2gg), BILBY (U-3cn), DALHART (U-4u), UE-3w#4, GASCON (U-4t), and UE-7ns to refine the model of radionuclide emplacement and the physical model of near-field rock properties relating to groundwater flow in the various lithologies in which testing was conducted. Incorporate the results into the hydrologic source term modeling and the transport modeling.

C.2.6.1 Activity A12: Existing Phenomenologic Data on Test Effects

Evaluate the measured dynamics of nuclear tests and pretest calculations to better understand potential radionuclide transport mechanisms (prompt injection), pathways (slow collapse, chimneys, tensile fracturing distances), and radionuclide



Geophysical interpretation of the PZ framework (current) 1 $366,000 $366,000

Geophysical interpretation of the PZ framework (proposed) 1 $170,000 $170,000

Compare interpretation with borehole and seismic data 1 $50,000 $50,000

Incorporation of the USGS work 1 $40,000 $40,000

Integrate USGS structural interpretation 1 $40,000 $40,000

Total $666,000



Variability of VCU mineralogy 1 $645,000 $645,000

Appendix C C-14


deposition (bedding planes, fractures, faults). This information would benefit near-field modeling for determining the hydrologic source term.

Specifications: Analyze pretest data, shock wave calculations, and post-test data to evaluate the impact of the tests on the media. Summarize the results of the conceptual phenomenological models in a report. Include the information in the near-field geochemical and hydrologic source term models where appropriate. Costs are shown in Table C-15.

C.2.6.2 Activity A13: Mineback Data from Rainier Mesa Tunnel Tests

Investigations of the effect of underground nuclear tests on the surrounding geologic media and post-test distribution of radionuclides have been conducted since the first underground nuclear test was conducted in 1951. Many reports such as Glasstone and Dolan (1977) and the USGS Trace Element Investigation series document the physical effects of nuclear tests observed in tunnels. Other records, such as field notes, document the physical effects of underground nuclear tests detonated in shafts. Several articles such as Smith et al. (1995) and Borg et al. (1976) document the distribution of glass and radioactivity within the tests. Prompt injection has been important as a mechanism to move radionuclides associated with tests in Yucca Flat. The INGOT (U-2gg), SANDREEF (U-7aq), and NASH (U-2ce) tests have been potentially implicated in such early time transport. The phenomenology of these tests should be better investigated, particularly with accompanying volcanostratigraphic data and information on clay and water content. A literature search of published and unpublished records would be conducted. The results would be compiled into one or more conceptual models of the physical effects and radionuclide distribution by evaluating dynamics of the tests, and investigating transport mechanisms (prompt injection, collapse rates, chimneys, and tensile fracturing zones) and radionuclide deposition (bedding planes, fractures, and faults). These results would be incorporated into the near-field models, thereby generating a more realistic simulation of the release of radioactive material into the surrounding groundwater.

Specifications: Synthesize the published and unpublished data into a conceptual model. Costs are shown in Table C-16.



Existing phenomenologic data on test effects 1 $45,000 $45,000



Compile information, synthesize data, report 1 $112,000 $112,000

Appendix CC-15


C.2.7 Subgroup S7: Hydrologic Source Term (Total $9,509,320)

Collect new data on the near-field environment of nuclear tests and determine the hydrologic source term for Yucca Flat modeling. There is a lack of physical and chemical information regarding the form and distribution of radionuclides within the cavity and chimney structure. Because of this, the results of the predictive geochemical and transport model within this region are highly uncertain. These uncertainties could be significantly reduced through data collection activities focused on determining the initial distribution of radionuclide, the geochemical properties of the rocks and melt glass within this environment, and the distribution and chemical form of radionuclides within the rocks and glass matrices. These data would not only improve the initial source term modeling, but also decrease the uncertainties in the hydrologic source term input that will be used in the CAU-scale model.

The proposed subgroups include field data collection to characterize the environment in and around a test cavity, and hydrologic source term modeling to produce the hydrologic source term for Yucca Flat transport modeling. A representative test would be selected for drilling two boreholes, one in the near field and one through the cavity.

Objective: Collect new data on the emplaced radionuclide distribution to determine the physical and geochemical effects on the cavity and near-field environment to improve modeling for the hydrologic source term. Incorporate the new data into the modeling and determine a representative hydrologic source term for Yucca Flat tests.

C.2.7.1 Activity A14: Near-Field Sampling

Analytical results from water quality sampling would provide dissolved radionuclide concentrations and information about the interactions between the radionuclides and colloids in the near-field environment. Cuttings and core samples would provide information on the physical parameters of the rock matrix after a nuclear test. The solid samples would be compared to the samples from the same stratigraphic interval in boreholes not associated with nuclear testing.

Specifications: Drill one borehole downgradient of a test cavity and rubble chimney, penetrating the subsurface two cavity radii above and below the working point. Multilevel well completion would be required to meet the sampling criteria. Collect water quality and solid samples from the borehole. Incorporate the data from this investigation into the hydrologic source term model. Costs are shown in Table C-17.

Appendix C C-16


C.2.7.2 Activity A15: Test-Cavity Sampling

Analytical results from water quality sampling would provide dissolved radionuclide concentrations and information about the interactions between the radionuclides and colloids in the near-field environment. Cuttings and core samples would provide information on the physical parameters of the rock matrix after a nuclear test. The solid samples would be compared to the samples from the same stratigraphic interval in boreholes not associated with nuclear testing.

Specifications: Drill into a test cavity to a depth of two cavity radii below the working point of the test. Multilevel well completion would be required to meet the sampling criteria. Collect water quality and solid samples from borehole. Incorporate the data from this investigation into the hydrologic source term model. Costs are shown in Table C-18.




Drilling plan 1 $15,000 $15,000



Support well development, sampling 1 $44,000 $44,000



Well development, sampling 1 $200,000 $200,000

Data analysis, modeling, and reporting 1 $150,000 $150,000

Total $2,672,160




Drilling plan 1 $15,000 $15,000



Support well development, sampling - near-field hole 1 $44,000 $44,000



Well development, sampling 1 $200,000 $200,000

Data analysis, modeling, and reporting 1 $500,000 $500,000

Total $3,357,160

Appendix CC-17


C.2.7.3 Activity A16: Hydrologic Source-Term Modeling

Determine an unclassified hydrologic source term for Yucca Flat and Climax Mine tests. This task would include laboratory testing for speciation, sorption, complexation, ion exchange, colloids, melt glass leaching, and matrix diffusion. In addition to samples collected from boreholes drilled into a cavity, archived saturated debris would be used for the laboratory experiments. The results from the laboratory testing might be adjusted for cavity geometry and phenomenology effects before incorporation into a model. Reactive transport would be linked to three-dimensional flow modeling to determine the hydrologic source term for Yucca Flat and Climax Mine. Costs are shown in Table C-19.

C.2.8 Subgroup S8: Transport Parameters - 1 (Total $3,653,270)

Determine transport parameters for the LCA from multi-well tracer tests. Determine transport parameters (i.e., diffusion parameters, effective porosity, and dispersivity) for the LCA. The effective values for these parameters at the CAU-scale is important for transport prediction. These values would be derived from the calibrated analysis of a multi-well tracer test. Two multi-well tracer tests have been conducted in the carbonates at the C-well complex in Yucca Flat and at the Amargosa site. A tracer test conducted using specifications similar to the BULLION test would provide better transport parameter values. The results from the tracer tests would be compared to the results of the BULLION test. This information would be used to calibrate laboratory tests to the LCA. Laboratory diffusion tests would also be used to refine tracer test analysis.

Objective: Develop data on transport parameters for the LCA that can be calibrated for use in the CAU-specific modeling.

C.2.8.1 Activity A17: Multi-Well Tracer Test in the LCA

Conduct a multi-well, multi-tracer test in the LCA at the BILBY location to provide a set of transport parameter values calibrated to Yucca Flat. This location is expected to be representative of the LCA beneath Yucca Flat.

Specifications: Drill one new well into the LCA near BILBY to be used in a tracer test. Assume the existing well penetrating the upper LCA will need minimal refurbishing to support the tracer test. Conduct a multi-tracer tracer test in the LCA near BILBY using specifications similar to the BULLION Forced Gradient Experiment (FGE). Analyze the results of the test using the numerical modeling methodology applied to the BULLION FGE. Incorporate the information into a report. Costs are shown in Table C-20.



Hydrologic source-term modeling 1 $3,480,000 $3,480,000

Appendix C C-18


C.2.8.2 Activity A18: Analysis of Two Existing Tracer-Test Datasets

Multi-well tracer tests have previously been conducted in the LCA at the C-wells (in southern Yucca Flat) and the Amargosa Tracer Calibration Site. These tests provide datasets that could be analyzed to determine dispersivity and effective porosity values for the LCA. Analysis of these tests using current knowledge of this HSU and a new test at BILBY to calibrate the analysis would provide good information on transport parameter values for the LCA in Yucca Flat.

Specifications: Analyze both datasets, using numerical modeling in a fashion similar to the BULLION FGE analysis, and prepare a report of the results. Costs are shown in Table C-21.

C.2.8.3 Activity A19: Laboratory Diffusion Studies

Laboratory measurements of diffusion coefficients on samples from the formation of interest provide data for constraining large-scale tracer tests. These tests would be conducted in a test cell to measure the diffusion rates for a variety of tracers.

Specifications: Conduct 12 laboratory diffusion tests on samples from the upper LCA. Include several core samples from the new well drilled near the BILBY site. Include core samples from the well at BILBY and the two wells at the tracer site, if available. Costs are shown in Table C-22.




Drilling plan 1 $15,000 $15,000



Support well development, hydraulic and tracer tests 1 $150,000 $150,000



Tracer test preparations 1 $135,000 $135,000

Well development, hydraulic testing, tracer tests 1 $395,110 $395,110


Total $3,163,270



Analysis of two existing tracer-test datasets 2 $120,000 $240,000

Appendix CC-19


C.2.9 Subgroup S9: Transport Parameters - 2 (Total $7,221,777)

Determine transport parameters for the VA and LCA from single-well tracer tests and earth-tide data. Values for transport parameters are not well known, either for the volcanic units or for the LCA. This subgroup independently addresses two of the transport parameters, diffusion and effective porosity. A single-well tracer test would provide information to determine the diffusion parameter, although such a test would not be as definitive as a multi-well tracer test. Because single-well tests are less expensive, more tests could be run to characterize variability. If multi-well tests were run, it would be useful to conduct a single well test and calibrate it against the results of the multi-well test. The methodology for deriving the transport parameters from other single-well tests might then be transferred to similar environments. If Subgroup S8 were conducted, one single-well test from Subgroup S9 should be performed at the BILBY site for this purpose.

An alternative approach for evaluating effective porosity of an aquifer is based on analysis of aquifer pressure-head response to transient pressure-head disturbance resulting from earth tides. The analysis of earth tides to determine effective porosity would require some development and calibration. The resulting information might not be as definitive as a multi-well tracer test. However, datasets for this analysis could be collected for nominal cost using existing wells and would allow more tests to characterize the variability of this parameter over the area of interest. A multi-well tracer test was conducted in fractured volcanics at the BULLION site on Pahute Mesa; it would useful for calibrating the earth-tide analyses for fractured volcanic rocks. Subgroup S8 includes a multi-well tracer test in the LCA that could be used to calibrate the subject tests for the LCA.

Objective: Develop CAU-specific data on transport parameters, effective porosity and diffusion parameter for the VA and LCA that can be calibrated within the context of the UGTA program data analysis and modeling.

C.2.9.1 Activity A20: Single-Well Tracer Tests in the VA and the LCA

Conduct six single-well, multi-tracer tests, three in the VA and three in the LCA, to provide data on the variability of the diffusion parameter values for these units. Test parameters would be specified to evaluate a large zone around the well, and might require one to two months per test.

Specifications: Identify six existing boreholes for testing the VA and the LCA. Recomplete and develop the wells as necessary to prepare them for testing. Conduct a multi-tracer drift/pumpback test in each well, assuming two months total duration. Analyze each tracer test using analytical models and compare with the BULLION results where appropriate. Costs are shown in Table C-23.



Laboratory diffusion studies 1 $250,000 $250,000

Appendix C C-20


C.2.9.2 Activity A21: Earth-Tide Analysis of the VA and the LCA (Total $199,153)

The wells in Yucca Flat will be evaluated to determine (1) whether the horizon is suitably isolated, and (2) the quality of the well completion to the groundwater system. The analysis of earth tides to determine porosity is experimental, and the analysis to relate the earth-tide response to effective porosity would require research and development.

Specifications: Develop the earth-tide analysis methodology, including calibration using data collected from the BULLION tracer-test site. Collect earth-tide data in Yucca Flat from eight wells, four completed in the VA and four in the LCA. Install water-level monitoring equipment in each well and collect measurements for three months. Analyze the datasets to determine effective porosity values for the two HSUs. Costs are shown in Table C-24.

C.2.10 Subgroup S10: Basin Structure (Total $1,550,000)

Characterize Yucca Flat basin structure using seismic methods. Information on the geologic structure in Yucca Flat has been obtained mainly from the boreholes used for nuclear testing, and thus is relatively sparse for areas in the south and along the west side of the basin. In particular, flux data are available for the identification of faults and their relative displacements, the extent of the LCA, and whether the surface of the LCA shows signs of karst development. Several seismic surveys have been conducted in northern Yucca Flat, and seismic data was




Drilling plan 6 $15,000 $90,000


Reinstall completion - condition hole 3 $375,000 $1,125,000

Reinstall completion - drill additional 500 ft 3 $520,000 $1,560,000

Support well development, hydraulic and tracer tests 6 $115,000 $690,000



Tracer test preparations 6 $45,000 $270,000

Well development, hydraulic testing, tracer tests 6 $284,244 $1,705,464


Total $7,022,624



Earth-tide analysis of the VA and the LCA 1 $199,153 $199,153

Appendix CC-21


recorded from the aftershocks of the nuclear tests. Existing data would be compared to the new data collected from seismic surveys conducted in the areas of Yucca Flat where information is sparse. Forward modeling would be conducted to determine a seismic array geometry suitable for karstic features of the LCA. Seismic methods provide good information on regional structure and are much more economical than drilling. Borehole data would be correlated to the seismic data for incorporation into the geologic model.

Objective: Use seismic information to evaluate the geologic structure in areas of the Yucca Flat basin where there is a need for greater definition due to sparse borehole data.

C.2.10.1 Activity A22: New Seismic Surveys

Seismic survey lines would provide the greatest amount of data on structure for areas where borehole data are sparse. The seismic data would be correlated to the borehole data enhancing the geologic interpretation for these areas. The specific data objectives of the surveys include defining the geologic units from the surface to the LCA, identifying the basin structure, defining the features along the basin edge, determining offsets on faults, and evaluating the upper LCA for karstic features.

Specifications: Conduct two-dimensional seismic surveys along three, 10-km lines, oriented east-west across the western and southern areas of the Yucca Flat basin. Refine the survey geometry using forward modeling to detect possible karstic features. Conduct a rigorous analysis of the seismic data and interpretation of the results within the framework of the Yucca Flat CAU geologic model, and incorporate new information into the model. Costs are shown in Table C-25.

C.2.10.2 Activity A23: Analysis of Existing Seismic Data

Existing seismic data, including aftershock mapping conducted during nuclear testing, have not been evaluated for possible contribution to the interpretation of the geologic structure of Yucca Flat. Two-dimensional seismic data have been collected in Yucca Flat in support of weapons testing. In addition, ground motion data have been collected from underground nuclear tests. These datasets would be evaluated to refine current structural interpretations in Yucca Flat.

Specifications: Evaluate all existing seismic data, including aftershock mapping, for additional information on geologic structure in Yucca Flat. Analyze, interpret, and incorporate the seismic data into the Yucca Flat CAU geologic model. Costs are shown in Table C-26.



Seismic surveys for the Yucca Flat Basin

1 $1,250,000 $1,250,000

Appendix C C-22


C.2.11 Subgroup S11: Basin Anomalies (Total $2,488,400)

Characterize the effects of the high-pressure zone in northern Yucca Flat and the elevated groundwater temperatures in eastern Yucca Flat on groundwater flow. Borehole logging and water level measurements within Yucca Flat indicate two anomalies that may affect groundwater flow and transport of radionuclides of concern within Yucca Flat. The first anomaly consists of extremely elevated groundwater heads in wells in several areas, particularly within Area 4. These heads range up to several hundred feet higher than water levels in surrounding areas (e.g., 1,250 ft higher at UE-4#1 and 550 ft at UE-3e4#1). The elevated head is thought to reflect high pore pressure resulting from the destruction of primary porosity of the low permeability tuff aquitard in which tests were conducted. The elevated heads, where present, alter the local groundwater flow regime. The second anomaly is elevated groundwater temperatures on the eastern side of Yucca Flat. Elevated temperatures decrease the density of the water, and fluctuating temperatures complicate the interpretation of the hydraulic head distribution. Neither anomaly is represented in the flow and transport model. The effects of the high-pressure zone and the fluctuating temperatures may need to be incorporated into the model. The temperature variation needs to be considered when using the measured heads, and groundwater flow models may need to include thermally driven groundwater flow.

Objective: Collect and evaluate data and conduct analyses to determine the nature and importance of the high-pressure zone and of the temperature gradient across Yucca Flat for the Yucca Flat CAU modeling.

C.2.11.1 Activity A24: High-Pressure Zone in Northern Yucca Flat

Although geologically and hydrologically well characterized by the Underground Test Containment Program, anomalous hydrological behavior in an area known as the Tuff Pile, which includes portions of Areas 3, 4, and 7, has never been well explained. Observations in several wells in zones of high fluid pressures, fluid levels much higher than expected (up to several hundred meters above the accepted static water level), and radionuclide contamination of groundwater in exploratory wells, sited within several hundred meters of historic underground test locations, document contaminant migration in the upper 650 m of the Tuff Pile stratigraphy. The main focus of this task is to determine the likelihood of contaminant migration near working points and the possibility of migration to the regional carbonate aquifer.

Analytical data from hydrological and radionuclide migration studies in this area show that contaminant migration is not well defined for this area. In fact, many straddle packer tests, piezometer readings, and radionuclide samplings have returned ambiguous data. These data can be interpreted within the framework of a



Analysis of existing seismic data 1 $300,000 $300,000

Appendix CC-23


geological model that considers the stratigraphy, rock properties, and structural information. Rock fracturing is both intrinsic and explosion generated and provides a plausible explanation of contaminant migration in zeolitic tuffs of low permeability associated with the working points at or below the static water table. Migration studies indicate cumulative effects of underground nuclear testing on groundwater behavior; hence, cumulative affects are greatest in areas of numerous underground tests as evidenced by elevated fluid levels and groundwater contamination.

Specifications: Incorporate the hydrogeological and radionuclide migration test data into the geological model. Test the model using a sophisticated numerical simulation called FEHM. Use data collected from future activities, which might include drilling a characterization well to explore the region below the test cavities down to the LCA, to validate and calibrate the model. Costs are shown in Table C-27.

C.2.11.2 Activity A25: Elevated Groundwater Temperatures in Eastern Yucca Flat

Several boreholes in Yucca Flat are suitable for thermal profiling to determine the temperature distribution of boreholes that penetrate the upper LCA. A small-diameter borehole is required for thermal profiling to preclude convection significantly affecting the temperature profile of the borehole. Temperature logging would be conducted in the boreholes that meet the requirements for thermal profiling. Multiple measurements from spatially varied boreholes are required for adequate thermal profiling. No drilling or borehole recompletions were considered for this task. A temperature correction for the head data is necessary to accommodate the density effects prior to using the data in a numerical simulation capable of solving groundwater advection and convection simultaneously.

Specifications: Identify 10 boreholes in the CAU suitable for thermal profiling. Conduct a thermal profile during two different time periods. Evaluate the data to determine the effect of the temperature distribution on the measured groundwater head distribution and the potential for convection-driven flow. Estimate the resulting effects on predicted flow paths. Costs are shown in Table C-28.



Data evaluation and modeling 1 $366,000 $366,000

Drilling exploratory hole and data analysis 1 $2,000,000 $2,000,000

Total $2,366,000

Appendix C C-24


C.2.12 Subgroup S12: LCA Characterization (Total $241,100)

Investigate possible karst development in the LCA and refine the depth/hydraulic conductivity relationship for the LCA. The LCA provides the primary transport pathway out of the Yucca Flat basin. The present conceptual model for the LCA contains a large-scale fracture network that facilitates circulation of contaminants in the LCA. The depth/hydraulic conductivity parameter in the model represents multiple physical features of the fracture system in the LCA; however, karst is not part of this model parameter. Karst could significantly affect the groundwater flow paths for radionuclides of concern by providing avenues for fast transport in addition to the fracture network. However, limited information is available on the presence of karst and its relationship to hydraulic conductivity with depth for the LCA. If karst were found to be a significant hydraulic feature of the LCA, karst representation would be added to the flow model.

Objective: Evaluate existing data to define the relationship between depth and hydraulic conductivity in the LCA. In particular, evaluate the possibility of karst development in the LCA beneath Yucca Flat and whether it is significant to the flow and transport of radionuclides of concern. If it is significant, incorporate it into the parameter in the flow and transport model.

C.2.12.1 Activity A26: Potential for Karst in the LCA

The development and continuity of karstic features in the LCA is unknown. Some argue that the LCA was not subjected to karst developing processes; however, this subject has not been studied in detail. A karst model indicates that karst features would be expected only in the upper 200 to 300 m of the LCA just below the depositional contact with the volcanics. There are indications of karstic features at outcrops along the basin margin and surrounding areas.

Specifications: Evaluate carbonate formations similar to the upper LCA that crop out along the basin margins and other areas at the NTS for karst development. Evaluate the paleohydrology associated with these formations to determine the likelihood of karst development in the LCA. Costs are shown in Table C-29.



Elevated groundwater temperatures in eastern Yucca Flat

1 $122,400 $122,400



Potential for karst in the LCA 1 $181,100 $181,100

Appendix CC-25


C.2.12.2 Activity A27: Hydraulic Conductivity with Depth

Core and borehole video logs of the LCA would be viewed for indications of karst development. Information obtained from these data would be used to further the understanding of hydraulic conductivity with depth.

Specifications: Review the literature in addition to field logs for evidence of karst in the LCA. Evaluate core and borehole video logs for evidence of karstic features in the LCA. Costs are shown in Table C-30.

C.3.0 Descriptions of Groups and Cost Estimates

Several groups were specified to evaluate different strategies for combining information collection. Each group is identified by a short title denoting the basic strategy. The intent was to specify different ways in which larger budgets could be spent to allow comparison of different strategies, and also to provide a well-spaced range of total budget to see if there was an optimum level of expenditure relative to improvement. The subgroups included in each group are based on the judgments of the expert panel concerning combinations that worked well together, or presented appropriate levels of investigation.

C.3.1 Groups

Table C-31 shows the subgroups and total cost for each group. No allowance was included for shared costs between subgroups in a group since there were generally no major common elements.

C.3.2 Group Summary

Table C-32 identifies the parameters addressed by each group. The character of each group can be intuited from the group title and the parameters addressed by the group. It is generally the case that the groups addressing the greater number of parameters cost the most.

Group C - Comprehensive

This group contains all of the subgroups except S6, the near-field shortcuts subgroup. Since all of the proposed subgroups were thought to have substantial merit, this group was intended to evaluate the maximum potential improvement. All parameters are addressed.



Hydraulic conductivity with depth 1 $60,000 $60,000

Appendix C C-26


Table C-31Group Cost Estimates

Group Group DescriptionSubgroup

Group TotalsS1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11

C Comprehensive x x x x x x x x x x $52,117,526

B Balanced x x x x x x $30,746,039

LEB Less-Expensive Balanced x x x x $25,543,238

ND No Drilling x x x x $4,567,531

V Value x x x x x $22,737,283

LCA LCA Transport x x x x $18,330,064

HST Hydrologic Source Term x x x $10,977,320

T5 Top 5 Choices x x x x x $27,600,853

FG Faults and Gradients x x $18,928,744

Table C-32Uncertain Parameters Addressed by Each Group

Group Group DescriptionFlux

NorthSlope

Multiplier


Effective Porosity

Diffusion (alpha)

Parameters Addressed

C Comprehensive x x x x x 5

B Balanced x x x x x 5

LEB Less-Expensive Balanced x x x 3

ND No Drilling x x x 3

V Value x x x 3

LCA LCA Transport x x x 3

HST Hydrologic Source Term x x 2

T5 Top 5 Choices x x x x x 5

FG Faults and Gradients x 1

Appendix CC-27


Group B - Balanced

The objective for this group was to evaluate all of the parameters, but at a lower cost. Subgroups were selected based on judgment of what would be most effective or useful. However, the group that was specified does not include the diffusion parameter because of the substantial cost associated with the subgroup that addresses the diffusion parameter.

Group LEB - Less-Expensive Balanced

The objective for this group was similar to the Balanced group, but with further reduced cost. This was accomplished by dropping those subgroups anticipated to be less effective where more than one subgroup addressed the same parameter. The effective porosity parameter was judged to be of limited importance when compare with the other parameters, so the subgroup addressing the effective porosity was eliminated to achieve a lower cost.

Group ND - No Drilling

The objective for this group was to concentrate on subgroups that did not require drilling due to the high cost of such activities. This group still addressed three parameters.

Group V - Value

This group was selected based on the concept of cost effectiveness rather than absolute effectiveness. Overall cost was reduced by eliminating the most expensive subgroups, even though they might have been judged to be most effective in reducing uncertainty. Three sensitive parameters are covered by this group, with subgroups requiring drilling limited to the hydrologic source term parameter for lack of an alternative approach to this parameter. The selected group was judged to have substantial overall merit.

Group LCA - LCA Transport

Focus on transport in the LCA guided the selection of subgroups for this group, based on the expectation that transport in the LCA primarily determined the contaminant boundary. This group addresses all three of the sensitive parameters related to transport in the LCA.

Group HST - Hydrologic Source Term

This group focused specifically on the hydrologic source term parameter and the path length parameter, which affects transport from the sources through the VCU to the LCA.

Appendix C C-28


Group T5 - Top 5 Choices

The five most popular subgroups which cover all five of the sensitive parameters were selected for this group. This group contains the optimal characterization activities chosen by the expert panel.

Group FG - Faults and Gradients

The internal hydrogeology of Yucca Flat relating to faults and the consequent water table configuration was the focus for this group. Two subgroups were selected that complemented each other, specifically to investigate the potentiometric trough and its implications. This group addresses only one sensitive parameter.

C.4.0 References

Borg, I.G., R. Stone, H.B. Levy, and L.D. Ramspott. 1976. Information Pertinent to the Migration of Radionuclides in Ground Water at the Nevada Test Site, Part 1: Review and Analysis of Existing Information, UCRL 52078 Pt 1. Livermore, CA: Lawrence Livermore National Laboratory.

Glasstone, S., and P.J. Dolan, eds. 1977. The Effects of Nuclear Weapons. Washington, DC: Government Printing Office.

Smith, D.K., B.K. Esser, and J. Thompson. 1995. Uncertainties Associated with the Definition of a Hydrologic Source Term for the Nevada Test Site. Livermore, CA: Lawrence Livermore National Laboratory.

Winograd, I.J., and W. Thordarson. 1975. Hydrogeologic and Hydrochemical Framework, South-Central Great Basin, Nevada-California, with Special Reference to the Nevada Test Site, USGS Professional Paper 712-C. Washington DC: U.S. Geological Survey.

Appendix CC-29

Appendix D

Assessment of Distributions Representing the Accuracy of Characterization Options


Appendix D: Assessment of DistributionsRepresenting the Accuracy ofCharacterization Options

The characterization options evaluated by the VOIA include component activities, subgroups of activities that address one (or at most two) parameters, and groups of activities (collections of subgroups) that address one or more parameters. To provide the necessary inputs for evaluating component activities and subgroups of activities, the panel estimated the ability of these activities or subgroups to provide accurate estimates of the parameters that they address. To provide inputs for evaluating groups of activities that address multiple parameters, the panel first decomposed the group into combinations of subgroups, with each subgroup addressing a different parameter. Thus, the expert panel was asked to assess the ability of the component activities, subgroups, and subgroup combinations to provide accurate estimates of the sensitive parameters. For the purposes of the assessment, a subgroup combination is defined as a set of subgroups within one or more of the nine groups, specifically, a subset composed of subgroups that address the same parameter (e.g., all subgroups in a group that impact the estimation of effective porosity). The reason for assessing subgroup combinations as well as component activities and individual subgroups is the need to understand how much more accuracy can be obtained if combinations of characterization options are conducted to resolve uncertainty on a specific parameter. Uncertainty reduction is not additive; that is, the amount of uncertainty reduction obtained from conducting two subgroups is not generally the sum of the uncertainty reductions achieved by each subgroup alone. Likewise, the uncertainty reduction resulting from a subgroup is generally not the sum of the uncertainty reductions of its component activities. Therefore, it was necessary to assess the joint accuracy and corresponding uncertainty-reducing potential for sets of subgroups as well as for individual subgroups and component activities.

The subgroup assessments were obtained in the following manner. First, each uncertain sensitive parameter was discretized; that is, the range of uncertainty was represented by three discrete levels. These levels were chosen to be the 5th, 50th, and 95th fractiles of the prior distributions. Next, the expert panel specified the distribution form for each of the test outcome variables. The panel agreed that the parameter estimates from tests addressing diffusion and hydrologic source would have a lognormal distribution, while the parameter estimates from tests addressing flux, porosity, and slope multiplier would have a normal distribution. Finally, the panel was asked to estimate the 5th and 95th fractiles of the parameter estimates that would result from each subgroup given each specified assumption for the actual parameter value.

The following sample questions from the assessment materials provided to the expert panel illustrates the logic for the assessment process:

• Assume Subgroup S8 (Transport Parameters) is conducted.

Appendix DD-1


tion m

the

ties as inly

e that

anel

alues ng to

l

ved

inear

e. rm

ior. o t

se for e

• Assume the diffusion parameter (alpha) along the flow path is X = 6.3 x 10-4

• Due to uncertainties associated with the fault zone characterization op(see influence diagram), the estimate of diffusion (alpha) resulting frothis subgroup is a random variable (assumed to be lognormal).

• Based on the subgroup results and your assessment of the relevant uncertainties as shown in the influence diagram, the estimate of the parameter will almost certainly be less than ____ times X (i.e., specify95th fractile).

Based on the subgroup results and your assessment of the relevant uncertainshown in the influence diagram, the estimate of the parameter will almost certabe greater than ____ times X (i.e., specify the 5th fractile).

As indicated by the above instructions, the expert panel was asked to assumthe parameter has a particular known value (e.g., X = 6.3 x 10-4), which was the 5th, 50th, or 95th fractile of the prior distribution. Performing the assessments assuming a variety of values for the uncertain parameter allowed the expert pto represent the sensitivity of the test’s accuracy to actual site conditions — toindicate, for example, that a test is more or less accurate at determining low vthan at determining high values. Thus, the panel provided inputs correspondithree different accuracy assessments for each subgroup.

As noted above, for each assessment of subgroup accuracy, the expert paneprovided the 5th and 95th fractiles of the distribution corresponding to the parameter estimate derived from the test. Since the parameter estimate derifrom the test is itself a random variable, the fractiles were then used to compute the parameters (e.g., mean and variance) of the test outcome distribution. A lregression on the parameters of the test outcome distribution provided a continuous estimate of the test accuracy for any specified true parameter valuNote that the expert panel assumed lognormal or normal as the distribution fofor the test outcome random variables depending on the distribution of the prThe only exception was slope multiplier, which was assigned a uniform prior treflect a lack of a priori information, but a normal test distribution, to indicate thaa subgroup could reduce uncertainty about the parameter. The normal and lognormal distributions are two parameter distributions, so the assigned fractiles were sufficient to compute the distribution parameter values needed to fit thedistributions.

The process of obtaining subgroup combination estimates was similar to the subgroup assessments. As discussed in Section 3.3, the expert panel was shown box plots indicating their assessments of the accuracy of each component subgroup and an estimate of the “composite” accuracy of the subgroup combination, assuming the subgroup accuracies could be combined as in independent sampling. The panel was then asked to provide the 5th and 95th fractiles of the distribution of the parameter estimate for the subgroup combination, conditional on assumed actual parameter value. As was the cathe assessment of the individual subgroups, for the subgroup combinations th

Appendix D D-2


racy

e p the

ividual is.

panel provided accuracy assessments corresponding to three specified values for the “actual” parameter, the 5th, 50th, and 95th fractiles of the corresponding prior distributions.

As discussed in Section 3.0, the process of obtaining component activity accuassessments was designed to leverage the efforts of the expert panel on the subgroup assessments. For each uncertain parameter, respondents rated thaccuracy of each activity as a percentage of the accuracy of the best subgrouaddressing the parameter. The “accuracy” for this exercise was defined to bevariance of the likelihood assuming the nominal true parameter value. The activity likelihoods were found by scaling the appropriate subgroup likelihoodvariances at low, nominal, and high true parameter values by the assessed percentage.

As also discussed in the main text of this report (Section 3.3), consensus judgments of the expert panel were obtained through a two-phase elicitation process wherein each panel member expressed and discussed his or her indbest estimates and then agreed upon a set of estimates to use for the analys

Tables D-1 through D-7 below provide parameter estimate ranges for all of the activities, subgroups, and subgroup combinations.

Table D-1Component Activity Accuracies, Assessed as a Percentage of the

Accuracy of the Best Subgroup Addressing Each Parameter (Page 1 of 2)

Parameter Activity Percent

Flux from the North

A1 43%

A2 66%

A4 58%

A5 23%

Slope Multiplier

A6 45%

A7 38%

A8 38%

A9 31%

A10 56%

A11 42%

A12 23%

A13 20%

A14 44%

A22 65%

A23 30%

A24 17%

A25 19%


A12 22%

A13 20%

A14 33%

A15 42%

A16 39%

Appendix DD-3


Porosity

A17 62%

A18 38%

A21 39%

Diffusion (alpha)

A17 61%

A19 40%

A20 57%

Table D-2Parameter Estimate Ranges for Subgroups Addressing Slope Multiplier

SubgroupSlope Multiplier = 0.1 Slope Multiplier = 0.5 Slope Multiplier = 0.9

5th 95th 5th 95th 5th 95th

S3 0.035 0.42 0.27 0.74 0.66 0.96

S4 0.032 0.49 0.24 0.74 0.62 0.95

S5 0.035 0.35 0.28 0.70 0.69 0.96

S6 0.028 0.56 0.20 0.81 0.44 0.97

S7 0.026 0.50 0.22 0.80 0.48 0.96

S10 0.035 0.39 0.27 0.69 0.66 0.96

S11 0.030 0.62 0.15 0.85 0.46 0.97

Table D-3Parameter Estimate Ranges for Subgroup Combinations

Addressing Slope Multiplier

Subgroup or Subgroup Combination

Slope Multiplier = 0.1 Slope Multiplier = 0.5 Slope Multiplier = 0.9


S3,S4,S5,S7,S10,S11 0.071 0.23 0.41 0.58 0.75 0.93

S3,S5,S7,S10 0.069 0.23 0.40 0.58 0.75 0.93

S3,S5,S7 0.059 0.27 0.37 0.62 0.73 0.94

S5,S6,S10 0.053 0.29 0.34 0.64 0.72 0.95

S5,S6,S7 0.044 0.32 0.31 0.67 0.71 0.96

S5,S7 0.038 0.33 0.29 0.68 0.70 0.96

S3,S4 0.035 0.37 0.28 0.71 0.68 0.96

Table D-1Component Activity Accuracies, Assessed as a Percentage of the

Accuracy of the Best Subgroup Addressing Each Parameter (Page 2 of 2)

Parameter Activity Percent

Appendix D D-4


Table D-4Parameter Estimate Ranges for Subgroups and Subgroup Combinations

Addressing Flux


Flux = 2130 Flux = 12000 Flux = 21900


S1 820 7,700 6,800 18,600 16,100 29,500

S2 1,200 6,000 6,200 18,600 14,000 30,200

S1, S2 1,450 4,900 8,200 15,900 17,000 26,300


Addressing Hydrologic Source


Hydrologic Source Term (HST)

HST = 1 HST = 32 HST = 1000


S6 0.004 270 0.17 7,900 6 210,000

S7 0.03 56 1.3 1,200 36 30,800

S6,S7 0.05 33 2.4 900 50 17,600


Addressing Porosity


Porosity = 0.02% Porosity = 1.1% Porosity = 2.2%


S8 0.02% 1.1% 0.3% 3.1% 0.6% 4.3%

S9 0.02% 2.0% 0.09% 4.6% 0.4% 6.1%

S8,S9 0.02% 0.57% 0.36% 2.8% 0.82% 4.1%


Addressing Diffusion (alpha)

Subgroup or Subgroup

Combination

Diffusion (alpha)

Alpha = 6.3 x 10-4 Alpha = 4.3 x 10-2 Alpha = 0.03


S8 7.0 x 10-5 6.2 x 10-3 5.1 x 10-4 3.7 x 10-2 3.9 x 10-3 0.22

S9 8.4 x 10-5 8.4 x 10-3 6.7 x 10-4 4.8 x 10-2 5.4 x 10-3 0.27

S8,S9 1.5 x 10-4 3.7 x 10-3 1.1 x 10-3 2.2 x 10-2 8.2 x 10-3 0.14

Appendix DD-5

Appendix E

Bayesian Analysis


me e

value e

ut

is

is

Appendix E: Bayesian Analysis

E.1.0 Bayesian Analysis

Bayesian analysis, or updating, is a method of measuring the ability of new information or data to reduce the uncertainty in an unknown quantity or parameter. Bayesian analysis is well-grounded mathematically as an extension of probability theory and was developed originally in 1759 by Rev. Thomas Bayes. Bayesian analysis has been successfully used in fields as diverse as oil prospecting, medical diagnosis, remote sensing, and astrophysics, as well as in previous VOIAs conducted for the NTS.

The basis of Bayesian analysis is the assumption that uncertainty about an unknown parameter may be quantified as a probability distribution. The more certainty there is about an estimate, the more narrow that probability distribution will be. Tests that can be performed to better measure the parameter may serve to further narrow the probability distribution describing uncertainty.

E.1.1 Priors, Posteriors, and Likelihoods

Information relevant to an uncertain quantity may be obtained in a variety of ways. For example, new information can come from gathering new data, analyzing current data, or building new models. The term “test” is used to refer to the general activity of collecting new information. Before performing some test, socurrent estimate of the value of an uncertain parameter must be obtained. Thprobability distribution that describes the current estimate of the value of the uncertain parameter is called the prior distribution. The prior distribution is basedon the current best judgment. This distribution serves as a baseline, and theof various experiments will be measured by the amount that the test makes thfinal probability distribution (the posterior distribution) more narrow than the prior. Note that even after a test is performed, there will generally be some uncertainty about the parameter. Unless the test is a perfectly accurate measurement of the uncertain parameter, it will not resolve all uncertainty abothat parameter.

Before the test is conducted, the outcome or information the test will producenot known. With Bayesian analysis, a third distribution, called the likelihood distribution, describes the range of outcomes of each test or experiment. If it assumed that the true value of a parameter is some value u, then the likelihood distribution gives the probability that a test would return a result y, given that the true value is u. If the test is highly accurate, the test result y will closely match u, given any true value of u. In other words, the likelihood distribution is used to measure the accuracy of a test, as well as account for biases or sensitivities the test might have to the parameter being measured. The likelihood function is sometimes referred to as being that family of distributions parameterized by u that yield probability distributions as a function of y.

Appendix EE-1


f nd nd

actly the ion

ue

te of rior he

ined d is is

E.1.2 Bayes’ Theorem

When a test is performed, the current state of knowledge (prior distribution) for the parameter and the accuracy (likelihood distribution) of the test are available for use. These are combined through Bayes’ theorem to compute a new state oknowledge (posterior distribution) after receiving a test outcome. To understathe value of Bayes’ theorem, consider the difference between the likelihood aposterior distributions. The likelihood tells what the test will report, if the true value of the parameter is known. In fact, the true value of the parameter is exthe information that is missing. The posterior is the probability distribution of true parameter, if what the test reported is known. That is, using the informatfrom the test along with the prior expectations for the parameter, a revised estimate of the uncertain parameter can be arrived at.

Bayes’ Theorem is derived and explained in most probability textbooks (for example, see von Neumann and Morganstern, 1953; Savage 1954; Schlaifer, 1959). Simply stated it is:

P0(u) is the prior distribution of u; that is, it is the prior probability estimate that any given u is the true value of the parameter. L(y | u) is the likelihood of measuring any value y, under the assumption that u is the true value of the parameter. P1(u | y) is the posterior probability estimate that any given u is the trvalue of the parameter, in light of a test which reported a result y. P(y) is a normalizing constant which ensures that all probabilities sum to one. Mathematically, it represents the probability that the test reports a value y, no matter what the true parameter value may be.

E.1.3 Determining the Value of Information

Bayes’ Theorem provides the appropriate posterior distribution for the estimathe parameter after some test has been performed and a result y has been obtained. The value of the test is easily measured after the fact by comparing the postedistribution with the prior distribution. However, for this analysis the value of tinformation obtained is evaluated by performing a test—one for which the outcome is not known. Therefore, all the various possible outcomes are combto determine the “expected” posterior distribution, which will then be comparewith the prior. It is desirable to calculate the weighted average of all possibleposteriors, weighted by the probability of the corresponding test outcome. Thsimply the integral of P(y) P1(u|y).

)(

)|()()|( 0

1 yP

uyLuPyuP = (E-1)

Appendix E E-2


the

ard

f e

be.

in

e

E.2.0 Application

This theoretical framework is applied to determine the expected posterior distributions of the geophysical parameters relevant to contaminant transport at Yucca Flat. These posterior distributions were directly compared to the prior distributions as well as provided as inputs to the contaminant transport model (Appendix F).

E.2.1 Approximations to Priors and Likelihoods

Because the forms of the various distributions were not convenient for closed-form integration, they were approximated with numerical equivalents. All distributions were represented by 100 discrete points from a domain sampling substantially all of the distribution. For parameters described by normal distributions, the numerical points were sampled from 3.5 standard deviations below the mean to 3.5 standard deviations above the mean. For parameters described by lognormal distributions, the numerical points were sampled from 3.5 standard deviations below the mean to 5 standard deviations above the mean. In all cases, physical constraints (such as nonnegativity) further constrained the range.

The likelihood distributions for three different assumed true values for each parameter were elicited from the expert panel (Appendix D). The likelihood distributions for other assumed true values were inferred by interpolation of the likelihoods assessed. An appropriate distribution (either normal or lognormal) was fit to the expert panel’s judgments of the likelihood distribution for each of three assumed true values. Linear regression on the means and standard deviations of these three assessed distributions provided the mean and standdeviation of the equivalent likelihood definition for any other assumed true valueof the parameter.

The above discussion describes the methods used to assess the numerator oEquation E-1. The denominator will be examined. This is the probability of thtest reporting a value y, no matter what the true value of the parameter mightThis may be found by integrating P0(u) L(y | u) over all u. This integral can be approximated numerically by computing the sum over each of the 100 points u of the prior times the likelihood.

E.2.2 Calculation of the Posteriors

Given the above approach for calculating the components of Equation E-1, thcalculation of the posterior distribution is straightforward. A test outcome y is assumed. For each of the 100 possible values of the true parameter u, multiply the prior probability of that u by the likelihood of measuring y given that u, and finally divide by P(y). The resulting distribution is normalized, and represents the estimate of the probability that any value u is the true parameter value, in light ofthe results of the test.

Appendix EE-3


E.2.3 Moment Matching

Given the above calculations, suppose that the test returns some value y. It is possible to determine the posterior probability from Equation E-1. However, it is desirable to value the test before knowing its outcome. Since the outcome of the test is not yet known, a weighted average of possible posteriors is computed to represent the information expected to be gained by performing the test. Since the posteriors to be used in the average will be used as inputs to the transport model, it was desirable to minimize the number of distributions. Therefore, instead of using the 100 point numerical approximation, three points were chosen to represent the distribution P(y). The discretization of continuous distributions is common practice in decision analysis, and there are several different approaches available (for example, see Smith 1993; Keefer and Bodily 1983; Miller and Rice, 1983). This analysis used a moment matching method based on Miller and Rice (1983). This method provides a distribution consisting of three value-probability pairs whose first five moments exactly match the first five moments of the original distribution. The moment matching value-probability pairs were calculated and then used to calculate the corresponding posterior distributions. Namely, each of the three values from the discretization process correspond to a test outcome, and, given a specific test outcome, a posterior distribution is then calculated. For Measure 1 (Section 3.0), the variances of these three posteriors were combined, weighted by their probabilities from the moment matching method. For the other measures, the three posteriors were used as inputs to the contaminant transport model. The results from the contaminant transport model were then weighted with the moment matching probabilities.

E.3.0 References

Keefer, D.L., and S.E. Bodily. 1983. “Three-Point Approximations for Continuous Random Variables.” In Management Science, 29 (5): 595. Stanford, CA: The Institute of Management Science.

Miller, A.C., and T.R. Rice. 1983. “Discrete Approximations of Probability Distributions.” InManagement Science, 29 (3): 352-362. Stanford, CA: The Institute of Management Science.

Savage, L.J. 1954. The Foundations of Statistics. New York, NY: John Wiley & Sons, Inc.

Schlaifer, R. 1959. Probability and Statistics for Business Decisions. New York, NY: McGraw-Hill Book Company.

Smith, J.E. 1993. “Moment Methods for Decision Analysis.” In Management Science, 39 (3): 340. Stanford, CA: The Institute of Management Science.

von Neumann, J., and O. Morganstern. 1953. Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.

Appendix E E-4

Appendix F

Transport Simulation Results for Activities and Groups


Appendix F: Transport Simulation Results for Activities and Groups

Tables F-1 through F-8 show the results of the transport simulations for three radionuclides: tritium, carbon-14, and plutonium 239/240 modeled for a 1,000-year time frame.

Table F-1Activity Simulation Results

(Page 1 of 3)

Activities Cases50th Fractile 95th Fractile

Distance (km) Time (yr) Distance (km) Time (yr)

A1

High 6.87E+01 7.35E+02 1.84E+02 1.00E+03

Low 3.38E+01 1.00E+03 6.60E+01 1.00E+03

Medium 5.61E+01 1.00E+03 1.28E+02 1.00E+03

A2

High 7.17E+01 1.00E+03 2.02E+02 1.00E+03

Low 3.12E+01 1.00E+03 5.02E+01 1.00E+03

Medium 5.61E+01 1.00E+03 1.50E+02 1.00E+03

A4

High 7.02E+01 1.00E+03 2.04E+02 3.90E+02

Low 3.01E+01 1.00E+03 5.61E+01 6.30E+02

Medium 5.41E+01 1.00E+03 1.28E+02 1.00E+03

A5

High 6.50E+01 1.00E+03 1.84E+02 1.00E+03

Low 3.96E+01 1.00E+03 7.90E+01 1.00E+03

Medium 5.69E+01 1.00E+03 1.35E+02 1.00E+03

A6

High 6.10E+01 1.00E+03 1.58E+02 1.00E+03

Low 5.44E+01 1.00E+03 9.45E+01 1.00E+03

Medium 5.64E+01 1.00E+03 1.23E+02 1.00E+03

A7

High 6.14E+01 1.00E+03 1.84E+02 1.00E+03

Low 1.00E+03 4.98E-01 1.00E+03 9.45E-01

Medium 5.83E+01 1.00E+03 1.11E+02 6.00E+02

A8

High 6.21E+01 1.00E+03 1.62E+02 1.00E+03

Low 5.39E+01 1.00E+03 1.06E+02 7.65E+02

Medium 5.46E+01 1.00E+03 1.39E+02 1.00E+03

A9

High 6.14E+01 1.00E+03 1.60E+02 1.00E+03

Low 5.45E+01 1.00E+03 1.03E+02 1.00E+03

Medium 5.81E+01 1.00E+03 1.53E+02 1.00E+03

A10

High 6.28E+01 1.00E+03 1.71E+02 1.00E+03

Low 5.60E+01 1.00E+03 9.96E+01 1.00E+03

Medium 5.62E+01 1.00E+03 1.20E+02 1.00E+03

Appendix FF-1


A11

High 6.36E+01 1.00E+03 1.78E+02 9.50E+02

Low 5.44E+01 1.00E+03 9.11E+01 1.00E+03

Medium 5.66E+01 1.00E+03 1.25E+02 1.00E+03

A12

sm6ah HST6ah 6.46E+01 1.00E+03 1.67E+02 1.00E+03

sm6ah HST6al 5.98E+01 1.00E+03 1.24E+02 8.90E+02

sm6ah HST6am 6.65E+01 1.00E+03 1.60E+02 1.00E+03

sm6al HST6ah 5.95E+01 1.00E+03 1.27E+02 1.00E+03

sm6al HST6al 5.56E+01 1.00E+03 1.00E+02 1.00E+03

sm6al HST6am 5.79E+01 1.00E+03 1.22E+02 1.00E+03

sm6am HST6ah 6.19E+01 1.00E+03 1.59E+02 1.00E+03

sm6am HST6al 5.75E+01 1.00E+03 1.02E+02 5.75E+02

sm6am HST6am 6.17E+01 1.00E+03 1.34E+02 1.00E+03

A13

sm6bh HST6bh 6.29E+01 1.00E+03 1.56E+02 1.00E+03

sm6bh HST6bl 5.96E+01 1.00E+03 1.32E+02 7.70E+02

sm6bh HST6bm 6.32E+01 1.00E+03 1.50E+02 6.70E+02

sm6bl HST6bh 5.98E+01 1.00E+03 1.45E+02 1.00E+03

sm6bl HST6bl 5.67E+01 1.00E+03 1.15E+02 1.00E+03

sm6bl HST6bm 5.89E+01 1.00E+03 1.24E+02 1.00E+03

sm6bm HST6bh 6.01E+01 1.00E+03 1.43E+02 6.40E+02

sm6bm HST6bl 5.81E+01 1.00E+03 9.70E+01 7.20E+02

sm6bm HST6bm 6.00E+01 1.00E+03 1.39E+02 9.05E+02

A14

sm7ah HST7ah 6.52E+01 1.00E+03 1.83E+02 1.00E+03

sm7ah HST7al 6.29E+01 1.00E+03 1.35E+02 1.00E+03

sm7ah HST7am 6.61E+01 1.00E+03 1.71E+02 1.00E+03

sm7al HST7ah 5.94E+01 1.00E+03 1.36E+02 6.25E+02

sm7al HST7al 5.51E+01 1.00E+03 9.88E+01 1.00E+03

sm7al HST7am 5.76E+01 1.00E+03 1.31E+02 1.00E+03

sm7am HST7ah 6.04E+01 1.00E+03 1.54E+02 9.30E+02

sm7am HST7al 5.95E+01 1.00E+03 1.17E+02 6.20E+02

sm7am HST7am 6.01E+01 1.00E+03 1.54E+02 1.00E+03

A15

High 6.33E+01 1.00E+03 1.83E+02 1.00E+03

Low 5.87E+01 1.00E+03 1.04E+02 1.00E+03

Medium 6.28E+01 1.00E+03 1.76E+02 1.00E+03

A16

High 6.34E+01 1.00E+03 1.52E+02 1.00E+03

Low 5.77E+01 1.00E+03 1.11E+02 1.00E+03

Medium 6.18E+01 1.00E+03 1.59E+02 1.00E+03


(Page 2 of 3)



Appendix F F-2


A17

alpha8ah poro8ah 4.50E+01 1.00E+03 7.01E+01 1.00E+03

alpha8ah poro8al 4.44E+01 1.00E+03 9.72E+01 1.00E+03

alpha8ah poro8am 4.61E+01 1.00E+03 7.83E+01 1.00E+03

alpha8al poro8ah 5.85E+01 1.00E+03 1.32E+02 1.00E+03

alpha8al poro8al 7.57E+01 1.00E+03 1.86E+02 1.00E+03

alpha8al poro8am 6.55E+01 1.00E+03 1.23E+02 8.55E+02

alpha8am poro8ah 4.71E+01 1.00E+03 7.42E+01 1.00E+03

alpha8am poro8al 5.66E+01 1.00E+03 1.03E+02 1.00E+03

alpha8am poro8am 5.08E+01 1.00E+03 7.81E+01 1.00E+03

A18

High 5.59E+01 1.00E+03 1.39E+02 1.00E+03

Low 6.53E+01 1.00E+03 1.97E+02 1.00E+03

Medium 5.88E+01 1.00E+03 1.52E+02 1.00E+03

A19

High 4.83E+01 1.00E+03 7.46E+01 1.00E+03

Low 6.26E+01 1.00E+03 1.57E+02 1.00E+03

Medium 5.34E+01 1.00E+03 8.49E+01 1.00E+03

A20

High 4.69E+01 1.00E+03 8.54E+01 1.00E+03

Low 6.36E+01 1.00E+03 1.46E+02 1.00E+03

Medium 5.42E+01 1.00E+03 8.16E+01 1.00E+03

A21

High 5.51E+01 1.00E+03 1.34E+02 8.70E+02

Low 6.50E+01 1.00E+03 1.62E+02 1.00E+03

Medium 5.98E+01 1.00E+03 1.65E+02 6.10E+02

A22

High 6.24E+01 1.00E+03 1.68E+02 1.00E+03

Low 5.50E+01 1.00E+03 1.05E+02 1.00E+03

Medium 5.75E+01 1.00E+03 1.13E+02 1.00E+03

A23

High 6.19E+01 1.00E+03 1.53E+02 1.00E+03

Low 5.53E+01 1.00E+03 1.06E+02 1.00E+03

Medium 5.68E+01 1.00E+03 1.27E+02 1.00E+03

A24

High 5.97E+01 1.00E+03 1.44E+02 1.00E+03

Low 5.51E+01 1.00E+03 1.11E+02 1.00E+03

Medium 5.88E+01 1.00E+03 1.23E+02 1.00E+03

A25

High 5.95E+01 1.00E+03 1.45E+02 1.00E+03

Low 5.58E+01 1.00E+03 1.53E+02 1.00E+03

Medium 5.70E+01 1.00E+03 1.59E+02 1.00E+03

Note: Activity A3 addresses flux from the west; it was not evaluated separately because it was determined not to be a sensitive parameter.


(Page 3 of 3)



Appendix FF-3


Table F-2Prior and Subgroup Simulation Results

(Page 1 of 2)

Subgroup Case50th Fractile 95th Fractile


Prior Distributions Prior 5.83E+01 1.00E+03 1.60E+02 6.50E+02

S1

High 7.56E+01 1.00E+03 2.00E+02 1.00E+03

Low 2.78E+01 1.00E+03 4.63E+01 1.00E+03

Medium 5.49E+01 1.00E+03 1.26E+02 1.00E+03

S2

High 7.35E+01 1.00E+03 2.10E+02 1.00E+03

Low 3.92E+01 1.00E+03 5.82E+01 1.00E+03

Medium 5.75E+01 1.00E+03 1.41E+02 1.00E+03

S3

High 6.26E+01 1.00E+03 1.76E+02 1.00E+03

Low 5.42E+01 1.00E+03 1.32E+02 1.00E+03

Medium 5.68E+01 1.00E+03 1.16E+02 1.00E+03

S4

High 6.46E+01 1.00E+03 1.58E+02 1.00E+03

Low 5.44E+01 1.00E+03 9.33E+01 9.15E+02

Medium 5.68E+01 1.00E+03 1.38E+02 1.00E+03

S5

High 6.21E+01 1.00E+03 1.61E+02 1.00E+03

Low 5.46E+01 1.00E+03 9.81E+01 1.00E+03

Medium 5.74E+01 1.00E+03 1.41E+02 1.00E+03

S6

6a H 6b H 6.84E+01 1.00E+03 1.78E+02 1.00E+03

6a H 6b L 6.23E+01 1.00E+03 1.49E+02 1.00E+03

6a H 6b M 6.48E+01 1.00E+03 1.69E+02 1.00E+03

6a L 6b H 6.10E+01 1.00E+03 1.08E+02 1.00E+03

6a L 6b L 5.62E+01 1.00E+03 9.00E+01 1.00E+03

6a L 6b M 5.86E+01 1.00E+03 1.04E+02 7.95E+02

6a M 6b H 6.53E+01 1.00E+03 1.73E+02 1.00E+03

6a M 6b L 5.95E+01 1.00E+03 1.30E+02 1.00E+03

6a M 6b M 6.32E+01 1.00E+03 1.60E+02 1.00E+03

S7

7a H 7b H 7.31E+01 1.00E+03 2.23E+02 1.00E+03

7a H 7b L 6.82E+01 6.00E+02 1.87E+02 1.00E+03

7a H 7b M 7.03E+01 1.00E+03 2.04E+02 1.00E+03

7a L 7b H 6.18E+01 1.00E+03 1.39E+02 1.00E+03

7a L 7b M 5.64E+01 1.00E+03 8.86E+01 1.00E+03

7a L 7b L 5.90E+01 1.00E+03 1.18E+02 9.10E+02

7a M 7b H 7.19E+01 9.35E+02 2.01E+02 1.00E+03

7a M 7b L 6.41E+01 1.00E+03 1.62E+02 1.00E+03

7a M 7b M 6.66E+01 1.00E+03 1.97E+02 1.00E+03

Appendix F F-4


S8

8a H 8b H 3.64E+01 1.00E+03 5.86E+01 1.00E+03

8a H 8b L 5.38E+01 1.00E+03 1.14E+02 1.00E+03

8a H 8b M 4.32E+01 1.00E+03 6.76E+01 7.30E+02

8a L 8b H 3.87E+01 4.95E+02 8.84E+01 1.00E+03

8a L 8b L 7.87E+01 1.00E+03 1.66E+02 1.00E+03

8a L 8b M 5.13E+01 1.00E+03 1.10E+02 8.55E+02

8a M 8b H 3.97E+01 1.00E+03 6.74E+01 1.00E+03

8a M 8b L 6.04E+01 1.00E+03 1.40E+02 1.00E+03

8a M 8b M 4.72E+01 1.00E+03 7.25E+01 1.00E+03

S9a

9b H 9d H 4.10E+01 1.00E+03 6.97E+01 1.00E+03

9b H 9d L 6.25E+01 1.00E+03 1.51E+02 1.00E+03

9b H 9d M 4.96E+01 1.00E+03 7.64E+01 1.00E+03

9b L 9d H 4.11E+01 6.50E+02 8.66E+01 1.00E+03

9b L 9d L 8.03E+01 1.00E+03 1.67E+02 1.00E+03

9b L 9d M 5.29E+01 1.00E+03 1.05E+02 1.00E+03

9b M 9d H 4.01E+01 9.30E+02 7.68E+01 1.00E+03

9b M 9d L 6.72E+01 1.00E+03 1.42E+02 1.00E+03

9b M 9d M 5.00E+01 1.00E+03 8.59E+01 1.00E+03

S10

High 6.26E+01 1.00E+03 1.65E+02 1.00E+03

Low 5.30E+01 1.00E+03 9.99E+01 1.00E+03

Medium 5.59E+01 1.00E+03 1.27E+02 8.10E+02

S11

High 6.19E+01 1.00E+03 1.66E+02 1.00E+03

Low 5.47E+01 1.00E+03 1.03E+02 1.00E+03

Medium 5.64E+01 1.00E+03 1.55E+02 1.00E+03

a Subgroups 9a and 9c were eliminated during the sensitivity analysis.S6 - hydrologic source term and slope multiplierS7 - hydrologic source term and slope multiplierS8 - effective porosity and diffusion in the LCAS9 - effective porosity and diffusion in the LCAH = High L = Low M = Medium

Table F-2Prior and Subgroup Simulation Results

(Page 2 of 2)

Subgroup Case50th Fractile 95th Fractile


Appendix FF-5


Table F-3Simulation Results for the Less-Expensive Balanced Group

Group Cases50th Fractile 95th Fractile


Less- Expensive Balanced

7a H 1 H 357 H 1.15E+02 1.00E+03 2.50E+02 5.30E+02

7a H 1 H 357 L 9.19E+01 1.00E+03 2.50E+02 7.90E+02

7a H 1 H 357 M 9.77E+01 1.00E+03 2.50E+02 6.25E+02

7a H 1 L 357 H 3.54E+01 1.00E+03 7.37E+01 1.00E+03

7a H 1 L 357 L 3.22E+01 1.00E+03 5.95E+01 1.00E+03

7a H 1 L 357 M 3.32E+01 1.00E+03 6.46E+01 1.00E+03

7a H 1 M 357 H 7.44E+01 1.00E+03 2.13E+02 1.00E+03

7a H 1 M 357 L 6.29E+01 1.00E+03 1.88E+02 1.00E+03

7a H 1 M 357 M 7.19E+01 1.00E+03 1.98E+02 1.00E+03

7a L 1 H 357 H 8.25E+01 1.00E+03 2.48E+02 7.20E+02

7a L 1 H 357 L 7.33E+01 1.00E+03 1.63E+02 1.00E+03

7a L 1 H 357 M 7.70E+01 1.00E+03 1.78E+02 1.00E+03

7a L 1 L 357 H 3.12E+01 1.00E+03 4.89E+01 1.00E+03

7a L 1 L 357 L 2.78E+01 1.00E+03 3.89E+01 1.00E+03

7a L 1 L 357 M 2.89E+01 1.00E+03 4.10E+01 1.00E+03

7a L 1 M 357 H 5.84E+01 1.00E+03 1.70E+02 1.00E+03

7a L 1 M 357 L 5.38E+01 1.00E+03 1.00E+02 1.00E+03

7a L 1 M 357 M 5.55E+01 1.00E+03 9.71E+01 7.80E+02

7a M 1 H 357 H 1.03E+02 8.95E+02 2.50E+02 7.00E+02

7a M 1 H 357 L 8.73E+01 1.00E+03 2.45E+02 1.00E+03

7a M 1 H 357 M 9.61E+01 1.00E+03 2.50E+02 9.00E+02

7a M 1 L 357 H 3.44E+01 1.00E+03 6.76E+01 1.00E+03

7a M 1 L 357 L 3.05E+01 1.00E+03 5.45E+01 1.00E+03

7a M 1 L 357 M 3.27E+01 1.00E+03 6.03E+01 1.00E+03

7a M 1 M 357 H 6.89E+01 1.00E+03 1.95E+02 1.00E+03

7a M 1 M 357 L 6.08E+01 1.00E+03 1.77E+02 1.00E+03

7a M 1 M 357 M 6.56E+01 1.00E+03 1.98E+02 1.00E+03

Explanation of Cases7a = S7 - hydrologic source term357 = S3, S5, and S7 slope multiplierH = High L = Low M = Medium

Appendix F F-6


Table F-4Simulation Results for the No Drilling Group



No Drilling

6b H 2 H 5610 H 9.61E+01 1.00E+03 2.50E+02 7.55E+02

6b H 2 H 5610 L 8.06E+01 6.75E+02 1.98E+02 1.00E+03

6b H 2 H 5610 M 8.63E+01 1.00E+03 2.50E+02 8.30E+02

6b H 2 L 5610 H 3.16E+01 1.00E+03 7.05E+01 1.00E+03

6b H 2 L 5610 L 2.85E+01 1.00E+03 4.23E+01 1.00E+03

6b H 2 L 5610 M 2.97E+01 1.00E+03 5.16E+01 1.00E+03

6b H 2 M 5610 H 6.84E+01 1.00E+03 2.01E+02 1.00E+03

6b H 2 M 5610 L 5.92E+01 1.00E+03 1.59E+02 9.60E+02

6b H 2 M 5610 M 6.40E+01 1.00E+03 1.65E+02 1.00E+03

6b L 2 H 5610 H 8.48E+01 1.00E+03 2.50E+02 8.40E+02

6b L 2 H 5610 L 7.07E+01 1.00E+03 1.62E+02 1.00E+03

6b L 2 H 5610 M 7.45E+01 1.00E+03 1.90E+02 1.00E+03

6b L 2 L 5610 H 2.96E+01 1.00E+03 5.16E+01 1.00E+03

6b L 2 L 5610 L 2.70E+01 1.00E+03 4.12E+01 1.00E+03

6b L 2 L 5610 M 2.81E+01 1.00E+03 4.19E+01 1.00E+03

6b L 2 M 5610 H 6.18E+01 1.00E+03 1.43E+02 1.00E+03

6b L 2 M 5610 L 5.42E+01 1.00E+03 1.10E+02 7.15E+02

6b L 2 M 5610 M 5.58E+01 1.00E+03 1.23E+02 1.00E+03

6b M 2 H 5610 H 8.86E+01 1.00E+03 2.50E+02 5.85E+02

6b M 2 H 5610 L 7.67E+01 1.00E+03 2.02E+02 7.45E+02

6b M 2 H 5610 M 8.58E+01 1.00E+03 2.14E+02 1.00E+03

6b M 2 L 5610 H 3.18E+01 1.00E+03 5.92E+01 1.00E+03

6b M 2 L 5610 L 2.80E+01 1.00E+03 4.72E+01 1.00E+03

6b M 2 L 5610 M 3.01E+01 1.00E+03 5.69E+01 1.00E+03

6b M 2 M 5610 H 6.60E+01 1.00E+03 1.93E+02 1.00E+03

6b M 2 M 5610 L 5.82E+01 1.00E+03 1.46E+02 1.00E+03

6b M 2 M 5610 M 6.09E+01 1.00E+03 1.57E+02 1.00E+03

Explanation of Cases6b = S6 - hydrologic source term2 = S2 - flux multiplier5610 = S5, S6, and S10 - slope multiplierH = High L = Low M = Medium

Appendix FF-7


Table F-5Simulation Results for the Value Group



Value

7a H 2 H 35710 H 1.18E+02 1.00E+03 2.50E+02 5.75E+02

7a H 2 H 35710 L 9.17E+01 1.00E+03 2.50E+02 8.70E+02

7a H 2 H 35710 M 9.46E+01 1.00E+03 2.50E+02 6.60E+02

7a H 2 L 35710 H 3.48E+01 8.65E+02 6.89E+01 1.00E+03

7a H 2 L 35710 H 3.00E+01 1.00E+03 5.55E+01 1.00E+03

7a H 2 L 35710 H 3.17E+01 1.00E+03 6.42E+01 1.00E+03

7a H 2 M 35710 H 7.51E+01 1.55E+02 2.50E+02 9.95E+02

7a H 2 M 35710 H 6.64E+01 1.00E+03 1.82E+02 1.00E+03

7a H 2 M 35710 H 7.22E+01 1.00E+03 2.07E+02 1.00E+03

7a L 2 H 35710 H 8.04E+01 1.00E+03 1.94E+02 7.05E+02

7a L 2 H 35710 H 7.29E+01 1.00E+03 1.49E+02 1.00E+03

7a L 2 H 35710 H 7.57E+01 1.00E+03 1.75E+02 1.00E+03

7a L 2 L 35710 H 3.02E+01 1.00E+03 5.04E+01 6.90E+02

7a L 2 L 35710 H 2.74E+01 1.00E+03 3.88E+01 1.00E+03

7a L 2 L 35710 H 2.79E+01 1.00E+03 4.08E+01 1.00E+03

7a L 2 M 35710 H 6.19E+01 1.00E+03 1.67E+02 1.00E+03

7a L 2 M 35710 H 5.59E+01 1.00E+03 9.70E+01 1.00E+03

7a L 2 M 35710 H 5.58E+01 1.00E+03 1.19E+02 1.00E+03

7a M 2 H 35710 H 1.04E+02 1.00E+03 2.50E+02 5.85E+02

7a M 2 H 35710 H 8.14E+01 8.25E+02 2.50E+02 9.55E+02

7a M 2 H 35710 H 8.95E+01 1.00E+03 2.50E+02 7.75E+02

7a M 2 L 35710 H 3.30E+01 1.00E+03 6.80E+01 1.00E+03

7a M 2 L 35710 H 2.93E+01 1.00E+03 6.04E+01 9.05E+02

7a M 2 L 35710 H 3.11E+01 1.00E+03 5.46E+01 1.00E+03

7a M 2 M 35710 H 7.50E+01 1.00E+03 2.18E+02 1.00E+03

7a M 2 M 35710 H 6.38E+01 1.00E+03 1.66E+02 1.00E+03

7a M 2 M 35710 H 6.88E+01 1.00E+03 1.82E+02 1.00E+03

Explanation of Cases7a = S7 - hydrologic source term2 = S2 - flux multiplier35710 = S3, S5, S7, and S10 - slope multiplierH = High L = Low M = Medium

Appendix F F-8


Table F-6Simulation Results for the LCA Transport Group



LCA Transport

p98 H a89 H f12 H 4.55E+01 1.00E+03 7.45E+01 1.00E+03

p98 H a89 H f12 L 2.32E+01 1.00E+03 3.30E+01 1.00E+03

p98 H a89 H f12 M 3.42E+01 1.00E+03 5.40E+01 1.00E+03

p98 H a89 L f12 H 8.09E+01 1.00E+03 1.66E+02 9.05E+02

p98 H a89 L f12 L 3.47E+01 1.00E+03 5.49E+01 1.00E+03

p98 H a89 L f12 M 5.67E+01 1.00E+03 1.12E+02 1.00E+03

p98 H a89 M f12 H 5.94E+01 1.00E+03 9.20E+01 1.00E+03

p98 H a89 M f12 L 2.76E+01 1.00E+03 3.72E+01 1.00E+03

p98 H a89 M f12 M 4.42E+01 1.00E+03 6.55E+01 1.00E+03

p98 L a89 H f12 H 4.81E+01 2.45E+02 1.13E+02 1.00E+03

p98 L a89 H f12 L 2.32E+01 3.70E+02 4.15E+01 9.50E+02

p98 L a89 H f12 M 3.59E+01 9.50E+02 7.59E+01 9.15E+02

p98 L a89 L f12 H 1.42E+02 1.00E+03 2.50E+02 4.70E+02

p98 L a89 L f12 L 5.10E+01 1.00E+03 1.17E+02 1.00E+03

p98 L a89 L f12 M 9.55E+01 1.00E+03 2.19E+02 1.00E+03

p98 L a89 M f12 H 6.94E+01 1.00E+03 1.50E+02 1.00E+03

p98 L a89 M f12 L 2.94E+01 7.15E+02 5.48E+01 1.00E+03

p98 L a89 M f12 M 4.94E+01 5.60E+02 1.20E+02 1.00E+03

p98 M a89 H f12 H 4.75E+01 1.00E+03 9.43E+01 1.00E+03

p98 M a89 H f12 L 2.36E+01 4.85E+02 3.63E+01 1.00E+03

p98 M a89 H f12 M 3.50E+01 8.80E+02 6.26E+01 1.00E+03

p98 M a89 L f12 H 9.55E+01 1.00E+03 2.09E+02 1.00E+03

p98 M a89 L f12 L 3.84E+01 1.00E+03 6.15E+01 1.00E+03

p98 M a89 L f12 M 6.61E+01 1.00E+03 1.29E+02 1.00E+03

p98 M a89 M f12 H 6.26E+01 1.00E+03 1.01E+02 1.00E+03

p98 M a89 M f12 L 2.83E+01 8.50E+02 4.28E+01 1.00E+03

p98 M a89 M f12 M 4.63E+01 1.00E+03 7.53E+01 1.30E+02

Explanation of Casesp98 = S8 and S9 - effective porosity in the LCAa89 = S8 and S9 - diffusion parameter in the LCAf12 = S1 and S2 - flux multiplierH = High L = Low M = Medium

Appendix FF-9


Table F-7Simulation Results for the Hydrologic Source Term Group




hst67 H 567 H 8.07E+01 1.00E+03 2.42E+02 1.00E+03

hst67 H 567 L 7.35E+01 1.00E+03 2.06E+02 1.00E+03

hst67 H 567 M 7.36E+01 1.00E+03 1.95E+02 1.00E+03

hst67 L 567 H 6.34E+01 1.00E+03 1.38E+02 6.75E+02

hst67 L 567 L 5.77E+01 1.00E+03 8.27E+01 1.00E+03

hst67 L 567 M 5.83E+01 1.00E+03 9.22E+01 1.00E+03

hst67 M 567 H 7.74E+01 1.00E+03 2.06E+02 1.00E+03

hst67 M 567 L 6.64E+01 1.00E+03 1.75E+02 1.00E+03

hst67 M 567 M 7.06E+01 1.00E+03 2.02E+02 1.00E+03

Explanation of Caseshst67 = S6 and S7 - hydrologic source term567 = S5, S6, and S7 - slope multiplierH = High L = Low M = Medium

Table F-8Simulation Results for the Faults and Gradients Group



Faults and Gradients

34 H 6.03E+01 1.00E+03 1.58E+02 1.00E+03

34 L 5.65E+01 1.00E+03 9.82E+01 1.00E+03

34 M 5.82E+01 1.00E+03 1.21E+02 1.00E+03

Explanation of Cases34 = S3 and S4 - slope multiplierH = High L = Low M = Medium

Appendix F F-10


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