Multi-Resource Analysis—

Methodology and Synthesis

U.S. Department of the Interior

U.S. Geological Survey

Circular 1442

By Karen E. Jenni, Emily Pindilli, Richard Bernknopf, Timothy L. Nieman, 

and Carl Shapiro

U.S. Department of the Interior

U.S. Geological Survey

Circular 1442

Multi-Resource Analysis—

Methodology and Synthesis
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2018

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as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Jenni, K.E., Pindilli, E., Bernknopf, R., Nieman, T.L., and Shapiro, C., 2018, Multi-Resource Analysis— 

Methodology and synthesis: U.S. Geological Survey Circular 1442, 81 p., https://doi.org/10.3133/cir1442. 

Library of Congress Cataloging-in-Publication Data

Names: Jenni, Karen E., author. | Geological Survey (U.S.), issuing body. 

Title: Multi-Resource Analysis--methodology and synthesis / by Karen E. Jenni 

 [and four others]. 

Other titles: U.S. Geological Survey circular ; 1442. 1067-084X 

Description: Reston, Virginia : U.S. Department of the Interior, U.S. 

 Geological Survey, 2018. | Series: USGS circular, ISSN 1067-084X ; 1442 | 

 Includes bibliographical references. 

Identifiers: LCCN 2018029664 | ISBN 9781411342361 (paperback) 

Subjects: LCSH: Land use--United States--Data processing. | Natural 

 resources--United States--Management--Data processing. | 

 Landscapes--United States--Data processing.

Classification: LCC HD205 .J46 2018 | DDC 333.730973--dc23 | SUDOC I 

 19.4/2:1442

LC record available at https://lccn.loc.gov/2018029664

ISSN 1067-084X (print)

ISSN 2330-5703 (online)

ISBN 978-1-4113-4236-1
 iii

Acknowledgments ........................................................................................................................................v

Abstract...........................................................................................................................................................1

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

Vision for the Multi-Resource Analysis.............................................................................................2

Background............................................................................................................................................6

Current Resource Assessments................................................................................................6

Enhancing and Integrating Resource Assessments..............................................................8

Supporting Studies and Activities...........................................................................................10

Proof-of-Concept Studies................................................................................................10

Synthesis Workshop.........................................................................................................16

Multi-Resource Analysis Process ...........................................................................................................18

Framing the Multi-Resource Analysis Study..................................................................................21

Multidisciplinary Multi-Resource Analysis Team.................................................................21

Defining the Scope of the Multi-Resource Analysis............................................................24

Analysis and Modeling.......................................................................................................................26

Running Models and Reporting Results..........................................................................................28

Development of Multi-Resource Analysis Components ......................................................................30

Multi-Resource Analysis Baseline ..................................................................................................31

Baselines—Synthesis of Previous Efforts.............................................................................31

Challenges for Future Multi-Resource Analysis Baselines................................................34

Multi-Resource Analysis Change Analysis ....................................................................................38

Scenarios and Scenario Analysis...........................................................................................38

Scenario Development—Synthesis of Previous Efforts ............................................44

Challenges for Future Multi-Resource Analysis Scenario Development................46

Integrated, Dynamic Models of Physical and Biological Interrelationships ...................48

Integrated Dynamic Models—Synthesis of Previous Efforts ...................................50

Challenges for Future Multi-Resource Analysis Modeling........................................56

Economic Analysis.....................................................................................................................58

Economic Analysis—Synthesis of Previous Efforts ...................................................60

Challenges for Future Economic Analysis....................................................................62

Multi-Resource Analysis Outlook and Information Delivery .......................................................64

Outlook and Information Delivery—Synthesis of Previous Efforts ...................................64

Challenges for Future Multi-Resource Analysis Information Delivery .............................68

Potential Next Steps...................................................................................................................................70

References....................................................................................................................................................72

Figure Credits...............................................................................................................................................78

Contents 
iv 

Highlights

1. The Powder River Basin Proof-of-Concept Study.................................................................12

2. The Piceance Basin Proof-of-Concept Study........................................................................14

3. A Multi-Resource Analysis Team Structure...........................................................................22

4. A Multi-Resource Analysis Framing Workshop.....................................................................25

5. The Baseline as an Interim Product........................................................................................32

6. Example: User-Specified Energy Development Scenario....................................................40

7. Example: Potential Development Restrictions.......................................................................42

8. Examples of Outlook Presentations.........................................................................................66

Figures

1. Multi-Resource Analysis process and components...............................................................2

2. Steps in conducting a Multi-Resource Analysis ...................................................................20

3. Overview of the Powder River Basin Multi-Resource Analysis models of 

interrelationships among resources .......................................................................................52

4. Overview of the Piceance Basin Multi-Resource Analysis models of

interrelationships among resources and other factors .......................................................54

Abbreviations

CBNG coal-bed natural gas

DA decision analysis

MRA Multi-Resource Analysis

PRB Powder River Basin

USGS U.S. Geological Survey

Contents 
 v

The authors thank David Brookshire of the University of New Mexico, Lynne 

Koontz of the National Park Service, Ione Taylor of Queens University, and 

Jim Coleman and Vito Nuccio of the U.S. Geological Survey for support, 

encouragement, and many discussions about Multi-Resource Analysis problems 

and solutions over the years. We thank Lynne Koontz and Jay Diffendorfer 

(U.S. Geological Survey) for the helpful reviews of this document. We would 

also like to thank the many participants in the proof-of-concept studies and 

synthesis workshop that advanced Multi-Resource Analysis concepts and 

provided the foundation for this document, including Dadhi Adhikari (University 

of New Mexico); Christopher Babis (University of New Mexico); Craig Broadbent 

(Brigham Young University—Idaho); Steve Garman (Bureau of Land Management); 

Rebecca Moore (Bureau of Land Management); Vince Tidwell (Sandia National 

Laboratories); and Steve Aulenbach, Zachary Bowen, Sky Bristol, Natasha Carr, 

Sarah Carter, Monica Dorning, Susan Hall, Sarah Hawkins, Seth Haines, 

Cerecia Martinez, Mark Mihalasky, Darius Semmens, Paul Pierce, and 

Katherine Walton-Day of the U.S. Geological Survey. Final thanks go to 

Sally Almeria for her careful and helpful editing and to Carol Quesenberry 

for translating “we want it to look nice” into a layout nicer than we 

imagined possible.

Acknowledgments 
By Karen E. Jenni,1

 Emily Pindilli,1 Richard Bernknopf,2 Timothy L. Nieman,3 and Carl Shapiro1

Multi-Resource Analysis—

Methodology and Synthesis
This document introduces the Multi-Resource Analysis 

(MRA), a set of products that are being designed to integrate 

information on multiple natural resources in a region, combine 

that information with models of resource interrelationships 

and scenarios of change, and provide meaningful insights on 

the implications of those changes to people and the resources 

they value. The MRA builds from and enhances a wide range 

of existing U.S. Geological Survey assessment products. 

These enhancements will help natural resource managers 

better understand the connections among the resources they 

manage and the changes that might occur due to natural 

events and human decisions. This knowledge will help them 

identify solutions to landscape-scale management issues 

that best meet their objectives. MRA products are developed 

through a structured process that brings together scientists, 

decision makers, and other stakeholders to address relevant 

issues and decisions for a specified geographic region, 

ensuring that the analysis is directly relevant. This circular 

introduces the MRA, describes the envisioned process for 

developing a region-specific MRA, and discusses the various 

MRA components and products. The MRA process and 

products are shown through descriptions and examples 

drawn from two proof-of-concept studies and related work.

The Multi-Resource Analysis (MRA) is conceived as a 

next-generation suite of analytical products that can be used 

to inform land use and resource management decisions. The 

MRA integrates scientific information about natural resources 

and explicitly recognizes changes to those resources caused 

by natural events and human decisions. The MRA considers 

the effects of these changes on multiple natural resources, the 

natural resources’ interrelationships, and the economic and 

societal consequences of these changes to humans. It builds 

on the wealth of geologic, hydrologic, biologic, and social/

cultural resource assessments produced by the U.S. Geological 

Survey (USGS) and others to provide public-sector resource 

managers and other decision makers with landscape-scale 

science products and perspectives.

With the enhanced resource assessment information that 

results from the MRA, resource managers can identify more 

and better management options and have greater understanding 

of how those options will affect the landscape and its resources, 

and the people who depend on those resources. Decision makers 

almost always need to act without knowing exactly what the 

future will bring, and thus they benefit from tools that help 

them anticipate potential future conditions and the effects of 

various management options. The MRA provides an enhanced 

description of how the many resources in a specific region 

connect, as well as projections of future conditions under 

different scenarios. These products allow decision makers to 

consider both decision-driven and external or stressor-driven 

changes in resources and resource-related benefits.

This circular summarizes the vision, status, and results of 

efforts undertaken by the USGS Science and Decisions Center

to develop the MRA. The “Vision for the Multi-Resource Analysis” 

section of this document describes the vision and motivation 

for developing the MRA approach, and briefly describes a 

set of studies that were conducted as part of the development 

process. The “Multi-Resource Analysis Process” section describes 

the envisioned process for conducting an MRA, and the 

“Development of Multi-Resource Analysis Components” 

section outlines each of the MRA components and how they are 

to be developed and provides examples from experiences to date. 

The “ Potential Next Steps” section concludes with a set of next 

steps for moving the MRA from the conceptual and demonstration

stage to a real decision-support process and set of tools.

1

U.S. Geological Survey.

2

University of New Mexico.

3

Decision Applications, Inc.

Abstract Introduction
Change Analysis

Decision-relevant and decision-ready information. May include maps and data layers; descriptions

and illustrations of scenarios; models of physical, biological, and economic relationships; and

tabular and graphical summaries of the quantity, quality, and values of resources. 

Modeled projections

of future conditions

and the implications

of changes from 

the Baseline, for 

different scenarios. 

Outlook

Frame the MRA

Analyze and model

Process

Run models and report results

Components

Integrated scientific

information on the current 

status of multiple natural

resources. 

Baseline

Economic Analysis

Evaluation of the economic and 

societal impacts of changes to 

resources. 

MRA scope, boundaries, and desired information delivery.

All components address multiple natural resources within a defined geographic area. 

Physical and Biological Models

Models of the interrelationships 

among multiple natural resources 

as they respond to changes and 

disruptions over time. 

Scenarios and Scenario Modeling

Key decisions, stressors, disruptions, 

or other changes of interest. 

2 Multi-Resource Analysis—Methodology and Synthesis

Vision for the Multi-Resource Analysis 

The MRA combines information on multiple resources into a single analytical framework that includes (1) a baseline 

assessment of the quantity and quality of colocated natural resources in a defined geographic region, (2) models of those resources’ 

interrelationships and the effects on humans of changes in those resources, and (3) projections of the future conditions of those 

resources as they experience various disruptions and stressors. By providing this critical information through a timely, relevant, 

and scientifically sound set of data and tools, the MRA supports resource managers as they make decisions that affect our 

Nation’s natural resources and economic and societal well-being. 

An individual, regionally specific MRA is developed through a structured and iterative process that brings together scientists, 

decision makers, and other stakeholders to identify and address issues and decisions of most relevance for a specified geographic 

region. The process and major MRA components are shown in figure 1 and described on the following pages.

Figure 1. Multi-Resource Analysis (MRA) process and components.

Introduction
Introduction 3
4 Multi-Resource Analysis—Methodology and Synthesis

Baseline.—The MRA Baseline combines scientific 

information on multiple natural resources into an integrated 

framework so that the information can be reviewed, analyzed, 

and disseminated. The Baseline includes information on the 

quality and quantity of resources in a region at a point in time, 

as well as relevant history, trends, and the current economic 

context for the region and its natural resources. Developing an 

MRA Baseline requires integrating data from different disciplines

and at different scales and summarizing and presenting that 

information in ways that are relevant to decision-makers’ 

interests. The “Background” section discusses some of the 

variety of resource assessments that may be available for a 

particular region. Even when several assessments exist in 

a region, they are typically conducted for different purposes or 

over different geographic and temporal boundaries, may not 

be easy to access, and are not integrated. The MRA Baseline 

provides that integration and easy access. 

Change Analysis.—The Change Analysis component of 

MRA consists of models and analyses that connect the 

Baseline (current conditions) to the Outlook (potential future 

conditions). Change Analysis includes three types of analyses.

• Scenarios of change.—Natural processes and events 

and human decisions can change landscapes, their 

natural resources, and the services and benefits those 

resources provide. Resource managers must make 

decisions in the face of uncertainty without knowing 

what the future will bring. So, tools and approaches 

that help them anticipate future conditions, and how 

their management options might affect resources, 

landscapes, and people are needed. Scenarios of 

change are a key part of the MRA and the use of 

scenarios is a standard tool decision makers can use 

to better understand the risks and benefits of different 

actions given uncertainties about the future. Developed 

by scientists, decision makers, and other stakeholders 

with deep knowledge of the region being analyzed, 

the scenarios of change in an MRA are developed 

from an understanding of the most important stressors, 

disruptions, and decisions that may affect the future 

landscape. Scenarios help identify the issues that should 

be modeled further in the Change Analysis, and define 

and constrain what the MRA Outlook will produce. 

• Physical and biological models of natural resource 

interrelationships.—The MRA includes explicit 

descriptions and models of the interrelationships 

among natural resources. Quantifying information on 

the biophysical relationships between those resources 

allows analysts to model and project how a change 

in one resource (for example, extraction of an energy 

resource) affects other resources (for example, water 

availability and water quality). For the MRA, models 

of resource interrelationships address (1) spatial issues, 

to capture regionally and locally important connections; 

and (2) temporal issues, to capture changes in resources 

and impacts over time. An important part of the vision 

for the MRA is that uncertainty is considered directly 

in each component; that is, where there is lack of 

information about and (or) regional variability in 

resource relationships, MRA models address and 

communicate that uncertainty.

• Economic analysis.—The connections between natural 

resources and humans are a principal consideration in 

the MRA. As the landscape and resources in a region 

change over time, there can be direct economic

consequences (for example, effects of energy extraction, 

water availability for agriculture, and effects of 

recreational spending) and indirect societal benefits 

(for example, ecosystem services, benefits associated 

with preserving the heritage and ways of life for Native 

American and rural communities, and benefits associated

with fishable and swimmable waters). Through analysis 

and valuation of the effects of changes in resources, the 

economic analysis in the MRA brings together the Baseline, 

models of the physical and biological interrelationships 

among resources, and scenarios of change into a common 

reference system that crosscuts multiple natural 

resources and enables consideration of the economic 

and societal effects of changes in those resources. 

Introduction
Outlook.—The results of the Change Analysis constitute 

the MRA Outlook. The Outlook provides decision makers with 

information on potential outcomes of decisions, actions, and 

other factors that affect resources over time. Similar to the 

Baseline, the Outlook describes the quality and quantity of each 

resource, but under changed conditions rather than current 

conditions. Comparing the Outlook to the Baseline provides a 

picture of potential changes over time: spatially and temporally 

referenced effects on the landscape, including changes in colocated

resources and changes in the benefits that are being derived 

from that landscape. Comparing the Outlook under different 

scenarios of change provides resource managers and stakeholders 

with insights about the tradeoffs that might be incurred under 

different management options and different scenarios.

To maximize the usability of the MRA and ensure results 

are decision-relevant and decision-ready, all of the components 

are packaged in one or more accessible, easy-to-use and easyto-understand information-delivery platforms. These platforms 

can take the form of maps of colocated resources; descriptions 

and illustrations of scenarios considered; models of resource 

interrelationships; and (or) tabular and graphical summaries 

of the quantity, quality, and values of resources both in the 

Baseline and under different scenarios. Decision-support 

systems provide a decision maker or stakeholder with desktop 

access and a variety of ways to explore the multidimensional 

outputs of the MRA.

Introduction 5
6 Multi-Resource Analysis—Methodology and Synthesis

Background 

The USGS has been conducting resource assessments for 

a variety of natural resources for several decades as part of the 

agency’s mandated responsibilities stated in the Organic Act 

of 1879 and later amendments (20 Stat. 394; 43 U.S.C. 31, 

et seq.). Energy and mineral resource assessments were one of 

the earliest focuses of the USGS, which has been conducting 

such assessments almost since its founding (Ferrero and 

others, 2013). 

Quantities of undiscovered, technically recoverable 

energy resources are estimated based on historic data, current 

technologies, and well-established and vetted probabilistic 

modeling approaches. The USGS also assesses water and 

biologic resources by using resource-specific methodologies. 

The differences in the resources assessed, the methodologies 

used, and the focuses of the assessments make it challenging 

to compare and (or) integrate existing assessments of multiple 

natural resources in a common framework. 

The information provided by various types of resource 

assessments supports decisions in land management, water 

allocation, energy and mineral policy, and environmental 

protection in both Government and the private sector, but 

current resource assessments are typically conducted under 

single-discipline assumptions and are not conducive to 

evaluating interrelationships among resources and the 

consequences of change. However, the demand for more 

sophisticated analyses of those connections is increasing. 

To help meet this need, the MRA requires that multiple 

natural resources are assessed in a way that allows data and 

information on those resources to be compared, combined, and 

synthesized, resulting in an integrated understanding of the 

region being assessed. With the enhanced assessment capabilities 

of the MRA, colocated natural resources and the tradeoffs 

between different resource uses can be considered, and the 

multifaceted effects of changes in resources can be analyzed. 

Current Resource Assessments

Energy and mineral resource assessments are typically 

geologically based, probabilistic estimates of volumes of 

commodities that include conventional oil and gas, unconventional 

oil and gas, coal, uranium, and nonfuel minerals. In energy 

resource assessments, quantities of undiscovered, technically 

recoverable oil and gas resources are estimated based on 

historic data, current technologies, and well-established and 

vetted probabilistic modeling approaches (Charpentier and 

Cook, 2012). The goal of a quantitative mineral resource 

assessment is to identify and clarify factors that are relevant to 

mineral-related decisions (Singer and Menzie, 2010). USGS 

energy and mineral resource assessments provide information 

about the quantity and quality of energy resources and the 

uncertainty surrounding them (after Pierce and others, 2007) 

within a geologically defined boundary. Cost of extraction and 

resource prices are assessed for some, but not all, of these

resources and are only rarely included within resource 

assessments focused on quantity and quality. 

Water and biologic resource assessments are conducted 

for their own unique purposes and use approaches that are 

unique to those resources. They are not designed to be directly 

comparable to energy and mineral resource assessments or 

with each other. 

Water resource assessments include real-time and historical 

stream-stage and streamflow information from the National 

Water Information System (https://waterdata.usgs.gov/nwis/sw); 

national and regional water quality assessments from the 

National Water-Quality Assessment Project (https://water.usgs.

gov/nawqa/); and groundwater levels and trends based on a 

combination of observations from streamgages, groundwater 

wells, and models (https://water.usgs.gov/ogw/). Additionally, 

USGS assesses water use across the United States every 

5 years through the National Water-Use Information Program 

(Barber, 2014). 

Introduction
Biologic resource assessments use scientific and statistically 

reliable methods and protocols to assess the status and trends 

of the Nation’s biological resources, with focus on the abundance, 

distribution, productivity, and health of those resources. The 

USGS provides assessments of seasonal life-cycle events 

through the National Phenology Network (https://www.usanpn.org/) 

and remote sensing approaches (https://remotesensing.usgs.

gov/), and assessments of the status and trends of a variety of 

species- or species-group-specific studies (Mac and others, 

1998). The National Gap Analysis Project is working to map 

predicted species distributions and associated land ownership 

and land cover across the United States (https://gapanalysis.

usgs.gov/about-gap/). In a few regions, biologic resource 

assessments address multiple species and their connections 

to habitats (for example, the Upper Mississippi River 

Restoration Program [https://www.umesc.usgs.gov/ltrmp.

html]). Anthropogenic trends and the effects of these trends 

on biological resources may also be assessed.

In addition to assessments of energy and mineral resources, 

water resources, and biological resource systems, the USGS 

has made significant investments in systematically understanding 

changes to land itself, particularly under land remote sensing 

programs (https://remotesensing.usgs.gov/). These data enable 

understanding of both long-term and short-term changes in 

land resources, including shifts between types of land use, 

such as changes from forest to agricultural use, and shifts in 

the quality of resources, such as changes in forest density.

Introduction 7
8 Multi-Resource Analysis—Methodology and Synthesis

Most existing USGS resource assessment methods do not 

include assessment of the economic and societal consequences 

of changes in resources and do not consider ecosystem service

benefits that may be derived from natural resources. When 

ecosystem service assessments are conducted, they are largely 

conducted on an ad-hoc, demand-driven basis. For example, 

recent USGS ecosystem service assessments have focused 

on fire mitigation as one ecosystem service provided by the 

Great Dismal Swamp National Wildlife Refuge in Virginia 

(Parthum and others, 2017); on the employment, recreation, 

and nourishment services from inland fisheries (Deines and 

others 2017); and on habitat, soil carbon, and water supply for 

a subset of watersheds included in a larger scale study (Byrd 

and others, 2015). Existing resource assessments and methods 

also do not typically attempt to quantify impacts (cumulative 

or otherwise) of changes in those resources. Environmental 

impacts are regularly quantified under section 102 of the 

National Environmental Policy Act of 1969 (42 U.S.C. 4321 

et seq.) which “requires federal agencies to incorporate 

environmental considerations in their planning and decisionmaking through a systematic interdisciplinary approach” 

(U.S. Environmental Protection Agency, 2017). However, 

these analyses are conducted reactively in response to a 

specific proposed action, whereas the resource assessments 

described throughout this section are typically more general.

Enhancing and Integrating Resource Assessments

Although traditional resource assessment methods have 

matured and continue to provide important information, societal 

choices and decisions now demand enhanced information. 

The emergence of integrated approaches and examples of 

larger scale studies aimed at supporting decision making 

indicate an appetite for these more ambitious approaches. 

For example, strategic environmental assessment explicitly 

focuses on decision support, but most analyses are project 

specific (Lobos and Partidário, 2014). The U.S. Fish and 

Wildlife Service is using strategic habitat conservation as a 

framework for managing resources at larger geographic scales 

(U.S. Fish and Wildlife Service, 2016), but it tends to focus 

only on ecological resources. Integrated environmental 

modeling (Laniak and others, 2013) focuses on modeling the 

connections across resources and processes at a variety of 

scales, but it is less connected to decision making.

With enhanced resource assessment information, resource 

managers can identify more and better management options 

and have greater understanding of how those options will 

affect the landscape, its resources, and the people who depend 

on them—the MRA provides exactly this kind of enhanced 

resource assessment. The MRA brings together information on 

multiple natural resources, the economic and societal values 

associated with those resources, and the services and benefits 

those resources provide into a single decision-oriented 

framework. An MRA provides an enhanced description of how 

the many resources in a specific region connect, thus allowing 

decision makers to consider both decision-driven changes and 

changes that might be driven by external stressors in those 

resources and in resource benefits over time. 

Introduction
Introduction 9
10 Multi-Resource Analysis—Methodology and Synthesis

Supporting Studies and Activities

The Science and Decisions Center has been developing 

the MRA concept by reviewing related work and information 

from a series of workshops and studies held in the past several 

years. These efforts culminated in two separate proof-of-concept 

studies that were designed to determine the challenges, feasibility, 

and value of the MRA as a new type of USGS product. Based 

on the results of these studies, other related research, and an 

MRA synthesis workshop held in 2016, the Science and Decisions 

Center concludes that the MRA could be developed and 

implemented by the USGS. The proof-of-concept studies and 

synthesis workshop helped define the vision for the MRA and 

provide an important part of the basis for the MRA process 

and approach described in this document.

Proof-of-Concept Studies 

The two MRA proof-of-concept studies were designed 

to be multipurpose; they were intended to demonstrate the

feasibility of the MRA, identify approaches that are most

promising, and evaluate the challenges to conducting an

MRA. The independently conducted studies explored several 

perspectives on how to develop an MRA. One study, conducted 

by a team of USGS scientists and private consultants under 

contract, focused on the Powder River Basin (PRB) of 

northeastern Wyoming, where the main energy resource is 

coal and the impacts of coal mining and coal-bed natural 

gas (CBNG) development on water, ecology, and the local 

economy are important issues in the region. The second 

study, led by researchers from the University of New Mexico 

under a cooperative agreement with the USGS, focused on 

the Piceance Basin in northwestern Colorado, where the main 

energy resource is shale gas and the impacts of gas extraction 

and hydraulic fracturing on water quality and wildlife are 

important issues in the region. 

The proof-of-concept studies were designed as exploratory 

efforts; the focus was on research that supported development 

of the MRA using existing USGS data and models rather than 

on producing analyses ready to support specific decision making.

The primary objectives of the two studies were

• to develop and test an approach for integrating existing 

data and assessments of multiple natural resources, 

their interrelationships, and the impacts of changes on 

economic values and

• to identify and explore ways to make that integrated 

information useful to land and natural resource managers 

and stakeholders. 

Beyond these objectives, there were few constraints on 

exactly how the proof-of-concept studies were approached, 

and it was understood that each study would evolve during the 

process as the concepts of the MRA were being developed. In 

each case, the study team had to identify a geographic area, a 

set of natural resources, at least one potential source or type of 

change, and a future time horizon on which to focus the study. 

Because both studies were conducted by small teams with 

very few dedicated resources, in practice those studies were 

highly constrained in terms of the amount and type of work 

that could be done to accomplish the study goals. For example, 

both studies had to rely exclusively on existing and accessible 

data. The two studies took different approaches when no 

such data were available, choosing either to not evaluate that 

resource or impact or to use data and models from a different 

region for illustration purposes. Both studies were also of 

limited scope—only a few of the relevant natural resources 

were included and a few scenarios considered in each study; 

only one study included stakeholder interactions, and only one 

illustrated an economic valuation of an ecosystem service. 

These limitations mean that the “results” of these proofof-concept studies are illustrative only. Although decisionrelevant conclusions should not be drawn from them, these 

studies met their goals of advancing the MRA concepts, 

identifying promising approaches and challenges associated 

with MRA implementation. The two proof-of-concept studies 

are summarized in the highlights on pages 12–13 and 14–15.

Introduction
Introduction 11
The Powder River Basin is a rural, coal-rich region 

in Montana and Wyoming consisting primarily 

of grassland and containing some important habitat 

for greater sage-grouse (Centrocercus urophasianus). 

In developing a proof-of-concept study for the 

Multi-Resource Analysis (MRA) in this region, the 

study team sought broad input across U.S. Geological 

Survey disciplines and from partners and stakeholders, 

including local and regional scientists, headquarterslevel Federal agencies (for example, U.S. Department 

of the Interior, U.S. Fish and Wildlife Service, 

Bureau of Land Management, and Forest Service), 

and State and local constituents. These discussions 

were essential for understanding the scope of 

resources and stressors that are relevant in the 

region and provided highly valuable information 

about the kinds of information, modeling tools, 

and analysis results that would be most useful to 

on-the-ground decision makers. Each of the major 

components of the MRA was developed, at varying 

levels of detail, in this proof-of-concept study. Some 

specific details about the study are provided here.

• Baseline information collected and summarized 

included data on the quantity, quality, and 

location of coal, coal-bed natural gas (CBNG), 

surface water, groundwater, some wildlife 

species and associated habitats, grazing, 

land disturbance, and land ownership.

• Energy development, specifically of coal and 

CBNG, was identified as the key driver of 

change for this modeling effort, and several 

development scenarios were hypothesized 

and modeled.

• The Change Analysis evaluated the potential 

effects of coal and CBNG extraction on surface 

disturbance, groundwater and surface water, 

and specially designated habitats, as well 

as several basic economic factors such as 

development revenue.

• Several methods for presenting the results 

of the Change Analysis (that is, the Outlook) 

were explored, including maps comparing 

the Outlook under different scenarios and 

graphs illustrating resource changes over 

time under different scenarios.

The Powder River Basin Proof-of-Concept Study
2.5

2.0

1.5

1.0

0.5

0.0

2015 2020 2025 2030 2035

3.0

Year

Precentage of produced water relative to total recoverable groundwater

14

14

14

14

16

16

87 16

16

85

85

85

20

18

26

26 20

287

18

18

212

90

90

25

25

WYOMING

MONTANA

Sheridan

Buffalo

Casper

Douglas

Gillette

Newcastle

EXPLANATION

Scenario 1 only

Scenario 2 only

Both scenarios

Coal development in both

 scenarios

Coal-bed natural gas development

 through 2035

Coal mining through 2035

Powder River Basin boundary

0 10 20 30 40 50 KILOMETERS

0 10 20 30 40 50 MILES

107°

45°

44°

43°

106° 105°

Base from The National Map, digital data, 2017

Universal Transverse Mercator projection

Zone 13N 

WYOMING

MONTANA

Map

area

Produced water relative to total recoverable groundwater 

over time for a single development scenario. Each line 

represents an individual watershed.

Note: The Powder River Basin MRA was not intended to address a specific issue or question in the 

Powder River Basin region or to be a substitute for focused studies; rather, it was a feasibility test and 

exploration of the challenges and benefits of developing an MRA.

Map of study area illustrating areas 

with energy development under 

different scenarios.
The Piceance Basin is a rural, energy-rich region 

in northwest Colorado that contains coal, natural 

gas, and oil shale resources. It is also home to 

one of Colorado’s most important populations of 

mule deer (Odocoileus hemionus) (Colorado Parks 

and Wildlife, 2014). In developing a proof-of-concept 

study for the Multi-Resource Analysis (MRA) in this 

region, the study team focused on the development 

of an analytical decision-support tool (DST) that 

combines natural resource and economic information

 to model the potential effects of energy resource 

development on ecosystem services. The DST 

produces a “net resource value” that is the sum 

of the economic benefits from energy and mineral 

production and the economic value of the effects 

this development has on several other natural 

resources in the area. The Piceance Basin MRA 

explored all major elements of the MRA concept 

described in this circular but at different levels 

of depth and sophistication. Some specific details 

of this MRA are provided here.

• Baseline information collected and used included 

the location(s) of natural gas and surface water 

resources, mule deer habitat, and key surface 

conditions related to habitat suitability.

• The Piceance Basin study considered energy 

development, specifically natural gas extraction 

using hydraulic fracturing, to be a key driver 

of change in the region. The proof-of-concept 

study focused on the development of the DST, 

which allows users to define their own scenarios.

The study team illustrated this capability by 

considering how development might proceed 

under different hypothesized scenarios that 

varied the pace of development over time, and 

the number of wells installed per well pad. 

• The Change Analysis focused on the potential 

effects of gas extraction on water use, surface 

disturbances, and mule deer, which were all 

connected through a systems dynamics model 

that considers the interaction of geology, 

hydrology, ecology, and economics in the region 

over time. The study also included an economic 

analysis of reduced water availability and 

reduced fish and mule deer populations 

potentially resulting from development. 

The Piceance Basin Proof-of-Concept Study 
IDAHO

WYOMING

MONTANA

COLORADO NEVADA UTAH

NEW MEXICO ARIZONA

0 40 80 120 160 200 KILOMETERS

Base from The National Map digital data,1:2,000,000, 2018 0 40 80 120 160 200 MILES

Albers Equal-Area Conic projection

Standard parallels 20°30‘N. and 45°30‘N.

Central meridian 109°00‘W.

North American Datum of 1983 

44°

42°

40°

38°

36°

114° 112° 110° 108° 106° 104°

EXPLANATION

Shale play

Province boundary

Year round

Summer

Winter

Mule deer habitat

Gas assessment unit 200263

• The primary information delivery platform in 

this study is the DST itself. The user interface 

provides links for viewing several different 

outputs of interest as graphs, maps, or tables 

and as changes in specific outcomes both 

locally and in aggregate. These outputs can 

be viewed in physical terms (for example, 

change in mule deer population) in economic 

terms (for example, the social cost of fewer 

mule deer), in aggregated terms (for example, 

the net resource value), or in any combination 

of those terms.

Note: The Piceance Basin MRA was not intended to address a specific issue or question in the Piceance Basin 

or to be a substitute for focused studies; rather it was a feasibility test and exploration of the challenges 

and benefits of developing an MRA, with an emphasis on the economic valuation of resources.

Example map showing colocated natural 

resources and the area included in this 

MRA (black outline).
16 Multi-Resource Analysis—Methodology and Synthesis

Synthesis Workshop 

The Science and Decisions Center convened a 2-day 

MRA synthesis workshop to identify and discuss best practices 

and promising methods that can be used to implement the 

MRA. Participants from both proof-of-concept studies were 

joined by USGS and other U.S. Department of the Interior 

scientists who had interests in and experiences with other 

multidisciplinary studies that focus on understanding the 

interrelationships among natural resources (for example, 

Haines and others, 2013; USGS Assessment Team, 2015; 

Wyoming Landscape Conservation Initiative, 2015; USGS, 

2016). The workshop discussions informed and helped clarify 

the MRA vision; emphasized the importance of a clear, 

structured process for implementing an MRA and for decisionmaker involvement throughout that process; and identified 

promising methods (and challenges) for each component of the 

MRA. All of these insights are reflected in the MRA vision and 

process described in this circular. A few of the key takeaways 

from the MRA synthesis workshop are summarized here.

Overall use and structure.—The MRA is a multiuse 

product and the process should be undertaken for a given 

region with the goal of developing products that can be useful 

to multiple decision makers as they grapple with a variety of 

complicated land and resource management decisions. The 

MRA resembles traditional USGS resource assessments in 

that it is intended to be developed proactively rather than in 

response to a specific or single decision-support need. 

However, by building resource manager and stakeholder 

perspectives into the analyses from the beginning, the scope 

of the MRA can be better managed and the resulting analytical 

products will be useful to those intended end users. The MRA 

can then be useful as a starting point for more detailed decisionspecific studies, making them faster and easier to complete. 

Baseline data, models of resources interrelationships, and future 

scenarios of interest can then be tailored to specific decisions.

Partnerships, engagement, and leveraging related work.—

For an MRA study to achieve its primary goal of providing 

information to support resource management decision making, 

it is essential that it be developed in close collaboration with 

entities and individuals who manage the natural resources in 

a region. In many cases, regional and local partners may have 

data, models, and projects underway that address at least a 

subset of the issues to be considered in an MRA, and working 

with those groups will increase both the quality and the 

relevance of MRA products. 

Resources and data considered.—Individual MRAs for 

different regions should have some commonalities while also 

addressing regionally specific issues. Use of national datasets, 

where available, will increase the comparability of MRAs 

for different regions and may enable some cross-regional 

comparisons. Use of the best available local and regional data 

is also necessary to ensure decision relevance and credibility 

with local and regional decision makers and stakeholders. In 

addition to the basic information on quantity and quality of the 

natural resources, the MRA Baseline should include resource 

history and trend information, as well as information on the 

regulatory, economic, and societal context of the region. This 

contextual information is important for communicating a clear 

and accurate understanding of regional issues, is helpful for 

identifying potential scenarios of change that are of interest, 

and can inform the multiresource modeling efforts. 

Introduction
Uncertainty.—There are many sources of uncertainty in 

an MRA, including uncertainty arising from a lack of data, a 

lack of scientific understanding, and natural variability. 

Lack of data may mean that no data at all are available, that 

available data are only partially relevant to the region and 

resources being considered, or that the predictive power of 

existing data is poor. Lack of scientific understanding arises 

because many of the resource interrelationships have not been 

studied carefully; even the general nature of those relationships 

may not yet have been examined. In addition, when broad 

spatial and temporal scales are considered, there is natural 

variability across time and space in the underlying resource 

characteristics. The MRA will only represent reality adequately 

if it reflects these uncertainties. It should include uncertainties 

explicitly and propagate them through the models so that the 

full uncertainty in the final analysis results can be communicated 

clearly. Tracking the sources of uncertainty is also useful when 

determining where additional data and science can reduce 

the uncertainty that matters to decision makers and when 

establishing research priorities. 

Information delivery.—Important issues that require 

additional exploration are how, and what, information from 

the MRA can and should be delivered to end users. The goal is 

to provide products and analytical results to decision makers, 

either as tools they can use directly or as data and models 

developed for the MRA that they can use as initial information 

for more detailed analyses of specific decision problems. Multiple 

media and deliverables are necessary to deliver information to 

different stakeholders. A key message from the MRA synthesis 

workshop was to focus first on what needs to be communicated 

to users, and then to let the goals of the MRA determine what 

information delivery technologies can be used. 

Introduction 17
Multi-Resource Analysis Process
A structured decision-framing process benefits situations 

with both high organizational complexity and high analytical 

complexity. The MRA process combines elements of the 

Dialog Decision Process (Barabba, 1995; McNamee and 

Celona, 2008), scenario planning (Schoemaker, 1995; National 

Park Service, 2013), and formal approaches for assessing and 

reviewing expert judgments (Coppersmith and others, 2009). 

The Dialog Decision Process emphasizes the separate but 

connected roles of decision makers and analysts in creating 

meaningful products and informs the basic iterative, multiteam 

approach described in the “Multidisciplinary Multi-Resource 

Analysis Team” section. Scenario planning emphasizes the 

importance of bringing together multiple perspectives to 

consider problems associated with planning in the face of 

large-scale uncertainties and suggests tools and approaches by 

which multidisciplinary teams can work together to identify 

a useful set of future scenarios. The process for identifying 

scenarios of change for an MRA is based largely on these concepts. 

Finally, in modeling problems where significant uncertainty 

exists, it is common within the USGS and elsewhere to 

promote structured interactions and the review of expert 

judgments ranging from conceptual model development to 

specific numerical model parameters (Hanks and others, 2009; 

Charpentier and Cook, 2012); the MRA process includes 

similar interactions among the various scientific experts and 

disciplines throughout the analysis.

There are several steps for developing MRA products. 

Although the steps naturally overlap to some degree, we 

describe the process as three connected steps (fig. 2): 

(1) define the frame for the MRA, (2) analyze and model 

resource interrelationships, and (3) run those models and 

deliver results. In a full DA process, a final stage would 

involve making and implementing a decision using those 

results. The MRA process stops short of that stage. End users 

may use the information provided by an MRA to help make 

decisions and craft a strategy for implementation, but those 

steps are beyond the scope of the MRA and reside in the hands 

of the end users. Each of these steps is described in this 

section in somewhat idealized terms. In practice, we expect 

the MRA process to evolve as the first few decision-ready 

MRAs are completed. 

An MRA is intended to be a decision-relevant, decisionready, multidisciplinary set of analytical products. A clearly 

defined process for scoping, conducting, and communicating 

about the MRA is critical for ensuring both decision relevance 

and scientific soundness. The process and steps described here 

build on the methodologies used in the field of decision analysis

(DA). A broadly accepted and widely used process and set 

of tools, DA has been used by the USGS to support land and 

resource managers in making specific decisions (for example, 

Post van der Berg and others, 2014; Runge and others, 2015), 

to structure and integrate science to support policy (Coleman 

and others, 2006; Jenni and others, 2014), and to support science

planning and research (Woodward and Jenni, 2014). For policylevel decisions, the structured DA approach offers a unique 

advantage by combining decision-maker and stakeholder input

with scientific and technical expertise. Decision makers and 

stakeholders participate directly in establishing management 

objectives, identifying decision options or alternatives, and 

sometimes taking part in the technical evaluations. Their 

participation greatly enhances the usefulness of the resulting 

science work. The DA approach has also been demonstrated 

to be a useful method to integrate science disciplines and 

provide insight into how to enhance the value of science 

information for decision making (von Winterfeldt, 2013). 

A core concept in DA is a strong, early, and ongoing 

focus on decisions. A typical DA starts with identification 

of key decision makers and their pressing issues and upcoming

decisions. This emphasis is somewhat different from a 

traditional research approach, in which models and analysis 

are oriented first towards increased understanding of a system 

and less emphasis is placed on decision-support needs. The MRA 

approach uses the decision focus of DA as a strong bounding 

framework to help maintain emphasis on what the potential 

users of the analysis have indicated is most important to 

understand and to ensure that the science-based assessments 

produce information that is directly useful. An MRA, however,

is not focused on one specific decision; it is intended to be 

useful for land and resource managers as they make a set of 

connected decisions over time while their options may also 

be evolving. An MRA also brings into focus additional 

complexities related to the overlapping and sometimes 

conflicting management responsibilities and objectives of 

different resource managers and stakeholders.
Frame the MRA

• Region, stakeholder,

 interests, key questions

• Project team

• Initial scoping 

• Baseline

• Future conditions under 

 different scenarios

Analyze and model Run models and

report results

All teams 

review progress

and adjust as 

necessary 

through periodic

meetings. 

Information 

delivery 

approaches 

are developed 

throughout the 

analysis and 

modeling 

stages.

Baseline 

information 

includes status

of colocated 

resources, 

including 

history, trends,

and context.

Refined scoping

includes 

identifying 

scenarios and 

defining 

conceptual 

models of 

resource 

relationships.

Modeling is 

based on the 

refined scope 

and periodic 

review and 

feedback.

Periodic team interactions

MRA delivery platform development

Baseline information development

Refined scoping

Modeling

20 Multi-Resource Analysis—Methodology and Synthesis

Figure 2. Steps in conducting a Multi-Resource Analysis (MRA). Although shown here as if they are

sequential steps, in practice, framing, analysis and modeling, and running models and reporting results 

are connected and overlap. For example, analysis and modeling may produce new questions that suggest 

re-scoping of some questions and reporting results may raise new issues that should be modeled.

Multi-Resource Analysis Process
Framing the Multi-Resource Analysis Study

As with the construction of a good house or a good 

organization, good analysis and good decisions rest on a 

strong foundation. The framing step provides the foundation 

for how the MRA will be used and is the first step in developing 

products that will be useful to resource managers. Framing 

allows us to articulate the goals and purposes of an MRA before 

proceeding to product development. Essentially, we want to 

be sure we have the right people involved, we are treating the 

right problems, and we are doing it from the right perspectives. 

Because an MRA is used to support managers of natural 

resources, it will most likely be initiated in a region where 

one or more decision-making entities have or anticipate a 

need for the kinds of information and tools that an MRA will 

provide. An MRA is a multiuse set of products, so it will be 

especially appropriate in regions where there are multiple 

decision-making entities interested in the results and where 

there are several upcoming decisions that would benefit from 

having multiresource information available. In some cases, 

interest in conducting an MRA in a particular region may 

be triggered by the availability of new single-resource 

assessments, new scientific information, or even new national 

or regional policies. 

Multidisciplinary Multi-Resource Analysis Team 

Proper framing requires broad input from a variety of 

participants, including decision makers, scientists, and other 

stakeholders with interests in the study region. Each of these 

participants brings important knowledge and different 

perspectives to the analysis. Decision makers and other 

potential end users provide input on their intended use of 

an MRA, decisions they may need to make in the near future, 

their management objectives, and potential stressors and 

disruptions that are of concern for the region. Technical 

personnel provide scientific information on resource 

interrelationships and uncertainties and bring expertise 

required for building models. Developing a set of products 

that address resource interrelationships and resource values 

requires a multidisciplinary team of scientists; providing results 

in a form that is useful to decision makers requires a team with 

expertise in information processing and information delivery. 

A successful MRA team structure ensures solid and 

ongoing connections among the entire team and throughout 

the process. Decision makers and scientists will have joint 

responsibility for creating a successful MRA for a given study 

region, so they will need to interact closely throughout the 

development of the MRA. The specific team structure and 

process for interacting can be tailored to the needs and existing 

stakeholder relationships in any particular area. The highlight 

on pages 22–23 illustrates one possible team structure.

IDAHO

WYOMING

COLORADO 

VADA UTAH

NEW MEXICO ARIZONA

EXPLANATION

Shale play

Province boundary

Year round

Summer

Winter

Mule deer habitat

Gas assessment unit 200263

Multi-Resource Analysis Process 21
A successful Multi-Resource Analysis (MRA) 

requires participation from diverse individuals 

and groups, some of whom may not have worked 

together previously. Each individual and group 

brings unique skills and perspectives to the process. 

This mixture is one key to a creative and useful 

multidisciplinary set of products. However, diversity 

among participants may also bring management 

and communication challenges, as people learn 

about each other and their varied perspectives 

through collaborative work. Structured teams and 

processes can reduce those challenges and help 

pave the path to smooth working relationships 

and better integrated science. Although many 

project management approaches are possible, 

the team structure and interactions described 

here represent one model for how an MRA team 

might choose to organize. It has been used 

effectively to organize large multidisciplinary 

teams conducting complicated decision-focused 

analyses. Ongoing interactions between teams 

with distinct responsibilities emphasize the 

shared goals of the MRA while allowing efficient 

specialization where necessary. In this process, 

one team has primary responsibility for ensuring 

that the study is relevant for decision makers, a 

second team has primary responsibility for scientific 

and technical content, and ongoing interactions 

among the teams throughout the process ensure 

the usefulness of the final set of products.

The MRA Guidance Team represents the 

potential end users or “customers” for the MRA; 

the team is composed of decision makers and other 

stakeholders who are interested in the results of 

an MRA and who are willing and able to provide 

ongoing review and input into the MRA as it is 

developed. The Guidance Team members are the 

main contributors to the initial MRA Framing steps 

and generally serve in a review capacity throughout 

the MRA development process to ensure that the 

results will be useful. It may be helpful for this 

team to seek input from additional stakeholders at 

times during the development of the MRA.

A Multi-Resource Analysis Team Structure 
MRA Guidance Team MRA Core Team

• Additional stakeholders • Interated Modeling Team

• Information Delivery Team

•Subject Matter Expert Teams

Ongoing interactions throughout process

One possible team structure.

The MRA Core Team bears primary responsibility for developing the MRA and its products and for keeping 

the technical work and the needs and interests of the Guidance Team aligned. Much of the technical 

work will be done by various subteams. The subteam structure is intended to be flexible, with individual 

researchers often serving in multiple capacities and on several teams. At a minimum, the Core Team will 

be supported by an Integrated Modeling Team that will focus on identifying and modeling the links 

between resources, resource changes, and resource values and by an Information Delivery Team that will

take the lead on developing the platform(s) for delivering the MRA to the end user. Subject Matter Expert 

Teams may be used by any of the other teams when there is a need to explore an issue in greater depth.
24 Multi-Resource Analysis—Methodology and Synthesis

Defining the Scope of the Multi-Resource Analysis 

The final element of the framing stage is defining in 

more detail the initial scope and scale of the MRA. The MRA 

teams will need to clarify what upcoming decisions would 

benefit from the MRA, what decision makers would like to 

achieve (their management objectives), the factors that may 

make it difficult to achieve those objectives (such as stressors 

and disruptions outside of the decision makers’ control), and 

the challenges with predicting whether those objectives can 

be met (for example, knowledge gaps and uncertainties about 

resource interrelationships and resource values). 

In addition, during the framing part of the process, the 

MRA teams will need to identify past, current, and anticipated 

future drivers of change in the region. Those drivers of change 

may be natural or human forces, and there may be considerable 

uncertainty about them. Examples include the frequency or 

severity of natural hazards (such as floods or fires), changes 

in weather and precipitation patterns (such as droughts or 

changes in growing seasons), changes in energy and mineral 

leasing policies and practices, changes in agricultural practices, 

and so on. This scenario identification is intended to generate 

a robust list of potential drivers of change, which can then be 

narrowed to a smaller set for inclusion in the MRA analysis 

and modeling steps. Scoping also includes identifying an 

appropriate set of geographic and temporal boundaries for the 

MRA. Traditional resource assessments are defined around 

boundaries that make the most sense for single resources: 

geologic boundaries for energy resources, watershed boundaries 

for water resources, and viable economic zones for economic 

assessments. Rarely, if ever, do those boundaries coincide, so 

decision makers, scientists, and other stakeholders will have to 

make carefully considered decisions about how best to bound 

the analyses that will become part of the MRA.

The outcome of the initial framing steps consists of 

documentation of the mosaic of decisions, objectives, values, 

uncertainties, constraints, scenarios, and other factors that need 

to be considered; the geographic and temporal scope for the 

analysis; and identification of potential project participants. 

This information will provide a rich and clear context for the 

upcoming steps, a shared understanding of the context among 

all participants, and a draft schedule or project plan for completing 

the MRA. As the project evolves, this frame may be adjusted, 

potentially more than once. Establishing a good working 

relationship among participants during the initial framing

efforts will facilitate later discussions and adjustments. (See 

the highlight on page 25 for information on an MRA 

framing workshop.)

Multi-Resource Analysis Process
One approach that has been used successfully 

to help define the scope and overall framing for 

many projects is to hold dedicated in-person 

workshops where all participants provide input 

and structure. The Powder River Basin MultiResource Analysis (MRA) study (see the highlight 

on pages 12–13) began with such a workshop. The 

workshop approach offers several key benefits. In 

particular, it helps build a common sense of purpose 

among the project participants, and it allows for a 

considerable amount of work to be accomplished 

in a short amount of time. Through a workshop 

setting and a structured framing process, team 

members quickly develop an appreciation for the 

skills and perspectives of other participants and 

can make fairly quick progress on developing a 

shared vision for what an MRA for the region they 

all care about should include and address.

In a framing workshop, participants work 

together to define a specific vision for what an 

MRA for their region would include. Because 

each participant brings different knowledge and 

background to the process, having everyone 

together makes it easier to understand and 

communicate what is already known about the 

region, where there are information gaps, and 

what kinds of information will be most useful in 

the future. In addition to establishing a set of 

working relationships among the participants, 

it is likely that robust discussion and framing of 

the issues for the region will lead to identification 

of additional decision makers, stakeholders, 

and technical experts who would be valuable 

participants in the MRA. A modular team structure, 

such as that shown in the highlight on pages 22–23, 

makes it relatively easy to expand or contract teams 

as necessary throughout the process.

A Multi-Resource Analysis Framing Workshop
26 Multi-Resource Analysis—Methodology and Synthesis

Analysis and Modeling

A significant amount of data collection, synthesis, modeling, 

and analysis is necessary to produce the MRA products shown 

in figure 1 and described in detail in the “Development of 

Multi-Resource Analysis Components” section. The analysis 

and modeling step involves several related tasks (fig. 2). First 

and most importantly, the analysis and modeling is conducted 

iteratively and with feedback between those with a management 

and decision focus and those whose focus is on science and 

modeling. All of the detailed work necessary to create the 

MRA Baseline and the MRA Change Analysis components 

happens in the analysis and modeling step.

As discussed further in the “Multi-Resource Analysis 

Baseline” section, the MRA Baseline provides a “snapshot in 

time” of the status of colocated resources in the study region. 

The first part of the Analysis and Modeling step is for the 

scientists and modelers to begin collecting information on the 

relevant resources in the study region and working with data 

scientists on constructing one or more ways to communicate 

and explain the baseline information. This information collection

is bounded by the frame defined in the first step of the MRA 

process, especially the geographic and temporal boundaries 

and the resources to be considered. Both development of the 

Baseline and development of the overall information delivery 

platform(s) begin as soon as framing is far enough along to 

provide a starting point for data collection. Baseline information 

is a prerequisite for the later modeling, although some work in 

this area may continue throughout the Analysis and Modeling 

step. Methods for information delivery are considered from the 

very beginning, and development of delivery platforms continues 

throughout the entire MRA process. Ideally, information 

delivery serves as a tool to communicate intermediate modeling 

results to the decision-oriented participants, giving them an 

opportunity to suggest modifications and improvements in how 

information should ultimately be delivered to them and their peers.

The framing step described in the “Framing the MultiResource Analysis Study” section includes the initial definition 

of the study scope, which identifies some of the key stressors

and disruptions of concern or interest in the area. As the Baseline 

is developed, the MRA teams acquire new information on the 

resource history and trends in the region and its economic, 

social, and regulatory contexts. This increased understanding 

may suggest refinements and revisions to the MRA’s initial scope 

and framing, such as definitions of important scenarios of change 

to be considered in the MRA and conceptual models of resource 

interrelationships and resource values. This refinement precedes 

detailed modeling and may be repeated more than once as 

models are developed and tested.

Detailed and quantitative modeling of resource 

interrelationships and resource values is primarily a scientific 

and technical task and thus a major focus of the scientific 

participants in the MRA. Given the complexity of an MRA, 

modeling is a considerable challenge and requires close 

interaction between modelers and process scientists as they 

proceed through iterations of model development. Work 

from the proof-of-concept studies and other USGS scientists 

(Bristol and others, 2013) suggests that the complexity can be 

managed by using a modular approach (for example, energy 

resource modules, water modules, ecological modules) and 

by identifying variables that act as “links” from one module 

to another. Information delivery experts are also closely involved 

in this step, providing guidance on software tools and facilitating

data manipulation and interactions between different tools.

Multi-Resource Analysis Process
Multi-Resource Analysis Process 27
28 Multi-Resource Analysis—Methodology and Synthesis

Although model development and quantification is primarily 

undertaken by the technical team members, stakeholder input 

is also relevant. Reviewing the models with potential end users 

of the information at intervals throughout the development 

process can provide several benefits. 

• Stakeholders often have insights that were not captured 

or have changed since the initial framing. These insights 

should be reflected in the models. 

• Stakeholders can assist in structuring the model by 

identifying sets of connections and information needs 

most directly tied to their objectives or outcomes of 

interest. Their perspectives are often different from 

those of the scientists, who may identify other 

connections and uncertainties.

• Stakeholders often have important data and information 

sources for the models. Increased interaction helps 

ensure the modelers know about these sources and 

incorporate them into the model.

• Stakeholders, in turn, may learn from the modelers 

about the types of analyses that can be conducted and 

what information products the MRA can provide. Early 

identification of how stakeholder expectations might 

conflict with what is scientifically feasible provides an 

opportunity for early communication and alignment of 

expectations. This will improve working relationships 

and lead to products that are more useful.

• Ongoing modeling team interaction maintains 

stakeholders’ knowledge of the details of the MRA, 

which increases their comfort with the models’ 

capabilities, strengths, and weaknesses and the 

likelihood that they will use the MRA upon completion.

Running Models and Reporting Results

The final steps in the MRA process are to actually run 

the models developed in the previous steps, to evaluate and 

understand the results, and to develop useful information 

delivery products to clearly communicate the MRA results. 

The Baseline is one important MRA result; the other is the 

projections of the future status of colocated resources under 

different scenarios. These projections are essentially the 

results of running the model of resource interrelationships and 

values under the scenarios identified by the decision makers 

and stakeholders, or other scenarios of interest, in the region. 

A typical MRA can perform numerous types of analyses 

and generate a wide variety of outputs, each of which could be 

presented in numerous ways. Analyses performed at this stage 

benefit both internal and external MRA participants. Some 

analyses are used primarily by the analysis team to ensure the 

models are appropriate and validated and produce reasonable 

results and to determine if additional model changes are 

warranted or if additional information should be collected to 

reduce uncertainties. Other analyses, such as various model 

runs and sensitivity analyses, are directed more towards the 

end users. It is likely that additional model refinements could 

be made even during this run-and-report stage.

The full team is involved in generating the final analyses 

for delivery. Those who developed the integrated models 

conduct a variety of analyses and work with those more 

focused on information delivery to develop useful approaches 

to communicate the results. Those with a decision-making 

perspective review preliminary runs and results and provide 

input on what information they think is most useful and how 

they would like to view it. The teams then iterate the models. 

“Final” results could be in the form of formal reports or data 

layers packaged with the tools for displaying those data, 

though both types of products would likely be provided. The 

MRA teams could produce very focused analyses, specified 

by a stakeholder to explore particular management decisions 

or other investigations, or a more generalized information 

delivery platform designed to allow a stakeholder to perform 

model runs and generate outputs on their own with no direct 

guidance from the analysis team. 

Multi-Resource Analysis Process
Multi-Resource Analysis Process 29
Development of Multi-Resource Analysis Components 
The MRA Baseline is the primary building block for the MRA, 

but it is also a useful interim product on its own. As a central 

portal for accessing both summary and detailed information on 

the natural resources in a region, the MRA Baseline is a natural 

starting point for several different audiences and end users. 

Those interested in a big-picture view of the resources in a region,

 for example, would likely be interested in the summary 

information, which includes descriptions, graphics, and maps 

for the region’s resources, their locations, and the values 

associated with those resources. As the MRA is implemented 

in different regions around the country, the Baselines can be 

considered together to better understand aggregate resource 

quantity and quality and the relevant tradeoffs in different 

regions. For land and resource managers charged with a more 

detailed evaluation of specific policy or decision options in the 

region, the MRA provides a natural starting point for data and 

information collection, even if more detailed or more localized 

information may also be required.

As a key building block for the overall MRA, the Baseline

provides a consistent basis for comparing the effects of changes 

to the landscape over time. These comparisons allow stakeholders 

to explore the future changes in resources, services, and values 

within a given scenario and across scenarios.

Baselines—Synthesis of Previous Efforts

Team members participating in the Piceance Basin and PRB 

proof-of-concept studies used, analyzed, synthesized, and 

integrated available data and information for several of the key 

natural resources in their respective regions. The Piceance Basin 

study considered quantities of natural gas resources, stream 

locations, and information on the land surface relevant to mule 

deer habitat quality, such as elevation, slope, and vegetative 

cover. In this study, baseline data were used as input for later 

integrated-modeling steps and were not presented separately 

as a Baseline product. The PRB proof-of-concept study included 

similar data and information on multiple colocated resources. 

Specifically, the research team assembled information on coal, 

CBNG, surface water, groundwater, some wildlife species and 

associated habitats, grazing, land disturbance, and land ownership. 

The PRB Baseline was used as an input into integrated 

modeling and was also used directly to develop an interim 

product, as described in highlight on pages 32–33. 

This section describes each of the three components of the 

MRA in more detail: describing what the component is, giving 

examples and synthesizing results from the two proof-of-concept 

studies with respect to the component, and then discussing 

some challenges associated with developing each component.

Multi-Resource Analysis Baseline

The MRA Baseline is a compilation and synthesis of 

data pertaining to colocated natural resources in a study 

region. As described in the “Multi-Resource Analysis Process,” 

section, prior to assembling the baseline information, the MRA 

study team identifies the spatial boundaries of the MRA being 

conducted and develops an initial list of the important natural 

resources in the region that should be included in the MRA. 

A key aspect of the Baseline is that it is built up by 

integrating the best available scientific and technical information 

on relevant resources in the region. Summaries of detailed 

scientific databases and reports will help bridge the gap that may 

exist between the interests and information needs of stakeholders 

and the way data are often presented. The summaries should 

identify key resource interrelationships, discuss resource use 

and values, and include historical and trend information and 

information on the economic, policy, regulatory, and societal 

context for the region. In the past, this is the kind of information 

that might have been assembled into a book of information 

on a region, similar to the large volume “Everglades—The 

Ecosystem and Its Restoration” (Davis and Ogden, 1997). 

That book summarized everything that was then known about 

the Florida Everglades ecosystem and provided a common 

baseline of knowledge available to everyone involved with or 

interested in work in that region. The MRA Baseline aims to 

provide a similar level of comprehensive, relevant information 

but in a more accessible and flexible online format.

The MRA Baseline provides a snapshot of the status of 

colocated natural resources at a specified point in time rather 

than a real-time portal with evolving information or data. The 

MRA Baseline may be updated periodically depending on external 

factors, such as new discoveries or decision needs, or as defined 

during the framing process for an individual MRA study.
The Baseline is a fundamental component of the Multi-Resource Analysis, and during development of the 

Powder River Basin proof-of-concept study, it emerged as a potentially useful interim product on its own. 

The Powder River Basin team did not attempt to develop a fully functional data management and delivery 

system, but instead developed and tested various examples of potential information delivery methods. They 

envisioned a web- and map-based interface that would connect a user to successively more detailed 

information through links, ending at the underlying technical data or reports. Between the “map” and the 

“data” is summary-level information at varying levels of detail. In the Powder River Basin example, 

summary documents were prepared that briefly describe the quantity and quality of specific resources 

and the potential interactions between resources. These summaries, in turn, connect to the detailed reports.

The Baseline as an Interim Product

Example elements of an MRA Baseline display.

Powder River Basin

Coal Resources

Coal resources 

summary information

Powder River Basin Resources

Energy Resources

Coal
Coal Resources and Coal Mining 

in the Powder River Basin

Summary description and data

Different audiences and stakeholders may have very different ways of thinking about and accessing 

information. To address different perspectives, the information in Baselines produced as Interim Products 

can be organized in different ways. For example, the screenshots shown here illustrate a flow from 

a regional map with a listing of different types of resources, to a more detailed resource-specific map 

with summary statistics related to a single resource, to a longer descriptive summary document. Other 

audiences for the Baseline may relate better to a different flow, for example, from the regional map and 

resource listing to a summary table of the quantity and quality of all the listed resources, then from that 

table to resource-specific maps and summaries. 
34 Multi-Resource Analysis—Methodology and Synthesis

Challenges for Future 

Multi-Resource Analysis Baselines

A fully functional prototype of data management and 

delivery for the MRA Baseline was not constructed in either 

proof-of-concept study. Therefore, the challenges described in 

this section are based on (1) efforts to synthesize information 

into illustrative Baseline products and from feedback gained 

from talking to stakeholders about those PRB MRA Baseline 

illustrations and (2) the data challenges experienced in collecting 

baseline information in most if not all studies similar to MRAs. 

Challenge: Data availability, sources, quality, and 

sensitivity.—Selection of the data to be included in the Baseline 

is heavily dependent on the intended audience, identification 

of important stakeholders and their needs, and the specific goals 

of the individual MRA being developed. There are, however, 

several general issues regarding the source and quality of data 

to be included (or excluded) from a Baseline. A successful 

MRA requires consideration of the following questions regarding 

data to be included in the Baseline. 

• Should baseline data be limited to peer-reviewed 

sources? If non-peer-reviewed data are included, how 

do we communicate the quality of the data so that users 

are aware of its potential limitations?

• Should baseline data be limited to that produced and 

reviewed by USGS and (or) other Federal agencies? 

Stakeholders were largely complimentary of USGS 

data, especially for energy and water assessments, but 

indicated that there are often better sources of data for 

ecological resources and wildlife, including State and 

local databases. If non-Federal data are used, should 

they be limited to that produced by State and local 

agencies, or should data from nongovernmental 

agencies be allowed?

A geographic information system interface, in particular, 

raises questions about whether the MRA should (or can, 

practically or legally) include geographic locations of resources 

on private land or certain sites of archaeologic, cultural, or 

historic importance. When those resources are shown with 

location information, they are typically not located precisely 

on maps that are easily accessible by the public; additionally, 

datasets containing the information may be password restricted, 

or locations may be “fuzzed” in some way. On the other hand, 

these data are often important to the purpose of the MRA. 

Questions of how to present those data, make them available, 

and use them in the MRA will have to be resolved.

Data availability presented a major challenge in the 

proof-of-concept studies. Although some geologic, hydrologic, 

and ecologic data were available for both studies, considering 

the data at similar scales was complex. Ecological data in 

particular proved difficult to obtain, interpret, and present in 

both studies. Accessing socioeconomic data was also challenging.

Social and economic data are important to the MRA Baseline 

and even more so to the subsequent Economic Analysis 

component of the MRA. Because the proof-of-concept studies 

were limited to secondary data sources, it was necessary to 

find social and economic data that were already available and 

could be used within the overall framework. However, to the 

extent that an MRA developer can collect primary information 

and (or) partner with other researchers conducting primary 

research, this challenge can be overcome. As the MRA concept 

expands and MRAs become a more common product, it is also 

likely that the demand for data will encourage needed research.

Finally, several stakeholders raised the issue of data 

management and the interoperability of different data 

management systems and tools. Although a successful MRA 

includes a unique information delivery platform, it is important 

that data used in the MRA be available for use in other contexts. 

In practice, data used in an MRA would likely be derived from 

research conducted for other purposes, so the challenge would 

be to integrate data and information from different sources 

into the MRA Baseline. The MRA’s information delivery team 

would address any issues of data interoperability. 

Development of Multi-Resource Analysis Components 

36 Multi-Resource Analysis—Methodology and Synthesis

Challenge: Resource summary information.—A key 

aspect of the vision for the MRA Baseline is that it should 

provide more than just links to resource data; it should include 

some historic and trend information, as well as summary 

information about the natural resources included in the MRA. 

Creating appropriate summaries is not trivial. The skills 

required to summarize large quantities of information into a 

format equivalent to an executive summary are often not the 

same as those required to perform the original scientific work, 

but the originators of that work need to be involved to ensure 

appropriate and correct presentation of the data. In a successful 

MRA, some effort might be required to find or cultivate the 

skills needed to prepare these summaries. In addition, a 

successful MRA would need to address questions related to 

which types and levels of baseline data will most benefit from 

being summarized, whether such summaries can be used as 

separate or standalone documents, and if so, what level of peer 

review might be required for those products.

Challenge: Timing and mechanism for keeping the 

Baseline relevant.—Numerous stakeholders stated that a 

one-time creation of an MRA Baseline has only limited use, 

and without periodic updates, the Baseline would rapidly lose 

relevance and value as a standalone product. This criticism is 

somewhat in tension with the vision for the MRA Baseline

—that it represents the status of colocated resources in a 

region at a specific point in time. Ensuring that the Baseline 

is relevant would require clear communication about its purpose 

and how it fits within the full MRA; it would also require 

identifying a schedule or process for updating the baseline 

information periodically. The timing of updates to the baseline 

information may differ for different data types. For example, 

energy resource assessments are updated relatively infrequently, 

typically when new information or technologies are discovered 

that have the potential to significantly change previous 

assessments. In contrast, some stakeholders suggested that 

biology data go out of date quickly relative to the energy and 

mineral assessment time frame. The developers of an MRA 

would have to consider the planned timing of updates for 

different types of data in the context of funding and 

manpower constraints. 

Challenge: Displaying multiple types of data to multiple 

users.—In talking to stakeholders and both USGS and 

non-USGS scientists, it became apparent that there exists a 

wide variety of preferences for how data should be organized 

and presented. Some individuals prefer map views, others like 

charts, and others prefer tables and text. Most agreed that there 

should be more than one way to access and display the 

information. Because there is no single best format for a given 

type of information, the challenge is to decide how to display 

the data, how to provide multiple ways to view the same 

information, how to display several types of information 

together, and how to ensure that the user can understand data 

displayed in different views.

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 37
38 Multi-Resource Analysis—Methodology and Synthesis

Multi-Resource Analysis Change Analysis

Several types of analyses are required to help scientists, 

decision makers, and other stakeholders understand the range 

of potential future conditions of multiple colocated resources 

in a region. The MRA focuses on analysis of changes and 

includes elements related to identifying the broad-scale stressors, 

disruptions, and decisions that are relevant for the region; 

modeling the dynamic physical and biological interrelationships 

among resources so that the implications of those changes on 

future conditions can be projected; and analyzing the economics 

of those resources and resource changes. 

Scenarios and Scenario Analysis

Natural processes and events and human decisions can 

change landscapes, their natural resources, and the benefits 

those resources provide. Decision makers often need to make 

decisions despite the significant uncertainty about how the 

future will evolve. One hurdle is simply the vast number of 

potential stressors, disruptions, and decisions that have the 

potential to affect a region. Another hurdle is that, for many of 

these stressors and disruptions, there is insufficient information 

or knowledge to make even probabilistic estimates of the 

likelihood of occurrence.

The MRA uses a scenario approach to address the multitude 

of possible stressors, disruptions, and decisions. Use of scenarios

as a means to consider possible future conditions is a hallmark 

of scenario planning and scenario analysis, methods that are 

used in business and finance (Schwartz, 1996), geopolitical 

analyses (Hillebrand and Closson, 2015), projections of future 

climate conditions (Nakicenovic and Swart, 2000), and 

environmental decision making (Mahmoud and others, 2009). 

See the highlight on pages 40–41 for a description of a userspecified energy development scenario and the highlight 

on pages 42–43 for information on scenarios considering 

potential development restrictions.

Development of Multi-Resource Analysis Components 
In an MRA, scenarios are created and defined as part 

of the framing process and may be refined several times as 

the analysis is conducted. Scenarios are specific to the region 

being analyzed and depend on the spatial and temporal scope 

of the study as defined by the multidisciplinary MRA team. 

Decision-maker engagement in the scenario identification 

process is essential for decision relevance. Engagement by 

scientists is also essential to ensure technical relevance. 

Having different groups bring their own unique perspectives 

on future changes during scenario development is the best 

way to be comprehensive. Scenario analysis is a common 

tool used to help decision makers think broadly about what 

the future might bring, consider and evaluate the risks of 

different options more thoroughly, and even consider the 

effects of potential “unknown unknowns” (Olabisi, 2017). 

In some cases, there may be existing regional coordination 

groups that are well suited to developing locally and regionally 

relevant scenarios. Examples of such groups include the 

Cooperative Ecosystem Studies Units National Network 

(http://www.cesu.psu.edu/) and the Landscape Conservation 

Cooperative Network (https://lccnetwork.org); both are 

multiagency collaboration and cooperation groups that address 

science and decision making at large geographic scales.

Identifying drivers of change and important future scenarios 

for a region is beneficial even apart from the role those scenarios

play in the MRA. By bringing together the decision makers 

and stakeholders who have overlapping responsibilities and 

interests across a relatively broad geography, the scenario 

development process provides an opportunity for those decision 

makers to develop a joint vision of what different futures 

might look like and to identify things each of them might do 

to affect that future or respond to externally driven changes. 

In addition to identifying scenarios of change to be considered 

in the MRA, the process is likely to identify a large number 

of additional scenarios and potential changes that would be 

relevant at different scales and to different subsets of the 

participants. These scenarios and potential changes could become 

useful or important considerations for various planning exercises

undertaken by an agency or organization.

Developing a relevant and manageable set of scenarios is 

a critical component of an MRA. These scenarios determine 

the scope of resources and interrelationships that are modeled 

in the subsequent analysis steps; a defined scope ensures that 

the MRA focuses on resources and issues that are relevant for 

the region. Scenarios also allow an MRA to consider “what if” 

analyses and determine the types of comparisons that can be 

made, such as comparisons between current and future conditions

and between different possible future conditions. 

Development of Multi-Resource Analysis Components 39
The Piceance Basin proof-of-concept study developed a flexible decision-support modeling tool that allows 

a user to define a future energy development scenario with considerable specificity. Users are given 

the option to specify the number of wells per pad, the density of well pads in the development area, 

and the future price of natural gas, all through the main model interface. 

Example: User-Specified Energy Development Scenario 

Example model interface with user input options 

shown under “Model Input.”
Example user-specification of locations of energy development (red cells) over time.

Users then specify where and when gas extraction will occur in the region by indicating on a simple map 

the “cells” that would be developed each year. In the figure below, 10 years of development activity are 

shown and the new regions developed in each year are shown in red. By using this simple interface and 

selecting different combinations of options (single or multiple wells per pad, high density or low density 

of well distribution, and different development locations), a user can define and compare any scenario 

they wish to consider, perhaps representing alternative development plans. Year 3 Year 4 Year 5

Year 8 Year 9 Year 10
One of the issues of interest when considering

energy development is the effect of potential 

restrictions on the locations of future development. 

The proof-of-concept studies explored a limited 

number of these development-restriction scenarios.

In the Powder River Basin study, for example, existing 

restrictions on where development can occur are 

incorporated into all scenarios, and additional 

scenarios were defined either as “development 

allowed” or “development not allowed” in each of 

three types of special habitats of interest. Although

more nuanced restrictions are

possible, looking at these extreme

cases suffices to test and illustrate

the concepts in the Powder River 

Basin proof-of-concept study. 

Example: Potential Development Restrictions 
14

14

14

14

16

16

87 16

16

85

85

85

20

18

26

26 20

287

18

18

212

90

90

25

25

WYOMING

MONTANA

Sheridan

Buffalo

Casper

Douglas

Gillette

Newcastle

Assuming no development in

 specific subregions

Coal-bed natural gas development

 through 2035

Powder River Basin boundary

EXPLANATION

0 10 20 30 40 50 KILOMETERS

0 10 20 30 40 50 MILES

107°

45°

44°

43°

106° 105°

Base from The National Map, digital data, 2017

Universal Transverse Mercator projection

Zone 13N 

14

14

14

14

16

16

87 16

16

85

85

85

20

18

26

26 20

287

18

18

212

90

90

25

25

WYOMING

MONTANA

Sheridan

Buffalo

Casper

Douglas

Gillette

Newcastle

With existing surface restrictions

 only

Coal-bed natural gas development

 through 2035

Powder River Basin boundary

EXPLANATION

0 10 20 30 40 50 KILOMETERS

0 10 20 30 40 50 MILES

107°

45°

44°

43°

106° 105°

Base from The National Map, digital data, 2017

Universal Transverse Mercator projection

Zone 13N 

Maps comparing possible future development under A, unrestricted and B, restricted development scenarios. 

A B
Scenario Development—Synthesis of Previous Efforts 

The two proof-of-concept studies focused on energy 

development activities as primary drivers of change. The PRB 

study considered both coal mining and CBNG development. 

Additionally, environmentally driven surface restrictions on 

where such development activities would be permitted were 

considered as part of specific scenarios. The Piceance Basin 

proof-of-concept study used natural gas extraction by means 

of hydraulic fracturing as the driver of change. 

Several different approaches to developing scenarios 

were explored. Both research teams considered the input 

of stakeholders critical to a useful MRA and to creating 

informative scenarios. The PRB proof-of-concept team had 

close contact with stakeholders at the local, regional, and 

Federal level during the development of the proof-of-concept 

study; they designed scenarios based on input from stakeholders 

and documented potential energy scenarios in the PRB. The 

Piceance Basin proof-of-concept team did not have that level 

of stakeholder contact; they instead designed a model that 

would allow any user to specify the development scenario 

explicitly during use. 

Quantity, timing, location, and method of resource 

extraction and reclamation activities are the key elements that 

must be determined in a scenario that considers the effects 

energy development may have on other colocated resources 

and the ecosystem services and human values those resources 

provide. One approach to defining a development scenario is 

simply to specify all four elements explicitly. Both proof-ofconcept studies included these types of scenarios.

When dealing with large spatial and temporal scales, 

direct and detailed specification of the full development 

trajectory is not practical in many cases. Rather, it is common 

to specify some of these elements (for example, the amount 

and method of development) and model the remainder (such 

as timing and location) by using a combination of data and 

assumptions. The PRB proof-of-concept study explored these 

additional approaches to defining scenarios. For example, with 

CBNG, the issue of how much gas would be produced over 

time was separated from the question of where that production 

occurs. Production-specified scenarios and price-driven scenarios

were used to estimate how much gas would be produced over 

time. For locations, combinations of randomly located wells 

(following Haines and others, 2013) and drilling driven by 

returns on investment were explored. The proof-of-concept 

studies also explored a limited number of development 

restriction scenarios. 

There are benefits to having stakeholders define the 

relevant scenarios; there are also benefits to creating a highly 

flexible tool that allows any user to specify how development 

or other drivers of change will occur on the landscape or to 

revise input assumptions that will modify scenarios. A hybrid 

approach that utilizes stakeholder input while also maintaining 

flexibility is likely to be most useful. 

44 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 45
46 Multi-Resource Analysis—Methodology and Synthesis

Challenges for Future Multi-Resource Analysis 

Scenario Development 

Both of the proof-of-concept studies focused on energy 

development and its impacts. The synthesis workshop participants

asked if (and how) an MRA should include a common or 

consistent set of scenarios or if it should focus on only the 

most locally relevant scenarios. The current vision is that an 

MRA would include both scenarios that are relevant across 

regions and scenarios that are locally significant. A successful 

MRA would have to address several key challenges.

Challenge: Identifying, defining, and choosing scenarios.—

The importance of understanding the effects of energy development 

on colocated natural resources was emphasized in the USGS 

Energy and Minerals Science Strategy (Ferrero and others, 

2013) and was one of the motivations for development of the 

MRA concepts. However, there is an almost endless set of 

decisions, disruptions, and (or) stressors that affect a region 

and that could potentially be defined and explored in an MRA. 

In the MRA process, decision makers, scientists, and other 

stakeholders all have a role in identifying potential scenarios. 

Stakeholders are likely to focus on scenarios based on decisions 

of interest to them, and scientists are likely to identify scenarios 

based on important external drivers of change. In choosing a 

manageable set of scenarios to include in a specific implementation 

of an MRA, there are several issues to consider.

• There may be scenarios of interest that have already 

been defined by other agencies or stakeholders, as 

there were for some of the coal-development scenarios 

in the PRB MRA. If so, it may be desirable to include 

them and build on existing completed work.

• If there are scenarios being considered that might be 

relevant to other geographies, it may be desirable 

to include such scenarios in a future MRA study to 

increase the likelihood that this work could be leveraged 

in subsequent implementations.

• It may be challenging to prioritize scenarios, but it is 

important to focus initially on a somewhat constrained 

set of scenarios. Project participants with strong local 

knowledge and policy- and decision-making perspectives 

should have the strongest role in deciding what scenarios

should be included.

• Scenarios can be described at different levels of detail. 

For example, in the Piceance Basin proof-of-concept 

study, development scenario definitions were detailed, 

based on user input on specific locations that would 

be developed in each year of the model time horizon. 

In contrast, some scenario options in the PRB proofof-concept study were much more loosely described, 

such as those that specified only a price trajectory for 

CBNG. In such scenarios, significant modeling may be 

required to have a well-defined scenario; the “boundary” 

between scenario definition and integrated modeling is 

not always clear.

Development of Multi-Resource Analysis Components 
Challenge: Modeling scenarios.—Some scenarios likely 

will be identified and discussed at a high level; in those cases, 

detailed modeling would be necessary to translate the scenario 

concept to a level of detail sufficient for exploring the implications 

of that scenario on multiple natural resources. For example, a 

scenario on rapid energy development across a region would 

need detailed data on the number and locations of gas wells 

installed at various points in time. Some of the required 

modeling could become quite complex, and choices made in 

modeling scenarios could constrain or affect modeling choices 

elsewhere in the MRA development. Particular challenges that 

may arise in modeling scenarios are listed below.

• Balancing simplicity and complexity—Simple scenarios 

have the advantage of being easy for users to understand, 

but they may not capture important details. Complex 

scenarios may be more accurate and realistic, but it 

is a challenge to communicate those details to users 

effectively. Transparency and documentation are necessary 

for users to understand how scenario forecasts are 

constructed and what is and is not included.

• Selecting spatial- and time-scale increments—The 

selection of proper spatial- and time-scale increments 

for modeling is a challenge throughout the MRA process,

from discussion during framing, to issues raised during 

modeling, to deciding how to deliver information and 

results across different scales. Although this challenge 

is not specific to scenario modeling, the discussion of 

scenarios may be the first point in the MRA development 

process where modeling challenges related to scales 

are encountered. The challenges are similar to those of 

balancing simplicity and complexity. Time and spatial 

units that are too coarse may not capture important 

details, but those that are too fine present challenges 

of excessive detail and potential problems with model 

size and run times.

• Understanding and defining a scenario—The understanding 

and definition of a scenario may evolve during scenario 

modeling. At this step, the interactions among all 

perspectives represented in the multidisciplinary MRA 

team are critical to ensure that the scenarios remain 

relevant to the MRA audience.

• Forecasting change—Finally, models, methods, and 

expertise for forecasting change under some types of 

scenarios may not exist during an MRA, so approaches 

would need to be developed. This challenge is also 

faced when creating integrated and dynamic models of 

resources and their connections. 

A final challenge relates to communicating what the 

scenarios represent. Many scientists in the USGS, and scientists 

generally, are reluctant to make “predictions” or “forecasts” of 

the future. Because MRA scenarios are by definition forward 

looking, some participants in the MRA proof-of-concept 

studies and synthesis workshop expressed concern that MRA 

scenarios could be interpreted as official USGS predictions 

of what will happen in the future rather than as projections of 

what might happen. Clearly communicating what scenarios are 

(and what they are not) is likely an ongoing challenge.

Development of Multi-Resource Analysis Components 47
48 Multi-Resource Analysis—Methodology and Synthesis

Integrated, Dynamic Models of Physical and 

Biological Interrelationships

Understanding how stressors, disruptions, and decisions 

can change future conditions of colocated natural resources 

requires an understanding of the interrelationships among 

those resources. Integrated, dynamic models of the physical 

and biological relationships among resources are fundamental 

to the MRA; it is through these models that the MRA connects 

the Baseline (current conditions) to the Outlook (projected 

future conditions under different scenarios). Making this 

connection requires models that 

• are spatially explicit to identify colocated resources 

and resources for which location is relevant to their 

use and value;

• capture the physical and biological connections among 

resources so that the effect of changes in one resource 

on other resources can be considered;

• address the temporal dynamics of systems so that change 

over time can be considered and the effect of different 

scenarios on future outcomes is recognized; and

• recognize, incorporate (where possible), and communicate 

the uncertainties that exist in all of these interrelationships.

This type of integrated physical and biological modeling 

is an active area of research and focus, both within and 

outside the USGS. Laniak and others (2013) described the 

goal of integrated environmental modeling as “to develop and 

organize relevant knowledge and information and apply it to 

explain, explore, and predict the behavior of environmental 

systems in response to human and natural sources of stress” 

(page 3). Some examples of modeling relevant to the resource 

interrelationships that might be considered in an MRA include 

(1) models of how land-use change affects water quality 

(Booth and others, 2011), (2) spatially explicit population 

models that evaluate the links between landscape and habitat 

changes and the populations of specific animals of interest 

(Turner and others, 1995; Chandler and Clark, 2014; DeAngelis 

and Yurek, 2017), (3) models of the impacts of energy 

development on the landscape (Hernandez and others, 2015) 

and on habitats and species (Copeland and others, 2009), and 

(4) models that connect soil and water conditions to watershed

-scale issues and to ecosystem services (Francesconi and 

others, 2016). Although much exciting work is being done in 

this area, robust integrated modeling has also been identified 

as one of the “grand challenges” for integrated science at the 

USGS (Jenni and others, 2017).

An important product from the Change Analysis component

of an MRA will be one or more integrated models of resource 

interrelationships that allow scientists, decision makers, and 

other stakeholders to explore the implications of the various 

scenarios on multiple resources in the region. The dynamic 

physical and biological models focus on biophysical outcomes 

that are of interest to the decision makers and stakeholders, 

such as changes to the quantity and quality of the various 

resources identified in the Baseline. As these models are 

developed for an MRA, they also provide a starting point for 

more detailed analyses that might be desired. For example, an 

MRA-scaled set of models of water requirements for hydraulic 

fracturing of a shale-gas resource extraction might be scaled 

down both geographically and temporally to examine water 

needs for a proposed lease or play and then extended to consider 

site-specific water availability and alternative uses.

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 49
Integrated Dynamic Models—Synthesis of Previous Efforts 

Because of the relative complexity of modeling resource 

interrelationships, both proof-of-concept teams used a set of 

resource-specific models and identified a limited number of 

variables that act as “links” from one module to another. This 

modular approach allowed the teams to leverage existing data 

and models, where such models exist, and to focus efforts 

on connecting those models and carrying the implications 

through the entire set of resource connections and interactions. 

An advantage of the modular approach to building integrated 

dynamic models is that each module can also be used separately, 

if desired, and scaled up, scaled down, or modified in some 

other way to support a range of additional studies.

Both proof-of-concept studies used Esri ArcGIS to 

capture and summarize the spatially explicit nature of the 

resources. Both research teams also used a second model to 

create and run models of resource interrelationships and changes 

over time. One study used Analytica, a highly flexible Monte 

Carlo simulation engine that allows quantification of a wide 

range of complex relationships. The other study used PowerSim, 

a systems dynamics model. The interrelationships that are 

modeled in the PRB and Piceance Basin proof-of-concept 

studies are displayed in figure 3 and figure 4, respectively. 

Both proof-of-concept studies estimate the physical 

effects of energy extraction activities (coal, CBNG, and 

unconventional natural gas) on hydrological and ecological 

resources in the study region. The PRB proof-of-concept 

models include (1) water consumption and water production 

associated with coal and CBNG development; (2) changes to 

the land surface from development activities summarized in a 

“Terrestrial Development Index,” a proxy for ecological 

disturbance modified from Carr and Means (2013); and 

(3) greenhouse gas emissions. The Piceance Basin proof-ofconcept models include (1) water consumption for hydraulic 

fracturing of natural gas wells, (2) changes to the land surface 

from new well pads and roads and how those changes might 

affect mule deer use of the local habitats, and (3) how additional 

erosion from construction activities might affect stream 

quality and how those changes in stream quality might affect 

an illustrative fish species potentially present in local streams. 

The nature of resource relationships is not always well 

understood, and sometimes it is even uncertain if changes in 

one resource have any effect on another. Although it can be 

tempting to either ignore uncertainties or to delay modeling 

until uncertainties are resolved, neither approach is helpful to 

decision makers. Ignoring uncertainty often leads to incorrect 

conclusions and delaying modeling can leave decision makers 

without useful scientific and technical insights. Beginning the 

integrated modeling steps early in the overall MRA process, 

as well as being flexible in the tools and approaches used to 

address uncertainty, leads to more useful and more timely 

results. A variety of approaches for accommodating different 

types of uncertainties were explored in the proof-of-concept 

studies. They included the use of bounding analyses with 

different assumptions about the nature and level of resource 

interrelationships, sensitivity analyses to explore a large range 

of values, and in cases where there was some data to support 

quantified relationships, probability distributions to capture 

input uncertainties and simulation models to carry those initial 

uncertainties through to uncertainty in the outcomes of interest.

The models of land surface disturbance from energy 

development in both proof-of-concept studies are highly 

simplified and include only the amount of total disturbance 

for well pads and roads. Related modeling efforts have 

demonstrated that much more detailed modeling of surface 

disturbances from energy development is feasible. Garman 

(2015, 2018) describes an “energy footprint” model in which 

actual well pad and road networks from recent development 

in a region of interest are digitized and then sampled within 

a simulation framework to make more realistic predictions of 

what the surface disturbance for a given development buildout 

would look like on the ground. This computationally intensive 

approach provides fine-grained information that is likely to 

be more useful in detailed ecological modeling than simpler 

aggregated approaches. 

50 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 51
52 Multi-Resource Analysis—Methodology and Synthesis

Scenarios for new mining

Double Click to Enter after

reading the text on this screen

Scenarios for new drilling

Diagram - Coal Resources

Diagram - USGS MRA PRB Proof-of-concept Model 

Diagram - CBNG Resources

Coal scenario

Coal Review

devel scenarios

CBNG

development

scenarios

Price scenario

type

User defined

coal price

User Interface Coal

Enter

User Interface CBNG

Coal Value

Gas Value

Coal produced

water

Coal water

consumption

CBNG Produced

water

Coal production

and disturbance

CBNG production

and disturbance

EIA Price

scenario

CBNG scenario

Price scenario

type User defined

gas price

EIA Price

scenario

For demonstration purposes only 

Multi-Resource Analysis Proof-of-Concept Model:

Powder River Basin

The model is implemented in Analytica(R), a decision analysis modeling environment and simulation package. 

Analytica has a powerful graphical interface, so that the model structure can be diagrammed and communicated, and 

is designed to model uncertainty. The model is packaged with a “Free” version of Analytica - which can be used to

run the current model. This version allows the user to run many of the analyses in this report, to change input values 

and distributions and explore the implications of those changes. The Free version does not allow those changes to 

be saved, nor does it allow changes to the model structure.

For more information about Analytica software, visit Lumina Decisions Systems’ website at www.lumina.com.

USGS MRA PRB Proof-of-concept Model - - Analytica® Enterprise 

File Edit Object Definition Result Diagram Window Help
Development of Multi-Resource Analysis Components 53

Figure 3. Overview of the Powder River Basin Multi-Resource Analysis models of interrelationships among resources.

(USGS, U.S. Geological Survey; CBNG, coal-bed natural gas; EIA, U.S. Energy Information Administration; prodn, production; 

CO2, carbon dioxide; cf, cubic foot; GHG, greenhouse gas; TDI, Terrestrial Development Index; sq mi, square mile)

Development of Multi-Resource Analysis Components 53

Diagram - Enter

Diagram - Water resources

Diagram - Other resources

Diagram - Ecological Resources

Coal scenario

Coal production

Energy value

Water

resources value

Habitat/species

value

Ecosystem

services value

Social value

Water used for coal mining

Coal produced water

Coal disturbed area

CBNG production by time

Total CBNG produced water by time

CBNG disturbed area

Scenarios

(selected by

user)

READ FIRST: (double click)

User Help for Viewing Model Components

Energy

resource

submodels

For demonstration purposes only

Primary

connecting

variables

Additional

resource

submodels

Societal and

economic

impacts

Habitat or

species

protections

CBNG

scenario

Coal produced

water

CBNG

production by

time

Coal production

CO2 per cf of

gas production

Air emissions

per ton coal

production

Air emissions

- CBNG

Air emissions

- coal

GHG

emissions

CBNG produced

water by grid

and time

Water used for

coal mining

Cumul water

prodn on grid

Water value

Social cost of 

carbon

emissions

Total water

prodn on grid

Ecology

value Affected Crucial

Stream

Corridors

Percent of

habitat area

affected

Wildlife

Impact Score

Well pads

per sq mi

Unreclaimed

well pad in

cell

TDI forecast

CBNG new

wells total

disturbed area

New coal mining

total disturbed area

From the Coal and

CBNG production and

disturbance submodels

Habitat impacts

Terrestrial Development Index (TDI)

User Interface Ecology

Coal 

Resources

Ground

Water

Affected portions

of specified

habitats

TDI

calculator

Habitat area

definitions

Water 

Resources

Ecological 

Resources

Other

Resources

CBNG 

Resources

Multi-Resource Analysis Proof-of-Concept Model:

Powder River Basin
●ENERGY RESOURCE

►Energy production

♦ Cost

♦ Revenue

♦ Net economic benefit

■ Resource

 prices

■ Technology

● WATER RESOURCE

►Disturbance

■ Water use 

 in other sectors

● ECOLOGICAL

ENDPOINT

● ECOLOGICAL

RESOURCE ♦ Ecosystem

 services

►Mitigation 

EXPLANATION

Energy resources Decreasing

Increasing

RESOURCE

Activity or direct impact

Outside factor

Benefit metric

Economic resources

Water system

Ecological system

Overlap of economic, water, 

and ecological systems

►

♦

■

●

$

54 Multi-Resource Analysis—Methodology and Synthesis

Figure 4. Overview of the Piceance Basin Multi-Resource Analysis models of interrelationships among resources and other factors. 

The arrows in this causal loop diagram indicate how changes in one factor affect another. For example, the decreasing arrow from 

“Technology” to “Cost” means that costs will decrease as technology improves, and the increasing arrow from “Resource prices” to 

“Revenue” means that revenue will increase as resource prices increase. 

Development of Multi-Resource Analysis Components 
$$$ $
56 Multi-Resource Analysis—Methodology and Synthesis

Challenges for Future Multi-Resource Analysis Modeling 

The creation of integrated, dynamic models of resource 

interrelationships across long time horizons and landscape 

scales is a significant challenge. Both proof-of-concept studies 

were able to identify and model a limited number of links 

from resources through scenarios to some biophysical and 

economic outcomes. In both studies, the ability to identify, 

modify, or create such models was limited by uncertainty 

about the nature and strength of the relations and limited 

availability of data and models to support regionally specific 

integrated models. Local and regional stakeholders who provided

input on the PRB proof-of-concept study stressed the importance

of providing transparency and traceability in the conceptual 

and numerical models. Some of the challenges in defining and 

modeling these interrelationships are listed below. 

Challenge: Balance of complexity and simplicity/clarity.—

Multiresource models are inherently complex, and experience 

with the proof-of-concept studies revealed that there are a large 

number of choices that must be made to complete models of 

this complexity. Some if not most modelers have a tendency to 

continue to add detail as models develop, but it is important to 

strike a balance. Too much detail robs the end user of clarity; 

too little detail robs the end user of accuracy and insight. 

Transparency and documentation are critical in this step, as 

they are throughout the MRA process, because they allow users 

to readily understand how models are constructed and calculated.

Challenge: Integration across disciplinary modeling norms.—

Integrated modeling requires interdisciplinary modelers, and 

building a cohesive analysis team across disciplines is not a 

trivial matter. Subject matter experts are often not accustomed 

to considering or working with resources outside of their 

expertise, and models that connect those resources are often 

lacking. Interdisciplinary modelers with expertise in making 

these connections may not, initially, have sufficient credibility 

with the subject matter experts to have their trust. However, 

scientific support for the models of resource relationships is 

essential both for the accuracy and the credibility of the MRA 

products. A successful MRA would identify approaches and 

methods for facilitating this kind of integrated modeling work. 

In addition to the general challenge of integrated modeling, 

several challenges related to the use of varying scales for spatial 

and temporal data have been identified. The scale of the data 

commonly used in one domain may not conform to the scale 

used in the other domains being integrated. Methods for 

reconciling different scales may have to be developed. 

56 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Challenge: Accounting for and communicating uncertainty.—

Model uncertainty includes both scientific uncertainty about 

resource relationships and values and uncertainty that stems 

from natural variability in resource location, quality, and 

the strength and nature of the resource relationships. These 

uncertainties are often both numerous and very large, so 

modeling all of them completely poses implementation and 

communication challenges. However, ignoring them threatens 

the credibility of the MRA Baseline and Outlook results. The 

following issues could be addressed in a future MRA.

• Uncertainty in relationships—An MRA explores the 

interrelationships among different resources over 

temporal and spatial scales that are not traditionally 

modeled. These functional relationships are often not 

well understood, and more than one conceptual model 

may exist. This conceptual model uncertainty can be 

very large; accounting for and communicating that 

uncertainty is important. 

• Uncertainty in models and data—Even within a 

single conceptual model of resource connections and 

interactions, there may be significant uncertainty 

about the nature and strength of that relation because 

data may be lacking, incomplete, or conflicting. The 

proof-of-concept work showed that experts are often 

uncomfortable trying to quantify these relationships. 

To accurately convey this uncertainty, experts were 

encouraged to use probability to capture and express 

their state of knowledge, but the experts had varying 

levels of comfort with probabilistic approaches.

Identification and propagation of uncertainty through the 

modeling chain also provides an opportunity: it can clarify 

which “input” uncertainties lead to the greatest uncertainty 

in understanding the effects of change on the resources and 

values that are important to decision makers and stakeholders. 

This understanding may be useful in establishing research 

priorities and may provide opportunities to reduce costs and 

increase the value of science research by directing funding to 

the most relevant research. 

Challenge: Selection and integration of modeling 

software and tools.—Selection or development of appropriate 

modeling software is a challenge common to several MRA 

components, especially to developing scenario models; modeling 

physical, biological, and economic interrelationships; and 

information delivery. The challenges may be most acute for 

modeling the physical and biological systems and connections, 

but are also challenging for the information delivery component 

of the MRA. 

There are many choices for modeling tools: off-the-shelf 

modeling packages such as Analytica and PowerSim as used in 

the proof-of-concept studies; mathematical modeling languages 

that are suited to these types of models such as R and Python; 

or custom software code written in Fortran, C++, or other 

programming languages. In the proof-of-concept studies, modeling

software was chosen based primarily on the immediate goals 

of the studies and on familiarity of the investigators with 

particular tools. It is likely that in any MRA study, the subject 

matter experts and the modelers will all have personal preferences

and individual areas of tool and software expertise. The philosophy 

of the MRA is to build on, not replace, existing tools, just as it 

builds on existing data. The challenge is to use the best available 

software and tools to create a cohesive integrated model and 

information delivery system (see the section “Multi-Resource 

Analysis Outlook and Information Delivery”). 

Finally, computational limits played a role in limiting the 

spatial and temporal scale of the analyses in both proof-ofconcept studies. Especially in light of the need to account for 

uncertainty, and because of the very large geographic and temporal

scale of an MRA, these computational limits are likely to be 

an important consideration in software development and selection.

Development of Multi-Resource Analysis Components 57
Economic Analysis

The integrated dynamic models of resource relationships 

focus on the physical and biological connections among 

resources and on how changes in the quantity and quality of 

one resource affect the quantity and quality of other resources. 

Understanding these connections and changes is only part of 

the information that decision makers and stakeholders need 

when considering their management options. They also need 

information on how those changes affect and are valued by 

the people who live in the region or rely on the resources 

provided by the region. Accordingly, the connections between 

resources, the environment, and people will be a principal 

consideration in the MRA.

The MRA Economic Analysis is necessary to make the 

connection between changes in the quantity and quality of 

multiple resources and changes in the economic, ecosystem, 

and societal values that those resources provide. It has two 

elements. The first is to connect the physical outcomes from 

the physical and biological models to the fundamental features 

and services that regional decision makers and stakeholders 

value. For example, the integrated dynamic models of the 

physical and biological systems may lead to estimates or 

projections of the amount of groundwater extracted from an 

aquifer for a particular use, but stakeholders might be concerned 

with the near- and long-term availability of water from that 

aquifer for agricultural irrigation. Expressing future conditions 

in the context of the stakeholders’ values makes the outcomes 

more meaningful to those affected by the changes and allows 

them to consider the potential tradeoffs and how they value 

different outcomes. The second element is to connect the 

valued aspects of the resources to an economic value, where 

possible. In the previous example, water markets or the price 

of agricultural water from other sources may be used to translate 

changes in the availability of water for irrigation to an economic 

value (benefit or cost). Economic valuation includes assessing 

conventional, market-derived costs and benefits and using 

nonmarket valuation techniques to estimate economic values 

associated with natural resources and the ecosystem services 

they provide. 

Identifying the fundamental aspects of the ecosystem and 

natural resources that produce economic and social value for 

various stakeholders gives land and resource managers a powerful 

tool they can use to communicate with those stakeholders 

about the full range of issues they face and decisions they 

make. The identification of those valued aspects of resources 

in a given region is part of the initial framing process and may 

continue through the modeling steps. Existing studies and 

literature that describe the more general ecosystem services 

that humans derive from different resources (Millennium 

Ecosystem Assessment, 2005) can enhance the identification 

of stakeholder and region-specific economic and social 

resource values; site-specific studies can also provide a useful 

starting point. Although there may be additional factors that 

are relevant to management decisions at a smaller scale, starting 

with an understanding of what stakeholders want to hear about 

allows managers to provide information that is more consistent 

and more targeted to those participating in and affected by 

decisions. Economic valuation models provide a starting point 

for tradeoff analyses and other economic analyses and give 

decision makers an opportunity to scale up or down the MRA 

models to support specific decisions. The MRA valuation 

process also identifies areas where site-specific studies may be 

necessary to fully support economic valuation; those studies 

may then become relevant for more detailed analyses.

In the context of an MRA, economic analysis provides 

a powerful extension of the more common USGS research 

products of technical information and data. It also provides 

the primary integrated picture of how people may be affected 

by changes in multiple colocated natural resources in a region 

under the various scenarios being considered.

58 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 59
Economic Analysis—Synthesis of Previous Efforts

The two proof-of-concept studies focused on economic 

valuation to different extents. The Piceance Basin research 

team emphasized the economic valuation of resource extraction 

and changes to the impacted resources. The research team 

defined the concept of a “net resource value,” intended to 

combine the economic benefits of extracting energy resources 

with the economic costs of the associated adverse impacts on 

colocated resources. The PRB proof-of-concept study translated 

the physical effects of changes in resource conditions to economic 

effects for a limited set of physical outputs; it did not focus on 

producing a net value. 

The PRB proof-of-concept study estimated the economic 

costs and revenues from coal mining and CBNG development, 

the value of alternative uses for produced groundwater, and 

the social costs of greenhouse gas emissions. Neither of the 

main ecological endpoints used in the modeling, the Terrestrial 

Development Index and the wildlife impact score, was amenable 

to valuation (economic or other) that would inform tradeoffs 

between those impacts and other effects. The Terrestrial 

Development Index has a physical interpretation; that is, it 

shows how much of an area is in a disturbed versus a natural 

condition, but it has not yet been connected to outcomes that 

can be valued economically. The wildlife impact score is used 

by the State of Wyoming in conservation planning, but it is not 

very well defined in terms of actual effects on species and thus 

would be difficult to value through economic metrics or by 

considering tradeoffs with other more concrete metrics.

For the Piceance Basin proof-of-concept study, the 

monetary value of natural gas in the market provided a simple 

proxy for the economic value of natural gas production to 

society. Groundwater, mule deer, and aquatic species were 

used as examples of ecosystem elements affected by energy 

extraction where nonmarket valuation approaches could be 

used. Values for mule deer and aquatic species were derived 

from a metaanalysis of previous studies. The research team 

combined values from these metaanalyses with estimates of 

the physical effects on those species to yield an economic cost 

of changes in species populations. All of the economic values 

were considered temporally, with discounting, and aggregated 

to provide a net resource value. 

Feedback from stakeholders gathered prior to building 

the proof-of-concept models in the PRB study suggested 

that the aim should not be for a “single integrated value” of 

resources or effects on resources. Rather, the stakeholders 

indicated that they would prefer that evaluation and valuing 

each type of resource be based as clearly as possible on 

the state of the art for a specific resource, which may be a 

clear quantitative description of the biophysical state of the 

resource, an economic valuation of that resource state, or 

some other metric. Although a net resource value in isolation 

may not be enough information for a land manager to make a 

decision, the value fully integrates resource information and is 

useful in evaluating tradeoffs and providing one component 

of a suite of outputs to support land-use decisions. 

60 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 61
62 Multi-Resource Analysis—Methodology and Synthesis

Challenges for Future Economic Analysis 

Resource management decisions are complicated in part 

because they almost always involve tradeoffs among competing 

values. The key challenge for the Economic Analysis component 

of the MRA is how to provide clear and meaningful metrics 

that resource managers can use to make decisions. The goal 

is to find both clear and common metrics for comparing 

disparate impacts. 

Challenge: Facilitating understanding of tradeoffs.—

The Economic Analysis component of the MRA starts with 

identification of the fundamental value(s) that stakeholders 

derive from the natural resources on the landscape. Clearly 

connecting the biophysical changes to these fundamental 

stakeholder values poses some modeling challenges. If valuation

stops at this point, the results provide resource managers with 

important value-relevant information to support decision making. 

In some cases, they may be able to identify dominant options 

without having to address tradeoffs, but many if not most 

challenging decisions require tradeoffs between several things 

that decision makers and stakeholders care about. The Economic

Analysis component includes explicit consideration and analysis 

of tradeoffs among multiple objectives. These concepts might 

be explored in more depth in a future MRA study. 

Challenge: Lack of data to support monetary valuation 

of changes in resources.—A key challenge in both proof-ofconcept studies was estimating an economic value for changes 

in resource quantity and quality, particularly for resources that 

have not historically been valued in economic terms. Although 

the economics of energy development are well understood and 

readily modeled, estimating the economic value of changes 

in other natural resources is a newer science. It is generally 

believed that the economic value of a nonmarket good or 

service is locally or regionally specific; that is, the economic 

value depends on the value placed on the good or service by 

those people who benefit from the good or service. Primary 

social and economic valuation analyses are time consuming 

and expensive to conduct. Secondary analyses, especially 

benefit-transfer approaches, are considered a second-best 

approach but it may be difficult to find studies that are similar 

enough to the MRA study region to be appropriate for benefit 

transfer. Future MRA studies could address this issue in more 

depth than was done in the proof-of-concept studies.

Challenge: Integrated net metrics can be useful but 

require context.—The Piceance Basin proof-of-concept study 

demonstrated that it is possible to produce a net monetary value 

for different scenarios by summing the monetary value of 

impacts on several biophysical endpoints. The idea of a single 

metric that summarizes all the changes may be very attractive, 

and in some sense it simplifies communication of the MRA 

Outlook. However, combining the monetary values from several 

economic studies that have very different foundations may 

be problematic from both theoretical and practical perspectives, 

and work remains to be done to ensure that such combinations 

are meaningful and clearly understood. Decision makers and 

stakeholders who participated in the PRB MRA proof-ofconcept study uniformly agreed that limiting reporting to a 

single integrated value of the effects of changes on colocated 

resources would be viewed with suspicion. They expressed 

a strong preference for results that clearly communicate the 

outcomes in natural terms (relevant to the resource value) 

and in economic terms, where feasible, and with any basis for 

combining values clearly communicated. A future MRA could 

explore further when and how net values should be presented 

and how they can enhance presentations of multimetric results. 

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 63
Multi-Resource Analysis Outlook and 

Information Delivery

The results of the analysis components constitute the 

MRA Outlook, the culmination of the MRA. Comparing the 

Outlook to the Baseline allows land and resource managers to 

better understand the potential changes in colocated resources 

over time under a set of future scenarios that they have chosen 

as relevant for their region. 

The Outlook provides information about the projected 

state of the natural resources included in the Baseline under 

each of the scenarios being evaluated, as well as information 

on the economic and social implications of changes in those 

resources from their Baseline status to the projected future 

status. The Outlook gives decision makers information on the 

potential outcomes of decisions, actions, and other factors that 

affect resources over time. It uses the results of the integrated, 

dynamic models of the biophysical interrelationships to describe 

the quality and quantity of each resource under changed 

conditions. It also uses the results of the economic analysis 

and the scenarios of change to provide information on the 

effect and value of changes in those resources under the scenarios 

considered in the MRA. 

A key part of the vision for the Outlook and information 

delivery components of the MRA is that the results of the 

analysis are presented in several ways. At a minimum, the 

Outlook provides (1) maps to communicate spatial relations, 

(2) tables to summarize the outcomes in physical and value 

terms and facilitate comparison of those outcomes over time 

and (or) across scenarios, and (3) graphs to provide easier 

visual comparison of some of those outcomes. Ultimately, 

the information delivery may include all of these, as well as 

software tools that would allow an interested user to access the 

underlying data (from both the Baseline and the Outlook) and 

create their own customized displays or outputs.

Outlook and Information Delivery—

Synthesis of Previous Efforts

The PRB and the Piceance basin proof-of-concept studies 

explored various ways to illustrate the results of modeling and 

analyses. A brief summary and some examples are provided in 

the highlight on pages 66–67. A compact way to communicate 

the MRA Outlook is to summarize resource conditions in a single

table. Conditions can also be illustrated in graphical displays, 

which draw attention to relatively small changes in the total 

resource over time, especially for energy and groundwater 

resources, and terrestrial disturbance. Within-region differences, 

however, some of which might be important to decision makers 

and stakeholders, can be masked if the summary results 

are focused at the scale of the outer boundary of the MRA region 

(the PRB or the Piceance Basin in the two proof-of-concept 

studies). For example, the key issues and questions for water 

resources are typically considered at a watershed (or smaller) 

scale—a much smaller region than the entire PRB. A similar 

issue arises when considering the change in terrestrial 

disturbance. In this case, the overall effect is better communicated 

through maps.

Another way of communicating the Outlook is to focus 

on differences across scenarios at specific points in time. This 

approach may be particularly helpful to decision makers and 

stakeholders who want to consider and compare future outcomes

of different decisions or policy choices. 

64 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 65
2000

2001200220032004200520062007200820092010201120122013201420152016201720182019202020212022202320242025202620272028

50

100

150

200

250

300

Total volume of gas, in thousand cubic feet of gas

Year

0

Gas production

 development

 plan

1 year

5 year

10 year

20 year

EXPLANATION

By design, the Multi-Resource Analysis considers multiple colocated natural resources, both in present 

day and under several possible future scenarios. The analysis may span a large landscape and consider 

a time frame that spans years or decades. Presenting this information to end users in a way that is useful 

and understandable is a significant challenge. The two proof-of-concept studies highlighted in this circular 

each explored several ways of presenting the Outlook. 

Annual energy production for four development plans.

Examples of Outlook Presentations

Summary table comparing the Baseline and Outlook in terms of the quantity and quality of natural resources in the region.

[%, percent; CBNG, coal-bed natural gas; CO2 , carbon dioxide]

Resources Baseline value Future value Change

Energy Resources

Coal

Recoverable resources (billion short tons) 125.1 116 -7%

Reserves at 10:1 stripping ratio or less (billion short tons) 8.2 0 -100%

CBNG Technically recoverable resources (trillion cubic feet) 9.106 4.94 -46%

Water Resources

Groundwater Quantity potentially recoverable (millions of acre-feet) 826.4 825.7 -0.09%

Ecology and Biology Resources

Air quality Cumulative greenhouse gas emissions from development activities (million tons of CO2

) 50 268 +217

Terrestrial disturbance

< 1% terrestrial disturbance (million acres) 3.32 3.11 -6%

>20% terrestrial disturbance (million acres) 0.39 0.44 14%
14

14

14

14

16

16

87 16

16

85

85

85

20

18

26

26 20

287

18

18

212

90

90

25

25

WYOMING

MONTANA

Sheridan

Buffalo

Casper

Douglas

Gillette

Newcastle

Scenario 1 only

Scenario 2 only

Both scenarios

Coal-bed natural gas development

 through 2035

Powder River Basin boundary

EXPLANATION

0 10 20 30 40 50 KILOMETERS

0 10 20 30 40 50 MILES

107°

45°

44°

43°

106° 105°

Base from The National Map, digital data, 2017

Universal Transverse Mercator projection

Zone 13N 

1.4

1.2

1

0.6

0.4

0.2

2020 2025 2030 2035

0

0.8

Year

Coal-bed natural gas, in trillion cubic feet

Scenario 1

Scenario 2

CBNG development

 through 2035

EXPLANATION

3.0

2.5

2.0

1.0

0.5

A B C D

0

1.5

Watershed

Percentage of recoverable groundwater

Scenario 1

Scenario 2

CBNG development

 through 2035

EXPLANATION

Comparison of development scenarios. A, Map of development locations. B, The quantity of energy produced. 

C, Water use for each scenario. 

A B

C
Challenges for Future Multi-Resource Analysis 

Information Delivery 

The MRA addresses multiple resources and scenarios, 

spans large landscape scales and long time frames, and includes 

uncertainty in models where appropriate. The sizeable amount 

of information in an MRA yields Outlook results that are 

complicated and diverse. Presenting this information to end 

users in a way that is useful and understandable is a significant 

challenge. In developing an MRA, it is vitally important to 

work with decision makers and stakeholders to decide on 

appropriate and useful information-delivery vehicles. Some of 

the challenges in information delivery, especially with respect 

to delivering the Outlook, are described below. 

Challenge: Communicating multidimensional results.—

The Outlook results for an MRA encompass many dimensions; 

that is, they are spatially explicit and characterize the effects 

of change on multiple resources over time. All of those 

dimensions cannot easily be displayed at once, requiring ways 

to “slice” through the multidimensional results in different 

forms so the end user can gain a more complete understanding. 

At a minimum, we expect that end users will want to be able 

to see and compare changes in the quantity, quality, and value 

of multiple resources over time and across scenarios. Each of 

the proof-of-concept studies explored a variety of tools for 

displaying this information, including maps, tables, and charts. 

Participants in the synthesis workshop suggested providing 

access to “raw” data as part of the Outlook as well as a set of 

tools that would allow an end user to create their own views 

of that data. In a successful MRA, it would be critical to work 

with decision makers and stakeholders to explore the utility of 

different information displays and delivery methods.

Challenge: Data interoperability.—Both proof-of-concept 

studies faced significant challenges related to modeling software 

and data interoperability. A decision-ready MRA needs to 

address these issues for both modeling and information delivery. 

Ideally, a user would be able to slice, rotate, and otherwise 

format both the Baseline data and the Outlook results and view 

them in various forms with reasonable ease. Model-generated 

data would be translated efficiently into forms that are viewable, 

often by other software products (for example, generating 

model outputs into a geographic information system map).

Challenge: Delivery of Baseline summaries.—In the 

PRB study, one of the concepts used for effective delivery of 

the Baseline is to include summary information at each level 

as the user drills down into the hierarchy of information. This 

approach presents at least two challenges. The first is to 

identify the types of summary information end users will find 

most useful. The second is to actually summarize, in some 

cases, a large amount of very detailed information and data, 

which may have originally been developed for a different 

purpose, in a way that addresses the needs and interests of 

the end users. The skills required to provide this kind of 

decision-relevant summary information are not necessarily 

the same skills required to develop the detailed analyses that 

provide the foundation for the MRA Baseline and the Change 

Analysis components. Tension between the need for simple, 

straightforward summaries and the need to respect the strengths 

and limitations of the supporting data and information is likely.

Challenge: Information delivery and sustainability of 

the models.—The models themselves represent an additional 

information delivery challenge. End users are unlikely to 

trust and use the other MRA products, such as the Outlook 

projections, without understanding and trusting the basis of 

the often complex models used to create those results. In the 

PRB proof-of-concept study, conceptual model diagrams 

were used to illustrate model structure, with a hierarchical 

approach illustrating different levels of model detail. Other 

tools and approaches may be useful as well. Understanding 

these models and the Outlook itself requires an investment of 

time; the process of developing an MRA builds in stakeholder 

and decision-maker learning, but extending that learning and 

understanding to new users poses an additional challenge.

68 Multi-Resource Analysis—Methodology and Synthesis

Development of Multi-Resource Analysis Components 
Development of Multi-Resource Analysis Components 69
Potential Next Steps 
A full implementation of an MRA is the logical follow-up 

to the proof-of-concept studies that have led to this synthesis 

and initial methodology. A full implementation would have 

three goals: (1) to develop and provide a first-generation set of 

MRA products that would be relevant, helpful, and useful for a 

real set of natural resource managers; (2) to create a full MRA 

case study that would illustrate the concepts and results to 

other potential end users; and (3) to refine the MRA methodology 

for subsequent implementations.

The main criterion for where and when to conduct an 

MRA study should be the availability of a set of decision 

makers in a given region who are interested in having, 

participating in, and using the results of an MRA. The first full 

implementation of the MRA should also consider additional 

characteristics of the study location, issues, and scope. In 

addition to identifying an interested participant or “customer” 

for the MRA, gathering the full range of project participants—

an effective multidisciplinary project team consisting of 

scientists from multiple disciplines and technical facilitators 

and modelers who can help decision makers and scientists 

work together—would be critical. Additionally, identifying a 

study region where there is already a wealth of resource 

information available would enable the MRA to start more 

quickly. This first-of-a-kind, large-scale implementation would 

leverage the work and lessons learned from the MRA 

proof-of-concept and related studies; it would also explore 

new issues as a way to test the robustness of the methodology.
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Figure Credits 81

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Jenni and others—Multi-Resource Analysis—Methodology and Synthesis—Circular 1442, August 2018