NUREG/CR-6948, Vol. 1 

 

Integrated Ground-Water 

Monitoring Strategy for 

NRC-Licensed Facilities and 

Sites: Logic, Strategic 

Approach and Discussion 

Advanced Environmental 

Solutions, LLC 

U.S. Nuclear Regulatory Commission 

Office of Nuclear Regulatory Research 

Washington, DC 20555-0001 
DISCLAIMER: This report was prepared as an account of work sponsored by an agency of the U.S. Government. 

Neither the U.S. Government nor any agency thereof, nor any employee, makes any warranty, expressed or

implied, or assumes any legal liability or responsibility for any third party’s use, or the results of such use, of any

information, apparatus, product, or process disclosed in this publication, or represents that its use by such third

party would not infringe privately owned rights.

AVAILABILITY OF REFERENCE MATERIALS

IN NRC PUBLICATIONS

NRC Reference Material

As of November 1999, you may electronically access

NUREG-series publications and other NRC records at

NRC’s Public Electronic Reading Room at 

http://www.nrc.gov/reading-rm.html. Publicly released

records include, to name a few, NUREG-series

publications; Federal Register notices; applicant,

licensee, and vendor documents and correspondence;

NRC correspondence and internal memoranda;

bulletins and information notices; inspection and

investigative reports; licensee event reports; and

Commission papers and their attachments.

NRC publications in the NUREG series, NRC

regulations, and Title 10, Energy, in the Code of

Federal Regulations may also be purchased from one

of these two sources.

1. The Superintendent of Documents

 U.S. Government Printing Office

 Mail Stop SSOP

 Washington, DC 20402–0001

 Internet: bookstore.gpo.gov

 Telephone: 202-512-1800

 Fax: 202-512-2250

2. The National Technical Information Service

 Springfield, VA 22161–0002

 www.ntis.gov 

 1–800–553–6847 or, locally, 703–605–6000

A single copy of each NRC draft report for comment is

available free, to the extent of supply, upon written

request as follows:

Address: U.S. Nuclear Regulatory Commission

 Office of Administration 

 Mail, Distribution and Messenger Team

 Washington, DC 20555-0001

E-mail: DISTRIBUTION@nrc.gov 

Facsimile: 301–415–2289 

Some publications in the NUREG series that are 

posted at NRC’s Web site address

http://www.nrc.gov/reading-rm/doc-collections/nuregs 

are updated periodically and may differ from the last

printed version. Although references to material found

on a Web site bear the date the material was accessed,

the material available on the date cited may

subsequently be removed from the site.

Non-NRC Reference Material

Documents available from public and special technical

libraries include all open literature items, such as

books, journal articles, and transactions, Federal

Register notices, Federal and State legislation, and

congressional reports. Such documents as theses,

dissertations, foreign reports and translations, and

non-NRC conference proceedings may be purchased

from their sponsoring organization.

Copies of industry codes and standards used in a

substantive manner in the NRC regulatory process are

maintained at—

The NRC Technical Library 

Two White Flint North

11545 Rockville Pike

Rockville, MD 20852–2738

These standards are available in the library for 

reference use by the public. Codes and standards are

usually copyrighted and may be purchased from the

originating organization or, if they are American

National Standards, from—

American National Standards Institute

11 West 42nd Street

New York, NY 10036–8002

www.ansi.org 

212–642–4900

Legally binding regulatory requirements are stated

only in laws; NRC regulations; licenses, including

technical specifications; or orders, not in 

NUREG-series publications. The views expressed

in contractor-prepared publications in this series are

not necessarily those of the NRC.

The NUREG series comprises (1) technical and

administrative reports and books prepared by the

staff (NUREG–XXXX) or agency contractors

(NUREG/CR–XXXX), (2) proceedings of

conferences (NUREG/CP–XXXX), (3) reports

resulting from international agreements

(NUREG/IA–XXXX), (4) brochures

(NUREG/BR–XXXX), and (5) compilations of legal

decisions and orders of the Commission and Atomic

and Safety Licensing Boards and of Directors’

decisions under Section 2.206 of NRC’s regulations

(NUREG–0750).
 NUREG/CR-6948, Vol. 1 

 

 

Integrated Ground-Water 

Monitoring Strategy for 

NRC-Licensed Facilities and 

Sites: Logic, Strategic 

Approach and Discussion 

Manuscript Completed: August 2007 

Date Published: November 2007 

Prepared by 

V. Price, T. Temples, J. Tauxe, R. Hodges, R. Falta 

 

Advanced Environmental Solutions, LLC 

407 West Main Street 

Lexington, SC 29072 

T.J. Nicholson, NRC Project Manager 

Prepared for 

Division of Fuel, Engineering and Radiological Research 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001 

NRC Job Code Y6020 
 iii

ABSTRACT 

This document presents a logical framework for assessing what, how, where and when to 

monitor underground water in order to ensure that a licensed nuclear site or facility is 

behaving within the expected limits as described by the performance assessment. The 

Strategy is implemented as an iterative process beginning with analysis of any existing site 

and facility characterization and monitoring data, any existing conceptual site model (CSM), 

in this case generally a hydrogeologic model, and any existing risk assessment or PA model. 

The iterative nature of the Strategy results in a graded approach to development or evaluation 

of a monitoring program. Through analysis, an initial assessment of what, how, when, and 

where to monitor to evaluate system performance is made. Performance Indicators include 

chemicals, hydrogeologic attributes, and other features, events, or processes (FEPs) that may 

significantly influence contaminant flow and transport. These PIs may be directly 

measurable in a monitoring program or may be derived from compilations and interpretations 

of data. The integrated strategy benefits are: 

• Characterization allows development of CSM; 

• CSM allows modeling / simulation; 

• Modeling allows prediction; 

• Monitoring allows refinement; 

• Refinement allows confidence. 

Paperwork Reduction Act Statement 

This NUREG does not contain information collection requirements and, therefore, is not 

subject to the requirements of the Paperwork Reduction Act of 1995 (44 U.S.C. 3501 et seq.). 

Public Protection Notification 

The NRC may not conduct or sponsor, and a person is not required to respond to, a request 

for information or an information collection requirement unless the requesting document 

displays a currently valid OMB control number. 
 iv
 v

FOREWORD 

This research report was prepared by Advanced Environmental Solutions, LLC (AES), under a commercial 

research contract (NRC-04-03-061) with the U.S. Nuclear Regulatory Commission (NRC). As such, 

this two-volume report presents a logical framework for assessing what, when, where, and how to monitor 

with regard to subsurface ground-water flow and transport, in order to ensure that the environs of a licensed 

nuclear site or facility behave within the expected limits, as prescribed by the performance assessment (PA). 

Volume 1 provides the logic, strategic approach, and examples of how to integrate ground-water monitoring 

with modeling. Specifically, the integrated ground-water monitoring strategy is implemented in an iterative 

manner, beginning with analysis of any existing site and facility characterization and monitoring data 

and the relevant conceptual site model (CSM), hydrogeologic model, and/or risk assessment or PA model. 

The iterative nature of this strategy provides a graded approach for use in developing or evaluating 

a ground-water monitoring program. In so doing, the analyst derives an initial assessment of what, when, 

where, and how to monitor to evaluate system performance. The monitoring is then integrated with 

modeling through the identification, measurement, and analysis of performance indicators (PIs). These PIs 

include hydrogeologic conditions and process attributes; chemical conditions and constituents; and other 

features, events, or processes (FEPs) that may significantly influence contaminant flow and transport. 

As such, PIs may be directly measurable using a monitoring program, or may be derived from compilations 

and interpretations of geophysical or other indirect data. This integrated ground-water monitoring and modeling 

strategy offers the following benefits: 

• Characterization allows the development of a CSM. 

• The CSM allows modeling and/or numerical simulation. 

• Modeling allows prediction of system behavior, while monitoring allows refinement of models. 

• Refinement supports confidence in the performance assessment, as well as the need for 

(and selection of) remediation approaches in the event of a contaminant release. 

Volume 2 presents practical examples of the applications of this strategy, which provide practical means 

of testing, evaluating, and improving both the ground-water monitoring program and its related model. 

Although the strategy and its applications were originally planned for decommissioning sites, they are also 

very useful for assessing ground-water monitoring programs, remediating ground water, and identifying and 

selecting approaches to preclude offsite migration of abnormal radionuclide releases at nuclear facilities. 

This approach is consistent with the NRC’s strategic performance goal of making the agency’s activities 

and decisions more effective, efficient, realistic, and timely by characterizing and monitoring radionuclide 

transport in ground water. Toward that end, this report demonstrates, using examples relevant to nuclear 

facility performance, that ground-water monitoring and modeling can be integrated within a systems approach. 

This information will assist NRC licensing staff and regional inspectors, Agreement State regulators, 

and licensees in their decision-making by promoting a greater understanding of ground-water monitoring 

concepts that relate to PA models. Nonetheless, this report is not a substitute for NRC regulations, 

and compliance is not required. Consequently, the approaches and methods described in this report 

are provided for information only, and publication of this report does not necessarily constitute NRC approval 

or agreement with the information contained herein. Similarly, use of product or trade names in this report 

is intended for identification purposes only, and does not constitute endorsement by either the NRC or AES. 

Christiana Lui, Director 

Division of Risk Analysis 

Office of Nuclear Regulatory Research 
 vi

EXECUTIVE SUMMARY 

U.S. Nuclear Regulatory Commission regulations are designed so that licensees minimize risks 

associated with nuclear waste and facility decommissioning sites. The subject of this report is a 

strategy for understanding an important component of that overall risk -- the component 

associated with exposure to contamination through ground water. Application of the strategy will 

confirm site performance, reduce uncertainty in long-term predictions about site behavior or risk, 

and will permit communication of these predictions to all stakeholders. Fundamentally, the 

strategy is to use a CSM as the focus for evaluating and interpreting ground-water monitoring 

data and processing those data to infer ground-water conditions. In essence, all information 

concerning (or affecting) ground-water flow and contaminant transport is contained 

(institutionalized) in the CSM. Thus, future predictions made using the CSM always are based on 

the entirety of the available information and its best interpretation into the CSM. 

More specifically, this document recommends an integrated and systematic approach for 

monitoring subsurface water flow and contaminant transport to test and confirm Performance 

Assessments, or their functional equivalents. We call this approach “Performance Confirmation 

Monitoring”. Traditional monitoring to determine compliance with ground-water protection 

regulations is a mature field rich in technology, data interpretation methods, design optimization 

studies, and case histories. Much of the technology can be applied to performance confirmation 

monitoring, but the basic philosophy behind the design of monitoring networks and of data 

interpretation for performance confirmation is different from compliance monitoring. 

Interpretation of ground water data relies fundamentally on the development and testing of a 

Conceptual Site Model (CSM). However complex the site geology may be, rocks and their 

structural, stratigraphic, sedimentologic, weathering features and hydrologic and chemical 

properties can be understood in the context of well known geological processes. Use of geologic 

understanding and models to illuminate site characterization and monitoring is critical to develop 

conceptual site models. The CSM is developed initially from site characterization data, and then 

refined through an iterative process of modeling and monitoring.

Reduction of model uncertainty may require additional site characterization, or it might simply 

mean a different approach to data analysis is required. Features could include, but are not limited 

to intrinsic site characteristics, flux of water, concentrations of contaminants or indicator 

parameters. 

Monitoring might include traditional monitoring or geophysical methods, measurement of 

moisture distributions, and collection and analysis of environmental samples. The point is that 

compliance with ground water protection standards is not the goal of performance confirmation 

monitoring, but rather refinement of the concepts and data used to understand site performance 

and to develop and support a performance assessment in which the aggregate effects of many 

factors are evaluated. 

Evaluating performance indicators and establishing their data quality objectives are important 

parts of this strategy. Decisions are made on what, how, where, and when data should be 

collected through the evaluation of the performance assessment and the development of data 

quality objectives as well as conceptual and computer modeling. This can allow informed 

decisions to be made before wells are drilled or monitoring devices installed, leading to a reliable, 

efficient, and cost effective monitoring network design. The integrated monitoring approach can 

increase the efficiency of the monitoring system, both in terms of data quality and savings 

realized by selecting the critical monitoring points, adequate frequency, and time period. 
 vii

CONTENTS 

ABSTRACT.......................................................................................................................iii 

EXECUTIVE SUMMARY ............................................................................................... vi 

CONTENTS...................................................................................................................... vii 

FIGURES........................................................................................................................... ix 

TABLES ............................................................................................................................. x 

ACRONYMS..................................................................................................................... xi

1 Introduction...................................................................................................................... 1 

1.1 Scope and Objectives ................................................................................................ 1 

1.2 Introducing the Concepts........................................................................................... 3 

1.2.1 Performance Confirmation Monitoring............................................................... 3 

1.2.2 Performance Indicators ....................................................................................... 4 

1.2.3 Logical Approaches to Problem Solving ............................................................ 4 

1.2.4 Data Quality Objectives ...................................................................................... 4 

1.2.5 Graded Approach ................................................................................................ 5 

2 The Conceptual Site Model ............................................................................................. 7 

2.1 Development or Review of a Conceptual Site Model ............................................... 9 

2.1.1 Evaluation of Geologic Models......................................................................... 11 

2.1.1.1 Congruity with Regional Setting ................................................................ 11 

2.1.1.2 Review of Existing Site Characterization and Monitoring Data ................ 11 

2.2 Evaluate Model Uncertainty.................................................................................... 12 

2.2.1 Risk-Informed Sensitivity Analysis .................................................................. 13 

2.3 Model Parameter Development............................................................................... 14 

3 Performance Indicators .................................................................................................. 18 

3.1 Develop Preliminary List of Performance Indicators.............................................. 18 

3.2 Classification of Performance Indicators ................................................................ 21 

3.2.1 Class 1: Concentrations in Natural Media......................................................... 23 

3.2.1.1 Contaminants in Ground Water .................................................................. 23 

3.2.1.2 Contaminants or Indicator Parameters in Soil Gas..................................... 23 

3.2.1.3 Contaminants in Other Fluids ..................................................................... 24 

3.2.1.4 Surface Water.............................................................................................. 24 

3.2.1.5 Plants and Animals ..................................................................................... 24 

3.2.2 Class 2: Physical................................................................................................ 24 

3.2.2.1 Water Content ............................................................................................. 24 

3.2.2.2 Physical Properties of Rock or Sediment.................................................... 25 

3.2.2.3 Physical Properties of Subsurface Fluids.................................................... 26 

3.2.2.4 Features, Events, and Processes as Performance Indicators....................... 27 

3.2.2.5 Elicitation of PIs from Analysis of a Facility ............................................. 28 

3.2.3 Class 3: Indicators Derived from Data Analysis or Modeling.......................... 30 

3.2.3.1 Indicators from Performance Assessment .................................................. 30 

3.2.3.2 Derived Indicators from Flow Models........................................................ 37 

3.2.3.3 Performance Indicators from the Coupled Flow and Chemical Transport 

Model ...................................................................................................................... 37 

3.2.4 Performance Indicators from a Bioremediation Project.................................... 38 

4 Modeling to Support Site Performance Assessment...................................................... 42 
 viii

4.1 Introduction ............................................................................................................. 42

4.1.1 What is the Purpose of the Modeling Effort?.................................................... 42 

4.1.2 What is an Appropriate Level of Effort and Sophistication for the Modeling 

Study?......................................................................................................................... 43 

4.2 Selection of Appropriate Flow and Transport Models ............................................ 44 

4.2.1 Types of Flow and Transport Models ............................................................... 45 

4.2.1.1 Is the Flow Model Appropriate for the Site? .............................................. 49 

4.2.1.2 Is the Chemical Fate and Transport Ground Water Model Appropriate for 

the Site?................................................................................................................... 50 

4.2.1.3 How is the Flow Model Constrained? ........................................................ 51 

4.2.1.4 How is the Chemical Fate and Transport Model Constrained? .................. 52 

4.2.1.5 How is the Flow Model Calibrated? ........................................................... 52 

4.2.1.6 How is the Chemical Fate and Transport Ground Water Model Calibrated?

................................................................................................................................. 53 

4.3 System Parameters Derived from Modeling of Site Characterization Data............ 54 

4.3.1 Derivation of Modeling Input Parameters from Physical Properties ................ 54 

4.3.2 Data Analysis Pointing to Needed CSM Revision............................................ 56 

4.3.2.1 Incongruity with current model predictions................................................ 59 

4.4 Evaluate and Refine Conceptual Site Models ......................................................... 61 

4.5 Data Management and Model Visualization ........................................................... 61 

4.5.1 Data Management ............................................................................................. 63 

4.5.2 Modeling and Visualization Software Used in the Development of this 

Document ................................................................................................................... 64 

4.5.3 Data Analysis .................................................................................................... 65 

4.5.3.1 Trend Analysis............................................................................................ 66 

4.5.3.2 Data Scaling................................................................................................ 66 

4.5.4 Data and Decision Rule Reporting and Communication .................................. 67 

5 Performance Confirmation Monitoring System Design: What, Where, When, and How 

to Monitor ......................................................................................................................... 68 

5.1 Network Design for Performance Indicators........................................................... 70 

5.2 Design and Implementation of Unsaturated-Saturated Zone Monitoring Networks 

for Ground Water Constituents...................................................................................... 70 

5.2.1 Unsaturated Zone .............................................................................................. 71 

5.2.2 Saturated Zone................................................................................................... 71 

5.2.3 Soil Gas and Volatile Fluids.............................................................................. 72 

5.3 Monitoring Network Assessment ............................................................................ 72 

5.4 Demonstrating Site Performance With Respect to Site Performance Objectives ... 73 

5.4.1 Site Performance Confirmation......................................................................... 73 

5.4.2 Remediation Confirmation................................................................................ 73 

5.5 Selection of Monitoring Points: Where and When to Monitor ............................... 74 

5.6 Selection of Monitoring Devices: How to Monitor................................................. 75 

5.6.1 Saturated Zone Sampling .................................................................................. 76 

5.6.2 Geophysical Methods........................................................................................ 76 

5.6.3 Vadose Zone Devices........................................................................................ 79 

5.6.4 Other Monitoring Devices................................................................................. 79 

5.7 Modify and Optimize Monitoring System............................................................... 80 
 ix

6 Conclusion ..................................................................................................................... 81 

References......................................................................................................................... 82 

Bibliography ..................................................................................................................... 90 

Appendix A: Existing Guidance for Traditional Monitoring ......................................... A-1 

Appendix B: Types of Monitoring.................................................................................. B-1 

Appendix C: Identification of the Conceptual Site Models with Maximum Likelihood 

Bayesian Model Averaging Method ............................................................................... C-1 

Appendix D: Determination of Data Quality Objectives............................................... D-1 

Appendix E: Radionuclides and Radiochemistry Analysis Methods .............................E-1 

Appendix F: Introduction to Systems Engineering Basics .............................................F-1 

Appendix G: Management by Objectives...................................................................... G-1 

FIGURES 

Figure 1-1 Integrated Ground Water Monitoring Strategy logic diagram................................2 

Figure 1-2. Relationship between Performance Confirmation Monitoring and other groundwater monitoring requirements...........................................................................................4 

Figure 2-1 Evaluation of a conceptual site model. This process starts with evaluation of data 

to develop an initial simple model, and gradually moves to more complex interpretation 

if needed............................................................................................................................10 

Figure 2-2 Drawdown Curve. (Scott C. James, DOE / SNL, personal communication, 2007)

..........................................................................................................................................13 

Figure 2-3 Parametric updating example................................................................................16 

Figure 2-4 Plume refinement with addition of monitoring wells (after L. Deschaine, personal 

communication 2003) .......................................................................................................17 

Figure 3-1. Monitoring Strategy Logic Flow with expanded Analysis step. ..........................20 

Figure 3-2. Aquifer heterogeneity in fractured rocks (Hsieh and Shapiro, 1996). .................26 

Figure 3-3 Cross-section through a generic disposal trench....................................................29 

Figure 4-1 Flow and Transport Model Selection....................................................................48 

Figure 4-2 Chemical Fate and Transport Model Selection......................................................49 

Figure 4-3 Three dimensional display showing percent clay (reds grading to sandier blues)54 

Figure 4-4 Model showing complex lithofacies interpretation ..............................................56 

Figure 4-5 Pen Branch example (water level units reported in ft above msl) ........................57 

Figure 4-6 Bull’s-eye in contoured data .................................................................................58 

Figure 4-7 Critical monitoring points. The dashed lines indicate the water table and the 

shaded areas represent the contamination plume..............................................................59 

Figure 4-8 Geologic model from Ruby (1981) showing cross-section through a barrier bar 

system ...............................................................................................................................60 

Figure 4-9 Sample for 3-D (solid) model from Kingdom Suite .............................................62 

Figure 4-10 Quasi 3-D contouring and solid rendering of Cs in soil. Note that hot wells do 

not extend to the base of the modeled volume..................................................................63 

Figure 5-1 Steps in Monitoring Plan Development................................................................69 
 x

TABLES 

Table 2-1 Summary of information in a conceptual site model for contaminant fate and 

transport in ground water....................................................................................................8 

Table 3-1 Classification of Performance Indicators ...............................................................22 

Table 3-2 PIs for ground water that can be derived from the PA...........................................36 

Table 3-3 Examples of Recommended Information and Performance Indicators for 

Bioremediation Project (after Long and Yabusaki, 2007)................................................38 

Table 4-1 Categories of ground water flow models ...............................................................46 

Table 4-2 Categories of chemical fate and transport models. These are used with some type 

of flow model, and are almost always transient................................................................47 

Table 5-1 Summary of Possible Applications of Surface and Cross Borehole Geophysical 

Methods for Site Characterization. ...................................................................................78 
 xi

ACRONYMS 

ACAP EPA’s Alternative Cover Assessment Program 

AES Advanced Environmental Solutions, LLC 

ALARA As Low As Reasonable Achievable 

ASTM American Society of for Testing and Materials 

BMA Bayesian model averaging 

CFM conceptual facility model 

CFR Code of Federal Regulations 

CMP critical monitoring point 

COC constituent/chemical/contaminant of concern 

CSM conceptual site model 

DMS data management system 

DOE United States Department of Energy 

DQO data quality objective 

DSI Derived System Indicators 

EIS Environmental Impact Statement 

EM expectation-maximization 

EPA United States Environmental Protection Agency 

ET evapotranspiration 

FEPs features, events, and processes 

GIS geographic information system 

GMS Groundwater Modeling System 

IAEA International Atomic Energy Agency 

INEEL Idaho National Environmental Engineering Laboratory 

INL Idaho National Laboratory – formerly known as INEEL and INEL. 

KIC Kashyap Information Criterion 

LLW low level radioactive waste 

LTP License Termination Plan 

MBO Management by Objectives 

MCL maximum concentration limit 

MLBMA maximum likelihood Bayesian model averaging 

MLE maximum likelihood estimation 

MP monitoring point 

NEA Nuclear Energy Agency 

NORM naturally-occurring radioactive material 

NPL National Priority List 

NRC Nuclear Regulatory Commission 

NVLAP National Voluntary Laboratory Accreditation Program 

OECD Organization for Economic Co-operation and Development 

PA performance assessment 

PCE percholorethylene (or tetrachloroethylene) 

PI performance indicator 

PNNL Pacific Northwest National Laboratory 

PRA probabilistic risk assessment 

PUREX plutonium uranium extraction 

QC quality control 

RCRA Resource Conservation and Recovery Act 
 xii

RIPB risk-informed, performance-based 

SA sensitivity analysis 

SAGEEP Symposium on the Application of Geophysics to Engineering and 

Environmental Problems 

SDMP Site Decommissioning Management Plan 

SZ saturated zone 

SRS Savannah River Site 

SWOT Strengths, Weaknesses, Opportunities, and Threats 

TCE Trichloroethylene

TEDE total effective dose equivalent 

UCL upper control limit 

USACE U.S. Army Corps of Engineers 

USGS United States Geological Survey 

UZ unsaturated zone (vadose zone) 

WSRC Westinghouse Savannah River Company 
 1

1 Introduction 

The Integrated Ground Water Monitoring Strategy has been developed to guide the 

monitoring of a nuclear waste or decommissioning site to improve assessment of its 

performance. To achieve this goal, we present an integrated and systematic approach to 

monitoring water flow and contaminant transport from the land surface through the 

unsaturated (vadose) zone into underlying saturated zones. The Strategy can apply to new 

sites, and provides guidance for assessment of sites with existing monitoring systems. It 

should be generally applicable to any NRC-licensed site where public or environmental risk 

through ground-water pathways are to be assessed. 

Performance assessment modeling of a site may have a time scale of thousands of years into 

the future. Yet, the lifetime of a monitoring program may be measured in decades at most. It 

is for this reason that a strategic approach to identifying the most important indicators of site 

performance and where and how to measure them in a short period of time so that the longterm predictions can be most useful. 

1.1 Scope and Objectives 

Nuclear Regulatory Commission (NRC) regulations are designed to ensure that licensees 

minimize potential risk to the public associated with nuclear waste and facility 

decommissioning sites. NRC has identified a need for long-term monitoring guidelines for 

detection of current conditions and changes in system behaviors and guidance pertaining to 

detection, evaluation, and monitoring of releases from operating facilities via unmonitored 

pathways (Statement of Work for RES-02-051). In part, the articulation of this need is based 

on National Academy of Sciences reports (NAS, 2000a and NAS, 2000b). More recently, the 

NRC Liquid Radioactive Release Lessons Learned Task Force has issued a report 

emphasizing monitoring needs at licensed facilities with a focus on power plant sites (NRC, 

2006a). 

The principal objective of this project is to respond to this need. Specifically, the Strategy 

developed in this document provides a logical framework for assessing what, how, where and 

when to monitor in order to: 

• ensure that a licensed nuclear site or facility is behaving within the expected limits as 

described by the performance assessment, 

• develop and refine conceptual flow and transport models by graded approaches, 

• help identify and quantify uncertainties in conceptualization and performance 

assessment (PA) process and risk, 

• aid in evaluation of whether engineered waste isolation systems are operating within 

their design performance envelope, 

• evaluate whether natural attenuation is occurring according to expectations, 
 2

• communicate the monitored Performance Indicators (PIs) through effective data 

management, analysis, and visualization techniques to decision makers and 

stakeholders, 

• improve understanding of site-specific features, events, and processes controlling 

ground water and contaminant movement, 

• assess effectiveness of contaminant isolation systems and remediation activities, 

• identify the presence of contaminant plumes and preferential ground-water 

pathways, 

• test alternative conceptual flow and transport models, and 

• aid in the confirmation of the assumptions of the PA model, and hence increase 

confidence in the PA. 

This Strategy can be applied to a broad range of sites of varying complexity including any 

range of geologic and climatic settings, waste compositions, and site design. Figure 1-1 

outlines the Strategy logic at a very high level. 

Figure 1-1 Integrated Ground Water Monitoring Strategy logic diagram 

The Strategy is implemented as an iterative process beginning with analysis of any existing 

site and facility characterization and monitoring data, any existing conceptual site model 

(CSM), in this case generally a hydrogeologic model, and any existing risk assessment or PA 

model. The iterative nature of the Strategy results in a graded approach to development or 

evaluation of a monitoring program. 

Through analysis, an initial assessment of what, how, when, and where to monitor to evaluate 

system performance is made. Performance Indicators include chemicals, hydrogeologic 

attributes, and other features, events, or processes (FEPs) that may significantly influence 
 3

contaminant flow and transport. These PIs may be directly measurable in a monitoring 

program or may be derived from compilations and interpretations of data (as discussed in 

Section 1.2.2). 

1.2 Introducing the Concepts 

This Section will briefly define some of the terms and concepts used in this document. 

Further development of the concepts will be covered in succeeding sections. 

1.2.1 Performance Confirmation Monitoring 

Performance Confirmation Monitoring (PCM) is intended to verify that the data going into 

and the predictions coming out of the PA are sufficiently accurate and that a facility is 

behaving within expected limits. The objective of monitoring in the context of this Strategy 

is to understand the functioning of a hydrogeologic system. 

A conceptual model of a hydrogeologic system (including engineered components such as 

waste sites, and natural components such as geology) is developed from site characterization 

and monitoring data. This conceptual model serves as the basis for computer simulations of 

ground water flow and contaminant transport, and ultimately of risk via the ground water 

pathway. This risk prediction ultimately derived from a conceptual model and appropriate 

input parameters is called a Performance Assessment. Performance Confirmation 

Monitoring of ground water within the area of the model can test whether the computer 

simulations represent the actual subsurface system. 

Performance Confirmation Monitoring as discussed here differs fundamentally from other 

forms of monitoring in its objectives. For example, Detection Monitoring required for 

Treatment, Storage, and Disposal Facilities under the Resource Conservation and Recovery 

Act, is intended to detect if a release has occurred. Compliance Monitoring serves to 

determine if a particular standard (i.e., ground water protection standard, MCL etc.) has been 

exceeded once a release occurs. (CFR 40, Part 264/265, Subpart F et sequel) In addition, 

Appendix B contains descriptions of types of monitoring. 

While the fundamental objectives of these forms of monitoring differ, the information 

acquired through these monitoring programs may be quite useful to a Performance 

Confirmation Monitoring program as these data often can be used to refine and validate the 

Conceptual Site Model. As such, these sorts of monitoring activities are a subset of 

Performance Confirmation Monitoring as depicted in Figure 1-2. It is important to note, 

however, that the driver for selecting what, when, where, and how to monitor is different 

because the goal is to understand system performance rather than system compliance. 
 4

Figure 1-2. Relationship between Performance Confirmation Monitoring and other 

ground-water monitoring requirements. 

A useful discussion of existing NRC regulatory monitoring requirements is contained within 

The Liquid Radioactive Release Lessons Learned Task Force Final Report, published in 2006 

(NRC, 2006a). In addition, Appendix A includes some existing regulations and guidance 

pertaining to monitoring at NRC-regulated facilities and other hazardous waste sites and 

disposal facilities (e.g., CFR Title 10 for NRC; CFR Title 40 for EPA). 

1.2.2 Performance Indicators 

Site and system Performance Indicators (PIs) are measurable or observable features that 

provide insight into reliability of the CSM, and hence simulations and assessments based on 

the CSM. 

Performance Indicators can be grouped into three classes. The first two classes include 

chemicals that are the primary risk drivers and their surrogates, as well as chemical and 

physical controls on flow and transport (e.g., pH, rock permeability). All of these are 

measurable through sampling and analysis. The third class of PIs results from interpretation 

of results of measuring the first two classes; these PIs are referred to in this document as 

Derived System Indicators (DSI). Included in this third class would be statistical and spatial 

outliers, and risk-significant phenomena identified through PA modeling. Examples are 

given in Tables 3-1 through 3-3. 

1.2.3 Logical Approaches to Problem Solving 

Management by Objectives, Systems Engineering, and the Data Quality Objectives (DQO) 

process are examples of approaches to logical problem solving. Application of the Strategy 

outlined in Figure 1-1 will be aided by understanding these approaches to problem solving. 

Details of these approaches are beyond the scope of this document. Each of these is 

discussed briefly in Appendices D, F, and G to this document. 

1.2.4 Data Quality Objectives 

The DQO process outlined by the U.S. Environmental Protection Agency (EPA 2000) is an 

aid to environmental decision making, guiding practitioners through identifying the decision 

Performance 

Confirmation 

Monitoring 

Compliance 

Monitoring, 

Detection 

Monitoring (etc) 
 5

and developing support to make that decision defensible. The seven sequential steps of DQO 

are adapted for application in this document in Appendix D. The process is iterative with 

certain outputs requiring input back into a previous step. A component of the proposed 

Strategy is to use the DQO process to aid in the selection of PIs and monitoring points 

thereby reducing uncertainty. 

This process provides guidance useful for determining the type, quality, and quantity of data 

necessary for the design of an adequate monitoring system. Through the development of 

DQOs, decisions can be made on what (the PI), how (type of monitoring device), where and 

when (monitoring points) data should be collected relative to the monitoring objectives 

established. The process also establishes practical constraints on the monitoring program and 

aids in the establishment of an acceptable level of uncertainty. 

The product of the DQO process should be a series of statements, or in the case of this 

Strategy, a series of questions that are asked to define the objectives and specifications of the 

proposed or existing monitoring plan. The answers to these questions should determine the 

degree of acceptability or the amount of uncertainty that can be tolerated in the program and 

still meet the objectives. For example, the DQO should establish measurement needs in 

terms of sensitivity, accuracy, and precision so that the selection of monitoring devices—and 

the selection of spatial and temporal locations for monitoring—can be determined. 

Decision Rules can be developed by following the DQO process. These rules should be 

formulated into a series of “if.... then” statements that require a decision to be made. Rules 

can be expressed as stated questions, a series of flow charts, or as a decision tree. There are 

four parts of each decision rule: 

1. identification of the PI (e.g., arsenic concentration), 

2. identifiable requirement (What does the PI tell me about site performance?), 

3. an action level (What PI values are within a range consistent with the site PA?), 

4. determination if the requirement has been met so that further action – or no action – 

should be taken. (Do observed data match modeled predictions of the PA? If not, 

then the CSM and modeling/simulations should be revisited or refined to improve the 

PA.) 

The number of required decision rules is based on the nature and complexity of the site as 

well as the amount of risk associated with it. The established rules should be clear and 

concise so that they can be used as a basis for decisions.

1.2.5 Graded Approach 

In applying this Strategy to various facilities, it becomes necessary to make some informed 

decisions concerning the level of effort necessary to ensure the site is performing to design 

standards, utilizing a graded approach. The graded approach can be defined as a process of 

determining the level of investigation, characterization, or monitoring of a site necessary to 

ensure a degree of confidence needed to minimize risk and verify the performance of the 

system. 
 6

The graded approach ensures that the level of analysis, documentation, and actions used to 

comply with requirements are commensurate with: 

(1) The relative importance to safety, safeguards, and security (level of risk); 

(2) The magnitude of any hazard involved; 

(3) The lifecycle stage of a facility (age, status, and condition of facility or process); 

(4) The programmatic mission of a facility (complexity of products or service involved); 

(5) The particular characteristics of a facility; 

(6) The relative importance of radiological and non-radiological hazards; and 

(7) Any other relevant factors. (Ponke, 2002)

Use of the graded approach in the application of the Strategy to a given facility is an intrinsic 

part of the process, resulting in a constant re-evaluation of the level of effort required to 

characterize the site and ensure Performance Assessments are complete and accurate. 

Factors that will influence the level of investigation of a site will include: 

• waste inventory 

• distance to receptors 

• complexity of geology, and 

• intended use of the data. 

Additional discussion of this topic is included in Chapter 4, in the context of selection of an 

appropriate flow and transport model for a site. 
 7

2 The Conceptual Site Model 

Identifying the most appropriate CSM is the heart of the monitoring strategy application. 

The key word is “appropriate”. “Far better an approximate answer to the right question, 

which is often vague, than an exact answer to the wrong question, which can always be made 

precise.” (Tukey, 1962) 

A CSM is a hypothetical picture of the site used to organize and communicate information 

about site characteristics. Consistent with the methods of scientific inquiry, this 

conceptualization is subject to testing with new data. If new data are inconsistent, either the 

data need evaluation, or the model needs to be revised. Successful resolution normally 

requires an iterative process of data collection and analysis (Shaw, 2006; EPA, 1999). 

The CSM forms the basis for all geologic ground water flow and contaminant transport 

modeling for the site. In the Strategy we are largely concerned with constructing and 

confirming the CSM and computer simulations through monitoring. Our CSM and its 

simulation predictions are limited to where and when contaminants are distributed through a 

ground water pathway. It does not include some components important to risk estimates such 

as population distribution or behavior. The first step in the monitoring Strategy is to analyze 

the problem – that is, establish DQOs, and develop useful CSMs. Conceptual Site Models 

form the basis for performance modeling, including flow and transport modeling; estimated 

exposure routes, pathways, travel times, and contaminant concentrations in space and time. 

The CSM includes these components: 

1. a geologic model (framework for hydrogeologic model), 

2. a hydrogeologic model (hydraulics for flow and geochemistry for transport), and 

3. chemical release scenarios. 

Development of a CSM requires that, in addition to the facility-related waste inventory and 

release scenarios, the natural system in which the facility is located be evaluated. In this 

context, the CSM becomes the connection between performance confirmation monitoring, 

site characterization, and the PA. 

Table 2-1 below, from Pope et al. (2004), lists the information included in a general CSM. Its 

most critical elements are those that have the greatest influence on estimated risk. This table 

is a useful guide to the collection and analysis of site data. 
 8

Table 2-1 Summary of information in a conceptual site model for contaminant fate and 

transport in ground water 

Contaminant 

Inventory 

• contaminant inventory and uncertainty in the amount, location, and 

concentration, nature and history of contaminant releases 

• source release information, such as container type and expected 

degradation rates, source control and other proposed remedial actions 

Demographic* • location and information on human (and perhaps ecological) receptors 

under current and anticipated future conditions 

Geological 

• regional and site geologic settings, structures, faults, and fractures 

• depositional environments and paleogeology 

• lithologic facies, distribution, thickness, and transmissive features 

• borehole core descriptions, geologic units and boundaries 

• geophysical data such as log and seismic data interpretations 

• anthropogenic features including buried corridors and heterogeneous fill 

materials that control ground water flow and contaminant migration 

Hydrologic & 

Meteorological 

• characteristics of surface water bodies, locations, depths, flow rates and 

recharge and discharge analysis 

• seasonal or temporal variation of surface water bodies 

• precipitation distributions in time and space 

• meteorological and climatic records, evapotranspiration potential 

Hydrogeologic 

• characteristics of vadose zone and aquifer structures or heterogeneity, 

identification of preferential flow or barriers 

• water content variation in vadose zone 

• hydraulic gradients and the variation in temporal and spatial domain 

• hydraulic properties, including the conductivity, storage, porosity, 

transmissivity, homogeneity, and anisotropy of these parameters 

• aquifer boundary conditions 

• ground-water recharge and discharge 

• ground-water interaction with surface water 

• quantitative description of ground-water flow field 

• ground-water level trend analysis. 

• aquifer usage information, location and production data for water-supply 

wells, and estimates of future uses

Geochemical 

• general geochemical conditions at the site 

• chemical characterization of sources for ground water contamination 

• geochemical processes that affect or are indicative of contaminant transport 

and fate, and mineralogy 

• temporal trends and variations of contaminant sources and concentrations, 

mobility of contaminants in each phase 

• sorption information, distribution coefficients, and sorption mechanisms 

• potential for mobilization of secondary contaminants 

• contaminant attenuation processes 

Biological* 

• identification of plants and animals that could facilitate contaminant 

transport in the near surface 

• plants and animal communities that could occupy the site in the future 

modified from Pope et al. (2004) 

*Demography and biology are not integral parts of the CSM for the Integrated Ground Water 

Monitoring Strategy 
 9

2.1 Development or Review of a Conceptual Site Model 

Figure 2-1 below, is a high level flow chart of the logic path to evaluate (or construct) a CSM 

using a graded and iterative approach. It includes (Step C) interfacing with geologic and 

flow modeling software, which in turn provide computer renderings for visualization. 

Features of the model thought to be critical to monitoring should be identified and their basis 

in field data clarified. Some of these may be location of flow paths, ground water velocity, 

preferential pathways, etc. These, in turn, become the FEPs that influence results of the PA. 

Therefore evaluation of the CSM is basically an evaluation of the uncertainties in the model 

relative to how well the model is able to constrain the outcomes of the PA. 

Normally, a CSM will be available as a starting point, but if this is not the case, then step C 

of Figure 2-1 will result in developing such a model by entering and interpreting data using 

geologic modeling and visualization software. Further discussion and illustration of this 

software is included below and in the case studies of Volume II. 

The three major steps are directly related to the components of the CSM: 

1. Development or evaluation of the geologic model, 

2. Development or evaluation of the flow and transport model, and 

3. Evaluation of risk sources and pathways. 

As part of the evaluation process, the logic path contains a provision to take steps to reduce 

uncertainties if uncertainties in the model elements critical to performance assessment are 

found to exist in the evaluation. Development or evaluation of flow and transport should 

begin with simple exercises like plotting piezometric data and water chemistry data. This is 

illustrated in several chapters of Volume II. 

Note that the subsurface information classed as geology also includes anthropogenic features 

(Table 2-1). Such features have proven to be pathways for contaminant migration at power 

plants (NRC, 2006a). Pipe or cable trenches, backfilled with gravel, can provide highpermeability pathways for rapid spread of leaking contaminants. Pathways provided by such 

features may lead contaminants in directions not predicted by contouring a few points on a 

water-table map. Roof drains and water supply leaks can inject large amounts of water into 

the vadose zone in such a way as to drive ground water and contaminants in directions that 

would not be predicted based on water levels from scattered monitoring wells. 

Because data acquisition and characterization for geologic and flow and transport modeling 

can rapidly become resource intensive, the Strategy supports and encourages a graded 

approach to these activities. Therefore part of the evaluation process is to determine the 

appropriate level of effort and sophistication for the CSM. 

Model and monitoring requirements are a function of site complexity. The appropriate level 

of complexity for flow and transport models is discussed in Chapter 4. 

 
 10

Review of a CSM

A

Determine geologic setting.

C

Evaluate site geological model in regional

context (using geologic modeling and

visualization software).

D

Are site and

regional data consistent?

Is the geologic model

adequate?

YES

NO

E

Construct flow and transport

model.

Collect more site-specific

data as indicated.

Identify data gaps for

additional modeling data.

Select and apply tools and

activities for

characterization of critical

features and processes

appropriate to site geology.

B

Collect regional and local

geologic information.

G

Evaluate Conceptual Site

Model For Risk

Pathways(CSM)

F

Is flow and

transport model

adequate?

NO

YES

Figure 2-1 Evaluation of a conceptual site model. This process starts with evaluation of 

data to develop an initial simple model, and gradually moves to more complex 

interpretation if needed. 
 11

2.1.1 Evaluation of Geologic Models 

The site geologic model contains the critical hydrogeologic elements that form the 

underpinnings and control the behavior of the flow and transport models associated with the 

PA. For this reason, a thorough evaluation of the geologic facet of the CSM (particularly the 

hydrogeologic and geochemical elements) is a fundamental element of the strategy. The 

complexity of the geologic portion of the CSM should be adequate to characterize these 

elements and to reduce the uncertainties associated with them to an acceptable level. 

Computer simulation of flow and transport will require simplification and estimation of 

hydrogeologic features. The process of developing a computer model from a geologic model 

is called abstraction. “Model abstraction is defined as the methodology for reducing the 

complexity of a simulation model while maintaining the validity of the simulation results 

with respect to the question that the simulation is being used to address.”(NRC, 2006b) A 

computer simulation for a regional water resource issue might be quite simple, whereas a 

model to address a contamination problem on the scale of a nuclear facility may include very 

small features. With improvements in computing power, almost any needed level of detail 

can be realized. 

2.1.1.1 Congruity with Regional Setting 

Any decision loop based on geologic investigation begins with the regional picture. Has the 

geologic model fully integrated the local model into the regional framework? Once the 

regional framework is determined, all site data should be compared to the regional 

framework for congruity, and incongruous data accounted for. For example, if joints and 

fractures are present and intersect regional geology at 45 degrees, the local site geologic 

model must reflect this joint pattern or explain either why it deviates from the trend or is not 

important to the PA. This test of congruity may lead to a selection between competing 

geologic models. 

2.1.1.2 Review of Existing Site Characterization and Monitoring Data 

Site specific and other local characterization and monitoring information contained in reports 

by the site operator or subcontractor should be reviewed for internal consistency with the 

CSM. 

Some brief case studies are included in Chapter 4. Additionally, more extensive case studies 

are the focus of Volume II. No one site allowed thorough testing of the Strategy. Each site 

presented some limited data pieced together from available sources. Still, each site teaches 

something that can be applied generically to all sites. In all of these case studies, 

examination of site data revealed Derived System Indicators appropriate for the sites under 

study. Once revealed, these DSIs can be applied to evaluate the validity of data used in 

models and interpretations. 

Any consideration of the adequacy of a monitoring system at a site level must fit into the 

context of the regional geologic and hydrogeologic framework, including FEPs such as 

faults, fractures, channels, bars and other geologic features that have an impact on the 

distribution of significant parameters affecting the flow of ground water within the local 

system. The monitoring data must be examined to determine if regional system parameters 
 12

contain a representative range of values that effectively capture conditions at the site. 

Characterization and monitoring data should be evaluated for outliers and trends that might 

suggest alternative conceptual models. Examples might include water levels that deviate 

from smoothed contour potentiometric maps or trend surfaces. In the Savannah River Site 

case described in Chapter 4, the anomalous data were first discarded and only later identified 

as clues that indicated a need to revise the CSM. 

2.2 Evaluate Model Uncertainty 

Uncertainty analysis is the assessment of the effects of uncertainty in various forms on the 

modeling results. In the context of the Strategy, these results are estimated ground water 

concentrations or risk to some member of the public. Parameter uncertainty, be it from 

natural variability or from imperfect knowledge, is propagated through the model, causing 

uncertainty in the results. Conceptual model uncertainty is manifested in different computer 

models, also producing uncertainty in results. Through sensitivity analysis (described in the 

following section), opportunities can be identified to cost-effectively reduce these 

uncertainties either through data analysis or additional data acquisition. 

The natural variability of many hydrogeologic properties contributes greatly to uncertainties 

in the CSM. Because this variability is spatial, and in some cases temporal in nature, its 

distribution and properties can be estimated as spatiotemporal averages and used to update 

the geologic model. Every effort should be made to characterize this variability through data 

analysis in its geologic context. The use of a mean permeability, for example, may obscure 

preferential flow pathways that become evident only when displayed in the context of the site 

geology. 

Characterization data are often subject to non-unique interpretation. Thus a family of 

conceptual models is possible, all honoring the available data (Neuman and Wierenga, 2003). 

It should be possible to conduct field tests to determine which of the possible models is most 

likely to represent reality, though it must be recognized that we cannot know if we have a 

“correct” model. Multi-well pumping tests, tracer tests, and cross-well geophysics can fill in 

data gaps that might help discriminate between models. 

An example is a long-term pumping test that suggests a permeable zone at some distance 

from the pumping well (Figure 2-2). The pump test, combined with knowledge of the 

geologic depositional setting (Walker, 1984) from regional data, might direct additional site 

characterization toward looking for buried stream channels, oyster beds, pumice beds, or 

other high permeability lithofacies. 

In Figure 2-2, drawdown reaches a maximum of about 0.4m in about 2 months, but then 

partially recovers and levels out after 3 months at about 0.25m. In this case, interconnected 

fractures begin to supply water after pressures at the pumping location are reduced. 

Examples can also be found in the literature or on the internet of somewhat similar response 

from wells near streams where the stream begins to supply water once the cone of depression 

from pumping reaches it. (Discussion of dual-porosity, dual permeability, and recharge 

boundary interpretation of pumping tests in fractured rocks is given in Craig and Reed, 1989) 
 13

Figure 2-2 Drawdown Curve. (Scott C. James, DOE / SNL, personal communication, 

2007) 

2.2.1 Risk-Informed Sensitivity Analysis 

A sensitivity analysis focuses on the question of which uncertainties (parameter or modeling 

uncertainties) are the most significant in contributing uncertainty to a selected result, such as 

an estimate of a ground water concentration of some contaminant, or future potential risk to a 

member of the public. A global sensitivity analysis systematically evaluates the correlation 

between each source of uncertainty in the model to the uncertainty in the result. Even in 

complex models, results are typically influenced significantly by only a handful of 

parameters or modeling constructs (or, more generally, FEPs). 

If a parameter is identified as having a strong influence on determining a risk endpoint, for 

example, that parameter is said to have risk-informed sensitivity. In other words, the value of 

that parameter is important in determining the estimated risk. In the context of the Strategy, 

such a parameter would be a candidate for a PI, or might suggest a PI that is influential in 

determining the value of that parameter. 

As discussed in the following section, the identification of sensitive parameters assists the 

iterative process of updating the model by improving (i.e. reducing the uncertainty of) 

parameter distributions. The cycle of identification of sensitive parameters, improvement in 

characterizing the uncertainty of those parameters, rerunning the probabilistic model, and 
 14

performing another sensitivity analysis continues until some stopping rule is met. Typically, 

this stopping rule is the outcome of some cost/benefit analysis applied to the updating of the 

parameters. It may be found that the uncertainty cannot be practically reduced, or that doing 

so would incur excessive cost. If the point is reached where the uncertainty of the most 

sensitive parameters is irreducible, then the iterative process is terminated and the model 

uncertainty is considered minimized. 

A global sensitivity analysis should be performed to identify the critical FEPs characterized 

by the model to which the PA is the most sensitive and could potentially impact the outcome 

of a release to the environment or risk to members of the public. It should be noted that those 

parameters to which one result is sensitive will generally be different from those for another 

result. For example, the parameters that are most influential in determining the peak 

concentration of tritium in the ground water will have little to do with long term potential 

doses, which would be caused by some other contaminant, given tritium’s relatively short 

half-life. This illustrates the importance of thoughtful selection of modeling endpoints for 

sensitivity analysis. 

2.3 Model Parameter Development 

The sensitivity analysis described above requires a probabilistic model, with stochastic 

definitions of input parameters. Any parameters that are defined deterministically (i.e. as a 

single value, implying no uncertainty) cannot be evaluated for sensitivity. This does not mean 

that the result may not depend heavily on the value of the parameter; rather, it means only 

that the degree of dependence remains unknown. It is important to appreciate this fact, 

recognizing that although the model may in fact be sensitive to a deterministic parameter, a 

sensitivity analysis can reveal only sensitivities produced by stochastic parameters, 

conditional on their distribution. In general, modifying the statistical distribution used to 

define a parameter value will change its role in result uncertainty. In some cases, a reduction 

in the uncertainty of a parameter (by including new information, for example) may remove 

the parameter from the list of sensitive parameters altogether. 

A model will typically go through several iterations of uncertainty reduction by improving 

the definition of sensitive stochastic parameters. There is generally nothing to be gained from 

improving distribution definitions for those parameters that have little influence on the result 

of interest. 

In Figure 2-3 an example is given of a modeling parameter (porosity) for which there is 

initially no data so that a uniform distribution between 0 to 100% porosity is chosen for the 

model. The first step of improvement is the choice of a more likely distribution (triangular) 

based on expert opinion. Additional data from field measurements can improve the quality 

of the probability density function (PDF) until a representative set of values is available for 

model input. 

An optimization example after Deschaine (personal communication, 2003) uses geostatistics 

to reduce the uncertainty in plume location. In this case, the initial estimate of the plume 

location identified as baseline in Figure 2-4 was inadequate. The existing monitoring 

network defined the plume with 93% confidence. Adding one well in an area of uncertainty 

increased the confidence to 98%. Further wells do not increase confidence significantly 
 15

enough to warrant installation. This technique can be used to derive stopping rules for 

additional characterization and monitoring. 

Both of these examples illustrate, on different scales, the Bayesian concept of iteratively 

updating the knowledge base of the PA and associated analyses, gradually improving the 

model and reducing overall uncertainty by obtaining more field or laboratory data. This 

knowledge base includes not only parameters, but sub-models (e.g., the model for 

unsaturated hydraulic conductivity discussed earlier) and conceptual models as well. As 

better knowledge is gained, through fieldwork, interpretation, or even improved insight into 

the workings of the PA model itself, the project team will be faced with revising the project 

at many levels. In simple cases, a new distribution for a parameter may be devised and 

modified in the PA model. If more global information is gained, then major revisions in the 

conceptual model may be in order. 

Techniques used to monitor for spatially distributed parameters have the usual concerns 

associated with accuracy and precision. However, in realistic settings many of these 

parameters show short-range spatial correlation characteristics so that the cost effectiveness 

of the technique becomes even more of an issue. Also, the temporal nature of these 

parameters must be accounted for. Some are relatively constant such as transmissivity and 

can be calculated in a one time sampling event. However, others, such as moisture content, 

may need to be monitored at specific intervals or due to some triggering event. 
 16

Figure 2-3 Parametric updating example 

Parametric updating example 

In this example of the iterative development of a 

distribution for a stochastic parameter, porosity of a 

porous medium is used. Based on its geometrical 

definition as the ratio of the volume of voids to the 

entire volume, the physical limits for porosity are 0 

to 1, 0 meaning no porosity (a solid with no void 

space in it) and 1 meaning an empty volume with 

no solid material in it. A prior distribution based on 

that information alone would be uniform(0, 1). 

A hydrogeologist’s first guess at porosity may be 

0.3, based only on the knowledge that the material 

is a sandstone, for example. This single value is 

represented here as a discrete distribution. 

Combining the knowledge of the physical limits 

with this point estimate leads to the triangular 

distribution tri(0, 0.3, 1). We know that a porous 

medium cannot actually have a porosity of exactly 

0 or 1, and that 0.3 is expected, and the triangular 

distribution reflects that elementary state of 

knowledge. This might be a reasonable starting 

point for a distribution for a probabilistic model, 

such as a PA. 

As site-specific data are obtained for field 

investigations, these data can be represented by 

some parametric distribution (a normal distribution 

is used in this example), which can be refined as 

more data are acquired. 

In this example, a beta distribution may in fact be 

more appropriate, since truncating the normal at 0 

and 1 will slightly alter the mean value. 
 17

Figure 2-4 Plume refinement with addition of monitoring wells (after L. Deschaine, 

personal communication 2003). 
 18

3 Performance Indicators 

As noted above, site and system Performance Indicators are measurable or observable 

features that provide insight into reliability of the Conceptual Site Model (CSM), and hence 

simulations and assessments based on the CSM. 

This chapter outlines some considerations in developing indicators of site performance. 

These include developing PIs (including Derived System Indicators [DSIs]) from analysis of 

site characteristics, modeling, analysis of the PA, statistical analysis of the monitoring data, 

and from the CSM. 

Effort should be focused on identification and application of selected site-specific PIs that are 

critical to the review and or development of a monitoring plan. Through implementation of 

the Strategy, appropriate PIs, monitoring devices, and decision criteria can be identified. PIs 

should also identify the relevant information needed to assist the decision maker in the 

determination of the adequacy of the existing or proposed system for reduction of risk and 

uncertainty. 

While it is possible to construct general lists of possible PIs, actual PIs are always site 

specific. While some may be obvious, others must be developed through expert analysis of 

site features and available data. Chapter 8 of Volume II discusses some data analysis 

methods that may lead to selection and evaluation of site PIs. 

3.1 Develop Preliminary List of Performance Indicators 

The purpose of this exercise is to develop a preliminary list of site and system PIs through 

analysis of the site characterization, the facility design, and the PA. Site-specific data on 

inventory, potential leak points, known surface and subsurface contamination, analysis of 

characterization and monitoring data, and modeling for risk-significant contaminants and 

FEPs will suggest a number of site-specific PIs. 

We find it useful to think of Performance Indicators in three classes. The first two classes 

include chemicals that are the primary risk drivers and their surrogates as well as chemical 

and physical controls on flow and transport (i.e., pH, rock permeability). All of these are 

measurable through sampling and analysis. The third class results from interpretation of 

results of measuring the first two classes of PIs. Included in this third class would be 

statistical and spatial outliers. 

Confirming site performance includes evaluation of risk-sensitive PA modeling assumptions 

related to FEPs. These FEPs, such as hydrologic system geometry and behavior and 

associated uncertainties, are evaluated through analysis of site characterization and 

monitoring data, including analysis of the distribution of primary PIs. This analysis can also 

yield a set of indicators that show whether the actual system is behaving as predicted by the 

models. We refer to these derived indicators as DSIs, because they function at the system 

level rather than at the component level. An analogy to a car’s indicators would be to use the 

oil temperature gauge as a primary indicator, and the check engine soon light as a Derived 

System Indicator. 
 19

Primary PIs are the direct risk drivers if present in sufficient concentration at points of 

exposure. All the others may be just as critical to estimating risk as the primary list because 

they are either phenomenon in need of quantification in order to estimate transport or they are 

indicators of model validity. Because risk ultimately exists only given certain behaviors at 

the point of exposure, these others that may control flow and transport are no less important 

than concentration measurements of a regulated substance. 

In Figure 1-1 the step called Analysis reviews the PA, the Site, and the Facility for potential 

PIs. The process is iterative - reviewing data gathered for the PA, risk related contaminants 

and FEPs from the PA, as well as characteristics of the facility under study to produce an 

exhaustive list of PIs and DSIs. In Figure 3-1 below, we have expanded the Analysis step to 

show, with more detail, what is involved in the selection of PIs. 
 20

Figure 3-1. Monitoring Strategy Logic Flow with expanded Analysis step. 

-analyze site characteristics  contaminant inventory  regional and site geology  site hydrology and meteorology  site hydrogeology  site geochemistry  site biology and ecology -analyze site monitoring data -review and construct CSM  geologic models  hydrogeologic models  chemical release scenarios -select and simulate flow and transport based on CSM 

data management and model visualization incorporate the state of knowledge through uncertainty determine the most significant FEPs though probabilistic Performance Assessment, probabilistic risk assessment and sensitivity analysis 

HOW: 

MDs 

WHERE & 

WHEN: 

MPs 

WHAT: 

PIs 

PERFORMANCE 

CONFIRMATION 

MONITORING: 

Data Collection & 

Analysis 

FEEDBACK to update: 

• CSM • PA • Choose PIs, MDs, MPs • Stopping Rules 

 Identify indicators of site performance* 

- 1st and 2nd Class PIs 

-DSIs including FEPs 

* Iterative process 

 Evaluate & refine CSM and flow and transport models* 

Do simulations match observed data? Do simulations 

confirm assumptions used in the PA?  Refine, identify, and evaluate PIs and DSI * Iterative process

ANALYSIS 

MONITORING NETWORK 

DESIGN 

MONITORING & FEEDBACK 
 21

3.2 Classification of Performance Indicators 

Listed in this section are the items to be considered in the development of a preliminary PI 

list. This section is structured around the class types in Table 3-1. 

Table 3-2, below gives examples of PIs that might be chosen based on a Performance 

assessment. Table 3.3 in section 3.4, below, is adapted from a recent paper by Long and 

Yabasuki discussing PIs that are specific for a bioremediation project The PIs they use are 

cross-referenced to provide site and project-specific examples of the generic PIs in Table 3-1.
 22

Table 3-1 Classification of Performance Indicators 

Class 1: Chemical 

A. regulated and direct drivers of risk – i.e., U, Cs-137, Pu, Sr-90, tritium, Rn 

• these are Primary PIs 

B. surrogates and indicators that a process is occurring - 

• gross alpha for uranium 

• chloride or nitrate from same source as risk drivers 

• degradation products – i.e. Am-241 for Pu-241, organic breakdown products 

for MNA 

C. process control chemical indicators needed to model transport 

• pH, alkalinity, conductivity, major cations, major anions, redox indicators... 

Class 2: Physical 

• examples include water content, pressure distributions 

• physical properties of rocks (porous/fractured media) 

• physical properties of subsurface fluids 

Class 3: DSIs modeled or derived from data analysis 

A. distribution of uncertainty 

• this would be determined by examining the distribution of characterization 

data available to develop a CSM and flow model. (areas of sparse or 

questionable data would have high uncertainty.)

B. lack of congruity 

• tests of flow and transport models 

• Do actual plume maps match predicted plumes? 

• Does site geology match regional geology? 

• Does site geology match geology reported from adjacent areas? 

C. outliers 

• spatial - for example: 

• “bulls eyes” around data points on contoured maps 

• areas of high characterization uncertainty 

• statistical (with no spatial component) 

• univariate, including control chart anomaly, 

• multivariate, including single-sample cluster 

D. risk-informed sensitive parameters (defined in text) 

• can identify which probabilistic parameters are the most significant 

in estimating risk. (some may be suitable as PIs or DSIs). 
 23

3.2.1 Class 1: Concentrations in Natural Media 

PIs that could indicate leaking waste or undesired/unexpected water flux through radioactive 

materials in the ground will be manifested as contaminant concentrations in: ground water, 

surface water, the vadose zone, (bulk soil or as soil gas), and in local flora and fauna. 

3.2.1.1 Contaminants in Ground Water 

The most important PI is the concentration of contaminants found in ground water, including 

the vadose zone. Once waste has reached the water table, transport of waste constituents in 

solution or suspension may be monitored through direct sampling of ground water. This is 

normally done through permanent wells, but can also be done with direct-push technologies. 

Typically, samples are analyzed in the field for highly unstable parameters (in terms of 

transporting samples), such as pH, alkalinity, redox potential, etc., which may be early 

indicators of system failure. After stabilization, samples can be transported to a laboratory for 

analysis for other constituents. 

Daughter products from decay of uranium, thorium, plutonium, tritium or other parent 

radionuclides may be more mobile, or may produce radiation that is more readily detected 

than the primary waste constituents. For example, 208Tl, 214Bi, and 241Am are gamma emitters 

that may provide evidence for the presence of U, Th, or Pu. 3

He can be used to detect tritium 

at depth. These can be measured and used as early warnings of a potential system release. In 

Table 3-1 these are classed as surrogates for primary contaminants. 

Even though 90Sr is not a gamma emitter, low energy gamma rays are emitted from some 

materials when irradiated by 90Sr’s beta radiation through a process called bremsstrahlung. 

This radiation has been detected and used in monitoring programs at Hanford’s high-level 

waste tank farm. (Hartman et al., 2003) 

3.2.1.2 Contaminants or Indicator Parameters in Soil Gas 

Some radioactive and organic liquids waste constituents may move without the assistance of 

water, including mercury, iodine, tritium, radon, argon, krypton. 

While most radionuclides do not exist in the gaseous phase, several do partition between 

water and gas phases, including tritium (as tritiated water), 14C (as 14CO2 and other volatile 

carbon compounds), and the noble gases Ar, Kr, and Rn. Many RCRA constituents are 

volatile or semi-volatile, and are detectable in soil gas. 

3

He is produced by decay of 3H, and as a noble gas, is mobile in soil. Helium and radon in 

soil gas and ground water have been used to prospect for uranium deposits. 3

He/4He ratios in 

soil gas have recently been used by Pacific Northwest National Laboratory (PNNL) to trace a 

tritium plume at the water table (Gee et al., 2001). 

Gases such as helium or sulfur hexafluoride can be used as tracers to detect leak locations in 

underground tanks, lines, or other structures. 
 24

3.2.1.3 Contaminants in Other Fluids 

Kerosene containing traces of t-butyl phosphate and uranium or plutonium (from the PUREX 

process) has been disposed of at some sites. Biodegradation of kerosene will produce 

methane and halogenated solvents (as well as their degradation products) which are readily 

detectable in soil gas. Carbon tetrachloride and other halogenated organic solvents may 

represent issues at some waste or decommissioned nuclear sites. Dense non-aqueous phase 

liquids such as trichloroethylene (TCE) are also a special problem at many sites. Since they 

exist in a separate phase from water, they sink through ground water due to their high 

density, and present a long-term source for slow dissolution into natural waters. They are 

notoriously difficult to clean up. 

3.2.1.4 Surface Water 

Surface water sampling may provide a method of locating preferred pathways of ground 

water. For example, sampling along a seep line, or of shallow subsurface water along a creek 

edge (where upwelling is expected) may provide more relevant information than sampling 

the flowing stream. 

3.2.1.5 Plants and Animals 

Vascular plants can transport waste constituents, and may extend roots significant depths to 

obtain water. Insects, especially ants and termites, and burrowing vertebrates can invade 

buried waste and may redistribute hazardous or radioactive constituents (Hooten and Myles, 

2006). 

Plants have been used in geochemical exploration for many years (Brooks, 1968). 

Phytoaccumulation of Cr, U, and Pu was the topic of a literature review by DOE’s Amarillo 

National Resource Center for Plutonium (Hossner et al. 1998). The focus of this work was 

soil remediation, but plants that accumulate metals are also useful integrating samplers for 

detection of metals. Sagebrush (Erdman and Harrach, 1981) and fir (Dunn, 1980) are known 

to be useful for uranium exploration, and should also serve as indicators of actinides in 

ground water near waste sites. Andraski et al. (2002) used tritium concentrations in creosote 

(Larrea tridentata) as a system indicator to map tritium plumes in the unsaturated zone in the 

Amargosa desert (See Chapter 4 of Volume II). 

3.2.2 Class 2: Physical 

3.2.2.1 Water Content 

Waste disposal sites are generally designed to keep water from making contact with or 

flowing through the waste. Water is reactive with some waste components, promotes 

biological activity, and can transport waste components in solution or suspension. These 

aqueous reactions vary greatly depending on the waste form, the method of disposal, and the 

packaging materials used. 

Water can react directly with waste metals such as sodium, aluminum, lithium, uranium, or 

plutonium, releasing potentially explosive hydrogen gas and producing transportable ions. 

Water can promote corrosion or degradation of containment devices or structures. For 
 25

example, at WIPP, halite may react with steel containers, releasing hydrogen gas. MgO 

added to fill is expected to ameliorate the situation (NAS/NRC, 2001). Water can promote 

biodegradation of organic packaging materials and produce organic or inorganic corrosive, 

complexing, and solubilizing agents. 

The ground-water flow and transport model will be based on a water budget. This budget 

will include some fraction of precipitation flowing through a cap or cover – thus the PIs 

include water flux at selected points. 

The amount of water found in the unsaturated zone or in engineered components of a 

disposal system can also be an indicator of the performance of a disposal system or the 

potential for transport in the unsaturated zone. Water can transport waste constituents both

downward and upward through infiltration, capillary action in the unsaturated zone, or 

fluctuations in water table elevation. In addition to these purely advective processes, water 

presents an opportunity for contaminant migration through diffusion. 

For these reasons much of the published work and research on waste site monitoring, 

especially in the western U.S., has focused on detection of water and assessment of water 

flux. (e.g., NRC sponsored research at the University of Arizona and PNNL, Young et al., 

1999a.) 

Pressure head distribution must also be understood in order to model flow and transport. An 

example is shown in Section 4.4.2 below where an anomalous water level led ultimately to 

major revision of the CSM. 

3.2.2.2 Physical Properties of Rock or Sediment 

These include transport-controlling features such as, porosity, permeability, stratification, 

fractures, etc. Heterogeneity is a function of the interrelationship of these controlling 

features. 

Radar data (Wyatt and Temples, 1996) typically collected as part of the geotechnical phase 1 

of characterization can be used to map fractures and other small scale features present at a 

site. Data can be collected and reviewed to determine if the condition is present in the data 

set, but missed during the initial interpretation. Existing monitoring points can be used to 

collect new information that will be used to update the CSM. Chapter 4 of Volume II 

includes a discussion of the application of three-dimensional computer modeling techniques 

to previously published geophysical data for the Amargosa Desert Research Site. Reevaluation of the data resulted in the identification of a previously unidentified structure that 

could act as a vertical pathway for the migration of contaminants and was subsequently used 

to update the CSM. 

When the CSM fails to produce satisfactory results, existing data can be reviewed to assess 

the possibility of a FEP that was missed in the original interpretation or new data can be 

obtained to update the CSM. 
 26

Figure 3-2. Aquifer heterogeneity in fractured rocks (Hsieh and Shapiro, 1996). 

Figure 3-2 provides an example of heterogeneity in fractured rocks and the potential for high 

permeability pathways based on pump tests from the Mirror Lake FSE Well Field in New 

Hampshire. Fractures with a high degree of interaction create highly transmissive zones 

within less permeable fractured rock. The highly transmissive fracture clusters in the FSE 

well field exert a strong influence on multiple-borehole hydraulic tests. In the Figure 3-2 the 

top left panel shows drawdown in other wells when FSE-6B is pumped. Multiple plumes 

may be present in an aquifer and isolated from each other due to these heterogeneities. At the 

Mirror Lake FSE Well Field, there are at least four distinct transmissive fracture clusters 

present (Hsieh and Shapiro, 1996). Dershowitz et al., 1998, is an excellent reference on the 

issues related to modeling flow in fractured rocks. 

Because geology controls certain distributions of properties such as porosity and permeability 

rather than these being associated with random events, we can analyze existing data to 

determine if there is a spatial component to the data. If trends are present, data can be 

evaluated and reinterpreted to improve confidence in its distribution. Most of this type of 

analysis has been pioneered by the petroleum industry (Yarus and Chambers, 1994). 

3.2.2.3 Physical Properties of Subsurface Fluids 

Fluid properties such as density and viscosity are important inputs to the modeling process. 

These, as well as temperature and salinity, are generally assumed to be constant throughout 
 27

the modeling domain, but in some cases these parameters must be allowed to vary in time 

and/or space in order to sufficiently mimic natural conditions. 

3.2.2.4 Features, Events, and Processes as Performance Indicators 

Performance Assessment of long-term monitoring systems requires the identification and 

evaluation of all the FEPs that could potentially affect the risk to potential receptors over 

very long timescales (e.g., millennia). To assess the potential impact of these FEPs, it is 

possible to conceive different scenarios in which combinations occur, and to develop 

conceptual models to represent the behavior of the system in those scenarios. 

The FEPs are classified according to their probability of occurrence in the timescale of 

interest. FEPs that are quite likely to occur are used to define the natural, or expected, 

evolution of the site and its environment, in the absence of any major disturbances. This is 

known as the “base scenario”. The less likely FEPs are used to define a number of alternative 

scenarios. 

In 10 CFR 61, Section 50 (disposal site suitability requirements for land disposal), NRC has 

identified characteristics of sites that might be conducive to facility siting. These include a 

geologic and hydrologic setting that presents no undue hazards, and is simple enough to be 

characterized, modeled, analyzed, and monitored. Our concern in this section is with 

hydrogeologic FEPs that control or influence water-borne radionuclide transport, and which 

are or can be identified as part of site characterization. 

The International Atomic Energy Act (IAEA) has also published guidance on the same topic 

(IAEA, 1999). IAEA/NEA has published a database of FEPs that have been considered at 

nuclear sites in many countries (OECD/NEA, 2000). FEPs to be included in the PA must be 

evaluated on a site-specific basis, and confirmed in the PA examination process. A number 

of groups have compiled lists of site FEPs and scenarios under which these may be important 

(Guzowski, 1990, and Guzowski and Newman, 1993). 

Idaho National Laboratory (INL) has published an exhaustive summary of unsaturated zone 

FEPs for which there is considerable uncertainty (Wood et al., 2000). The document reviews 

unsaturated zone investigations conducted at INL between 1960 and 1999. It makes 

recommendations for programs to address issues with spatial variability, data, numerical 

models, conceptual models, source terms, geochemistry and microbiology, as well as 

organization and communication. 

Some of the FEP uncertainties that can be reduced through monitoring are: 

• changes in redox conditions along flow path, 

• uncertainties in Kd, and the conditions under which it is valid, 

• uncertainty in fluid-matrix interactions, 

• possible chemical alteration of sediments from reaction with leaked fluids (Pruess 

et al., 2002), and 

• uncertainty in assumptions about dispersion, both horizontal, and vertical 

(dispersion is often used as a proxy to account for flow controls). 
 28

Each hydrogeologic FEP represents a factor to be considered in a PA, and may be critical to 

site-specific monitoring plans. Such FEP lists should be reviewed, and appropriate PIs and 

DSIs selected for the preliminary PI list. 

DSIs derived from analysis of site characteristics or data analysis are discussed in Chapter 8 

of Volume II, including examples of the identification of outliers through multivariate data 

analysis. 

3.2.2.5 Elicitation of PIs from Analysis of a Facility 

A systems analysis of the facility should be performed to determine normal operating ranges 

and possible failure modes, and what could be measured to indicate a failure. For reactor or 

processing facilities, at least part of this analysis should be available from the Safety Analysis 

Report. 

Floor joints, floor-to-wall seals, drains, wall or floor piping openings, and other potential 

points of release to the environment should be considered as potential monitoring points. 

PI definition is a function of technical requirements for the facility design envelope. The 

facility design envelope technical requirements are quantitative values identifying optimal 

performance. The facility design envelope defines the technical requirements that outline the 

expected performance ranges and identifies those parameters that are critical to overall 

system performance; e.g., hydraulic performance, structural stability, projected system 

durability, etc. To illustrate this overall process, a hypothetical Low Level Radiological 

Waste Disposal Facility will be examined in the following paragraph. 

The facility footprint is static but the overall system is dynamic. An example to illustrate 

these concepts for selecting appropriate performance parameters is a near surface radiological 

waste disposal facility. The facility is a complex system incorporating various components 

and materials; the design of which is driven by its primary intended functions. The covered 

trench is an old and simple disposal concept consisting of waste in trenches covered with soil. 

Disposal sites using this concept frequently have retrofitted engineered barriers. A generic 

cross-section is shown in Figure 3-3. 
 29

waste

backfill soil

cover

infiltration of

surface water

ground water

intrusion

leachate

native 

soil

Figure 3-3 Cross-section through a generic disposal trench 

There are a number of pathways by which radionuclides and hazardous waste constituents 

may migrate from the waste to ground water, including: 

• infiltration of surface water, 

• ground water intrusion, 

• migration of contaminated water (leachate), and 

• movement of gases in vadose zone (An example is discussed in Volume II, 

Chapter 4 for the USGS ADRS). 

Each of these pathways provides opportunities for monitoring. Moisture distribution in a cap 

or cover, changes in water table elevation, and contaminants or indicator parameters detected 

in lysimeters or wells are all potential system PIs. 

For an engineered system, a PI could be any characteristic or consequence that would 

indicate degraded performance. These should be derived from a careful review of the facility 

design and performance requirements of the engineered system. The PI provides a 

measurable quantity related to performance to support an overall system understanding and to 

assess the quality, reliability or effectiveness of an engineered system, as a whole or of 

particular aspects or components (IAEA, 2003). 

An example of a measurable quantity for an engineered system component is the hydraulic 

performance of an engineered barrier. Degradation of barrier component hydraulic 

performance is a predominant effect and may be a Derived System Indicator in shallow waste 

disposal facilities. EPA, 2006, reports on final results of the evaluation of a compacted clay 

barrier near Albany, N.Y. as part of the Alternative Covers Assessment Program (ACAP), 

and suggests that the site’s wet/dry cycles and root penetration caused the clay barrier to 

begin failing within the first eight months of service. These findings are similar to those of 

other ACAP laboratory and field studies showing that conventional clay barriers also degrade 

quickly in cool/humid or warm/dry environments and only minimally reduce percolation. An 
 30

evapotranspiration cover seemed to perform better than compacted clay alone at the Albany 

site. (EPA, 2006). This effect portrays the degraded effectiveness of the engineered system as 

a whole and the necessity to adapt a monitoring strategy that can indicate degrading 

performance conditions. A direct impact of this effect is an unexpected accelerated release of 

short lived radionuclide contaminants into the biosphere. Engineered system designs should 

incorporate a multiple barrier configuration to compensate for component degradation. 

From the standpoint of monitoring system design, it should be assumed that landfill covers 

will fail far short of any 30-year design lifetime. Monitoring should be planned at the time of 

landfill design and, if possible, integrated into the engineered facility. 

3.2.3 Class 3: Indicators Derived from Data Analysis or Modeling 

Derived System Indicators (Class 3 PIs) are derived from data analysis or are components of 

ground water or geologic modeling to which PA results are sensitive. Data analysis may be 

as simple as producing a contoured map; may involve computer methods such as cluster or 

factor analysis; or may include evaluation of computer model predictions. 

Some examples are presented as part of the discussion, and others are described in detail in 

Chapter 8 of Volume II. Additional discussion illustrating derivation of performance 

indicators is in Section 4.3.2, below. 

The discussion in this section illustrates the process of deriving indicators through data 

analysis and modeling. Many of the PIs actually called out in this section are Class 1 

(Chemical) or Class 2 (Physical PIs), but in document construction, it seemed more logical to 

keep the PA and facility discussion (above – including Class 2 and 3 indicators) intact. This 

document is a review draft, it follows that we invite clarifying reviewer comments. 

3.2.3.1 Indicators from Performance Assessment 

This section discusses the PA process in a context of PI development. A more generalized 

discussion is in Chapter 4. 

A site-specific PA can be used to identify PIs which may be used to test the assumptions and 

results of the PA. In order for this to work, the PA must be subjected to a sensitivity analysis. 

The process of iterative PA development, as outlined in Figure 3-4, assumes a probabilistic 

approach. NRC has developed a methodology for development of PAs of LLW disposal 

facilities (NRC 2000). 

PA development begins with some conceptual understanding of the facility and the site, 

based on what data are initially available from site characterization (see Figure 3-4). For an 

operating facility, some information should be available, but for legacy sites (or for proposed 

sites), a site characterization may have to be performed as a starting point. Given one or more 

CSMs and (if a facility exists) a conceptual facility model (CFM), one may embark on a 

preliminary version of the PA. As the PA is developed, the analyst should consider its 

weaknesses and data gaps as potential PIs. 
 31

As emphasized in Neuman and Wierenga (2003) and their discussion of Bevin's work (Bevin 

1993, Bevin 2000, Bevin and Freer 2001), the analyst must be aware that many CSMs may 

fulfill the requirement of agreement with available data. The analyst must pick a starting 

point and proceed, without the misconception that the selected model is correct a priori, but 

with the intention of testing the CSM through monitoring (and the PA, which is the modeled 

implementation of the CSM) as a working hypothesis. 

Those input parameters or PA models that are highly uncertain will be subjects for an 

uncertainty analysis, which identifies how the parameter uncertainty affects the uncertainty of 

the PA results (being ground water concentrations, dose, or risk). A sensitivity analysis 

identifies which of these parameters are the most significant to some endpoint in the model 

(e.g. dose to a hypothetical future human receptor) and appropriate indicators (i.e. a 

measurable component) should be developed. A flexible PA is desirable—one that will 

accommodate the most probable conceptual models, perhaps even simultaneously. 

The PA must also be constructed to produce specific results. Most importantly, it must 

evaluate the site’s compliance with performance objectives, which are defined by the 

regulations driving the PA. For NRC’s work, these regulations are derived from 10 CFR 61, 

but associated regulations for disposal of radioactive wastes (which could be construed to 

include the residuals of a decommissioned facility) include 40 CFR 191, 40 CFR 194, and 

DOE Order 435.1. The performance objectives are all rooted in risk and can be used to 

derive system PIs.
 32

Develop conceptual site model(s)

(CSM) through site characterization

and synthesis.

Have

sensitive parameters

changed or been

redefined

?

NO

YES

For each CFM: Determine the performance indicators (PIs) that would indicate

a failure.

For each CSM: Determine the PIs to evaluate the CSM and to discriminate

between alternative CSMs.

For each sensitive parameter in the PA: Determine PIs to monitor or refine

definition of a parameter.

Perform designated monitoring and data analysis.

Continue

monitoring

?

NO

YES

STOP

Update

the PA.

*Nuclear Regulatory Commission's

Performance Assessment Methodology

(NUREG-1573)

Critically examine facility and

CFM for failure modes.

Critically examine CSM for potential

weaknesses and alternate CSMs.

Determine which could be subject to

uncertainty/sensitivity analysis in the

performance assessment (PA).

START

Develop PA (see NRC PAM*) to

accommodate different CSMs, if

applicable. 

Determine performance objectives.

Perform risk-informed PA sensitivity

analysis in order to identify sensitive

parameters.

Do

CSM or CFM

need modification

?

YES

NO

Develop conceptual facility

model (CFM) through

systems analysis.

Figure 3-4 The relationship between performance assessment and monitoring
 33

Through sensitivity analysis, the PA relates the CSM to the performance objectives, and 

provides information on what is most important to the process. Those parameters and 

processes that are most significant are the ones to examine as possible PIs. In some cases, 

the PA monitoring plan depends entirely on the existing regional monitoring network at a 

site, thereby saving substantial costs in developing new monitoring wells (INEEL 2002, 

WSRC 2000). 

At this point, different kinds of uncertainty will be identified. Each of the contributors to 

uncertainty in the PA can be used as an indicator of performance. Some examples are given 

below and in Table 3-2. 

• Precipitation: an example of aleatory uncertainty (natural variability). In some 

cases the nature of the distribution is not shaped by ignorance, but rather by the 

variability in space and/or time. An example of this is precipitation. Even 

though recording of precipitation will not reduce its variability, if it is a sensitive 

parameter in the model, it could be selected as an appropriate PI. Such 

monitoring will help to identify when some precipitation event lies outside the 

assumed PA distribution, or it may suggest that a PA model that assumes some 

spatiotemporal average precipitation be modified to account for transient effects 

in infiltration. 

• Disposed inventory: an example of epistemic uncertainty (incomplete 

knowledge). There are many examples in the radioactive waste management 

world where our knowledge of the makeup of a legacy disposed inventory is very 

poor. Short of a thorough excavation and physical analysis of the disposed 

materials, a process which most site managers are loathe to undertake, the 

analysis may have to rely on poor records and aging memories of site workers. In 

this case gross alpha, non-volatile beta, or gamma ray detection could be used a 

PI. 

While the PA may have to accept these uncertainties, the sensitivity analysis will always 

identify other parameters whose distributions can be refined (narrowed), resulting in a 

reduction of overall uncertainty in performance objective results. Some examples follow: 

• Soil/water partition coefficients (Kds, or distribution coefficients) are generally 

not known for a specific site. Preliminary values are often taken from the 

literature, and have wide (generally lognormal) distributions. Because 

contaminant transport is heavily dependent on site-specific Kds, these are likely to 

show up as sensitive parameters, and are good candidates for uncertainty 

reduction through site-specific in situ or laboratory analyses. Such analyses have 

been performed in support of LLW disposal at Los Alamos National Laboratory, 

the Savannah River Site, and the Nevada Test Site. 

• An example of a sub-model that may be a sensitive component in the overall 

model is the unsaturated zone hydraulic conductivity modeled as a function of 

water saturation. The unsaturated flow component model of a given PA may 

implement one of several mathematical models, including van Genuchten (1980), 

Brooks and Corey (1964), or the Rossi and Nimmo (1994) modification to 

Brooks and Corey. In essence, this represents conceptual model uncertainty 
 34

manifested in mathematical form. If there is any question about which of these 

models is most appropriate for a given site, the PA may incorporate all three, 

selecting different models for different realizations in the stochastic Monte-Carlo 

runs. One of these models may be found to produce more reasonable results, 

depending on site conditions, and the material properties associated with that 

model may be subject to additional investigation, further refining the performance 

objective estimates. 

Once a sensitivity analysis has identified those parameters or processes to which the model’s 

outcome is sensitive, the analyst must identify which of those might be suitable for further 

investigation as PIs (Figure 3-5). The resulting list of potential PIs needs to be discussed 

with the rest of the project team (geologists, hydrologists, hydrogeologists, ecologists, and 

environmental engineers) to determine which are feasible for implementation as monitoring 

subjects. 

Other PI candidates are intermediate results in the PA, such as aqueous concentrations in 

ground water, surface soil concentrations of constituents of concern (COCs), or other media 

concentrations. These DSIs are useful intermediate results that provide indicators of system 

performance. 

If a nuclear facility is not expected to release radionuclides to the environment for some 

centuries, and if the site behaves as expected, monitoring will provide only a partial 

confirmation of the PA, if nothing is ever detected. The monitoring should still be done, 

however, since detection in such a circumstance would prompt a revision of the PA. In the 

spirit of Karl Popper’s philosophy of science, the PA can never be proven, but it can be 

invalidated (Popper, 1963). PIs should be chosen with the highest likelihood of disproving 

the PA hypothesis. 

Table 3-2 is a list of PIs that could be derived from a PA. These could be monitored if they 

are determined to be risk-significant (by the sensitivity analysis following a probabilistic PA 

that includes risk assessment) or if they fill potentially significant data gaps. Most of these 

are dynamic, meaning that they have a temporal component. The changes through time will 

have to be evaluated in order to determine whether the variation needs to be modeled, or if 

perhaps some sort of temporal average will suffice. Many are spatially variable as well, and 

a similar determination will have to be made. Note that a preliminary list of PIs developed 

from a PA may well have some of the same entries as a list developed from site or facility 

analysis. This redundancy in the list development process helps assure that important PIs are 

not missed. 
 35

Performance Indicators Derived from Performance Assessment

Run the PA model probabilistically,

generating a collection (perhaps

thousands) of realizations, propagating the

input and modeling uncertainty into a

distribution of results containing

uncertainty. These results are a

representation of the state of knowledge.

Examine the resulting realizations with a

multivariate sensitivity analysis (SA), which

will identify which stochastic elements

(parameters, submodels) are correlated to

the result of interest. A sophisticated SA

can also reveal the ranges over which

inputs are significant to the results.

Develop the computer-based probabilistic

Performance Assessment (PA) model,

with preliminary stochastic input

distributions reflecting state of knowledge

through uncertainty and perhaps multiple

Conceptual Site Models (CSMs) or

sub-models if uncertain.

Indentify results of interest. These could be

contaminant concentrations, receptor risk, or

some other model output.

The most significant sensitive parameters 

are candidate Performance Indicators (PIs),

and may be suitable for monitoring.

Figure 3-5 Performance Indicators derived from Performance Assessment 
 36

Table 3-2 PIs for ground water that can be derived from the PA 

Performance Indicator Significance 

water chemistry (e.g. pH, alkalinity, major 

cations and anions) 

affects contaminant transport through 

speciation, solubility, partitioning, etc. 

unsaturated zone (UZ) moisture content 

profile 

affects UZ hydraulics and water flow 

water table elevation affects UZ travel times and “smearing” of 

contamination at the water table 

aquifer (saturated zone, SZ) properties, such 

as hydraulics, flow rates, dispersivities 

affects SZ transport: dispersion and travel 

times 

contaminant aqueous concentration in 

leachate 

output of waste sub-model; input to UZ 

model 

contaminant aqueous concentration in 

recharge water 

output of UZ sub-model; input to SZ model 

contaminant aqueous concentration in SZ, 

perhaps in various places 

output of SZ sub-model; summary output to 

the whole transport model; input to the dose 

model (ground water ingestion and irrigation 

pathways) 

contaminant gas phase concentration in the 

UZ 

immediate data for volatiles (e.g. 3H, TCE); 

may be useful in tracer work or at sites 

where RCRA volatiles are involved 

contaminant concentrations in surface water, 

if ground water discharges are modeled 

provides data for comparison to ground 

water discharges 

ecological assemblages, concentrations in 

ecological media (plant and animal tissues) 

aids in evaluation of biotically-induced 

transport (disruption and translocation of 

contaminants) and in ecological exposure 

(risk assessment) 

human health risk assessment exposure 

parameters, such as demographics, land use, 

individual behaviors, dose conversion 

factors, dose pathways, etc. 

these are critical inputs to the dose 

assessment model 

Atmospheric dispersion parameters 

(meteorological data, such as stability 

arrays) 

critical input to atmospheric dispersion 

models 
 37

3.2.3.2 Derived Indicators from Flow Models 

The flow and transport models will estimate water heads and concentrations of chemical 

components at points within the modeled area. Thus the comparison of the parameters 

predicted from the model to measurements at monitoring points is indicative of model 

validity. Variation between predicted and measured heads or predicted and measured 

concentrations indicates that the system is not performing as modeled. 

While the reliability and accuracy of even a calibrated model is inherently unknown 

(Neuman and Wierenga, 2003), it can be tested against PIs. A performance indicator in this 

case would be a measured field quantity such as hydraulic head or spring discharge rate that 

has not been used in the calibration process. Typically, historical data are used in the 

calibration process, while new data (in space, time, or space and time) are used as PIs. The 

key distinction between calibration targets and PIs is that the model must be able to predict

the value of the PI without any adjustment. Successful prediction of PIs is known as model 

validation. Unfortunately, this validation step is often omitted in studies. 

If only hydraulic data are used as PIs to validate the flow model, then the model is only valid 

for prediction of these types of hydraulic variables. Since the main goal of the flow model is 

usually to provide an accurate distribution of flow velocities to the transport model, 

additional flow model validation using the coupled flow and chemical transport model is 

required. Some basic challenges to the flow model validation using hydraulic PIs are: 

Was the model adjusted to match the PI? If so, this is not validation but just further 

calibration. 

Is the PI sensitive to site conditions and behavior? If the PI does not change much with 

changing conditions, then matching it is not very demanding. 

Is the PI relevant to important site behavior? If the PI is largely irrelevant, then so is the 

validation performed using it. Unfortunately, common hydraulic measurements, such as 

hydraulic head, may not be of primary importance in the successful prediction of 

chemical transport. While it is still necessary to correctly predict the heads, this does not 

guarantee that the flow model is predicting the correct velocity distribution. On the other 

hand if the rate of water discharge to a sensitive wetland is important, then this discharge 

makes an excellent PI. 

3.2.3.3 Performance Indicators from the Coupled Flow and Chemical 

Transport Model 

It is best to validate the flow and transport models simultaneously using all available 

hydraulic and chemical data. Chemical data used as PIs consists mainly of chemical 

concentrations measured in observation wells (that have not been used in the calibration 

process). Natural or forced gradient tracer tests can be performed at uncontaminated sites to 

provide these data. If chemical concentration data of some type are not available, the 

coupled model cannot be validated. The same basic questions apply to the validation of the 

coupled model as to the flow model: 

Was the model adjusted to match the PI? Not allowed for validation. 
 38

Is the PI sensitive to site conditions and behavior? This is less likely in the case of 

chemical concentration data PIs. 

Is the PI relevant to important site behavior? The most relevant data are contaminant 

concentrations, but reactive or non reactive tracer concentrations are also useful. Again, 

the model is only validated for the processes that it actually predicts. 

3.2.4 Performance Indicators from a Bioremediation Project 

Long and Yabusaki (2007) discuss evaluation of a uranium bioremediation project at the 

DOE Hanford Site. They focus on some monitoring needs and present a table of indicators 

of performance for the project. This table is adapted below to provide an illustration of siteand project-specific PIs. 

The first column in Table 3-3 lists the PI type from Table 3-1 which best matches the PI 

described by Long and Yabusaki (op. cit., Table 5.1). Note that their Table 5.1 includes not 

only performance indicators, but CSM features and recommended information for project 

planning. 

Table 3-3 Examples of Recommended Information and Performance Indicators for 

Bioremediation Project (after Long and Yabusaki, 2007) 

AES PI Class Information 

area/Parameter/Method

Desired Range Comments 

 Remediation process 

conceptual model 

NA Fundamental to 

prioritization of 

monitoring parameters 

1 / CSM Depth-discrete data for 

mandatory geochemical 

parameters 

NA Characterizes spatial 

distribution of 

fundamental 

biogeochemistry in 

aquifer 

1 / CSM Depth-discrete data for 

desirable monitoring 

parameters 

NA Characterizes spatial 

distribution of desired 

biogeochemical reactions 

in aquifer 

1 A/C In situ redox status of U 

using in situ sediment 

incubators (ISIs) 

Significant U(IV) present Evidence that 

precipitation of U(IV) is 

occurring in situ obtained 

via differential U 

extraction.

1 B/C Organic and inorganic 

carbon analyses 

NA More accurate 

documentation of 

carbonate geochemistry 

1A / CSM Background U(VI) 

concentration, monthly or 

bi-monthly and eventbase (e.g., high water 

table) 

NA Number of sampling 

points based on plume 

and treatment zone 

complexity and size 

(including depth) 

1A / CSM Treatment zone and 

down-gradient U(VI) 

concentration 

Below MCL Number of sampling 

points based on plume 

and treatment zone 

complexity and size 
 39

1A / CSM / 3B Depth discrete U(VI) data 

(upper/mid/lower part of 

contaminated zone) 

Regulatory Compliance 

Criteria 

Decreased effectiveness 

of treatment in the 

uppermost part of the 

saturated zone may be 

problematic 

1B Major dissolved gas 

components in ground 

water 

NA Evidence for key TEAPs 

and microbial metabolism 

1B – ratio is indicator of 

process 

In situ redox status of U 

by direct sampling of in 

situ materials 

U(IV)/U(VI) 

measurements on in situ 

sediments provide 

"ground truth" for U 

bioreduction 

1B / 1C Tracer for electron donor >0 in treatment zone Typically Br is used for 

conservative tracer, 

accurate indication of 

donor distribution 

1C DO, ORP, specific 

conductivity, and pH 

measured hourly to 4 

times daily in background 

and treatment zone 

(autonomous 

multiparameter probes) 

DO<0.5, ORP<0, 

conductivity initial 

increase, pH ~ steady 

Values used as overall 

dynamic indicator of 

impact of bioremediation 

on subsurface 

geochemistry 

1C DO, ORP, specific 

conductivity, and pH 

measured at time of 

ground-water sampling in 

background and treatment 

zone using flow-cell with 

multiparameter probe 

See above Linkage of U(VI) 

concentrations with 

parameter change 

evidence for 

bioremediation process 

conceptual model 

1C Aqueous electron 

acceptors and reduction 

byproducts in background 

and treatment zone: 

nitrate, nitrite, 

ammonium, Mn(IV/II), 

sulfate, sulfide 

NA Significant concentrations 

of oxygen and/or other 

electron acceptors above 

the U TEAP on the redox 

ladder must be addressed 

by the bioremediation 

strategy and their 

reduction products 

monitored. Sulfur 

isotopic analyses may 

provide supplemental 

information. 

1C Fe(III) mineral abundance NA Fe(III) minerals provide 

sorption sites for Fe(II) & 

U(VI), terminal electron 

acceptor for Fe-reducing 

bacteria, dissolved Fe(II) 

source 

1C Fe(II), sulfide measured 

in field at time of 

sampling for U(VI) (up 

gradient, treatment zone, 

and down gradient) 

Increasing Fe(II); sulfide 

indicator of sulfate 

reduction 

Maintaining metal 

reduction may optimize 

U(VI) removal from 

ground water; sulfate 

reduction may enhance 

long-term immobilization 

in sulfate-rich systems 
 40

1C Electron donor 

concentration in treatment 

zone 

>0 Evidence of delivery and 

treatment zone 

distribution; consumption 

calculation based on 

tracer data 

1C Alkalinity (measured in 

the field) 

NA Indicator of carbonate 

geochemistry, dissolved 

carbonate/bicarbonate 

forms strong anionic 

complexes with U(VI) to 

decrease its adsorption 

and increase its solubility 

and mobility 

1C Major cations and anions NA Provides additional 

evidence for dominant 

geochemical aqueous 

complexation and mineral 

solubility reactions 

1C Microbiological 

assessment using coupons 

or in situ incubators 

Shift to metal and/or 

sulfate reduction 

Evidence for desired in 

situ microbial respiration 

obtained from deploying 

coupons or in situ 

incubators in well bores 

and periodically 

measuring microbial 

parameters (see text for 

additional discussion) 

1C Microbiological 

assessment performed 

directly on sampling of 

treatment zone materials 

Shift to metal and/or 

sulfate reduction 

Measurements directly on 

ground-water filtrates or 

sediment cores provide 

"gold standard" 

assessment of microbial 

community structure 

(e.g., PLFA, 16S, DNA / 

RNA chip arrays, or 

functional chip arrays) 

1C Form and 

mobility/lability 

± 30% of estimate Experiments and 

sediment extractions to 

identify uranium form 

and potential for future 

mobility based on labile 

fraction 

1C / CSM Depth-discrete sediment 

sampling/extraction for 

U, Fe, AVS 

NA Evidence for conversion 

of terminal electron 

acceptors 

2 Temporal recharge ±20% Seasonal and episodic 

impact to unsaturated 

flow, extreme recharge 

event and impact must be 

considered if flooding 

probable at the site 

2 Ground-water flow 

velocity (Darcy flux) and 

direction 

±30% of estimate Seasonal and episodic 

impact critical 
 41

2 Water table dynamics 

(use hourly data as eventbased geochemical 

sampling driver) 

NA Relationship between 

water table and U 

concentration critical 

2 Site hydrogeology: 

hydraulic conductivity, 

porosity, dispersivity, 

hydrofacies 

NA Fundamental to both site 

and process conceptual 

model 

2 Particle size 

characteristics 

NA Reactive surface area, 

clays, upscaling lab to 

field 

2 Impact of treatment 

process on ground-water 

flow directions (hourly 

water level at minimum 4 

points) 

Dependent on 

background flow 

Helps to provide 

assurance that ground 

water is not rerouted 

around treatment zone 

2 Time-lapse GPR Crosswell or Electrical 

Measurements 

Shift in geophysical 

attributes in zone of 

electron donor 

Indicates 2-D distribution 

of electron donor, 

although impact of other 

transformations on 

geophysical signatures 

must be assessed and 

errors associated with 

tomographic inversion 

procedures can ‘smear’ 

amendment boundary. 

2 / CSM / 3? Impact of treatment 

process on hydraulic 

properties 

<15% change Documents possible 

system clogging of pores 

2/ CSM Vadose zone 

hydrogeology: porosity, 

water retention function 

parameters 

±20% of estimate Seasonal and episodic 

impact to flow direction 

critical 

3 Time-Lapse Electrical 

Resistivity and Self 

Potential Tomography 

NA Can indicate the 3-D 

distribution of dominant 

TEAP's 

3 Time-Lapse Seismic 

Tomography 

NA Sensitive to gas evolution 

and secondary mineral 

precipitation 

3B / Rem Design Spatial extent of 

contamination zone 

(plume geometry) 

±20% of estimate Differentiate between 

vadose zone and aquifer 

concentrations; aqueous 

and sediment associated 

uranium; geometry drive 

layout of bioremediation 

system 

NA/CSM Site conceptual model for 

uranium source term 

NA Consideration of 

alternative conceptual 

models critical 

NA/CSM Site conceptual model for 

subsurface (vadose zone 

and ground-water) flow 

and contaminant transport 

NA Consideration of 

alternative conceptual 

models critical 
 42

4 Modeling to Support Site Performance 

Assessment 

4.1 Introduction 

Performance Assessment for most sites requires a quantitative analysis that uses the CSM in 

conjunction with flow and transport modeling. For example, the Groundwater Modeling 

System (GMS), a commercial modeling/visualization software package, can be used for 

developing water flow and contaminant transport models and presenting the results visually. 

A very simple site may be assessed qualitatively by reviewing the CSM and quantitatively by 

using an analytical solution of contaminant transport. 

The PA process is conducted iteratively starting with the analysis of generic and limited sitespecific information in support of relatively simple conceptual models, and progressing to 

more realistic, site-specific and detailed analysis, such as a numerical and stochastic 

modeling, to reduce uncertainty in assessing performance of the site (NRC, 2000). Modeling 

begins with an assessment of the purpose, goals and objectives of the PA. 

Statistical analysis to determine if existing populations of data can be extrapolated using 

predictive models can be used to fill data gaps based on regional geologic/hydrogeologic 

concepts. This is illustrated in Section 4.3.1, below. Statistical methods can also be used to 

evaluate data uncertainty if current values are unacceptable. 

After the local model is verified to be in compliance with the regional model, critical flow 

paths relevant to the PA should be identified. As a further check these local flow paths 

should be compared to regional flow paths to determine if there is a reasonable fit, and if not 

an explanation should be developed that can be supported with actual data. 

4.1.1 What is the Purpose of the Modeling Effort? 

The most important issues to address during the review of any modeling report are 1) What is 

the purpose of this modeling effort? And 2) Does the model reliably accomplish this 

purpose? 

It is important to determine if the purpose of the modeling effort is reasonable in view of the 

site history and conditions. The typical reasons for performing a modeling study at a site are: 

a) to describe possible contaminant pathways and travel times to human or ecological 

receptors at a site where contaminant releases have not yet occurred, 

b) to predict pathways, travel times, and concentrations at receptor locations for a site 

where a release has occurred, but where the current extent of contamination is 

limited, 

c) to identify and refine the CSMs and to estimate model parameters, 
 43

d) to identify data to which model results are most sensitive, through sensitivity 

analysis 

e) to predict plume behavior, and to help document natural attenuation processes at a 

site where ground water contamination is extensive, 

f) to design and manage remediation systems to control or remove ground water 

contamination, 

g) to evaluate and update the existing monitoring network, 

h) to evaluate regulatory compliance, 

i) to estimate potential doses to a receptor at some point in the future. 

Modeling studies whose purpose is to satisfy goals a), b) or c) usually are smaller and less 

sophisticated than those whose purpose is to satisfy goals e), f) or g). 

4.1.2 What is an Appropriate Level of Effort and Sophistication for 

the Modeling Study? 

An important and obvious metric for choosing the level of effort given to a modeling study is 

an assessment of the current or potential threat the site poses to human or ecological health. 

The first question to ask is: What is the overall mass and toxicity of the chemicals at the site? 

If either the chemical mass or the chemical toxicity is very low, then obviously there is little 

reason for computer simulation of flow and chemical transport. Potential risk may be 

demonstrated to be low with a simple “back of the envelope” calculation. If the contaminant 

mass and toxicity are high, then the next task should be to asses the potential risk of chemical 

transport in the ground water. This potential can be approximately quantified by 

threat = (likelihood of a release) × (mobility of chemical) × (persistence of 

chemical) × (ground water velocity) × (inverse of distance to sensitive receptor) 

If each of these five variables is high, then the threat posed by the site is very large. If three 

or four of the five variables are high, the threat is lower, but still significant, while if only one 

or two of the variables are high, the threat is smaller. The modeling effort should be 

consistent with the threat level, and sites where the threat is high deserve a comprehensive 

flow and chemical transport modeling effort. These cases should use flow models such as 

class F4c or higher (as identified in Table 4-1), coupled with chemical transport models C3 or 

higher, depending on site conditions. If a site has a low threat level, then a comprehensive 

modeling effort is likely not justified, and simpler models such as F0 through F3a and C0 

through C3 may be appropriate. 

The amount and quality of site geologic, hydrogeologic, and chemical data should also be 

consistent with the magnitude of the ground water threat. Thus it should be expected that 

sites that pose lower risks will have less field data for constraining and calibrating models. 

This would be an additional justification for use of simpler models that do not have large data 

needs. Sites that pose higher threats typically (but not always) have more field data for 
 44

constraining and calibrating more complex models. The sophistication and level of detail in 

the models must be consistent with the quality and quantity of field data. 

4.2 Selection of Appropriate Flow and Transport Models 

Site specific ground water models, like the CSM geologic portion, must also be developed in 

the context of the regional ground water flow and transport paths. All too often the ground 

water model developed for a site is in total disagreement with the regional data. As the local 

model is developed it must be compared and adjusted so that it fits into the regional ground 

water flow regime. 

Conceptual hydrogeologic models are developed for a site as abstractions of existing 

characterization and monitoring data. Each possible interpretation of the data is a working 

hypothesis, to be confirmed, improved, or rejected as new data are obtained (Chamberlain, 

1890). 

It is very important to select a model or models that are consistent with the modeling 

purpose, and with the quantity and quality of the site data. Many modeling efforts give poor 

or unreliable results because these basic considerations are not observed. 

Models are constructed from site characterization data that can include, but are not limited to: 

local and regional geologic setting, geophysical logs, cone penetrometer (CPT) logs, core 

descriptions, core property measurements, field mapping, fracture studies, geophysical 

surveys (e.g. seismic, resistivity, electromagnetic, magnetic, gravity, natural radiation), 

aquifer test data, piezometer data, and water chemistry. The objective of a hydrogeologic 

conceptual model is to represent all the physical features of the site that can control 

subsurface movement of water or contaminants. 

Analytical and numerical mathematical models of ground water flow and chemical fate and 

transport are commonly used at both contaminated and uncontaminated sites. The 

complexity and capabilities of available models vary tremendously, ranging from simple onedimensional, steady-state analytical solutions, to comprehensive three-dimensional transient 

numerical solutions. Analytical solutions are exact algebraic solutions to governing 

differential equations. Derivation of these solutions always requires making simplifications 

to the problem geometry, parameter distributions, and time dependency. In many instances, 

these simplifications or assumptions are reasonable, and in these cases, analytical solutions 

provide very efficient and easy-to-use tools. Analytical solutions are commonly used in 

cases where field data are limited, or where rough estimates are sufficient for decision 

making. 

In many instances, the problem geometry, boundary conditions, parameter distributions, or 

chemistry may be too complicated to be represented by an analytical solution. In these cases, 

numerical flow and transport models are commonly used. A numerical model recasts the 

governing differential equations into discrete algebraic equations, and the variables of interest 

(hydraulic heads, chemical concentrations, etc.) are solved for at discrete points in space and 

time. Numerical solutions are always approximate solutions to the governing differential 

equations, but in most cases, they can be mathematically very accurate. Numerical models 

have the advantage of allowing for flexible three-dimensional geometries, complex parameter 

distributions, and fully transient behavior. This flexibility and complexity, however, must be 
 45

balanced with the quality and quantity of site data. In other words, a sophisticated numerical 

model can only provide results that are as reliable and complete as the data used to help 

construct the model. 

As a general rule, it is advisable to use a graded approach, starting with the simplest possible 

model for a site and increasing the complexity of the model only as the site data and site 

needs justify it. At each stage in the process, consistency checks must be made, and the 

different models should generally support each other. 

4.2.1 Types of Flow and Transport Models 

The three basic types of models used at field sites are ground water flow models, chemical 

fate and transport models, and multiphase flow models. These three categories of models are 

often used independently, but they can be combined in many instances as well. For example, 

a chemical fate and transport model requires information on the ground water velocity field. 

One approach is to simply assume a simple one-dimensional uniform ground water flow 

field, and then solve the chemical fate and transport equations. 

A more flexible approach couples a chemical fate and transport model with a ground water or 

multiphase flow model. With this approach, the flow field is calculated by the appropriate 

flow model, and the velocity field is passed on to the chemical transport code. Depending on 

the problem, the chemical transport may affect the flow field (for example if the fluid density 

changes due to high dissolved solids), and in this case the flow and transport should be fully 

coupled. One-way coupling, where the flow field is assumed to be independent of the 

chemical transport is used for most problems involving ground water contamination, while 

full coupling is commonly used in multiphase flow problems such as high-level waste 

isolation modeling. 

Table 4-1 lists different categories of ground water flow models, and Table 4-2 lists different 

categories of chemical fate and transport models. The various flow and chemical transport 

models can be combined in many ways, depending on the site. For example, it may be 

appropriate to use a very simple flow model with a very complex chemical fate and transport 

model at a site where the ground water flow is nearly uniform, but where the contaminant 

fate and transport involves complex reactions of multiple species. Similarly, it may also be 

appropriate to use a very complicated flow model with a very simple chemical fate and 

transport model at a site where the geology is complex, but well understood, and 

contamination is either nonexistent, or characterized by simple advective transport. Figure 

4-1 and Figure 4-2 illustrate the model selection tables in decision tree format. 
 46

Table 4-1 Categories of ground water flow models 

Model 

Category 

Solution Method, 

flow type 

Dimensionality Time Dependency Permeability 

Distribution 

Infiltration 

F0 None; GW flow 

velocity assumed 

One Steady-state Homogeneous Not considered 

F1a Analytical GW One Steady-state Homogeneous Variable in space 

F1b Analytical GW One Transient Homogeneous Variable in 

space, time 

F2 Analytical GW Two (x-y) Steady-state Homogeneous Constant 

F3a Numerical GW Two (x-y) Steady-state Homogeneous Constant 

F3b Numerical GW Two (x-y) Steady-state Heterogeneous Variable in space 

F3c Numerical GW Two (x-y) Transient Homogeneous constant 

F3d Numerical GW Two (x-y) Transient Heterogeneous Variable in space 

and time 

F4a Numerical GW Three Steady-state Homogeneous constant 

F4b Numerical GW Three Transient Homogeneous Variable in time 

F4c Numerical GW Three Steady-state Layered constant 

F4d Numerical GW Three Transient layered Variable in time 

F4e Numerical GW Three Steady-state Fully 

heterogeneous 

Variable in space 

F4f Numerical GW Three Transient Fully 

heterogeneous 

Variable in space 

and time 

F5 Numerical 

multiphase 

Three with dual 

domain capability 

Transient Fully 

heterogeneous 

Variable in space 

and time 

F6 Numerical fracture 

flow 

Three Transient Heterogeneous 

fracture apertures 

Not usually 

considered 
 47

Table 4-2 Categories of chemical fate and transport models. These are used with some 

type of flow model, and are almost always transient 

Model 

Category 

Solution Method AdvectionDispersion Adsorption Reactions Coupling with 

flow 

C0 Numerical, particle 

tracking 

Only pathlines, twoor three-dimensionalNone None One-way using 

GW velocity 

C1a Analytical One-dimensional for 

both 

Constant Kd First order parent 

daughter 

One-way, uniform 

GW velocity 

C1b Analytical One-dimensional for 

advection, 3-D for 

dispersion 

Constant Kd First order parent 

daughter 

One-way, uniform 

GW velocity 

C2 Numerical 2-D for both Variable Kd First order parent 

daughter 

One-way with 

GW 

C3 Numerical 3-D for both Variable Kd First order parent 

daughter 

One-way with 

GW 

C4a Numerical 3-D for both Nonlinear 

adsorption 

isotherms, variable 

in space 

Multiple species 

nonlinear reactions 

One-way with 

GW 

C4b Numerical 3-D for both, dual 

domain system with 

mass transfer 

Nonlinear 

adsorption 

isotherms, variable 

in space 

Multiple species 

nonlinear reactions 

One-way with 

GW 

C5 Numerical 3-D for both Nonlinear 

adsorption 

isotherms, variable 

in space 

Multiple species 

nonlinear reactions 

Fully coupled 

with GW 

C6 Numerical 3-D for both, dual 

domain system with 

mass transfer 

Nonlinear 

adsorption 

isotherms, variable 

in space 

Multiple species 

nonlinear reactions 

Fully coupled 

with multiphase 

flow 
 48

Figure 4-1 Flow and Transport Model Selection 
 49

Figure 4-2 Chemical Fate and Transport Model Selection 

4.2.1.1 Is the Flow Model Appropriate for the Site? 

Assuming that the site poses a moderate to high threat to ground water, the following 

hydrogeologic considerations should guide the selection of the fluid flow model. Note that 

the model must be able to simultaneously address all of these considerations, so each 

consideration acts as a “filter”, reducing the number of acceptable models. 

Site Geometry and Boundary Conditions 

a) Site has a very small vertical thickness compared to area, and flows are believed 

to be essentially one- or two-dimensional (e.g., wholly vertical or wholly horizontal). 

One or two-dimensional models may be appropriate (F0 through F3d). If the site has 

irregular boundary conditions, a numerical model (F3a-F3d) will be needed 

b) Site has a large vertical thickness, or significant horizontal and vertical flows. A 

three-dimensional model is probably needed (F4a through F6) 

Geologic Nature 

a) Approximately homogeneous, unconsolidated – any of the flow models may be 

appropriate in this case, but be aware that no sites are truly homogeneous. 
 50

b) Unconsolidated, approximately layered. Models F4c or F4d could be appropriate, 

but more flexible models that consider full heterogeneity could also be used. 

c) Unconsolidated, strongly heterogeneous. Models F4e, F4f, or F5 should be used. 

These types of models will have very large data requirements, however. 

d) Fractured rock, low matrix porosity. If the flow can be considered as an effective 

continuum, then a standard ground water flow code can be used but if the individual 

fracture flows are important, then a fracture flow code (F6) should be used. 

e) Fractured rock, high matrix porosity. The same flow codes usually apply here as 

in the low matrix porosity case, but the chemical fate and transport code will need to 

account for fracture-matrix interactions (C4b or C6). 

Ground Water Gradients 

a) Relatively uniform in space and time. Simple models (F0 through F3b) could be 

appropriate. 

b) Variable in space, but constant in time. Steady-state models (F2, F3a, F3b, F4a, 

F4c, F4e) could be appropriate. 

c) Variable in space and time. Transient models required (F3c, F3d, F4b, F4d). 

Recharge 

a) Recharge is not dominant, and doesn’t vary much in space or time. Models F2, 

F3a, F3c, F4a, or F4c may be appropriate. 

b) Recharge is important, and is highly variable in space but not time. Models F1a, 

F3b, F4e could be appropriate. 

c) Recharge is important, and is highly variable in both space and time. Models F3d, 

F4f, F5, or F6 would be needed. 

Vadose zone 

a) Thick – the model may need to simulate flow through the vadose zone. If this 

effect of the thick vadose zone is expected to be dominant, then a multiphase flow 

code (F5) is required. Otherwise the vadose zone flow may be estimated. 

b) Thin – a standard ground water model should be sufficient. 

4.2.1.2 Is the Chemical Fate and Transport Ground Water Model 

Appropriate for the Site? 

Because the chemical transport model is coupled in some way to the flow model, its choice is 

largely driven by the choice of the flow model. Beyond this, the transport model choice is 

driven mainly by the nature and complexity of the contaminant reactions. 
 51

Contaminant Geochemistry 

a) Very simple conservative, or unknown. Particle tracking algorithm (C0) will 

show the likely path and travel time for a conservative tracer, but it will not predict 

concentrations. 

b) Simple first order decay with linear adsorption, constant Kd and reaction rates. 

Models C1a through C3 could be appropriate, depending on the flow model. 

c) Simple first order decay with linear adsorption, but variable Kd and reaction rates. 

Models C2 or C3 would be appropriate 

d) More complex, involving multiple species, pH, Eh dependencies and kinetic 

(nonlinear) reactions. Model C4 would apply. 

e) Transport of chemicals alters ground water density. Fully coupled models C5 or 

C6 would be needed. Multiphase flow (vadose zone) is needed. Model C6 is 

required. 

g) There are strong fracture-matrix interactions. A dual domain model (C4b or C6) 

should be used. 

4.2.1.3 How is the Flow Model Constrained? 

A mathematical model may allow for a large range of values in its parameters. Numerical 

models further allow for flexibility in geometry and parameter distributions. In order to be 

meaningful and mathematically unique, the model must be constrained by hydrogeologic 

features or measurements. Even under the best of circumstances, there will still be some 

uncertainty in the values for some parameters, especially distributed parameters such as 

permeability. These uncertainties can be reduced by model calibration, which is discussed 

later. Some specific considerations: 

Are the dominant geological features incorporated in the model? It is critical that the 

flow model, to the extent possible, include significant known geologic features that are 

believed to affect the hydrogeology. This is often an ongoing process where 

mathematical models are revised as more information becomes available. 

Are the model boundary conditions well-defined? The mathematical model boundary 

conditions can have a tremendous influence on the calculated results, so they must be 

justified by hydrogeological features at the site such as constant head boundaries (rivers 

and lakes and swamps), or no-flow boundaries (very low permeability rock boundaries, 

sealed faults, permanent ground water divides). If the model boundary conditions are not 

well-defined, then it must be demonstrated that the model is insensitive to the boundary 

condition. 

Are the spatially distributed parameters reasonable? Important parameters such as 

permeability and recharge can be highly variable in space (and time for recharge), but 

there are typically few measurements available. Nonetheless, it is important to at least 
 52

show that these parameters are within expected ranges for the site location. These 

parameters are typically calibrated to develop the final site flow model. 

4.2.1.4 How is the Chemical Fate and Transport Model Constrained? 

Given a correct representation of the ground water flow field, the chemical transport model is 

constrained by measured chemical properties and concentrations from the laboratory, and 

from the field site. Chemical properties include basic properties such as density, molecular 

weight, solubility, radioactive (or biochemical) half-life, as well as more complex properties 

such as speciation, partition coefficients, and multi-species solubility relationships. These 

properties may be obtained from the literature, or they may be measured in batch 

experiments, though it has been shown that batch studies are poor estimators of adsorption 

behavior in the field. Other laboratory experimental data may include soil water distribution 

coefficients (Kd) and other local scale transport parameters. Field site constraints are often 

lacking or poorly defined. For example, if a ground water plume does not exist at a site, 

there will be few or no chemical field data for model constraint. Still, there are 

“reasonableness” checks that can be made on the model. The most important term in a 

chemical transport model is usually the chemical source term. This is the contaminant 

loading to the subsurface as a function of time and space. Unfortunately, this most important 

parameter is usually poorly constrained, and is often almost completely unknown. The 

uncertainty in the past and future source strength can lead to large uncertainties in the 

relevance of the model output. Some specific considerations for constraining a chemical fate 

and transport model are: 

Are the dominant chemical reactions and transport phenomena accurately represented in 

the model? The model must be able to reproduce laboratory experimental results, and the 

transport phenomena must be consistent with site conditions. Consideration should also 

be given to specialized chemical environments, such as conditions within a degrading 

concrete slab, or those of the surrounding natural environment. 

Is the source term well defined by the field data? In cases where the source term is 

known to a reasonable degree of certainty, it should be used as a primary constraint (i.e. 

boundary condition) on the model. If it is not well known, it may be used to calibrate the 

model, along with other parameters. For cases without any contamination, the source 

strength can be varied to show likely effects of various releases. 

Are the boundary conditions realistic? Chemical transport boundary conditions (other 

than the source term) usually consist of inflow and outflow conditions. The inflow 

condition would typically be a fixed background concentration or mass flux. The outflow 

condition should not artificially influence concentration phenomenology inside the model 

domain. 

4.2.1.5 How is the Flow Model Calibrated? 

The spatially distributed parameters (mainly permeability and recharge) are usually calibrated 

using an automated or manual technique. This process involves selecting measurements in 

space and time such as hydraulic heads in observation wells, or discharge from springs or 

creeks. The model parameters are then adjusted to get a best fit of the data. Note, however, 

that this best fit does not guarantee that the parameter set is unique, or that some other 
 53

possible model configuration couldn’t produce a better fit. Some questions that arise in 

model calibration are: 

How sensitive is the calibration to the parameter? Calibration works best when a model 

fit of the calibration data is very sensitive to the parameter, but this is often overlooked. 

Do the calibration data points give a good representation of the important site behavior? 

The calibration is meaningful only if it is relevant to the important site processes. 

Unfortunately, these are often not measured. For example, the ground water velocity 

distribution is of key interest to most sites, but this is difficult and expensive to measure 

(using, for example, tracer tests), so hydraulic heads are typically used as calibration 

targets. 

Does the calibrated model fit the calibration data? Sometimes, a model configuration is 

incapable of fitting the site data well. In this case, the configuration should be discarded, 

and new conceptual models should be tested until a sufficiently good fit is achieved. 

4.2.1.6 How is the Chemical Fate and Transport Ground Water Model 

Calibrated? 

Calibration of the chemical transport model is similar to calibration of the flow model. 

Because the two models are coupled, calibration of the two is often done together. For 

example, the ground water flow velocities and directions determine the movement of 

dissolved chemical plumes. Therefore, the flow model can be calibrated by hydraulic heads, 

but it must also produce the correct flow field so that the chemical transport model matches 

the chemical plume. Aside from the flow field, the main chemical transport parameters are 

reaction rates, adsorption coefficients, dispersion coefficients, diffusion coefficient (for small 

Péclet numbers,) and in some cases, the source term, salinity, density, and temperature. 

Consideration should be given to the potential significance of changes in these properties in 

space and/or time. The primary data for model calibration are measured concentrations of 

chemicals (or tracers) from observation wells. If the site is not contaminated, and there have 

been no tracer tests, then there are essentially no data available for calibration, and the 

reliability of the model will be low. The basic questions to be asked of the calibration 

process are the same as for flow model calibration: 

How sensitive is the calibrated model to the parameters? 

Do the calibration points give a good representation of the important site behavior? 

Does the calibrated model fit the calibration data? 
 54

4.3 System Parameters Derived from Modeling of Site 

Characterization Data 

4.3.1 Derivation of Modeling Input Parameters from Physical 

Properties 

Another relevant question to ask is, “Can existing data be used to derive needed data?” For 

example, can percent clay (Figure 4-3) be used to estimate hydraulic conductivity? If data 

conversion is used it must be checked against known conditions within the regional 

framework to determine if the derived data is reasonable and adequately reflects the local 

conditions. Neuman and Wierenga (2003) proposed an iterative approach to ground-water 

modeling that from a logic flow is relevant and can be used for the process of refining 

conceptual and numerical models. 

Computer programs designed for modeling have been refined to accept greater geologic 

heterogeneity. By using this software to handle greater complexities, flow path models can be 

developed that more accurately represent conditions in the subsurface and thereby reduce 

uncertainty in the monitoring program. With greater confidence that the models are more 

accurately reflecting true conditions in the flow regimes, a higher confidence can be achieved 

that the data collected though the monitoring activity adequately capture the FEPs as defined 

by the PA. 

Figure 4-3 Three dimensional display showing percent clay (reds grading to sandier 

blues). 
 55

Through an understanding of the regional geologic/ hydrogeologic framework that constrains 

a given site; appropriate data can be derived to assist in bounding uncertainty and can 

identify data gaps to be filled. Sperry and Pierce (1995) outline a method by which hydraulic 

conductivity can be derived from grain shape, size and porosity. 

This method uses simple regression analysis to establish a relationship between physical 

properties and a derived hydrologic property that is usually not measured during 

characterization or monitoring. Temples and Waddell (1996) used the relationship of 

porosity to permeability to extrapolate porosity data into hydraulic conductivity form well 

logs in place of traditional pump testing. 

Miller et al. (2000) outlined procedures to spatially represent the hydrogeologic 

characteristics of a site by utilizing sequence stratigraphic concepts to map the distribution of 

lithofacies constraining uncertainty in geologic properties that control fluid flow in the 

subsurface (Figure 4-4). Software developed by Roxar was used to develop a three 

dimensional framework of key geologic parameters. The software uses both deterministic 

and stochastic methods to derive a continuous solid earth model of the critical parameters for 

prediction of plume migration. 

Using both stochastic and deterministic methods offers a practical solution to incorporate real 

measured data and verified derived data into aquifer models for the predicting of monitoring 

points. Monte Carlo simulations are the most prevalently used statistical methods to predict 

uncertainty of a model. The method is relatively straightforward and can be used to predict 

both linear and nonlinear properties that are critical to flow and transport models, which are 

the basis for the selection of monitoring points both spatially and temporally. Neuman and 

Wierenga (2003) outline the logic and the statistics behind using this method for model 

development and quantifying the uncertainty associated with this approach. Data derived 

form this type of analysis can assist in decision making about the adequacy of monitoring 

relative to the PA.
 56

Figure 4-4 Model showing complex lithofacies interpretation. 

Several computer codes (such as Plumefinder, Deschaine, 2004) use various statistical 

methods to predict plume configuration emanating from waste sites. These models are only 

as good, however, as the information input. Most of the input data used for these predictions 

are based on data collected from characterization and monitoring. We therefore consistently 

return to the validity and accuracy of the CSM as the basis for the identification of 

appropriate FEPs, ground water model development and the subsequent monitoring program 

to confirm the assumptions that establish the bounding conditions identified as relevant in the 

PA. Generically derived probability distributions of key parameters such as soil moisture, 

hydraulic conductivity, or porosity can be given a higher confidence thorough Bayesian 

updating. Since we know that geology controls the distributions of certain properties such as 

porosity and permeability rather than these being associated with random events, we can use 

existing data to analyze and estimate spatial trends using geostatistics. If trends are present, 

data can be evaluated and reinterpreted to improve confidence in its distribution. Most of this 

type of analysis has been pioneered by the petroleum industry (Yarus and Chambers, 1994). 

4.3.2 Data Analysis Pointing to Needed CSM Revision 

In most cases plotting the data followed by visual inspection will suggest whether there are 

obvious things to be checked. Figure 4-5 illustrates water table levels and heads in an 

underlying confined unit at the DOE Savannah River Site. The data points out the abnormal 

water level from the confined aquifer (Falls et al 1997 and Price et al. 1991). Figure 4-6 

shows contoured water levels for the lower aquifer, and illustrates the concept of a bull’s-eye

in contoured data. 
 57

Figure 4-5 Pen Branch example (water level units reported in ft above msl) 

In Figure 4-5, all well data have been projected onto a north-south plane. The water table is 

above 250 ft msl in all wells. Pressure heads in the underlying confined unit range between 

180 and 210 ft, except for one well at the right of the figure where the head is 268 ft. 

Geologic study revealed that a fault displaces strata in such a way that the usually confined 

unit is connected to the water table across (north of) the fault. 
 58

-42 -40 -38 -36 -34 -32 -30

-42 -40 -38 -36 -34 -32 -30

10

12

14

16

18

20

22

10

12

14

16

18

20

22

213

219 209

228

155 150

184 145 140

130

165 120

136150

156

144

170

179

194

167 268

114

139

185

177

191

176

153

134

Scale (meters)

0 2000 4000

Figure 4-6 Bull’s-eye in contoured data 

When the anomalously high water level was first noted, it was attributed to bad well 

construction, and the suspect data were deleted from a regional contour map. However, it 

was an error to delete the result simply because it did not fit with the rest of the data. An 

evaluation of other associated data may reveal the anomalous information is indicative of a 

previously unrecognized situation. Detailed geologic study later revealed the fault and 

suggested a connection between the water table and the deeper water-bearing zone. By 

extension, it can be inferred that similar connection exists along the trend of the regional 

fault, and that shallow waters can flow freely into the deeper aquifer anywhere along the 
 59

fault. The anomalous water level is a DSI pointing to what would become a critical issue if a 

new facility were planned for the area.

This leads to the concept of critical monitoring points. A new geologic model including the 

regional fault has been developed. The hydrologic implications of this geologic model can be 

tested by selecting well locations along the fault, or perhaps by testing at existing wells. 

A second example of conceptual model development leading to revision of a monitoring well 

layout involves an unlined waste site located near the top of a hill. Several borings defined 

the water table as shown in Figure 4-7. Then, following standard procedures, one well was 

placed up-gradient and three down-gradient of the waste site. Well installation involved 

augering until returns were wet, then waiting for a water level to stabilize inside the hollow 

stem to establish a current static water table level. After the water table was determined, a 

well was set so that the 15 ft screened zone penetrated the water table by 10 ft. The wells 

were then sampled and only the down-gradient wells showed contamination, as would be 

expected based on this original CSM. 

Figure 4-7 Critical monitoring points. The dashed lines indicate the water table and 

the shaded areas represent the contamination plume. 

4.3.2.1 Incongruity with current model predictions 

A soil gas survey was conducted of a large area around the waste site to help search for 

contaminant discharge points. Surprisingly, a significant soil gas anomaly indicated 

movement of a chlorinated solvent in the assumed up-gradient direction from the waste site. 

Further geologic study indicated that strata dipped in the up-gradient direction (left on Figure 

4-7). 
 60

This strongly suggested that contaminants moving downward from the waste site were 

deflected along dipping beds to reach the saturated zone to the left, and to bypass the upgradient well. This was later confirmed with additional monitoring wells. 

This waste site also provides an example where discarded data were later determined to be 

critical to model development. Pumping tests were performed to produce data for 

incorporation into a ground water flow model. Existing dedicated pumps were used to pump 

the wells at about 10 to 15 gpm. Drawdown was measured to the nearest 0.1 inch with a 

minimum detection limit of 0.1 inch. About 15 wells were tested. The hydraulic conductivity 

was reported as averaging 400 gpd/ft2

, but several wells had no values reported because the 

field data were not subjected to interpretation. Closer inspection of the report revealed that 

the data were not interpreted because no drawdown could be detected! Even the simplest 

calculation at the minimum 0.1 inch detection limit would have yielded a conductivity of a 

minimum of 15,000 gpd/ft2

. (SRS Internal Memorandum, V. Price to Tracy Killian, 1986) 

In this case, careful reading of the report revealed this huge error of interpretation was missed 

by reviewers and by the project engineer with responsibility for the site. The explanation for 

the extreme variation in conductivity observed is clear when the site geologic setting is 

understood. Figure 4-8 is a cross-section through a typical barrier bar depositional setting 

from the recent SC / GA coast (Ruby, 1981). Oyster beds and shell lags are interbedded with 

beach sands and back-barrier tidal flat clays and fine sands. These interbedded carbonates 

have a documented history in the literature of being highly leached after burial. This is 

similar to the geologic setting of the waste site area, but Figure 4-7 does not include detailed 

geology. Eocene oyster beds at that site have been leached to the point that they are highly 

transmissive. 

Figure 4-8 Geologic model from Ruby (1981) showing cross-section through a barrier 

bar system 
 61

4.4 Evaluate and Refine Conceptual Site Models 

The key step for a successful modeling of ground water flow and reactive solute transport 

modeling is to identify a most probable CSM from the constructed alternate CSMs. Current 

inverse models usually can only adopt one of these alternate conceptual models at one step, 

which may lead to statistical bias and underestimation of uncertainty (Neuman, 2003). Trial 

and error is the most common method for conceptual model identification and it is usually 

performed using qualitative criteria. CSMs that fail to reproduce measured data are regarded 

as inappropriate. Quite often, this allows one to be left with a single CSM (Dai et al., 2004a; 

Dai et al., 2005). 

However, in some cases, several models match the observation data equally well. In such 

cases, we need some quantitative criteria for model identification. A comprehensive strategy 

for constructing alternative conceptual-mathematical models of subsurface flow and 

transport, selecting the best among them, and using them jointly to render optimum 

predictions under uncertainty has been developed by Neuman and Wierenga (2003) and 

Meyer et al. (2004). They proposed a Maximum Likelihood Bayesian Model Averaging 

(MLBMA) method to accomplish this goal. In Appendix C, the MLBMA method is applied 

to develop conceptual models for ground water flow and reactive transport, to identify 

essential hydrogeological and geochemical processes and to estimate related parameters. 

Proper Performance Confirmation Monitoring (PCM) will result in a database that can be 

used to identify and test alternative conceptual models, and to evaluate conceptual and 

numerical model assumptions and results. For example, if the PA’s ground water transport 

calculations are based on a certain pH range, then pH would be monitored to detect any 

changes. If the site PA assumes 10% soil water content, and a rainfall or snow melt event 

causes a large exceedance from this value as shown by unsaturated zone monitoring, this 

information would need to be considered for revising the CSM and PA. 

Monitoring data should be continuously updated in the CSM and analyzed for outliers or 

transient events indicating potential conditions outside the envelope established in the PA, 

and suggesting a need to update the CSM. Traditional approaches to monitoring assume a 

static CSM. Our process (Figure 1-1) proposes to establish the idea that the CSM is dynamic, 

constantly being revisited with new information, particularly monitoring data. The CSM 

should constantly be updated and compared to FEPs in the PA to determine if conditions 

outside the PA envelope are detected. 

4.5 Data Management and Model Visualization 

Geologic, hydrogeologic and contaminant transport models can require analysis and 

representation of large amounts of data. The model should also be able to represent the 

uncertainties in both the data and the modeled results. In addition the typical model is 

required to integrate many different types of information ranging from boring logs to 

geophysical studies and laboratory derived parameters. Also, in order to maintain the model 

credibility and transparency, documentation of Quality Control and Quality Assurance 

information must be provided. In order to manage the information associated with the models 

and to communicate the model results to both specialists and non-specialists in an efficient 

way, data management and model visualization become key considerations. This is 

particularly true as the sophistication and complexity of the models increase. 
 62

Geologic modeling software should be selected that will produce a geologic model of 

sufficient detail to meet the requirements discussed in the previous sections. The most 

powerful commercial modeling software has been produced for the petroleum or mining 

industry, and some of these packages have integrated fluid flow modeling modules. Most of 

them will allow data fusion by accepting seismic and other geophysical data, geologic core 

logs, and geophysical logs. Stochastic and geostatistical methods are used to integrate the 

data to best represent geology and hydrologic variables such as permeability. A sample of 

one of the many such software packages is illustrated in Figure 4-9. Utilization of these 

packages greatly improves the speed of evaluation while at the same time reduces uncertainty 

by the use of multiple data sources. This process would be difficult if done without the aid of 

a computer. 

Figure 4-9 Sample for 3-D (solid) model from Kingdom Suite 

The CSM could be built in a commercial modeling/visualization package such as Petra and 

Groundwater Modeling System (GMS). Visualization helps the modeler (as well as managers 

and other stakeholders) understand the interrelationships of site, facility, and plume 

geometries as well as their associated uncertainties. Displaying ground water data in a quasi3-dimensional format makes it much easier to see areas of interest that require the placement 

of critical monitoring points. 

Figure 4-10 is one frame taken from a visualization of radioactivity distribution at a tank 

farm. Three-dimensional computer contouring alone indicates no significant contamination 

at the base of the modeled volume. Visualization reveals to the operator that the hot wells do 

not extend to the base of the volume, whereas the cold wells do extend deeper, and control 
 63

contouring at that depth. There is still a real possibility that contamination extends below the 

depth of measurement at the hot locations. This also calls into question the contouring 

method, which is probably on a plane-by-plane basis, rather than truly in 3-D. Also an area 

to the right of the plume does not contain any monitoring points. This area could be a 

candidate for placement of a monitoring point to reduce uncertainty in contaminant 

concentration. 

1 pCi/g 10 pCi/g 30 pCi/g 100 pCi/g

Plume at 50.0 pCi/g Cesium

Figure 4-10 Quasi 3-D contouring and solid rendering of Cs in soil. Note that hot wells 

do not extend to the base of the modeled volume. 

4.5.1 Data Management 

In order to facilitate efficient analysis and communication of the data obtained by the 

monitoring network, any monitoring program should incorporate a Data Management System 

(DMS). The DMS should contain provisions for efficient data storage and retrieval, in 

addition to incorporating techniques for data analysis, and Quality Control. Closely 

associated with these techniques are data querying tools that allow the data to be easily 

accessed by the analysis, reporting and modeling software used by the decision rule 

evaluation and PA model. In addition, the DMS should integrate and provide methods to 

communicate the results of the monitoring plan and decision rules in a clear and concise way 

to concerned parties such as management or stakeholders. The decision rules can be 

programmed in a relational database so that, as new data are added, the project manager is 

quickly apprised of the meaning of the data in the context of site-specific requirements. 

The provisions in the DMS for data storage and retrieval potentially range from simple 

spreadsheets for small-scale data-poor intensive monitoring programs to large scale 

integrated databases. In most cases however, the DMS will be implemented in a 

commercially available relational database that will readily accommodate geologic, 

hydrologic, geophysical, and geochemical data. This can include geochemical data, 3-D 
 64

seismic data, maps, well logs, periodic sampling data, and data analysis reports. Database 

software such as Oracle or Access makes custom data sets easy to design and program. 

Since the data and subsequent analyses will serve as the basis for all of the technical and 

many of the managerial decisions, the degree of confidence in the data is crucial to both the 

decision makers and the stakeholders. The data must be in a format that is accessible and 

understandable to all that are involved in the PA process. This is a necessity if the data are to 

withstand the scrutiny demanded by the PA acceptance process. This requirement 

immediately brings to focus the advantages of storing and managing the data in a relational 

database. Data querying tools can easily retrieve the data in any standard format. Many 

modeling and reporting tools can be implemented on top of a database structure so that the 

data can be directly accessed. This capability potentially leads to many efficiency gains. For 

instance, since the DQO process provides that detailed analysis techniques be associated with 

the decision rules, and these rules are formulated in a precise analytical way, in many cases 

the evaluation of the decision rules could be automated. Also this makes automated 

standardized report generation and GIS updates possible, as data and analytical results 

become available in the DMS. 

In summary, depending upon the needs of the decision rule and PA modeling process, the 

DMS will need to be able to import data into a variety of software packages, including 

statistical analysis, geologic process models, geophysical interpretation packages, ground 

water flow and transport models, GIS and others. Thus it is critical that the database structure 

be well designed and robust. 

4.5.2 Modeling and Visualization Software Used in the Development 

of this Document 

Several software packages used during the preparation of the test cases detailed in Volume II 

are listed below. This listing is not comprehensive and is not an endorsement of any of this 

software. Some additional software is discussed in Volume II case studies. 

• ArcGIS (with 3D Analyst and Spatial Analyst) - Standard GIS application, ArcGIS 

is useful for analysis, mapping and data presentation. When used in conjunction with 

the 3D Analyst package, it is also good for 3-D visualization. The Spatial Analyst 

raster GIS allows complex environmental modeling directly within the GIS 

environment, including ground water contaminant transport advection and dispersion 

modeling (Tauxe, 1994). 

• Global Mapper - A tool for manipulating maps. Allows correction of problems with 

existing maps, or merging of several maps from different coordinate systems into one 

map with a single set of coordinates. Has an easy to use interface for geo-registering 

aerial photographs, or other images. Can be used to overlay several different types of 

maps in translucent format. 

• GMS - Provides complete integration of numerous modeling programs 

(MODFLOW, RT3D, etc...) and 3-D visualization. We typically use GMS for 

visualization and modeling. Data and interpretations are loaded from other programs 

(excel, or Petra), and then modeling programs such as MODFLOW and RT3D are 

run. 
 65

• GoldSim – Systems analysis simulation software. GoldSim is an object-oriented 

graphical probabilistic systems analysis program. It is most useful in the context of 

the Strategy for constructing PA models, with inputs based on process model results 

where indicated. Being natively probabilistic, GoldSim is ideal for modeling where 

uncertainty and sensitivity analyses are of interest, and was used for developing a 

hypothetical probabilistic radiological PA for demonstration purposes. 

• HydrogeoAnalyst (HGA) - HGA provides true 3-D solid contouring capabilities, 

which was utilized in the evaluation presented in Chapter 4 of Volume II. None of 

our other available software provides true 3-D contouring capabilities. True 3D 

gridding allows the production of solid models for visualization. This is very 

different from pseudo-3D perspective views. It also serves to directly link a database 

system (designed around environmental data) to GIS mapping capabilities. 

• Microsoft Access - Widely used database system for data compilation and storage. 

• Microsoft Excel - Spreadsheet program used for both the organization of data and for 

simple modeling and data analysis. Also provides good graphing capabilities and 

some statistical tools. The same functionality is also found in OpenOffice Calc, an 

open source spreadsheet. 

• Petra – Petroleum industry software used for data storage as well as for geologic 

interpretation of well log and seismic data. Petra can also be used with the Petraseis 

module to interpret seismic data, and incorporate the results with your well 

interpretations. After geologic model development, the interpreted layers are 

exported as grid files, and imported into GMS for modeling. Also use Petra for 

contouring data, and making maps. 

• R – An open source statistical programming language. This was used for 

development of sensitivity analysis for the results of the GoldSim model. 

• RockWorks - Suite of various geologic based tools. It has a spread-sheet like format 

for storing and organizing data, and has mapping capabilities. 

• SAS - High end statistical analysis package. 

• Surfer (Golden Software) Contouring and basic GIS functions 

4.5.3 Data Analysis 

Many of the data analysis methods will have been specified or developed as part of the DQO 

development process. However, a fundamental requirement is that data analysis methods be 

able to distinguish between the natural variability in the data and the actual response in the 

parameter being evaluated. In the case where a monitoring hypothesis is being applied, an 

appropriate statistical method can help to resolve these types of issues (see Chapter 8 of 

Volume II for discussion of some specific techniques). The selection of the statistical 

approach should be tied to the monitoring objectives, hypotheses, and decision rules (EPA, 

2004). The specific type of statistical tests will depend upon the data type and collection 
 66

methods, and the desired level of decision error. Many of these issues are addressed as part of 

the DQO process. 

4.5.3.1 Trend Analysis 

Trend analysis is used to evaluate the magnitude and direction of change in the monitored PIs 

over time. In order for a trend analysis to be valid, sufficient data over the time period of 

interest must be obtained. In quality control programs trend analysis charts are called 

Shewhart Charts. 

Trend analysis can be used to determine how the parameters of interest are progressing 

toward their predicted values, and in some cases be used to predict parameter response to a 

change in conditions based on a previous response to a similar change, such as a large rainfall 

event. In some cases, trend analysis could be used to refine the PA model, or to modify site 

operations. 

Trend analysis can be used in resolving the issue of apparent outliers and anomalies. If a data 

point exhibits a divergence from the trend of the expected parameter values, this should 

trigger an investigation. There are three distinct outcomes resulting from this investigation: 

1) revision of the conceptual site model that will be used to update the PA model, 

2) removal of a specific reading due to collection/recording errors, or 

3) removal of the data point and all associated data. 

In short, trend analysis can be a valuable tool in PA model confirmation and evaluating 

model assumptions. More discussion is included in Chapter 8 of Volume II. 

4.5.3.2 Data Scaling 

The objective of up-scaling is to use information collected in detail to develop another data 

set that is representative of a given property at a larger scale. This process must be performed 

correctly in order to preserve the critical property of the detailed set in the larger scale 

version. 

The problem is one of grid size and the appropriate spatial and/or temporal averaging of 

parameter values. Even with current processor speeds and computer memory, there is still 

insufficient computational capacity to perform modeling at the resolution of most input data. 

Well logs, for example, are typically sampled at 0.5 feet or less. For calculation purposes it is 

necessary to reduce the data volume to some manageable amount of data. This is achieved by 

creating a coarser grid and by assigning appropriate physical property or state values to the 

coarser grid elements. 

This grid coarsening should be accomplished in a manner that preserves the character of the 

original data density (Mansoori, 1994). For parameters that can be treated as additive, such as 

porosity or saturation, simple arithmetic means can be used. More complex parameters like 

permeability or hydraulic conductivity require a more refined method. King (1989) outlines a 

method of up-scaling permeability data using grid replacement. 
 67

4.5.4 Data and Decision Rule Reporting and Communication 

The ultimate reason for the existence of the DMS is to provide input to the decisions based 

on rules engineered in the DQO process and to communicate the results of the evaluation of 

these rules. However, in order for management and stakeholders to understand the ultimate 

consequences of the decision and the rule makeup, it is desirable that the DMS also be able to 

communicate other important information about the PA. This would include: 

• features, events, and processes associated with the CSM to include assessment of 

model uncertainties, 

• how the monitoring network interfaces with the CSM, 

• general features and characteristics of the monitoring data and how it relates to 

the CSM, and 

• general features and characteristics of the analytical results and how they relate to 

the CSM. 

The conceptualization of the information associated with the items listed above can be quite 

complex. Most engineers and scientists have been trained and are experienced in 

conceptualizing this type of information from rudimentary inputs such as tables of 

coordinates or geologic cross sections. In most instances this requires synthesizing a three- or 

four-dimensional image from one- or two-dimensional input media. In order to make this 

process more efficient for management and stakeholders the DMS should implement 

capability to communicate this information in a fully three or four dimensional sense with 

associated visualization. The cost and requirements for operating resources of modern 

software for three- and four-dimensional imaging are well within the limits of most personal 

computers and would be an inconsequential budgetary item to most projects. 
 68

5 Performance Confirmation Monitoring System 

Design: What, Where, When, and How to Monitor 

This Section describes how to translate the information learned through, data analysis, CSM 

development, and Performance Indicator selection into an operational Performance 

Confirmation Monitoring plan. Monitoring network design is directly determined by the 

requirements for data acquisition systems used to make meaningful measurements of 

identified and selected PIs for testing of PA modeling assumptions. The details of the 

monitoring network design are determined by the characteristics of the data and the 

requirements imposed by the DQOs and decision rules. 

In general, the objective of monitoring network design is to choose monitoring locations and 

frequencies that meet DQOs while minimizing cost. Also, in certain instances it may be 

necessary for the network design to incorporate provisions to characterize background levels 

in addition to the ability to measure the levels of the relevant PI at locations where the system 

performance is being evaluated. 

 The performance confirmation monitoring system design process is outlined in Figure 5-1. 

What to monitor is determined from the preliminary list of PIs and DSIs through a down 

selection process. The down selection should be on the basis of risk implications determined 

by the application of established decision rules. 

Where to monitor: Monitoring locations are selected to optimally reduce the level of 

uncertainty in the CSM. Keep in mind that the overarching goal is to develop an accurate 

understanding of the natural system to reduce risk. 

When to monitor: Monitoring times or frequency will be determined by site conditions, and 

perhaps by regulations. Examples are given in Volume II of variability that would not be 

captured in a quarterly or even a monthly monitoring program, and of changes in 

contaminant concentrations and trends caused by rainfall events. Based on the conceptual 

site model, triggering events may set a monitoring plan into action. For example snow melt 

might release water into a contaminated vadose zone and require monitoring at the water 

table. 

How to monitor: The selection of monitoring devices will be controlled by the PI selected, 

the location where the device is to be placed, the performance requirements defined through 

the DQO process, and by the overall site conditions. Qualified experts must be employed for 

the selection, application, and interpretation of subsurface measurement technologies. 
 69

Preliminary PI

and

DSI List

Examine Site Geology

for Potential Pathways

Examine Facility

for Test Points and

Release Points

Model All Likely

Plume Scenarios

for PI List

and also

Identify Areas with

High Uncertainty

Select Critical

Monitoring Points (CMPs)

to Test Plumes and

Address Uncertainty

Select Monitoring

Device to Meet PI

DQO and Function

at CMP

Steps in

Monitoring Plan Development

for

Chemical PIs and DSIs

What

Where

and

When

How

Figure 5-1 Steps in Monitoring Plan Development 
 70

5.1 Network Design for Performance Indicators 

Generally, a monitoring strategy would be to sample for the presence of the PI, either 

continuously or at specified intervals (“when”), in pathways associated with its mobility at 

monitoring point locations (“where to monitor”) selected so that detection of the constituent, 

if present, is as certain as possible. Note that the success in the implementation of this 

strategy requires that the uncertainties in the existence, location, geometry, and transport 

characteristics of the constituent mobility pathways be well known. This knowledge is the 

essence of the connection between site characterization and monitoring network design. 

The site characterization and CSM must be robust enough so that all risk-related pathways 

are identified and located so that monitoring locations can be effectively identified. Also, the 

pathway transport properties need to be quantified well enough that the sampling time 

intervals can be effectively determined. Emphasis should be placed on sampling at locations 

in which the CSM exhibits large uncertainties in pathway characterization. In practice this 

Strategy would be implemented by analyzing the possible source locations for the constituent 

associated with the PI, and modeling the locations for potential plumes for these sources 

using the flow and transport model developed in the CSM. 

When selecting how to monitor for a particular constituent, the accuracy and precision of 

techniques should be considered (to ensure that the DQOs are satisfied), as well as cost and 

ease of implementation. The AES 2003 report included a review of measurement devices. 

This is a very actively developing field, so a review of new devices and their applicability is 

necessary. 

5.2 Design and Implementation of Unsaturated-Saturated Zone 

Monitoring Networks for Ground Water Constituents 

Once the chemical constituent identified with the PI has reached the water table, transport of 

constituents in solution or suspension may be monitored through direct sampling of ground 

water. This is normally done through permanent wells, but can be done with direct-push 

technologies also (e.g. ‘geo-probe’, and CPT). 

In a uniform isotropic world, ground water is monitored by establishing the slope of the water 

table and placing one well up-gradient, one well to each side, and one well down-gradient of 

a site. Generally, flow is assumed to be horizontal, and down the water table gradient. These 

conditions are probably never truly realized in nature especially at the scale of a waste 

disposal site. They are nevertheless, and unfortunately, implicit in many designs of ground 

water monitoring systems. 

The assumption of uniform aquifer properties might be appropriate for a water resource 

study. Normally, water production wells are screened across all productive zones of an 

aquifer, so the quality and quantity of water produced follow properties of the entire 

productive thickness. Thus, at the scale of a regional aquifer, horizontal flow to producing 

wells may, in such cases be a reasonable assumption. 

At the scale of a typical site, however, local subsurface heterogeneity controls the shape and 

extent of any plume emanating from the waste site and controls the zone of capture of 

monitoring wells. Furthermore, vertical flow may be important (Hubbert, 1940). Design of 
 71

monitoring systems at the scale of a waste facility or decommissioned nuclear site must 

therefore incorporate consideration of facility design and site characteristics. 

5.2.1 Unsaturated Zone 

Bodvarsson et al. (2000) presents a detailed review of flow and transport in the unsaturated 

zone. They tabulate all important flow and transport processes, each of which presents a 

challenge or an opportunity for monitoring. They note that heterogeneity in the unsaturated 

zone is poorly understood, and may confound modeling efforts. From a strategic standpoint, 

a modeling strategy will have to be sufficiently robust to overcome the issues of 

heterogeneity and variability of properties. 

In the unsaturated zone, the monitoring objective is frequently detection of water. This can 

be done with tensiometers or with various geophysical methods that measure soil electrical 

properties related to moisture content. Chapter 4 of Volume II mentions the use of 

thermocouple psychrometers to measure soil moisture in the vadose zone at the Amargosa 

Desert Research Site. 

A great deal of NRC-supported research has focused on unsaturated zone monitoring in arid 

climates. Work at the Maricopa site (Young et al. 1999a & b) has evaluated trenches, 

islands, boreholes, and geophysics for monitoring. Borehole and surface geophysical 

methods offer flexibility because instrumentation can be maintained or replaced, and 

boreholes can be advanced to depth, or can be angled beneath a waste site. 

Work at the Apache Leap Tuff, the Las Cruces Trench, and other sites has been reported in 

NRC, open literature and INTRAVAL Project reports. Larsson et al. (1997) reported that 

data from the Las Cruces Trench were valuable for testing modeling against field results. 

5.2.2 Saturated Zone 

Location of wells in the saturated zone is an important component of designing a monitoring 

network for any waste site. The most effective monitoring network design for any given site 

is heavily dependent on the type of media through which the ground water will travel (i.e. 

sand, sands and clays, fractured bedrock, karst, fractured chalk). 

Spruill and Candela (1990) propose that two different approaches be used to design a 

monitoring network depending on the type of information required. In some cases one may 

be looking for quantification of typical concentrations in a given area and in other cases 

looking for potential problem areas. In this reference a method is introduced to determine the 

sample size necessary to describe any selected quantile, and kriging was used to determine 

the minimum number of wells necessary to retain spatial information. However, this 

geostatistical technique generally requires larger sample sizes than networks designed by 

traditional techniques. 

Ben-Jemaa and Marino (1994) have applied statistical tools to determine the number of wells 

and samples necessary to characterize properties of the aquifer and the water it yields. It 

might be possible to use this method for estimating a range of constituent concentrations 

from samples of a regional aquifer, then to spot probable anomalous values in data from a 
 72

given well. This approach should work at the scale of an aquifer because resolution of local 

water-bearing zones is not needed. 

In Volume II, Chapter 3, we recommend that monitoring purposes be integrated into process 

water systems. Instead of one high-capacity well, a number of lesser capacity wells close to 

the facility are recommended. These are pumped more or less continuously so that each 

develops a zone of capture much more extensive than that of a monitoring well that is only 

pumped a few times a year. In this way, leaks that might ordinarily go undetected for years 

as a narrow plume passed between monitoring wells would be detected by the active 

monitoring system. In the event of detection the well can be taken out of service for process 

(or drinking) water, but still pumped as a plume control well until a remediation plan is fully 

developed. 

Selection of monitoring points at the site or facility scale must be based on understanding 

details of site hydrogeologic features or controls. Without this detail, statistical approaches 

will give false guidance. 

5.2.3 Soil Gas and Volatile Fluids 

Many chemicals may move without the assistance of water. These include mercury, iodine, 

tritium, radon, argon, krypton and other waste components such as organic liquids and their 

vapors. Soil gas can be sampled and analyzed in a number of ways, and the term soil gas is 

often applied to any soil constituents that got to where they are found in the form of a vapor. 

In a regulatory context, soil gas can be used to identify contaminated areas, but cannot 

directly quantify contaminants in regulated media such as drinking water. Soil gas surveys 

are broadly divided into free gas and soil-adsorbed –vapor methods. Actual soil, or an 

absorbent buried for some period, e.g., two weeks, can be removed to a lab or portable 

analytical device. Free gas can be tested with portable devices, or transported for laboratory 

analysis. Both approaches are used as screening techniques to identify areas for further 

study. 

Implementation of a soil gas survey generally begins with a grid-based sampling plan with 

additional locations near potential contaminant sources such as pipes or trenches. Data 

turnaround is normally a matter of hours so that confirmation and infill sampling can be done 

if constituents of interest are detected. Access methods vary from truck-mounted probes to 

hand-pushed or hammered probes or augers to spades. Simple methods may work as well as 

expensive methods. 

In a case example discussed in Section 4.3.2, above, soil gas provided information leading to 

revision of a CSM, and later placement of additional wells. 

5.3 Monitoring Network Assessment 

The general adequacy of a monitoring network design may be assessed by consideration of 

the following points: 

1. Do monitoring networks sample at appropriate locations and frequencies to provide a 

high degree of confidence that chemical constituents associated with the PIs will be 
 73

detected if present? In practice this means that the network must sample at locations 

identified in all plume scenarios defined in the CSM and simulations based on the 

CSM. The sampling frequency at these locations must also be commensurate with 

the transport rates for the chemical constituent as determined by the flow and 

transport model. 

2. Do network sample locations and intervals accommodate areas of high uncertainty in 

the flow and transport model? 

3. Does the network design provide for identification of background levels if necessary 

for identifying elevated levels of the constituent associated with the PI? 

The use of visualization software allows the project manager to develop insights into the 

observed or predicted 3-D relationships between the facility, site geology, and plume 

geometries. Visualization of modeling output can be used to determine areas where there is a 

high degree of uncertainty in plume mapping or site characterization. This visualization also 

reveals the placement of MPs in the context of site, facility, and contaminant FEPs. For 

example tritium plumes are represented in 3D renderings in the Brookhaven Chapter of 

Volume II. 

5.4 Demonstrating Site Performance With Respect to Site 

Performance Objectives 

Each engineered facility has design requirements, derived from release restrictions, which 

specify limits of releases to the environment. These requirements become objectives to be 

achieved. A CSM is developed, and release scenarios modeled as part of the PA process. 

Whether or not the objectives are being met is determined through Performance Confirmation 

Monitoring. 

The purpose of material in this document is to aid NRC in their effort to demonstrate that a 

licensed nuclear site or facility is behaving within the expected limits as described by the 

performance assessment. 

5.4.1 Site Performance Confirmation 

Much of the discussion above addresses this topic. MPs and PIs are selected to verify that 

the site is performing as designed or modeled, and within acceptable limits. 

5.4.2 Remediation Confirmation 

Performance Confirmation Monitoring can also demonstrate that natural attenuation or 

remediation is occurring according to expectations. Although remedial expectations and, 

consequently, appropriate performance monitoring analyses are site specific in nature, 

reduction in contaminant concentrations to specified levels is generally expected for most 

selected remedies (Pope et al., 2004). Data analyses useful in evaluating progress toward 

contaminant reduction objectives include evaluation of temporal trends in contaminant 

concentrations or mass, comparisons of observed contaminant distributions with predictions 

or required milestones, and in some cases, comparison of calculated attenuation rates with the 
 74

range of rates required to meet remedial objectives within the required time frame. 

Evaluations of adequate progress toward restoration objectives are difficult due, in large 

measure, to subsurface variability and to a lesser extent, measurement variability. This will 

often necessitate use of multiple lines of evidence (e.g., temporal trends and estimates of 

contaminant mass loss as discussed in Pope et al., 2004) and relatively dense monitoring 

networks to reduce uncertainty to acceptable levels. 

The remedial action objective of attaining permitted standards throughout the plume should 

be demonstrated before monitoring is terminated to ensure that the required standards are 

actually achieved (Pope et al., 2004). The techniques for determining compliance with 

remedial action objectives under a natural attenuation remedy are similar to those used for 

determining compliance following application of other remediation methods. The 

demonstration of the attainment of cleanup objectives should include sufficient verification 

monitoring (e.g., three to five years) once the standards are met to evaluate the effects of 

natural variations in site conditions, based on objective statistical analyses of the data. 

Statistical methods useful in these evaluations include analyses of temporal trends in 

contaminant concentrations and comparisons with the specified concentration standard. 

Guidance regarding verification of compliance with cleanup objectives is provided in Cohen 

et al. (1994) and EPA (1992a). 

5.5 Selection of Monitoring Points: Where and When to Monitor 

There are uncertainties inherent in all PA models. Poor understanding of site hydrogeology 

may lead to inappropriate monitoring point selection. Transient events such as rainfall or 

snow-melt, long-term climate, and infiltration rates may change drastically over time, making 

previous computer modeling obsolete and decreasing the validity of data gathered from 

sampling points devised based on original site assumptions. 

Heterogeneity in general was noted above as a challenge for modelers. PA model parameters 

that are important to risk include spatially distributed parameters such as chemical properties 

used to model transport (absorption coefficients, or transmissivity) and physical properties 

such as moisture content or pressure distributions. These types of parameters are not related 

to mobile constituents; consequently the main issue in the monitoring network design is no 

longer related to pathways but to spatial heterogeneity. 

Where to monitor for these indicators is mainly dependent on having adequate sampling 

density in locations where these parameters are important to the flow and transport risk, for 

example risk associated constituent pathways and hydrologic confining units. The sampling 

density in these locations should be enough to drive down the uncertainty in the spatial 

correlation of these parameters to acceptable levels so that a high degree of confidence will 

result from the flow and transport modeling. This can be determined by various statistical 

methods. The monitoring wells are usually assigned in the following critical locations: 

1) Source areas, within and immediately down-gradient of source area; 

2) Transmissive zones with highest contaminant concentrations or hydraulic 

conductivity; 

3) Fringe portions and boundary of the plume; 

4) Areas representative of contaminated and uncontaminated geochemical settings; 

5) Areas supporting the monitoring of site hydrogeology; or 
 75

6) Regulatory points of compliance. 

The predicted time-series trends of the contaminant concentrations provide most of the 

information for us to determine the monitoring frequency or time intervals. The most 

appropriate frequency is determined based on the predicted rate with which contaminant 

concentrations change due to ground water flow and natural attenuation processes, the degree 

to which the causes of this variability are known, the types of evaluations to be performed, 

the locations of possible receptors, and the remedial action objectives for the site. In 

situations where the hydrologic, geochemical and contaminant trends are stable and the CSM 

is verified by existing monitoring data, reductions in sampling frequency may be warranted. 

In situations where the variability is high, increases in monitoring frequency may be 

warranted. More frequent monitoring of ground water elevations may be warranted, 

particularly during the establishment of baseline conditions, to improve the characterization 

of ground water flow patterns. 

The other factors for determining monitoring frequency include the relevance of PIs and the 

information redundancy. If a PI is not expected to significantly influence the performance 

evaluations in a site, then monitoring frequency for that PI could be greatly reduced or even 

eliminated. If over a period of several years data trends are stable, a reduction in monitoring 

frequency may be warranted. In Volume II of this document, examples are presented to 

illustrate pitfalls of premature reduction or termination of sampling, as well as to demonstrate 

when monitoring points may be abandoned. 

In addition to considerations associated with uncertainties related to heterogeneities of the 

model parameters, the PA model assumptions and uncertainties may also be evaluated by 

analysis of congruity, as discussed in Section 3.2.3. This analysis is mainly concerned with 

evaluating the CSM for consistency relative to new or unconsidered information. The 

monitoring network design in this case is mainly concerned with making sure that new 

information is obtained when available and that the congruity review occurs on a timely 

basis. 

A feedback loop must exist so that new information obtained from the monitoring network is 

used to refine the CSM and to continually optimize the network itself. Details are dependent 

on the type of new information, but may consist only of a periodic review and assessment by 

qualified experts. For instance new information about features in the locally surrounding 

geology resulting from nearby characterization or monitoring activities need to be evaluated 

to ensure that they validate the existing CSM. 

5.6 Selection of Monitoring Devices: How to Monitor 

Selection of an appropriate monitoring device will de driven by what the PI is and what 

property of the PI is best measured. For example, early detection of leakage of conductive 

fluid would best be accomplished using electrical methods. These devices measure directly 

the electrical conductivity of a volume of soil. With minimal assumptions, this measured 

parameter can be easily converted to the PI of interest, i.e. soil moisture. The important 

issue, from a strategic standpoint, is that the resulting data collected by a monitoring device 

address the issue of interest. 
 76

Given the constraints of the engineered facility, what is the best type of device to gather the 

needed data? Given the physical features of the site, what devices can be used to make the 

desired measurements of critical FEPs? If subsurface emplacement is required a number of 

access technologies are available as well as guidance for their application. 

The AES (2003) report included a review of measurement devices. This is a very actively 

developing field, so a review of new devices and their applicability must be done 

periodically. EPA/625/R-93/003a (EPA, 1993) is an extensive desk reference guide to 

sampling and monitoring devices. The discussion below is not intended t give thorough 

coverage of all devices and methods, but should give the reader a general overview of 

methods. Because each monitored location is unique to some degree, experts should be 

consulted to select the best available technology for the specific application. 

Monitoring Devices fall into several general categories: devices that directly measure a 

primary or secondary PI and those that measure some associated property that can be 

converted to an estimate of a PI parameter. These two general types of monitoring devices 

can be further divided into how the device collects the data and whether the data to be 

collected is in the vadose zone or the saturated zone. Listed below are some examples of 

monitoring devices. How these devices are deployed is strictly a function of the PI and the 

location. 

5.6.1 Saturated Zone Sampling 

Once a contaminant has reached the water table, transport of the constituents in solution or 

suspension may be monitored through direct sampling of ground water. 

Many States and Federal agencies have issued guidance for well installation that covers most 

well construction issues. For years the standard guide for well drilling and installation was 

The Johnson Water Well Manual, which has been replaced by Driscoll, 1986. These sources 

contain details of well materials, installation, pumps, and testing. 

United States Geological Survey (USGS), State, and EPA guidance documents also exist and 

were referenced in AES (2003). A USGS reference (Lapham et al., 1997) describes the 

supporting documentation, or metadata, that should be collected during well installation or 

other types of borings. 

There is extensive literature on saturated zone sampling for hydrologic and chemical data 

including EPA, U.S. Army Corps of Engineers (USACE), DOE, and State guidance 

documents. It includes permanent and temporary (e.g., penetrometer) methods. The reader is 

referred to Sara (1994) and Driscoll (1986). 

5.6.2 Geophysical Methods 

Geophysical methods can be thought of in two broad categories, those which require access 

to the subsurface through wells or direct push methods and those which are non-invasive and 

are applied from the surface. In the first category are geophysical logs such as resistivity and 

radiation logs. 
 77

Devices requiring direct access to the subsurface by wells (including piezometers) or direct 

push methods: 

• geophysical logs, 

• in situ chemical probes, and 

• sensors for many elements or compounds. 

Geophysical methods measure an associated property which can then be extrapolated to the 

PI of interest. Geophysical methods and what each technique measures are described in a 

large number of textbooks. One of the best for environmental (shallow) applications is 

Telford, Geldart, Sheriff, and Keys (1976). Annual publications of the Symposium on the 

Application of Geophysics to Engineering and Environmental Problems(SAGEEP) record 

new applications of geophysics to environmental problems. EPA published a guidance 

document (EPA, 1993) that has fact sheets or short descriptions of almost any conceivable 

subsurface monitoring and characterization technique including geophysical methods. 

Sara (1994) has an excellent summary of geophysical techniques and several figures that 

illustrate which geophysical techniques are best for which applications. In general, 

geophysical methods are intended to supplement other methods. 

While geophysical methods can be applied without any subsurface data, interpreting the 

results is improved if independent information on geologic strata, properties, or structures can 

be obtained. For example, a single well can provide sonic velocities for seismic 

interpretation, or layer thicknesses and electrical conductivity for electrical or 

electromagnetic methods. 

Kowalsky et al., 2006, report on combining geophysical and hydrological data for improved 

subsurface characterization. They illustrate time-lapse (4D) methods to detect changes in 

subsurface plumes. Several books specifically related to geophysical methods in 

hydrogeology are available. 

Geophysics can be used to provide information between boreholes or to quantify some 

physical property where access is an issue for boreholes, such as under impermeable covers. 

Table 5-1 presents commonly applied geophysical methods. 

 
 78

Table 5-1. Summary of Possible Applications of Surface and Cross Borehole Geophysical Methods for Site Ch

Surface/ Crosshole Method: Application 

Resistivity EM 

Induction 

GPR Seismic 

Reflection 

Crosshole Seismic 

Seismic Refraction 

Depth to Water Table 2 2 4 2 0 4 Fresh/Salt Water Interface 4 4 2 1 0 2 Depth to Bedrock 4 4 4 4 0 4 Gross Hydrostratigraphy 4 4 4 4 1 4 Detailed Hydrostratigraphy 2 3 4 4 4 1 Significant Fault Detection 4 4 4 4 4 4 Cavity Detection 2 1 3 2 3 0 Porosity, Permeability Estimation 3 3 3 3 3 1 Water Content Estimation 3 3 3 1 0 1 Contaminant Detection 3 3 3 1 3 0 Detection of Buried Metallic Objects 

2 4 4 2 1 0 

Landfill Delineation 4 4 4 2 2 2 Depth of investigation – a function of design and equipment selected 

Shallow to km 

Shallow to km. 

Shallow 0-20m 

Shallow to km Between wells Shallow to km 

Resolution – often a function of design of data acquisition 

10 10 Good By design By design - 

fuzzy 

Good 

Key: 0 = not considered applicable 1 = limited use 2 = used, or could be used, but not the best approach or has limitations 3 = excellent potential but not fully developed 4 = generally considered an excellent approach, techniques are well developed 10 – resolution approx 10% of depth Modified from Looney and Falta, 2000, Volume I, p223 
79

5.6.3 Vadose Zone Devices 

Water samplers for the unsaturated zone have long been used in agricultural research to 

evaluate nutrient and pesticide movement in soils. Listed below are some of the monitoring 

devices currently being used to: 

measure soil water content and distribution 

• neutron probes 

• time domain reflectometry 

• frequency domain reflectometry 

• electrical resistance sensors 

• tensiometers 

• thermocouple psychrometers 

• heat dissipation sensors or thermal conductivity sensors 

• microgravimeters 

measure constituents 

• soil gas 

o mercury 

o iodine 

o tritium 

o radon, argon, krypton, etc. 

o organic vapors 

• gamma (radiation) detectors 

5.6.4 Other Monitoring Devices 

Surface water sampling may provide a method of locating preferred pathways of ground 

water. For example, sampling along a seep line, or of shallow subsurface water along a creek 

edge (where upwelling is expected) may provide more information than sampling the flowing 

stream. Current-operated integrating samplers are available for streams. Stream flow may 

drive a paddle wheel that turns gears to periodically add a sample of the stream water to a 

container. Samples are retrieved and analyzed to detect contaminants seeping into the 

stream. 

Once PIs have been identified, then appropriate measurements, measuring devices and access 

technology must be selected. While we again iterate through the PA, facility and site, the 

analysis assumes that the PIs are chosen, and that instrumentation or measurement type is 

now the focus. 

This process is interactive and iterative, involving representatives of all parts of the total 

process. The modelers need to be involved during the design of the sampling and analysis 

plan to make sure that information crucial to the model is collected during site 

characterization. 
80

5.7 Modify and Optimize Monitoring System 

As part of the data analysis process, opportunities exist to increase the efficiency of the 

monitoring system, both in terms of data quality and savings realized. Applying an 

optimization technique to analyze an existing well network can potentially reduce the number 

of wells that need to be sampled and possibly the sampling frequency, while increasing the 

detection and delineation of contaminant plumes. On the other hand, some techniques (Ross 

and Vieux, 2000) can reveal inadequacies in the existing design and indicate the need for a 

greater well density to improve the monitoring performance. 

Optimization methods can be used as the technical basis for a regulatory permit modification 

(Tuckfield et al., 2001). This is necessary when the long term use of a monitoring well has set 

a precedent for its continued use, whether it contributes to an understanding of the overall 

system or not. 

The optimization methods used to analyze the network design vary. Some employ a 

geostatistical method of kriging (Journel and Huijbregts, 1978, Ben-Jemaa et al., 1994, 

Spruill and Candela, 1990), some use geological setting analysis (Nativ et al., 1999) 

combined with geostatistics (Tuckfield et al., 2001), and others use a combination of multiple 

criteria decision analysis combined with geostatistics (Woldt and Bogardi, 1992). In this last 

method there exists the opportunity to consider the performance and cost-effectiveness of 

different designs. 

We caution against the use of statistical methods to justify elimination of monitoring wells. 

In complex hydrogeological systems (and they all are) wells assigned to the water table 

aquifer may actually be screened in several water-bearing zones. Head differences might be 

trivial, but transport pathways might be complex. 

Chapters 2, 3 and 8 of Volume II include discussions of deleting wells from a monitoring 

network. Chapter 2 of Volume II provides an evaluation for the Charleston Naval Weapons 

Station showing how application of the strategy provides for the revision of the monitoring 

well network to increase the monitoring system performance and reduce uncertainty. 
81

6 Conclusion 

Performance Assessment modeling of a site may make predictions of potential contaminant 

levels many millennia into the future. Yet, the lifetime of a monitoring program may be 

measured in decades at most. For this reason, a strategic approach must be applied that 

ensures that the facility is, in fact, behaving as expected and within limits described by the 

performance assessment and that there is high confidence that it will continue to do so. 

Risk or performance assessments are based on a sufficiently accurate understanding of flow 

and transport in the subsurface. It is most important to determine what things to measure and 

where and how to measure them in a short period of time so that the long-term predictions 

can be most accurate. 

The discussions and recommendations of the preceding pages are intended to help guide a 

site manager in a logical way toward an effective ground water monitoring system. A logical 

or strategic approach to system design, including what, where, when, and how to monitor, is 

the best path toward reducing uncertainty regarding system performance. 

Although the high level logic of the approach is simple, a great deal of specialized knowledge 

is required for implementation of the logic. The Strategy requires application of tools from 

geology, hydrogeology, geochemistry, geophysics, statistics, computer simulations and 

access and sampling technologies. An objective in this document has been to present enough 

information in each area for a responsible party to appreciate the need for expertise in each 

area. Our recommendation is that the Strategy be implemented with the assistance of a team 

of technical experts. 

The DQO process can be a powerful tool supporting goal setting and evaluation of whether 

goals are achieved. Unfortunately the first step in the DQO process is unwritten. The first 

step is to assemble a really good technical team to write the DQOs. Poorly written DQOs can 

result in poor goals and poor execution. This differs a bit from the MBO process in which all 

stakeholders are asked to contribute ideas and which involves less critical thinking. The 

strength of MBO results from its inclusiveness. 

Ultimately a manager, together with relevant stakeholders or regulators will make decisions 

based on ground water data. Following a logical approach, including application of the DQO 

process, will allow increased confidence in decisions, and reduce the risk of reaching the 

wrong decision. 
82

References 

Advanced Environmental Solutions, LLC. A Review of Ground-Water Monitoring Issues, 

Strategies, and Technologies. Washington, DC. May 30, 2003. U.S. Nuclear 

Regulatory Commission, ADAMS accession no. ML072740153 

 

Andraski, B.J., et al. “Importance and Use of Plants in Evaluation Water Flow and 

Contaminant Transport in Arid Environments.” Eos Trans. AGU, Fall Meet. Suppl., 

Abstract H52A-0836. Vol. 83. No.47. 2002. 

 

Ben-Jemaa, F. and M.A Marino. “Multivariate Geostatistical Design of Ground-Water 

Monitoring Networks.” Water resources Planning and Management. Vol.120 No. 4: 

505-522. 1994. 

 

Bevin, K.J. Prophecy, Reality, and Uncertainty in Distributed Hydrological Modeling. 

Advance Water Resource. pp. 16, 41-51. 1993. 

 

Bevin, K.J. Uniqueness of Place and Non-Uniqueness of Models in Assessing Predictive 

Uncertainty. Computational Methods in Water resources XIII. Bentley et al. 

Balkema, Rotterdam: The Netherlands. pp. 1085-1091, 785-792. 2000. 

 

Bevin, K.J., and J. Freer. “Equifinality, Data Assimilation, and Uncertainty Estimation in 

Mechanistic Modeling of Complex Environmental Systems Using the GLUE 

Methodology.” Journal of Hydrology. pp. 249, 11-29. 2001. 

 

Blanchard, B.S., and W.J. Fabryky. Systems Engineering and Analysis, Third Edition.

Prentice Hall International Series in Industrial and System Engineering. 1998. 

 

Bodvarsson, G., et al. Flow and Transport Modeling of the Vadose Zone. 2000. 

 

Brooks, R.H. and A.J. Corey. Hydraulic Properties of Porous Media. Hydrol. Fort Collins, 

CO: Colorado State University. Paper 3. 1964. 

 

Brooks, R.R. “Biogeochemical Methods of Prospecting.” Australian Mining. Vol. 60: 81-83. 

1968. 

 

Carrera, J. and S.P. Neuman. “Estimation of aquifer parameters under steady-state and 

transient conditions: 2. Uniqueness, stability, and solution algorithms.” Water 

Resource Res. Vol. 22. No2: 211-227. 1986. 

 

Chamberlain, T.C. The Method of Multiple Working Hypotheses. Science. pp. 754-759. 

(1890) 1965. 

 

Cohen, R.M., et al. EPA/600/R-94/123. “Methods for Monitoring Pump-and-Treat 

Performance.” EPA, Office of Research and Development :Ada, Oklahoma. 1994 

http://www.epa.gov/r10earth/offices/oea/gwf/issue20.pdf

 
83

Cooley, R.L. “Some new procedures for numerical solution of variably saturated flow 

problems.” Water Resource Res. Vol. 19 No.5: 1271-1285. 1983. 

 

Craig, R.W. and R.L. Reed. ISGS/WRIR-89-4025. “Geohydrology of Rocks Penetrated by 

Test Well USW-h6, Yucca Mountain, Nye County, Nevada.” pp. 48. 1989. 

 

Dai, Z., et al. Evaluating Conceptual Site Models with Multicomponent reactive Transport 

Modeling, Abstract and Poster. New Orleans, Louisiana: AGU 2005 Joint Assembly. 

2005. 

 

Dai, Z. and J. Samper. “Inverse problem of multicomponent reactive chemical transport in 

porous media: Formulation and applications.” Water Resource Res. Vol.40, W07407, 

doi: 10.1029/2004WR003248. 2004. 

 

Dai, Z., et al. “Transport in heterogeneous sediments with multimodal conductivity and 

hierarchical organization across scales.” Journal of Hydrology. Vol.294 No 1-3:68-

86. 2004a. 

 

Dai, Z., et al. “Estimating Parameters for Hierarchical permeability Correlation Models.” 

Aquifer Characterization, Eds. J. Bridge and D. Hyndman. SEPM Publishers. No. 80: 

41-54. 2004b. 

Dershowitz, W.S., et al. DOE/PC/91008-5 (DE98000469). “Fractured Reservoir discrete 

Feature Network Technologies.” DOE: Oklahoma. 1998. 

 

Driscoll, F.F. Ground Water and Wells. Johnson Division. 1986. 

 

Dunn. “Bibliography of Uranium biogeochemistry.” Misc. Rept. Miner. Resour.

Saskatchewan. Vol. 80 No.4: 59. 1980. 

 

Environmental Protection Agency. EPA/230/R-92/014. “Methods for Evaluating the 

Attainment of Cleanup Standards, Volume 2: Ground Water.” U.S. EPA, Office of 

Policy, Planning, and Evaluation, Environmental Statistics and Information Division. 

Washington, DC. 1992a. 

http://www.epa.gov/superfund/health/conmedia/gwdocs/per_eva.htm

EPA. Ground-Water Monitoring: Draft Technical Guidance. Washington, DC: U.S. EPA. 

1992b. 

 

EPA. EPA-625/R-93/003a “Subsurface Characterization and Monitoring Techniques, A Desk 

reference Guided, Volume 1: Solids and Ground Water Appendices A and B.” 

EPA:Washington, DC. 1993. 

 

EPA. EPA QA/G-4HW, EPA/600/R-00/007. “Data Quality Objectives Process for Hazardous 

Waste Site Investigations.” EPA: Washington, DC. pp. 1-143. 2000. 

 

EPA. OSWER Directive No. 9355.4-28. “Guidance for Monitoring at Hazardous Waste 

Sites: Framework for Monitoring Plan Development and Implementation.” 2004. 

 
84

EPA. Compacted Clay and ET Landfill Covers Compared in Humid Climate. Technology 

Innovation Program, Publication and Studios, Technology News and Trends. May 

2006. http://www.clu-in.org/products/newsltrs/tnandt/view.cfm?issue=0506.cfm

 

Erdman, J.A. and G.H. Harrach. “Uranium in Big Sagebrush from Western U.S. and 

Evidence of Possible Mineralization in the Owyhee Mountains of Idaho.” Journal of 

Geochemical Exploration. Vol.14 No.1:83-94. 1981. 

 

Falls, W., et al. Geology and Hydrogeology of Cretaceous and Tertiary Strata, and 

Confinement in the Vicinity of the U.S. Department of Energy Savannah River Site, 

South Carolina and Georgia. Water-Resources Investigations Report. U.S. Geology 

Survey, SC. 1997. 

 

Gee, G.W., et al. PNNL-13712. “Installation of a Hydrologic Characterization Network for 

Vadose Zone Monitoring of a Single-Shell Tank Farm at the U.S. Department of 

Energy Hanford Site.” Pacific Northwest National Laboratory: Richland, 

Washington. 2001. 

 

Guzowski, R.V. SAND89-7149. “Preliminary Identification of Scenarios that May Affect the 

Escape and Transport of Radionuclides from the Waste Isolation Pilot Pant, 

Southeastern New Mexico.” Sandia National Laboratories: Albuquerque, New 

Mexico. 1990. 

 

Guzowski, R.V. SAND93-7100. “Preliminary Identification of Potentially Disruptive 

Scenarios at the Greater Confinement Disposal Facility, Area 5 of the Nevada Test 

Site.” Sandia National Laboratories: Albuquerque, NM. 1993. 

 

Hartman M.J., L.F. Morasch, and W.D. Webber. Hanford Site Groundwater Monitoring for 

Fiscal Year 2002. PNNL-14187. Richland, Washington: Pacific Northwest National 

Laboratory. 2003. 

Hooten, M.M. and T.G. Myles. Biotic Processes Relevant to Near-Surface Pedoturbation 

and Contaminant Transport in Alluvial Soils of the Nevada Test Site. Los Alamos, New 

Mexico: Neptune and Company. December 2006. 

http://www.neptuneandco.com/pubs/Rev_Final_Biotic_Trans_Soils_NTS.pdf 

Hill, M. C. Methods and Guidelines for Effective Model Calibration. U.S. Geological Survey, 

Water-Resources Investigations Report 98-4005.1998. 

Hoeting, J.A., et al. “Bayesian model averaging: A tutorial.” Statist. Sci.. Vol. 4 No.4: 382-

417. 1999. 

Hossner, L.R., et al. Literature Review: Phytoaccumulation of Chromium, Uranium, and 

Plutonium in Plant Systems. ANRCP-1998-3. 1998. 
85

Hsieh, P.A. and A.M. Shapiro. “Hydraulic characteristics of fractured bedrock underlying the 

FSE well field at the Mirror Lake Site, Grafton County, New Hampshire, in Morganwalp, 

D.W., and Aronson, D.A. eds., U.S. Geological Survey Toxic Substances Hydrology 

Program—Proceedings of the technical meeting, Colorado Springs, Colorado, September 

20-24, 1993.” U.S. Geological Survey Water-Resources Investigations Report 94-4015. 

Vol. 1: 127-130. 1996.

Hubbert, M.K. “The Theory of Ground-Water Motion”. Journal of Geology. Vol. 48 No.8 

Part 1:781-944. 1940. 

International Atomic Energy Agency (IAEA). Hydrogeological Investigation of Sites for the 

Geological Disposal of Radioactive Waste. Vienna: IAEA. 1999. 

IAEA. IAEA-TECDOC-1372. “Safety Indicators for the Safety Assessment of Radioactive 

Waste Disposal.” IAEA:.Austria. 2003. 

Idaho National Engineering and Environmental Laboratory (INEEL). INEEL/EXT-02-01209. 

“RWMC Performance Assessment / Composite Analysis Monitoring Report - FY-2002.” 

INEEL: Idaho. 2002. 

James, Scott C., Sandia National Lab, Personal Communication, 2007. The figure shown 

here was also presented in James and Umari, Saturated Zone Anisotropy Near the Cwells Complex, a paper presented at the Geological Society of America annual meeting, 

November 2003. 

Journel, A.G., and Ch.J. Huijbregts. Mining Geostatistics. Academic Press. 1978. 

Kashyap, R.L. “Optimal choice of AR and MR parts in autoregressive moving average 

models.” IEEE Trans. Pattern Anal. Mach. Intel. PAMI. Vol. 4 No.2: 99-104. 1982. 

King, P.R. The Use or Renormalization for Calculating Effective Permeability: Transport in 

Porous Media. Vol. 4: 37-58. 1989. 

Lapham, W.W., F. D. Wilde, and M.T. Koterba. Guidelines and Standard Procedures for 

Studies of Ground-Water Quality: Selection and Installation of Wells, and Supporting 

Documentation, U.S. Geological Survey Water Resources Investigations Report 96-4233. 

VA.1997. 

Larsson, A., et al. INTRAVAL Project: To Study Validation of Geosphere Transport Models 

for Performance Assessment of Nuclear Waste Disposal, Phase 2, Summary Report. 

Nuclear Energy Agency, Organization for Economic Cooperation and Development. 

1997. 
86

Long, P.E. and S.B Yabusaki. PNNL-16385. “Evaluating the Efficacy of Uranium 

Bioremediation in the Subsurface: Technical Bases and Performance Indicators.” Letter 

Report to the Nuclear Regulatory Commission. pp. 62. 2007. 

Mansoori, J. A Review of Basic Upscaling Procedures: Advantages and Disadvantages, in 

Yarus and Chambers, eds. Stochastic Modeling and Geostatistics. AAPG Computer 

Applications in Geology. No. 3: 65-74. 1994. 

Meyer, P.D., et al. NUREG/CR-6843. “Combined Estimation of Hydrogeologic Conceptual 

Model and Parameter Uncertainty.” p42, prepared for U.S. Nuclear Regulatory 

Commission. Washington, DC. 2004. 

Miller, R. B., J.W. Castle, and T.J. Temples. “Deterministic and Stochastic Modeling of 

Aquifer Stratigraphy.” Groundwater. Vol. 38, No. 2. SC. 2000. 

National Academy of Sciences (NAS). Research Needs in Subsurface Science. Washington, 

DC: National Academy Press. 2000a. 

NAS. Natural Attenuation for Ground-Water Remediation. Washington, DC: National 

Academy Press. 2000b. 

NAS/NRC. Improving Operation and Long-Term Safety of the Waste Isolation Pilot Plant. 

Washington, DC: National Academy Press. pp.142. 2001. 

Nativ, R., E. Adar, and A. Becker. “A monitoring network for ground water in fractured 

media.” Ground Water. Vol. 37: 38– 47. 1999. 

Neuman, S.P. “Maximum likelihood Bayesian averaging of uncertain model predictions.” 

Stochastic Environ. Res. and Risk Assess. Vol. 17: 291-305. 2003. 

Neuman, S. P and P.J. Wierenga. NUREG/CR-6805. “A Comprehensive Strategy of 

Hydrogeological Modeling and Uncertainty Analysis for Nuclear Facilities and Sites.” 

Arizona: University of Arizona. 2003. 

Nielsen, D. M. Practical Handbook of Ground-water Monitoring. Michigan: Lewis 

Publishers. 1991. 

Nuclear Regulatory Commission (NRC). NUREG-1573. “A Performance Assessment 

Methodology for Low-Level Radioactive Waste Disposal Facilities.” NRC:Washington, 

DC. 2000. 

NRC. NUREG-1569. “Standard Review Plan for In Situ Leach Uranium Extraction License 

Applications.” NRC:Washington, DC 2003a. 
87

NRC. NUREG-1620, Rev. 1. “Standard Review Plan for the Review of a Reclamation Plan 

for Mill Tailings Sites Under Title II of the Uranium Mill Tailings Radiation Control Act 

of 1978.” NRC:Washington, DC. 2003b. 

NRC. Liquid Radioactive Release Lessons Learned Task Force Report (NRC Document 

Accession # ML062650312,). NRC:Washington, DC. 2006a. 

NRC. NUREG/CR-6884. “Model Abstraction Techniques for Soil-Water Flow and 

Transport.”. NRC:Washington, DC. 2006bOrganisation for Economic Co-operation and 

Development (OECD). “Features, Events and Processes (FEPs) for Geologic Disposal of 

Radioactive Waste.” Nuclear Energy Association: Paris. 2000. 

Ponke, D., Implementing the Graded Approach Process at the Department of Energy 

Facilities. 2002. 

Pope, D.F., et al. EPA/600/R-04/027. “Performance Monitoring of MNA Remedies for 

VOCs in Ground Water.” Technique Report for Office of Research and Development. 

U.S. Environmental Protection Agency. p74. April, 2004. 

Popper, K. Conjectures and Refutations: The Growth of Scientific Knowledge. New York: 

Harper & Row.1963. 

Price, V., W.C. Fallaw, and J.B McKinney. WSRC-RP- 91-96, ESH-EMS-90171. “Geologic 

Setting of the New Production Reactor Reference Site within the Savannah River Site 

(U).” 1991. 

Pruess, K., et al. “Fluid Flow, Heat Transfer, and Solute Transport at Nuclear Waste Storage 

Tanks in the Hanford Vadose Zone.” Vadose Zone Journal. Vol.1:68-88. 2002. 

Radian International. Guide to Optimal Groundwater Monitoring. Prepared for the Naval 

Facilities Engineering Service Center in Port Hueneme, CA. January 2000. 

Ross, R. R and B.E. Vieux. “A Probabilistic Method for Estimating Monitoring Point Density 

of Containment System Leak Detection.” Ground Water. Vol. 38, No. 4: 533-540. JulyAugust 2000. 

Rossi, C. and J.R. Nimmo. “Modeling of soil water retention from saturation to oven 

dryness.” Water Resources Research. Vol.30: 701-708. 1994. 

Ruby, C. H. Clastic Facies and Stratigraphy of a Rapidly Retreating Cuspate Foreland. Cape 

Romain, South Carolina: University of South Carolina.1981. 

Sara, M. N. Standard Handbook for Solid and Hazardous Waste Facility Assessments. 

Florida: Lewis Publishers. 1994. 
88

Shaw, T., USACE, Seattle District Joint Services Environmental Management Conference & 

Exposition Denver, CO March 2006, EPA, 

http://www.epa.gov/correctiveaction/workshop.htm, RCRA Corrective Action Workshop 

on Results-Based Project Management, June 10, 1999. 

Sperry, J. M. and J.J. Peirce. A Model for Estimating the Hydraulic Conductivity of Granular 

Material Based on Grain Shape, Grain Size, and Porosity. North Carolina: Duke 

University. 1995. 

Spruill, T.B. and L. Candela. “Two Approaches to Design of Monitoring Network.” Ground 

Water. Vol. 28, No. 3: 430-442. 1990. 

Sun, N-Z. Inverse Problems in Groundwater Modeling. Netherlands: Kluwer Academic 

Publishers. pp. 364. 1994. 

Taplin, R.H. “Robust likelihood calculation for time series.” J. Roy. Statist. Soc. Ser. Vol. B 

55: 829-836.1993. 

Tauxe, J.D. Porous Medium Advection-Dispersion Modeling in a Geographic Information 

System. Center for Research in Water Resources Technical Report No. 253, Bureau of 

Engineering Research. Austin, Texas: The University of Texas. May 1994. 

Telford, W.M., et al. Applied Geophysics. Cambridge: Cambridge University Press.1976. 

Temples, T.J. and M.G. Waddell. Application of Petroleum Geophysical Well Logging and 

Sampling Techniques for Evaluating Characteristics. Vol. 34, No. 3. 1996. 

Tuckfield, R.C., et al. Using Geosciences and Geostatistics to Optimize Groundwater 

Monitoring Networks at the Savannah River Site. Tennessee: U. S. Department of 

Energy. 2001. 

Tukey, J.W. “The Future of Data Analysis.” Annals of Mathematical Statistic. Vol. 33: 1-67. 

1962. 

van Genuchten, M. Th. “A closed-form equation for predicting the hydraulic conductivity of 

unsaturated soils.” Soil Science. Soc. American. Journal. Vol. 44:892–898. 1980. 

Wagner, B.J. and S.M. Gorelick. “Optimal ground water quality management under 

parameter uncertainty.” Water Resource. Res. Vol. 23, No.7: 1162-1174. 1987. 

Wagner, B.J. “Simultaneous parameter estimation and contamination source characterization 

for coupled ground water flow and contaminant transport modeling.” Journal of. 

Hydrology. Vol. 135: 275-303. 1992. 
89

Walker, R.G. Facies Model. Toronto, Canada: Geological Association of Canada. 1984. 

Williams, J.D. The Compleat Strategyst. New York: McGraw-Hill. p. 234. 1954. 

Woldt, W. and I. Bogardi. “Ground Water Monitoring Network Design Using Multiple 

Criteria Decision Making and Geostatistics.” Water Res. Bull. Vol. 28 No.1:45-62. 1992. 

Wood, T.R., et al. INEEL/Ext-99-0984. “Deficiencies in Vadose Zone Understanding at the 

Idaho National Engineering and Environmental Laboratory.” 2000. 

Westinghouse Savannah River Company (WSRC). WSRC-RP-2000-00326, Rev. 0. 

“Composite Analysis Monitoring Plan for the E-Area Low-Level Waste Facility and the 

Z-Area Saltstone Disposal Facility.” WSRC. 2000. 

Wyatt, D.E. and Temples, T.J. “Ground-Penetrating Radar Detection of Small-Scale 

Channels, Joints and Faults in the Unconsolidated Sediments of the Atlantic Coast Plain.”

Environmental Geology. Vol. 37, No. 3. 1996. 

Yarus, J.M. and R.L. Chambers. Stochastic modeling and geostatistics: principles, methods, 

and case studies. Tulsa, Oklahoma: American Association of Petroleum Geologists. 

p.379. 1994. 

Young, M.H., et al. NUREG/CR-5694. “Results of Field Studies at the Maricopa 

Environmental Monitoring Site, Arizona.” NRC: Washington, DC. 1999a. 

Young, M.H., et al. NUREG/CR-5698 “Comparing Monitoring Strategies at the Maricopa 

Environmental Monitoring Site, Arizona.” NRC: Washington, DC. 1999b. 
90

Bibliography 

ASTM, 2002, Standard Guide for Risk-Based Action Applied to Petroleum Release Site, 

ASTM E1739-95. 

Atwood, C. L., et al. NUREG/CR-6823, SAND2003-3348P. “Handbook of Parameter 

Estimation for Probabilistic Risk Assessment.” Sandia National Laboratories, 

Albuquerque, New Mexico.2003. 

Bailey, L.E.F., and D.E Billington. Overview of the FEP Analysis Approach to Model 

Development, NIREX Science Report S/98/009.1998. 

Beardsley, C., et al. WSRC-TR-98-00191. “Groundwater Sampling Reduction at the 

Savannah River Site.” Westinghouse Savannah River Company, Aiken, SC. 1998. 

Black, P.K., et al. “Assessing the Probability of Inadvertent Human Intrusion at Nevada Test 

Site Waste Management Sites: Final Report.” U.S. Department of Energy, Report 

Number DOE/NV-593-Vol. 1. 2001. 

Carle, S. F., M. Zavarin, and G.A Pawloski. Geostatistical Analysis of Spatial Variability of 

Mineral Abundances and Kd in Frenchman Flat. NTS, Alluvium, Draft Report, 

NNSA/NV.2002. 

Compton, R. R. Manual of Field Geology. John Wiley & Sons. pp. 1-378.1962. 

Coward, M. P., T.S Daltaban, and H. Johnson. Structural Geology in Reservoir 

Characterization. The Geological Society, London.1998. 

Curtis, G.P. NUREG/CR-6871. “Documentation and Applications of the Reactive 

Geochemical Transport Model RATEQ.” U.S. Geological Survey.2005. 

Daniel, D. E. Surface Barriers: Problems, Solutions and Future Needs. In- Situ Remediation: 

Scientific Basis for Current and Future Technologies. Washington: Battelle Press. pp. 

441-487. 1994. 

Davis, J.A. and G.P Curtis. NUREG/CR-6820. “Application of Surface Complexation 

Modeling to Describe Uranium (VI) Adsorption and Retardation at the Uranium Mill 

Tailings Site at Naturita, Colorado.” Nuclear Regulatory Commission.2003. 

Department of Environmental Protection (DEP). Groundwater Monitoring Guidance 

Manual. Pennsylvania Department of Environmental Protection.2001. 
91

DeSchaine, L.M. Mathematical Simulation and Optimization of Subsurface Environmental 

Contamination Events. Chalmers University of Technology.2003. 

Department of Energy (DOE). Summary Report of Performance Monitoring Workshop. 

Office of Environmental Cleanup Technologies. 2005. 

Environmental Protection Agency (EPA). EPA/600/R-04/027. “Performance Monitoring of 

MNA Remedies for VOCs in Ground Water.” National Risk Management Research 

Laboratory, Office of Research and Development, Cincinnati, OH. 2004a. 

EPA. EPA/542/R-04/013. “Survey of Technologies for Monitoring Containment Liners and 

Covers.” Office of Solid Waste and Emergency Response, Washington DC. 2004b. 

EPA. OSWER 5102G, EPA 542-R-04-001b. “Demonstration of Two Long-Term 

Groundwater Monitoring Optimization Approaches.”2004. 

EPA. EPA 542-R-05-003. “Roadmap to Long-Term Monitoring Optimization.” 2005. 

Fayer, M.J., et al. PNL-5604. “Model Assessment of Protective Barrier Designs.” Prepared 

for the Department of Energy.1985. 

Gregg, D.O. “An Analysis of Ground-Water Fluctuations Caused by Ocean Tides in Glynn 

County, Georgia.” Ground Water. Vol. 4, No 3.1966. 

Harbaugh, J. W., J.H Doveton, and J.C Davis. Probability Methods in Oil Exploration. New 

York: John Wiley& Sons, Inc. 1977. 

Hogan, J.F., F.M Phillips, and B.R Scanlon. Groundwater Recharge in a Desert Environment 

– The Southwestern United States. American Geophysical Union. Washington, DC. 2004. 

Horton, D.G., S.P Reidel, and G.V Last. PNNL-11958, Rev. 2. “Monitoring Guidance for 

Vadose Zone Monitoring of Liquid Waste Disposal Facilities for the Hanford 

Groundwater Project.” 1999. 

International Atomic Energy Agency (IAEA). Safety Assessment for Near Surface Disposal 

of Radioactive Waste. No. WS-G-1.1. International Atomic Energy Agency, Vienna. 

1999. 

IAEA. Scientific and Technical Basis for the Near Surface Disposal of Low and Intermediate 

Level Waste. Technical Report Series, No. 412. International Atomic Energy Agency, 

Vienna.2002. 

INCORE. Integrated Concept for Ground water Remediation. Contract No. EVK1 CT-1999-

00017. 1999. 
92

Kotra, J.P., et al. Branch Technical Position in the Use of Expert Elicitation in the HighLevel Radioactive Waste Program, NUREG-1563, U.S. Nuclear Regulatory Commission, 

D.C. 1996. 

Last, G. V., et al. PNNL-34515. “Use of Process Relationship Diagrams in Development of 

Conceptual Models.” Pacific Northwest National Laboratory. 2001. 

Last, G.V., et al. “A Comprehensive and Systematic Approach to Developing and 

Documenting Conceptual Models of Contaminant Release and Migration at the Hanford 

Site.” Journal of Stochastic Environmental Research and Risk Assessment. 2004. 

Leroy, L.W. Subsurface Geologic Methods. 2nd Edition. Colorado School of Mines, CO. 

1951. 

Looney, B. WSRC-RP-2002-00077. “Technical Targets – A Tool to Support Strategic 

Planning in the Subsurface Contaminants Focus Area.” Westinghouse Savannah River 

Company, Aiken, SC. 2002. 

Mackin, P.C., et al. NUREG/CR-6733. “A Baseline Risk-Informed Approach for In Situ 

Leach Uranium Licensees.” 2001. 

McDonald, M.G. and A.W Harbaugh. A Modular Three-Dimensional Finite- Difference 

Ground-Water Flow Model: U.S. Geological Survey Techniques of Water- Resources 

Investigations. Book 6, Chap. A1, p. 586.1988. 

Meyer, P.D., G.W Gee., and T.J. Nicholson. PNNL-13091, NUREG/CR-6656. “Information 

on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources 

for Dose Assessments at Decommissioning Sites.” Pacific Northwest National 

Laboratory, Washington. November 1999. 

Meyer, P.D. and R.Y Taira. NUREG/CR-6695, PNNL-13375. “Hydrologic Uncertainty 

Assessment for Decommissioning Sites: Hypothetical Test Case Applications.” Pacific 

Northwest National Laboratory, Washington. 2001. 

Meyer, P.D., M.L. Rockhold, and G.W Gee. Uncertainty Analyses of Infiltration and 

Subsurface Flow and Transport, PNNL-11705, NUREG/CR-6565. September 1997. 

National Academy of Sciences (NAS)/NRC. Rock Fractures and Fluid Flow. Washington, 

D.C: National Academy Press. pp. 1-557. 1996. 

National Academy of Sciences (NAS)/NRC. Conceptual Models of Flow and Transport in 

the Fractured Vadose Zone. Washington, D.C: National Academy Press. pp. 1-365. 2003. 

NEA. The Handling of Timescales in Assessing Post-closure Safety. ISBN 92-64-02161-2. 

2004. 
93

Nicholson, T. J. and V. Price. “Development of an integrated ground-water monitoring 

strategy for supporting performance assessments of nuclear facilities.” Eos Trans. AG. 

Vol. 84 No.46, Fall Meet. Suppl. 2003. 

NRC. NUREG/CP-0177, PNNL-13654. “Proceedings of the Environmental Software 

Systems Compatibility and Linkage Workshop.” Nuclear Regulatory Commission. 

2000a. 

Nuclear Regulatory Commission (NRC). NUREG-1724. “Standard Review Plan for the 

Review of DOE Plans for Achieving Regulatory Compliance at Sites with Contaminated 

Ground Water Under Title I of the Uranium Mill Tailings Radiation Control Act.” 

Nuclear Regulatory Commission.2000b. 

NRC. Land Disposal of Radioactive Waste. 10 CFR 61, Nuclear Regulatory Commission, 

Subpart C - Performance Objective. pp. 167. 2003. 

Pachepsky, Y., et al. Letter Report: Task 3 - Develop Test Plan for Testing and Confirming 

Model Abstraction Techniques. U.S. Nuclear Regulatory Commission. 2003. 

Reed, P.M. Cost Effective Long-Term Groundwater Monitoring Design Using A Genetic 

Algorithm and Global Mass Interpolation. Master’s Thesis, University of Illinois at 

Urbana-Champaign. 1999. 

Saltelli, A.S., S. Tarantola, and K/ P.-S Chen. “About the Use of Rank Transformation in 

Sensitivity Analysis of Model Output.” Reliability, Engineering, and System Safety. pp. 

50, 225-239. 1999. 

Sobol, I.M. “Sensitivity Analysis for Nonlinear Mathematical Models.” Mathematical 

Modeling & Computational Equipment. pp. 1, 40-414. 1993. 

U.S. Department of Agriculture. NUREG/CR- 6729. “Field Studies for Estimating 

Uncertainties in Ground-Water Recharge Using Near-Continuous Piezometer Data.” U.S. 

Nuclear Regulatory Commission: Washington D.C. 2001. 

White, G.J, et al. NUREG/CR-5615, EGG-2604. “Low-Level Radioactive Waste Disposal 

Facility Closure.” 1990. 

Wilson, J.L., and W.R Linderfelt.. Groundwater Quality in Pumping Wells Located Near 

Surface Water Bodies. New Mexico Institute of Mining and Technology. New Mexico 

Water Resources Research Institute. 1991. 

Wood, T.R. Impact of Surface Water Recharge on the Design of a Groundwater Monitoring 

System for the Radioactive Waste Management Complex, Idaho National Engineering 

Laboratory. Proceedings of the Annual Symposium on Engineering Geology and 

Geotechnical Engineering. April 1990. 
94

Zheng, C. and P.P Wang. MT3DMS: A Modular Three-Dimensional Multispecies Transport 

Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants 

in Groundwater Systems; Documentation and User’s Guide. University of Alabama. U.S. 

Army Corp of Engineers, SERDP-99-1. 1999. 

.
A-1 

Appendix A: Existing Guidance for Traditional 

Monitoring 

Guidance documents that cover some aspects of environmental monitoring have been issued 

by several different governmental agencies and other entities. In most cases the guidance is 

regulatory driven, though some describe the use of monitoring as a tool, such as for decision 

making. Presented here are summaries of the sections that pertain to monitoring. This 

includes many of the regulatory driven guidance documents affecting NRC regulated 

facilities and those that pertain to other hazardous waste sites and disposal facilities. 

10 CFR 20, Subparts E and F 

Subpart E: The NRC issued radiological criteria for license termination in July 21, 1987. The 

general provisions and scope include: 

(a) The criteria in this subpart apply to the decommissioning of facilities licensed under Parts 

30, 40, 50, 60, 61, 63, 70, and 72 of this chapter, and release of part of a facility or site for 

unrestricted use in accordance with § 50.83 of this chapter, as well as other facilities subject 

to the Commission's jurisdiction under the Atomic Energy Act of 1954, as amended, and the 

Energy Reorganization Act of 1974, as amended. For high-level and low-level waste disposal 

facilities (10 CFR Parts 60, 61, 63), the criteria apply only to ancillary surface facilities that 

support radioactive waste disposal activities. The criteria do not apply to uranium and 

thorium recovery facilities already subject to Appendix A to 10 CFR Part 40 or to uranium 

solution extraction facilities. 

(b) The criteria in this subpart do not apply to sites which: 

(1) Have been decommissioned prior to the effective date of the rule in accordance with 

criteria identified in the Site Decommissioning Management Plan (SDMP) Action Plan of 

April 16, 1992 (57 FR 13389); 

(2) Have previously submitted and received Commission approval on a license termination 

plan (LTP) or decommissioning plan that is compatible with the SDMP Action Plan criteria; 

or 

(3) Submit a sufficient LTP or decommissioning plan before August 20, 1998 and such LTP 

or decommissioning plan is approved by the Commission before August 20, 1999 and in 

accordance with the criteria identified in the SDMP Action Plan, except that if an EIS is 

required in the submittal, there will be a provision for day-for-day extension. 

(c) After a site has been decommissioned and the license terminated in accordance with the 

criteria in this subpart, or after part of a facility or site has been released for unrestricted use 

in accordance with § 50.83 of this chapter and in accordance with the criteria in this subpart, 

the Commission will require additional cleanup only, if based on new information, it 

determines that the criteria of this subpart were not met and residual radioactivity remaining 

at the site could result in significant threat to public health and safety. 
A-2 

(d) When calculating Total Effective Dose Equivalent (TEDE) to the average member of the 

critical group the licensee shall determine the peak annual TEDE dose expected within the 

first 1000 years after decommissioning. 

Subpart F: Issued in May 21, 1991, Subpart F is the regulations for surveys and monitoring: 

(a) Each licensee shall make or cause to be made, surveys that— 

(1) May be necessary for the licensee to comply with the regulations in this part; and 

(2) Are reasonable under the circumstances to evaluate— 

(i) The magnitude and extent of radiation levels; and 

(ii) Concentrations or quantities of radioactive material; and 

(iii) The potential radiological hazards. 

(b) The licensee shall ensure that instruments and equipment used for quantitative radiation 

measurements (e.g., dose rate and effluent monitoring) are calibrated periodically for the 

radiation measured. 

(c) All personnel dosimeters (except for direct and indirect reading pocket ionization 

chambers and those dosimeters used to measure the dose to the extremities) that require 

processing to determine the radiation dose and that are used by licensees to comply with § 

20.1201, with other applicable provisions of this chapter, or with conditions specified in a 

license must be processed and evaluated by a dosimetry processor— 

(1) Holding current personnel dosimetry accreditation from the National Voluntary 

Laboratory Accreditation Program (NVLAP) of the National Institute of Standards and 

Technology; and 

(2) Approved in this accreditation process for the type of radiation or radiations included in 

the NVLAP program that most closely approximates the type of radiation or radiations for 

which the individual wearing the dosimeter is monitored. 

10 CFR 61 

The NRC has issued regulations for the land disposal of low-level radioactive waste in 10 

CFR 61. While the monitoring strategy is not constrained to disposed radioactive wastes, the 

language in the regulation can be readily applied to decommissioned nuclear facilities as 

well, if they are imagined to be a specialized form of disposal. As far as PA is concerned, it 

doesn’t matter if the inventory of radionuclides on the surface or buried below is the result of 

intentional waste disposal activities, or of residual and perhaps inadvertent contamination of 

a nuclear facility. The site characterization and modeling of material properties, both man 

made and natural, and of radionuclide transport is independent of these distinctions. Indeed, 

DOE 435.1-1, discussed in a following section, recognizes that, “For deactivated high-level 

waste facilities or sites that are closed as low-level waste sites, the disposal facility 
A-3 

performance objectives [for low-level waste] should be met.” [DOE G 435.1-1 

II.U.(3)(a)(5)]. 

These regulations are oriented principally toward waste classification and the operations of a 

shallow land burial (SLB) disposal site, and provide only general guidance for monitoring. 

The regulation defines monitoring as “...observing and making measurements to provide data 

to evaluate the performance and characteristics of the disposal site.” This general definition is 

at the core of this monitoring strategy, in that it frames monitoring in the context of 

evaluation of the site both in reality and in its modeled representation. Later in the text of the 

regulation, monitoring is assigned the more specific purpose of “...providing early warning of 

releases of radionuclides from the disposal site before they leave the site boundary” 

[§61.53(d)]. 

Several other references to monitoring are made in 10 CFR 61. Section 61.7(a)(2) suggests a 

500-year time frame for the consideration of site characteristics. This hints at the regulation’s 

emphasis on modelability [§61.50(a)(2)] and stability [§61.7(b)(2) and §61.44] of the site. 

Confidence in the stability of site characteristics throughout the modeled time window is of 

paramount importance. While this should be used as a criterion for selection of a new site, 

such conditions are by no means guaranteed at any given decommissioned nuclear facility or 

legacy waste site. Easily modeled sites will have straightforward monitoring plans, but those 

sites with more challenging conditions will require monitoring strategies involving many 

contingencies and allowances for various conceptual models. 

Subpart B of the regulation concerns licensing, and mentions monitoring only briefly, in the 

context of a monitoring plan accompanying the license application. The specific technical 

information required includes “A description of the environmental monitoring program to 

provide data to evaluate potential health and environmental impacts and the plan for taking 

corrective measures if migration of radionuclides is indicated,” [§61.12(l)]. The following 

section discussed technical analyses, requiring an analysis of specific environmental transport 

pathways, including “...air, soil, ground water, surface water, plant uptake, and exhumation 

by burrowing animals” [§61.13(a)]. This monitoring strategy is concerned with only one of 

these pathways: ground water. Presumably, additional strategies will be developed for the 

other pathways, which in some cases (e.g. arid sites with deep water tables) will be of much 

greater significance. 

Post-closure monitoring is also discussed in Subpart B. Section 61.29 states “Following 

completion of closure authorized in §61.28 [Contents of application for closure] the licensee 

shall observe, monitor, and carry out necessary maintenance and repairs at the disposal site 

until the license is transferred by the Commission in accordance with §61.30 [Transfer of 

license]. Responsibility for the disposal site must be maintained by the licensee for 5 years.” 

That said, one presumes that site monitoring will continue for a far greater period than that. 

Yet, curiously, the time frame of interest is not specified in 10 CFR 61. 

Subpart D is entitled “Technical Requirements for Land Disposal Facilities”, and includes a 

section (61.53) on Environmental monitoring. In §61.53(c), a longer time frame is hinted: 

“Measurements and observations must be made and recorded to provide data to evaluate the 

potential health and environmental impacts during both the construction and operation of the 
A-4 

facility and to enable the evaluation of long-term effects and the need for mitigative 

measures.” The meaning of “long-term” is not defined. 

10 CFR 40, Appendix A 

This regulation establishes criteria relating to the siting and design of uranium tailings for 

permanent isolation. This involves minimizing the potential for disturbance and dispersion by 

natural forces of the site without ongoing maintenance. 

The specific ground water protection standards set forth are defined in Criterion 5, which 

incorporates the basic standards imposed by the EPA in 40 CFR Part 192, Subparts D and E. 

The ground water monitoring to comply with these standards is required in Criterion 7. 

Criterion 7 states that “At least one full year prior to any major site construction, a preoperational monitoring program must be completed to provide baseline data on a milling site 

and its environs”. The operational monitoring program is required to “measure or evaluate 

compliance with applicable standards and regulations, to evaluate performance of control 

systems and procedures, to evaluate environmental impacts of operation, and to detect 

potential long term effects”. 

The licensee is directed to establish a detection monitoring program. The main reason for this 

monitoring is to comply with the ground water standards set in paragraph 5B (1) by the NRC 

for this site. In the event that a contaminant exceeds the set standards, a corrective action 

program must be implemented. In that event, “the licensee shall establish and implement a 

corrective action monitoring program”. The monitoring program in support of the corrective 

action “may be based on existing monitoring programs to the extent that the existing 

programs can meet the stated objective of the program” (WSRC, 2000; INEEL, 2002). 

NUREG-1569 

The NRC has recently issued guidance on how to conduct a ground water monitoring 

program at an in situ uranium leach facility (NRC, 2003a). This includes both new facilities 

and those facilities applying for renewal. The intent of the document is to ensure that an 

excursion (i.e., an unplanned lixiviant migration) from the uranium leach zones is detected 

early enough to allow correction and minimize aquifer degradation. The original intent of the 

document was to offer guidance to the NRC staff reviewing the license application, but it 

offers valuable guidance to the licensee in preparing an application. 

The licensee is responsible for establishing the criteria used in the number, screen length, and 

placement of monitoring wells, and their sampling schedule. The licensee is responsible for 

determining the ground water parameters that will serve as the excursion indicator 

constituents and their upper control limits. 

The NRC staff responsible for reviewing the in situ leach application are directed to use 

American Society for Testing and Materials (ASTM) standards when applicable to ensure 

acceptable procedures by the applicant. In addition, the document contains numerous other 

specific guidelines to be used in the review process. For example, it states that at least four 

independent sets of baseline samples should be collected by the licensee. 
A-5 

Three distinct phases of monitoring are indicated, though the standard review plan section 

deals specifically with the operating phase. 

In the pre-operational phase, ambient monitoring is used to adequately evaluate the natural 

spatial and temporal variations in water quality. From this, a set of water quality parameters 

and their upper control limits are established. 

In the operational phase, detection monitoring is used to ensure the timely detection of 

unplanned subsurface migration (an excursion) away from the production zone, in either the 

horizontal or vertical direction. An excursion is defined when two or more excursion 

parameters in any monitor well exceed the defined upper control limits (UCLs). If an 

excursion is detected, corrective actions must be made and the NRC must be notified. 

Remediation monitoring is conducted after the in situ operations if necessary. The portion of 

the aquifer used for active leach operations are exempted from cleanup by the EPA, however, 

the ground water adjacent to the leach zone must be remediated. In the remedial phase, 

monitoring is conducted just for the indicator constituents. 

NUREG-6733 

This document (Macklin et al., 2001) provides the foundation for implementing a riskinformed, performance-based (RIPB) regulatory program at in situ leach uranium operations. 

Much of this document follows the guidance set forth in the draft version of NUREG-1569, 

but contains specific details for corrective actions in the case of lixiviant excursions. 

Along with specific actions to take in case of an excursion such as converting some injection 

wells to production wells to change the hydraulic gradient (which should be monitored to 

confirm the performance of such a corrective action), the sampling frequency for a monitor 

well on “excursion status” should be increased to weekly, and the number of monitored 

parameters may be expanded. When the excursion indicator drops below the UCL for three 

consecutive weekly sampling events, the excursion is considered to be corrected. 

With regard to the standards for restoring the aquifer outside of the leach zone, the primary 

goal is to return all parameters to the average pre-extraction baseline conditions. If this goal 

can not be met with “reasonable” restoration efforts, a secondary goal is the maximum 

concentration limits (MCLs) for drinking water specified by the EPA. 

In addition to local conditions (the leach field), detailed water sampling programs should be 

implemented to identify potential impacts on the regional ground water system. Historical 

results indicate though that this has not been a problem. 

NUREG-1620 

This recent publication (NRC, 2003b) provides guidance to NRC staff on protecting water 

resources when reviewing uranium tailings reclamation plans and operating license renewals/ 

amendments as required by Title II of the Uranium Mill Tailings Radiation Control Act of 

1978 as amended. The review plan is written in a general manner to cover a variety of site 

conditions. 
A-6 

This document instructs reviewers to ensure that operations at uranium mills do not 

contaminate ground water and to mitigate the spread of contaminants to the public. The 

ultimate goal of the review process is to ensure long term compliance with 10 CFR Part 40, 

Appendix A. 

The water resource protection review is divided into four parts: 

• Site Characterization 

• Ground water protection standards for the hazardous constituents 

• Use of alternative hazardous chemical concentration limits 

• Ground water corrective action and compliance monitoring. 

In the compliance monitoring section, reviewers are instructed to “evaluate whether the 

ground water monitoring system is sufficient to verify the performance of the selected 

cleanup strategy and to monitor the long term performance of any on-site tailings disposal 

cells”. Yet the focus here is on compliance monitoring for corrective action. In support of 

constructing a defensible corrective action plan, the document offers a thorough treatment of 

the process involved. 

In contrast, there is little guidance in how to monitor for long term performance of waste 

units. While the techniques presented are applicable to the design and success of performance 

monitoring, they are not presented in this light. The reviewer would most likely need to seek 

other guidance documents to assess the success of such monitoring. 

DOE Order 435.1 

The Department of Energy (DOE) regulates many of its own radioactive waste disposal 

practices under Order 435.1, Radioactive Waste Management, and its associated Manual and 

Implementation Guide. Although the DOE regulations are not directly applicable to NRC 

activities, there may be useful parallels in how the two agencies conceive of monitoring. 

In general terms, DOE 435.1 requires that “Radioactive waste management facilities, 

operations, and activities shall meet the environmental monitoring requirements of DOE 

5400.1, General Environmental Protection Program, and DOE 5400.5, Radiation Protection 

of the Public and the Environment [DOE Manual 435.1-1 §I.1.E.(7)]. The Manual and 

Implementation Guide are subdivided into sections for high-level, transuranic, and low-level 

waste classifications, each with its own section concerning monitoring. For high-level waste, 

the focus of §II.T is on the operations of pretreatment, treatment, storage, and transportation 

facilities, rather than on disposal facilities. Implementation Guide recognizes as an issue that 

DOE has no extensive guidance on disposal, but follows NRC licensing requirements. 

While the environmental monitoring mandated by DOE 5400.1 and DOE 5400.5 is adequate 

to detect after-the-fact releases of high-level waste to the environment, additional 

requirements are necessary to improve the detection of conditions that could provide warning 

of impending releases that could increase worker exposure and/or impact the environment. 

An effective monitoring program is dependent on the frequency and the rigor of the 

monitoring operations, and the effectiveness of the systems and devices in detecting changes 

and abnormal conditions. Therefore, facility managers must take these factors into 
A-7 

consideration when designing the monitoring program to ensure that the high-level waste 

systems are being operated according to design. 

The specified parameters to be monitored are selected based on their significance for 

anticipating and identifying undesirable conditions and the availability of a means for 

monitoring them. In addition, parameters to be monitored include those to ensure the 

protection of public health, the environment, and workers due to releases of radioactivity in 

ventilation exhausts and liquid effluent streams, and from unsafe concentrations of 

flammable and/or explosive gases in the waste. The accuracy and precision of measurement 

required is dictated by the expected variations in the parameters and the level of accuracy 

and precision needed to identify problems. The monitoring frequency for specific parameters 

is likewise determined based on the possible time variation of the parameter and the response 

time required to take mitigating action. [DOE G 435.1-1 II.T] 

Similarly, for transuranic wastes, the focus in the order is not on disposal, since that is 

regulated by the Environmental Protection Agency (EPA) under 40 CFR 191. Again, 

however, the Implementation Guide recognizes the need for monitoring: 

While the environmental monitoring mandated by DOE 5400.1 and DOE 5400.5 is adequate 

to detect environmental releases, it was determined that due to the long storage times that 

occur with transuranic waste, monitoring of additional systems or parameters was needed.

[DOE G 435.1-1 III.Q.(2)] 

Only for low-level wastes does DOE specifically consider monitoring of disposal facilities, in 

§IV.R.(3), Disposal Facilities. Therein, the regulation encourages monitoring of site 

parameters as well as PIs (performance objectives): 

a. The site-specific performance assessment and composite analysis shall be used to 

determine the media, locations, radionuclides, and other substances to be monitored. [This 

corresponds to the what and where of this strategy.] 

b. The environmental monitoring program shall be designed to include measuring and 

evaluating releases, migration of radionuclides, disposal unit subsidence, and changes in 

disposal facility and disposal site parameters which may affect long-term performance. 

The environmental monitoring programs shall be capable of detecting trends in performance 

to allow application of any necessary corrective action prior to exceeding the performance 

objectives. 

OSWER Directive 9355.4-28 

This document outlines a framework for monitoring at hazardous waste sites (EPA, 2004). A 

general approach is offered, as opposed to specific instructions. Its goal is to treat monitoring 

in a holistic manner, as opposed to merely something to satisfy a regulatory requirement. 

In particular, this document is geared towards the use of monitoring in making or facilitating 

management decisions about the waste site. The entire monitoring program is developed in a 
A-8 

framework called “Scientific Management Decision Points” that culminate in a decision 

document. 

Practical Handbook of Ground Water Monitoring 

This textbook (Nielsen, 1991) relates the regulatory concepts surrounding monitoring at 

hazardous waste sites, specifically those for RCRA (40 CFR 264.97) with the need for a 

holistic approach in ground water system design. It states for example, that the EPA specified 

minimum of one up-gradient and three down-gradient wells, will be adequate for only a very 

small number of sites. 

It stresses the need to understand the geologic controls on ground water movement, as the 

degree of complexity will govern the amount of effort required to characterize the site and 

develop a conceptual model to guide the monitoring design. Examples of monitoring well 

designs in both simple and complex geologic settings are presented. 

EPA/625/R-93/003a 

The document (EPA, 1993) entitled Subsurface Characterization and Monitoring Techniques 

is a comprehensive list of techniques and devices available for use in a ground water and/or 

vadose zone monitoring program. It does not offer guidance on the design and 

implementation of a monitoring program, just on the accepted techniques and devices. 

Optimal Ground Water Monitoring 

The focus of this document (Radian, 2000) is on determining ways to design and optimize 

ground water monitoring programs to maximize their cost-effectiveness without 

compromising quality. Some of the strategies used to ensure cost effectiveness include 

reducing the number of monitoring points, reducing monitoring duration and frequency, and 

simplifying analytical protocols. Ideally, the application of these strategies is continually 

revisited as the monitoring program progresses. 

Ground Water Monitoring: Draft Technical Guidance 

This EPA, 1992b manual provides guidance to permitted hazardous waste disposal facilities 

in conducting RCRA ground water monitoring programs to meet the requirements of 40 CFR 

Parts 264 and 270. The three distinct phases of ground water monitoring are defined: 

detection, compliance, and corrective action monitoring. The manual describes procedures 

that the EPA believes are the most appropriate for designing, installing, and operating 

monitoring systems. 

The approach favored is one that relies heavily on the development and refinement of 

conceptual models. Strongly emphasized is the idea that the process of developing a 

conceptual model is ongoing, and that it should be revisited and refined by data gained from 

ground water sampling over a period of time. 
A-9 
B-1

Appendix B: Types of Monitoring 

Ambient Monitoring 

Ambient monitoring typically is a short term activity conducted to determine background 

conditions in the ground water. The goal is to establish both natural variations and any manmade impacts to the system. The results of this monitoring will form a basis against which 

future monitoring results will be compared. The purpose is to establish background values for 

specific chemicals of concern (COCs), develop ground water trend analyses, or determine 

compliance. 

Detection Monitoring 

Detection monitoring is used at sites not believed to be releasing hazardous wastes or 

constituents into the ground water. This typically involves semi-annual monitoring of ground 

water parameters or constituents that would indicate the presence of hazardous substances. If 

monitoring indicates a release, analysis of all COCs is generally required, and the site or 

facility enters the compliance phase. 

Compliance Monitoring 

Compliance monitoring typically is conducted at established intervals for those constituents 

detected in the detection monitoring phase. In addition, this phase monitors for all COCs that 

are associated with the site or facility, whether or not they have been detected. Compliance 

monitoring can also be used to determine Performance Indicators. 

Assessment Monitoring 

If compliance monitoring identifies an unauthorized release into ground water, assessment 

monitoring is initiated to establish the nature and extent and to determine the origin of the 

release. Assessment monitoring can lead to a determination that sampling and/or analytical 

anomalies exist. 

Performance Confirmation Monitoring 

Performance Confirmation Monitoring (PCM) is intended to verify that the data going into 

and the predictions coming out of the PA are sufficiently accurate and that a facility is 

behaving within expected limits. The objective of monitoring in the context of this Strategy 

is to understand the functioning of a hydrogeologic system. It is important to note that the 

driver for selecting what, when, where, and how to monitor is to understand system 

performance rather than system compliance. 

Remediation Monitoring 

Ground water remediation monitoring is initiated when an unauthorized release has been 

detected and characterized through assessment monitoring. Remediation monitoring is 

usually implemented as part of a clean-up effort to determine how effective the clean-up 

activities are and if this effort has attained the remediation goals. 

Post-Closure Monitoring 
B-2

Post-closure monitoring is required for permitted facilities. Post closure monitoring is 

conducted to detect any changes in the ground water after the cessation of an activity. 

Analytes to monitor for include those that were monitored during compliance and/or 

remediation monitoring. Post closure monitoring also is performed to determine when 

remediation is complete 
C-1

Appendix C: Identification of the Conceptual Site 

Models with Maximum Likelihood Bayesian Model 

Averaging Method 

Maximum Likelihood Bayesian Model Averaging (MLBMA) method includes a 

comprehensive strategy combining the statistic analysis and numerical modeling technique to 

identify and select the best CSM from a group of alternate CSMs. We will introduce this 

method starting from Bayesian model averaging. 

Bayesian Model Averaging 

Hoeting et al. (1999) and Neuman (2003) presented Bayesian model averaging (BMA) by 

noting that if ∆ is a quantity one wants to predict, then its posterior distribution given a 

discrete set of hydrologic and geochemical measurement data D is 

( ) ( , ) ( )

1

D k D k D

N

k

p ∆ = ∑ p ∆ M p M =

(C1) 

where M = (M1, ..., MN) is the set of all models or possible geochemical processes 

considered, at least one of which must be valid. p(∆ D)is the average of the posterior 

distributions ( ,D) Mk p ∆ under each model, weighted by their posterior model probabilities 

( D) Mk p . The posterior probability for model Mk is given by Bayes’ rule, 

 ( 1, 2,..., )

( ) ( )

( ) ( ) ( )

1

k N

p M p M

p M p M

p M

i i

N

i

k k

k = =

∑=

D

D

D

 (C2) 

where: 

 (C3) 

is the integrated likelihood of model Mk, θ k is the vector of parameters associated with model 

Mk, ( ) k Mk p θ is the prior density of θ k under model Mk, ( , ) k Mk p Dθ is the joint likelihood 

of model Mk and its parameters θ k , and p (Mk) is the prior probability that Mk is the correct 

model or geochemical process. 

The components of parameter vector θ k include: (1) flow parameters; (2) transport 

parameters: molecular diffusion coefficient, longitudinal and transverse dispersivities, 

distribution coefficient, Kd, and total and accessible porosity (the latter may differ from total 

porosity when anion exclusion takes place); (3) geochemical parameters; and (4) kinetic 

reaction parameters such as rate constants. 

p M k p k k p k M k k ( ) ( θ , ) (θ ) dθ ∫ D = D
C-2

Maximum Likelihood Bayesian Model Averaging 

To make BMA computationally feasible, we will adopt the suggestions by Taplin (1993), 

Hoeting et al. (1999) and Neuman (2003) to approximate ( ,D) Mk p ∆ by , ) ˆ p(∆ Mk ,θ k D , 

where θ k

ˆ is the maximum likelihood estimate of θ k based on the likelihood ( , ) k Mk p Dθ . 

Maximum likelihood estimation provides an approximate covariance matrix for the 

estimation errors of θ k

ˆ . So, we can determine , ) ˆ p(∆ Mk ,θ k D by Monte Carlo simulation of 

∆ through random perturbation of the parameters. According to suggestions of Kashyap 

(1982), we propose to select one among several competing models which minimizes the 

Kashyap Information Criterion (KIC) 

, ) ˆ ln ( 2

1 ) 2 ln( 2 ) ˆ , ) ln ( ˆ ln ( ˆ k k k

D

Pk

k k k k k F M

N

N d p M p M θ π = − Dθ − θ − + D (C4) 

where, NPk is the dimension of parameter vector θ k

ˆ associated with model Mk, ND is the 

dimension of hydrologic and geochemical data D, and Fk is the normalized Fisher 

information matrix. Equation (C4) makes a connection between the model identification and 

parameter estimation. Solving it can provide information for us to make a decision on 

selecting a more parsimonious model with fewer parameters or selecting a more complex 

model with more parameters under the condition that the simulated values match well to the 

measurements. 

Numerical Solutions 

The general numerical methods to evaluate θ k

ˆ by calibrating a deterministic model Mk

against hydrologic and geochemical data D is described by Cooley (1983), Carrera and 

Neuman (1986), Wagner and Gorelick (1987), Wagner (1992), Sun (1994), Hill (1998), and 

Dai and Samper (2004). The approach yields a negative log likelihood criterion that includes 

two weighted square residual terms: a generalized sum of squared differences between 

simulated and observed state variables, and a generalized sum of squared differences between 

posterior and prior parameter estimates. Within the first term, Dai and Samper (2004) derived 

a two-level weighting system to balance the different type of measurements (such as 

hydraulic heads or pressures, total dissolved concentrations, total concentrations including 

liquid and solid phases, cumulative water inflows and water contents) and the concentrations 

of different chemical components. Such a weighting system will be incorporated into the 

objective function for handling the different type of measurements. 

The second term in the likelihood criterion deals with prior information. Without prior 

information, most complex inverse problems are ill posed. Such information can be derived 

from available data and incorporated into the objective function. It can be also used also to 

provide a range of permissible values that a parameter can take within its lower and upper 

bounds during the optimization process. Moreover, incorporating prior information into the 

objective function is mathematically equivalent to taking extra measurements, may alter the 

numerical predominance of parameters over the measurements and thus provide the system 

with the ability to supply a unique set of parameter estimates. In some cases, a set of highly 

correlated parameters can be varied simultaneously with no significant effect in the objective 
C-3

function (Dai and Samper, 2004; Dai et al. 2004a, b). This can lead to nonunique parameter 

estimates. Sometimes nonuniqueness can even lead to numerical instability and failure of the 

estimation process. However, if prior information is available for at least one of the 

parameters, it should be included in the objective function to overcome nonuniqueness and 

provide stability. 

The processes for identifying CSMs by maximum likelihood Bayesian model averaging 

(MLBMA) are plotted in the flow chart (Figure C1) 

Figure C1. Identifying conceptual site model by maximum likelihood Bayesian model 

averaging (modified from Meyer et al., 2004). 

Input the constructed 

CSMs with variant 

scenarios of FEPs 

Assign a prior probability to each CSM 

and prior probability distribution to each 

parameter to be estimated

Save parameter sensitivity (Jacobian) matrix 

and covariance matrix for evaluating the 

existing monitoring network 

Calculate posterior probability for each 

model 

Calibrate each CSM by inverse modeling of 

flow and transport 

Evaluate model information criteria, KIC Posterior parameter estimation 

Estimate posterior probability distributions of 

the estimated parameters 

Evaluate model-averaged prediction 

weighted by model probabilities (MLBMA 

result) 

Combined estimates of CSM and 

parameter uncertainty 
D-1

Appendix D: Determination of Data Quality 

Objectives 

Step 1. Problem Definition 

Step one in DQO development involves developing a concise statement of the problem and a 

conceptual model. Other activities identified under step one are to identify team members 

and available resources. 

Step 2. Identify the Decision 

Identification of the decision involves three facets, identification of the principal study 

question, identifying alternative actions, and formulation of a decision statement. The 

principal study question in the context of Performance Assessment would depend on the 

exact nature of the PI or model assumption to be tested. However, a representative study 

question for a typical PI might be: “Does the concentration of a particular constituent in 

ground water indicate that the system under study is outside its performance envelope?” Or 

if the PA model is being evaluated: “Is the CSM consistent with information obtained from 

an adjacent site?” 

Alternate actions specify what actions are to be taken given the answer to the principal study 

question. For example an action might be to refine the CSM, to gather additional data on the 

site, to reevaluate uncertainties to lower values, or to do nothing. 

The last part of step 2 is to combine the principal study question and the alternate actions to 

form a concise decision statement employing an If ..., Then format. For example, “If the 

CSM is consistent with new information, then reevaluate and lower associated uncertainties”, 

or “If not consistent with new information then refine conceptual model with new 

information. 

Step 3. Identify Inputs to Decision 

Step three involves the implementation of the decision statement formulated in step two. This 

requires the identification of the kinds and sources of information that will be used to 

evaluate the decision statement. For most PIs this would be fairly straight forward and 

involve measurement of the concentration level of a constituent in ground water at some 

location, etc. However, this may be a more complicated question when it comes to 

evaluating the assumptions made in the PA model and would possibly involve a sensitivity 

analysis in order to determine which parameters associated with the model were important to 

the question. 

It is also necessary to determine the basis for setting the action level associated with the “If..” 

part of the decision statement. Most often for a PI, the action level would be specified by a 

regulatory limit or exceedance of background conditions. However, in the example given 

above concerning the assessment of the PA conceptual model related to new information the 

action level may be based on the significance of the new information to the elements of the 

conceptual model related to the most risk. 
D-2

Once the practical aspects of the implementation of the decision statement have been 

determined it must be ascertained if sampling and analysis methods exist or can be developed 

with sufficient resolution and precision to meet the requirements of the specified action level. 

Step 4. Define Boundaries of the Study 

Step 4 is concerned with the sampling details such as the sizes of samples necessary and the 

boundaries of the area to be sampled. Of particular importance in the application of 

performance confirmation monitoring is the duration of the sampling program. This step also 

involves such considerations as to practical constraints that may interfere with the sampling 

program. These issues become important when the specific design for sampling data is 

developed. 

Step 5. Develop Decision Rule 

In step 5 the team develops a theoretical decision rule in the form of an unambiguous “If..., 

Then...” statement. This rule is theoretical in the sense that it assumes no uncertainty in any 

of the information involved as input to the decision statement. In this statement the decision 

based parameter (i.e. sample mean, etc) is specified and the particular action level quantified. 

Detailed sample analysis methods are also specified and it is verified that the action level 

magnitude chosen for the decision statement is above the detection limit and resolution for 

the sampling methods chosen. The actual statement is formulated by combining the value of 

the decision parameter assuming no uncertainty with the alternative actions. 

Step 6. Identify Limits on Decision Errors 

Evaluation of the decision statement requires that the magnitude of some data parameter be 

compared to the specified action level. The measurement and modeling of all real data has 

associated uncertainties and these uncertainties must be dealt with in evaluation of the 

decision statement. These uncertainties mean that errors will be made in the decision as to 

exceedance of the action level. Step 6 determines a methodology for managing these 

decision errors. The essence of this methodology is to define a baseline CSM that is assumed 

to be true unless the data indicate otherwise. The critical issue to this process is to decide 

what probability levels are significant for abandoning the baseline condition. The significant 

probability level is decided based on risk as evaluated by the team. 

Step 7. Optimize Design for Obtaining Data 

In step 7 the details of the data acquisition system design are specified taking into account all 

of the issues identified in the preceding steps. The issues in step 7 are basically those 

involved in designing the monitoring network. 
E-1

Appendix E: Radionuclides and Radiochemistry 

Analysis Methods1

The purpose of this appendix is to provide a generalized list of radionuclides that might be 

expected at NRC-licensed sites as fuel cycle-related contaminants. Any ground-water 

monitoring program at a nuclear site should test for the listed (Table 1) materials at least once 

per year. 

Radionuclides2

Radionuclides, broadly, are produced in four ways. They may be natural, they may result 

from radioactive decay, they may be the products of nuclear fission, or they may result from 

activation, a process of capturing an energetic particle by a nucleus. 

Naturally-occurring radioactive material, or NORM, includes uranium, thorium, and their 

decay series daughters. Other NORM includes potassium-40 and rare substances such as 

samarium-147, but these are not fuel cycle-related. 

Decay

Radioactive decay occurs when atomic nuclei spontaneously disintegrate and emit particles 

such as electrons (beta radiation), 2-proton-2-neutron particles (alpha radiation), or neutrons, 

accompanied by electromagnetic energy (gamma radiation). The time for half of a material 

to decay is called its half-life. The shorter the half-life the more radioactive a substance is, 

or the more disintegration occurs per unit time. 

In alpha decay, an alpha particle consisting of two neutrons and two protons leaves the 

nucleus. An alpha particle is the same as a helium nucleus or a helium atom without its 

electrons. Note that the atomic number decreases by two and the mass decreases by four. 

For example when U-238 decays, Th-234 is produced and one alpha particle is emitted. 

In beta decay, a neutron splits into an electron and a proton, and the electron is emitted as a 

beta particle. Because an electron has almost no mass, the atomic number is not changed, but 

the number of nuclear protons increases by one. For example tritium has two neutrons and 

one proton, for a nuclear mass of 3 – the single proton being a characteristic of hydrogen. 

When tritium decays by emitting a beta particle, it is left with one neutron and two protons --- 

it is now helium with a mass of 3. (Most helium has a mass of 4, with two protons and two 

neutrons.) Note that the atomic number goes up by one, but the mass does not change. 

In addition to alpha and beta particles, gamma photons resembling high-energy x-rays, may 

be produced. Alpha and beta particles are easily stopped by shielding, but gamma rays can 

travel readily through construction materials and special shielding (e.g., leaded glass, lead 

and steel containers) must be provided to protect personnel. 

 

1

 Table 2, below, is adapted from www.gel.com. The reference is not intended as an endorsement of General 

Engineering Laboratories, LLC. There are a number of qualified (e.g. NELAP-certified) laboratories for mixed 

waste. 

2

 A more thorough discussion of radionuclides in the environment, including definitions and discussion of terms 

may be found at : http://www.epa.gov/narel/radnet/glossary.html 
E-2

Some nuclear decay involves emission of neutrons (neutron decay). Neutrons, because they 

have no charge, may also penetrate materials and require shielding. 

The products of decay are called daughter products. Some daughters in the U-238 decay 

chain include Ra-226 and Rn-222, Bi-214, and finally Pb-206. Gamma rays emitted by Bi214 provide an analytical method for U-238. These gamma rays can be detected and 

quantified with portable equipment. 

Thorium-232 also decays through isotopes of radium and radon. These isotopes have much 

shorter half-lives than the corresponding U-series isotopes. In this series, Tl-208 provides a 

diagnostic gamma ray, and the final stable isotope is Pb-208. 

Fission

The energy produced in nuclear reactors comes from the fission of the fuel, commonly U235. In fission a nucleus splits when hit by a neutron. The split produces nuclei of lighter 

elements such as cesium and strontium. A small fraction of the total mass disappears and 

converts to energy. 

U-235 spontaneously splits and emits a neutron occasionally. When the concentration of 

spontaneously fissioning atoms is high enough, the neutrons emitted by spontaneous fissions 

can hit other atoms and induce splitting. This is known as a chain reaction. The chain 

reaction can be controlled by limiting the ability of neutrons from one atom of U-235 to reach 

other atoms. This is done in solution by dilution and controlled geometry (e.g., slab-shaped 

tanks). In a reactor, control rods are inserted and withdrawn to control the rate of fission and 

hence the power production. In an emergency, materials such as cadmium nitrate can be 

added to the primary cooling water. Cadmium strongly absorbs neutrons and poisons the 

chain reaction. 

Activation

In reactor environments, neutrons can be absorbed by ordinarily stable isotopes to produce 

radioactive isotopes. Common materials like sodium, titanium, and iron can become 

radioactive if exposed to a high neutron flux. The most commonly mentioned activation 

product is Co-60 which is produced when Co-59 absorbs a neutron. 

Plutonium is produced after U-238 captures a neutron. 

Radionuclides at waste sites 

The EPA maintains a list of waste sites called the National Priority List, or NPL under the 

Superfund program. These are waste sites that have been abandoned. Not all contain 

radioactive material, but the following table lists radionuclides that have been identified, and 

the number of sites where they have been found. Fission and activation products typical of a 

reactor site are present in this table. 
E-3

Table 1 Distribution of Radionuclides Present at NPL Sites (EPA, 1996)

Radionuclide Half-life Prevalence Radionuclide Half-life Prevalence Radionuclide Half-life Prevalence 

Actinium-227 21.773 yr 2 sites Iodine-129 15.7 

million yr 

3 sites Ruthenium106 373.59 d 2 sites 

Americium-241 432 yr 4 sites Iodine-131 8 d 1 site Selenium-79 650,000 

yr 

1 site 

Antimony-125 2.758 yr 1 site Krypton-85 10.72 yr 1 site Strontium-90 29.1 yr 11 sites 

Carbon-14 5700 yr 2 sites Manganese-54 312.7 d 1 site Technetium99 212,000 

yr 

7 sites 

Cobalt-60 5.27 yr 7 sites Nickel-63 100.1 yr 1 site Thorium-228 1.9 yr 3 sites 

Cerium-144 284.893 

d 

1 site Plutonium-238 87.8 yr 10 sites Thorium-230 80,000 

yr 

14 sites 

Cesium-134 2.1 yr 2 sites Plutonium-239 24,000 yr 10 sites Thorium-232 1.41 × 

1010 yr 

14 sites 

Cesium-135 3 million 

yr 

1 site Plutonium-240 6560 yr 5 sites Tritium 12.3 yr 11 sites 

Cesium-137 30 yr 10 sites Protactinium231 32,500 yr 1 site Uranium-234 247,000 

yr 

32 sites 

Curium-244 18 yr 1 site Radium-226 1602 yr 29 sites Uranium-235 0.71 

billion yr 

10 sites 

Europium-152 13.516 yr 1 site Radium-228 5.76 yr 9 sites Uranium-238 4.51 

billion yr 

30 sites 

Europium-154 8.593 yr 1 site Radon-220 55.6 s 5 sites 

Europium-155 5.0 yr 1 site Radon-222 3.825 d 23 sites 
E-4

RADIOCHEMISTRY ANALYSES 

Several commercial analytical laboratories have facilities for analysis of samples for 

radionuclides. The following Table 2 was adapted from the web site of one such lab. 

* w - water 

s/s/v/af /m - soils/sludges/vegetation/air filters/milk 

Table 2. Analytical Methods 

ANALYSIS METHOD MATRIX 

*see key

Americium 241 DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

RAD-A011 by Alpha Spectroscopy w 

Americium 243 RAD-A011 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

Carbon 14 RAD-A003 by Liquid Scintillation Counting 

 EPA EERF C-01 w 

 EPA EERF C s/s/v/af/m 

Chlorine 36 RAD-A033 w 

s/s/v/af/m 

Curium 242, 243/244, 245/246 RAD-A011 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

Gamma Spectrometry Many isotopes detectable – DOE HASL-300 manual currently 

available at http://www.eml.st.dhs.gov/publications/procman/ 

 RAD-A013 by Gamma Spectrometry w 

 EPA 901.1 af 

 DOE EML HASL 300 4.5.2.3 s/s/v/m 

Gross Alpha RAD-A001 by Gas Flow Proportional Counting w 

 EPA 900.0/9310 s/s/v/af/m 

 RAD-A001c by Coprecipitation w 

 EPA EERF 00-02-1 af 

Gross Alpha and Nonvolatile Beta RAD-A001 by Gas Flow Proportional Counting w 

 EPA 900.0/9310 swipe direct count s/s/v/af/m 

Iodine 129 RAD-A006 by X-ray Spectroscopy w 

 LANL EM-9 s/s/v/af/m 

Iodine 131 RAD-A013 by Gamma Spectroscopy w 

 EPA 901.1 s/s/v/af/m 

Iron 55 RAD-A040 by Liquid Scintillation Counting w 

 DOE EML HASL-300 (Fe-01-RC) modified s/s/v/af/m 

Lead 210 RAD-A018 by Gas Flow Proportional Counting w 

 DOE EML HASL 300 (Pb-01-RC) modified af 

 RAD-A013 by Gamma Spectroscopy s/s/v/m 

 DOE EML HASL 300 

Neptunium 237 RAD-A032 by Alpha Spectroscopy w 

 DOE EML HASL 300 modified s/s/v/af/m 

Nickel 59 RAD-A022 by X-ray Spectroscopy w 

 DOE RESL Ni-1 s/s/v/af/m 

Nickel 63 RAD-A022 by Liquid Scintillation Counting w 

 DOE RESL Ni-1 s/s/v/af/m 

Nonvolatile Beta RAD-A001 by Gas Flow Proportional Counting w 

 EPA 900.0/9310 s/s/v/af/m 

Plutonium 238, 239/240 RAD-A011 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 s/s/v/af/m 

Plutonium 241 RAD-A035 by Liquid Scintillation Counting w 

 DOE EML HASL 300 4.5.4 s/s/v/af/m 

Plutonium 242 RAD-A011 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 s/s/v/af/m 
E-5

Polonium 208, 210 RAD-A016 by Alpha Spectroscopy w 

Polonium 209 RAD-A016 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 s/s/v/af/m 

Promethium 147 RAD-A020 by Liquid Scintillation Counting w 

 EPA EERF PM-01-1 (modified) s/s/v/af/m 

Radium 223 RAD-A024 by Gamma Spectroscopy s/s/v/m 

 DOE EML HASL 300 4.5.2.3 

Radium 226 RAD-A008 by Radon Emanation w 

 EPA 903.1 modified af 

 RAD-A013 by Bismuth Ingrowth and Gamma Spectroscopy 

 DOE EML HASL 300 4.5.2.3 s/s/v/af/m 

Radium 228 RAD-A009 by Gas Flow Proportional Counting w 

 EPA 904.0 modified/9320 af 

 RAD-A013 by Actinium Ingrowth and Gamma Spectroscopy 

 DOE EML HASL 300 s/s/v/af/m 

Radon 222 RAD-A007 by Liquid Scintillation Counting w 

 SM 7500-Rn B (modified) 

Selenium 79 RAD-A031 by Liquid Scintillation Counting w 

 NERC ORD s/s/v/af/m 

Strontium 89 RAD-A004 by Gas Flow Proportional Counting w 

 EPA 905.0 modified s/s/v/af/m 

Strontium 90 RAD-A004 by Gas Flow Proportional Counting w 

 EPA 905.0 modified s/s/v/af/m 

Strontium 89 & 90 (Total 

Radiometric Strontium)

RAD-A004 by Gas Flow Proportional Counting w 

 EPA 905.0 modified s/s/v/af/m 

Technetium 99 RAD-A005 by Liquid Scintillation Counting w 

 DOE EML HASL 300 (TC-01-RC) s/s/v/af/m 

Tellurium 125m RAD-A031 by X-ray Spectroscopy w 

 NERC ORD s/s/v/af/m 

Thorium 228, 230, 232 RAD-A012 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

Thorium 229 RAD-A012 by Alpha Spectroscopy w 

 DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

Thorium 234 RAD-A013 by Gamma Spectroscopy w 

 EPA 901.1 s/s/v/af/m 

Total Activity RAD-A041 by Liquid Scintillation Counting w/s/s/v/af/m 

Total Alpha Radium RAD-A010 by Gas Flow Proportional Counting w 

 EPA 900.1, EPA 903.0, 9315 af/s/s/v/m 

Total Uranium RAD-A023 by Laser Kinetic Phosphorimetry w 

 ASTM D5174 s/s/v/af/m 

Tritium (H-3) RAD-A002 by Liquid Scintillation Counting w 

EPA 906.0 s/s/v/af/m 

Uranium 232 RAD-A011 by Alpha Spectroscopy w 

DOE EML HASL 300 4.5.4 modified s/s/v/af/m 

Uranium 233/234, 235/236, 238 RAD-A011 by Alpha Spectroscopy w 

DOE EML HASL 300 4.5.4 modified s/s/v/af/m 
F-1

Appendix F: Introduction to Systems Engineering 

Basics 

The terms “systems engineering” and “systems analysis” are applied in many fields and with 

many meanings. Fundamentally, however, the concepts refer to a structured approach to 

design or analysis of some system. 

Broadly, a system is any portion of the universe isolated for study. A system may include 

sub-systems and components, each of which has a function in the overall system. 

The structured approach can begin with establishing requirements of a system to be designed 

and constructed. The approach can also begin in reverse by dividing a complex system into 

subsystems and components. An engineered system will have a function that it is required to 

perform. There may be some latitude in the system requirements, for example a home 

heating system might be required to operate within a 5-degree range, whereas an eye 

surgeon’s laser might be required to operate with almost zero tolerance. These are 

performance requirements. A waste site might be required to retain 90% of its soluble 

contaminants for 100 years. It could be designed for zero leakage and perform at a very 

small actual leakage that still meets the performance requirement. 

A system to be designed might be an airplane. Generally a customer specifies the overall 

performance requirements of the system. These could include speed, range, cargo and or 

passenger load, weaponry, and so forth. They could also include factors in the external 

environment such as altitude and weather, water landing and take off, short runways, etc. 

Then a design team would determine what subsystems would be needed to meet these 

requirements, and in more detail, what were the components of the subsystems and materials 

of the components. From this design, models would be built, including physical and 

computer models. Simulations in test chambers or mathematical abstractions would test the 

models. Prototypes would be built and tested to determine whether the customer’s 

requirements were met. 

For each component there would be some performance requirement, a contribution to 

meeting the overall performance requirement. 

An analysis of the system would determine what components were capable of seriously 

degrading the overall system performance. For example, an engine oiling subsystem might 

contain a critical pump. 

Here is where performance monitoring comes in. Critical components should be monitored 

to determine whether they are meeting the specified operating requirements. Oil pressure 

could be measured to ensure that the oiling subsystem was performing as needed. 

Once in the customer’s hands, the operator – in our example, the pilot – would need to be 

aware of fuel reserves, cabin pressure, engine temperature, oil pressure, hydraulic pressures, 

etc. Automatic alarms could be designed to alert the operator to a decline in system 

performance below some safety factor. 
F-2

The systems approach is applied in many disciplines, and is in fact implicit in any attempt to 

study natural systems. A textbook on forest ecology may not mention systems analysis, but 

understanding of the interactions between bugs, bunnies, grass, trees, rain, streams and so 

forth is an exercise in systems analysis. Once these interactions are understood, first 

qualitatively, then through measurements, they can be simulated with computer models. 

Once simulated, the consequences of some perturbation, such as deforestation, or introducing 

a predator, can be estimated and any attendant risk quantified. In this way, risk-informed 

decisions can be made about some course of action. 

But – let’s throw in a very large word of caution. Even in an engineered system it is hard to 

predict how well all the components and subsystems will function together. Problems appear 

in testing, and some components are redesigned before production is begun. In complex 

natural systems it is sheer folly for us to think we understand all the components and 

subsystems in a quantitative way. 

We can test each component. For example, 100 motors could be run continuously until they 

fail. The first failure, last failure, and the mean time of failure can be recorded. This 

information can be used to establish the probability that a motor will fail after some number 

of hours. The same can be done with bearings, tires, brakes, and so forth. Sometimes the 

occurrence rate for some event can only be guessed using expert opinions. The failure 

probability of all components or subsystems can be combined into a failure prediction model 

for the final system. This approach is that of a probabilistic risk assessment, or PRA. 

Some natural processes operate too slowly or infrequently for us to take much note of them. 

We know to design components for earthquakes – at least on a probabilistic basis. The 

thought process of systems analysis must be applied in designing or evaluating a monitoring 

program. 

For further reading see: 

Blanchard, B.S., and Fabryky, W.J. Systems Engineering and Analysis, Third Edition.

Prentice Hall International Series in Industrial and System Engineering. 1998. 
G-1

Appendix G: Management by Objectives 

Management by Objectives (MBO) is an enterprise planning paradigm that can be thought of 

as having several stages of increasing detail. The first stage is to articulate the purpose of the 

enterprise. The second stage is to articulate high-level goals for the enterprise. The next 

level of detail is to develop an understanding of the steps that must be undertaken to reach 

each goal. 

The concept can be applied at any level within an organization – or even at home to plan a 

vacation. Most of us think in these terms whether we list goals and objectives on paper or 

not. 

Purpose 

The purpose can also be called the mission. A lot of lip service has been paid to mission, 

vision, and principles – we will focus only on mission. 

The others generally fail in execution and become statements endorsing motherhood, 

patriotism, and regular church attendance – worthwhile, but not contributing to critical 

thinking about the enterprise. That may be because they are passed down from the top, and 

have no meaning at the level at which work is accomplished. They should be distilled from 

the objectives. The purpose is analogous to the needs definition that drives the systems 

engineering process. 

Goals 

Here we begin to focus on elements of the enterprise that fit together to make the whole. For 

example – if our purpose is to make automobiles, then a goal would be to make good ones, 

another goal might be to sell many. We might divide our enterprise into divisions based on 

the goals, and the goals of the enterprise might become the purpose of these subdivisions. In 

the systems engineering paradigm, we are defining subsystems. 

Objectives 

These are definable milestones on the road to reaching a goal and are developed for each 

goal. These must be understood in any organization at the operational level. In systems 

engineering, we are reaching the component level. 

Objectives may be best defined by brainstorming involving all levels of the enterprise. In a 

restaurant, the cook, the wait staff, the cashier, and the dishwasher should be involved in 

developing and ranking a list of objectives. 

A manager might have a goal of building a culture where all personnel embrace the goals of 

the enterprise. Getting personnel involved in setting goals and defining objectives for the 

enterprise should be an objective for the manager’s path to institutional unity. 

In systems engineering we are at the component level, but the analogy is not complete. 

Objectives are milestones along a path to reaching goals. 
G-2

Pathways 

There may be many roads to reach a goal. Each will have milestones, and some may branch 

or cross. Choosing the optimal pathway becomes an exercise in game theory. At each 

crossroads, there may be opportunity costs. This is best understood by reading Frost’s poem 

that begins “Two roads diverged in a yellow wood...” 

The classic book on enterprise strategy is Williams, 1954. The book introduces concepts of 

win-win strategies to optimize the outcome of decisions that must be made in any enterprise. 

Strengths, Weaknesses, Opportunities and Threats 

Once objectives are defined one must determine whether one has the resources to reach them. 

Resources is used here as a broad term to include the overall business environment. The 

Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis should be conducted in 

a brainstorming setting by all with a stake in or knowledge of reaching the objective. 

Ultimately a manager will decide how or whether or when to proceed on a given pathway. 

The SWOT analysis should provide a thorough checklist of issues that must be analyzed to 

reach a decision. 

Strengths may lie in personnel, budgets, previous experience, strategic location, available 

equipment, subcontractors, or other factors developed in a stakeholder meeting, traditions 

that give one a competitive edge should also be included. Weaknesses are generally in the 

same categories as strengths. Opportunities, in addition to those strictly related to the 

targeted objective, could include secondary targets, new markets or collateral benefits. 

Threats include opportunity costs, competition, enemy gun emplacements, foreign labor 

rates*, injury from dangerous equipment or processes, risks deriving from failure to achieve 

the target. Risks could include fines from regulators. SWOT analysis may identify critical 

issues that must be addressed before an objective can be achieved. (* In the 1980s, market 

analyses for the future of copper prices included such things as projected demands and 

current inventories – but did not predict that Peruvian workers would go to the mines to 

produce copper at any price rather than face unemployment.) 

Summary 

The MBO concepts can be applied at any level of an activity. For ground-water monitoring, 

the purpose is to provide assurance that a facility is operating in a safe manner with respect to 

any public or environmental risk through ground-water pathways. 

Goals would include: to understand the risk sources, the ground-water pathways, controls on 

flow and transport; and to develop integrated conceptual and computer models of the system. 

Predictions from the models would help set other goals and objectives. 

Objectives would be methods to develop and feed the predictive model with characterization 

and monitoring data. The data would allow refinement of the models and thus contribute to 

understanding and assurance. 

A SWOT analysis or something analogous should be undertaken and reviewed regularly. 
G-3

Reference 

Williams, J.D., 1954, The Compleat Strategyst, McGraw-Hill, New York, 234p.
BIBLIOGRAPHIC DATA SHEET

(See instructions on the reverse)

NRC FORM 335

(9-2004)

NRCMD 3.7

U.S. NUCLEAR REGULATORY COMMISSION 1. REPORT NUMBER

 (Assigned by NRC, Add Vol., Supp., Rev.,

 and Addendum Numbers, if any.)

NUREG/CR-6948, Vol 1

3. DATE REPORT PUBLISHED

MONTH

November

YEAR

2007

4. FIN OR GRANT NUMBER

Y6020

2. TITLE AND SUBTITLE

Integrated Ground-Water Monitoring Strategy for NRC-Licensed Facilities and Sites: Logic,

Strategic Approach and Discussion

5. AUTHOR(S)

Van Price, Tom Temples, John Tauxe, Rex Hodges and Ronald Falta

6. TYPE OF REPORT

Technical

7. PERIOD COVERED (Inclusive Dates)

February 2003 - September 2007

8. PERFORMING ORGANIZATION - NAME AND ADDRESS (If NRC, provide Division, Office or Region, U.S. Nuclear Regulatory Commission, and mailing address; if contractor,

Advanced Enivironmental Solutions, LLC

9. SPONSORING ORGANIZATION - NAME AND ADDRESS (If NRC, type "Same as above"; if contractor, provide NRC Division, Office or Region, U.S. Nuclear Regulatory Commission, 

Division of Fuel, Engineering and Radiological Research

provide name and mailing address.)

and mailing address.)

10. SUPPLEMENTARY NOTES

T.J. Nicholson, NRC Project Manager

11. ABSTRACT (200 words or less)

This document presents a logical framework for assessing what, how, where and when to monitor underground water in order to

ensure that a licensed nuclear site or facility is behaving within the expected limits as described by the performance assessment.

The Strategy is implemented as an iterative process beginning with analysis of any existing site and facility characterization and

monitoring data, any existing conceptual site model (CSM), in this case generally a hydrogeologic model, and any existing risk

assessment or PA model. The iterative nature of the Strategy results in a graded approach to development or evaluation of a

monitoring program. Through analysis, an initial assessment of what, how, when, and where to monitor to evaluate system

performance is made. Performance Indicators include chemicals, hydrogeologic attributes, and other features, events, or

processes (FEPs) that may significantly influence contaminant flow and transport. These PIs may be directly measurable in a

monitoring program or may be derived from compilations and interpretations of data. The integrated strategy benefits are: 

- Characterization allows development of CSM;

- CSM allows modeling / simulation;

- Modeling allows prediction;

- Monitoring allows refinement; and

- Refinement allows confidence.

12. KEY WORDS/DESCRIPTORS (List words or phrases that will assist researchers in locating the report.)

conceptual site model

geophysics

ground water

hydrogeology

monitoring

Performance Assessment

Performance Indicator

risk

tritium

visualization

14. SECURITY CLASSIFICATION

13. AVAILABILITY STATEMENT

unlimited

(This Page)

unclassified

(This Report)

unclassified

15. NUMBER OF PAGES

16. PRICE

NRC FORM 335 (9-2004) PRINTED ON RECYCLED PAPER

407 West Main Street

Lexington, SC 29072

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

Washington, DC 20555-0001