EPA/600/R-11/011F | August 2011 | www.epa.gov
United States
Environmental Protection
Agency
Aquatic Ecosystems, Water Quality, and Global
Change: Challenges of Conducting Multi-stressor
Global Change Vulnerability Assessments
National Center for Environmental Assessment
Office of Research and Development
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EPA/600/R-11/011F
August 2011
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-stressor Global Change
Vulnerability Assessments
Global Change Research Program
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
Preferred Citation:
U.S. Environmental Protection Agency (EPA). (2011) Aquatic ecosystems, water quality, and
global change: challenges of conducting multi-stressor global change vulnerability assessments.
National Center for Environmental Assessment, Washington, DC; EPA/600/R-11/01 IF.
Available from the National Technical Information Service, Springfield, VA, and online at
http://www.epa.gov/ncea.
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CONTENTS
LIST OF TABLES v
LIST OF FIGURES vi
PREFACE vii
AUTHORS, CONTRIBUTORS, AND REVIEWERS ix
1. INTRODUCTION 1
2. SYNERGIES WITH OTHER EPA EFFORTS 6
3. INDICATORS CONSIDERED FOR THIS REPORT 10
3.1. LITERATURE SEARCH 10
3.1.1. Core Literature 10
3.1.2. Protocol for Collecting Additional Relevant Literature 15
3.2. CREATION OF A COMPREHENSIVE LIST OF INDICATORS 16
3.2.1. Identifying Indicators of Water Quality and Aquatic Ecosystem Condition 16
3.2.2. Selection of Indicators 17
3.2.3. Exclusion of Certain Indicators and Studies 20
3.2.4. Deletion of Duplicate Indicators 20
4. CHALLENGES PART I: INDICATOR CLASSIFICATION 22
4.1. DEFINING VULNERABILITY 22
4.1.1. Determinants of Vulnerability 23
4.1.2. Defining a Vulnerable Situation 24
4.1.3. Biophysical and Socioeconomic Domains 25
4.1.4. Predictability and Uncertainty 26
4.2. CLASSIFYING VULNERABILITY INDICATORS 27
4.3. HOW DO THESE INDICATORS REFLECT VULNERABILITY? 31
5. CHALLENGES PART II: DETERMINING RELATIVE VULNERABILITY 55
5.1. VULNERABILITY GRADIENTS AND THRESHOLDS 55
5.2. MODIFYING AND REFINING INDICATORS TO INCORPORATE 60
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CONTENTS (continued)
6. CHALLENGES PART III: MAPPING VULNERABILITY 67
6.1. ASSESSMENT OF INDICATOR DATA AVAILABILITY AND MAPPABILITY
AT THE NATIONAL SCALE 67
6.1.1. Identification of Data Sources for Indicators 67
6.1.2. Description of Major Data Sources 68
6.1.3. Supporting Information Collected for Data Sources 75
6.1.4. Lack of Data and Other Unresolved Data Problems 76
6.1.4.1. Data Availability Issues 76
6.1.4.2. Data Sets Without National Coverage 81
6.1.4.3. Non-uniform Spatial Distribution of Data 82
6.1.4.4. Temporal Gaps 83
6.1.5. Data Problems that Could be Resolved 83
6.2. CREATION OF EXAMPLE MAPS 85
6.3. SPATIAL AGGREGATION 88
6.3.1. Local Variation 89
6.3.2. Extent of Spatial Units (HUC Levels) 89
6.3.3. Alternate Spatial Frameworks 93
6.3.3.1. Watersheds (and hydrologic units) 93
6.3.3.2. Ecoregions 93
6.3.3.3. Coastal Areas 94
6.4. CATEGORICAL AGGREGATION 95
7. CHALLENGES PART IV: COMBINING INDICATORS 99
7.1. COMBINING INDICATORS WITH OTHER DATA 99
7.2. COMPOSITES OF VULNERABILITY INDICATORS 102
7.2.1. Creating a Composite Map 103
7.2.2. Characterizing Vulnerability Profiles 104
8. SUMMARY AND RECOMMENDATIONS 109
8.1. SUMMARY OF CHALLENGES 109
8.1.1. Challenges Part I: Indicator Classification 110
8.1.2. Challenges Part II: Determining Relative Vulnerability Ill
8.1.3. Challenges Part III: Mapping Vulnerability 112
8.1.3.1. Data and Mappability 112
8.1.3.2. Spatial Aggregation 113
in
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8.1.4. Challenges Part IV: Combining Indicators 114
8.2. RECOMMENDATIONS FOR FUTURE RESEARCH 116
8.2.1. Assessment of Non-mappable Indicators 116
8.2.2. Identifying Opportunities to Enhance Source Data 117
8.2.3. Development of New Indicators from Available Data Sets 118
8.2.4. Need for Additional Study and Data Collection in Coastal and Other Areas 119
8.2.5. Use of Indicators for Future Studies 120
8.2.6. Establishment of Stress-response Curves, Vulnerability Thresholds, and Baseline
Conditions 121
8.2.7. Drawing on Other Established Approaches for Combining Indicators 121
8.2.8. Incorporating Landscape and Land Use Metrics 122
8.2.9. Incorporating Information Based on Remote Sensing Technologies 122
8.2.10. Incorporating Metrics of Adaptive Capacity 122
9. REFERENCES 124
10. APPENDICES IN A SEPARATE PDF
A. LIST OF LITERATURE REVIEWED
B. COMPREHENSIVE LIST OF INDICATORS
C. DATA SOURCES, SUPPORTING INFORMATION, AND TECHNICAL NOTES
D. MAPPING METHODOLOGY
E. EXAMPLE MAPS FOR INDICATORS OF WATER QUALITY AND AQUATIC
ECOSYSTEM VULNERABILITY, DISPLAYED USING 4-DIGIT HYDROLOGIC
UNITS
F. EXAMPLE MAPS FOR INDICATORS OF WATER QUALITY AND AQUATIC
ECOSYSTEM VULNERABILITY, DISPLAYED USING ECOREGIONS
G. VULNERABILITY CATEGORY MATRIX
H. EVALUATION AND POTENTIAL MODIFICATION OF VULNERABILITY
INDICATORS
IV
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LIST OF TABLES
3-1. List of core literature 11
3-2. Indicator primary and secondary categories 20
3-3. Rationale for exclusion of certain indicators 21
4-1. List of vulnerability indicators 29
5-1. Indicators with objective thresholds and their vulnerability categories 60
5-2. Vulnerability indicators categorized in the National Environmental Status and Trend
(NEST) Framework 63
6-1. Distribution of data source 69
6-2. Indicators eliminated due to lack of data or unresolved data problems 76
6-3. Data gaps 84
6-4. List of mapped vulnerability indicators 86
7-1. Principal components loadings for the twenty four indicators included in the PC A
analysis 106
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LIST OF FIGURES
3-1. Flowchart of methodology used to identify and map vulnerability indicators 12
3-2. Indicator definition from EPA's 2008 Report on the Environment 17
5-1. Mapping data relative to regulatory thresholds 64
5-2. Modification of indicator definitions using existing data 65
5-3. Modification of indicator definitions using existing data 66
6-1. Limitations of data sets containing self-reported data 81
6-2. Aggregation, precision, coverage, and data density 92
6-3. Data represented by different spatial frameworks 96
6-4. Spatial framework for coastal zone indicators 97
6-5. Different breaks to distinguish data classes 98
7-1. Current and future vulnerability to water shortages 102
7-2. Vulnerability profile similarity 108
8-1. Indicator evaluation process 115
VI
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PREFACE
This report investigates the issues and challenges associated with identifying, calculating,
and mapping indicators of the relative vulnerability of water quality and aquatic ecosystems,
across the United States, to the potential adverse impacts of external forces such as long-term
climate and land-use change. We do not attempt a direct evaluation of the potential impacts of
these global changes on ecosystems and watersheds. Rather, we begin with the assumption that a
systematic evaluation of the impacts of existing stressors will be a key input to any
comprehensive global change vulnerability assessment, as the impacts of global change will be
expressed via often complex interaction with such stressors: through their potential to reduce
overall resilience, or increase overall sensitivity, to global change. This is a well established
assumption, but to date there has been relatively little exploration of the practical challenges
associated with comprehensively assessing how the resilience of ecosystems and human systems
in the face of global change may vary as a function of existing stresses and maladaptations. The
work described in this report is a preliminary attempt to begin such an exploration.
To do so we gathered, from the literature, a set of more than 600 indicators of water
quality and aquatic ecosystem condition and changes in condition, along with numerous datasets
from EPA, other federal agencies, and NGOs, and we have used all of this as a testbed for
identifying best practices and challenges for calculating and mapping vulnerability nationally.
We investigated gaps in ideas, methods, data, and tools as well. Specifically, we explored:
Challenges associated with identifying those indicators that speak specifically to
vulnerability, as opposed to those reflecting simply a state or condition;
Challenges associated with calculating and estimating the values of these
vulnerability indicators, including establishing important indicator thresholds that
reflect abrupt or large changes in the vulnerability of water quality or aquatic
ecosystems;
Challenges associated with mapping these vulnerability indicators nationally,
including data availability and spatial aggregation of the data; and
Challenges associated with combining and compositing indicators and developing
multi -indicator indices of vulnerability.
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We hope that this report will be a useful building block for future work on multi-stressor
global change vulnerability assessments. Ultimately, we believe the work described here can
contribute to bridging disconnects between the decision support needs of the water quality and
aquatic ecosystem management communities and the priorities and capabilities of the global
change science data and modeling communities. In addition, we hope it will help to synthesize
lessons learned from more detailed, place-based, system-based, or issue-based case studies. Such
studies include those conducted on individual watersheds, on wetlands, and on urban
ecosystems. This synthesis will be used to obtain national-scale insights about impacts and
adaptation; and to prioritize future work in developing adaptation strategies for global change
impacts.
We would like to acknowledge the excellent work of the Cadmus Group, Inc. in their
collaboration with NCEA to develop this draft report. In addition, a team of external expert
advisors provided critical insights that have informed all of our work in the project to date: Drs.
David Allan, Kathleen Miller, John Day, David Gochis, David Yates, and Thomas Meixner.
Many thanks as well to Mike Slimak, whose substantial contributions greatly improved this
report, as well as to our external and EPA reviewers. Finally, we would like to thank all the
NCEA Global Change Research Program staff for their numerous and significant inputs to this
project.
This final document reflects a consideration of all comments received on an External
Review Draft dated February 8, 2011 (EPA/600/ R-l 1/01 A) provided by an expert panel, and
comments received during a 45-day public review and comment period (February 28, 2011 -
April 14,2011).
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
AUTHORS
Julie Blue
Nupur Hiremath
JeffMaxted
Matthew Diebel
Charles Hernick
Jonathan Koplos
Chris Weaver
The Cadmus Group, Inc.
EPA/ORD/NCEA
CONTRIBUTORS
David Allan
John Day
Thomas Meixner
David Gochis
Kathleen Miller
David Yates
University of Michigan
Louisiana State University
University of Arizona
National Center for Atmospheric
Research
EPA REVIEWERS
Robert Hall
Rachael Novak
Douglas Norton
Betsy Smith
Rick Ziegler
EPA/R9
EPA/OW/OST
EPA/OW/OWOW
EPA/ORD/NERL
EPA/ORD/NCEA
EXTERNAL PEER REVIEWERS
Paul H. Kirshen
Michael Mallin
Hans Paerl
Battelle Memorial Institute
Center for Marine Science\ University of
North Carolina at Wilmington
University of North Carolina at Chapel Hill
Institute of Marine Sciences
IX
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1. INTRODUCTION
The U.S. Environmental Protection Agency (EPA) Global Change Research Program
(GCRP), located within the Office of Research and Development (ORD), is a national-scale
program that supports decision-making about adapting to potential climate change and other
global change impacts on air and water quality, aquatic ecosystems, and human health. GCRP
collaborates with EPA Program and Regional offices, and state, local, municipal, and tribal
natural resource managers, to provide scientific support for these efforts. There is a large body of
literature suggesting that improvements to measuring, modeling, and understanding climate
changes relevant to the hydrologic cycle, water quality, and aquatic ecosystems are needed (e.g.,
Barsugli et al., 2009; Bates et al., 2008; Lettenmaier et al., 2008; Kundzewicz et al., 2007;
Miller and Yates, 2005; Poff et al., 2002). The management strategies of the past will not
necessarily be adequate given increased awareness of stressors such as climate change and land-
use change. As emphasized by a number of recent publications, top-down, prediction-based
assessments of the interactions between climate change and hydrologic systems, ecosystems, and
human communities will likely be of limited usefulness for local decision-making. This is due to
current and foreseeable limits on reducing climate uncertainties, and because these kinds of
assessments are not necessarily compatible with conclusions from the social sciences about how
information is used in decision-making (e.g., see Dessai et al., 2009; Johnson and Weaver, 2009;
NRC, 2009; Moser and Luers, 2008; Sarewitz et al., 2000; Fischhoff, 1994).
Effective decision support will instead start with a commitment to understand the systems
we manage or aim to protect and a willingness to use what we know now for decision-making,
while working to learn more. In general, comparing relative vulnerabilities fits in well with this
framework, because direct evaluation of the absolute effects of climate change on water quality
and aquatic ecosystems is out of reach given the state of the science for many of our
vulnerability indicators. Yet policy decisions must continue to be made in the absence of perfect
information. Understanding the current condition of and threats posed to our environment now
can be the lens through which we view the potential risks posed by global change. This can be
achieved through systematic, quantitative planning frameworks that help us to understand and
evaluate various management strategies across a wide range of plausible futures. The result of
such planning should be the selection of management strategies that alleviate, or at least do not
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exacerbate, existing and anticipated vulnerabilities of water quality and aquatic ecosystems. In
other words, we should seek strategies that are robust with respect to the inherent uncertainties of
the problem (e.g., Brown et al., 2010; Lempert et al., 2004).
Informed by this philosophy, GCRP has developed and is implementing a multi-year
research effort designed to improve national-scale understanding of the multiple complex
interactions between global change and the nation's waters. Part of this work is a major effort
devoted to the development of scenarios of future climate, land-use, and hydrologic change. For
example, GCRP is conducting hydrologic modeling in 20 large, U.S. watersheds in an attempt to
provide broad, national-scale scenarios of streamflow and nutrient/sediment loading across a
wide range of potential climate and land-use changes, to improve our understanding of the
plausible range of hydrologic sensitivity to global change. Such scenarios can be used, in
principle, to investigate the potential negative water quality and aquatic ecosystem impacts that
we must prepare to remedy, nationally, given existing and likely future vulnerabilities of our
aquatic ecosystems.
But what are these existing vulnerabilities? The idea for this report began with a
seemingly simple question: How easy would it be to assess, and map, the relative vulnerability of
watersheds, across a number of dimensions, for the whole United States in a meaningful, self-
consistent way? In this report, we summarize the lessons learned to date in our attempts to
answer this question.
There are two main outcomes that we report on here. First, we have collected, evaluated
the quality of, processed, and aggregated a large quantity of data on water quality and aquatic
ecosystem indicators across the nation. Second, we have attempted to identify best practices,
challenges, and gaps in ideas, methods, data, and tools for calculating and mapping vulnerability
nationally. In both contexts, we hope that this report will be a useful building block for future
work on multi-stressor global change vulnerability assessments.
To measure relative vulnerability, we identified indicators that reflect the three
components of vulnerability as identified by the IPCC (2007a): sensitivity, exposure, and
adaptive capacity. Sensitivity is the extent to which a system responds either positively or
negatively to external stimuli; exposure is the degree to which a system is exposed to stressors
(and in some cases, specifically climatic variations); and adaptive capacity is the ability of a
system to cope with stress. Most vulnerability indicators identified in this report measure the
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exposure or sensitivity of water quality and aquatic ecosystems to stressors. An understanding of
exposure and sensitivity may facilitate the development of adaptive capacity within a system.
It is important to clarify here that this report does not evaluate impacts of climate change
on ecosystems and watersheds. Instead, it deals only with the question of how to estimate the
relative effects of other, existing stressors and their potential to reduce overall resilience, or
increase overall sensitivity, to climate change. It examines this question by looking at indicators
of vulnerability to such stressors. We argue that a systematic evaluation of the impacts of
existing stressors is a key input to any comprehensive climate change vulnerability assessment,
as the impacts of climate change will be expressed via interaction with such stressors.
While the idea that existing stressors reduce resilience and increase vulnerability to
climate change remains an assumption for many systems, it is an established one, deeply
embedded in recent large climate change assessment efforts. For example, the IPCC 4th
Assessment Working Group II report states that: "Vulnerability of ecosystems and species is
partly a function of the expected rapid rate of climate change relative to the resilience of many
such systems. However, multiple stressors are significant in this system, as vulnerability is also a
function of human development, which has already substantially reduced the resilience of
ecosystems and makes many ecosystems and species more vulnerable to climate change through
blocked migration routes, fragmented habitats, reduced populations, introduction of alien species
and stresses related to pollution" (IPCC, 2007a). It then goes on to provide examples from
terrestrial, marine, and coastal ecosystems.
Reducing the impact of current stressors is also frequently considered to be a "no regrets"
adaptation strategy for enhancing ecosystem resilience to climate change. The U.S. Climate
Change Science Program (USCCSP, 2008) reviewed adaptation options for six federally
managed programs in the United States: national forests, national parks, national wildlife
refuges, national estuaries, marine protected areas, and wild and scenic rivers. Adaptation
options were studied by reviewing available literature, data, and models, as well as by assessing
the consensus within the scientific community. Decreasing current anthropogenic stresses was
the adaptation approach deemed most likely to lead to good outcomes in the face of climate
change uncertainties. Numerous studies confirmed that this approach was likely to be the most
successful of those considered.
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The idea that existing stressors reduce resilience and increase vulnerability to climate
change informs both the definition of "vulnerability" that we use, and the selection of individual
indicators we examine. It is key to providing the link between what these indicators measure and
an understanding of the ecological and watershed impacts of climate change, and we expand
upon this idea at other points in this report.
Returning to our framing question, "How easy would it be to assess, and map, the relative
vulnerability of watersheds, across a number of dimensions, for the whole United States in a
meaningful, self-consistent way?'\ our strategy for addressing it was as follows:
We conducted a literature search and compiled a comprehensive list of broadly defined
indicators of the vulnerability of water quality or aquatic ecosystems, including those relating to
ambient surface and groundwater quality, drinking water quality, ecosystem structure and
function, individual species, and the provision of ecosystem services. This then formed the set of
indicators for exploring a number of subsequent challenges. These challenges fall into four broad
categories:
1. Challenges associated with identifying those indicators that speak specifically to
vulnerability as opposed to those reflecting simply a state or condition.;
2. Challenges associated with calculating and estimating the values of these
vulnerability indicators, including establishing important indicator thresholds that
reflect abrupt or large changes in the vulnerability of water quality or aquatic
ecosystems;
3. Challenges associated with mapping these vulnerability indicators nationally,
including data availability and spatial aggregation of the data; and
4. Challenges associated with combining and compositing indicators and developing
multi-indicator indices of vulnerability.
For this work, we relied on published research and on studies by EPA, other federal
agencies, and well-respected institutions like the Heinz Center and the Pew Center, both for
indicator definitions and for the data to support the mapping of indicators. While each study
reviewed had a slightly different objective, much of the information was relevant to the goals of
this project. The intent was to examine what could be accomplished with existing indicators and
data sets, and for the most part we did not attempt at this point to conceive of new indicators or
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collect new data. As part of this work we developed a number of example maps, and we use
some of these maps in this report for illustrative purposes. We recognize that approaches other
than the one we took are possible, but the lessons we learned while developing strategies for
compiling and mapping national-level indicator data sets under this project would likely be
useful for an array of alternative approaches. This project was a starting point and its findings
have broad applicability.
The next section (Section 2) briefly describes a number of EPA efforts that informed this
work, and with which we could usefully integrate the ideas in this report more closely in the
future. Section 3 describes the compilation and examination of the extensive set of indicators for
water quality and aquatic ecosystems that was the starting point for the analyses in this report.
Sections 4 through 7 then discuss the four broad categories of challenges described above. We
summarize our findings and propose some recommendations in Section 8. Finally, several
appendices document the following: the literature reviewed (Appendix A); the full set of more
than 600 indicators initially evaluated (Appendix B); the data sources and supporting information
for the 53 vulnerability indicators that were evaluated for data availability and mapping potential
(Appendix C); the methodological details for how the various maps were produced (Appendix
D); example maps displayed using 4-digit Hydrologic Units and their descriptions (Appendix
E); example maps displayed using ecoregions and their descriptions (Appendix F); vulnerability
categories for each indicator by each HUC (Appendix G); and the steps for evaluating and
modifying vulnerability indicators (Appendix H).
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2. SYNERGIES WITH OTHER EPA EFFORTS
There are a number of EPA efforts devoted to indicator-based assessment of
environmental condition and impairment. This report draws from these efforts in a number of
direct and indirect ways. In addition, greater integration of the work described here with these
efforts has the potential for a number of significant benefits. Here, we briefly summarize some of
these connections.
The valued role of environmental indicators in environmental resource assessment and
management is evidenced in recent years by several prominent reports from both within the
government sector and outside it (e.g., Heinz Center, 2008). Notably, EPA tracks roughly 83
indicators of environmental and human health for its Report on the Environment (U.S. EPA,
2008a). For example, Chapter 3 of the ROE is a report card on trends in the extent and condition
of the nation's waters (U.S. EPA, 2008a). The ROE indicators are revisited roughly once every
three to four months and subsequently updated online to assess changes over time. They are
generally reported as national averages or representative examples, rather than as mapped
distributions. The long-term goal for the ROE is to report all indicators as temporal trends. The
ROE has its roots in the Environmental Monitoring and Assessment Program (U.S. EPA, 2010a),
a research program within EPA's Office of Research and Development that was designed to
develop the tools necessary to monitor and assess the status and trends of national ecological
resources. EMAP collected field data from 1990 to 2006, and focused on developing the
scientific understanding for translating environmental monitoring data from multiple spatial and
temporal scales into assessments of current ecological condition and forecasts of future risks to
our natural resources. We drew a number of the indicators discussed in this report, as well as
general indicator definitions, from the ROE.
Monitoring of the nation's aquatic resources is now conducted by the EPA Office of
Water's National Aquatic Resource Surveys (U.S. EPA, 201 Ob), which publishes a series of
studies that report on core indicators of water condition. These studies use standardized field and
lab methods that are designed to yield unbiased, statistically-representative estimates of the
condition of the whole water resource, such as rivers and streams, lakes, ponds, reservoirs, and
wetlands. Products of this program include the National Coastal Condition reports, the National
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Wetland Condition Assessment, the Wadeable Streams Assessment, and a number of other
reports. Again, as with the ROE, we drew a number of indicators from these assessments.
One of the largest and most important efforts within the agency that has relevance for
indicator-based work is the Impaired Waters listing (U.S. EPA, 2010c). Section 303(d) of the
Clean Water Act (CWA) requires states, territories, and authorized tribes to assess their waters
and identify all water bodies (e.g., streams and rivers) that are impaired. Impaired waters are
those that do not meet water quality standards because they are too polluted or otherwise
degraded. Waters that do not meet state, territory, or tribal Water Quality Standards due to such
impairments are placed on the CWA Section 303(d) list, scheduled for Total Maximum Daily
Load (TMDL) development, and eventually restored. EPA maintains responsibility for
implementing the 303(d) regulations by ensuring that impaired waters lists are developed. All
impaired waters information is then provided to the public via EPA's online data system known
as ATTAINS (U.S. EPA, 2010d). For this report, we considered using or developing indicators
based on the 303(d) impaired waters lists from each state. Our intent was to use these lists to
determine the degree to which waters are impaired for a given unit of spatial aggregation and to
frame these identified impairments within a vulnerability context. This link has been previously
discussed by EPA during evaluations of how water programs may need to adapt to changes in
climate - e.g., EPA's National Water Program Strategy: Response to Climate Change report
states that warmer air and water temperatures may lead to "increased pollutant concentrations
and lower dissolved oxygen levels will result in additional waterbodies not meeting water quality
standards and, therefore, being listed as impaired waters requiring a total maximum daily load
(TMDL)" (U.S. EPA, 2008b, p. 9). However, we decided to forego using 303(d)-based
indicators because of significant gaps in the impaired waters data, which are not comprehensive.
This lack of national data is compounded by the variation in assessment programs across states.
See Section 6.1.4 and Figure 6-1 for additional discussion of these issues.
EPA's Regional Vulnerability Assessment (ReVA) program (U.S. EPA, 2009a) seeks to
characterize vulnerability through investigation of ecosystem dynamics, the connectivity
between ecosystems and the broader landscape, and ecosystem interactions with socioeconomic
factors. The purpose of the ReVA program is to examine the probability of future problems at a
regional scale, even when precise environmental conditions at a given location cannot be
predicted. The ReVA program also aims to help decision-makers assess the degree and types of
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stress posed by human actions on a region's environmental resources. The program's
methodology evaluates indicators of vulnerability, aggregates them into indices, and evaluates
the likelihood of exacerbation of vulnerability as a result of future stressors. To date, the ReVA
program's methodology has been applied to a comprehensive analysis of the Mid-Atlantic region
(U.S. EPA, 2000a). EPA plans to conduct similar assessments in other regions.
The ReVA program is an outstanding source of vulnerability metrics and indicators. The
present study complements the ReVA program by building on its extensive work on
vulnerability and investigating a similar methodology for national scale investigations of
vulnerability focused on climate change. Both the ReVA program and the current study present
relative measures of vulnerability and identify future research opportunities that would result in
measures of absolute vulnerability. Future efforts may include integration of ReVA tools and
data with the indicators presented in the current report.
EPA's just-released 2010 report, Climate Change Indicators in the United States (U.S.
EPA, 2010e), is a new effort that is intended to track and interpret a set of 24 indicators, each
describing trends related to the causes and effects of climate change. It focuses primarily on the
United States, but in some cases also examines global trends. EPA intends to begin using these
indicators to monitor the effects and impacts of climate change in the United States, assist
decision-makers on how to best use policymaking and program resources to respond to climate
change, and assist EPA and its constituents in evaluating the success of their climate change
efforts. We did not use these indicators in this report, but we envision integrating them with the
methodologies discussed here in future efforts to assess vulnerability of water quality and aquatic
ecosystems to climate change.
Finally, there is a pressing need for objective strategies to prioritize agency efforts by
comparing different geographic locations in terms of their expected responses to future
conditions and various management options. This can be done with regard, for example, to
stream restoration (Norton et al., 2009) and to climate change adaptation (Lin and Morefield,
2011). As Norton et al. (2009) write, "Tens of thousands of 303(d)-listed waters, many with
completed TMDLs, represent a restoration workload of many years. State TMDL scheduling and
implementation decisions influence the choice of waters and the sequence of restoration.
Strategies that compare these waters' recovery potential could optimize the gain of ecological
resources by restoring promising sites earlier." Norton et al. (2009) then explore ways that states,
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tribes, and territories can use measurable metrics of ecological, stressor, and social context to
estimate the relative recovery potential of sites, as a key input into decisions that set priorities for
the selection and sequence of restoration efforts. Similarly, Lin and Morefield (2011), using the
Atlantic and Gulf Coast National Estuaries as their example, propose a framework for assessing
and prioritizing management recommendations that might be made in response to communities'
vulnerability to climate change and their wishes to develop adaptation strategies. In our view,
attention to the issues and challenges discussed in this report is likely to aid in the task of
developing objective measures that can inform a broad range of prioritization decisions.
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3. INDICATORS CONSIDERED FOR THIS REPORT
This section describes the approach used to compile a comprehensive list of potential
indicators of water quality and aquatic ecosystem vulnerability from those identified in published
sources. Figure 3-1 outlines the general methodology in the selection of indicators for this study.
3.1. LITERATURE SEARCH
We performed an extensive literature search to identify recent studies related to the
monitoring and evaluation of water quality and ecosystem conditions. The types of literature
reviewed included journal articles, studies, and reports. The literature ranged widely in study
area, from local to international. It ranged in technical field from biological, hydrological, and
chemical, to human aspects, and included both primary and secondary literature. The literature
sources also varied, including individual researchers, public institutions, and non-governmental
organizations. Studies reviewed spanned a decade of relevant literature from 1998 through 2008.
The literature reviewed was primarily obtained from the GCRP research team members
and through internet and library database searches conducted by Cadmus. Literature identified by
GCRP as relevant was considered to be "core literature" and was given high priority in the
review process. Thereafter, other references were reviewed to identify additional indicators for
possible inclusion. The citations within the core literature were also useful as sources of
additional relevant literature.
3.1.1. Core Literature
As noted above, the GCRP research team identified a short list of studies as core
literature that served as a starting point for identifying vulnerability indicators. These studies are
listed in Table 3-1.
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Table 3-1. List of core literature
List of Core Literature (see Appendix A for full references)
Coastal States Organization, 2007 Millennium Ecosystem Assessment, 2005b
Ebi et al., 2007 Millennium Ecosystem Assessment, 2005c
Frumhoff et al., 2007 National Assessment Synthesis Team, 2000a
Gilliom et al., 2008 National Assessment Synthesis Team, 2000b
Gleick and Adams, 2000 Poff et al., 2002
Hamilton et al., 2004 . U.S. EPA, 2006
Heinz Center, 2002 . U.S. EPA, 2008a
Heinz Center, 2008 . U.S. EPA, 2008b
Kurd etal., 1998 . USGAO, 2005
Kurd et al., 1999 . United States Geologic Survey (USGS), 1999
Lettenmaier et al., 2008 Zogorski et al., 2006
Millennium Ecosystem Assessment, 2005a
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Figure 3-1. Flowchart of methodology used to identify and map vulnerability indicators.
Step 1: Conduct literature search.
Extensive literature search conducted.
- 4 86 documents
Step 2: Identify indicators of water quality and
aquatic ecosystem condition.
Literature review conducted and indicators of water
quality and aquatic ecosystem condition identified.
Step 3: Delete duplicate indicators
If identical indicators cited by different literature
sources, a single best indicator selected for further
evaluation. Remaining duplicate indicators (which were
either not defined, poorly defined, or were specific to a
geographic region) deleted.
66 indicators N
eliminated
Step 3: Classify indicators of vulnerability
Indicators of vulnerability identified. State variables
(i.e., those measuring condition at a point in time)
504 indicators N
eliminated
Step 4: Assess data availability.
Data sources identified and some indicators eliminated
because: (a) no indicator data were available; (b) data
collection was in progress; (c) data were not national;
(d) data were not recent, or were a projection; (e)
combination of multiple data sets entailed complex
methods; (f) indicator required extensive modeling
using raw data.
I
28 indicators \
eliminated
Step 5: Create example maps.
Data obtained and manipulated to create maps using
CIS software for readily mappable indicators.
FINAL 25
MAPPABLE
INDICATORS
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Some studies, typically those that were specifically geared towards identifying indicators
of ecosystem change or documenting the results of national environmental monitoring studies,
served as a source for many of the indicators in this EPA study. Some key studies in the core
literature and how they were used are described below.
Hurdetal, 1998 andHurdet al, 1999
The report, Water Climate Change: A National Assessment of Regional Vulnerability,
prepared for EPA by Kurd et al. (1998), identified key aspects of water supply and quality that
could be adversely affected by climate change, developed indicators and criteria useful for
assessing the vulnerability of regional water resources to climate change, created a regional
database of water-sensitive variables consistent with the vulnerability measures, and applied the
criteria in a comparative national study of the vulnerability of U.S. water resources. The result of
this study was a series of national-scale maps attempting to demonstrate the vulnerability of
different U.S. regions to climate change for each indicator of vulnerability of water supply and
quality. An abbreviated version of this study, presenting a few select indicators and outlining the
general methodology used in creating national-scale maps for each indicator, was later published
in the Journal of the American Water Resources Association (Kurd et al., 1999). The spatial
resolution of vulnerability estimates used by Kurd et al. (1998) was a 4-digit Hydrologic Unit
Code (HUC) or hydrologic subregion, of which there are 222 nationwide.
Heinz Center, 2002 and Heinz Center, 2008
The State of the Nation's Ecosystems 2008: Measuring the Land, Waters, and Living
Resources of the United States prepared by the H. John Heinz Center for Science, Economics,
and the Environment (hereafter referred to as the Heinz Center), was the most recent publication
in an effort aimed at developing a comprehensive evaluation of the condition of the nation's
ecosystems. Aspects of this effort were a model for the methodology used in the present study.
We also used an older publication from the same effort (Heinz Center, 2002) to incorporate
indicators that were not considered in the Heinz Center 2008 study.
The indicators in the Heinz Center reports often described the state of ecosystem
attributes. Because current state was considered a component of vulnerability, the selection of
these indicators typically represented the first screening step in identifying useful vulnerability
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indicators. The state indicators used by the Heinz Center did not explicitly describe stressors that
affected those indicators, although stressors were implied for ecosystem attributes that were in a
degraded state.
The Heinz Center described several indicators for which adequate data were not
available. We also adopted the approach of identifying ongoing collection efforts or proposing
data collection priorities for indicators of potential importance. The Heinz Center report includes
terrestrial ecosystem types; the present study does not. However, the "Coasts and Oceans" and
"Fresh Waters" sections of the Heinz Center report included many specific indicators that we
used here.
U.S. EPA, 2006
Wadeable Streams Assessment (WSA): A Collaborative Survey of the Nation's Streams
summarizes the results of a collaborative effort led by EPA (2006) to provide a statistically
defensible report on the condition of the nation's smaller streams. Standardized methods were
used to measure several physical, chemical, and biological attributes at 1,392 sites that represent
the small streams in the U.S.
The database that accompanied WSA was used as a data source for mapping several of
the indicators in the present study. As with some indicators from the Heinz reports, the measures
reported in EPA's WSA report (2006) reflect the current condition of the wadeable streams,
rather than their specific vulnerability to future changes.
U.S. EPA, 2008a
As described in Section 2, EPA tracks roughly 83 indicators of environmental and human
health, and reported on those indicators in U.S. EPA's 2008Report on the Environment. The
Report on the Environment (ROE) is published less frequently in hardcopy form, but continually
updated online (www.epa.gov/roe). Chapter 3 of the ROE is a report card on trends in the extent
and condition of the nation's waters. The indicators in this report were generally reported as
national averages or representative examples, rather than mapped distributions. Some indicators
were reported as temporal trends. Indicator data were derived from multiple sources, and no new
data were collected as part of this chapter. The indicators in this report are revisited roughly once
every three to four months and subsequently updated online to assess changes over time. The
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ROE provided several indicators for this report. Some ROE indicators of temporal trends are
closely tied to the concept of vulnerability.
United State Geologic Survey (USGS), 1999
The Quality of our Nation's Waters: Nutrients and Pesticides, the first summary report
from the USGS' National Water-Quality Assessment (NAWQA) program, reports on the
geographic distribution, environmental drivers, and temporal trends of nutrients and pesticides in
surface waters. The NAWQA data include several useful summary statistics from the broad
range of physical and chemical water quality parameters measured as a part of the NAWQA
program.
Under the NAWQA program, 51 sites are broken up into smaller groups that are sampled
in multiple rounds (20 study units in 1991; 16 study units in 1994; and 15 study units in 1997).
NAWQA is also considered the best source of information on the occurrence of pesticides in
surface and groundwater. However, even with the full complement of study units (including
units that were not completed at the time of the present study), the spatial coverage of NAWQA
sites is relatively sparse. As with most of the literature used in the present study, NAWQA
reports primarily on current condition, rather than vulnerability to future change.
3.1.2. Protocol for Collecting Additional Relevant Literature
To develop a comprehensive list of indicators cited in the published literature, an
extensive and representative sample of recent studies was needed. We conducted a literature
search using publicly available (e.g., Google Scholar) and non-public (e.g., ScienceDirect)
search tools to identify studies with a primary or secondary focus on water quality and aquatic
ecosystems. We selected studies based on their likelihood of containing water quality and aquatic
ecosystem indicators.
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Along with the core literature, we identified 86 studies that could be used as potential
sources of indicators, including:
19 government reports;
40 peer-reviewed journal articles; and
27 other reports including those by non-governmental or inter-governmental
organizations.
See Appendix A (List of Literature Reviewed) for a complete list of the reviewed
literature.
3.2. CREATION OF A COMPREHENSIVE LIST OF INDICATORS
We reviewed the literature collected and identified indicators relevant to the present
study. This section describes the guidelines we used to identify relevant indicators, and the
details of the choices we made to select only certain indicators from particular studies based on
these general guidelines.
We use the term, "indicator" in this report as it is commonly used in the published
literature (Adger et al., 2004; Villa and McLeod, 2002; Kurd et al., 1998), to define a variable or
a combination of variables that can be used to measure the change in an environmental attribute.
Similar terms, such as "metric" are also widely used in the literature (Norton et al., 2009; Luers,
2005), while metric and indicator are used interchangeably in other studies (Adger, 2006;
Nicholson and Jennings, 2004). For the purposes of this report, we use the terms metric and
indicator interchangeably.
3.2.1. Identifying Indicators of Water Quality and Aquatic Ecosystem Condition
We reviewed all of the studies indentified in the literature search to develop a
comprehensive list of indicators. Unlike a typical literature review, we reviewed these studies for
indicators of water quality and aquatic ecosystem condition, rather than for their contributions to
the body of knowledge on this topic. Therefore, they were reviewed for their explicit or implicit
16
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description of indicators that could potentially be used to assess the vulnerability of water quality
and aquatic ecosystems to environmental change. We selected indicators following the
guidelines for good indicators from EPA's Report on the Environment (ROE) as presented in
Figure 3-2 (Indicator definition from EPA's 2008 Report on the Environment).
Useful It answers (or makes an important contribution to answering) a question in the ROE.
Objective. It is developed and presented in an accurate, clear, complete, and unbiased manner.
Data Quality. The underlying data are characterized by sound collection methodologies, data management
systems to protect their integrity, and quality assurance procedures.
Data Availability. Data are available to describe changes or trends, and the latest available data are timely.
Representative Data. The data are comparable across time and space and representative of the target
population. Trends depicted in this indicator accurately represent the underlying trends in the target
population.
Transparent and Reproducible Data. The specific data used and the specific assumptions, analytical
methods, and statistical procedures employed are clearly stated.
Figure 3-2. Indicator definition from EPA's 2008 Report on the Environment.
This selection process resulted in a comprehensive list of 623 indicators (presented in
Appendix B: Comprehensive List of Indicators). Each indicator was assigned a unique indicator
identification number (Indicator ID#) - this was necessary given the large number of indicators
and to avoid confusion among indicators with similar names. In subsequent sections of this
report, each indicator name is associated with its parenthetical ID# (e.g., Acid Neutralizing
Capacity [#!]). These identification numbers also facilitate easier referencing of each indicator in
the appendices of this report.
Most water quality and aquatic ecosystem indicators found in the literature were included
in the comprehensive list. However, it is important to discuss why we excluded some indicators
from this list and chose not to examine them in subsequent steps of this methodology. We
discuss these reasons immediately below.
3.2.2. Selection of Indicators
In the interest of thoroughness, we made broad determinations regarding whether or not
each indicator, measure, or metric in a particular study could be used to characterize, evaluate, or
assess water quality or aquatic ecosystems. On the rare occasions when we excluded indicators
17
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from a particular study from the comprehensive list, we documented the reasons for such
exclusions - for example, indicators related to air quality were generally not considered relevant
to this project, and have been well-studied elsewhere. The wide range of characteristics that
describe the comprehensive list of indicators for this project can be summarized as follows:
Indicators covered a variety of different disciplines;
Indicators were of varying scales, from local to national;
Indicators had varying amounts of data associated with them;
Indicators were aggregated (made up of smaller input indicators) or disaggregated;
Indicators were drinking water indicators or indicators related to aquatic ecosystems;
Some were indicators related to infrastructure; and
Indicators were potentially important to decision-makers at a variety of levels,
ranging from federal, to regional and local levels.
Indicators included in the list were vetted in the literature, although to varying extents.
Some studies focused solely on identifying robust water quality and ecosystem condition
indicators that could be used to observe and explain changes in the natural environment. Other
studies merely provided a theoretical rationale for needing the development of new indicators.
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In addition to selecting specific indicators, we also reviewed the literature to obtain the
following indicator-related information:
Indicator definition, as specified in the literature, or written based on supporting text
in the literature;
Level at which it is adopted (i.e., local, state, or national);
Whether the indicator is currently in use;
Geographic scope (i.e., local, state, or national);
Spatial resolution;
Target audience (e.g., scientists, policymakers, risk analysts); and
Rationale for the indicator's inclusion on the comprehensive list of indicators (based
on information in the literature) to corroborate the indicator's relevance as an
indicator of the vulnerability of waterbodies to environmental degradation.
In addition, a team of technical experts classified the potential application of each
indicator to climate change as high, medium, or low. These experts, listed on page iii of this
report, represent multi-disciplinary fields related to the impacts of climate change on various
aspects of human life and the natural environment.
In addition to the steps described above, we took two specific actions to ensure the most
comprehensive indicator list possible:
Creation of Indicator Categories: Different indicators measure different aspects of
potential vulnerability. By grouping like indicators, it was possible to determine
which aspects of water quality and aquatic ecosystem condition were reasonably
covered by the selected indicators and to identify potential coverage gaps. Therefore,
to facilitate reviews of the indicator list, we established indicator categories and sub-
categories, as shown in Table 3-2 (Indicator primary and secondary categories).
Review of Indicator List by Technical Experts: The technical advisors reviewed a
draft list of indicators and were asked to add indicators where they perceived gaps.
Through this process, one indicator (Total Withdrawal Information by Source & Type
of Use [#622]) was added to the comprehensive list, and a significant amount of
additional detail and new information was added for the indicators already in the
comprehensive list.
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Table 3-2. Indicator primary and secondary categories
Ecological (161)
Condition of Plant Species
Distribution of Plants
Exposure to Contaminants
Habitat Condition
Non-Native Species
Species at Risk
Species Diversity
Species Populations
Land Cover/Use (61)
Agricultural
Coastal
Forest
Freshwater
Glaciers
Grasslands/Shrublands
Natural Cover
Urban/Suburban
Wetlands
Other (2 1
Hydrological (104)
Duration of Natural Events
Engineered Structures
Precipitation
Sea Level Rise
Temperature
Water Flow
Water Levels
Waves
Socioeconomic (57)
Housing
Policy
Recreation
Resource Use
Air (19)
Aerosols
Ozone
Temperature
Human Populations (14)
Population Size
Susceptible Populations
Chemical (96)
Carbon
Chlorophyll a
Contaminants in Sediment
Microbes
Multiple Contaminants
Nutrients
Oxygen
Pesticides
pH
Salinity
Turbidity/Clarity
Extreme Weather Events (16)
Drought
Fire
Flood
Storm
Soil (27)
Composition
Erosion
Sediment
Note: The "Other" category has no secondary categories.
3.2.3. Exclusion of Certain Indicators and Studies
In some cases, we excluded from the comprehensive list particular indicators, groups of
indicators, or all indicators from a particular study. Table 3-3 (Rationale for exclusion of certain
indicators) presents the rationale for not selecting some indicators from particular studies.
3.2.4. Deletion of Duplicate Indicators
As indicators for the comprehensive list were identified from various literature sources,
some redundancy was noted in some groups of indicators. When two or more indicators were
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identified as being very similar, one was selected to represent the group, and the others were
removed from further consideration for mapping. Selected representative indicators were most
often those that had a clear definition, were relevant at the national level (i.e., not limited to a
small geographic region), could be quantified easily, or were obtained from this study's core
literature sources. Sixty-six indicators were deleted because they were redundant with other
indicators in the comprehensive list.
Table 3-3. Rationale for exclusion of certain indicators
Reasons for Exclusion of
Indicators
Literature Sources
(see Appendix A for full references)
Indicators were modeled
projections, specific to a non-U.S.
location, or were too broadly
defined.
Arnell, 1998
Arnell, 1999
Barrett etal, 2005
Bergstrometal.,2001
Conway and Hulme, 1996
de Wit and Stankiewicz, 2006
Gleick and Adams, 2000
Kundzewicz et al., 2008
Lettenmaier et al., 2008
Nicholls and Hoozemans, 1996
Palmer et al., 2008
Roderick and Farquhar, 2002
Indicators were of human
adaptive capacity or
socioeconomic indicators, rather
than of aquatic ecosystems or
water quality.
Adger at al., 2004
Brooks et al., 2005
Ebi et al., 2007
Fnimhoff etal, 2006
Fnimhoff atal, 2007
Gleick and Adams, 2000
Jacobs et al., 2000
Kling et al., 2003
Millennium Ecosystem
Assessment, 2005a
Millennium Ecosystem
Assessment, 2005b
Twilley etal., 2001
Indicators were identical or very
similar to those in another study,
or indicators were better defined
in another study.
Bradbury et al., 2002
Bunn and Arthington, 2002
Chesapeake Bay Program, 2008
Dai etal., 1999
Fnimhoff etal., 2007
Grimm etal., 1997
Hamilton et al., 2004
Hay slip etal., 2006
Huntingtonetal., 2004
Kurd etal., 1998
Kling et al., 2003
Long Island Sound Study, 2008
Ojimaetal., 1999
U.S. EPA, 1995
U.S. EPA, 2002
Zogorski etal., 2006
Indicators and their associated
data sources were not adequately
detailed as the study was primarily
a policy/funding-oriented
document.
Coastal States Organization, 2007
Luers et al., 2006
Murdoch et al., 1999
National Assessment Synthesis
Team, 2000b
Poff etal., 2002
U.S. EPA, 2008c
USGAO, 2000
USGAO, 2002
USGAO, 2004
USGAO, 2005
Vincent and Pienitz, 2006
Yamin et al., 2005
Indicators were large aggregates
of smaller indicators.
Gleick and Adams, 2000
U.S. EPA, 2008d
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4. CHALLENGES PART I: INDICATOR CLASSIFICATION
This section describes how we evaluated the indicators introduced in the previous section
to determine whether they were suitable, in principle, for assessing relative vulnerability to large-
scale environmental degradation due to external stressors (of which climate change would be one
example). First we considered how to define vulnerability. We then applied that definition to
each of the 623 indicators that resulted from the process described in the previous section,
resulting in a small subset being classified as "vulnerability" indicators.
4.1. DEFINING VULNERABILITY
There has been considerable debate in the literature on the meaning of vulnerability in the
context of environmental systems and stressors (climate change in particular) and the elements of
which it is composed. We summarize some of that discussion here as background.
It has been argued that the lack of a common definition has hindered interdisciplinary
discourse on the topic and the development of a common framework for vulnerability
assessments (Fiissel, 2007; Brooks, 2003). Others have argued that the purpose of the analysis
should guide the selection of the most effective definition or conceptualization (Kelly and Adger,
2000).
Some of the purposes for which climate change vulnerability assessments may be
performed include: increasing the scientific understanding of climate-sensitive systems under
changing climate conditions; informing the specification of targets for the mitigation of climate
change; prioritizing political and research efforts to particularly vulnerable sectors and regions;
and developing adaptation strategies that reduce climate-sensitive risks independent of their
attribution. Each of these purposes has specific information needs and thus might require a
targeted approach to provide this information.
Below is a summary of discussions about the definition of vulnerability in the literature
on climate change, including:
Determinants of vulnerability;
Defining a vulnerable situation;
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Biophysical and socioeconomic domains; and
Predictability and uncertainty.
4.1.1. Determinants of Vulnerability
The IPCC definition of vulnerability is: "The degree to which a system is susceptible to,
or unable to cope with, adverse effects of climate change, including climate variability and
extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to
which a system is exposed, its sensitivity, and its adaptive capacity." (IPCC, 2007a, p. 995)
(IPCC Def 1). Three terms are defined further in the IPCC report: sensitivity, exposure, and
adaptive capacity.
The IPCC defines sensitivity as "the degree to which a system is affected, either
adversely or beneficially, by climate-related stimuli." This definition is generally supported by
much of the literature on the topic, but there are two subtly different interpretations. The first
considers sensitivity as the probability or likelihood of passing a critical threshold in a variable
of interest (e.g., the probability of exhausting water supplies) (Fraser, 2003; Jones, 2001). The
second considers sensitivity to be the degree to which outputs or attributes change in response to
changes in climate inputs (Moss et al., 2001). This second interpretation incorporates an
understanding that some stresses may increase gradually, instead of emphasizing the passing of
one critical threshold value as the only kind of important change. In both cases, a system's
sensitivity to stress is separate from its exposure to stress.
Similarly, exposure is "The nature and degree to which a system is exposed to significant
climatic variations." A system may be currently exposed (or predicted to be exposed in the
future) to significant climatic variations. Because there are multiple factors related to climate and
climate change that may cause stress (e.g., temperature, precipitation, winds, changes in spatial
and temporal variability and extremes, etc.), the type of exposure ("hazard" in Fiissel's [2007]
terminology) should be specified. In this definition, exposure is separate from sensitivity. A
system may be exposed to significant climate changes, but if it is not sensitive to those changes,
it is not vulnerable. The socioeconomic literature on vulnerability tends to lump these factors
together (e.g., "Social vulnerability to climate change is defined as the exposure of groups or
individuals to stress as a result of the impacts of climate change" [Adger, 1999]).
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Finally, adaptive capacity is "The ability of a system to adjust to climate change
(including climate variability and extremes) to moderate potential damages, to take advantage of
opportunities, or to cope with the consequences." In the socioeconomic literature, vulnerability is
often defined primarily by adaptive capacity, particularly as it is linked to poverty (e.g., ".. .the
vulnerability of any individual or social grouping to some particular form of natural hazard is
determined primarily by their existent state, that is, by their capacity to respond to that hazard,
rather than by what may or may not happen in the future." Kelly and Adger, 2000; see also
Olmos, 2001; and Tompkins and Adger, 2004). This conceptualization views sensitivity to most
hazards as a given, exposure to some hazard(s) as inevitable, and therefore the need for
adaptation will arrive sooner or later. Other authors have argued that because adaptive capacity is
not necessarily static (i.e., it can be developed), vulnerability assessments should focus on
sensitivity and exposure, with the goal of identifying locations to focus the development of
adaptive strategies (O'Brien et al., 2004; Kelly and Adger, 2000).
4.1.2. Defining a Vulnerable Situation
There is general agreement in the literature that the term, "vulnerability," by itself, may
not be sufficiently descriptive (Moreno and Becken, 2009; Fiissel, 2007; Polsky et al., 2007;
Brooks, 2003). Instead, a vulnerable situation should be defined. This definition should include
the following components (Fiissel, 2007):
Temporal reference: the point in time or time period of interest. Specifying a
temporal reference is particularly important when the risk to a system is expected to
change significantly during the time horizon of a vulnerability assessment, such as for
long-term estimates of climate change.
Sphere: Internal (or 'endogenous' or 'in place') vulnerability factors refer to
properties of the vulnerable system or community itself, whereas external (or
'exogenous' or 'beyond place') vulnerability factors refer to something outside the
vulnerable system that adds to the vulnerability of the system.
Knowledge domain: socioeconomic (e.g., poverty) vs. biophysical (e.g., flow regime
sustainability).
System: the system of analysis, such as a coupled human-environment system, a
population group, an economic sector, a geographical region, or a natural system.
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Attribute of concern: the valued attributes of the vulnerable system that are
threatened by its exposure to a hazard. Examples of attributes of concern include
human lives and health; the existence, income and cultural identity of a community;
and the biodiversity, carbon sequestration potential, and timber productivity of a
forest ecosystem.
Hazard: a potentially damaging physical event, phenomenon, or human activity that
may cause the loss of life or injury, property damage, social and economic disruption,
or environmental degradation.
An example of a fully specified vulnerable situation is: 'vulnerability of the incomes of
the residents of a specific watershed to drought'. In practice, only the components of the
definition that are not clear from the context (or uniformly applied to multiple situations) need be
defined. The advantage of a specific definition of a vulnerable situation is that it is unambiguous.
The disadvantage is that it makes it difficult to conduct holistic vulnerability comparisons among
locations.
4.1.3. Biophysical and Socioeconomic Domains
In the climate change literature, the term "vulnerability" has more frequently been
applied to socioeconomic situations; the term "risk" has been used to describe biophysical
condition situations (e.g., Jones, 2001). Biophysical vulnerability or risk is primarily related to
sensitivity and exposure, while socioeconomic vulnerability is more a function of adaptive
capacity. Biophysical vulnerability may encompass effects on humans, such as increase in
population at risk of flooding due to sea level rise. However, it is related to human exposure to
hazard rather than to the ability of people to cope with hazards once they occur (Brooks, 2003).
The view of vulnerability as a state (i.e., as a variable describing the internal state of a system)
has arisen from studies of the structural factors that make human societies and communities
susceptible to damage from external hazards. Social vulnerability encompasses all those
properties of a system independent of the hazards to which it is exposed that mediate the
outcome of a hazardous event (Brooks, 2003). In theory, this idea could be applied to biophysical
systems, inasmuch as previous stress has rendered the system more susceptible to any new
hazard.
Most of what we define as "vulnerability indicators" in this report are biophysical
indicators. They therefore primarily encompass sensitivity and exposure to environmental
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stresses. Adaptive capacity can be developed in locations that are sensitive and exposed to stress.
In addition, while much of the literature on ecosystem vulnerability, particularly as it relates to
climate change, focuses exclusively on the degradation of ecosystem components that directly
serve human needs (Fiissel, 2007), several of the indicators in this report focus on the direct,
inherent vulnerability of the aquatic ecosystems themselves, independent of the ecosystem
services provided to humans. We also examine other indicators that focus on the vulnerability of
drinking water quality, and are thus more obviously and directly related to human needs.
4.1.4. Predictability and Uncertainty
The future behavior of socio-ecological systems is difficult, or perhaps impossible, to
predict because the components of these systems are constantly adapting to changing conditions.
As a result, a system may contain non-linearities, inter-dependencies, and feedback loops that
make its overall behavior unpredictable (Moreno and Becken, 2009; Fraser et al., 2003; Holling,
2001). A vulnerability assessment itself may reduce future vulnerabilities by helping target the
development of adaptive capacity in systems that are sensitive and exposed to external stressors
such as climate change.
For climate change in particular, many of the adverse effects on ecosystems and human
systems are expected to occur as a result of stochastic events that may or may not happen, but to
which a subjective probability of occurrence could in principle be assigned. Because these
probabilities are conditioned on, for example, predictions of future climate and on models of how
the system will respond to climate changes (Jones et al., 2001), it may not be possible to
constrain them very much given the current limitations of climate prediction, as discussed in the
Introduction. This report focuses on the challenges associated with assessing vulnerability across
the nation without depending on accurate environmental prediction. That is, for most of the
report we evaluate the vulnerability of water quality and aquatic ecosystems in the absence of
specific future scenarios of global climate, population, and land use changes. This bottom-up
approach of focusing on indicators vetted in the scientific literature, available data, and current
vulnerability, can be used in follow-up studies in combination with approaches focused on
improving our ability to predict environmental changes.
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4.2. CLASSIFYING VULNERABILITY INDICATORS
In the early phases of this project, we held a workshop1 to develop rules of thumb for
classifying the comprehensive suite of 623 indicators into two broad categories. The first
category is "vulnerability indicators" that, at least in principle, could measure the degree to
which the resource being considered (e.g., watershed, ecosystem, human population) is
susceptible to, and unable to cope with, adverse effects of externally forced change. Such change
could potentially include climate or any other global change stressor. The second category
constitutes state variables or indicators of condition that merely measure the current state of a
resource without relating it to vulnerability.
Informed by the literature above, the workshop participants concluded that, in practical
terms, to qualify as a measure of "vulnerability," an indicator should inherently include some
relative or value judgment. Examples include comparing one watershed to another, comparing
the indicator to some objectively defined threshold or possible state, or reporting on the
indicator's change over time. Measures of water quality or ecological condition at a point in
time without reference to a baseline would not make good vulnerability indicators. Viewed from
the perspective of indicator measurement, this can be achieved by such methods as computing a
ratio of two quantities, at least one of which is a time rate of change or a measure of variation, or
computing the portion of a distribution that lies above or below a defined threshold. Examples
abound, including the ratio of the standard deviation of annual streamflow to mean annual
streamflow (to measure degree of variability in the stream), the ratio of stream withdrawals of
water to mean annual streamflow (to measure the portion of the flow that is being used), the ratio
of mean annual baseflow to mean annual total flow (to measure the susceptibility to dry periods),
and the average number of days in a year that a metric such as temperature, dissolved oxygen, or
salinity in coastal wetlands exceeds a particular threshold.
Applying these rules of thumb is straightforward for some of the indicators and less so
for others. Many could arguably fall into either the "vulnerability" or the "state" category. For
example, when assessing vulnerability to flooding, we might examine the total number of people
lrThe workshop took place at the National Center for Environmental Assessment (NCEA), in Washington, DC, on
December 18, 2008. Participants included members of the Cadmus team, members of the EPA Global Change
Research Program (GCRP) staff from NCEA, and the outside expert consultants acknowledged in this report.
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living within the 100- or 500-year floodplain in a given watershed; when measuring ecosystem
health, we might look at the total number of species in each watershed classified as "at risk." The
key for these examples is that, by embedding an implied threshold in these indicators - i.e., by
choosing the particular flood frequency (e.g., 100-year or 500-year) that we consider to be
damaging, or a particular classification of "at risk" - we have made a judgment about the system
that goes beyond assessing its condition to assessing its susceptibility to harm. Not all
vulnerability indicators incorporate implied thresholds, and those that vary over a gradual
gradient are still of great value and can inform assessments of relative vulnerability, as discussed
in Section 5.1.
This classification exercise winnowed the original list of 623 indicators down to 53
indicators shown in Table 4-1 (List of vulnerability indicators). Examples illustrating these
classification principles include the following:
Vulnerability Indicators:
Stream Habitat Quality (#284) - compares stream habitat conditions in a given area to
those in a relatively undisturbed habitat in a similar ecosystem;
Groundwater Depletion (#121) - compares the average groundwater withdrawals to
annual average baseflow, reflecting the extent to which groundwater use rates may be
exceeding recharge; or
Wetland Species At-Risk (#326) - examines the number of threatened and
endangered species inhabiting a particular wetland area.
State Variables:
Nitrogen and Phosphorus - large rivers (#186) - measurement of nitrogen and
phosphorus in all streams without a reference value; or
Instream fish habitat (#138) - a measure of instream fish concealment features (e.g.,
undercut banks, boulders, large pieces of wood, brush) within a stream and along its
banks, without specifying reference conditions, such as, for example, concealment
features at undisturbed sites.
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Table 4-1. List of vulnerability indicators
Indicator
(See Appendix B for definitions)
Acid Neutralizing Capacity (ANC) (#1)
Altered Freshwater Ecosystems (percent miles changed) (#17)
At-Risk Freshwater Plant Communities (#22)
At-Risk Native Freshwater Species (#24)
At-Risk native marine species (relative risk) (#27)
Coastal Vulnerability Index (to sea level rise) - CVI (#51)
Commercially important fish stocks (size) (#55)
Fish and Bottom-Dwelling Animals (comparison to baseline) (#95)
Flood events (frequency) (#100)
Freshwater Rivers and Streams with Low Index of Biological Integrity
(ecosystem condition) (#116)
Groundwater Depletion - Ratio of Withdrawals/ Baseflow (#121)
Groundwater reliance (#125)
Harmful algal blooms (occurrence) (#127)
Invasive species - Coasts affected (area, ecosystem condition) (#145)
Invasive species in estuaries (percent influenced) (#149)
Low flow sensitivity (mean baseflow) (#159)
Meteorological drought indices (#165)
Number of Dry Periods in Grassland/Shrubland Streams and Rivers (Percent
of streams with dry periods over time) (#190)
Ratio of Snow to Precipitation (S/P) (#218)
Ratio of water withdrawals to annual streamflow (level of development)
(#219)
Riparian Condition (Riparian Condition Index) (#231)
Status of Animal Communities in Urban and Suburban Streams (Percent of
urban/suburban sites with undisturbed and disturbed species) (#276)
Streamflow variability (annual) (#279)
Stream habitat quality (#284)
Water Clarity Index (real vs. reference) (#318)
Water Quality Index (5 components) (#319)
Waterborne human disease outbreaks (events) (#322)
Wetland loss (#325)
Literature Source
(See Appendix A for full
citations)
U.S. EPA, 2006
Heinz Center, 2008
Heinz Center, 2008
Heinz Center, 2008
Heinz Center, 2008
Day et al., 2005
Heinz Center, 2008
Heinz Center, 2008
Lettenmaier et al., 2008
Heinz Center, 2008
Kurd etal., 1998
Hurdetal., 1998
Heinz Center, 2008
Heinz Center, 2008
Heinz Center, 2008
Hurdetal., 1998
Jacobs etal., 2000
Heinz Center, 2008
Lettenmaier et al., 2008
Hurdetal., 1998
Heinz Center, 2008
Heinz Center, 2008
Hurdetal., 1998
Heinz Center, 2008
NEP, 2006
NEP, 2006
Heinz Center, 2008
MEA, 2005
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Indicator
(See Appendix B for definitions)
Wetland and freshwater species at risk (number of species) (#326)
Ratio of water use to safe yield (#328)
Erosion rate (#348)
Instream use/total streamflow (#351)
Total use/total streamflow (#352)
Snowmelt reliance (#361)
Pesticide toxicity index (#364)
Population Susceptible to Flood Risk (#209)
Herbicide concentrations in streams (#367)
Insecticide concentrations in streams (#369)
Organochlorines in Bed Sediment (#371)
Herbicides in Groundwater (#373)
Insecticides in Groundwater (#374)
Salinity intrusion (coastal wetlands) (#391)
Heat-Related Illnesses Incidence (#392)
Precipitation Elasticity of Streamflow (#437)
Ratio of reservoir storage to mean annual runoff (#449)
Runoff Variability (#453)
Macroinvertebrate Index of Biotic Condition (#460)
Macroinvertebrate Observed/Expected (O/E) Ratio of Taxa Loss (#461)
Coastal Benthic Communities (#462)
Threatened & Endangered Plant Species (#467)
Vegetation Indices of Biotic Integrity (IB I) (#475)
Instream Connectivity (#620)
Water Availability: Net Streamflow per capita (#623)
Literature Source
(See Appendix A for full
citations)
Hurdetal., 1998
Schmitt et al, 2008
Murdoch et al., 2000
Meyer etal., 1999
Meyer etal., 1999
IPCC, 2007
USGS, 2006
Hurdetal., 1998
USGS, 1999
USGS, 1999
USGS, 1999
USGS, 1999
USGS, 1999
Poff etal., 2002
Pew Center, 2007
Sankarasubramanian et al., 2001
Lettenmaier et al., 2008
Lettenmaier et al., 2008
U.S. EPA, 2006
U.S. EPA, 2006
U.S. EPA, 2008
U.S. EPA, 2008
U.S. EPA, 2008
Heinz Center, 2008
Hurdetal., 1998
All of the indicators listed in Table 4-1 were further examined for data availability and
mappability, as discussed in detail in Section 6.
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4.3. HOW DO THESE INDICATORS REFLECT VULNERABILITY?
All of the 53 vulnerability indicators vary in their responses to environmental stress and
in the degrees to which they reflect vulnerability of water quality and aquatic ecosystems. Here
we discuss, for the subset of 25 vulnerability indicators that were mappable at the national scale,
how the literature characterizes the link between each indicator and the potential vulnerability of
ecosystems or human systems.
Acid Neutralizing Capacity (#1)
Definition: The Acid Neutralizing Capacity or ANC (#1) indicator is a measure of the
ability of stream water to buffer acidic inputs (U.S. EPA, 2006). Streams may be naturally acidic
due to the presence of dissolved organic compounds (U.S. EPA, 2006). However, acid deposition
arising from anthropogenic sources may increase the acidity of the stream (U.S. EPA, 2006).
Acid mine drainage, formed by water passing through mines and mine tailings, is the primary
source of acid in surface water, and results in the formation of concentrated sulfuric acid. Acidity
is also caused by acid rain formed by dissolution of industrial and automotive emissions, such as
nitrogen oxide and sulfur dioxide, in rain water (U.S. EPA, 2006).
Measurement/Calculation: The ANC indicator is calculated as the percent of stream sites
that have been deemed to be at risk, i.e., that have ANC values of 100 milliequivalents (a
baseline condition) or less. The data used to map this indicator were collected every five years.
Impacts of global climate change and other stressors: Acid deposition from
anthropogenic sources may lower the pH of a stream with low ANC, thereby affecting aquatic
vegetation and organisms, as well as water quality, particularly in sensitive watersheds. Changes
in precipitation due to global climate change may result in increased acid deposition or drainage
from acid mines. Areas with a low percentage of streams with suitable buffering capacity could
experience disproportionately large adverse effects resulting from increased acid exposure. In
contrast, well-buffered streams with higher ANC may not be as sensitive to increased acidity
from external sources.
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At-Risk Freshwater Plant Communities (#22)
Definition: This indicator describes the risk of elimination faced by wetland and riparian
plant communities. The condition of these communities is considered important because of the
ecosystem services they provide, including habitat for a variety of species, flood storage, water
quality improvements, carbon storage, and other benefits (Heinz Center, 2008; Johnson et al.,
2007; NRC, 1992). Loss of community types reduces ecological diversity and may eliminate
habitat for rare and endangered species. At-risk status is a vulnerability indicator for aquatic
ecosystems by definition, identifying communities that may have less resistance to stressors
because they are already compromised.
Measurement/Calculation: Identifying which communities are at risk and their degree of
endangerment is useful for planning conservation measures (Grossman et al., 1998). The Heinz
Center (2008) describes three risk categories: vulnerable (moderate risk), imperiled (high risk),
and critically imperiled (very high risk). Factors that were used to assign these risk categories
include range, the number of occurrences, whether steep declines have occurred, and other
threats.
Impacts of global climate change and other stressors: A number of environmental
changes might alter the risk status of a plant community. Changes in land use and climate-related
changes may decrease the range of a given plant community. The ranges of some plants may
shift with temperature changes. Drying would reduce the ranges of some plants, but increased
precipitation may allow some species to expand their ranges. Sea level rise associated with
global climate change or a reduction in the input of freshwater may allow drought-resistant or
salt-resistant plants to move into areas once dominated by freshwater plants (Lucier et al., 2006).
Many potential effects on at-risk freshwater plant communities are poorly understood, including
alterations in biogeochemical cycling and the effects of increased severity of storms.
At-risk Native Freshwater Species (#24)
Definition: Similar to the previous entry, this indicator describes the risk of extinction
faced by 4,100 native freshwater species, including fish, aquatic mammals, aquatic birds, reptiles
and amphibians, mussels, snails; crayfishes, shrimp, and insects (Heinz Center, 2008). Plants are
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not included. The status of these species is important because of their value both individually
(e.g., as food or for other purposes) and as part of aquatic ecosystems. The at-risk status assigned
to these species again directly reflects vulnerability, identifying organisms that may have less
resistance to stressors because they are already compromised and have experienced a decline;
further declines for some may result in extreme rarity or even extinction.
Measurement/Calculation: The Heinz Center (2008) describes four risk categories:
vulnerable, imperiled, critically imperiled, and extinct. Assignment to the "vulnerable,"
"imperiled," and "critically imperiled" categories is based on up to twelve factors, including
population size, number of populations, range, steep or widespread decline, or other evidence of
risk.
Impacts of global climate change and other stressors: A number of external stressors
might affect risk category. For example, changes in the hydrologic cycle, whether induced by
climate or land-use change, may reduce available habitat and alter the range and number of
locations where species occur. Sea level rise may flood freshwater habitats. Degradation of water
quality and presence of certain contaminants may affect the health and long-term stability of
sensitive species. If habitat is already fragmented by land use, further stress may further
endanger freshwater species.
Various taxa may be sensitive to environmental change, including climate change. Fish
are sensitive to temperature, and changes in temperature may shift the ranges of some species,
possibly causing local extinctions (Fiske et al., 2005). Changes in water chemistry and limnology
may also affect fish. For example, increased temperature reduces dissolved oxygen and increases
thermal stratification (Fiske et al., 2005). Some amphibians may experience reproductive issues,
such as interference with their life cycles or temperature effects on gender determination (Lind,
undated). Climate-related changes in the ranges of pathogens or increases in emerging pathogens
may also endanger freshwater species.
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Coastal Vulnerability Index (#51)
Definition: The Coastal Vulnerability Index, created by Thieler and Hammar-Klose
(2000), is intended to be a measure of the relative vulnerability of U.S. coastal areas to the
physical changes caused by relative sea-level rise (RSLR) (Thieler and Hammar-Klose, 2000).
Measurement/Calculation: The CVI at a particular location is calculated based on the
values of six variables at that location: geomorphology, coastal slope, rate of RSLR, shoreline
erosion and accretion rates, mean tidal range, and mean wave height (Thieler and Hammar-
Klose, 2000). Each location on the coastline is assigned a risk value between 1 (low risk) and 6
(high risk) for each data variable. The CVI is then calculated as the square root of the product of
the ranked variables divided by the total number of variables: CVI = [(a*b*c*d*e*f*)/6)]Al/2.
Thus, a higher value of the CVI indicates a higher vulnerability of coast at that location. The data
for each of the six variables used to map this indicator were collected at various frequencies.
The CVI changes based on changes in the following variables (see Thieler and Hammar-
Klose, 2000):
Geomorphology, which is a measure of the relative erodibility of different landforms.
Landforms may be of the following types, listed in order of increasing vulnerability to
erosion or increasing value of CVI: rocky, cliffed coasts, fiords, or fiards; medium
cliffs or indented coasts; low cliffs, glacial drifts, or alluvial plains; cobble beaches,
estuaries, or lagoons; barrier beaches, sand beaches, salt marshes, mud flats, deltas,
mangroves, or coral reefs. For instance, the value of the CVI is relatively higher along
the Louisiana coast due to its lower-lying beaches and marshy areas with shallow
slopes that are more prone to erosion.
Coastal slope (percentage), which is a measure of the relative risk of inundation and
of the rate of shoreline retreat. Shallower slopes are more vulnerable as they retreat
faster than steeper ones, and will result in a higher value of the CVI. The lower and
upper bounds for the coastal slope are <0.025% and >0.2% for the Atlantic Coast,
<0.022% and >0.115% for the Gulf Coast, and <0.6% and >1.9% for the Pacific
Coast.
Rate of RSLR (mm/year), which is the change in mean water elevation at the coast.
Higher rates of RSLR, resulting in a higher value of the CVI, cause loss of land and
destruction of the coastal ecosystem. The lower and upper bounds for RSLR are <1.8
mm/yr and >3.16 mm/yr for the Atlantic Coast, <1.8 mm/yr and >3.4 mm/yr for the
Gulf Coast, and <-1.21 mm/yr and >1.36 mm/yr for the Pacific Coast. In contrast, the
value of CVI is relatively lower along the Eastern Gulf of Mexico coast mostly due to
lower rates of RLSR.
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Shoreline erosion and accretion rates (m/year), which is the rate at which the
shoreline changes due to erosion or sediment deposition. Positive accretion rates
(resulting in lower values of the CVI) lead to more stable shorelines that are less
vulnerable to erosion, while positive erosion rates (resulting in higher values of the
CVI) lead to loss of coastal land. The lower and upper bounds for shoreline erosion or
accretion rates are <-2.0 m/yr (erosion) and >2.0 (accretion) for all U.S. coasts.
Mean tidal range (m), which is the average distance between high tide and low tide.
Coastal areas that have higher tidal ranges (resulting in lower CVI values) are less
vulnerable to sea-level rise (Kirwan and Guntenspergen, 2010). The lower and upper
bounds for mean tidal range are <1.0 m and >6.0 for all U.S. coasts.
Mean wave height (m), which is a measure of the energy of the wave. A higher
energy wave (resulting in higher values of CVI) has a greater tendency to mobilize
sediments along the coasts, thereby increasing erosion. The lower and upper bounds
for mean wave height are <0.55 m and >1.25 for the Atlantic Coast and the Gulf
Coast, and <1.1 and >2.60 for the Pacific Coast.
Impacts of global climate change and other stressors: The CVI is, as noted above, a
direct measure of the vulnerability of coastal ecosystems to RSLR induced by climate change,
and it also captures a change in the ecological condition of the coastal area with respect to
previous conditions (e.g., lower sea-levels). RSLR, exacerbated by long-term temperature
increases, is expected to increase flooding duration as well as salinity stress caused by saltwater
intrusion (Mendelssohn and Morris, 2000, as cited in Day et al., 2005). These factors, in turn,
will lead to increased RSLR, destroying coastal wetlands, which may not be able to accrete
upwards at the same rate (Day et al., 2005).
Erosion Rate (#348)
Definition: Erosion rate is a measure of the rate of long-term soil loss due to erosion.
Land use patterns, such the use of land for agricultural purposes or deforestation, can also cause
erosion (Yang et al., 2003). Soil erosion is a major non-point pollution source of surface water
(Yang et al., 2003). Erosion from runoff events may cause higher levels of nutrients, dissolved
organic carbon, and sediment loads in surface water sources (Murdoch et al., 2000). The Erosion
Rate indicator can, thus, be used to assess differences in the potential vulnerability of surface
water sources as a result of erosion effects.
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Measurement/Calculation: The Erosion Rate can be estimated using Yang et al.'s (2003)
Revised Universal Soil Loss Equation (RUSLE). This estimate is based on four independent
variables: rainfall erosivity, soil erodibility, topography, and vegetation. This indicator only takes
into account soil erosion caused by rainfall and flowing water, and for a grid cell with
coordinates (i, j) it can be calculated as follows (Yang et al., 2003):
where R = average rainfall erosivity factor,
LS = average topographical parameter,
K = average soil erodibility factor,
C = average land cover and management factor,
P = average conservation practice factor.
These variables affect the Erosion Rate in the following manner:
Average topographical parameter is a measure of the slope length and steepness.
Erosion Rate increases with steeper slopes and greater slope length.
Soil erodibility is the average long-term erosive tendency of rainfall and runoff. This,
in turn, depends on the texture, proportion of organic matter, soil structure, and
permeability. Erosion rate increases with greater erodibility.
Rainfall erosivity represents the erosive force caused by rainfall and runoff. This, in
turn, is dependent on the annual precipitation. Greater rainfall erosivity causes a
higher rate of soil erosion.
Average land cover and management factor is a measure of land use and is calculated
as the average soil-loss ratio weighted by the distribution of annual rainfall.
Average conservation practice factor is a measure of practices that control erosion.
For RUSLE, P is assigned a value of 0.5 for agricultural land and 0.8 for mixed
agricultural and forest land. Erosion rate decreases with active conservation practices.
Impacts of global climate change and other stressors: Increased precipitation and greater
storm intensities induced by global climate change may result in increased transport of sediment,
leading to higher erosion rates.
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Groundwater Reliance (#125)
Definition: Groundwater Reliance is a measure of the dependence of a community on
available groundwater resources. It is defined as the share of total annual withdrawals from
groundwater. This indicator is particularly important as a measure of vulnerability in those
regions that depend primarily on groundwater for drinking water, irrigation, and industrial and
commercial purposes, because surface water supplies may be limited, contaminated, or
expensive to use (Kurd et al., 1998).
Measurement/Calculation: This indicator is calculated as the ratio of withdrawals from
groundwater to total annual withdrawals from groundwater and surface water (Kurd et al., 1998).
The data used to map this indicator were collected every five years.
Impacts of global climate change and other stressors: Long-term changes in the
hydrologic cycle, specifically groundwater recharge and surface flows, may make regions with
higher groundwater reliance more vulnerable to water shortages. In addition, urbanization may
have a significant impact on groundwater availability and stream baseflow. Increased impervious
surface area may intercept rainfall that would normally recharge aquifers. The intercepted
rainfall may be directed into storm drains and carried to streams, urban lakes, or estuaries (Klein,
1979; Simmons and Reynolds, 1982).
Herbicide Concentrations in Streams (#367) and Insecticide Concentrations in Streams (#369)
Definition: These indicators are defined as the average concentrations of herbicides and
insecticides, respectively, in US streams. Pesticides are of acknowledged concern for human
health as well as the health of aquatic organisms. Their ingestion may lead to a number of health
concerns, including kidney problems, reproductive problems, and cancer. These compounds have
been studied primarily in laboratory animals, although some information is based on
epidemiological data. Pesticides are a primary drinking water quality indicator, with Maximum
Contaminant Levels (MCLs) in place for 24 pesticides, mostly in the |ig/L range.
Measurement/Calculation: This indicator is calculated as the average concentration of
herbicides (herbicides, herbicide degradates, and fungicides) or insecticides (insecticides,
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insecticide degradates, and acaricides) for all sampling sites and all sampling events. The data
used to map this indicator were collected at various frequencies depending on purpose and
collection site.
Impacts of global climate change and other stressors: Environmental changes that may
affect the concentrations of pesticides in streams include alterations to the hydrologic cycle
(Noyes et al., 2009). Lower precipitation in the summer may lower streamflow and reduce
dilution, leading to higher concentrations, although higher temperatures may offset this by
increasing pesticide degradation (Bloomfield et al., 2006). If winter precipitation increases,
dilution will tend to increase as well. Climate change may also alter how water moves over the
land. For example, increased precipitation, or more extreme wet events, may increase overland
flow because the capacity of the soil to infiltrate water will be exceeded. Intense summer storms
may promote increased runoff if the antecedent conditions are dry because the soil will be more
hydrophobic (Boxall et al., 2009). These effects may promote a greater input of suspended solids
into streams, increasing the loading of particle associated pesticides. Climate-induced changes to
pest migration or ranges may prompt changes in pesticide usage, which may be reflected in
inputs to surface water (Chen and McCarl, 2001). Bloomfield et al. (2006) note, however, that
direct climate change effects would be difficult to predict, and that secondary effects from land
use changes associated with climate change may be more important as controls on inputs of
pesticides to surface water.
Herbicides in Groundwater (#373) and Insecticides in Groundwater (#374)
Definition: These indicators are defined as the average concentrations of herbicides and
insecticides, respectively, in shallow groundwater. Because groundwater can contribute
herbicides and pesticides to streams, concentrations of these compounds in groundwater need to
be considered in evaluations of surface waters and aquatic ecosystems. The presence of these
toxics provides an indication of potential contributions of these chemicals to streams. As
described in the previous entry, they are also a primary drinking water concern, and EPA has set
MCLs for 24 of these compounds.
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Measurement/Calculation: This indicator is calculated as the average concentration of
herbicides (herbicides, herbicide degradates, and fungicides) or insecticides (insecticides,
insecticide degradates, and acaricides) for all sampling sites and all sampling events. The data
used to map this indicator were collected at various frequencies depending on purpose and
collection site.
Impacts of global climate change and other stressors: Changes in precipitation brought
on by global climate change may affect groundwater herbicide and insecticide concentrations.
Greater winter precipitation would promote the movement of these substances through the soil
towards the water table, and large storms in particular may rapidly transport them into
groundwater. In addition, during drier summers, less biodegradation occurs in the unsaturated
zone, leaving greater amounts of pesticides available to be transported to groundwater. Finally,
herbicide and insecticide use may increase if climate change leads to increased prevalence of
pests and weeds.
Instream Use/ Total Streamflow (#351)
Definition: A primary consideration for healthy aquatic ecosystems is having adequate
water to maintain fish and wildlife habitat, and competing demands for water can be a significant
stressor to these ecosystems (Meyer et al., 1999). This indicator describes the competition by
expressing instream water needs for fish and wildlife as a percentage of total available
streamflow.
Measurement/Calculation: The ratio of instream use to total streamflow can be calculated
using three variables: total groundwater withdrawals, mean annual runoff, and groundwater
recharge. Groundwater overdraft values can be calculated based on the definition in the WRC
(1978) report: Groundwater Recharge - Groundwater Withdrawals. Instream use can be
calculated based on the definition in the WRC (1978) report: Streamflow * 0.6. Streamflow is
assumed to be equal to runoff. This indicator is then calculated using the formula described in
WRC (1978): Instream use / (Streamflow - Groundwater overdraft). The data for these variables
were collected at various frequencies: data on groundwater withdrawals were collected every 5
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years, data on mean annual runoff were collected as a one-time effort in 1975, and groundwater
recharge data were collected as a one-time effort between 1951 and 1980.
Impacts of global climate change and other stressors: Changes in water withdrawals due
to population change can decrease the streamflow available for instream use. Alterations in the
hydrologic cycle due to climate change might also decrease streamflow in some areas. This
would cause the instream use/total streamflow ratio to increase. A WRC (1978) report notes that
a ratio > 100 (based on 1975 data) indicates that withdrawals of water are having a deleterious
effect on the instream environment. DeWalle et al. (2000), however, discuss the scenario of
concurrent urbanization and climate change. They note that urbanization can significantly
increase mean annual streamflow and may offset reductions in flow caused by climate change.
This indicator serves as a good vulnerability indicator because regions with greater competition
between instream flow uses and consumptive uses are more vulnerable to decreases in
streamflow resulting from climate change.
Macroinvertebrate Index ofBiotic Condition (#460)
Definition: The Macroinvertebrate Index ofBiotic Condition indicator (#460) is a
composite measure of the condition of macroinvertebrates in streams. Assessing the condition of
these macroinvertebrate species is a good measure of the overall condition of the aquatic
ecosystem as they often serve as the basic food for aquatic vertebrates and are, therefore,
essential to aquatic ecosystems with vertebrate species (U.S. EPA, 2010f; U.S. EPA, 2006; U.S.
EPA, 2004).
This indicator allows qualitative measurements of macroinvertebrate condition to be
represented as a numerical value. It can be considered a good indicator of relative vulnerability
as it compares macroinvertebrate condition at study sites with those at undisturbed reference
sites located in similar ecoregions (U.S. EPA, 2006). Furthermore, this indicator may be tracked
over time to determine temporal changes in vulnerability relative to a baseline (U.S. EPA,
201 Ob).
Measurement/Calculation: The Macroinvertebrate Index indicator is represented by the
average Macroinvertebrate Index value in a given area. It depends on field observations of six
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variables: taxonomic richness, taxonomic composition, taxonomic diversity, feeding groups,
habits, and pollution tolerance (U.S. EPA, 2006). Each variable is assessed using the benthic
macroinvertebrate protocol in which stream samples are collected and the characteristics of
macroinvertebrates in them are assessed (U.S. EPA, 2004). Each variable is assigned a score
based on field observations and individual scores are summed to obtained the value of the
Macroinvertebrate Index, ranging from 0 to 100 (U.S. EPA, 2006). The data used to map this
indicator were collected every five years.
The Macroinvertebrate Index changes based on the following variables:
Taxonomic richness, which is the number of distinct taxa or groups of organisms. A
stream with more taxa, which indicates a wider variety of habitats and food
requirements, will be less vulnerable to stress.
Taxonomic composition, which is a measure of the relative abundance of ecologically
important organisms to those from other taxonomic groups. For example, a polluted
stream will likely have a higher abundance of organisms that are resilient to pollution
with lower representation from other taxa and will be more vulnerable to stress.
Taxonomic diversity, which is a measure of the distribution of organisms in a stream
amongst various taxonomic groups. Higher taxonomic diversity represents a healthier
stream that is less vulnerable to stress.
Feeding groups, which is a measure of the diversity of food sources that
macroinvertebrates depend on. A more diverse food chain is representative of a more
stable aquatic environment that is less vulnerable to stress.
Habits, which is measure of the characteristics of different organisms and their
preferences for different habitats. A stream environment with more diverse habitats
(e.g., streambed sediment, rocks, woody tree roots, debris) supports a wider variety of
macroinvertebrates and will be less vulnerable to stress.
Pollution tolerance, which is a measure of the degree of resilience to pollution of
macroinvertebrate species in a stream. Highly sensitive organisms will be more
vulnerable to contamination in streams, compared to pollution-resistant ones.
Impacts of global climate change and other stressors: The structure and function of
macroinvertebrate assemblages is a reflection of their exposure to various stressors over time, as
these organisms have long life-cycles over which they change in response to stress (U.S. EPA,
2004). Stable ecosystems are likely to contain a variety of species, some of which are sensitive to
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environmental conditions. These sensitive taxa are most likely to be subject to local extirpations
when exposed to climate-induced changes in temperature or flow conditions. Similarly, these
species may not tolerate increases in precipitation or temperature variation, which subsequently
increase the frequency of disturbance events.
Macroinvertebrate Observed/Expected (O/E) Ratio of Taxa Loss (#461)
Definition: The Macroinvertebrate Observed/Expected (O/E) Ratio of Taxa Loss
indicator (#461; also known as O/E Taxa Loss) is a measure of the biodiversity loss in a stream
(U.S. EPA, 2006). The O/E Taxa Loss directly reflects the vulnerability of an ecosystem based
on its loss of biodiversity (U.S. EPA, 2006). It also reflects a change in ecological condition
relative to undisturbed reference sites (U.S. EPA, 2006).
Measurement/Calculation: This indicator is represented by the ratio of the taxa observed
at a site to the ratio of the taxa expected to be present at that site as predicted by a region-specific
model (U.S. EPA, 2006). Observed taxa are assessed using the benthic macroinvertebrate
protocol in which stream samples are collected and the characteristics of macroinvertebrates
present in them are assessed (U.S. EPA, 2004). Expected taxa are predicted by models developed
from data collected at undisturbed or least disturbed reference sites within a region, for each of
three major U.S. regions - Eastern Highlands, Plains and Lowlands, and the West (U.S. EPA,
2006). O/E Taxa Loss ratios are represented as a percentage of the expected taxa present, and
they range from 0% (i.e., none of the expected taxa are present) to greater than 100% (i.e., more
taxa than expected are present) (U.S. EPA, 2006). The data used to map this indicator were
collected every five years.
Impacts of global climate change and other stressors: Stable ecosystems are likely to
contain a variety of species, some of which are sensitive to environmental conditions. These
sensitive taxa are most likely to be subject to local extirpations when exposed to climate-induced
changes in temperature or flow conditions. Similarly, these species may not tolerate increases in
precipitation or temperature variation, which subsequently increase the frequency of disturbance
events. A measure of the loss of sensitive species may thus serve as an important indicator of
vulnerability to climate change and other stressors.
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Meteorological Drought Indices (#165)
Definition: This indicator is defined as the average value of the Palmer Drought Severity
Index between 2003 and 2007. Meteorological Drought Indices provide a representation of the
intensity of drought episodes brought on by a lack of precipitation (Heim, 2002). For example,
the Palmer Drought Severity Index (PDSI) takes into account precipitation and soil moisture data
from a water balance model as well as a comparison of meteorological and hydrological drought
(Heim, 2002). The PDSI can be used as a proxy for surface moisture conditions and streamflow
(Dai et al., 2004). PDSI trends are also linked to climate patterns such as the El Nino-Southern
Oscillation (Dai et al., 1998).
Measurement/Calculation: PDSI values can be calculated per the methodology in Karl et
al., 1996. Calculated PDSI values can be obtained from NOAA's NCDC Divisional Data for
each of 344 climate divisions. The data used to map this indicator were collected monthly.
Impacts of global climate change and other stressors: Because drought is a well
recognized stressor for natural and human systems, indicators of the spatial and temporal
distribution of drought severity are relevant to vulnerability to additional external stressors. This
is particularly true for climate change, as drought is directly linked to changes in meteorology
that themselves are likely to be affected by climate change.
Organochlorines in Bed Sediment (#371)
Definition: This indicator is defined as the average concentrations of organochlorines in
bed sediments. As part of its National Water Quality Assessment (NAWQA) program, the U.S.
Geological Survey has analyzed organochlorines in bed sediment (USGS, 1999). Although they
have not been used for decades, organochlorine insecticides linger in sediments, posing a
potential threat to humans and aquatic organisms. For example, any increase of organochlorines
in shellfish may find its way into the human food chain. As a vulnerability indicator,
organochlorines in sediment are deleterious compounds that can cause ecological condition to
deviate from what would be expected in an undisturbed system.
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Measurement/Calculation: This indicator is calculated as the average concentration of
organochlorines in bed sediments for all sampling sites and all sampling events. The data used to
map this indicator were collected at various frequencies depending on purpose and collection
site. Long et al. (1995) derived critical levels or breakpoints for sediment metals and chemical
contaminants such as pesticides, PAHs, and PCBs for estuarine systems. MacDonald et al.
(2000) conducted similar work for freshwater systems.
Impacts of global climate change and other stressors: Any environmental factor that
disturbs bed sediment or affects its transport may affect the exposure of humans or aquatic
organisms to organochlorines. Dredging of rivers and harbors may resuspend sediments,
increasing contact with aquatic organisms. More intense storms may also resuspend sediment.
On the other hand, climate-related increase of sediment input to larger water bodies may provide
some "burial" of contaminated sediments, especially if the new sediment is uncontaminated.
Pesticide Toxicity Index (#364)
Definition: This indicator combines pesticide concentrations for a stream water sample
with toxicity estimates to produce a number (the Pesticide Toxicity Index or PTI value) that
indicates the sample's relative toxicity to aquatic life. This method, developed by Munn and
Gilliom (2001), allows data for multiple pesticides to be linked to the health of an aquatic
ecosystem, and it allows streams to be rank ordered by their PTI values (Gilliom et al., 2006).
The PTI value for a stream increases as pesticide concentrations increase. It is a suitable
vulnerability indicator in that it attempts to estimate the potential damage to an ecosystem's
resilience as a result of pesticides.
Measurement/Calculation: The PTI for each sampling event is calculated by summing
the toxicity quotients for all pesticides. The toxicity quotient is the measured concentration of a
pesticide divided by its toxicity concentration from bioassays (e.g., a Lethal Dose 50 (LD50) or
Effective Concentration 50 (EC50) value) for a selected species. For the present study, the
toxicity quotient used was an ECso value for each pesticide for the species Daphnia.
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Impacts of global climate change and other stressors: Concentrations may change due to
environmental factors such as urbanization, whereby increased streamflow may decrease
concentrations due to greater dilution or produce greater pesticide inputs through increased
sediment input. Potential climate-related effects include decreased streamflow, which may
increase concentrations through reduced dilution, or increased precipitation, leading to increased
streamflow and hence sediment inputs. Conversely, increased temperature may accelerate
pesticide degradation, leading to lower concentrations. However Noyes et al. (2009) note that if
water temperature increases, pesticides can become more toxic to aquatic organisms. It is not
known if this effect would apply to humans. Determining the toxicity of mixtures of pesticides to
humans is extremely challenging; exploring toxicity changes as a result of climate change is an
important direction for future research.
Precipitation Elasticity of Streamflow (#437)
Definition: The Precipitation Elasticity of Streamflow indicator is designed to assess the
sensitivity of streamflow to changes in precipitation patterns. It measures the sensitivity of
streamflow to climate change and is useful in assessing the vulnerability of regions where
maintaining relatively constant streamflow is critical (Sankarasubramanian et al., 2001).
Measurement/Calculation: The Precipitation Elasticity of Streamflow (Ep) is defined as a
change in streamflow caused by a proportional change in precipitation. It can be calculated as
follows:
dP Q
where P = precipitation and Q = streamflow.
An indicator value greater than 1 indicates that a large change in precipitation is
accompanied by a relatively smaller change in streamflow, and thus, streamflow is elastic or
sensitive to precipitation changes. An indicator value of less than 1 indicates that a small change
in the precipitation is accompanied by a relatively larger change in the streamflow, and thus
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streamflow is inelastic or less sensitive to precipitation changes. The data for these variables
were collected at various frequencies: data on streamflow were collected annually, and data on
precipitation were collected monthly.
Impacts of global climate change and other stressors: Streams do not respond uniformly
to increased precipitation due to underlying differences in geology, terrain, and other factors.
Precipitation elasticity can be used to predict how increased precipitation brought on by global
climate change might affect streams in a given region. Increases in precipitation and storm
intensity could result in disproportionately large adverse effects, such as flooding, in areas with
high precipitation elasticity. Further, these effects could be enhanced or offset by changes in
temperature. Climate change, as well as anticipated increases in urbanization, both contribute to
the expected increase in the intensity of storms in some areas, leading to more flooding and
severe erosion in flashier stream systems.
Ratio of Reservoir Storage to Mean Annual Runoff (#449)
Definition: The Ratio of Reservoir Storage to Mean Annual Runoff indicator is a measure
of the storage capacity of reservoirs relative to runoff within the basin (Graf, 1999). Dams can be
used to manage water resources to ensure a reliable supply of water to regions that depend on
surface water (Lettenmaier et al., 2008). On the other hand, dams can also alter riparian
ecosystems and hydrologic processes, causing unnatural variability in streamflow when water is
released, fragmenting aquatic ecosystems, and causing erosion and sedimentation (Graf, 1999).
The ability to store a large portion of water from land runoff indicates that a community already
has the capacity to harness more surface water if needed and may, therefore, be less vulnerable to
changes in hydrologic processes. Arid or semi-arid regions, where water is scarce, tend to have
larger reservoirs, some of which may be able store up to three or four times the volume of annual
runoff (Graf, 1999). This indicator is a good indicator of the vulnerability of water supply.
However, it may have a limited ability to predict the vulnerability of water quality and aquatic
ecosystems as dams tend to adversely affect both these variables, while they benefit water supply
or availability.
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Measurement/Calculation: The Ratio of Reservoir Storage to Mean Annual Runoff is
determined by the magnitude of its individual components, reservoir storage capacity and mean
annual runoff. The storage capacity of reservoirs in a given region is determined by the size of
the dam, and the mean annual runoff is determined largely by precipitation and snowmelt. The
data used to map this indicator include runoff data that were collected as a one-time effort
between 1951 and 1980, and dam inventory data for which the collection frequency is unknown.
Impacts of global climate change and other stressors: Climate change may introduce
increased inter- and intra- annual variation in runoff. Areas with relatively low reservoir storage
compared to the availability of runoff may be more vulnerable to intense and prolonged droughts
or changes in the seasonal timing of runoff.
Ratio of Snow to Precipitation (#218)
Definition: The Ratio of Snow to Precipitation is the ratio of the amount of snowfall to
the amount of total precipitation. It can also be described as the percentage of precipitation
falling as snow. As such, a decreasing ratio can indicate either a relative decrease in snowfall or
relative increase in rainfall, although annual trends in the Ratio of Snow to Precipitation
primarily reflect the former (Huntington et al., 2004).
Measurement/Calculation: The data used to map this indicator were collected annually.
Impacts of global climate change and other stressors: Changes in the Ratio of Snow to
Precipitation are driven by temperature variations (Karl et al., 1993). Thus, the ratio will be
affected by temperature changes associated with global climate change. Trends in the Ratio of
Snow to Precipitation can lead to changes in runoff and streamflow patterns because of the effect
on the timing and amount of spring snowmelt (Knowles et al., 2006; Huntington et al., 2004).
Because of this, areas with decreasing ratios can be more vulnerable to summer droughts (Feng
and Hu, 2007).
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Ratio of Water Withdrawals to Annual Streamflow (#219)
Definition: The Ratio of Water Withdrawals to Annual Streamflow indicator is a measure
of a region's water demand relative to the potential of the watershed to supply water. This
indicator is defined as the share of total annual water withdrawals (from surface water and
groundwater) to the unregulated mean annual Streamflow (Kurd et al., 1998). Streamflow is
important for the sustenance of surface water supply as well as for riparian ecosystems. It is also
important for aquifers that are fed by Streamflow.
Measurement/Calculation: Unregulated mean annual Streamflow is calculated based on
drainage area, mean annual precipitation, and mean annual temperature using regional regression
models specified by Vogel et al. (1999). The ratio of water withdrawals to annual Streamflow can
then be calculated using water-use data. The data for these independent variables were collected
at various frequencies: mean annual precipitation data were collected monthly, mean daily
maximum temperature data were collected monthly, and water-use data were collected every five
years.
Impacts of global climate change and other stressors: Regions with higher water demand
will withdraw higher amounts of water from Streamflow both for immediate use as well as for
storage in reservoirs. These regions also rely on institutional management to maintain the critical
flow in rivers and streams (Kurd et al., 1998). In the long-term, such regions are likely to be
more vulnerable to climate changes that lead to large changes in Streamflow. Regions where
water demand is a smaller proportion of the unregulated Streamflow are likely to be less
vulnerable to climate-induced changes in Streamflow because there is greater available supply
from which to draw without affecting the critical flow (Kurd et al., 1998).
Runoff Variability (#453)
Definition: Runoff Variability is defined as the coefficient of variation of annual runoff.
This indicator largely reflects the variation of annual precipitation (Lettenmaier et al., 2008;
Maurer et al., 2004). Small or moderate changes in precipitation can lead to larger changes in
runoff amounts, increasing runoff variability (Burlando and Rosso, 2002; Karl and Riebsame,
1989). Runoff is also linked to and affected by other factors, such as temperature,
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evapotranspiration, snowmelt, and soil moisture, and it is a critical component of the annual
water balance (Gedney et al., 2006; Maurer et al., 2004; Wolock and McCabe, 1999; Karl and
Riebsame, 1989). Furthermore, clearcutting and urbanization also lead to increased runoff.
Measurement/Calculation: Annual runoff can be calculated by aggregating the monthly
runoff values for each year. Mean and standard deviation of the annual runoff are calculated,
following which the coefficient of variation (i.e., the runoff variability) is calculated by dividing
the standard deviation by the mean annual runoff. It is easier to measure runoff than it is to
measure other variables in the water balance, such as precipitation and evapotranspiration, thus
making it a more reliable indicator (Wolock and McCabe, 1999). The data used to map this
indicator were collected every three hours.
Impacts of global climate change and other stressors: Understanding inter-annual
variation in runoff is important for future scenarios in which climate change will affect
precipitation and temperature, both of which affect runoff (Maurer et al., 2004). The spatial and
temporal variability of runoff is also essential for predicting droughts and floods (Maurer et al.,
2004).
Stream Habitat Quality (#284)
Definition: The Stream Habitat Quality (#284) indicator is used to assess the condition in
and around streams. Physical features such as instream vegetation, sediment, and bank
vegetation create diverse riparian habitats that can support many plant and animal species (Heinz
Center, 2008). Streams degraded by human use are characterized by decreased streambed
stability, increased erosion of stream banks, and loss of instream vegetation. Such streams are
marginal habitats for most species (Heinz Center, 2008) and hence may be particularly
vulnerable to additional stresses. Stream habitat can be altered quickly due to stochastic events
such as major flooding, or slowly over time due to subtle changes in flow regime.
Measurement/Calculation: The Stream Habitat Quality indicator is represented by the
Rapid Bioassessment Protocol score, an index that can be used to assess the condition of
underwater and bank habitats. The Rapid Bioassessment Protocol is a methodology developed by
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EPA to assess habitat conditions based on field observations often variables: epifaunal substrate/
available cover, embeddedness (for riffles) or pool substrate characterization (for pools), velocity
and depth regimes (for riffles) or pool variability (for pools), sediment deposition, channel flow
status, channel alteration, frequency of riffles or bends (for riffles) or channel sinuosity (for
pools), bank stability, bank vegetative protection, and riparian vegetated zone width (U.S. EPA,
2004). Each of these variables is observed and assigned a qualitative category and score: Poor (0-
5), Marginal (6-10), Sub-optimal (11-15), or Optimal (16-20) (U.S. EPA, 2004). The scores for
all the parameters are summed to obtain the Rapid Bioassessment Protocol score for that stream
(U.S. EPA, 2004). A higher Rapid Bioassessment Protocol score indicates higher Stream Habitat
Quality, while a lower Rapid Bioassessment Protocol score indicates a degraded stream.
Stream Habitat Quality changes based on changes in the following variables (U.S. EPA,
2004):
Epifaunal substrate or available cover, which measures the relative quantity and
variety of natural structures in the stream, such as cobble (riffles), large rocks, fallen
trees, logs and branches, and undercut banks, available as refugia, feeding, or sites for
spawning and nursery functions of aquatic macrofauna. The abundance of these
structures in the stream creates niches for animals and insects, and it allows for a
diversity of species to thrive in the same habitat.
Embeddedness in riffles, which measures the extent to which rocks (gravel, cobble,
and boulders) and snags are buried in the silt or sand at the bottom of the stream.
Fewer embedded features increase the surface area available to macroinvertebrates
and fish for shelter, spawning, and egg incubation. Similarly, pool substrate
characterization is a measure of the type and condition of bottom sediment in pools.
Firmer sediment, such as gravel, and rooted aquatic vegetation support more
organisms.
Velocity and depth regimes for riffles, which measure the variety of habitats caused
by different rates of flow and stream depth, such as slow-deep, slow-shallow, fast-
deep, and fast-shallow. The ideal stream habitat will exhibit four patterns which
represent the stream's ability to maintain a stable environment. Pool variability is a
measure of the different pool types, such as large-shallow, large-deep, small-shallow,
and small-deep. The more diverse the pool types, the greater the diversity of the
habitat that can be supported by the stream.
Sediment deposition, which is a measure of the amount of sediment accumulation in
streams. More sediment deposition is indicative of unstable streambeds which are an
unfavorable environment for aquatic organisms.
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Channel flow status, which is the extent to which the stream channel is filled with
water. Low channel flow may not cover the streambed and vegetation, leaving them
exposed and reducing available habitat for organisms. Optimal channel flow covers
the streambed, creating more available habitat in which organisms can thrive.
Channel alteration, which is a measure of the significant changes, typically human-
induced, in the shape of the stream channel, such as straightening, deepening,
diversions, or conversion to concrete. Altered channels are often degraded and limit
the natural habitat available to organisms.
Frequency of riffles, which is a measure of the number of riffles in a stream. Riffles
provide diverse habitats in which many organisms can thrive. Similarly, channel
sinuosity in pools is a measure of the degree to which the stream meanders. More
sinuous streams allow for diverse natural habitats and can also adapt to fluctuations in
water volumes, thereby providing a more stable environment for aquatic organisms.
Bank condition, which measures the extent to which banks are eroded. Eroded banks
indicate moving sediments and unstable stream habitat for aquatic animals and plants.
Bank vegetative protection, which is a measure of the vegetative cover of the stream
bank and near stream areas. Banks with dense plant growth prevent erosion, control
nutrients in the stream, and provide shade, thus maintaining a healthier riparian
ecosystem. In contrast, banks that are covered with concrete in urban areas or
experience high grazing pressure from livestock in agricultural areas prevent
vegetative growth along the stream, thereby creating a poorer aquatic environment.
Riparian vegetated zone width, which is a measure of the extent of the vegetative
zone from the edge of the stream bank through to the outer edge of the riparian zone.
The riparian vegetated zone buffers the riparian environment from surrounding areas,
minimizes runoff, controls erosion, and shades the riparian habitat.
The Stream Habitat Quality indicator allows qualitative measurements of habitat
condition to be represented as a numerical value. However, most measurements of independent
variables that affect the score are "visual-based", that is they are dependent on the visual
assessment by the field team that will score the study sites for each variable (U.S. EPA, 2004).
Despite this, Stream Habitat Quality can be considered a good indicator of relative vulnerability
for our purposes as it compares stream conditions at study sites with those at undisturbed
reference sites located in similar regions (Heinz Center, 2008; U.S. EPA, 2006). Furthermore,
this indicator may be tracked over time to determine temporal changes in relative vulnerability,
thus allowing one to assess the impacts of future stressors in relation to present stressors. The
data used to map this indicator were collected every five years.
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Impacts of global climate change and other stressors: Climate-induced changes in storm
intensity, runoff seasonality, average flows, or flow variation could result in disproportionately
large negative effects on high quality stream habitats.
Total Use/Total Streamflow (#352)
Definition: This is the second indicator expressing the competition between water needs
and water availability in streamflow. According to WRC (1978), the ratio of total use to total
streamflow is a measure of the water available for "conflict-free development of offstream uses."
It is similar to Indicator #351 (Instream Use/Total Streamflow), except that the numerator
includes the needs for both instream and offstream use. It is a good vulnerability indicator
because regions that have high offstream needs may be less able to withstand decreases in
streamflow that may occur due to climate change.
Measurement/Calculation: The ratio of total use to total streamflow can be calculated
using three variables: mean annual runoff, groundwater recharge, and water use. Groundwater
overdraft values can be calculated based on the definition in the WRC (1978) report:
Groundwater Recharge - Groundwater Withdrawals. Instream use can be calculated based on the
definition in the WRC (1978) report: Streamflow * 0.6. Streamflow is assumed to be equal to
runoff. This indicator is then calculated using the formula described in WRC (1978): (Instream
use + Total Consumptive Use) / (Streamflow - Groundwater overdraft). The data for these
variables were collected at various frequencies: mean annual runoff data were collected as a one-
time effort from 1951-1980, groundwater recharge data were collected as a one-time effort in
1975, and water-use data were collected every five years.
Impacts of global climate change and other stressors: Meyer et al. (1999) note that
climate-induced changes in water availability will occur in a context in which human-induced
changes in water demand are also occurring. A reduction in streamflow (e.g., due to changes in
climate) or an increase in offstream use (due to greater withdrawals for consumptive use) will
increase this ratio. According to WRC (1978), a ratio > 100% indicates a conflict between
offstream uses and instream flow needs. As with instream use/total streamflow, total streamflow
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may be increased by urbanization. This is presumably due to increased impervious area. This
may offset any flow reductions due to climate change in areas undergoing population expansion.
Wetland and Freshwater Species at Risk (#326)
Definition: The Wetland and Freshwater Species at Risk is a measure of the level of
stress that a watershed is experiencing based on the number of water-dependent species "at risk"
(Kurd et al., 1998). The Wetland and Freshwater Species at Risk indicator is defined as the
number of aquatic and wetland species that are classified by NatureServe (a non-profit
conservation organization that maintains biological inventories for animal and plant species in
the U.S.) as vulnerable, imperiled, or critically imperiled. A watershed with a higher value of this
indicator might be considered to be more vulnerable than a watershed with the lower value of
this indicator.
Assessing the condition of species in a watershed can be a good indication of the health
of the watershed. However, the indicator is not necessarily a very strong indicator of the
vulnerability of aquatic ecosystems, as it only looks at the absolute number of at-risk species,
regardless of the total number of species that occupy that habitat (Kurd et al., 1998).
Furthermore, this indicator does not account for the inherent diversity in the watershed;
watersheds that historically have more species may be less vulnerable to species loss (Kurd et al.,
1998).
Impacts of global climate change and other stressors: Watersheds may be stressed due to
changes in the hydrological cycle related to global climate change and encroachment or other
disturbances from human activities (Kurd et al., 1998). This may cause populations dependent on
affected niches to diminish, and may even lead to extinction of species in some cases (Kurd et
al., 1998).
Water Availability: Net Streamflow per Capita (#623)
Definition: Water availability is a measure of the availability of freshwater resources per
capita to meet water demand for various human consumptive uses (Kurd et al., 1998). This
indicator is defined as the net streamflow per capita.
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Measurement/Calculation: This indicator can be calculated as follows:
Water Availability = (Unregulated annual streamflow - Annual water withdrawals)
Population
This indicator depends on three variables: unregulated mean annual streamflow, water
withdrawals, and population living in the watershed. Unregulated mean annual streamflow is
calculated based on drainage area, mean annual precipitation, and mean annual temperature
using regional regression models specified by Vogel et al. (1999). The data for these variables
were collected at various frequencies: mean annual precipitation data were collected monthly,
mean daily maximum temperature data were collected monthly, and water-use data were
collected every five years.
Impacts of global climate change and other stressors: We might reasonably assume that
regions with abundant per capita water availability are less vulnerable to long-term changes in
the hydrologic cycle brought on by climate change as well as to population growth, and,
conversely, regions with lower per capita water availability are more vulnerable.
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5. CHALLENGES PART II: DETERMINING RELATIVE VULNERABILITY
5.1. VULNERABILITY GRADIENTS AND THRESHOLDS
A variety of approaches are available to water quality and natural resource managers who
must interpret indicator values and indicator-based vulnerability assessments. These approaches
vary depending on the state of available knowledge for a given indicator. In many cases, research
suggests that responses of water quality or ecosystem condition to external stressors are linear,
meaning that changes in condition (or in indicators of condition) occur over a gradual gradient
rather than abruptly. Thus, management decisions can be made based on the value of the
indicator along the gradient. In other cases, the response may be non-linear, but the thresholds
that distinguish acceptable from unacceptable conditions are not yet fully understood. Given this
state of knowledge, management decisions to prevent ecosystem degradation or a risk to human
health may be based on the relative value of an indicator along the gradient of known values. For
example, managers may act out of an abundance of caution when the value of an indicator
increases following a long period of stability, even if the risks associated with inaction are
unclear. Managers may also choose to act if an indicator value appears to be significantly
different from values in other, more pristine locations.
Another approach is the use of known thresholds to facilitate indicator interpretation by
indicating points at which management action is required to prevent adverse impacts to human
health and the environment (Kurtz et al., 2001). Vulnerability thresholds reflect abrupt or large
changes in the vulnerability of water quality or aquatic ecosystems. EPA's Office of Research
and Development (ORD) Evaluation Guidelines, which describes key concepts in environmental
indicator development, describes the role that thresholds can play in interpreting the values of
indicators of ecological condition:
To facilitate interpretation of indicator results by the user community, threshold
values or ranges of values should be proposed that delineate acceptable from
unacceptable ecological condition. Justification can be based on documented
thresholds, regulatory criteria, historical records, experimental studies, or
observed responses at reference sites along a condition gradient. Thresholds may
also include safety margins or risk considerations. (U.S. EPA, 2000b).
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In this study, we attempted to divide the range of values calculated for appropriate
indicators into different classes based on evidence in the literature of abrupt or large changes in
vulnerability associated with certain values of the indicator. These functional break points (i.e.,
objective thresholds that distinguish between acceptable and unacceptable conditions) can be
highly useful to decision makers. The literature reviewed for this study, however, most often
presented arbitrary cutoffs based on round numbers or frequency distributions. It is not surprising
that functional break points do not currently exist for many indicators. Groffman et al. (2006)
point out that determining such break points can be challenging due to the non-linear response of
many indicators and the multiple factors that can affect the value of functionally relevant
indicator break points. For example, natural variation in water chemistry and ecosystem types
across the nation leads to spatial variation in critical thresholds for dissolved oxygen (DO).
Persistently low DO levels in any one ecosystem can yield a community of flora and fauna that
are unaffected by DO levels that would be detrimental to another ecosystem. Blackwater river
systems of the Southeastern U.S. illustrate this variation. These systems have high levels of
dissolved organic matter that may exceed ecologically relevant thresholds elsewhere in the
nation, but locally these are high quality systems that are free from the impoundments that alter
other systems in the U.S. (Meyer, 1990).
In some cases, objective break points in non-linear system responses may be
characterized through additional research, either through meta-analysis of previous research
efforts or through new data collection and analysis. In either case, collection of indicator values
associated with a range of ecological responses is required to establish functionally relevant
break points. There are several statistical approaches for identifying thresholds in non-linear
relationships, including regression tree analysis (Breiman et al., 1984) and two-dimensional
Kolmogorov-Smirnov techniques (Garvey et al., 1998). Future research may yield additional
insights into how these break points vary spatially (Link, 2005).
In general, we considered three different types of thresholds for the suite of indicators
evaluated in this project.
Human health-based thresholds, such as Maximum Contaminant Level Goals (MCLGs)
or Health Advisories (HAs), which are set based on scientific studies, can potentially be used as
thresholds for water quality indicators. EPA establishes MCLGs for contaminants detected in
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drinking water based on an extensive review of available data on the health effects of these
contaminants.
The MCLG is the maximum concentration of a contaminant in drinking water which has
no known or anticipated adverse health effect on the population consuming this water, (U.S.
EPA, 2010g; U.S. EPA, 2009b). MCLGs for carcinogens are set to zero, based on any evidence
of carcinogenicity, as these effects typically manifest over a lifetime of exposure. MCLGs for
non-carcinogens are often based on a Reference Dose (RfD), which is the amount of contaminant
that a person can be exposed to daily without experiencing adverse health effects over a lifetime
(expressed in units of mg of substance/kg body weight/day). MCLGs are non-enforceable and
are based purely on the risk posed by a contaminant to human health (U.S. EPA, 2010c; U.S.
EPA, 2009a). The MCLG is, thus, a threshold based on scientific data (as opposed to a
Maximum Contaminant Level [MCL] that takes other factors into account2).
Similarly, HAs are estimates of acceptable concentrations of drinking water contaminants
that are developed by EPA as guidelines to help Federal, State, and local entities better protect
their drinking water quality (U.S. EPA, 2009a). Like MCLGs, HAs are not enforceable, but are
determined solely based on health effects data, such as exposure and toxicity. Unlike MCLGs,
HAs are revised from year to year as new data become available.
Other parameters could also be used to assess the toxicity of a drinking water
contaminant (U.S. EPA, 2009c):
Median Lethal Dose (LD50), which is the oral dose of a contaminant that will cause
50 percent of the population it is administered to die (expressed in mg per kg of body
weight);
Cancer Potency (for carcinogens), which is the concentration of a contaminant in
drinking water that poses a risk of cancer equivalent to 1 in 10,000 individuals or
In contrast MCLs are National Primary Drinking Water Regulations (NPDWRs) established by EPA as legally
enforceable standards that can be applied to public water systems to ensure safe drinking water supply to the public
(U.S. EPA, 2010c). An MCL is defined as the "highest level of a contaminant that is allowed in drinking water"
(U.S. EPA, 2009a). While the MCL is set such that it is as close to the MCLG as possible, it is typically higher than
the MCLG as it is determined based not only on health considerations, but also on the sensitivity of analytical
techniques available to detect the contaminant as well as on the availability of treatment technologies and the extent
to which they can remove the contaminant from drinking water (U.S. EPA, 2009a).
57
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No Observed Adverse Effect Level (NOAEL), which is the highest dose at which no
adverse health effects are observed; and
Lowest Observed Adverse Effect Level (LOAEL) associated with the RfD, which is
the lowest dose at which adverse health effects are observed.
These parameters are considered preliminary or less developed thresholds than an RfD
value but could still, potentially, be used as thresholds for drinking water indicators.
Ecological thresholds are central to the ecological theory of "alternate stable states"
(Scheffer et al., 2001; May, 1977; Sutherland, 1974; Rolling, 1973; Lewontin, 1969), where the
biotic and abiotic conditions within an ecosystem can reach multiple equilibria. It is believed that
the transition between stable states occurs when a significant perturbation results in the
breaching of one or more ecological thresholds. The "ball-in-cup" model is commonly used to
illustrate this concept (Beisner et al., 2003). A stable ecosystem can be thought of as a ball that
resides at the bottom of a cup. There may be many adjoining cups (i.e., the alternate stable states)
that the ball could reside in. Small perturbations may push the ball up the side of the current cup,
but the ball will eventually return to the bottom - this steep slope illustrates the concept of
resilience. If the perturbation is large enough, the ball may be pushed across the lip of the cup
(i.e., the ecological threshold) and eventually settle into the bottom of a different cup.
Identifying precise ecological thresholds is widely considered to be a difficult task.
Ecosystems can be, and often are, a complex mix of biotic and abiotic elements that are difficult
to evaluate. Aside from the complex logistics of examining multiple variables simultaneously
over ecologically-relevant timescales, ecosystem evaluations can be complicated by the
influence of exogenous factors (e.g., climate, human interference) that introduce uncertainty into
observations. Furthermore, it is reasonable to believe that many ecosystems are truly unique,
meaning that even if ecological thresholds are well understood, they are not widely applicable
for the purposes of understanding vulnerability at broad scales. Finally, in many cases, ecological
thresholds are difficult to observe unless breached, and the alternate stable state may not be
desirable for social, environmental, or economic reasons. Thus, experiments designed to observe
ecological thresholds through artificial induction of an alternate stable state are not commonly
implemented.
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As the science of alternate stable states advances, it may be possible to define objective
thresholds for some of the aquatic ecosystem vulnerability indicators in this study. In the
meantime, relative comparisons of indicator values can be made, and the range of values may or
may not extend across thresholds that could be used to distinguish between vulnerable and less
vulnerable areas.
Sustainability thresholds differentiate between sustainable and unsustainable conditions.
In the context of this study, Sustainability thresholds are most useful in determining where a
water resource may currently be being used unsustainably. The construction of indicators that
use Sustainability thresholds differs somewhat from other indicators. Instead of directly
measuring an environmental condition, they frequently use ratios that attempt to identify whether
or not a system is in balance. These ratios may help answer basic questions for a given area, such
as "Do groundwater withdrawals exceed groundwater recharge?" Or "Do surface water
discharges equal surface water withdrawals?"
The critical value for many ratios centered on these questions is one. For example, for a
theoretical indicator evaluating the balance between groundwater withdrawals and groundwater
recharge, the indicator values may be calculated as Recharge / Withdrawals. Areas where the
value of this ratio is greater than one have more groundwater available than is currently be used
and could be considered sustainable (i.e., providing a "safe yield"). These areas could also be
considered less vulnerable to additional exposure to stresses that reduce groundwater availability.
Conversely, values less than one indicate areas where groundwater withdrawals exceed recharge
- a potentially unsustainable condition. These areas would be more vulnerable to further
exposure to climate-related stresses that reduce recharge.
We calculated values and produced maps for the 25 indicators described in Section 4.3,
and included in Appendix E (displayed using 4-digit Hydrologic Units) and Appendix F
(displayed using ecoregions). When available, we applied objective threshold values identified in
the literature, as shown in Table 5-1. In these cases, data were divided into two or more
categories as specified in the literature. Appendix H includes an evaluation of the extent to which
objective functional thresholds may be applicable for each of the mapped indicators. In cases
where objective thresholds were not available and visualization of changes in indicator values
59
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along a gradual gradient was more appropriate, we produced maps using a continuous grayscale
color ramp.
Table 5-1. Indicators with objective thresholds and their vulnerability categories
Indicator
Literature
Source
Vulnerability Categories and Thresholds
Instream Use/
Total
Stream/low
(#351)
Meyer et al.,
1999
No thresholds were provided in Meyer et al. (1999). However, the original
data source (WRC, 1978) used a threshold of one to indicate regions where
water exports are already adversely affecting the instream environment. We
displayed this indicator in Appendices E and F with the following categories:
<1.00 (sustainable) and>1.00 (unsustainable).
Precipitation
Elasticity of
Stream/low
(#437)
Sankarasubr
amanian et
al., 2001
Sankarasubramanian (2001) identified a value of one as a breakpoint between
elastic and non-elastic responses in streamflow to precipitation. We displayed
this indicator in Appendices E and F with the following categories: <1
(inelastic) and >1 (elastic).
Total Use/ Total
Streamflow
(#352)
Meyer et al.,
1999
No thresholds were provided in Meyer et al. (1999). However, the original
data source (WRC, 1978) used a threshold of one to indicate a potential
conflict between offstream uses and the estimated instream flow needs. We
displayed this indicator in Appendices E and F with the following categories:
<1.00 (sustainable) and >1.00 (unsustainable).
5.2. MODIFYING AND REFINING INDICATORS TO INCORPORATE
THRESHOLDS
A major strength of the approach pursued in this study is the use of readily available data,
much of which has been vetted by other researchers, agencies, or institutions. Few indicators,
however, directly incorporate objective thresholds. Such thresholds, as noted above, can be
highly useful to decision makers, especially when they distinguish between acceptable and
unacceptable conditions. In some cases, slight modification of an indicator definition can
facilitate the identification of objective thresholds. For example, the pesticide indicators (#367,
#369, #371, #373, and #374) do not incorporate regulatory or human health thresholds because
these indicators are calculated as aggregates of multiple pesticides, some of which are
unregulated, and whose health effects are less well understood. As an alternative, a predictive
model (Larson et al., 2004) is used to map the average probability of exceeding the human health
threshold (maximum contaminant level (MCL)) for atrazine, which is the most commonly used
herbicide (Figure 5-1). The predictive modeling approach is currently being expanded by USGS
to other pesticides. Because these models are built from variables that may be affected by climate
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change, they may be particularly well-suited to assessing changes in vulnerability across
different scenarios of climate and land-use change.
In addition, new indicators may be developed by integrating multiple existing data sets.
For example, methylmercury production potential could be a useful indicator of vulnerability of
aquatic animals to anthropogenic waste. Currently, there is no existing data source that describes
methylmercury potential across the entire U.S. However, a new analysis could be conducted
using data for wet soils, temperature, and methylmercury deposition, to assess exposure of
aquatic life to this contaminant. Existing data sets could be used for the variables in such an
analysis, such as wet soils data from the United States Department of Agriculture-Natural
Resources Conservation Service (USDA-NRCS, http://soils.usda.gov/); temperature data from
NOAA's NCDC (http://www.ncdc.noaa.gov/oa/ncdc.html); and atmospheric deposition data
from the University of Illinois Urbana-Champaign's National Atmospheric Deposition Program
(NADP; http://nadp.sws.uiuc.edu/). Development of such aggregate indicators using easily
available existing data sets may yield additional useful indicators that are critical for assessing
regional vulnerability.
An alternative approach would be to define ideal water quality and aquatic ecosystem
vulnerability indicators, and then appropriately transform existing data or collect new data to
assess vulnerability. Development of indicators that more directly compare the sensitivity and
exposure components of vulnerability would facilitate a quantitative comparison of their relative
importance. For instance, in an effort to understand the relative importance of temperature and
population changes on groundwater availability, water use indicators may have to be scaled
relative to water availability or per capita demand. As an example, groundwater availability per
capita could accommodate adjustments from these diverse influences: precipitation effects on
recharge, temperature effects on evaporation, and population effects on demand. The hydrologic
component of this evaluation would require a model whose drivers include climate variables,
scenarios of whose future values can be developed. Creating primary indicators of ecological
function would allow for similar evaluations. Although an approach that defines ideal indicators
may yield objective thresholds/breakpoints and clear connections to the three aspects of
vulnerability, it is likely that difficulties in collecting all requisite data would limit the number of
indicators that could be constructed. However, Figure 5-2 and Figure 5-3 represent examples of
two indicators that can be developed using existing data. Figure 5-2 depicts total water use
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efficiency, a modification of the industrial water use efficiency indicator cited in Kurd et al.,
1998. Figure 5-3 depicts total water demand for human uses. Both indicator maps were created
using the USGS National Water-Use Dataset to provide a more complete picture of U.S. water
use.
The National Environmental Status and Trend (NEST) Indicator Project used another
approach to assemble a suite of indicators. The process used in that project included the
distillation of many perspectives on water into five categorical questions (Table 5-2) that
guided the search and development of indicators. All of the questions are addressed to some
extent by the indicators mapped during this project, although some key subcategories do not
have representative indicators. Some of these indicator classes could be filled by further
examination of existing data, but others would require additional data collection efforts. Several
published examples of these indicator classes were included in the comprehensive list of
indicators first assembled for this project, but were subsequently eliminated based on a lack of
data, data gaps, or unreliable quality of the available data sets, or inadequate or incomplete data
collection efforts. Data collection or manipulation efforts geared specifically towards informing
these indicators, such as those discussed below, might provide the necessary data for creating
national-scale maps.
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Table 5-2. Vulnerability indicators categorized in the National Environmental Status and
Trend (NEST) Framework
Vulnerability indicators from this project categorized according to the question framework from
the National Environmental Status and Trend (NEST) Indicator Project.
NEST Question
How much water do we
have?
How much water do we
use?
What is the condition
of aquatic ecological
communities?
What is the physical
and chemical quality
of our water?
Is the water we have
suitable for human use
and contact?
No clear fit to above
questions
Example Indicators
Meteorological Drought Indices (#165)
Ratio of Snow to Precipitation (S/P) (#218)
Precipitation Elasticity of Streamflow (#437)
Ratio of Reservoir Storage to Mean Annual
Runoff (#449)
Runoff Variability (#453)
Groundwater Reliance (#125)
At-Risk Freshwater Plant Communities (#22)
At-Risk Native Freshwater Species (#24)
Stream Habitat Quality (#284)
Wetland and Freshwater Species at Risk (#326)
Macroinvertebrate Index of Biotic Condition
(#460)
Macroinvertebrate Observed/Expected Ratio of
TaxaLoss (#461)
Acid Neutralizing Capacity (ANC) (#1)
Herbicide Concentrations in Streams (#367)
Insecticide Concentrations in Streams (#369)
Organochlorines in Bed Sediment (#371)
Herbicides in Groundwater (#373)
Insecticides in Groundwater (#374)
Coastal Vulnerability Index (#5 1)
Subcategories Not
Represented
Flooding (e.g., Population
Susceptible to Flood Risk
[#209])
Groundwater availability
(e.g., Groundwater Depletion
[#121])
Total water use (e.g., Ratio of
Water Use to Safe Yield
[#328])
Habitat Fragmentation (e.g.,
InStream Connectivity
[#620])
Nutrients (e.g., Water Quality
Index [#319])
Recreational water quality
Waterborne pathogens (e.g.,
Waterborne Human Disease
Outbreaks [#322])
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This map displays the probability of predicted concentrations ofatrazine, a pesticide, exceeding its regulatory threshold (i.e., its
Maximum Contaminant Level or MCL). The resulting map places pollutant concentrations into a human health context.
Average Probability of Exceeding
the Atrazine MCL (3 ug/L)
| | 0.00 - 0.03
P ^ 0.04 - O.C6
| 0.07 - 0.09
j^B 0.10-0.12
^B 3.13-0.1-
II States
100 200 300 400 500 Miles
I I I I I
Figure 5-1. Mapping data relative to regulatory thresholds.
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This map of 1995 Water Use Efficiency is a refinement of indicator #135. This example demonstrates how minor refinements using
existing data sets may result in indicators that more directly assess vulnerability.
% of Withdrawals Released
(Withdrawals - [Conveyance Loss + Consumption]) / Withdrawals
| | 94% - 98%
I I 82% - 93%
0 100 200 300 40G 500 Mites
I I I I I I
29% - 45%
| | States
Figure 5-2. Modification of indicator definitions using existing data.
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This map of 1995 Water Demand was developed using data sets that were also used to develop indicators #125 and #135. Many of the
available data sets used to develop the indicator maps can be used to develop additional indicators of vulnerability.
Total Water Withdrawals
MGal/Day
| | 2.1 - 330
920-1.700
1.800-3,200
3300-11,000
I States
0 100 200 300 400 500 Miles
I I I I I I
Figure 5-3. Modification of indicator definitions using existing data.
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6. CHALLENGES PART III: MAPPING VULNERABILITY
Producing a single map to represent numerical data from disparate sources in an accurate
and unbiased manner is a classic cartographic challenge. This challenge is rooted in the fact that
"a single map is but one of an indefinitely large number of maps that might be produced.. .from
the same data" (Monmonier, 1996). The choices made with regard to the metrics calculated, the
categories used to generalize those metrics, the spatial units used to aggregate localized data, and
the symbols used to display map features can all lead to substantially different maps.
Furthermore, these choices can be used to emphasize or minimize spatial trends and patterns.
The effort to produce indicator maps for this study was met with these same cartographic
challenges. The following sections discuss these challenges in greater detail and provide example
maps, using the indicators discussed above, to illustrate how these challenges can affect use of
indicators for assessments of vulnerability across the nation.
Mapping the above-described indicators at the national scale requires the compilation of
multiple reliable data sets that provide consistent sample density at this scale. In recent years,
agencies such as EPA, USGS, and NOAA have invested considerable resources to develop such
data sets. These are immensely informative and were used to develop many of the maps
contained in this report.
6.1. ASSESSMENT OF INDICATOR DATA AVAILABILITY AND MAPPABILITY
AT THE NATIONAL SCALE
We examined the 53 vulnerability indicators (see Table 4-1 and Figure 3-1) for data
availability and mappability, in the process identifying existing, available data that could
potentially be used for creating national maps for each of these indicators.
6.1.1. Identification of Data Sources for Indicators
We determined data availability for each indicator by re-examining the literature in which
the indicator was cited. In most cases, the study that cited the indicator also cited a data set,
either one that was collected and assembled during the study itself or a publicly available data set
containing data compiled by the authors of the study or by one or more private or public entities.
If no specific data set was cited in the original literature, data sets recommended by team
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members or technical advisors were used. If a data set was not available or could not be
recommended, the indicator was marked as having no associated data and was not evaluated for
mapping.
Data availability was the most serious limitation in evaluating whether or not we could
produce maps for the 53 vulnerability indicators. Of these, only 32 indicators were initially
assessed as having adequate data (using data sources identified in the literature) for nationwide
mapping. Furthermore, not all of these 32 indicators could be mapped, as the data sources
referenced in the literature were not always tailored specifically to the indicator. This was
frequently the case with indicators that were identified by one entity and whose data were
collected by another entity. In contrast, several indicators identified in USGS' The Quality of
Our Nation's Waters report (e.g., Herbicide Concentrations in Streams [#367]; Insecticide in
Groundwater [#374]; Organochlorines in Bed Sediment [#371]) are based on NAWQA data that
are also collected by USGS.
For indicators that met minimum criteria for availability and for which we identified data
sets, nationwide mappability at the level of 4-digit HUC watersheds (as a minimum screening
criterion) was assessed simultaneously with data availability. This was because we found that it
was not possible to establish mappability without beginning the process of manipulating and
mapping the data to determine what obstacles there may be to mapping.
6.1.2. Description of Major Data Sources
The data sets identified for these 53 indicators varied in size, level of detail, quality, and
relevance to the indicator. Some data sets were collected specifically with the concerned
indicator in mind; in other cases, the indicator was designed with a specific data source in mind.
From an initial assessment of data sources, it was evident that major national organizations, such
as EPA, USGS, NOAA, and NatureServe, were key players in national-scale data collection
efforts for indicators of water quality and aquatic ecosystems. For some indicators, we used data
sets produced by other organizations or published in peer-reviewed literature.
A distribution of how often we used data sources from these organizations and other
entities for assessing indicator mappability is shown in Table 6-1 (Distribution of data sources).
The following 14 indicators (out of 53) had no data available and are, therefore, not included in
the 39 indicators in the table: Flood Events (#100), At-Risk Native Marine Species (#27),
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Freshwater Rivers and Streams with Low Index of Biological Integrity (#116), Harmful Algal
Blooms (#127), Invasive Species-Coasts Affected (#145), Invasive Species in Estuaries (#149),
Riparian Condition (#231), Status of Animal Communities in Urban and Suburban Streams
(#276), Streamflow Variability (#279), Snowmelt Reliance (#361), Salinity Intrusion (#391),
Threatened and Endangered Plant Species (#467), Vegetation Indices of Biotic Integrity (#475),
and Instream Connectivity (#620). See Appendix C for a complete and more detailed listing of
data sources for each of the 39 indicators in Table 6-1.
Table 6-1. Distribution of data source
Indicator
Acid Neutralizing
Capacity (ANC)
(#1)
Altered
Freshwater
Ecosystems (#1 7)
At-Risk
Freshwater Plant
Communities
(#22)
At-Risk Native
Freshwater
Species (#24)
Coastal Benthic
Communities
(#462)
Coastal
Vulnerability
Index - CVI (#51)
Commercially
Important Fish
Stocks (#55)
Data Source Organization
EPA
X-
Wadeable
Streams
Assessment
X-
National
Land Cover
data set
(NLCD)
X-
Sampling
data in
National
Coastal
Assessment
(NCA)
database
USGS
X - National
Hydrography
data set (NHD)
NOAA
X - Annual
Commercial
Landing
Statistics
NatureServe
X-
Customized
data set
X-
Customized
data set
Other
X- U.S. Fish &
Wildlife Service's
(USFWS) National
Wetlands Inventory
(NWI)
X - Carbon Dioxide
Information Analysis
Center's (CDIAC)
Coastal Hazards
Database
69
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Indicator
Erosion Rate
(#348)
Fish and Bottom-
Dwelling Animals
(#95)
Groundwater
Depletion (#121)
Groundwater
Reliance (#125)
Heat-Related
Illnesses
Incidence (#392)
Herbicide
Concentrations in
Streams (#367)
Herbicides in
Groundwater
(#373)
Insecticide
Concentrations in
Streams (#369)
Insecticides in
Groundwater
(#374)
Instream Use/
Total Stream/low
(#351)
Low Flow
Sensitivity (#159)
Macroinvertebrate
Index ofBiotic
Condition (#460)
Data Source Organization
EPA
X-
Wadeable
Streams
Assessment
(WSA)
X-
Wadeable
Streams
Assessment
USGS
X - National
Water-Use
Dataset
X - National
Water-Use data
set
X - NAWQA
X -NAWQA
X -NAWQA
X -NAWQA
X - National
Water-Use
Dataset
NOAA
NatureServe
Other
X-Yang,D. W., S.
Kanae, T. Oki, T. Koike,
andK. Musiake. 2003.
Global Potential Soil
Erosion with Reference
to Land Use and Climate
Changes. Hydrological
Processes 17:2913-
2928.
X - National Center for
Health Statistics
(NCHS)'s Mortality data
X - Water Resources
Council. 1978. The
Nation's Water
Resources: The Second
National Water
Assessment, 1975-2000.
Volume 2.
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Indicator
Macroinvertebrate
Observed/
Expected (O/E)
Ratio of Taxa Loss
(#461)
Meteorological
Drought Indices
(#165)
Number of Dry
Periods in
Grassland/
Shrubland
Streams and
Rivers (#190)
Organochlorines
in Bed Sediment
(#371)
Pesticide Toxicity
Index (#364)
Population
Susceptible to
Flood Risk (#209)
Precipitation
Elasticity of
Streamflow (#437)
Ratio of Reservoir
Storage to Mean
Annual Runoff
(#449)
Ratio of Snow to
Total Precipitation
(#218)
Ratio of Water
Use to Safe Yield
(#328)
Data Source Organization
EPA
X-
Wadeable
Streams
Assessment
USGS
X- Hydro
Climatic Data
Network
(HDCN) &
Stream Gauge
Data
X -NAWQA
X -NAWQA
X - HDCN
X - Mean
Annual Runoff
Data
NOAA
X-
Divisional
Data on the
Palmer
Drought
Severity Index
(PSDI)
X - Monthly
Climate Data
NatureServe
Other
X-EPA'sECOTOX
database
X-FEMA'sQ3 Flood
Data & ESRI ArcUSA's
U.S. Census tract data
X - Oregon State
University's PRISM
Climate Modeling
System
X- USACE's National
Inventory of Dams
(NID)
X-Schmitt, C. V.,
Webster, K. K,
Peckenham, J. M.,
Tolman, A. L., and J. L.
McNelly. 2008.
Vulnerability of Surface
Water Supplies in Maine
to the 2001 Drought.
Journal of the New
England Water Works
Association. 122(2):
104-116.
71
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Indicator
Ratio of Water
Withdrawals to
Annual
Streamflow (#219)
Runoff Variability
(#453)
Stream Habitat
Quality (#284)
Total Use/ Total
Streamflow (#352)
Water Availability:
Net Streamflow
per Capita (#623)
Water Clarity
Index (#318)
Water Quality
Index (#319)
Waterborne
Human Disease
Outbreaks (#322)
Wetland and
Freshwater
Species at Risk
(#326)
Wetland Loss
(#325)
Data Source Organization
EPA
X-
Wadeable
Streams
Assessment
X-NCA
X-NCA
USGS
X - National
Water-Use
Dataset
X - National
Water-Use
Dataset
NOAA
NatureServe
X-
Customized
data set
Other
X - Oregon State
University's PRISM
Climate Modeling
System
X - University of
Washington's Variable
Infiltration Capacity
(VIC) Land Surface
Data Set
X - Water Resources
Council. 1978. The
Nation's Water
Resources: The Second
National Water
Assessment, 1975-2000.
Volume 2.
X - Oregon State
University's PRISM
Climate Modeling
System
X - Centers for Disease
Control and Prevention
(CDC)'s Waterborne
Disease and Outbreak
Surveillance System
(WBDOSS)
X -USFWS National
Wetlands Inventory
(NWI)
As can be seen in Table 6-1, some data sources furnished data for multiple indicators.
These major data sources are discussed in greater depth below.
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EPA 's Wadeable Streams Assessment (WSA)
EPA's WSA was designed to be the first statistically defensible summary of the condition
of the nation's streams and small rivers. Chemical, physical, and biological data were collected
at 1,392 wadeable perennial stream locations in the coterminous United States. Data were
collected by field crews during summer index periods between 2000 and 2004. Sample sites
were selected using a probability-based sample design; rules for site selection included weighting
based on the 1st- through 5th-order stream size classes and controlled spatial distribution. Due to
this sampling system, the sampling effort for the WSA varies across HUC-4 units. Because a
probability-based sampling design was used, the WSA data set may have avoided the bias that
may occur with ad hoc data sets. However, it is still less than ideal for mapping average
conditions in 4-digit HUCs because lakes, reservoirs, and large rivers were not sampled, and
because some HUCs had few or no sampling sites.
USGS's National Water-Quality Assessment (NAWQA) Program
USGS's NAWQA Program collects chemical, biological, and physical water quality data.
From 1991 to 2001, the NAWQA program collected data from 51 study units (basins) across the
United States; after 2001, data collection continued at 42 of the study units. Although the
program spanned 10 years, not all 51 sites were sampled every year, but were, instead, broken up
into smaller temporal frames (20 study units in 1991; 16 study units in 1994; and 15 study units
in 1997).
The NAWQA data warehouse currently contains sampling information from 7,600
surface water sites (including 2,700 reach segments for biological studies) and 8,800 wells. The
NAWQA sampling design uses a rotational sampling scheme; therefore, sampling intensity
varies year to year at the different sites. In general, about one-third of the study units are
intensively investigated at any given time for 3-4 years, followed by low-intensity monitoring.
Due to this sampling scheme, the sampling effort for the NAWQA Program varies across HUC-4
units.
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USGS' National Water-Use Dataset
USGS's National Water-Use Dataset contains water-use estimates for each county in the
United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. USGS
publishes reports every five years (starting in 1985) that present water-use information
aggregated at the county, state, and national levels. USGS study chiefs from each state are
responsible for collecting and analyzing information, as well as making estimates of missing data
and preparing documentation of data sources and methods used to collect those data. The study
chiefs are also responsible for determining the most reliable sources of information available for
estimating water use for each state. Because of this, data sources and quality may vary by
location.
NOAA 's Monthly Climate Data
NOAA's National Climatic Data Center (NCDC) is the world's largest active archive of
weather data. NCDC's Monthly Climate Data Set contains information collected for 18,116 sites
across the United States from 1867 to the present. The data set includes an assortment of
parameters such as measurements of rain, snow, evaporation, temperature, and degree days.
NCDC Monthly Climate data are primarily intended for the study of climate variability and
change. NOAA reports that, whenever possible, NCDC observations have been adjusted to
account for effects from factors such as instrument changes, station relocations, observer practice
changes, and urbanization.
NatureServe Data Set Customized for EPA
NatureServe collects and manages detailed local information on plants, animals, and
ecosystems though natural heritage programs and conservation data centers operating in all 50
U.S. states, Canada, Latin America, and the Caribbean. The data sets were originally customized
for the Heinz Center for publication in the 2008 State of the Nation's Ecosystems report. We
obtained updated state-level data on At-Risk Native Freshwater Species (#24) and on At-Risk
Freshwater Plant Communities (#22) to produce the maps for these indicators in this study.
These data sets were provided in Excel format by NatureServe on July 29, 2009. Data on
freshwater species were updated from those presented in the Heinz Center, 2008 report, and
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included counts of at-risk (GX-G3) and total native freshwater animal species by state for the
U.S. Due to incomplete state distribution, the data set did not include giant silkworm moths,
royal moths, sphinx moths, or grasshoppers. NatureServe did not update data on plant
communities as they determined that plant community data have not changed significantly since
the original analysis for the Heinz Center.
6.1.3. Supporting Information Collected for Data Sources
To assess data availability, we isolated information about the underlying data on which
the indicators were based. This information is also presented in Appendix C (Data Sources,
Supporting Information, and Technical Notes). Information considered when assessing the
mappability of data included:
Data sets used and the organizations or individuals who published or own the data;
How to obtain the data (download online or contact a specific person/organization)
and whether or not payment was necessary to obtain the data set;
Spatial resolution of data (e.g., state, study sites, HUC level, ecoregion);
Temporal resolution of data (i.e., frequency of data points and duration of data
collection);
Extent of coverage of data (e.g., national, regional, state, local);
Type of data source (e.g., survey, census, database, modeled data set);
Format of data (e.g., Excel tables, GIS shapefiles); and
Relevant metadata (either as a website or a supporting document).
In many cases, the supporting documentation accompanying the data did not provide all
of the abovementioned details. However, the available information has proven useful for
prioritizing indicators for further investigation into their mappability.
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6.1.4. Lack of Data and Other Unresolved Data Problems
6.1.4.1. Data Availability Issues
To streamline the process of determining indicator mappability, we identified issues with
data availability and how data were presented as early in the process as possible. We encountered
problems both in the effort to locate, access, and download indicator data and in the effort to
manipulate, transform, or modify the data so that they could be mapped using GIS software at
the appropriate scale. Based on our assessment of data availability, 28 indicators were
determined to be non-mappable. Although data sets were available for a few of these indicators,
the problems with the data sets could not be reconciled, even with greater time and effort spent
on data manipulation and mapping, and, therefore, these indicators were considered non-
mappable. These 28 indicators presented one or more of the problems listed in Table 6-2
(Indicators eliminated due to lack of data or unresolved data problems).
Table 6-2. Indicators eliminated due to lack of data or unresolved data problems
Data
Availability
Problem
Description of the
Problem
Example
Indicators
Specific Data Availability Problem
Data
reported by
individual
states
Reporting, sampling, and
assessment methods vary
between states. These
indicators are likely to
reflect programmatic
differences instead of
differences in
vulnerability.
Fish and Bottom-
Dwelling Animals
(#95)
The indicator is derived from STORET, a
database that relies substantially on self-
reported data.
Waterborne
Human Disease
Outbreaks (#322)
The WBDOSS datasets relies on voluntary
reporting from public health departments
within the United States.
303(d) Impaired
Waters3
The ATTAINS database relies on data
reported by individual states.
Multiple
Data Sets
Complete data set could
only be obtained by
combining more than one
data set, as specified in the
literature. The effort
necessary to combine the
data ranges widely.
Population
Susceptible to
Flood Risk (#209)
This would require combining digital flood
data from FEMA (unavailable at time of
inquiry) and Census Bureau demographic data.
Water Quality
Index (#319)
Five data sets combined into an index.
Wetland Loss data
(#325)
USFWS' National Wetlands Inventory data are
at different scales at different locations.
3This indicator was not assigned an indicator ID# because it was not derived from the scientific literature. The
indicator was added to incorporate EPA's extensive water quality assessment database.
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Data
Availability
Problem
Description of the
Problem
Example
Indicators
Specific Data Availability Problem
Coastal Benthic
Communities
(#462)
Benthic indices vary by region and it is unclear
whether regional indices are comparable.
Data set
derived from
extensive
modeling
Complete data set needed
to be recreated with
extensive modeling using
raw data.
Groundwater
Depletion (#121)
Indicator based on a modeled base-flow data
set developed by Vogel et al. (1999) and
presented in Hurd et al. (1998).
Low Flow
Sensitivity (#159)
Indicator based on a modeled base-flow data
set developed by Vogel et al. (1999) and
presented in Hurd et al. (1998).
Streamflow
Variability (#279)
Indicator based on a model developed by
Vogel et al. (1999) and presented in Hurd et al.
(1998).
Data
collection in
progress
Data are unavailable
because collection efforts
are in progress.
Instream
connectivity
(#620)
USGS is currently collecting data on indicator
as a part of its National Hydrography Dataset.
Not national
in scope
Data are unavailable
nationally
Number of Dry
Periods in
Grassland /
Shrubland
Streams and
Rivers (#190)
The data set identified by the Heinz Center
contained an analysis of grassland and
shrubland watershed areas for Western
ecoregions only.
Water Clarity
Index (#318)
Data are only available for certain U.S. coastal
regions.
Invasive Species -
Coasts Affected
(#145)
This indicator evaluates invasive species
within the context of local land use, a scale
that is relatively uncommon. No national
datasets have been identified that
simultaneously evaluate local land
management and the presence of invasive
species.
Ratio of Water
Use to Safe Yield
(#328)
Data set identified by the source only contains
data for the state of Maine.
Salinity Intrusion
(#391)
Data sources cited in the information source,
(Poff et al., 2002) are local studies with limited
(and non-comparable) data sets. No
comprehensive national data sets are known to
exist.
Data no
longer
collected, or
are not for
Data are not recent enough
(cutoff date varies with the
indicator) or are based on
future projections.
Waterborne
Human Disease
Outbreaks (#322)
Data are no longer reported (most recent data
are from 2006).
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Data
Availability
Problem
Description of the
Problem
Example
Indicators
Specific Data Availability Problem
the current
time period.
Heat-Related
Illnesses
Incidence (#392)
Data consist of projections for the years 2020
and 2050.
No data set
available.
Data for the indicator are
unavailable.
At-Risk Native
Marine Species
(#27)
The Heinz Center (2008) study, which is the
source of this indicator, identifies NatureServe
as a potential source of information relevant to
this indicator, but acknowledges that data
availability is limited to a small set of species.
Flood Event
Frequency (#100)
No data source was identified in this study that
could be used to map this indicator at a
national scale.
Freshwater Rivers
and Streams with
Low Index of
Biological
Integrity (#116)
There are currently no regional or national data
bases that assemble this information for a
broad range of taxa.
Harmful Algal
Blooms (#127)
Currently, there are no nationwide monitoring
or reporting programs for harmful algal events.
Invasive Species
in Estuaries
(#149)
Currently, there are no national monitoring
programs for invasive species in estuaries and
no agreed-upon methods for combining
information on the number of species and the
area they occupy into a single index.
Status of Animal
Communities in
Urban and
Suburban Streams
(#276)
The Heinz Center (2008) study, which is the
source of this indicator, states that currently
available data are not adequate for national
reporting.
Riparian
Condition Index
(#231)
The Heinz Center (2008) study, which is the
source of this indicator, identifies four
literature sources that outline various ways to
create such an index, but acknowledges that no
raw data are currently available.
Snowmelt
Reliance (#361)
The information source (IPCC, 2007a) only
has theoretical discussion of indicator. No
specific data source is cited.
Threatened &
Endangered Plant
Species (#467)
This indicator was provided as an example
EPA's National Wetland Condition
Assessment. This report does not identify a
specific data source for this indicator.
Vegetation Indices
of Biotic Integrity
(#475)
This indicator was provided as an example
EPA's National Wetland Condition
Assessment. This report does not identify a
specific data source for this indicator.
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Data
Availability
Problem
Description of the
Problem
Example
Indicators
Specific Data Availability Problem
Altered
Freshwater
Ecosystems
(percent miles
changed) (#17)
A national database with the number of
impounded river miles does not exist. Data
from three sources need to be integrated, one
of which currently does not provide data in
electronic form.
Commercially
important fish
stocks (#55)
Data for change in fish stock size over time are
not currently available. The change in a fish
stock size over time would need to be
calculated for each area where fish stock data
are available.
Duplicate
Indicator
Data are available, but the
indicator was a duplicate
of another indicator.
Fish and Bottom
Dwelling Animals
(#95)
This table highlights two challenges to the adoption and use of indicators at a national
scale. First, it draws attention to the issue of measurability. In many cases, a measurable
indicator requires a substantial effort to calculate the value at a single location. This may be due
to the need for prolonged observation periods, complex sampling protocols, or other factors. For
example, Vegetation Indices of Biotic Integrity (#475) uses the relationships between
anthropogenic disturbances and observations of plant species, plant communities, plant guilds,
vegetation structure, etc. to describe wetland condition. Typically, the highest IBI values
represent reference standards or least-disturbed ecological conditions. To collect the data
required to calculate an IBI, a trained observer must record multiple parameters in the field for
each local IBI score. Though the indicator is measurable and highly useful in the locations where
data exist, the effort required to collect data for this indicator at a national scale may be
prohibitive.
Second, Table 5-2 highlights how data sources that may otherwise be excellent may be
problematic for the purposes outlined in this study. We will discuss the issue of self-reported
data in further detail as an example. Data sets that rely on individual state reports are problematic
for three reasons. First, the monitoring activities and subsequent reporting may be limited by the
availability of the state's resources. This can result in data gaps stemming from varying levels of
reporting activity across states. Second, state-based assessments that require sampling from a
population (e.g., stream assessments) may not rely on statistically rigorous sampling methods,
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resulting in sampling that may not be representative. Third, assessment methods may vary from
state to state. For example, the assessment and classification methods used by states during the
development of the 303(d) impaired waters lists vary substantially among states. Together, these
inconsistencies in reporting, sampling, and assessment result in maps that may reflect
programmatic differences instead of actual differences in vulnerability. For these reasons,
indicators based on national data sets that had national coverage but rely on individual entities to
voluntarily report data, (e.g., EPA's Storage and Retrieval [STORET] database for water quality
data, CDC's Waterborne Disease and Outbreak Surveillance System [WBDOSS], and EPA's
Assessment, TMDL Tracking and ImplementatioN System [ATTAINS] database), were not used
in the present study.
Figure 6-1 shows a national map that relies on one such national data set, the ATTAINS
database. Panel A shows a map that relies on the total stream-miles designated as 303(d)
impaired waters. This first map is problematic because it does not account for large differences
in assessment rates across states, or for the fact that overall assessment rates are low. According
to the EPA ATTAINS database, only 26.4% of the nation's streams and rivers and 42.2% of the
nation's lakes and reservoirs have been assessed for impairments, making it difficult to create
national-scale indicators. Panel B attempts to account for differences in assessment rates by
showing the percentage of assessed stream-miles that are designated as 303(d) impaired waters.
Though this second map is an improvement over the first because it normalizes the assessment
effort, the programmatic differences still result in areas that may not appear to be vulnerable
simply because sampling and assessment methods vary substantially between states.
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The follow ing maps display number (panel A) and percent (panel B) of stream-miles designated
as 303(d) impaired waters using data from EPA 's Assessment, TMDL Tracking and
Implementation System (ATTAINS) database.
A.
Stream-miles designated as impaired
[TOO- 1,590
11,600 - 20,900
1 States
B.
Percent of assessed stream-miles designated as impaired
| 0% - 20.7%
^11 20.8% - 44.6%
^B > 44.6%
I States
0 200 400 600 800 1,000 Miles
I I I I I I
Figure 6-1. Limitations of data sets containing self-reported data.
6.1.4.2. Data Sets Without National Coverage
In some cases, the data required to calculate indicator metrics were incomplete in terms
of national coverage. Indicators based on a particular ecosystem or land cover type (e.g.,
grassland or shrubland) may not extend to all parts of the country. For example, few, if any,
streams in Eastern ecoregions are grassland or shrubland streams. Other national coverage data
gaps stemmed from data availability. For example, although 500 year flood plains can be
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identified for all parts of the country, GIS-compatible digital flood plain data from FEMA are
only available for certain parts of the country where paper maps have been digitized.
Other data gaps were the result of incomplete data collection. For example, for the
indicator Commercially Important Fish Stocks (#55), the Heinz Center (2008) study evaluated
only about 21% of the commercially important fish landings found in U.S. waters. Similarly, for
the indicator Number of Dry Periods in Grassland/Shrubland Streams and Rivers (#190), the data
set provided by the Heinz Center contained an analysis of grassland and shrubland watershed
areas for Western ecoregions only. Although the reasons for mapping Western ecoregions only
are unclear, it is likely that few, if any, sites in Eastern ecoregions satisfied the definition of a
"grassland" or "shrubland" watershed used in the 2001 National Land Cover Dataset.
In some cases national coverage was unavailable because data collection efforts are still
in progress. For the indicator Wetland Loss (#325), wetlands in 13 states are either unmapped or
are recorded only on hardcopy maps. Similarly, data for the indicator Coastal Benthic
Communities (#462) (from EPA's National Coastal Assessment [NCA]) and digital flood data
for the indicator Population Susceptible to Flood Risk (#209) (from the Federal Emergency
Management Administration [FEMA]) were not available at the time of this study for several
areas within the U.S.
6.1.4.3. Non-uniform Spatial Distribution of Data
In some cases, the national-scale data required to calculate a vulnerability metric are
available, however the data are not distributed homogeneously across the country. As a result,
varying amounts of data are available within each of the HUC-4 units. This variation can be
substantial, and in cases where only few sample points are available within a HUC-4 boundary,
individual sites may exert a large influence on the calculated metric value.
The indicator Acid Neutralizing Capacity (#1), for example, is calculated using data from
1,601 stream sites across the country that were sampled as part of EPA's Wadeable Streams
Assessment. The number of sites sampled within each of the 204 HUC-4 units varies from 0 to
93, with a median value of 5 sample sites. The calculated vulnerability metrics for HUC-4 units
containing the median number of samples (or fewer) are particularly sensitive to measurements
at individual sites. A change in the status of a single site from "not at risk" to "at risk" changes
the calculated metric (percentage of "at risk" sites) by 20%. This could result in the entire HUC-
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4 unit being placed in a different category of vulnerability as a result of a single measurement. A
mapping challenge emerges when vulnerability metrics calculated from a small pool of data are
mixed with those calculated from a larger pool. It is difficult, and sometimes impossible, to
illustrate on a single map where low density would be most likely to result in an erroneous
vulnerability classification.
6.1.4.4. Temporal Gaps
Many indicators are derived by comparing data contained in two separate data sets, or by
comparing data from one data set collected over two distinct time periods. In the first case, it is
important to consider the time period in which the data are collected, especially if the
information collected may change over time. Temporal gaps between data sets may result in
erroneous vulnerability assessments and inaccurate maps. For example, Net Streamflow
Availability per Capita (#623) depends on time-sensitive information from a range of data sets.
Evaluating streamflow, withdrawals, and population figures from different time periods may
provide a different assessment of vulnerability when compared to data collected from the same
year. In the second case, indicators based on comparisons to a historical condition are dependent
on the existence of historical data. For some indicators considered during the course of this
project, this historical information was not available. The Wetland Loss (#325) indicator
provides an example of such a case. Information regarding wetland extent is not available at the
national scale in a format suitable for mapping with a GIS.
Another issue related to temporal gaps pertains to future data collection. One objective of
this project is to identify indicators that can be updated over time to track changes in
vulnerability. In cases where data collection and reporting have been discontinued, the indicator
no longer meets this key objective. The Waterborne Human Disease Outbreaks (#322) and
Runoff Variability (#453) indicators fall into this category. If future data collection efforts are
proposed, these indicators may become more useful for national level assessments.
6.1.5. Data Problems that Could be Resolved
Of the 53 indicators that were examined for data availability, twenty-five indicators were
mapped. Data sources and supporting information for 32 indicators that had some form of data
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available that could be examined for mapping are presented in Appendix C (Data Sources,
Supporting Information, and Technical Notes).
We identified various types of data gaps in the search for data to represent our
vulnerability indicators at the national scale. In some cases, additional assessment of an indicator
suggested that there were too many obstacles to nationwide mapping at the present time. Because
one rule of thumb for this project was to identify those vulnerability indicators that could be
readily mapped, we did not consider indicators that appeared to be mappable but only with
extensive data processing efforts. The extent of the data gaps that affected the production of
maps differed from one indicator to another, and prohibited production of maps for some
indicators. In other cases the problems were minor and maps could be produced (with a few
accompanying caveats). The data gaps for this project could typically be placed into one of the
three categories shown in Table 6-3.
Table 6-3. Data gaps
Data
Availability
Problem
Data Sets
Without
National
Coverage
Non-
uniform
Spatial
Distribution
ofData
Temporal
Gaps
Description of the
Problem
National data collection
is incomplete or indicator
is location-specific.
Data are not distributed
homogeneously across
the country (therefore,
number of data points
within each HUC varies).
Lack of historical data
(which are needed as a
baseline) or time-
sensitive data which
must be updated
frequently.
Example Indicators
Population
Susceptible to Flood
Risk (#209)
Number of Dry
Periods in
Grassland/Shrubland
Streams and Rivers
(#190)
Acid Neutralizing
Capacity (#1)
Wetland Loss (#325)
Water Availability:
Net Streamflow
Availability per
Capita (#623)
Specific Data Availability Problem
At time of inquiry, GIS-compatible digital
flood plain data from FEMA were only
available for certain parts of the country.
Heinz Center data identifies grassland and
shrubland watershed areas for Western
ecoregions only.
EPA Wadeable Streams Assessment data
were collected at 1,601 sites. However, the
number of sites within HUC-4 units ranged
between 0 and 93 sites.
Historical data on the extent of wetlands is
not available.
Variables that this indicator depends on
(streamflow, water withdrawals, and
population) are all time-sensitive. Indicator
maps are not useful if recent data are not
available.
Mapped indicators typically used nationally recognized data sets or data sets created by
national agencies, such as EPA, USGS, and NOAA. While these data sets are comprehensive in
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nature and cover the entire country, they still have data gaps as well as data quality issues.
Nevertheless, the data issues associated with the mapped indicators were either resolved or
considered minor enough that a map would still provide useful information for a vulnerability
assessment. Minor data issues were carefully documented for the mapped indicators.
6.2. CREATION OF EXAMPLE MAPS
We evaluated for mapping purposes 32 indicators for which national data had been
collected. Twenty-five indicators were considered to be mappable (Table 6-4). Six of the
remaining indicators were not mapped for this project due to challenges with acquiring data or
representing the source data spatially. One of these indicators was mappable, but had substantial
gaps in coverage that limited our ability to assess relative vulnerability at a national scale.
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Table 6-4. List of mapped vulnerability indicators
Indicator
(See Appendix B for definitions)
Acid Neutralizing Capacity (ANC) (#1)
At-Risk Freshwater Plant Communities (#22) '
At-Risk Native Freshwater Species (#24)'
Coastal Vulnerability Index (CVI) (#51) 2
Erosion Rate (#348)
Groun dwater Reliance (#125)
Herbicide Concentrations in Streams (#367) '' s
Herbicides in Groundwater (#373)'' 3
Insecticide Concentrations in Streams (#369) '' 3
Insecticides in Groundwater (#374)'' 3
Instream Use/Total Stream/low (#351)
Macroinvertebrate Index ofBiotic Condition (#460)'
Macroinvertebrate Observed/Expected (O/E) Ratio ofTaxa Loss
(#461)
Meteorological drought indices (#165)2
Organochlorines in Bed Sediment (#37 1)1' 3
Pesticide Toxicity Index (#364)
Precipitation Elasticity of Stream/low (#437)
Ratio of Reservoir Storage to Mean Annual Runoff (#449)'' 3
Ratio of Snow to Total Precipitation (#218)2
Ratio of Water Withdrawals to Annual Stream/low (#219)3
RunoffVariability (#453)
Stream Habitat Quality (#284)'
Total Use / Total Stream/low (#352)
Water Availability: Net Stream/low per Capita (#623)'' 3
Wetland and freshwater species at risk (number of species) (#326)'
Literature Source
(See Appendix A for full citations)
U.S. EPA, 2006b
Heinz Center, 2008
Heinz Center, 2008
Day etal., 2005
Murdoch et al., 2000
Kurd etal., 1998
USGS, 1999
USGS, 1999
USGS, 1999
USGS, 1999
Meyer etal., 1999
U.S. EPA, 2006b
U.S. EPA, 2006b
National Assessment Synthesis Team, 2000a
USGS, 1999
Gilliom etal., 2006
Sankarasubramanian et al., 2001
Lettenmaier et al., 2008
Lettenmaier et al., 2008
Hurdetal., 1998
Lettenmaier et al., 2008
Heinz Center, 2008
Meyer etal., 1999
Hurdetal., 1998
Hurdetal., 1998
Indicator definition changed based on available data.
Indicator not defined in information source. Definition obtained from primary literature cited in the information
source or new definition created based on available data.
Indicator name changed to more appropriately match its definition or the available data.
The software we used for creating the maps for the 25 indicators was ArcMap 9.2 (©
1999-2006 ESRI). For most indicators, data were available either in a GIS format, such as
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shapefiles, or in tabular form. In some cases, we processed tabular data in Microsoft Excel 2002
or Microsoft Access 2002 prior to importing into ArcMap. In other cases, we manipulated these
data and calculated summary statistics directly in ArcMap. We used ArcMap to overlay different
data sets, and we ultimately overlaid all data sets with HUC-4 boundaries. The data layer for
such boundaries was obtained from the USGS.
For illustrative purposes, we had to choose a spatial unit of analysis. We chose to use
USGS hydrologic units at the 4-digit scale here, for three practical reasons. First, USGS
hydrologic units provide complete, continuous coverage of the continental U.S., which we
established as a requirement of this project. Second, hydrologic units are usually synonymous
with watersheds. Using a spatial unit with an inherent link to existing hydrography seems
appropriate for a project that is evaluating indicators of vulnerability for drinking water and
aquatic ecosystems. HUCs are frequently used by EPA, USGS, and other agencies to monitor
water-related phenomena across the country. Finally, 4-digit HUCs were chosen because they
balance the need to convey interpretable regional patterns with the objective of providing
detailed local information. In other words, in our judgment, they do not over-generalize regional
patterns and they do not over-extend the underlying data by providing more local resolution than
is warranted. However, we reiterate that the maps we show are to illustrate the various issues we
discuss, and we are not advocating any particular spatial aggregation as a matter of best practice.
Alternative spatial frameworks or resolutions of course exist, and we discuss the implications for
mapping of using such alternatives in more detail in sub-section E (Spatial Aggregation) below.
We aggregated or dis-aggregated the data, depending on their native scale (e.g., state-
level data [where there is one data value provided for each state] vs. point data), to obtain a
single value of the indicator for each HUC-4 watershed. Using Symbology, we assigned different
colors or gray shades to represent the HUC-4 watersheds in different vulnerability categories on
each indicator map. The detailed step-by-step methodology for each indicator is documented in
Appendix D (Mapping Methodology).
We produced 25 complete example maps by HUC-4 watershed (see Appendix E). In
addition, we produced an incomplete map for one indicator for which data suitable for mapping
were available for portions of the country. However, substantial gaps in national coverage limit
the ability to assess the relative vulnerability of ecosystems to environmental change at a national
scale using this indicator. The remaining five indicators were not mapped for this project due to
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challenges with acquiring data or representing the source data spatially. These issues are
discussed in detail below.
The mapped indicators fall into five categories established during the evaluation of the
literature (see Section 3), though the indicators we mapped are not distributed evenly across
these categories. The categories (with number of indicators mapped shown in parentheses) are:
chemical (7); ecological (6); hydrological (8); soil (1); socioeconomic (3).
Assuming that vulnerability can be inferred from metric values that were at the high (or
low, depending on the indicator) end of the range of mapped values, regional differences in
relative vulnerability were apparent for some of the mapped indicators. For example, the map for
the indicator Meteorological Drought Indices (#165) displays high vulnerability in the Western
United States, an area that has historically been exposed to prolonged drought. The map also
shows high vulnerability for the Southeastern U.S., an area that has experienced a severe drought
in recent years.
In some cases, there are no strong regional patterns. For example, the map for Stream
Habitat Quality (#284) displays a spatially heterogeneous pattern, with no particular portion of
the country strongly distinguished from any other.
Regions for which a single indicator might suggest greater vulnerability may not appear
as vulnerable across a full suite of indicators. An examination of the full set of maps by HUC-4
watershed in Appendix E suggests determining overall water quality- and aquatic ecosystem-
related vulnerability across all of these dimensions may be complicated. Appendix E also
contains detailed descriptions of each of the 25 maps created for the mappable indicators. We
return to the issue of combining indicators in more detail in Section 7.
6.3. SPATIAL AGGREGATION
To create a national map illustrating an indicator of vulnerability, it is necessary to
aggregate data collected at discrete locations and calculate summary statistics that describe
conditions across a larger area. Examples of such statistics may include the mean value of an
indicator or the percentage of sites that exceed a threshold value. In many cases, this aggregation
process results in a slightly different metric. For example, Acid Neutralizing Capacity is reported
in milliequivalents/L at the site scale. However, an aggregate statistic that can be calculated, and
is both referred to in EPA's Wadeable Streams Assessment report and mapped for this report, is
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the percentage of sites with ANC less than 100 milliequivalents/L. When developing maps using
aggregated metrics, it is important for both the producers and consumers of maps to understand
how the underlying data and the aggregation methods may affect the validity of objective
thresholds and the patterns illustrated in the final map. In the above example, the threshold of
100 milliequivalents/L is a relevant threshold at the scale of an individual site. However, no
objective thresholds are defined for the range of aggregated percentage values calculated for
each HUC. Appendix H includes an evaluation of the effects of aggregation on the validity of
theoretical breakpoints for each of the mapped indicators. These issues of aggregation
underscore the concept that a single set of data can be used to produce many different maps. The
following sections discuss additional factors to be considered when aggregating data.
6.3.1. Local Variation
Measurements at individual sample sites are affected by local factors such as land use,
the presence of an industrial facility, an urban center, a protected region (e.g., a National Park),
or other features that exist in a heterogeneous landscape. Within a large area (like a HUC-4 unit)
that contains a wide variety of these local factors, measurements collected at individual sites may
vary substantially. When a group of values within such an area are aggregated into a single
value, local variation can be masked. Understanding the degree of local variation is an important
component of interpreting vulnerability. For this reason, it may be necessary to simultaneously
consider maps that illustrate the vulnerability metric and the variation in raw data values present
within each spatial unit.
6.3.2. Extent of Spatial Units (HUC Levels)
Aggregation of individual local measurements into a single metric frequently involves the
extrapolation of information. Extrapolation may be appropriate in areas where sampling density
is large enough to accurately describe the conditions, and that the extent of the local
measurements coincides with the extent of the larger areal unit used to aggregate data. However,
extrapolation may also result in the masking of low data density in cases where the extent of the
aggregate unit is significantly different from the extent of the underlying data. The producers of
maps must be sensitive to the limits of aggregation (and subsequent extrapolation) when
choosing a spatial framework to represent a data source comprised of local measurements.
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For example purposes, we rely here on 4-digit HUCs to illustrate patterns of vulnerability
- we apply it consistently to compare across indicators. For some indicators, however,
aggregation of data into this framework may mask low data density. Figure 6-2 illustrates this
issue using 3 different scales of HUC units and the same underlying data set. The visual contrast
between the top and bottom maps demonstrates how low data density can be masked through
aggregation into larger spatial units.
All of the indicators we selected for mapping were chosen based on their ability to
provide information on the relative vulnerability of water quality and aquatic ecosystems. As
environmental measurements, the data collected and used for each indicator has an inherent level
of uncertainty and error associated with it. Selecting a particular unit for presenting information
in a set of maps is useful for making comparisons across the set. However, the data collected for
the indicators were not available at consistent scales across the set of indicators. The data for
most of the indicators was thus altered to present it at a consistent scale. Although manipulating
the data changes the accuracy of the information, the manipulations help make the information
presented more useful. For the most part, data manipulation required either a scaling up or down
of data or transformation of the data from different geographic boundaries.
Data needing to be scaled up included point data. In all cases, the sample data used to
calculate metrics for these indicators is not distributed homogeneously. As a result, dissimilar
amounts of data are available within the HUC-4 unit boundaries. In cases where there are few
sample points within a HUC-4 boundary, individual sites have a greater influence on the metric
value that is calculated.
Data presented at the state level needed to be scaled down or transformed to match the
HUC-4 geographic boundary. Transforming the data from a state-based representation to a HUC-
4 representation requires an assumption that the distribution of the indicator is uniform within
each state. Although this assumption is unlikely to be accurate, it allows for area-weighted
metrics to be calculated for HUC-4 units that intersect more than one state.
Coastal data presented a unique challenge in mapping. As a watershed geographic unit,
HUC-4 has limited or no coverage for coastal and nearshore area data. This makes aggregation
for the purposes of reporting at the HUC-4 scale problematic. To address this issue, we
developed a special reporting unit for one indicator, the Coastal Vulnerability Index (#51).
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Although necessary for creating useful and comparable maps, data manipulations change
the quality of the data presented through assumptions about coverage and the representativeness
of the data to nearby geographic areas. In most cases, data manipulations are likely to yield
greater error and uncertainly than the original data. However, problems associate with data
manipulation are likely to be more important for some indicators than others. For example, an
indicator based on fine-scale data within a HUC-4 boundary will likely present a more accurate
picture of relative regional vulnerability than an indicator based on transformed state-level data.
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The following maps display the Stream Habitat Quality (#284) indicator at various scales of
HUC units, illustrating how low data density can be masked through aggregation into larger
spatial units.
Average Rapid Assessment Score
40 - 109
^B 126~ 13S
^^| 137- 147
148 - 190
4-digit HUC units
Bioassessuient Sample Location
8-digit HUC units
12-digit HUC units
0 200 400 600 800 1.000 Miles
I I I I I I
Figure 6-2. Aggregation, precision, coverage, and data density.
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6.3.3. Alternate Spatial Frameworks
The selection of the spatial framework used to evaluate geographically-based data can
have a significant influence on the graphical display of spatial information and for the
assessment and management of resources (Omernik and Griffith, 1991). In some cases, different
units of analysis can result in maps that provide difference perceptions using the same set of
underlying data. Two spatial frameworks, watersheds and ecoregions, are often associated with
ecosystem management. Each of these frameworks has advantages, and the tradeoffs between the
two systems reinforce the concept that there is no single best spatial framework for displaying
indicators of water quality and aquatic ecosystem condition or vulnerability.
6.3.3.1. Watersheds (and Hydrologic Units)
Watersheds are often advocated as the appropriate unit for ecosystem management
because they encompass the area of land that influences a connected system of water bodies
(Montgomery et al., 1995; U.S. EPA, 1995). To address the practical need for a system of
management units that serve as a standardized base for inventorying hydrologic data, the US
Geological Survey delineated hydrologic units. These units are commonly identified by their
hydrologic unit codes (HUCs) (Seaber et al., 1987). The term "HUC" is often used to describe
the hydrologic unit, not just the unit code). HUCs are assigned at several hierarchical spatial
scales. The HUC-4 units (n = 204) used in this study have a mean area of 38,542 km2.
It is noteworthy that many HUCs are true watersheds, while others are combinations of
multiple smaller watersheds or segments of a larger watershed. HUCs provide non-overlapping,
continuous coverage of a given area, and are typically used in place of true watersheds for
mapping environmental data.
6.3.3.2. Ecoregions
Ecoregions are alternative spatial units, introduced by Omernik (1987), that are
specifically designed to be internally homogeneous with regard to factors that affect water
quality, such as vegetation, soils, land forms, and land use. Similar to HUCs, ecoregions are
designated at several hierarchical spatial scales. The size of individual ecoregions varies more
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than individual HUCs. For example, the 87 ecoregions at the Level 3 scale range in size from
649 to 357,000 sq. km.
The shortcoming of ecoregions is that they rarely encompass a single hydrologically
connected area, making it difficult to identify the location(s) where cumulative stresses will be
felt.
Figure 6-3 illustrates differences resulting from the use of different spatial frameworks.
Although the national spatial patterns are similar, there are local differences that may influence
vulnerability interpretations. Specifically, differences between the maps are most evident in the
western United States - particularly within the Rocky Mountains - and in northern Wisconsin.
These differences are reasonable, given the basis for delineating individual areas within each of
these frameworks. HUCs, which are based loosely on watershed boundaries, tend to integrate a
wider range of physical/topographical characteristics than ecoregions. These local physical
characteristics may have a significant influence on the ratio of snow to total precipitation at any
one point, resulting in a wide range of values within a HUC. Ecoregions, on the other hand, are
specifically intended to describe regions with physical/topographical similarities. Thus, one
would expect that ecoregions would contain less within-unit variation for Indicator #218. Maps
of the 25 mappable indicators by ecoregion are presented in Appendix F. Appendix F also
contains detailed descriptions of each of these maps. From a visual comparison of these maps
with the HUC maps presented in Appendix E, it is evident that the choice of similarly sized
spatial units (i.e., HUC4 vs. Ecoregion Level 3) has little effect on our results at the national
scale.
6.3.3.3. Coastal Areas
Coastal areas are worthy of focus in national scale vulnerability assessments because they
are of great national importance and pose unique challenges. Coastal areas may be more prone to
the effects of climate change, but the limited geographic extent of coastal areas necessitates the
use of a different analysis framework. For example, the indicator Coastal Vulnerability Index
(#51) uses data available from a USGS database. The data are limited to only coastal and
nearshore areas. Although this indicator provides complete coverage of coastal areas,
aggregation into HUC-4 units or ecoregions would not provide meaningful results. To address
this issue, a set of special reporting units for coastal areas was developed for this indicator. Each
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unit extends approximately 20 miles inland and includes approximately 150 miles of coastline
(Figure 6-4).
6.4. CATEGORICAL AGGREGATION
It is common to symbolize numerical data using chloropleth maps, which use a range of
colors that correspond to the underlying data values. Determining how each color is assigned to
the range of data values is classic cartographic challenge that applies to most any mapping
project, this study included. For numerical data, the methods used to delineate breaks between
data classes can affect the spatial patterns conveyed in a map, and the subsequent interpretation
of those data. Thus, care must be taken in the development of maps based on numerical data,
especially if the resulting spatial patterns may be used to develop policy.
Figure 6-5 illustrates how a single set of data can be used to create alternate maps simply
by altering the number of data classes and the breaks used to distinguish between individual data
classes.
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The following maps display the Ratio of Snow to Precipitation (#218) indicator using 4-digit
HUC units and Omernik's (1987) ecoregions, illustrating how the same underlying data appear
different when using different spatial frameworks.
Riitio of Total Snowfall to Total Precipitation
| 0% - 5%
r~^i 5.01%-10%
^H 10.01%-15%
^H 15.01%-20%
^H 20.01%-100%
4-digit HUC unit
Ecoregions
Figure 6-3. Data represented by different spatial frameworks.
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The following map displays the Coastal Vulnerability Index (#51), a coastal indicator, for which a set of special reporting units for
coastal areas was developed. Each coastal unit extends 20 miles inland and includes approximately 150 miles of coastline.
Coastal Vulnerability Index
Low Vulnerability
Medium Vulnerability
High Vulnerability
0 100 200 300 400 500 Miles
I I I I I
Figure 6-4. Spatial framework for coastal zone indicators.
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The following map displays the Stream Habitat Quality (#284) indicator, illustrating how the
same underlying data appear different when displayed using three different data breaks
(quantiles, equal intervals,, and natural breaks or jenks).
3 Quantiles
Average Rapid Assessment Score
^40-119
^M 120 - 138
139 - 190
Equal Interval
Average Rapid Assessment Score
40-50
51 - 75
76 - 100
101 - 125
126- 150
151 - 175
176 - 200
Natural Breaks (Jenks)
5 Classes
Average Rapid Assessment Score
1 40 - 97
_ 98-120
^| 121 - 136
^| 137 - 153
^H 154- 190
Figure 6-5. Different breaks to distinguish data classes.
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7. CHALLENGES PART IV: COMBINING INDICATORS
7.1. COMBINING INDICATORS WITH OTHER DATA
Exposure to future stresses associated with external stressors such as climate and land-use
change is likely to vary spatially. Scenarios derived from climate models can be used to map
changes in exposure across the plausible range of future changes. A more comprehensive
evaluation of future stresses could directly incorporate such scenarios in a vulnerability
indicator-based assessment. Figure 7-1 displays an approach for combining indicators identified
in this report with other variables. This approach allows the identification of locations that are
both vulnerable to stress and are likely to experience additional stress in the future. Four
indicators that are related to potential water shortages are presented in the context of simulated
changes in temperature and precipitation derived from the IPCC 4th Assessment Report (IPCC,
2007b) and population derived from EPA's Integrated Climate and Land Use Scenarios (ICLUS)
project (U.S. EPA, 2009d). Increasing temperature and population and decreasing precipitation
all tend to increase the likelihood of water shortages. These plots are examples meant to illustrate
how one might go about highlighting regions where we might see a convergence between an
already stressed water supply system, a warmer, drier climate, and significant population growth.
While all of the indicators in Figure 7-1 relate to water supply, they deal with different
aspects of vulnerability. For example, Precipitation Elasticity of Streamflow (#437) is based only
on natural variation in water availability, whereas Groundwater Reliance (#125), Ratio of
Withdrawals to Streamflow (#219), and Water Availability: Net Streamflow per Capita (#623)
either directly incorporate current rates of water use or infer it through population. These plots
illustrate how high water withdrawals in some regions may be unsustainable under a given
temperature and precipitation scenario, or how locations that have low water availability per
capita might also be places where we expect to see the greatest population increases in the future.
In general, under the scenarios used here, current sensitivity and future exposure tend to co-vary,
and thus the places that are vulnerable now are likely to become more vulnerable in the future.
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The following plots displays values of some example indicators with a sample scenario of
temperature and precipitation (based on the Bl greenhouse gas storyline) drawn from the IPCC
Summary for Policymakers (IPCC, 2007b) and a population scenario from the Integrated
Climate and Land Use Scenarios (ICLUS) project. All variables are scaled as changes over a
100 year period from 2000 to 2100. Each point represents a single HUC-4 and is shaded
according to values of the indicator.
A. Groundwater Reliance (#125) (white, 0-10%; grey, 11-60%; black, 61-100%).
20
V
300
Figure 7-1. Current and future vulnerability to water shortages.
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B. Ratio of Withdrawals to Streamflow (#219) (white, 0 0.11; grey, 0.12-0.75; red, 0.75-59).
20
300
C Precipitation Elasticity of Streamflow (#437) (white, 0.43-1.59; grey, 1.60-2.06; black, 2.07-
2.96).
20
300
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D. NetStreamflowper Capita (#623) (white, 8,493-1,779,536; grey, 888-8,493; black, 0-877).
20
300
Figure 7-1. Current and future vulnerability to water shortages, (continued)
7.2. COMPOSITES OF VULNERABILITY INDICATORS
Because individual indicators only provide information on limited dimensions of aquatic
ecosystem and water quality vulnerability, effective management planning would likely require
that these dimensions be integrated into a more holistic perspective on vulnerability. Assuming
issues specific to individual indicators can be resolved, there are several possible quantitative
methods for integrating multiple indicators.
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7.2.1. Creating a Composite Map
Mapped indicators could, potentially, be overlayed into a composite map, such that the
averages of all indicator values for each of the HUC units are represented on a single map. This
is the approach taken in Kurd et al. (1999). This is challenging, however, for a number of
reasons. One major reason is that the distinction between relative and real (i.e., functionally
significant) differences in vulnerability, while not necessarily as critical for interpretation of
individual indicator maps, is extremely important for the construction of a composite
vulnerability map. For example, if the range of values for an indicator only reflect one category
of vulnerability (e.g., very high vulnerability), differences in relative vulnerability may be
functionally insignificant. If this type of indicator is given equal importance in a composite score
to one whose values span a functionally significant range, the composite score will be inaccurate.
As a consequence, the vulnerability of individual locations may be under- or over-estimated,
depending on the relative frequency of high vulnerability values from these two classes.
Another way to aggregate indicators could be by identifying geographic units where
further stresses (including climate change) will cause the most harm across all system
dimensions (e.g., see Lin and Morefield, 2011). This can be done as follows:
Assign numeric scores to the vulnerability categories (e.g., 3 for highest, 2 for
medium, and 1 for lowest). Sum the scores across all indicators.
For each geographic unit, calculate the percentage of indicators that are in the highest
vulnerability category.
Once any technical deficiencies and data gaps have been addressed through data
collection efforts, construction of a composite vulnerability map should consider the following:
The relative importance of system dimensions. The relative weighting of individual
indicators is dependent on management objectives and the degree to which indicators
are redundant with one another.
Range of indicator values. Only indicators whose values span functionally significant
ranges should be used for a composite vulnerability map. This will lead to a more
accurate representation of relative vulnerability.
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How an integrated vulnerability rating will translate into management or adaptation
efforts. Locations with high integrated vulnerability may either be moderately
vulnerable for most attributes, or highly vulnerable for a few attributes. While both of
these scenarios point to the need for planning, the specific suite of relevant strategies
would differ. Thus, the production of multiple visualization tools may often be a
helpful exercise.
7.2.2. Characterizing Vulnerability Profiles
The aim of this type of integrative procedure is to identify commonalities in the types of
vulnerabilities among regions. A vulnerability profile for a given location can be defined as the
set of values for all the vulnerability indicators. Such an analysis allows watersheds with similar
vulnerability profiles to be identified, and might be useful in the transfer of successful
management or adaptation strategies from one location to another. Specifically, if a selected
watershed is vulnerable in certain ways and in need of an adaptation strategy, other locations
with similar vulnerability profiles could be identified. Successful adaptation strategies in those
other locations could then be assessed for their applicability in the selected watershed.
Similarities in vulnerability profiles among locations can be summarized numerically
through multivariate statistical analyses useful for finding patterns in data, such as Principal
Components Analysis (PCA). PCA is used to consolidate the information in a large number of
variables into a smaller number of artificial variables (called principal components) that will
account for most of the variability in the original variables. The first component extracted in a
PCA accounts for the greatest amount of total variance in the original variables, and the second
and subsequent components account for progressively less variance.
The principal components (PCs) are described in terms of loadings of the original
variables. A PC may be heavily loaded on at least one variable, and usually on more than one. A
high loading indicates that the PC is strongly related to that variable (either negatively or
positively depending upon the sign of the loading). Variables for which a PC is heavily loaded
are correlated with each other, creating clusters of related variables that should be interpretable
from a conceptual standpoint. The PCs themselves, however, are uncorrelated with one another.
One benefit of conducting a PCA for this study is that reducing the full set of indicators to its
principal components helps to avoid overemphasis on system properties that are represented by
multiple similar indicators.
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As an example, we conducted a PC A on 24 of the 25 mapped indicators (we excluded the
Coastal Vulnerability Index (#51) because of its unique spatial units). We normalized indicators
with non-normal frequency distributions with log or square root transformations. We inverted the
scales of some indicators so that high vulnerability was always represented by high values of the
indicator. We used the correlation matrix of these standardized variables for the PC A. When no
data were available for an indicator, the HUC was assigned the median value for that indicator.
We rotated the PCA (Varimax) and specified a maximum of six principal components - these six
cumulatively account for about 57% of the total variance, with 35 % coming from the first three.
Table 7-1 shows the six PCs generated in the PCA analysis. These PCs help demonstrate
which types of processes or environmental factors are driving a large part of the variability in the
data. PCI is heavily loaded on indicators related to at-risk species, which are negatively
correlated with the ratio of snow to total precipitation (see bolded loadings in Table 7-1). PC2 is
correlated with variables indicative of streamflow availability and usage. PCS represents
pesticides in surface water. PC4 is loaded on indicators related to macroinvertebrates and stream
habitat quality. For PCS, the most heavily loaded indicator is meteorological drought indices,
which is moderately correlated with at-risk freshwater plant communities. Finally, PC6 is loaded
on herbicides in groundwater, but not pesticides in groundwater.
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Table 7-1. Principal components loadings for the twenty four indicators included in the
PCA analysis
Indicator
Acid neutralizing capacity (#1)
At-risk freshwater plant communities (#22)
At-risk native freshwater species (#24)
Groundwater reliance (#125)
Meteorological drought indices (#165)
Ratio of snow to total precipitation (#218)
Ratio water withdrawal to annual streamflow
(#219)
Stream habitat quality (#284)
Wetland species at risk (#326)
Erosion rate (#348)
Instream use/total streamflow (#351)
Total use/total streamflow (#352)
Pesticide toxicity index (#364)
Herbicide concentrations in streams (#367)
Insecticide concentrations in streams (#369)
Organochlorines in bed sediment (#371)
Herbicides in groundwater (#373)
Insecticides in groundwater (#374)
Precipitation elasticity of streamflow (#437)
Ratio of reservoir storage to mean annual runoff
(#449)
Runoff (variability) (#453)
Macroinvertebrate index of biotic condition
(#460)
Macroinvertebrate observed/expected (#461)
Water availability: streamflow per capita (#623)
Proportion of variability explained
PCI
0.166
0.401
0.863
0.087
0.006
-0.774
-0.071
0.092
0.789
0.387
0.132
0.017
0.082
0.078
0.070
0.092
0.018
0.191
0.628
-0.117
0.160
0.051
-0.156
-0.150
0.120
PC2
-0.367
0.220
0.167
0.196
0.182
0.033
0.873
-0.018
-0.102
-0.056
0.262
0.753
0.009
-0.112
0.025
0.089
0.212
0.080
-0.073
-0.250
0.504
0.074
0.030
0.839
0.117
PC3
-0.231
-0.007
0.068
0.242
-0.138
-0.167
-0.089
0.170
0.017
-0.058
0.144
0.048
0.889
0.769
0.870
0.515
0.160
0.078
0.156
-0.090
0.036
-0.043
0.080
-0.127
0.113
PC4
-0.071
0.153
0.051
0.291
-0.038
-0.193
0.036
0.687
0.026
-0.076
-0.104
0.126
-0.027
0.111
-0.033
0.016
-0.009
-0.139
0.207
0.074
-0.056
0.845
-0.754
0.002
0.085
PCS
-0.233
0.604
0.149
-0.033
0.771
0.120
0.035
0.196
-0.204
0.131
0.005
-0.052
-0.003
-0.028
-0.020
-0.358
-0.239
-0.537
0.153
-0.151
0.256
0.007
0.066
-0.009
0.073
PC6
-0.265
0.090
0.117
-0.313
0.019
0.300
0.056
0.056
0.200
0.504
-0.456
-0.211
-0.041
-0.112
0.033
0.109
0.721
0.355
-0.107
0.110
0.137
-0.112
-0.055
-0.005
0.065
The map in Figure 7-2 is another way of using and displaying the results of the PCA.
This map shows the similarity of an example focal watershed (shown in blue) to watersheds
across the U.S. We defined the similarity of two watersheds as the weighted Euclidean distance
(Dw) among the values of the first six principal components:
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where x; and y; are the values of component i for the two watersheds, and w; is the weight
for component i, which is defined as the proportion of the total variance in the entire dataset
explained by that component. This approach is similar to the methods used by Iran et al. (2006).
As discussed above, because this kind of analysis and map allows watersheds with
similar vulnerability profiles to be identified, it might be useful in the transfer of successful
adaptation strategies from one location to another. Specifically, the map could help to identify
locations with the most similar multi-dimensional vulnerability profiles to that of a selected focal
watershed in need of adaptation strategies. Successful adaptation strategies in those other
locations could then be assessed for their applicability at the focal watershed.
While relative similarity could identify the closest matches to the focal watershed, its
mean absolute similarity to all other locations would be a measure of its uniqueness. The
similarity of all pairwise combinations of watersheds could be cataloged in a vulnerability
similarity matrix to expand the applicability of this approach. Such a matrix would include every
watershed on the horizontal axis, and these same watersheds on the vertical axis. Each central
cell of the matrix would contain a value that documents (according to the formula above) the
similarity of the two watersheds defined by that cell. In addition, the vulnerability profile
approach could be further refined by applying weights to indicators to account for differences in
accuracy or relevance to climate change or other stressors of interest.
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The following map displays the results of the PCA conducted on 24 of the 25 mapped indicators. It shows the similarity of the focal
HUC watershed (blue) to the remaining 203 watersheds.
\ | States
^B Focal HUC
Vulnerability Profile Similarity
I Similar
0 100 200 300 400 500 Miles
I I I I I I
Figure 7-2. Vulnerability profile similarity.
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8. SUMMARY AND RECOMMENDATIONS
This report investigates issues, challenges, and lessons associated with identifying,
calculating, and mapping indicators of the relative vulnerability of watersheds across the United
States to the potential adverse impacts of external stresses such as long-term climate and land-
use change. It is our hope that this report will be a useful building block for future work on
multi-stressor global change vulnerability assessments.
It is important to clarify here that this report does not attempt any kind of direct
evaluation of the potential impacts of climate change or other global change stressors on
ecosystems and watersheds. Instead, it deals only with the question of how to estimate the
impacts of current stressors. We argue that a systematic evaluation of the impacts of existing
stressors is a key input to any comprehensive climate change vulnerability assessment, as the
impacts of climate change will be expressed via often complex interaction with such stressors -
i.e., through their potential to reduce overall resilience, or increase overall sensitivity, to climate
change. This argument is not new, and in fact it has been a staple of writing on climate change
impacts, vulnerability, and adaptation, particularly of large assessments like those of the IPCC
and U.S. Global Change Research Program. However, to date there has been relatively little
exploration of the practical challenges associated with comprehensively assessing how the
resilience of ecosystems and human systems in the face of global change may vary as a function
of existing stresses and maladaptations.
8.1. SUMMARY OF CHALLENGES
Our approach in this report has two basic elements. First, we have collected, evaluated
the quality of, processed, and aggregated a large quantity of data on water quality and aquatic
ecosystem indicators across the nation that have been reported on in the ecological, hydrological,
and management literature. Second, we have used this set of indicators as a testbed for
identifying best practices, challenges, and gaps in ideas, methods, data, and tools for calculating
and mapping vulnerability nationally.
Specifically, we compiled a list of 623 indicators of the vulnerability of water quality or
aquatic ecosystems that were defined in the literature, focusing our search on expanding the list
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of indicators rather than reviewing literature for its more general contributions to the body of
knowledge on a topic. The indicators compiled relate to drinking water and source water quality,
ecosystem structure and function, individual species, and ecosystem services. We explored
challenges associated with using these indicators to assess vulnerability of water quality and
aquatic ecosystems nationally. These challenges fall into four broad categories:
1. Challenges associated with identifying those indicators that speak specifically to
vulnerability, as opposed to those reflecting simply a state or condition;
2. Challenges associated with determining relative vulnerability using indicators,
including interpreting gradients of indicator values, and, when possible, establishing
important indicator thresholds that reflect abrupt or large changes in the vulnerability
of water quality or aquatic ecosystems;
3. Challenges associated with mapping these vulnerability indicators nationally,
including data availability and spatial aggregation of the data; or
4. Challenges associated with combining and compositing indicators and developing
multi-indicator indices of vulnerability.
Sources of indicator definitions and data used to map the indicators included published
research and studies by EPA, other federal agencies, the Heinz Center, the Pew Center, etc. We
limited the study to existing indicators and datasets, and for the most part did not attempt to
develop new indicators or collect new data. As part of this work, we developed a number of
example maps, and we use some of these maps in this report for illustrative purposes. We hope
that the lessons we learned while developing strategies for compiling and mapping national-level
indicator datasets under this project will be useful for indicator-based vulnerability assessments
in general. Here we summarize the main findings of the report, organized according to the four
challenges listed above.
8.1.1. Challenges Part I: Indicator Classification
There is on ongoing debate in the literature on the meaning of vulnerability and the
elements of which it is composed, particularly in the context of climate change. For the purposes
of this report, we generally took as our starting point the IPCC definition, i.e., "The degree to
which a system is susceptible to, or unable to cope with, adverse effects of climate change,
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including climate variability and extremes. Vulnerability is a function of the character,
magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its
adaptive capacity" (IPCC, 2007a). Most of what we define as "vulnerability indicators" in this
report primarily encompass sensitivity and exposure to environmental stresses, and we do not
focus on adaptive capacity. The indicators we discuss relate generally to the vulnerability of
aquatic ecosystems, ecosystem services, and drinking water supplies.
Our first challenge was to identify guidelines for classifying the comprehensive suite of
623 indicators. The goal was to divide them into vulnerability indicators versus those indicators
that merely measure the current state of a resource. The vulnerability indicators, at least in
principle, could measure the degree to which the resource being considered (e.g., watershed,
ecosystem, human population) is susceptible to, and unable to cope with, adverse effects of
externally forced change. Such change potentially includes climate or any other global change
stressor.
We determined that, in practical terms, the essence of a vulnerability indicator is that it
should inherently include some kind of relative or value judgment, e.g., comparing one
watershed to another, comparing it to some objectively defined threshold or possible state, or
reporting on its change over time, as opposed to measuring water quality or ecological condition
at a point in time without reference to anything else. Applying these criteria, we winnowed the
original list of 623 indicators down to 53, and in the report we discuss the degree to which
indicators from this reduced set might reflect vulnerability of water quality and aquatic
ecosystems to challenges from long-term global change stresses.
8.1.2. Challenges Part II: Determining Relative Vulnerability
Determination of the relative vulnerability of a particular location using a given
vulnerability indicator (or an index, if multiple indicators have been combined), can be
accomplished by comparing the value of the indicator to a gradient of values measured at
different locations. Alternatively, one can capitalize on objective vulnerability thresholds for
some indicators. Such thresholds reflect abrupt or large changes in the vulnerability of water
quality or aquatic ecosystems in response to a small change in a stressor, sometimes but not
always associated with a particular regulatory threshold. Such thresholds are most useful when
they distinguish between acceptable and unacceptable conditions.
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We searched for thresholds for our 53 vulnerability indicators from three different
categories: human health-based thresholds, ecological thresholds, and sustainability thresholds.
In the literature, we most often encountered the use of arbitrary cutoffs to separate relative
vulnerability categories (e.g., high, medium, and low). We were only able to map objective
thresholds for a small subset of the indicators, though in some cases we suggested modification
of an indicator definition to facilitate the identification of thresholds. The lack of available
functional break points for most indicators is to be expected. Many indicators respond to stress
linearly or along a gradual gradient. For others, objective break points may be characterized
through additional research, either through meta-analysis of previous research efforts or through
new data collection and analysis. Future research may also yield additional insights into how
break points for some indicators vary spatially (Link, 2005).
8.1.3. Challenges Part III: Mapping Vulnerability
The effort to produce indicator maps for this report faced a number of classic
cartographic challenges. Most of these challenges fell into the following two major categories:
data availability and mappability, and spatial aggregation.
8.1.3.1. Data and Mappability
Data availability and suitability were the most serious limitations in evaluating whether
or not we could produce maps for the 53 vulnerability indicators. Issues we encountered included
the following:
Lack of national coverage;
Varying scales of the data;
Varying duration of the data records;
Multiple datasets needed to be combined;
Extensive modeling effort was required to generate values for the indicator;
No dataset available for the indicator; and
Data collection was in progress.
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These data availability and suitability issues were often identified during the literature
review. For example, study authors sometimes explicitly noted the need for data for particular
indicators that were potentially useful. In other cases, these issues emerged only after beginning
the process of attempting to create maps. For example, the limited spatial extents of some
datasets were identified during the mapping process. A major lesson we learned from this project
was that it may often be impossible to establish mappability without beginning the process of
manipulating and mapping the various datasets involved.
Overall, these data and mappability issues reduced the starting set of 53 vulnerability
indicators to a set of 25 vulnerability indicators for which we were able to create example maps.
8.1.3.2. Spatial Aggregation
To create a national map for a given indicator of vulnerability, one must aggregate data
collected at discrete locations and calculate summary statistics that describe conditions across a
larger area, such as the mean value of an indicator or the percentage of sites that exceed a
threshold value. As noted above, a major research gap is the lack of objective, functional
thresholds between "vulnerable" and "not vulnerable" for most of the indicators we investigated.
A complementary challenge is that, even if such functional breakpoints can be found, it may be
difficult to aggregate in such a way that these breakpoints remain meaningful.
The major issues we encountered were the following:
Local variation and spatial heterogeneity in data collection sites;
The choice of spatial frameworks (e.g., watersheds, ecoregions, coasts); and
The extent (resolution) of the spatial unit chosen.
As illustrated with a variety of example maps, these methodological choices can lead to
very different results, and hence different conclusions about relative vulnerability in one location
compared to another.
A systematic process for refining or re-defining indicators of vulnerability to account for
the challenges summarized above is likely to be valuable. Such a process is presented in Figure
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7-2. For example, the Acid Neutralizing Capacity (#1) indicator is defined as the ability of a
stream to buffer acidic inputs from acid rain or acid mine drainage. This indicator can be refined
to measure the percentage of sites that with ANC less than 100 millequivalents/L to account for
the aggregation challenge. In addition, indicators can be refined to more explicitly incorporate
the exposure component of vulnerability. If elements of environmental change, such as
temperature or precipitation, can be explicitly incorporated into the indicator, then future changes
in this indicator can be modeled using predicted changes in the values of these elements. This
strengthens the ties between the indicator and changes that may occur in the future, and
facilitates the generation of more useful forecasts for decision-makers.
8.1.4. Challenges Part IV: Combining Indicators
Ultimately, the value for global change assessments of a database of indicators, and their
maps, rests in how they can be examined holistically. Such indicators and their maps can also be
examined in combination with scenarios of changes in critical external stressors, such as climate
and land use. We showed some simple examples of how one might use such scenario data to
highlight locations around the country where, for example, we might see a convergence between
an already stressed water supply system, a warmer, drier climate, and significant population
growth. One of several more sophisticated approaches involves designing indicators that
explicitly include a functional dependence on a stressor that is expected to change over time,
such as temperature, precipitation, or population.
We also considered the challenges associated with compositing multiple indicators in
some way and mapping the result. This brings up issues of determining the functional
equivalency of the different levels of relative vulnerability measured by the very different
indicators, with no absolute standard as an anchor point for weighting their contributions.
Creation of a uniform scoring system (e.g., 1, for lowest, and 5 for highest, vulnerability)
resolves the practical difficulties of mapping but not the conceptual ones of establishing the
relative contribution of each indicator to overall vulnerability. Appendix H includes an
evaluation of the effects of aggregation on the validity of theoretical breakpoints for each of the
mapped indicators based on the process outlined in Figure 8-1.
A possible way forward is in the development of what we refer to as "vulnerability
profiles," based on multivariate statistical analyses such as PCA. As a simple example, we
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conducted a PCA on the mapped indicators. The six principal components we extracted tended to
be associated with different potential dimensions of vulnerability: i.e., PCI with at-risk species;
PC2 with streamflow availability and usage; PCS with pesticides in surface water; PC4 with
macroinvertebrates and stream habitat quality; PCS with meteorological drought indices; and
PC6 with herbicides in groundwater. This kind of analysis allows the identification of watersheds
or other geographic units with similar vulnerability profiles. This has the potential to be useful in
the transfer of successful management or adaptation strategies from one location to another.
This process can be used to evaluate and guide the modification of potential indicators. The
questions are oriented around the definition of vulnerability and the suitability of the indicator
for mapping. Appendix H provides an evaluation of each of the 25 mappable indicators within
the framework of the five questions presented in this flowchart.
Indicatorselection
Can the indicatoi
be modified to
describe
vulnerability?
Does the
indicator describe
vulnerability?
Notappropriate for
vulnerability assessments.
Indicatordisplay
Are objective
breakpoints in
the range of
vulnerability
documented?
Can objective
breakpoints be
identified?
Mappable with arbitrary
breakpoints
Are me
breakpoints sti
valid when the
data are
aggregated?
Mappable with objective
breakpoints
Figure 8-1. Indicator evaluation process.
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8.2. RECOMMENDATIONS FOR FUTURE RESEARCH
As a result of exploring the challenges and issues described above, we have identified a
number of areas where additional research is likely to contribute significantly to our ability to
carry out indicator-based vulnerability assessments - both in the specific context of the
indicators discussed in this report, and more generally.
8.2.1. Assessment of Non-mappable Indicators
Some indicators were designated as non-mappable due to the need for additional
processing of available data, statistical analyses, evaluation of modeled data, or other tasks that
were beyond the scope of this study. Enhanced modeling efforts that combine probabilistic
(Bayesian) and mechanistic approaches may be particularly useful in defining minimum data
collection requirements and for characterizing the interactions between physical, chemical, and
biological processes. Additional effort to address these needs may yield highly useful maps of
these indicators.
Examples of the data evaluation needs include:
Acquiring and assembling national-scale wetland data: Wetlands may be significantly
affected by climate and land-use change. Unfortunately, one important indicator for
wetlands, Wetland Loss (#325), was designated as non-mappable, due to the effort
required to download and process the data from the National Wetlands Inventory
(NWI). The online ordering system requires users to download individual datasets at
the 7.5 minute (1:24K) or 15 minute (1:100K) scales. In the lower 48 states, the
USGS has designated approximately 56,500 l:24K-scale quadrangles. It may be
possible to acquire national wetlands coverage from the U. S. Fish & Wildlife
Service, and conduct subsequent analyses that would result in a national wetlands
indicator.
Assessment of the National Inventory of Dams database: Instream connectivity
(#620) is an important measure that can be used to make inferences about drinking
water availability (e.g. large reservoirs) and aquatic ecosystem functions (e.g.
migration of species). To produce an accurate assessment of connectivity, it is
important to have a comprehensive source of dam locations and diversions in the
United States. The National Inventory of Dams, managed by the U.S. Army Corps of
Engineers, is an attempt at such a data set, but some data (especially data pertaining
to small dams) is absent from the database, available digital maps of the stream
network are of varying quality and detail across the country, and the available data for
dams are frequently inaccurate. An assessment of this database is needed and, if
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possible, additional dam data should be obtained to produce a map for this indicator.
Work by the USGS on the National Hydrography Dataset and the NHD-plus is
currently underway and should provide useful data in the coming years. A challenge
to reporting this indicator will be evaluating what percentage of dams is omitted
because they are too small to be registered in the national database on dams.
Digitization and analysis of national flood plain data: The Population Susceptible to
Flood Risk (#209) indicator evaluates the human population currently residing within
a 500-year flood plain. A map for this indicator could be obtained by overlaying
estimates of the 500-year flood plain from the Federal Emergency Management
Agency (FEMA) with population data from the U.S. Census Bureau. However,
according to FEMA's Map Service Center, GIS-compatible digital flood plain data
were not available at the time of this study for several areas within the U.S. FEMA is
currently working on a multi-year project to update and digitize national flood plain
data. In the absence of a national flood plain data set, it would be useful to utilize
existing digital flood plain data for urbanized areas to evaluate the percentage of
metropolitan populations that may be prone to flooding.
8.2.2. Identifying Opportunities to Enhance Source Data
The indicators evaluated during this study were associated with data sets with varying
degrees of completeness, ranging from large national assessment efforts, to indicators with no
clear data source. Additional research is needed to identify opportunities to enhance the utility of
national data sets and fill significant data gaps.
Examples of large national data sets that were used for this study include the EPA
Wadeable Streams Assessment or the USGS National Water Quality Assessment (NAWQA)
Program. These are unique data sets that yield high-quality data, but even these excellent data
collection efforts fall short of providing the data density required to produce robust analyses of
vulnerability over large scales, e.g., at the scale of a 4-digit HUC unit, as calculated values may
be highly sensitive to a few or even a single measurement taken at a discrete location within the
spatial aggregation unit. Additional research is needed to evaluate data collection effort required
to enhance the statistical power of these key datasets.
In addition, some example maps produced for this study could be improved by addressing
significant gaps in the source data. For example, the data set used to produce Instream Use /
Total Streamflow (#351) did not include estimates of groundwater recharge, one of the input
variables for this indicator, for some regions. For these regions, we assumed recharge was equal
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to withdrawals. The accuracy of this indicator in these areas would be improved by acquiring
better estimates for the missing variable.
Furthermore, some data sets that are regularly updated through ongoing data collection
activities may have quality problems. For example, the Centers for Disease Control and
Prevention's (CDC) Waterborne Disease and Outbreak Surveillance System (WBDOSS), a
potential data set for the Waterborne Human Disease Outbreaks (#322) indicator, relies on
voluntary reporting of water-related disease outbreaks by public health departments of U.S.
states, territories, and local governments. The data are inconsistent and of variable quality.
Ideally, data would be reported regularly for all parts of the country and consistently documented
by a single responsible entity. Alternatively, if voluntary data collection by multiple entities
continues, stringent guidelines might be set forth to ensure the quality of the data in this
database.
Finally, some of the indicators that we deemed to be non-mappable because we could not
identify any existing data source have the potential to be highly useful measures. Additional
research to identify the data needed to calculate appropriate vulnerability metrics, collect new
data, or transform existing data to calculate and map these indicators would be valuable.
8.2.3. Development of New Indicators from Available Data Sets
A direct follow-up effort to the methodology employed for this study would be a review
of existing national-scale environmental data sets to determine which might lend themselves to
the development of new, useful indicators. This would allow for more opportunities to create
indicators that are specifically tailored to the needs of local planners and decision-makers. For
example, a new indicator, Water Demand, defined as the total water withdrawals in millions of
gallons per day, can be created based on data available from the USGS' National Water-Use
Data set. A map of this indicator is shown in Figure 5-3. Assessment of vulnerability using this
indicator, perhaps in combination with indicators of water availability such as Groundwater
Depletion (#121) and Net Streamflow per Capita (#623), may be useful at a variety of scales,
from national to local, for understanding the water budgets of communities. This would facilitate
responses with, for example, improved conservation policies in areas subject to severe water
shortages.
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Using available data as a starting point would also enhance our ability to work with
indicators with objective thresholds that distinguish between acceptable and degraded condition.
For example, in the present study a set of five pesticide indicators [#367, #369, #371, #373, and
#374] were mapped using USGS' NAWQA data set. These indicators were designed by USGS
to provide a cumulative assessment of multiple pesticides present in ambient water by
calculating an average concentration. It is difficult to determine thresholds for these indicators
given the diversity of pesticides and the varying levels of risks they pose. Instead, the
development of new indicators for individual pesticides, using the same data set, would allow us
to map the data using established thresholds, such as MCLs, to categorize vulnerability.
Individual pesticide indicators may present regional patterns and identify regional water quality
concerns, whereas the combined indicators developed by USGS and used in this study may mask
local and regional vulnerability.
8.2.4. Need for Additional Study and Data Collection in Coastal and Other Areas
We note that the example indicators mapped for this study do not represent an even
distribution across the possible categories of water quality and aquatic ecosystem vulnerability
indicators. Our heavy focus on areas such as water quantity, freshwater ecosystems, and certain
aspects of water quality is a result of the methodology applied, and not a reflection of bias on the
part of the investigators or advisors selecting indicators and mapping data. Furthermore, as we
have emphasized throughout the report, the selection of indicators that were mapped is not
intended to imply anything about which indicators are inherently more important for assessing
vulnerability to global climate change and other stressors. Rather, the example maps are for
illustration of our methodology, and the selection of indicators for mapping was based on the
ready availability of data.
Data on the location of streams and quantity of surface water flow were generally readily
available in readily usable formats. There are several critical areas within the study of water
quality and aquatic ecosystems, however, which suffer more than other areas from the challenges
and data limitations discussed in this report. Additional research is needed in the areas of coastal
aquatic systems, wetlands, freshwater tidal marshes, and the fish and animal habitats they
support. Additional data collection over longer time periods and greater spatial extents is needed
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to capture the characteristics and trends in the condition and vulnerability of these important
systems.
8.2.5. Use of Indicators for Future Studies
The focus of the present study was to identify indicators of water quality and aquatic
ecosystem condition that represented vulnerability and could be mapped at the national scale.
598 indicators were eliminated from the original comprehensive list of indicators for various
reasons that made them unsuitable for a national-scale vulnerability assessment. However, many
of these indicators may be valuable for other studies or purposes.
Many indicators were eliminated because their associated data sets did not have
comprehensive national coverage or may only be relevant in some areas. Although these
indicators had limited utility for the present study, they are likely to be valuable for conducting
vulnerability assessments at regional or local scales. For example, EPA National Coastal
Assessment data for the Water Clarity Index [#318] and Water Quality Index [#319] indicators
are only available for the Gulf coast region. Similarly, Snowpack Depth [#440] is only measured
in regions where rivers and other surface water sources are primarily fed by snowmelt, such as in
the Colorado River basin. Mangrove Cover [#63] is only relevant where these trees grow - a
small portion of the Gulf Coast. Each of these indicators may be highly useful for monitoring
changes over time in local systems and for guiding local decisions in response to observed or
expected changes. A useful follow-up effort to this study would be the development of an
indicator compendium that would describe the geographic extent and available data sources for
indicators that are relevant at local and regional scales. Local decision makers could use this
resource in conjunction with the national-scale indicators presented in this study to guide local
planning efforts.
Indicators whose data were based on future projections were also eliminated because the
present study only examined current vulnerability. For example, data for Heat-Related Illnesses
Incidence [#392] are available as estimates of mortality from the National Center for Health
Statistics (NCHS) based on three climate change scenarios for the years 2020 and 2050. Data for
land cover or land use indicators, such as Coastal Wetlands (acreage) (#52) and Urban and
Suburban Areas (acreage) (#308), Population susceptible to flood risk (#209), and other
population-related indicators, may be projected into the future using output data from climate
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and earth system models. These data, while not useful for the present study, are useful in
understanding future vulnerability, particularly when taking into account the effects of climate
change on human and natural environments. Understanding future vulnerability is a crucial
component of many ongoing and planned research studies aimed at strategic planning for
adaptation to the effects of global climate change.
8.2.6. Establishment of Stress-response Curves, Vulnerability Thresholds, and Baseline
Conditions
In this report we focused on the development of methods to assess relative vulnerability.
Additional research to evaluate how individual indicators respond to stress (e.g., sensitivity,
threshold response, resistance, etc.) will facilitate assessments of absolute vulnerability linked to
system function. There is a large body of basic ecological and sociological research that will
need to be created before this issue can be comprehensively addressed. The issue of thresholds,
much discussed above, is of course intimately related.
Furthermore, observationally establishing baseline conditions, and implementing more
routine monitoring for locally relevant indicators, would enable water resource managers to
identify significant water quality and ecological changes over time, which would allow the
development of additional indicators, or more accurate calculation of existing indicators, for
assessment.
8.2.7. Drawing on other Established Approaches for Combining Indicators
In particular, a comparison of the traditional multivariate approaches for combining
indicators to the approaches used by EPA's ReVA program, such as the generalized weighted
distance method, may be fruitful. Future research efforts could apply the ReVA aggregation
methods to the indicators in this report, which are topically and spatially broader. Such
aggregation would also allow relationships between components of vulnerability for the
indicators specified in this study to be addressed. Future work could include the design of new,
robust indicators using existing data sources.
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8.2.8. Incorporating Landscape and Land Use Metrics
Landscape metrics, such as percent natural cover, roads crossing streams, and agriculture
on slopes, can provide additional context for the indicators presented in the report. Metrics such
as these may assist with the interpretation of sensitivity. Land use metrics that specify the
sources of polluted runoff (e.g., urban areas, Concentrated Animal Feeding Operation areas) and
of polluted groundwater (e.g., septic systems in low-lying areas) are useful for assessing the
vulnerability of surface and subsurface water quality, respectively. Measurements of human
impact may explain an indicator's vulnerability score or may suggest an alternative
interpretation. In addition, some metrics, such as population growth rate, can be used to assess
future exposure to stress (see, for example, Figure 7-1).
8.2.9. Incorporating Information Based on Remote Sensing Technologies
Remote sensing technologies have facilitated measurement of a variety of landscape and
land use indicators. They are commonly used to measure fragmentation of forests, the influence
of urbanization and suburbanization on the landscape, and for quantification of land cover / land
use categories (e.g., how the extent of forests or croplands have changed over time). Remote
sensing can also be used to investigate how local ecologies have been disturbed by human
encroachment. Remote sensing is currently being employed for the measurement of
chlorophyll a and turbidity.
8.2.10. Incorporating Metrics of Adaptive Capacity
Vulnerability to future changes depends in part on choices made by society today and
into the future. In the context of climate change in particular, adaptive capacity is the ability of
an ecosystem or society to continue to perform its range of functions despite changes in factors
that affect those functions. A system has inherent adaptive capacity when its natural attributes
make it resilient to stress, whereas institutional adaptive capacity includes policies, practices, and
infrastructure that create options for meeting human and ecosystem needs in the face of an
uncertain future. The specific attributes or actions that create adaptive capacity are largely
different for aquatic life and human uses of water, although there is some overlap among these
categories.
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Differentiating inherent and institutional adaptive capacity is useful because it points to
two different management approaches. Systems with inherent adaptive capacity are less
vulnerable, even when they are sensitive and exposed to stress. Thus, many advocate directing
planning and management efforts toward systems lacking this capacity. Institutional adaptive
capacity can be built in many ways (for examples, see IPCC, 2007a). Many of these strategies
require a significant shift from short to long term planning, which is typically resisted by
institutional and infrastructural inertia. Many specific practices involve diversification and the
creation of redundancy, which can be hard to justify in the context of current conditions. Some
also require acknowledgement of fundamental uncertainty about the future.
Community-based analyses have shown that the conditions that interact to shape
exposures, sensitivities, adaptive capacities, and hence create needs and opportunities for
adaptation, are community-specific (Smit and Wandel, 2006). This finding suggests that any
attempt to transfer adaptive strategies among regions must look for commonalities both in the
magnitude of vulnerability and in its qualitative, multi-dimensional profile. As described above,
some of the techniques described in this report (e.g., the development of vulnerability profiles
and similarity maps) could, in principle, be used to identify such commonalities among regions,
which, in combination with case studies of successful adaptation, would provide guidance for
potential policy transfer, or serve as a screening tool for the feasibility of adaptive strategy
transfer.
As we said above, we hope that this report will be a useful building block for future work
on multi-stressor global change vulnerability assessments. Ultimately, we believe the work
described here is a preliminary contribution toward bridging disconnects between the decision
support needs of the water quality and aquatic ecosystem management communities and the
priorities and capabilities of the global change science data and modeling communities; to the
synthesis of insights across more detailed, place-based, system-based, or issue-based case studies
(e.g., in individual watersheds, wetlands, urban ecosystems) to obtain national-scale insights
about impacts and adaptation; and to prioritization of future work in developing adaptation
strategies for global change impacts.
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X. Appendices
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The following appendices accompany the report entitled Aquatic Ecosystems, Water Quality, and
Global Change: Challenges of Conducting Multi-stressor Global Change Vulnerability
Assessments.
Appendix A. List of Literature Reviewed
Appendix B. Comprehensive List of Indicators
Appendix C. Data Sources, Supporting Information, and Technical Notes
Appendix D. Mapping Methodology
Appendix E. Example Maps for Indicators of Water Quality and Aquatic Ecosystem
Vulnerability, Displayed Using 4-digit Hydrologic Units
Appendix F. Example Maps for Indicators of Water Quality and Aquatic Ecosystem
Vulnerability, Displayed Using Ecoregions
Appendix G. Vulnerability Category Matrix
Appendix H. Evaluation and Potential Modification of Vulnerability Indicators
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The literature sources in the following list were reviewed to identify indicators of vulnerability of
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Chesapeake Bay Program. 2008. Chesapeake Bay: Health & Restoration Assessment 2007. EPA-
903-R-08-002 (CBP/TRS-291-08). A Report to the Citizens of the Bay Region.
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Climatic Change and Energy Cost and Availability for the Restoration of the Mississippi
Delta. Ecological Engineering. 24 (4): 253-265.
Day, J. W., D. F. Boesch, E. J. Clairain, G. P. Kemp, S. B. Laska, W. J. Mitsch, K. Orth, H.
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Lessons from Hurricanes Katrina and Rita. Science. 315 (5819): 1679-1684. March 23,
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Frumhoff, P. C., J. C. McCarthy, J. M. Melillo, S. C. Moser, and D. J. Wuebbles. 2006. Climate
Change in the U.S. Northeast. A Report of the Northeast Climate Impacts Assessment.
October 2006. Cambridge, MA: Union of Concerned Scientists (UCS).
*Frumhoff, P. C., J. C. McCarthy, J. M. Melillo, S. C. Moser, and D. J. Wuebbles. 2007.
Confronting Climate Change in the U.S. Northeast: Science, Impacts, and Solutions.
Synthesis Report of the Northeast Climate Impacts Assessment (NECIA). Cambridge,
MA: Union of Concerned Scientists (UCS).
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Gibson, J. J., T. D. Prowse, and D. L. Peters. 2006. Partitioning Impacts of Climate and
Regulation on Water Level Variability in Great Slave Lake. Journal of Hydrology. 329
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August 2011
The following table lists the 623 indicators gathered from a review of the 86 documents cited in Appendix A. Each indicator was
randomly assigned an Indicator ID#. This unique identifier serves as an easy way to identify and refer to the indicator throughout the
report. Indicator definitions included in this table were obtained, when possible, from the literature source that identified the indicator.
(Note: Some text is verbatim from the source.) Definitions for some indicators had to be revised when the data used were different
(e.g., more recent) than those cited by the literature. These revised definitions are marked with a * in the Indicator Definition column.
The references in the Literature Source column refer to the literature source from which the indicator was obtained. Full citations for
these references can be found in Appendix A. Some indicators from this list were determined to be duplicates; for these, the ID# of the
corresponding duplicate indicator is listed in the Duplicate Indicator column. After selection of one indicator for further assessment
from each duplicate group, the remaining duplicates were eliminated from further consideration. Selection of indicators from groups
of duplicates was based on completeness of the indicator definition, national (as opposed to regional or local) focus of the literature
source, etc. Eliminated duplicate indicators are marked with an x next to the indicator ID#.
The 53 indicators identified as vulnerability indicators in the report appear in boldface font in this table. (The remaining 559 indicators
were considered to be state variables). Of these, the 25 example indicators that were mapped have a ** next to the indicator ID#.
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
1**
Acid Neutralizing Capacity
(ANC)
Percent of streams with low acid-neutralizing capacity (ANC) i.e., below
100 milliequivalents/liter. ANC is a measure of the water's ability to
buffer additional acid deposition or drainage from acid mines."1
USEPA, 2006b.
Agricultural Inputs -
Durable Goods (Units of
durable goods per unit of
output)
Tractors are an example of durable goods. This indicator reports the
amount of inputs used to produce one unit of output, with 1975 as the
base year. For example, all fertilizers used on U.S. farms were divided by all
agricultural outputs even if different amounts of fertilizer were used to
produce each commodity. So, for any input, the index value for a given year
describes whether more or less of that input was used to produce a unit of
output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
Agricultural Inputs -
Energy (Units of energy
per unit of output)
This indicator reports the amount of energy inputs used to produce one
unit of output, with 1975 as the base year. For example, all fertilizers used
on U.S. farms were divided by all agricultural outputs even if different
amounts of fertilizer were used to produce each commodity. So, for any
input, the index value for a given year describes whether more or less of
that input was used to produce a unit of output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
Agricultural Inputs -
Fertilizers (Units of
fertilizers per unit of
output)
This indicator reports the amount of fertilizer inputs used to produce one
unit of output, with 1975 as the base year. For example, all fertilizers used
on U.S. farms were divided by all agricultural outputs even if different
amounts of fertilizer were used to produce each commodity. So, for any
input, the index value for a given year describes whether more or less of
that input was used to produce a unit of output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
Agricultural Inputs - Labor
(Units of labor per unit of
output)
This indicator reports the amount of labor inputs used to produce one unit
of output, with 1975 as the base year. For example, all fertilizers used on
U.S. farms were divided by all agricultural outputs even if different
amounts of fertilizer were used to produce each commodity. So, for any
input, the index value for a given year describes whether more or less of
that input was used to produce a unit of output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
Agricultural Inputs - Land
(Units of land per unit of
output)
This indicator reports the amount of land inputs used to produce one unit
of output, with 1975 as the base year. For example, all fertilizers used on
U.S. farms were divided by all agricultural outputs even if different
amounts of fertilizer were used to produce each commodity. So, for any
input, the index value for a given year describes whether more or less of
that input was used to produce a unit of output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
Agricultural Inputs -
Pesticides (Units of
pesticides per unit of
output)
This indicator reports the amount of pesticide inputs used to produce one
unit of output, with 1975 as the base year. For example, all fertilizers used
on U.S. farms were divided by all agricultural outputs even if different
amounts of fertilizer were used to produce each commodity. So, for any
input, the index value for a given year describes whether more or less of
that input was used to produce a unit of output in that year than in 1975.
Heinz Center, 2002; Heinz
Center, 2008
Agricultural Outputs -
Crops (Units of output per
year)
The indicator reports agricultural outputs over time, with 1975 as the base
year.
Heinz Center, 2002; Heinz
Center, 2008
Agricultural Outputs -
Meat, Dairy, Eggs, and
Other Products (Units of
output per year)
The indicator reports U.S. agricultural outputs over time, with 1975 as the
base year.
Heinz Center, 2002; Heinz
Center, 2008
10
Agricultural Outputs -
Total (Units of output per
year)
The indicator reports U.S. agricultural outputs over time, with 1975 as the
base year.
Heinz Center, 2002; Heinz
Center, 2008
11
Agricultural products
(economic production)
This indicator reports the production of food and fiber and the withdrawals
of water (agricultural products), using an index with 1980 as the base year.
Heinz Center, 2002; Heinz
Center, 2008
12
Agricultural water use
share
Agricultural sector withdrawals (QWag) as a share of total average annual
withdrawals. Method of calculation: QWag/QW
Hurd et al., 1998.
13
Air Quality - High Ozone
Levels: At least 1 day per
year (Percent of
urban/suburban air
monitoring stations with 1
day exceedance)
This indicator reports the percentage of air pollution monitoring stations in
urban and suburban areas with "high" ozone concentrations at least 1 day
a year. Ground-level ozone is considered high when the 8-hour average
concentration exceeds 0.08 parts per million (ppm).
Heinz Center, 2002; Heinz
Center, 2008
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
14
Air Quality - High Ozone
Levels: At least 2 days per
year (Percent of
urban/suburban air
monitoring stations with 2
day exceedance)
This indicator reports the percentage of air pollution monitoring stations in
urban and suburban areas with "high" ozone concentrations at least 2 days
a year. Ground-level ozone is considered high when the 8-hour average
concentration exceeds 0.08 parts per million (ppm).
Heinz Center, 2002; Heinz
Center, 2008
15
Air Quality - High Ozone
Levels: At least 3 days per
year (Percent of
urban/suburban air
monitoring stations with 3
day exceedance)
This indicator reports the percentage of air pollution monitoring stations in
urban and suburban areas with "high" ozone concentrations at least 3 days
a year. Ground-level ozone is considered high when the 8-hour average
concentration exceeds 0.08 parts per million (ppm).
Heinz Center, 2002; Heinz
Center, 2008
16
Air Quality - High Ozone
Levels: At least 4 days per
year (Percent of
urban/suburban air
monitoring stations with 4
day exceedance)
This indicator reports the percentage of air pollution monitoring stations in
urban and suburban areas with "high" ozone concentrations at least 4 days
a year. Ground-level ozone is considered high when the 8-hour average
concentration exceeds 0.08 parts per million (ppm).
Heinz Center, 2002; Heinz
Center, 2008
17
Altered Freshwater
Ecosystems (percent
miles changed)
This indicator of alteration reports the percentage of:
Heinz Center, 2002; Heinz
Center, 2008
18
Ambient toxicity (chemical
concentration)
Metals, pesticides, PCBs, and organic contaminants.
USEPA, 2006a.
19
Animal Deaths and
Deformities (events)
This indicator reports on unusual mortality events for waterfowl, fish,
amphibians, and mammals, and on deformity events for amphibians. Only
data on waterfowl mortality can be reported at this time.
Heinz Center, 2002; Heinz
Center, 2008
20
Aquatic life mobility
N/A
ME A, 2005c.
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
21
Areas with depleted
oxygen (percent monthly
exposure)
The percentage of brackish water exposed to a range of oxygen
concentrations for at least 1 month will be reported as anoxic (no oxygen),
hypoxic (>0 and <2 parts per million [ppm]), low (2-4 ppm), or sufficient
(>4 ppm).
Heinz Center, 2002; Heinz
Center, 2008
22*
At-Risk Freshwater Plant
Communities
This indicator reports on the percentage of wetland and riparian plant
communities that are at risk of extinction. These status ranks are based
on such factors as the remaining number and condition of occurrences of
the community, the remaining acreage, and the severity of threats to the
community type.*
Heinz Center, 2002; Heinz
Center, 2008
467
23
At-Risk Native Forest
Species (Percent of all
forest species that are at
risk)
This indicator reports on the relative risk of extinction of native forest
species. The risk categories are based on such factors as the number and
condition of individuals and populations, the area occupied by the species,
population trends, and known threats. Degrees of risk reported here range
from very high ("critically imperiled" species are often found in five or
fewer places or have experienced very steep declines) to moderate
("vulnerable" species are often found in fewer than 80 places or have
recently experienced widespread declines). In all cases, a wide variety of
factors contribute to the overall ratings. "Forest species" live in forests
during at least part of their life and depend on forest habitats for survival.
Heinz Center, 2002; Heinz
Center, 2008
24**
At-Risk Native Freshwater
Species (relative rank)
This indicator reports on percentage of native freshwater species that are
at risk of extinction. The risk categories are based on such factors as the
number and condition of individuals and populations, the area occupied
by the species, population trends, and known threats.*
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
25
At-Risk Native Grassland
and Shrubland Species -
By Category (Percent of all
at-risk species in a certain
category)
Categories include: Extinct, Critically Imperiled, Imperiled, Vulnerable, and
All At-Risk. This indicator reports on the status of native grassland and
shrubland species with respect to their relative risk of extinction. These
status ranks are based on multiple factors: the number and condition of
individuals and populations, the area occupied by the species, population
trends, and known threats. Degrees of risk reported here range from very
high ("critically imperiled" species often are found in five or fewer places or
have experienced very steep declines) to moderate ("vulnerable" species
often are found in fewer than 80 places or have recently experienced
widespread declines). In all cases, a wide variety of factors contribute to
overall ratings. "Grassland and shrubland species" live in these habitats
during at least part of their life cycle and depend on them for survival.
Heinz Center, 2002; Heinz
Center, 2008
26
At-Risk Native Grassland
and Shrubland Species -
By Region (Percent of all
at-risk species in a certain
region)
Regions include: Northeast/Mid-Atlantic, Southeast, Midwest, Southwest,
Rocky Mountain, Pacific Coast, and Hawaii. This indicator reports on the
status of native grassland and shrubland species with respect to their
relative risk of extinction. These status ranks are based on multiple factors:
the number and condition of individuals and populations, the area
occupied by the species, population trends, and known threats. Degrees of
risk reported here range from very high ("critically imperiled" species often
are found in five or fewer places or have experienced very steep declines)
to moderate ("vulnerable" species often are found in fewer than 80 places
or have recently experienced widespread declines). In all cases, a wide
variety of factors contribute to overall ratings. "Grassland and shrubland
species" live in these habitats during at least part of their life cycle and
depend on them for survival.
Heinz Center, 2002; Heinz
Center, 2008
27
At-Risk native marine
species (relative risk)
Relative risk of extinction of native marine species, both plants and
animals. The risk categories are based on such factors as the number and
condition of individuals and populations, the area occupied by the
species, population trends, and known threats.
Heinz Center, 2002; Heinz
Center, 2008
28
At-Risk native species
(relative rank)
This indicator reports on the relative risk of extinction of native plant and
animal species.
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
29
30
31
32X
33X
34
35
36X
37
Indicator
Bay grasses
Beach closings (driven by
bacterial contamination)
Benthic Index (several)
Benthic
Macroinvertebrates
Benthic organisms
(abundance, diversity)
Bottom habitat (diversity,
abundance, biomass)
Carbon Storage - Forests
(Weight of carbon stored
overtime)
Carbon Storage -
Grasslands/Shrublands
(Weight of carbon stored
overtime)
Chemical contaminants
(exceedence of regulatory
value)
Definition
N/A
Measure of bacterial contamination.
Shannon-Weiner Diversity Index, and other Indices.
Benthic communities are largely composed of macroinvertebrates, such as
annelids, mollusks, and crustaceans. These organisms inhabit the bottom
substrates of estuaries and play a vital role in maintaining sediment and
water quality. They also are an important food source for bottom-feeding
fish, invertebrates, and birds.
Benthic abundance, species richness/diversity.
Attainment of the benthic restoration goal was determined by examining:
benthic biodiversity measures, measures of assemblage abundance and
biomass, life history strategy measures, activity beneath the sediment
surface, and feeding guild measures.
This indicator reports how much carbon an essential component of all
organisms is stored in forests, including trees, soil, and plant litter on the
forest floor, and in wood products.
This indicator will report the total amount of carbon stored in soil and
plants in grasslands and shrublands.
Metals, PCBs, tributyltin, and priority organics found exceeding total
maximum daily loads (TMDLs).
Literature Source
[See Appendix A for full
citation]
Chesapeake Bay Program,
2008.
USEPA, 1995.
USEPA, 2006a.
USEPA, 2008b.
Hayslip et al., 2006.
Chesapeake Bay Program,
2008.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Chesapeake Bay Program,
2008.
Duplicate
461, 33
32, 461
36, 617
35, 617
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
38
Chemical Contamination
in Urban Streams -
Contaminant Occurrence
(Number of contaminants
detected)
This indicator reports on contaminants found in urban and suburban
streams. Compounds reported here include many pesticides, select
pesticide breakdown products, ammonia, and nitrate (because nitrate and
ammonia occur naturally, they are not included in the graphs showing
contaminant occurrence).
Heinz Center, 2002; Heinz
Center, 2008
39
Chemical Contamination
in Urban Streams-
Contaminant
Concentrations above
Standards or Guidelines
(Percent sites with
exceedances)
This indicator reports on contaminants found in urban and suburban
streams. Compounds reported here include many pesticides, select
pesticide breakdown products, ammonia, and nitrate (because nitrate and
ammonia occur naturally, they are not included in the graphs showing
contaminant occurrence).
Heinz Center, 2002; Heinz
Center, 2008
40
Chemical Contamination
in Urban/Suburban Soils
This indicator reports on contaminants found in urban and suburban soils.
Compounds reported here include many pesticides, selected pesticide
breakdown products, ammonia, and nitrate (because nitrate and ammonia
occur naturally, they are not included in the graphs showing contaminant
occurrence).
Heinz Center, 2002; Heinz
Center, 2008
41*
Chlorophyll a
N/A
Chesapeake Bay Program,
2008.
42
42
Chlorophyll a (surface
concentration)
Good: Surface concentrations are less than 5 ng/L (less than 0.5 ng/L for
tropical ecosystems). Fair: Surface concentrations are between 5 ng/L and
20 ng/L (between 0.5 ng/L and 1 ng/L for tropical ecosystems). Poor:
Surface concentrations are greater than 20 ng/L (greater than 1 ng/L for
tropical ecosystems).
USEPA, 2006a.
41
43
Chlorophyll
concentrations (within 25
miles of shore)
Chlorophyll concentration in estuaries and ocean waters within 25 miles of
shore. For ocean waters, the indicator reports the average value for the
season with the highest concentration, for each region. For estuaries, the
indicator reports the percentage of area in three ranges: below 5 parts per
billion (ppb), between 5 and 20 ppb, and above 20 ppb, using data for the
season with the highest average concentration.
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
44
Climate, storm, and
extreme event variability
Climate fluctuations, mostly related to flood and drought events.
Gleick and Adams, 2000.
45
Coastal Bird Populations
(2 indicator species)
Indicators consist of two birds (piping plover and least tern) that inhabit
Long Island Sound beaches, plus wading birds that forage in tidal marshes.
Long Island Sound Study,
2008.
46
46*
Coastal birds
N/A
MEA, 2005b.
45
47
Coastal erosion (managed
vs. unmanaged area)
How much of the U.S. coast is managed in an attempt to control erosion
and how much remains in a "natural" state, with no erosion control. For
unmanaged areas, the indicator reports what fraction is eroding, accreting
(gaining land area), or stable.
Heinz Center, 2002; Heinz
Center, 2008
48A
Coastal Fish Tissue
Contaminants
N/A
USEPA, 2008b.
58, 99,
579
49
Coastal land loss
N/A
Twilley et al., 2001.
50*
Coastal Sediment Quality
N/A
USEPA, 2008b.
250
51**
Coastal Vulnerability
Index (to sea level rise)
Index of coastal ecosystem vulnerability to sea level rise, he index allows
the six physical variables to be related in a quantifiable manner that
expresses the relative vulnerability of the coast to physical changes due
to sea-level rise. This method yields numerical data that cannot be
equated directly with particular physical effects. It does, however,
highlight those regions where the various effects of sea-level rise might
be the greatest. The six variables are: a = Geomorphology; b = Coastal
Slope (%); c = Relative sea-level change (mm/year); d = Shoreline
erosion/accretion (m/year); e = Mean tide average (m); e = Mean wave
height (m). Once each section of coastline is assigned a risk value based
on each specific data variable, the coastal vulnerability index is calculated
as the square root of the geometric mean, or the square root of the
product of the ranked variables divided by the total number of variables
as: CVI = [(a*b*c*d*e*f*)/6)]Al/2. *
Day eta I.,2005.
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
52
Coastal wetlands
(extent/acreage)
Acreage of coastal habitats whose defining feature is that they are
composed of living organisms (such as seagrasses, mangrove forests, and
coastal wetlands) or are built by them (such as coral reefs or shellfish
beds).
Heinz Center, 2002; Heinz
Center, 2008
53
53A
Coasts and Oceans
(extent/acreage)
This indicator presents the area coastal land as a percentage of the total
U.S. land area, for the most recent 50-year period and compared to
presettlement estimates.
Heinz Center, 2002; Heinz
Center, 2008
52
54
Commercial fish and
shellfish landings (weight)
Reports the weight of fish, shellfish, and other products taken from U.S.
waters. Landings, plus certain aquaculture harvests, are shown for five
regions that cover all waters out to the 200-mile territorial limit.
Heinz Center, 2002; Heinz
Center, 2008
55
Commercially important
fish stocks (size)
Tracks the percentage of commercially important fish species, or "stocks,"
that are increasing or decreasing in size. Only stocks whose population
increased or decreased by at least 25% are reported. Trends are based on
the estimated weight, or "biomass," of the entire stock.
Heinz Center, 2002; Heinz
Center, 2008
56
Condition of bottom-
dwelling animals (percent
area inhabited)
Describes the condition of worms, clams, snails, and shrimplike animals in
bottom sediments ("benthic communities") by reporting the percentage of
area in which these communities are in "undegraded," "moderate," and
"degraded" condition. The index reflects changes in benthic community
diversity and the abundance of pollution-tolerant and pollution-sensitive
species. A low benthic index rating indicates that the benthic communities
are less diverse than expected, are populated by more than expected
pollution-tolerant species, and contain fewer than expected pollution-
sensitive species. The data in this report reflect an assessment of benthic
communities as "good" (high index score), "fair" (moderate index score), or
"poor" (low index score).
Heinz Center, 2002; Heinz
Center, 2008
57
Constructed Materials -
30% or Greater Area
Covered by Constructed
Materials (area)
This indicator would report on the percentage of land area covered by 30%
or more constructed materials.
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
58
Contaminants in fish and
shellfish (chemical
concentration)
Measures the concentration of PCBs, mercury, and DDT in the edible tissue
of seafood from U.S. coastal waters.
Heinz Center, 2002; Heinz
Center, 2008
48, 99,
579
59
Contamination in bottom
sediments (concentration)
Information on the concentration, in coastal bottom sediments, of four
major classes of contaminants that can harm fish and other aquatic
organisms and can adversely affect human health if ingested while
consuming fish or shellfish.
Heinz Center, 2002; Heinz
Center, 2008
60
Cropland (total area of
land used for crops)
This indicator reports the amount of land used for crops, including pasture
and hay. Acreage that is enrolled in long-term set-aside programs, such as
the Conservation Reserve Program (CRP) is not considered to be part of this
indicator.
Heinz Center, 2002; Heinz
Center, 2008
61
Croplands
(extent/acreage)
This indicator presents the area of croplands as a percentage of the total
U.S. land area, for the most recent 50-year period and compared to
presettlement estimates.
Heinz Center, 2002; Heinz
Center, 2008
62
Cropped Land (area,
ecosystem condition)
This indicator would report on the percentage of land area that is cropped
land (not including interspersed natural areas).
Heinz Center, 2002; Heinz
Center, 2008
63
Delta accretion rate
The rate of sediment accretion in a river delta.
Day et al., 2008.
64
Depth to Shallow
Groundwater (Percent of
shallow groundwater with
a certain depth)
This indicator will describe the depth to shallow groundwater in grassland
and shrubland areas. Specifically, it will report the percentage of grassland
and shrubland areas where the depth to groundwater falls within several
ranges (less than 5 feet, 5 to 10 feet, 10 to 20 feet and more than 20 feet).
Heinz Center, 2002; Heinz
Center, 2008
65
Disruptive Species - By
Region (Number of
disruptive species by
region)
This indicator would report the number and type of "disruptive" species
found in metropolitan areas. Disruptive species are those that have
negative effects on natural areas and native species or cause damage to
people and property. This indicator would report the number of disruptive
native and non-native plant and animal species on a regional basis, for the
most current year.
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
66
67X
68
69
70
71X
72x
73X
Indicator
Disruptive Species in
Metropolitan Areas
(Number of disruptive
species overtime)
Dissolved inorganic
nitrogen
Dissolved Inorganic
Nitrogen (DIN) (surface
concentration)
Dissolved Inorganic
Phosphorus (DIP) (surface
concentration)
Dissolved Organic Carbon
Dissolved oxygen
Dissolved oxygen
Dissolved oxygen
Definition
This indicator would report the number and type of "disruptive" species
found in metropolitan areas. Disruptive species are those that have
negative effects on natural areas and native species or cause damage to
people and property. Specifically, the indicator will report the number of
larger metropolitan areas with 5 or fewer, from 6 to 10, from 11 to 20, and
more than 20 disruptive plant and animal species.
N/A
Good: Surface concentrations are less than 0.1 mg/L (NE, SE, Gulf), 0.5
mg/L (West), or 0.05 mg/L (tropical). Fair: Surface concentrations are 0.1-
0.5 mg/L (NE, SE, Gulf), 0.5-1.0 mg/L (West), or 0.05-0.1 mg/L (tropical).
Poor: Surface concentrations are greater than 0.5 mg/L (NE, SE, Gulf), 1.0
mg/L (West), or 0.1 mg/L (tropical).
Good: Surface concentrations are less than 0.01 mg/L (NE, SE, Gulf), 0.01
mg/L (West), or 0.005 mg/L (tropical). Fair: Surface concentrations are
0.01-0.05 mg/L (NE, SE, Gulf), 0.01-0.1 mg/L (West), or 0.005-0.01 mg/L
(tropical). Poor: Surface concentrations are greater than 0.05 mg/L (NE, SE,
Gulf), 0.1 mg/L (West), or 0.01 mg/L (tropical).
N/A
Good: Concentrations are greater than 5 mg/L. Fair: Concentrations are
between 2 mg/L and 5 mg/L. Poor: Concentrations are less than 2 mg/L.
N/A
N/A
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Hayslipetal., 2006.
USEPA, 2006a.
USEPA, 2006a.
Lettenmaier et al., 2008.
USEPA, 2006a.
Chesapeake Bay Program,
2008.
Hayslipetal., 2006.
Duplicate
68
67
72, 73,
74, 75,
337, 131
71, 73,
74, 75,
337, 131
71, 72,
74, 75,
337, 131
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August 2011
Indicator
ID#
74
75*
76X
77
78
79
80
81
82
83
Indicator
Dissolved Oxygen
Dissolved oxygen
concentration
Dissolved solids (total)
Drained or Impounded
Wetlands (area,
ecosystem condition)
Drought events (severity)
Dryness Ratio (ratio of
precipitation/evapotransp
iration)
Duration of Dry Periods in
Grassland/Shrubland
Streams and Rivers
(Percent of streams with
substantially
shorter/longer dry-
periods overtime)
Ecosystem extent
(classification of area)
Ecosystem heat sensitivity
Ecosystem ice cover
sensitivity
Definition
Percent of observations of ambient concentrations less than 5 mg/L
N/A
N/A
This indicator would reports the percentage of land area drained or
impounded wetlands (areas that remain wetlands but have been highly
altered).
N/A
Share of total average annual precipitation (P) that is lost through
evapotranspiration (ET), where ET is defined as P-QS.
Duration of dry-period compared to a 50-year average). The indicator
tracks the frequency and duration of zero-flow conditions for streams and
rivers in grassland/shrubland regions. It reports the percentage of streams
and rivers that have at least one no-flow day per year, and the percentage
where the duration of zero-flow periods for a given period is substantially
longer or shorter than the long-term (50-year) average.
Type of ecosystem.
The average annual number of days with maximum temperatures
exceeding 90°F (32°C). *
The average annual number of days with average temperatures below 32°F
(0°C). *
Literature Source
[See Appendix A for full
citation]
Hurdetal., 1999.
USEPA, 1995.
USEPA, 2006b.
Heinz Center, 2002; Heinz
Center, 2008
Lettenmaier et al., 2008.
Hurdetal., 1999.
Heinz Center, 2002; Heinz
Center, 2008
U.S. Climate Change
Science Program, 2008.
Hurd et al., 1998.
Hurd et al., 1998.
Duplicate
71, 72,
73, 75,
337, 131
71, 72,
73, 74,
337, 131
345
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August 2011
Indicator
ID#
84
85
86
87X
88
89
90
Indicator
Electrical Conductivity
Estuarine Waters
Contamination (chemical
occurrence)
Evaporation and
Transpiration
Evaporation and
Transpiration
Expenditure on Dredging
Activities in Waterways
(economy)
Extreme temperatures
(number)
Extreme/heavy rainfall
events (number)
Definition
N/A
This indicator reports on contaminants found in estuarine waters.
Contaminants reported here include many pesticides, selected degradation
products, polychlorinated biphenyls (PCBs), polyaromatic hydrocarbons
(PAHs), volatile organic compounds, other industrial contaminants, trace
elements, nitrate, and ammonium. (Because nitrate, ammonium, and trace
elements such as cadmium and chromium occur naturally, they are not
included in the contaminant occurrence graphs).
N/A
N/A
Average annual expenditures on dredging activities in navigable
waterways.
Number of Threshold Exceedances per Year Thresholds: Daily Maximum
Temperature of 97°F/36°C.
Number of 24-hour and 7-day intense rainfall events.
Literature Source
[See Appendix A for full
citation]
USEPA, 2006b.
Heinz Center, 2002; Heinz
Center, 2008
Lettenmaier et al., 2008.
Gleick and Adams, 2000.
Hurdetal., 1999.
Kling et al., 2003 using
data from Wuebbles and
Hayhoe, 2003.
Kling et al., 2003 using
data from Wuebbles and
Hayhoe, 2003.
Duplicate
603
87
86
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
91
Farmland Landscape
(Cropland as a percentage
of total farmland)
This indicator reports the percentage of the farmland landscape that is
actively used for crop production, pasture, or haylands. The "farmland
landscape" includes croplands and the forests or woodlots, wetlands,
grasslands and shrublands, and the like that surround or are intermingled
with them. This indicator describes the degree to which croplands
dominate the landscape, or, conversely, the degree to which these other
lands are intermingled. This indicator also describes the composition of the
noncropland portion of the farmland landscape by reporting the
percentage of these lands that are forests, grasslands and shrublands,
wetlands, developed areas, and other lands and waters. The noncropland
elements of the farmland landscape (other than developed) provide
wildlife habitat, serve as streamside buffers and windbreaks, and lend a
distinctive visual character to the landscape. (Pasture and haylands are
intermediate in character between "natural" grasslands and cultivated
croplands; for this indicator, they are counted as croplands.)
Heinz Center, 2002; Heinz
Center, 2008
92
Finfish (abundance,
biomass)
Indicators include abundance of popular sport fish (such as striped bass
and bluefish), fish biomass, and abundance offish that spawn in rivers in
the Sound's watershed.
Long Island Sound Study,
2008.
93
Fire Frequency (Percent of
forest land burned over
time)
This indicator describes the frequency with which forests are burned by
wildfire. It would report the fraction of forest lands that experience wildfire
much more or less frequently, moderately more or less frequently, or with
about the same frequency as in presettlement times. Thus, a forest that,
historically, burned every 50 years on average will be considered
moderately altered if it burns every 100 years, and significantly altered if it
burns only every 150 years, and about the same if it burns once every 50
years.
Heinz Center, 2002; Heinz
Center, 2008
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August 2011
Indicator
ID#
94
95
96
97
98
ggX
Indicator
Fire Frequency (Percent of
grasslands/shrublands by
area that are burned over
time)
Fish and Bottom-Dwelling
Animals (comparison to
baseline)
Fish and shellfish
populations (5 species)
Fish range distribution
(species)
Fish Tissue Contaminants
Index (exceedence of
regulatory value)
Fish-tissue contaminants
(chemical levels)
Definition
This indicator will describe how often grassland and shrublands are burned
by wildfire. Specifically, it will report the fraction of grassland and
shrubland areas that burn much more or less often, moderately more or
less often, or about as often as before European settlement. So, for
example, an area that historically burned every 5 years on average might
be considered moderately altered if it now burns every 10 years and
significantly altered if it now burns only every 25 years. An area that
historically burned every 80 years might be considered moderately altered
if it now burns every 40 years and significantly altered if it now burns every
20 years. (Presettlement conditions are used here as a reference against
which to compare current conditions, not as an implied management goal).
This indicator reports on "biological integrity" the degree to which the
suite offish and bottom-dwelling animals in a lake or stream resembles
what one might find in a relatively undisturbed lake or stream in the
same region. Tests assess the number of different species, the number
and condition of individuals, and food chain interactions for fish and
bottom-dwelling (or benthic) animals, which include insects, worms,
mollusks, and crustaceans.
Blue Crab, Oyster, Striped Bass, Shad, and Juvenile Menhaden population
characteristics.
Northern/southern limit.
Good: Concentrations of all chemical contaminants fall below the range of
the EPA Advisory Guidance. Fair: Concentration of at least one chemical
contaminant falls within the range of the EPA Advisory Guidance. Poor:
Concentrations of at least one chemical contaminant exceeds the
maximum value in the range of the EPA Advisory Guidance.
Inorganic arsenic, cadmium, lead, mercury, selenium, silver, zinc, DDT.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Chesapeake Bay Program,
2008.
Kling et al., 2003.
USEPA, 2006a.
Hayslipetal., 2006.
Duplicate
58, 48,
579
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Indicator
ID#
100
101
102
103
104
105
Indicator
Flood events (frequency)
Forest Age (Percent of
forest lands by age group)
Forest Area and
Ownership (Area of forest
lands)
Forest Community Types
with Significantly Reduced
Area (Area occupied by
community type)
Forest Disturbance: Fire,
Insects, Disease (Area
affected by disturbance)
Forest Management
Categories (Change in
percent of forested area
with time)
Definition
N/A
This indicator reports the percentage of forest lands with stands in several
age classes.
This indicator reports how much forest land there is in the United States
and who owns it.
This indicator would report whether those forest community types that
cover significantly fewer acres than they did in presettlement times are
increasing or decreasing in area, and by how much. It would also report the
total area occupied by these much-reduced forest community types those
that have been reduced by 70% or more in area.
This indicator reports the acreage of forest affected each year by several
important types of disturbance: forest fires, insects, and diseases of trees.
This indicator reports the percentage of forest area in several different
management categories. These range from "reserved lands" (forests in
national parks, wilderness areas, and other similar areas) to forests under
intensive management involving replanting after harvest. Other forest
lands in intermediate categories are subject to a wide variety of both
management practices and restrictions on use.
Literature Source
[See Appendix A for full
citation]
Lettenmaier et al., 2008.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
Page B-19
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August 2011
Indicator
ID#
106
107
108
109
110
111
Indicator
Forest Pattern and
Fragmentation (Percent of
a tree's surroundings that
are forested)
Forest products
(economic production)
Forest Types (Area
covered by a certain
forest type)
Forests (extent/acreage)
Forests with Nursery
Stock (area, ecosystem
condition)
Fragmentation and
Landscape Pattern
Definition
This indicator describes a tree's forest neighborhood according to the
degree of forest cover within various distances. Thus, the "immediate
neighborhood" of a particular tree is everything within about 250 feet in all
directions. This "immediate neighborhood" is "mostly forest" if the land is
at least 90% forested. A tree's "local neighborhood" extends about 1/4
mile in all directions, and its "larger neighborhood" extends about 2 1/2
miles. This analysis relies upon computer analyses of satellite data on
millions of individual forest points. While these points (called "pixels") are
not individual trees they are squares about 100 feet on a side they serve
much the same purpose.
This indicator reports the production of food and fiber and the withdrawals
of water (forest products), using an index with 1980 as the base year.
This indicator reports the acreage of a variety of forest "cover types." Cover
types describe the dominant species of trees found in the forests (e.g.,
oak-hickory forests are dominated by oaks and hickories, but include other
kinds of trees as well).
This indicator presents the area of forests as a percentage of the total U.S.
land area, for the most recent 50-year period and compared to
presettlement estimates.
This indicator reports the percentage of land area that is comprised of
forests planted with nursery stock.
N/A
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
Page B-20
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August 2011
Indicator
ID#
112
113
114
115
116
117
118
Indicator
Fragmentation of
Farmland Landscapes by
Development (Ratio of
cropland: developed land)
Fresh Water Resources
Contamination (chemical
occurrence)
Fresh Waters
(extent/acreage)
Freshwater input to
coastal ecosystems
Freshwater Rivers and
Streams with Low Index
of Biological Integrity
(ecosystem condition)
Glaciers
Grassland and Shrubland
Areas (extent/acreage)
Definition
This indicator would report the degree to which suburban development
and other built-up areas break up (fragment) the farmland landscape
(defined as croplands plus intermingled "natural" areas such as forests,
wetlands, and grasslands and shrublands). Areas with a mosaic of cropland
and intermingled natural areas but little or no development would be
rated as "low" on the "fragmentation index" used for this indicator, while
those in which small patches of cropland are mixed into a backdrop of
suburban development would be rated as "high."
This indicator reports on contaminants found in freshwater resources.
Contaminants reported here include many pesticides, selected degradation
products, polychlorinated biphenyls (PCBs), polyaromatic hydrocarbons
(PAHs), volatile organic compounds, other industrial contaminants, trace
elements, nitrate, and ammonium. (Because nitrate, ammonium, and trace
elements such as cadmium and chromium occur naturally, they are not
included in the contaminant occurrence graphs).
This indicator presents the area of fresh waters.
Rate of freshwater input into coastal ecosystems.
This indicator would report on the percentage of freshwater rivers and
streams with low IBI (Index of Biological Integrity, a species-based
measure of disturbance).
N/A
This indicator presents the area of grasslands and shrublands as a
percentage of the total U.S. land area, for the most recent 50-year period
and compared to presettlement estimates.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Day et al., 2008.
Heinz Center, 2002; Heinz
Center, 2008
Gleick and Adams, 2000.
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
Page B-21
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Aquatic Ecosystems, Water Quality, and Global Change:
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Final Report
August 2011
Indicator
ID#
119
120
121
122
123
124X
125**
126
Indicator
Grassland Patches
(Percent of grassland
patches that cover a
certain area)
Grasslands and
Shrublands (Area covered
by grasslands/shrublands)
Groundwater Depletion -
Ratio of
Withdrawals/ Basef low
Groundwater Levels - Area
of Aquifer subject to
Change in Groundwater
Levels
Groundwater recharge
Groundwater recharge
Groundwater reliance
Growing season length
Definition
This indicator will describe the fraction of grassland area and shrubland
area that is in patches of different sizes. The total area occupied by patches
of a certain size will be reported as a percentage of the total area of either
grasslands or shrublands.
This indicator reports the acreage of U.S. grasslands and shrublands
(although data are not available for Hawaii).
Ratio of average groundwater withdrawals (QGW) in 1990 to annual
average baseflow (QBase), reflecting the extent that groundwater use
rates may be exceeding recharge.
This indicator would report the percentage of the area of the nation's
major regional aquifers in which water levels are increasing, decreasing, or
stable. The indicator would report what fraction of the aquifer area
declined, increased, or remained stable in comparison to a previous period,
and it would be reported every 5 years.
Rate of groundwater recharge (mm/year) based on WaterGAP Global
Hydrology Model and mapped on a 0.5 degree grid. It accounts for spatial
variation in precipitation, infiltration capacity, hydrogeology, topography,
and permafrost. *
N/A
Share of total annual withdrawals derived from groundwater in 1995.
Method of calculation: QGW/QW. *
Number of days between last spring frost and first autumn frost.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Hurd et al., 1999.
Heinz Center, 2002; Heinz
Center, 2008
Lettenmaier et al., 2008.
Gleick and Adams, 2000.
Hurd et al., 1998.
Kling et al., 2003 using
data from Wuebbles and
Hayhoe, 2003.
Duplicate
124
123
Page B-22
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August 2011
Indicator
ID#
127
128
129
130
13 lx
132
133
134X
135
136
Indicator
Harmful algal blooms
(occurrence)
Highest spring streamflow
date
Hydrologic isolation
Hydropower capacity
Hypoxia (dissolved oxygen
concentration)
Ice cover duration (freeze
and ice-out dates)
Ice cover on rivers
Ice -out date
Industrial water use
(average annual share
consumed)
Inland water extent
Definition
Defined as (1) an increase in the abundance of species that are known to
produce toxins harmful to marine animals or humans; (2) the occurrence
of lesions or mass mortalities of marine animals caused by HAB species;
and (3) the occurrence of human pathologies caused by HAB species. A
single event counts only once toward the relative intensity scale, even if it
produces multiple impacts (e.g., an increase in the abundance of a HAB
species that causes mass mortalities and an increased human health risk
will be counted as a single event).
Peak streamflow in early spring, indicative of maximum baseflow and/or
maximum snowmelt rate in a basin.
Indicates whether a system is a closed basin, or "isolated water" (i.e., a
single spring that flows for a short distance before re-infiltrating into
ground).
Distribution of regional hydroelectric power capacity, in megawatts (MW).
Levels of dissolved oxygen below 2 milligrams per liter (mg/L).
Number of days waterbodies are covered by ice, or are ice-free.
N/A
Date of spring ice-out on lakes in the Northeast.
Share of total industrial water use that is consumed (i.e., not returned to
the system). *
Total areal extent of inland waters (could be divided by area to calculate
density).
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Frumhoff etal., 2006.
MEA, 2005b.
Hurd et al., 1998.
USEPA, 2008b.
Kling et al., 2003.
Frumhoff etal., 2006.
Frumhoff etal., 2006.
Hurd etal., 1999.
MEA, 2005b.
Duplicate
71, 72,
73, 74,
75, 337
275, 423
Page B-23
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August 2011
Indicator
ID#
137
138
139
140
141
142
143X
144
145
146
Indicator
Institutional Barriers to
Water Trading
Instream fish habitat
Intactness of coastal
buffer
Intensively Grazed
Grassland (area,
ecosystem condition)
Invasive Plant Cover -
Grasslands and
Shrublands (Percent of
non-native plant cover)
Invasive plant coverage -
Forests (Percent area of
non-native cover)
Invasive species
Invasive species
Invasive species - Coasts
affected (area, ecosystem
condition)
Invasive species - Forests
Definition
Flexibility score (on a scale of integers from zero to five) is assigned to each
state based on the relative degree of barriers to water trading.
Instream fish concealment features consisting of undercut banks, boulders,
large pieces of wood, brush, and cover from overhanging vegetation within
a stream and its banks.
The degree to which natural coastal landforms and vegetation are intact.
This indicator would report on the percentage of land area that is
intensively grazed grassland/shrubland.
This indicator will report the percentage of plant cover in grasslands and
shrublands that is made up of non-native species. The indicator will report
on both invasive non-native species (those that spread aggressively) and all
non-native species.
This indicator describes the degree to which non-native plants are found in
U.S. forests. It will report the percentage of the total area covered by
overstory (large trees that form the canopy) and understory (shrubs,
ground plants, and smaller trees) that is made up of non-native plants.
N/A
This indicator reports the percentage of watersheds with different numbers
of nonnative species with established breeding populations. "Non-native"
includes species not native to North America and those that are native to
this continent but are now found outside their historic range.
This indicator would report on the percentage of coastline length that is
heavily affected by invasive species.
This indicator would report on the percentage of land area that is forests
Literature Source
[See Appendix A for full
citation]
Hurdetal., 1999.
USEPA, 2006b.
Day et al., 2007.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Twilley et al., 2001.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Duplicate
463, 144,
618
618, 143,
463
Page B-24
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August 2011
Indicator
ID#
147
148
149
150X
15 lx
Indicator
Affected (area, ecosystem
condition)
Invasive species -
Grasslands and
Shrublands Affected (area,
ecosystem condition)
Invasive species (range
expansion)
Invasive species in
estuaries (percent
influenced)
Irrigation withdrawal rate
Lake and Stream Acidity
Definition
heavily affected by invasive species.
This indicator would report on the percentage of land area that is
grasslands and shrublands heavily affected by invasives.
N/A
Percentage of major estuaries with high, medium, or low influence by
non-native species. Ratings of the degree of influence should incorporate
both the number of different species present and the degree to which
they occupy available habitat.
N/A
N/A
Literature Source
[See Appendix A for full
citation]
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
USEPA, 2008b.
Duplicate
444
604, 621
Page B-25
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August 2011
Indicator
ID#
152
153X
154
155
156
157
158
159
160
161
162
163
Indicator
Lake Levels and
Conditions
Lake stratification
(thermal structure)
Land use
Lands and Waters with
Highly Altered Species Mix
(area, ecosystem
condition)
Latitude/altitude
Lined and Culverted
Streams (area)
Living Planet Index
Low flow sensitivity
(mean baseflow)
Low-flow events
M&l water use share
Major Crop Yields (Tons or
bushels per acre of land)
Mangrove cover
Definition
N/A
N/A
N/A
This indicator would report on the percentage of lands and waters with
highly altered species mix, such as would be characteristic of altered fire or
hydrologic regimes.
N/A
This indicator would report on the percentage of land area and stream
length that is lined and culverted streams.
N/A
Unregulated mean baseflow in cfslmiZ, i.e. the amount of streamflow
originating from groundwater outflow.
N/A
Municipal and industrial sector share of total average annual withdrawals.
This indicator reports the yields of corn, soybeans, wheat, hay, and cotton
(which account for 95% of crop production in the US), as an index with
1975 as the base year. Values above 1.0 indicate higher yields, typically
measured as tons or bushels per acre, than in 1975; values below 1.0
indicate lower yields than in 1975.
N/A
Literature Source
[See Appendix A for full
citation]
Gleick and Adams, 2000.
Kling et al., 2003.
U.S. Climate Change
Science Program, 2008.
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
Hurd et al., 1999.
Frumhoff etal., 2006.
Hurd et al., 1998.
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
Duplicate
343
Page B-26
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Final Report
August 2011
Indicator
ID#
164
165**
166
167
168
169
170
171
172
173
Indicator
Marine fish landings
(economic production)
Meteorological drought
indices
Mid-channel clarity
Migratory bird use
Miles of Hardened
Coastline
Miles of Streams
Impounded to Lakes
(area, ecosystem
condition)
Modification of water
regimes
Monetary Value of
Agricultural Production -
Time-related (Billions of
1999 $ per year)
Monetary Value of
Agricultural Production -
Area-related (Thousands
of 1999 $ per square mile)
Native Vegetation in Areas
Dominated by Croplands
(Percentage of vegetation
native to a specific area)
Definition
Specifically refers to marine fish landings. This indicator reports the
production of food and fiber and the withdrawals of water, using an index
with 1980 as the base year.
Average Palmer Drought Severity Index value, 2003-2007. *
N/A
N/A
This indicator would report on the percentage of coastline length that is
hardened coastline.
This indicator would report on the percentage of stream length (at some
last baseline date) that has since been impounded into lakes.
A measure of the degree of anthropogenic influence, which may be
worsened by climate change.
This indicator reports the dollar value of the annual output of major crops
and livestock, in billions of 1999 dollars per year. The value is determined
by multiplying the amount of output by the prices received by farmers (in
1999 dollars).
This indicator reports the dollar value of the annual output of major crops
and livestock, in thousands of 1999 dollars per square mile. The value is
determined by multiplying the amount of output by the prices received by
farmers (in 1999 dollars).
This indicator would report, for areas where croplands account for a large
percentage of the land cover, how much of the remaining vegetation
(outside of croplands) is native to the area.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
National Assessment
Synthesis Team, 2000a.
Chesapeake Bay Program,
2008.
MEA, 2005b.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
PageB-27
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August 2011
Indicator
ID#
174
175
176X
177
178
179
Indicator
Natural coastal wetlands
Natural Ecosystem
Services
Natural Ecosystem
Services -
Urban/Suburban Lands
Nitrate and Pesticides in
Shallow Ground Water in
Agricultural Watersheds
Nitrate concentration -
major aquifers on
agricultural lands (Percent
of samples with drinking
water exceedances)
Nitrate concentration -
shallow groundwater
Definition
N/A
This indicator would report on the levels of key services provided by
"natural" ecosystems forests, grasslands and shrublands, fresh waters,
and coasts and oceans. The goods, or products, these ecosystems
provide such as fish, wood products, and food can be counted, and a
monetary value often placed upon them.
Urban and suburban areas are defined by what people have built, but the
remaining "natural" components trees, meadows, streams, wetlands, and
the like provide valuable services to the residents of these developed
areas. Ecosystem services are the benefits, both tangible and intangible,
that these natural elements provide. For example, forested areas reduce
stormwater runoff, when compared to paved areas, and trees cool streets
and buildings, reducing energy consumption; trees also reduce urban noise
levels. Natural areas, including forests, grasslands and shrublands, beaches,
lakes, streams, and wetlands, also provide recreational opportunities,
increase property values and community amenities, and are aesthetically
pleasing.
N/A
Percentage of samples exceeding drinking-water standard for nitrate (10
milligrams per liter).
Median concentration of nitrate (in milligrams per liter).
Literature Source
[See Appendix A for full
citation]
MEA, 2005b.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
USEPA, 2008b.
USGS, 1999.
USGS, 1999.
Duplicate
176
175
Page B-28
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August 2011
Indicator
ID#
180
181
182
183
184
185
186X
187
188
Indicator
Nitrate in Farmland
Streams and Groundwater
Nitrate in Grassland and
Shrubland Groundwater
(Percent of groundwater
sites tested)
Nitrate in Streams
(Average nitrate
concentration)
Nitrate in Urban and
Suburban Streams
(Percent of streams with
certain level of detects)
Nitrogen (movement,
yield/load)
Nitrogen (total)
Nitrogen and Phosphorus
-large rivers
Nitrogen and Phosphorus
-streams in agricultural
watersheds
Nitrogen and Phosphorus
-wadeable streams
Definition
This indicator reports on the concentration of nitrate in representative
farmland streams and groundwater sites. Specifically, the indicator reports
the percentage of streams and groundwater wells with average nitrate
concentrations in one of four ranges, in areas that are primarily farmland.
This indicator reports on the concentration of nitrate in groundwater in
grassland and shrubland areas. Specifically, the indicator reports the
percentage of groundwater sites with average nitrate concentrations in
one of four ranges, in areas that are primarily grassland or shrubland.
This indicator reports on the concentration of nitrate in representative
streams in forested areas. Specifically, the indicator reports the percentage
of streams with average nitrate concentrations in one of four ranges, for
streams draining watersheds that are primarily forested.
This indicator reports the concentration of nitrate in streams in
representative urban areas. Specifically, the indicator reports the
percentage of streams with average nitrate concentrations in one of four
ranges, for streams draining watersheds that are primarily urban.
This indicator reports the yield of nitrogen from major watersheds: pounds
of nitrogen per square mile of watershed area that enters rivers and
streams through discharges, runoff, and other sources. It also reports the
load of nitrate, a common form of nitrogen, from major rivers: tons of
nitrate carried to the ocean each year by the four largest U.S. rivers.
This indicator reports the total nitrogen concentration in a system.
This indicator reports the nitrogen and phosphorus content of large rivers.
This indicator reports the nitrogen and phosphorus content of streams and
rivers in agricultural watersheds.
This indicator reports the nitrogen and phosphorus content of wadeable
streams.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
USEPA, 2006b.
USEPA, 2008b.
USEPA, 2008b.
USEPA, 2008b.
Duplicate
459
Page B-29
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August 2011
Indicator
ID#
189
190
191
192
193
194
195
196
Indicator
Nitrogen concentration -
streams (total)
Number of Dry Periods in
Grassland/Shrubland
Streams and Rivers
(Percent of streams with
dry periods over time)
Nutrient enrichment
(coastal wetlands)
Open Mines, Quarries,
and Gravel Pits (area)
Patches of Forest,
Grassland and Shrubland,
and Wetlands - By Region
(Percent area of natural
lands in urban/suburban
areas by region)
Patches of Forest,
Grassland and Shrubland,
and Wetlands - National
(Percent area of natural
lands in urban/suburban
areas)
Permafrost
Permafrost temperatures
Definition
Average annual concentration of total nitrogen (in milligrams per liter).
The indicator tracks the frequency and duration of zero-flow conditions
for streams and rivers in grassland/shrubland regions. It reports the
percentage of streams and rivers that have at least one no-flow day per
year, and the percentage where the duration of zero-flow periods for a
given period is substantially longer or shorter than the long-term average.
Nutrient enrichment of coastal wetland ecosystems.
This indicator would report on the percentage of land area that is open
mines, quarries, and gravel pits, measured from satellite.
This indicator reports how much of the "natural" area within urban and
suburban lands is in patches of varying size, from less than 10 acres to
greater than 10,000 acres. Natural areas include forests, grasslands and
shrublands (including most pasturelands especially in the west), and
wetlands.
This indicator reports how much of the "natural" area within urban and
suburban lands is in patches of varying size, from less than 10 acres to
greater than 10,000 acres. Natural areas include forests, grasslands and
shrublands (including most pasturelands especially in the west), and
wetlands.
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USGS, 1999.
Heinz Center, 2002; Heinz
Center, 2008
Day et al., 2008.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Gleick and Adams, 2000.
Lettenmaier et al., 2008.
Duplicate
Page B-30
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August 2011
Indicator
ID#
197
198
199X
200
201
202
203
204
Indicator
Pesticide Exceedances in
Farmland Streams and
Groundwater (Percent of
waterbodies with
regulatory exceedances)
Pesticide Occurrence in
Farmland Streams and
Groundwater (Number of
pesticides detected in
waterbodies)
Pesticides in Streams in
Agricultural Watersheds
Phosphorus (total)
Phosphorus in Farmland
Streams
Phosphorus in Urban and
Suburban Streams
(Percent of streams with
certain level of detects)
Phosphorus -lakes,
reservoirs (concentration)
Phosphorus -large rivers
Definition
This indicator reports on pesticides and pesticide degradates found in
farmland streams and groundwater as the percentage of streams and
shallow groundwater wells with contaminant concentrations that exceeded
standards and guidelines (benchmarks) set for the protection of human
health or aquatic life. Data report currently used agricultural pesticides and
selected breakdown products of these pesticides, as well as selected
organochlorine insecticides that were widely used in the past but whose
use is no longer permitted in the United States.
This indicator reports on pesticides and pesticide degradates found in
farmland streams and groundwater as the average number of such
contaminants detected throughout the year in streams and shallow
groundwater wells. Data report currently used agricultural pesticides and
selected breakdown products of these pesticides, as well as selected
organochlorine insecticides that were widely used in the past but whose
use is no longer permitted in the United States.
N/A
Indicator reports total phosphorus concentration in water body
This indicator reports on the concentration of phosphorus in representative
farmland streams. Specifically, the indicator reports the percentage of
streams with average annual concentrations in one of four ranges, for
streams draining watersheds that are primarily farmland.
This indicator reports the concentration of phosphorus in representative
streams in urban areas. Specifically, the indicator reports the percentage of
streams with average annual concentrations in one of four ranges, for
streams draining watersheds that are primarily urban.
This indicator reports the average concentration of phosphorus in lakes and
rivers.
This indicator reports the average concentration of phosphorus in large
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
USEPA, 2008b.
USEPA, 2006b.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Duplicate
199
198
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August 2011
Indicator
ID#
205
206
207
208
209
210
211
212X
213X
Indicator
(concentration)
Phytoplankton
Plant Growth Index
Population
Population (human)
Population (human)
susceptible to flood risk
Population Trends in
Invasive and Non-invasive
Grassland/Shrubland Birds
(Percent change in
population size of species
overtime)
Potential for wetland
migration
Precipitation
Precipitation
Definition
rivers.
N/A
This indicator reports a plant growth index, based on satellite
measurements of the amount of solar energy absorbed by vegetation and
potentially used for photosynthesis. The index shows, for any given year,
whether plant growth in a region or for an ecosystem type was above or
below the 11-year average
N/A
This indicator reports the human population, using an index with 1980 as
the base year.
Population within the 500-year flood plain.
This indicator describes population trends for selected grassland/shrubland
bird species by comparing trends for selected "invasive" species with those
that are not invasive.
Area of tidal wetlands compared to area of land within one-half tide range
above spring high water. Ratio of tidal wetlands to dry land and all land.
N/A
N/A
Literature Source
[See Appendix A for full
citation]
Center, 2008
Chesapeake Bay Program,
2008.
Heinz Center, 2002; Heinz
Center, 2008
Twilley et al., 2001.
Heinz Center, 2002; Heinz
Center, 2008
Hurd et al., 1999.
Heinz Center, 2002; Heinz
Center, 2008
U.S. Climate Change
Science Program, 2008.
Twilley et al., 2001.
Lettenmaier et al., 2008.
Duplicate
208
207
360
213, 214,
215
212, 214,
215
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August 2011
Indicator
ID#
214*
215
216
217
218**
219**
220
221
Indicator
Precipitation
Precipitation (average
daily)
Production of Cattle on
Grasslands and
Shrublands (Number of
cattle overtime)
Publicly Accessible Open
Space per Resident
(Percent of metropolitan
areas with a certain
amount of open space per
resident)
Ratio of Snow to
Precipitation (S/P)
Ratio of water
withdrawals to annual
streamflow
Recreation - Outdoor
(number of activities)
Recreation - Participation
in freshwater activities
(number of days)
Definition
N/A
N/A
This indicator reports the number of cattle grazing on grasslands and
shrublands (including pastures), rather than at feedlots, during July of each
year.
This indicator would report the amount of open space land that is
dominated by "natural" surfaces, like grass or woods, along with lakes,
rivers, beaches, and wetlands that is accessible to the general public in
large metropolitan areas. Specifically, the indicator would report the
percentage of metropolitan areas with different amounts of open space
per resident.
Average annual ratio of snowfall (in inches) to total precipitation (in
inches). *
Ratio of total annual surface and groundwater withdrawal in 1990 (QW)
to unregulated mean annual streamflow (Qs). Method of calculation:
QW/Qs
This indicator reports the number of times Americans over the age of 15
took part in a variety of outdoor recreational activities. (Each time
someone took part in an activity is counted: if the activity took place over
multiple days, each day counts as a separate event, and if a person took
part in several activities on a single day, each activity is counted as a
separate event.)
This indicator shows the number of days that people took part in a variety
of freshwater activities. A "recreation day" for this measure is any day
during which a person was engaged in the activity, whether for only a few
Literature Source
[See Appendix A for full
citation]
Gleick and Adams, 2000.
Kling et al., 2003 using
data from Wuebbles and
Hayhoe, 2003.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Lettenmaier et al., 2008.
Hurd et al., 1999.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
212, 213,
215
212, 213,
214
259, 260
608
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August 2011
Indicator
ID#
222
223
224
225
226
227
228X
Indicator
Recreation in Forests
(Number of days of
recreational activity per
year)
Recreation on Farmlands
(Number of days of
engagement in
recreational activities)
Recreation on Grasslands
and Shrublands (Number
of days of recreational
activity per year)
Recreational water quality
(beach-mile-days affected
by Enterococcus)
Red List Index
Relative Bed Stability
(RBS)
Relative sea level rise
(RSLR)
Definition
minutes or for many hours.
This indicator would report the number of days per year that people
engage in a variety of recreational activities in forests. Activities such as
walking, hiking and backpacking, fishing and hunting, wildlife viewing,
cross-country and downhill skiing, and snowmobiling would be included.
This indicator would report the number of days spent fishing, hunting,
viewing wildlife, or engaged in other recreational activities on farmland.
This indicator will report the number of days per year that people engage
in a variety of recreational activities on the nation's grasslands and
shrublands. Activities will include: hunting; off-road vehicle (ORV) driving,
motorsports, mountain biking, and snowmobiling; bird watching and
nature study; and hiking and camping.
This indicator will report the percentage of "beach-mile-days" affected by
various levels of Enterococcus, a bacterium that indicates contamination
with human or animal waste. A "beach-mile-day" is one mile of beach
affected for one day 100 miles of beach affected for one day would count
the same as 1 mile affected for 100 days.
Index of threatened/endangered status for birds, based on a 1988 baseline.
Ratio that compares measures of particle size of observed sediments to the
size of sediments that each stream can move or scour during its flood stage
(based on measures of the size, slope, and other physical characteristics of
the stream channel).
Function of absolute sea level, changes in land level, and sediment delivery
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
MEA, 2005b.
USEPA, 2006b; USEPA,
2008b.
MEA, 2005b.
Duplicate
229, 241,
405, 412
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August 2011
Indicator
ID#
229
230
231
232
233
234
235X
236
237
238
239
240X
Indicator
Relative sea level rise
(RSLR)
Riparian Areas (extent,
acreage)
Riparian Condition
(Riparian Condition
Index)
River channel and
geomorphology
River flow and nitrogen
loads
Road surface (area)
Runoff
Runoff from precipitation
and snowmelt (mean
annual)
Runoff -large areas
Runoff patterns
Runoff -regions
Salinity
Definition
The net change in the relative elevations of the sea surface and coastal
lands
For streams and rivers, the indicator reports on the type of land cover on
their shorelines and adjacent areas ("riparian" areas): forest; grasslands,
shrublands, or wetlands; and urban/suburban or agricultural land.
This indicator will describe the condition of riparian (streamside) areas.
The condition of these areas will be rated using an index that combines
key factors such as water flows, streambed physical condition, riparian
vegetation composition and structure, and use by various species. Such a
measure should take into account multiple factors, including hydrology
(e.g., relationship to natural flow patterns), geomorphology (e.g., stream
sediment transport), and biology (e.g., canopy cover) to provide an
overall index of condition.
N/A
Indicator reports total nitrogen arriving in a water body (e.g., from the
Mississippi River system to the Gulf of Mexico).
This indicator would report on the percentage of land area that is road
surface (including unpaved roads).
N/A
Water-balance model (monthly precipitation and potential
evapotranspiration) and output from the two GCMs to estimate the effects
of climate change
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
Day et al., 2008.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Gleick and Adams, 2000.
Chesapeake Bay Program,
2008.
Heinz Center, 2002; Heinz
Center, 2008
Twilley et al., 2001.
National Assessment
Synthesis Team, 2000a.
Gleick and Adams, 2000.
Lettenmaier et al., 2008.
Gleick and Adams, 2000.
USEPA, 2006b.
Duplicate
228, 241,
405, 412
236
235
603, 84
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August 2011
Indicator
ID#
241*
242
243X
244
245
246
247
248X
249
Indicator
Sea level rise
Sea surface temperature
(difference from average)
Sea surface temperature
(trend)
Seagrass cover
Seals (number)
Sediment contaminants
(chemical levels)
Sediment Contamination
(exceedance of regulatory
value)
Sediment delivery
Sediment discharge (river
to coast)
Definition
N/A
Indicator describes whether sea surface temperature (SST) is above or
below average. Method used: (1) the seasonal average sea surface
temperature (SST) of near-shore water (shoreline out to 25 miles) was
calculated for the warmest season in each region (termed the "seasonal
mean maximum"), which typically occurred during summer or fall; (2) the
long-term mean (during the warmest seasons) for the period of
observation (1985-1998) was calculated; and (3) the long-term mean was
then subtracted from the seasonal mean maxima. Thus, values greater than
zero are positive "anomalies" (i.e., deviations from the long-term average),
and those less than zero are negative anomalies.
N/A
N/A
Number of seals observed in winter months at two monitoring locations.
Total organic carbon, metals, polynuclear aromatic hydrocarbons (PAH),
PCBs (Polychlorinated Biphenyls), pesticides.
Using ERM and ERL guidelines. Good: No ERM values are exceeded, and
fewer than five ERL values are exceeded. Fair: No ERM values are
exceeded, and five or more ERL values are exceeded. Poor: One or more
ERM values are exceeded.
N/A
Sediment load discharged from riverine systems into estuarine and coastal
systems
Literature Source
[See Appendix A for full
citation]
Gleick and Adams, 2000.
Heinz Center, 2002; Heinz
Center, 2008
Twilley et al., 2001.
MEA, 2005b.
Long Island Sound Study,
2008.
Hayslipetal., 2006.
USEPA, 2006a.
MEA, 2005b.
Day et al., 2008.
Duplicate
228, 229,
405, 412
243
242
249
248
Page B-36
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Final Report
August 2011
Indicator
ID#
250
251
252
253
254
255
256
Indicator
Sediment Quality Index (3
components)
Sediment Total Organic
Carbon (TOC)
Sediment toxicity
Sediment Toxicity (species
test)
Shape of "Natural"
Patches (Ratio of
perimeter to area of
patch)
Shellfish (acreage,
harvest)
Shoreline types
(miles/category)
Definition
Based on three sediment quality component indicators: sediment toxicity,
sediment contaminants, and sediment TOC. Good: Less than 5% of the NEP
estuarine area is in poor condition, and more than 50% of the NEP
estuarine area is in good condition. Fair: 5% to 15% of the NEP estuarine
area is in poor condition, or more than 50% of the NEP estuarine area is in
combined poor and fair condition. Poor: More than 15% of the NEP
estuarine area is in poor condition.
Good: The TOC concentration is less than 2%. Fair: The TOC concentration
is between 2% and 5%. Poor: The TOC concentration is greater than 5%.
Acute sediment toxicity test.
Using a 10-day static toxicity test with the amphipod Ampelisca abdita.
Good: Mortality is less than or equal to 20%. Poor: Mortality is greater than
20%.
This indicator describes the shape of patches of "natural" lands in the
farmland landscape, by reporting on the percentage of patch area that is
found in "compact" patches (e.g., like a circle), "elongated" patches (e.g.,
like a long narrow rectangle), and an intermediate class of patch shape.
These classes are defined based on the ratio of the perimeter, or edge, of
each patch to its area; these perimeter-to-area ratios will be divided by
patch area for the sake of comparison. "Natural" areas include forest,
grasslands and shrublands, wetlands, and lands enrolled in the
Conservation Reserve Program (CRP).
Indicators include acreage of shellfish beds and harvests of important
commercial mollusks and crustaceans (oyster and lobster).
Reports the miles of coastline in several categories, including: beach; mud
or sand flats; steep sand, rock, or clay cliffs; wetlands; and coastline
"armored" with bulkhead or riprap. The coastline includes ocean-front
areas and the shoreline of estuaries and bays.
Literature Source
[See Appendix A for full
citation]
USEPA, 2006a.
USEPA, 2006a.
Hayslipetal., 2006.
USEPA, 2006a.
Heinz Center, 2002; Heinz
Center, 2008
Long Island Sound Study,
2008.
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
50
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August 2011
Indicator
ID#
257
258
259X
260X
261
262
263X
264
265
266X
267
Indicator
Shrubland Patches
(Percent of grassland
patches that cover a
certain area)
Silt-Clay content of
sediment
Snow influence
Snow Water Equivalent
(SWE)
Snowmelt event dates
Snowmelt runoff volume
Snowpack
Snowpack
Snowpack density
Snowpack depth
Soil Biological Condition
(Percentage of croplands
with variety of worms)
Definition
This indicator will describe the fraction of grassland area and shrubland
area that is in patches of different sizes. The total area occupied by patches
of a certain size will be reported as a percentage of the total area of either
grasslands or shrublands.
The proportion of fine grained materials (silt and clay) in the estuarine
sediments.
Share of average annual precipitation (P) that falls as snow (Ps) (i.e., snow
water equivalent).
N/A
N/A
N/A
N/A
N/A
N/A
N/A
This indicator reports the percentage of croplands in three different ranges
on the Nematode Maturity Index (NMI), an index that measures the types
of roundworms, or nematodes, in the soil. Calculation of the NMI is based
on the proportion of nematodes with different levels of tolerance for
disturbance. A map showing the percentage of cropland in each major
cropland region with low index values (indicating disturbed soils) would
accompany the nationwide map.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Hayslipetal., 2006.
Hurd et al., 1998.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Frumhoff etal., 2006;
Frumhoffetal., 2007.
Gleick and Adams, 2000.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
218, 260
259, 218
264
263
438, 440
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August 2011
Indicator
ID#
268
269*
270
271
272
273
274
275X
Indicator
Soil Erosion (Percentage
of U.S. farmlands prone to
erosion)
Soil moisture
Soil moisture
Soil Organic Matter
(Percentage of organic
matter by soil weight)
Soil Salinity (Percentage of
croplands with high
salinity)
Species Status (Percent of
metropolitan areas with
species at risk)
Spring high water
Spring ice-out dates
Definition
This indicator reports the percentage of U.S. farmlands according to their
potential for erosion by wind or water. These data are based on an index
that combines information on soil characteristics, topography, and
management activities such as tillage practices and whether crop residue is
left on the field or not. This indicator covers croplands (excluding pastures)
and Conservation Reserve Program (CRP) lands.
N/A
N/A
This indicator reports the amount of organic matter partially decayed
plant and animal matter in the top 4-6 inches of cropland soil. Soil
organic matter is usually measured as the percentage of organic matter (by
dry weight) in the top 4-6 inches of the soil, where human activities have
most influence on soil condition.
This indicator reports the percentage of cropland with different levels of
salt content, measured in decisiemens per meter (dS/m). A map showing
the percentage of land in each major cropland region with elevated salt
levels (i.e., over 4 dS/m), would accompany the nationwide map.
This indicator reports the degree to which "original" plants and animals are
either absent entirely or are at risk of being lost from metropolitan areas.
Original species are those that, before European settlement, inhabited the
lands now occupied by metropolitan areas. Specifically, the indicator will
report on the fraction of metropolitan areas where 25% or more, 50% or
more, and 75% or more of original species are at risk of being displaced or
are absent.
This indicator is the average high tide during a full or new moon. It
approximates the boundary between tidal wetlands and dry land.
This indicator reports the date at which lake ice cover ends.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Twilley et al., 2001.
Gleick and Adams, 2000.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
U.S. Climate Change
Science Program, 2008.
Lettenmaier et al., 2008.
Duplicate
270
269
134, 423
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August 2011
Indicator
ID#
276
277
278
279
280X
281
282X
Indicator
Status of Animal
Communities in Urban
and Suburban Streams
(Percent of
urban/suburban sites
with undisturbed and
disturbed species)
Status of Animal Species
in Farmland Areas
Stream Bank Vegetation
Streamflow variability
(annual)
Streamflow variability
(daily and weekly)
Streamflow variability
(daily and weekly)
Streamflow variability
(daily)
Definition
This indicator reports on "biological integrity" in urban and suburban
streams. Biological integrity is a measure of the degree to which the suite
offish and bottom-dwelling (or benthic) animals (including insects,
worms, mollusks, and crustaceans) resemble what one might find in a
relatively undisturbed stream in the same region. Tests assess the
number of different species, number and condition of individuals, and
food chain interactions. High scores indicate close resemblance to
"reference" or undisturbed conditions, and low scores indicate significant
deviation from them.
This indicator reports the status of wildlife in farmland areas.
This indicator describes the percentage of miles of stream (stream-miles) in
urban and suburban areas that are lined with trees, shrubs, and other
plants.
The coefficient of variation (CV) of unregulated Streamflow is an indicator
of annual Streamflow variability. It is computed as the ratio of the
standard deviation of unregulated annual Streamflow (oQs) to the
unregulated mean annual Streamflow (QS)1.
Trends in Streamflow volumes based on daily flow data (same as "Changing
Stream Flows" from (2) Heinz Center, 2002) are indicators of daily and
weekly Streamflow variability.
This indicator describes changes in the amount and timing of river and
Streamflow by reporting the percentage of monitored streams or rivers
with major, moderate, and minimal changes in low flow, high flow, and the
timing of these two extreme events. The indicator also describes the nature
of major flow changes. Four subindicators were included in the analysis:
N/A
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Hurd et al., 1999.
USEPA, 2008b.
Heinz Center, 2002; Heinz
Center, 2008
Lettenmaier et al., 2008.
Duplicate
413, 415
281, 282
280, 282
280, 281
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August 2011
Indicator
ID#
283*
284**
285
286
287
288
289
Indicator
Stream Habitat Quality -
Farmland Streams
(Presence of certain
attributes compared to
undisturbed streams)
Stream habitat quality
(comparison to baseline)
Streamflow (discharge)
Submerged Aquatic
Vegetation (SAV)
Suburban/Rural Land Use
Change
Suspended solids (total, 4
substances)
Temperature (average
annual)
Definition
This indicator describes the habitat quality of farmland streams by
comparing a number of key attributes to those of relatively undisturbed
streams in the same general area. The index would incorporate the
presence of riffles and pools, the size of streambed sediments and the
degree to which larger gravel and cobbles are buried in silt, the presence of
branches, tree trunks, and other large woody pieces, and the stability of
the bank.
This indicator is represented by the Rapid Bioassessment Protocol score,
an index that can be used to assess the condition of underwater and bank
habitats. The Rapid Bioassessment Protocol score is used to assess
habitat conditions based on field observations often variables: epifaunal
substrate/ available cover, embeddedness (for riffles) or pool substrate
characterization (for pools), velocity and depth regimes (for riffles) or
pool variability (for pools), sediment deposition, channel flow status,
channel alteration, frequency of riffles or bends (for riffles) or channel
sinuosity (for pools), bank stability (condition of banks), bank vegetative
protection, and riparian vegetated zone width.*
Discharge data from a national network of stream gages
Rooted aquatic plants that support the health of ecosystems by generating
food and habitat for waterfowl, fish, shellfish, and invertebrates.
This indicator describes the pattern and intensity, or density, of
development, both at the outer edge of suburban development around
cities, and in rural areas that, despite the lack of a large town center, are
growing rapidly toward suburban densities.
Suspended materials include soil particles (clay and silt), algae, plankton,
and other substances. Total suspended solids (TSS) refer to the matter that
is suspended in water.
Mean annual temperature.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
National Assessment
Synthesis Team, 2000a.
USEPA, 2008b.
Heinz Center, 2002; Heinz
Center, 2008
Hayslipetal., 2006.
Gleick and Adams, 2000.
Duplicate
284
283
549
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August 2011
Indicator
ID#
290
291
292
293
294
295
296
297
298
Indicator
Temperature (average
daily)
Temperature -streams
Thermal sensitivity
(changes in extremes)
Tidal wetlands area
Timber Growth and
Harvest- Private (Volume
of timber harvested vs.
grown overtime)
Timber Harvest - By
Region (Volume of timber
overtime)
Timber Harvest - By Use:
Fuelwood (Volume of
timber overtime)
Timber Harvest - By Use:
Logging Residues/Other
(Volume of timber over
time)
Timber Harvest - By Use:
Other Products (Volume
of timber over time)
Definition
N/A
N/A
This indicator is based on the sensitivity to changes in extreme
temperatures. It combines the vulnerability of two sub-indicators: (1) heat
(the average annual number of days with maximum temperatures
exceeding 35 degrees C) and (2) cold (the average annual number of days
with average temperatures below O degrees C).
N/A
This indicator reports the annual amount of new wood grown and the
annual amount of wood harvested on public and private timberlands, by
region.
This indicator reports trends in timber harvest, by regions East and West,
and by primary product category (sawlogs, pulpwood, etc.).
This indicator reports trends in timber harvest, by region and by primary
product category (sawlogs, pulpwood, etc.).
This indicator reports trends in timber harvest, by region and by primary
product category (sawlogs, pulpwood, etc.).
This indicator reports trends in timber harvest, by region and by primary
product category (sawlogs, pulpwood, etc.).
Literature Source
[See Appendix A for full
citation]
Kling et al., 2003 using
data from Wuebbles and
Hayhoe, 2003.
Lettenmaier et al., 2008.
Hurdetal., 1999.
Chesapeake Bay Program,
2008.
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
Page B-42
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August 2011
Indicator
ID#
299
300
301
302
303
304
305
306
307
Indicator
Timber Harvest - By Use:
Pulpwood (Volume of
timber overtime)
Timber Harvest - By Use:
Sawlogs (Volume of
timber overtime)
Timber Harvest - By Use:
Veneer logs (Volume of
timber overtime)
Topography/Elevation
(LIDAR)
Total Impervious Area
(Percent of
urban/suburban areas
having a certain amount
of impervious area)
Trophic state
Trophic state (coastal
waters)
Tropical storm frequency
Unusual marine
mortalities
Definition
This indicator reports trends, by region, in the harvest of pulpwood.
This indicator reports trends, by region, in the harvest of sawlogs.
This indicator reports trends, by region, in the harvest of veneer logs.
This indicator includes: (1) Light Detection and Ranging (LIDAR) mapping,
and (2) shallow water-penetrating LIDAR.
This indicator classifies urban and suburban areas according to their
percentage of impervious surface (e.g., roads, parking lots, driveways,
sidewalks, rooftops, etc.). The indicator uses several thresholds: less than
10% impervious surface in the region, at least 10%, at least 20%, and at
least 30%.
This indicator integrates water clarity, phosphorus concentration, and
chlorophyll a.
This indicator refers to aspects of aquatic systems associated with the
growth of algae, decreasing water transparency, and lowering oxygen
levels in the lower water column that can harm fish and other aquatic life.
Frequency and intensity of tropical storms (current and predicted with
climate change)
Unusual mortality events (UME) are characterized by an abnormal number
of dead animals or by the appearance of dead animals in locations or at
times of the year that are not typical for that species.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Heinz Center, 2002; Heinz
Center, 2008
Coastal States
Organization, 2007.
Heinz Center, 2002; Heinz
Center, 2008
USEPA, 1995.
USEPA, 2008b.
Day et al., 2007.
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
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Final Report
August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
308
Urban and Suburban
Areas (extent/acreage)
This indicator presents the extent/acreage of urban and suburban areas as
a percentage of the total U.S. land area, for the most recent 50-year period
and compared to pre-settlement estimates. It also reports on a key
component of freshwater ecosystems (freshwater wetlands) and will report
on the area of brackish water, a key component of coastal and ocean
ecosystems when data become available.
Heinz Center, 2002; Heinz
Center, 2008
309
Urban and Suburban
Lands - By Region (Area
covered by
urban/suburban lands by
region)
This indicator reports the extent of urban and suburban lands, in acres.
Heinz Center, 2002; Heinz
Center, 2008
310
Urban and Suburban
Lands- By Region (Percent
area covered by
urban/suburban lands by
region)
This indicator reports the extent of urban and suburban lands as a
percentage of all land area in a region.
Heinz Center, 2002; Heinz
Center, 2008
311
Urban and Suburban
Lands - Composition of
Undeveloped Urban and
Suburban Lands (Percent
area of undeveloped lands
by region)
This indicator reports on the extent and composition of undeveloped lands,
such as wetlands, croplands, forest, or grassland and shrubland, contained
within urban and suburban areas.
Heinz Center, 2002; Heinz
Center, 2008
312
Urban Heat Island
(Percent of metropolitan
areas with having certain
differences in air
temperature between
rural and urban areas)
This indicator describes the difference between urban and rural air
temperatures for major U.S. metropolitan areas. Temperatures within
urban areas will be compared to those in less-developed surrounding
areas.
Heinz Center, 2002; Heinz
Center, 2008
313
Vegetative cover -riparian
(area of 3 classes)
This indicator reports the sum of the amount of woody cover provided by
three layers of riparian vegetation: the ground layer, woody shrubs, and
USEPA, 2006b.
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August 2011
Indicator
ID#
314
315
316X
317X
318
319
320
321
Indicator
Water Availability:
Streamflow per Capita
(Vulnerability of Domestic
Water Uses)
Water Clarity
Water Clarity (seed,
remote sensing)
Water clarity (secci,
transmitivity)
Water Clarity Index (real
vs. reference)
Water Quality Index (5
components)
Water use to storage
Water withdrawals
Definition
canopy trees.
A measure estimating per capita water availability based on per capita
average annual streamflow (QS). Method of calculation: 1. QS/pop.
This indicator reports the percentage of lake and reservoir area with low-,
medium-, and high-clarity water. (Ponds are not included because of their
shallow depth.)
This indicator presents correlations between water clarity, as measured by
the Secchi disk, and light in the blue and red bands of the spectrum
reflected from lake water surfaces and measured as "brightness" by
satellite sensors.
Light transmitivity, secci depth.
Water clarity index (WCI) is calculated by dividing observed clarity at 1
meter by a regional reference clarity at 1 meter. This regional reference is
10% for most of the U.S., 5% for areas with naturally high turbidity, and
20% for areas with significant submersed aquatic vegetation (SAV) beds
or active SAV restoration programs. Good: WCI ratio is >2. Fair: WCI ratio
is between 1 and 2. Poor: WCI ratio < 1.
This indicator is based on 5 water quality component indicators (DIN, DIP,
chlorophyll a, water clarity, and dissolved oxygen).
This indicator is the ratio of total annual average surface and groundwater
withdrawals in 1990 (Qw) to total active basin storage (S). Method of
calculation: QW/S.
This indicator reports the total amount of surface water and groundwater
withdrawn for use in the municipal, rural, industrial, thermoelectric, and
Literature Source
[See Appendix A for full
citation]
Hurd et al., 1998.
Heinz Center, 2002; Heinz
Center, 2008
Brezoniketal., 2007.
Hayslipetal., 2006.
USEPA, 2006a.
USEPA, 2006a.
Hurd et al., 1998.
Heinz Center, 2002; Heinz
Center, 2008
Duplicate
316, 317
315, 317
315, 316
447
Page B-45
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Final Report
August 2011
Indicator
ID#
322
323
324
325
326**
327
328
329
Indicator
Waterborne human
disease outbreaks
(events)
Watershed forest cover
(area)
Wetland Extent, Change,
and Sources of Change
Wetland loss
Wetland species at risk
(number of species)
Wetlands, Lakes,
Reservoirs, and Ponds
(extent, acreage)
Ratio of water use to safe
yield
Percent urbanized
Definition
irrigation sectors.
This indicator reports the number of disease outbreaks (i.e., at least two
people getting sick) attributed to drinking water that is untreated or
where treatment has failed to remove disease-causing organisms, or to
swimming or other recreational contact at lakes, streams, and rivers.
N/A
N/A
Rate or total extent of wetland loss relative to original extent of wetland.
Number of wetland and freshwater species at risk, either threatened or
endangered. *
This indicator reports the area of wetlands and lakes, reservoirs, and ponds
and the length of small, medium, and large streams and rivers.
Safe yield provides an estimate of the maximum quantity of water that
can be withdrawn during an extended dry period without depleting the
source beyond its ability to be replenished in naturally 'wet years.' It is
measured as the water balance of inflows, usable storage, and
evapotranspiration determined between Jun-Oct in a median year, using
an 80% likelihood of water level recovery by the following spring. This
indicator reports the ratio of water use to safe yield.
N/A
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2002; Heinz
Center, 2008
Chesapeake Bay Program,
2008.
USEPA, 2008b.
MEA, 2005b.
Hurd et al., 1999.
Heinz Center, 2002; Heinz
Center, 2008
Schmitt et al., 2008.
Schmitt et al., 2008.
Duplicate
Page B-46
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Final Report
August 2011
Indicator
ID#
330
331
332
333
334
335
336
337X
338
339
Indicator
Summer retail sales
Seasonal housing
Peak drinking water use
nitrate in drinking water
select pesticides in
drinking water
Total Trihalomethanes
(TTHMs) in drinking water
Cyanotoxins (the 3 on the
Contaminant Candidate
List SorCCLS) in drinking
water
Dissolved oxygen
concentration
Lake stratification timing
Lake volume
Definition
N/A
N/A
Peak drinking water use at individual public water systems.
Percent of public water systems (PWSs) nationally with at least one
compliance monitoring sample detection of nitrate at a concentration
greater than one-half the nitrate drinking water standard.
Percent of PWSs nationally with at least one detection of alachlor, atrazine,
carbofuran, endothall, and/or simazine at a concentration greater than
one-half the respective drinking water standard.
Percent of PWSs nationally with at least one compliance monitoring sample
detection of Total Trihalomethanes (TTHMs, disinfection byproducts)
greater than one-half the TTHM drinking water standard. TTHMs include
chloroform, bromodichloromethane, dibromochloromethane, and
bromoform.
Percent of PWSs sites with at least one detection of one of the three
cyanotoxins.
N/A
The occurrence and timing of thermal stratification in lakes.
N/A
Literature Source
[See Appendix A for full
citation]
Schmittetal., 2008.
Schmittetal., 2008.
Schmittetal., 2008.
USEPA, 2008d.
USEPA, 2008d.
USEPA, 2008d.
USEPA, 2008d.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Duplicate
71, 72,
73, 74,
75, 131
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August 2011
Indicator
ID#
340
341
342
343
344
345
346
347
348**
349
350
351**
352**
353
Indicator
Ice cover duration
Metabolic rate
Epilimnion volume
Hypolimnion temperature
Permafrost and glacial
extent
Solute concentration
Surface water extent
Dominant flowpath
Erosion rate
Water clarity
Water residence time
Instream use/total
streamflow
Total use/total
streamflow
Flow regime
Definition
N/A
Rates of productivity, decomposition, and chemical reactions in surface
waters
Volume of the upper mixed layer in stratified lakes.
Temperature of the bottom water in stratified lakes.
N/A
Concentrations of dissolved materials in water
Areal extent of surface waters
Relative importance of various hydrological pathways (i.e., surface runoff,
shallow subsurface flow, deep groundwater flow)
Rate of soil erosion from watershed lands
N/A
Rate of water inflow to and outflow from a water body
Ratio of instream use to total streamflow. Method of calculation:
(Instream flow requirements to meet the needs of fish and
wildlife)/(1975 streamflow + 1975 consumption - 1975 groundwater
overdraft)
Method of calculation: (Instream flow requirements to meet the needs of
fish and wildlife + 1975 consumption)/(1975 streamflow + 1975
consumption - 1975 groundwater overdraft)
This indicator is a composite of several measurable streamflow variables,
including magnitude, frequency, duration, seasonal timing, and rate of
change of flows
Literature Source
[See Appendix A for full
citation]
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Murdoch et al., 2000.
Meyer etal., 1999.
Meyer etal., 1999.
Meyer etal., 1999.
Duplicate
153
76
Page B-48
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Aquatic Ecosystems, Water Quality, and Global Change:
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Final Report
August 2011
Indicator
ID#
354
355
356
357
358
359
360X
361
362
363
Indicator
Broad geographic region
Cyanobacteria abundance
Enterococci abundance
Peatland extent
Isolated wetland extent
Water quality criteria
Vulnerability to floods
Snowmelt reliance
Pesticide concentration
Predicted pesticide
concentration
Definition
Regions are Arctic and subarctic North America, Laurentian Great Lakes and
Precambrian Shield, Rocky Mountains, Mid Atlantic and New England,
Southeastern US, Pacific Coast and Western Great Basin, Great Plains, Arid
Southwest
Relative abundance of cyanobacteria in phytoplankton as characterized by
photopigments. High performance liquid chromatography (HPLC), coupled
to photodiode array spectrophotometry (PDAS).
Number of colony forming units of enterococcal bacteria per volume of
water.
Extent or density of peatland.
Extent or density of isolated wetlands (alpine wetlands, prairie potholes).
Water quality criteria are developed for specific chemicals to evaluate
whether a water body is supporting aquatic life uses. Such criteria describe
the minimum level of water quality necessary to allow a use to occur. EPA
has developed water quality criteria for 157 pollutants to protect a variety
of water body uses.
Percent of population that lives in floodplains.
Dependence of water uses on seasonal melting of snow and/or glaciers.
Pesticide concentration in surface water, groundwater, bed sediments, or
fish tissue relative to a health benchmark (human or aquatic life). Acute
and chronic benchmarks have been determined for most commonly used
pesticides.
Predicted pesticide concentration for selected pesticides (atrazine,
dieldrin). The indicator values are predicted by a regression model.
Literature Source
[See Appendix A for full
citation]
Meyer etal., 1999.
Paerl et al., 2003.
Paerl et al., 2003.
Burkett and Kusler, 2000.
Burkett and Kusler, 2000.
USEPA, 2002.
Intergovernmental Panel
on Climate Change, 2007.
Intergovernmental Panel
on Climate Change, 2007.
Gilliom etal., 2006.
Gilliom etal., 2006.
Duplicate
209
Page B-49
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Aquatic Ecosystems, Water Quality, and Global Change:
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Final Report
August 2011
Indicator
ID#
364**
365
366
367**
368
369**
370
Indicator
Pesticide toxicity index
Groundwater contribution
to baseflow
Phosphorus concentration
-streams (total)
Herbicide concentrations
in streams (Percent of
streams with highest
concentration)
Herbicides Use (Weight
per unit of agricultural
land)
Insecticide
concentrations in streams
(Percent of streams with
highest concentration)
Insecticide Use (Weight
per unit of agricultural
land)
Definition
The Pesticide Toxicity Index (PTI) accounts for multiple pesticides in a
sample, including pesticides without established benchmarks for aquatic
life. The PTI combines information on exposure of aquatic biota to
pesticides (measured concentrations of pesticides in stream water) with
toxicity estimates (results from laboratory toxicity studies) to produce a
relative index value for a sample or stream. The PTI value is computed for
each sample of stream water by summing the toxicity quotients for all
pesticides detected in the sample. The toxicity quotient is the measured
concentration of a pesticide divided by its toxicity concentration from
bioassays (such as an LC50 or EC50).
Proportion of baseflow in streams that is contributed by groundwater
discharge
Average annual concentration of total phosphorus (in milligrams per liter).
Average concentrations of herbicides in US streams. *
Herbicide use in pounds, per acre of agricultural land.
Average concentrations of insecticides in US streams. *
Insecticide use in pounds, per acre of agricultural land.
Literature Source
[See Appendix A for full
citation]
Gilliom et al., 2006.
Hayashi and Rosenberry,
2002.
USGS, 1999.
USGS, 1999.
USGS, 1999.
USGS, 1999.
USGS, 1999.
Duplicate
Page B-50
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Aquatic Ecosystems, Water Quality, and Global Change:
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Final Report
August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
371*
Organochlorines in Bed
Sediment (Percent of
streams with highest
concentration)
Average concentrations of organochlorines in bed sediments.
USGS, 1999.
372
Organochlorines Use
(Weight per unit of
agricultural land)
Organochlorines use in pounds, per acre of agricultural land.
USGS, 1999.
373*
Herbicides in
Groundwater (Percent of
aquifers with highest
concentration)
Average concentrations of herbicides in shallow groundwater and
aquifers. *
USGS, 1999.
374*
Insecticides in
Groundwater (Percent of
aquifers with highest
concentration)
Average concentrations of insecticides in shallow groundwater and
aquifers. *
USGS, 1999.
375
Occurrence of One or
More VOCs in Aquifers
(Concentration detected
by aquifer)
Detection of VOCs in aquifer samples demonstrates the vulnerability of
many of the Nation's aquifers to VOC contamination.
Zogorski et al., 2006.
376
Occurrence of One or
More VOCs in Aquifers -
by Aquifer Study (Percent
of aquifer studies with
detects)
Percentage of the 98 aquifer studies conducted as part of the Study-Unit
investigations that had detections of one or more VOCs at an assessment
level of 0.2 ug/L
Zogorski et al., 2006.
377
Occurrence of One or
More VOCs in Aquifers -
by Principal Aquifer
(Percent of aquifers with
detects)
Detection frequencies, expressed as a percentage, by principal or other
aquifer, of one or more VOCs at an assessment level of 0.2 ug/L
Zogorski et al., 2006.
Page B-51
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Aquatic Ecosystems, Water Quality, and Global Change:
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August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
378
Occurrence of One or
More VOCs in Aquifers -
by Aquifer Lithology
(Percent of detects per
aquifer type)
Detection frequencies, expressed as a percentage, by aquifer lithology
category, of one or more VOCs at an assessment level of 0.2 ng/L
Zogorski et al., 2006.
379
Occurrence of VOC
Groups in Aquifers
(Percent of aquifers with
detects)
Detection frequencies, expressed as a percentage, for various VOC groups
at assessment levels of 0.2 ng/L and 0.02
Zogorski et al., 2006.
380
Occurrence of Individual
VOCs in Aquifers
Detection frequencies, expressed as a percentage, for various VOCs at
assessment levels of 0.2 ng/L and 0.02 ng/L The 15 most frequently
detected VOCs represent most of the use groups and include 7 solvents, 4
THMs, 2 refrigerants, 1 gasoline oxygenate, and 1 gasoline hydrocarbon.
Zogorski et al., 2006.
381
Population Served by
Domestic Wells (Percent
of population relying on
domestic wells for
drinking water)
Domestic wells are privately owned, self-supplied sources for domestic
water use.
Zogorski et al., 2006.
382
Population Served by
Public Wells (Percent of
population relying on
public wells for drinking
water)
Public wells are privately or publicly owned and supply ground water for
PWSs. As defined by the USEPA, PWSs supply drinking water to at least 15
service connections or regularly serve at least 25 individuals daily at least
60 days a year.
Zogorski et al., 2006.
383
Pesticide detection
frequency
Atrazine, DEA, metolachlor, prometon, and simazine were "were detected
with sufficient frequency to perform statistical analysis of temporal
changes in both detection frequency and concentration."
Bexfield, 2008.
384
Playas (shallow ephemeral
lakes)
The existence of lakes in these valleys depends on local (watershed)
precipitation, playa surface evaporation and, except in endorheic basins
(especially prevalent in the Great Basin and north-central Mexico), alluvial
hydrological drainage.
Grimm et al., 1997.
Page B-52
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Final Report
August 2011
Indicator
ID#
385
386
387
388
389
390
391
392
393
394
395
Indicator
Ecosystem thermal regime
(latitude, shift in miles)
Migration corridor
restriction
Thermal - Habitat suitable
for trout
Stream baseflow
(summer)
Wetland hydroperiod
Fens (area, abundance)
Salinity intrusion (coastal
wetlands)
Heat-Related Illnesses
Incidence
Populations at Increased
Risk - Older and Younger
Age Groups
Populations at Increased
Risk - Using of Certain
Drugs
Populations at Increased
Risk- Dehydrated
Individuals
Definition
N/A
Dams and reservoirs, deforestation, diversion of water for offstream uses
such as irrigation and urban development.
N/A
Water in stream channel.
Patterns of water depth, and the duration, frequency, and seasonality of
flooding.
Groundwater-dominated wetlands
N/A
N/A
Heat-related mortality in individuals over 65 years of age, and in babies and
infants.
Certain drugs (such as stimulants, beta-blockers, anticholinergics, digitalis,
and barbiturates) interfere with the body's ability to cope with high
temperatures.
Populations of individuals that have a tendency to consume fewer non-
alcoholic fluids.
Literature Source
[See Appendix A for full
citation]
Poffetal., 2002.
Poffetal., 2002.
Poffetal., 2002.
Poffetal., 2002.
Poffetal., 2002.
Poffetal., 2002.
Poff et al., 2002.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi etal., 2007.
Ebi et al., 2007.
Duplicate
Page B-53
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Aquatic Ecosystems, Water Quality, and Global Change:
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Final Report
August 2011
Indicator
ID#
396
397
398
399
400
401
402
403
404
405X
Indicator
Populations at Increased
Risk - Individuals with Low
Fitness
Populations at Increased
Risk - Individuals Who
Engage in Excessive
Exertion
Populations at Increased
Risk - Individuals Who Are
Overweight
Populations at Increased
Risk- Reduced
Adjustment to High
Outdoor Temperatures
Populations at Increased
Risk- Urban Populations
Populations at Increased
Risk - Individuals with
Lower Socio-economic
Status
Populations at Increased
Risk- Individuals Living
Alone
Temperature Increases
Precipitation Increases
Sea level rise
Definition
N/A
Outdoor workers and those who maintain a vigorous exercise regimen
during a heat wave are particularly at risk.
N/A
N/A
N/A
N/A
N/A
Impact of increased temperature on hypoxia in wetlands.
Impact of increased precipitation on hypoxia in wetlands.
Impact of increased sea level rise on hypoxia in wetlands.
Literature Source
[See Appendix A for full
citation]
Ebi et al., 2007.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi etal., 2007.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi etal., 2007.
Ebi etal., 2007.
Ebi etal., 2007.
Duplicate
228, 229,
241, 412
Page B-54
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August 2011
Indicator
ID#
406
407
408
409
410
411
412X
413X
414
415X
416
Indicator
Strength of Summer Wind
Hurricane Intensity
Hurricane Frequency
Frequency and Magnitude
of El Nino/Southern
Oscillation (ENSO)
Peak Flows
Intensity of Rainfall
Sea level rise
Streamflow Variability
(annual)
Days of 99th Percentile
Flow
Streamflow Variability
(annual min, med, max
value)
Seasonal Streamflow
Timing (Month of annual
min, med, max)
Definition
Impact of stronger summer winds on hypoxia in wetlands.
N/A
N/A
N/A
N/A
N/A
N/A
Annual minimum, median, and maximum daily Streamflow values for 1941-
1999 at 400 sites in the coterminous U.S. considered to have natural
Streamflow.
Number of days per year that meet or exceed 99th percentile Streamflow
values for 1941-1999 at 400 sites in the coterminous U.S. considered to
have natural Streamflow. 99th percentile flow value calculated for each site
from HCDN Streamflow data. 99th percentile value = 0.99 (highest flow
value for site during period of record).
Annual minimum, median, and maximum daily Streamflow values from
1940-1999 at 435 sites in the coterminous U.S. and southeastern Alaska
considered to have natural Streamflow.
Month of the annual minimum, median, and maximum streamflows for
1940-1999 at 435 sites in the coterminous U.S. and southeastern Alaska
considered to have natural Streamflow.
Literature Source
[See Appendix A for full
citation]
Ebi et al., 2007.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi et al., 2007.
Ebi etal., 2007.
Ebi etal., 2007.
Ebi et al., 2007.
McCabe and Wolock,
2002.
McCabe and Wolock,
2002.
Lins and Slack, 2005.
Lins and Slack, 2005.
Duplicate
228, 229,
241, 405
279, 415
279, 413
421
Page B-55
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August 2011
Indicator
ID#
417
418
419
420
42 lx
422
423
424
425
426
427
428
Indicator
Center of Volume Date
Peak Flow Date
Ratio of Seasonal to
Annual Flow
Magnitude of Monthly
Flows
Timing of Seasonal Peak
Flows
Median Total Seasonal
Snowfall
Lake Ice-Out Date
Monthly Average
Streamflow
Winter Average
Streamflow
Winter Average
Temperature
Winter Average
Precipitation
Surface Air Temperature
Definition
Date by which half of the total water volume for a time period has gone by
a river gauging station. Time periods were winter/spring (Jan 1 to May 31)
and fall (Oct 1 to Dec 31). Analyzed for 27 rural, unregulated river gauging
stations in New England with an average of 68 years of record.
Winter/spring center of volume date = WSCV.
Date of the highest daily mean flow within a season: winter/spring (Jan 1 to
May 31) and fall (Oct 1 to Dec 31). Analyzed at 27 rural, unregulated river
gauging stations in New England with an average of 68 years of record.
N/A
N/A
N/A
The median accumulation of snow (usually in units of height) over a
particular snow season.
Lake ice-out dates are the dates of ice break-up, i.e. the annual dates in
spring when winter ice cover leaves a lake.
Daily Streamflow data averaged into monthly time series data for 37 sites in
New England considered to have natural Streamflow.
Daily Streamflow data averaged into winter seasonal Streamflow data for
37 sites in New England considered to have natural Streamflow.
Monthly divisional temperature averaged into "winter average
temperature" values for the period 1895-1999.
Monthly divisional precipitation averaged into "winter average
precipitation" values for the period 1895-1999.
N/A
Literature Source
[See Appendix A for full
citation]
Hodgkinsetal., 2003.
Hodgkinsetal., 2003.
Hodgkins et al., 2003.
Hodgkins et al., 2003.
Hodgkins et al., 2003.
Hodgkinsetal., 2003.
Hodgkinsetal., 2003.
Bradbury et al., 2002.
Bradbury et al., 2002.
Bradbury et al., 2002.
Bradbury et al., 2002.
Bradbury et al., 2002.
Duplicate
416
134, 275
Page B-56
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Final Report
August 2011
Indicator
ID#
429
430
431
432
433
434
435
436
437**
438
439
Indicator
Storm-Track Patterns
Snowfall Variability
Tree-Ring Chronology
Diurnal Range of Surface
Air Temperature (DTR)
Daily Mean Cloud Cover
Top 5-cm Soil Moisture
Content
Surface Specific Humidity
Dew Point
Precipitation Elasticity of
Streamflow
Snowpack Depth
Concentration of
Particulate and Colored
Dissolved Organic
Material (CDOM)
Definition
Spatial variations in the paths followed by centers of low atmospheric
pressure.
N/A
N/A
The difference between the daytime maximum temperature and the
nighttime minimum temperature for a 15 km by 15 km site on the Konza
Prairie near Manhattan, KS, in 1987-1989.
The daily average amount of the sky covered by clouds (reported in eighths
or oktas of sky covered) for a 15 km by 15 km site on the Konza Prairie near
Manhattan, KS, in 1987-1989.
The moisture content of the top 5 cm of soil for a 15 km by 15 km site on
the Konza Prairie near Manhattan, KS, in 1987-1989.
The ratio of the mass of water vapor within a given mass of air near the
Earth's surface (reported in g of water vapor per kg of air at specified
temperature) for a 15 km by 15 km site on the Konza Prairie near
Manhattan, KS, in 1987-1989.
The temperature to which a given unit of air must be cooled (at constant
pressure) in order for vapor to condense into water.
The proportional change in streamflow (Q) divided by the proportional
change in precipitation (P) for 1,291 gauged watersheds across the
continental US.
The regional average snowpack depth, in inches.
Concentration of pigmented compounds, dissolved in a waterbody that
derives from organic material.
Literature Source
[See Appendix A for full
citation]
Bradbury et al., 2002.
Bradbury et al., 2002.
Bradbury et al., 2002.
Daietal., 1999.
Daietal., 1999.
Daietal., 1999.
Daietal., 1999.
Roderick and Farquhar,
2002.
Sankarasubramanian et
al., 2001.
Sankarasubramanian et
al., 2001.
Warwick and Pienitz,
2006.
Duplicate
266, 440
PageB-57
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Final Report
August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
440
Snowpack Depth (2)
Regional snowpack for the Western United States.
Barnett, et al., 2005.
266, 438
441
Aerosols
The amount of aerosol pollution (particulate matter) in a unit of air.
Barnett, et al., 2005.
442
Water withdrawals: Public
Supply (Volume of water
used per day)
Volume of water consumed in the public supply per day. This is different
from water withdrawals, as one of the largest uses of water is for cooling of
thermoelectric power plants, and much of that water is returned to the
streams from which it is withdrawn (use of water for hydroelectric power
generation, virtually none of which is consumptively used, is not included in
this category). On the other hand, a much higher fraction of the water
withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
443
Water withdrawals: Rural
Domestic and Livestock
(Volume of water used
per day)
Volume of water consumed by rural domestic and livestock per day. This is
different from water withdrawals, as one of the largest uses of water is for
cooling of thermoelectric power plants, and much of that water is returned
to the streams from which it is withdrawn (use of water for hydroelectric
power generation, virtually none of which is consumptively used, is not
included in this category). On the other hand, a much higher fraction of the
water withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
444
Water withdrawals:
Irrigation (Volume of
water used per day)
Volume of water consumed for irrigation purposes per day. This is different
from water withdrawals, as one of the largest uses of water is for cooling of
thermoelectric power plants, and much of that water is returned to the
streams from which it is withdrawn (use of water for hydroelectric power
generation, virtually none of which is consumptively used, is not included in
this category). On the other hand, a much higher fraction of the water
withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
150
445
Water withdrawals:
Thermoelectric Power
(Volume of water used
per day)
Volume of water consumed for thermoelectric power generation per day.
This is different from water withdrawals, as one of the largest uses of water
is for cooling of thermoelectric power plants, and much of that water is
returned to the streams from which it is withdrawn (use of water for
hydroelectric power generation, virtually none of which is consumptively
used, is not included in this category). On the other hand, a much higher
fraction of the water withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
Page B-58
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
446
Water withdrawals: Other
Industrial Uses (Volume of
water used per day)
Volume of water consumed for other industrial uses per day. This is
different from water withdrawals, as one of the largest uses of water is for
cooling of thermoelectric power plants, and much of that water is returned
to the streams from which it is withdrawn (use of water for hydroelectric
power generation, virtually none of which is consumptively used, is not
included in this category). On the other hand, a much higher fraction of the
water withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
447
Water withdrawals: Total
(Volume of water used
per day)
The data compiled by the USGS are somewhat limited in that they are for
water withdrawals, rather than consumptive use. The distinction is
important, as one of the largest uses of water is for cooling of
thermoelectric power plants, and much of that water is returned to the
streams from which it is withdrawn (use of water for hydroelectric power
generation, virtually none of which is consumptively used, is not included in
this category). On the other hand, a much higher fraction of the water
withdrawn for irrigation is consumptively used.
Lettenmaier et al., 2008.
321
448
Extent of reservoir storage
(Volume of water stored
per area)
N/A
Lettenmaier et al., 2008.
449*
Ratio of reservoir storage
to mean annual flow
(Volume of water stored
per unit flow)
A small storage-to-runoff ratio usually indicates that the reservoir is
primarily used to shape within-year variations in runoff, and a large
storage-to-runoff value usually indicates that the reservoir is primarily
used to smooth interannual variations in runoff. *
Lettenmaier et al., 2008.
450
Precipitation (variability)
Variability of mean annual precipitation, expressed as the Coefficient of
Variation, C sub v (the standard deviation divided by the mean), for the
continental U.S.
Lettenmaier et al., 2008.
451
Runoff Ratio
Annual mean runoff divided by annual mean precipitation, for the
continental U.S. and Alaska.
Lettenmaier et al., 2008.
452
Snow to runoff ratio
Ratio of maximum mean snow accumulation to mean annual runoff, for the
continental U.S. and Alaska.
Lettenmaier et al., 2008.
Page B-59
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
Indicator
ID#
453**
454
455
456
457
458
459
460**
Indicator
Runoff (variability)
Runoff (variability -
persistence)
Variability of April-Sept.
streamflow
Incidence of "surplus"
flow days
Length of snow season
Warm Season Surface
Water Supply
Phosphorus and nitrogen
concentration
Macroinvertebrate Index
of Biotic Condition
Definition
Variability of annual runoff, expressed as the Coefficient of Variation, C
sub v (the standard deviation divided by the mean), for the continental
U.S. and Alaska.
Variability of annual runoff, expressed as the "lag one" correlation
coefficient, for the continental U.S. This correlation coefficient reflects the
correlation between two values of the same variable (i.e. runoff) at
different points in time.
Variability of April-Sept, streamflow at 141 unregulated sites across the
Western U.S.
Number of days with flows above a station-dependent surplus threshold,
for 42 HCDN sites in the central and southern U.S.
Length of the snow season in the Ohio Valley over the second half of the
20th century.
Defined as precipitation minus potential evapotranspiration, for several
sites on the Arctic coastal plain over the past 50 years.
Concentrations of total P and N from 1975 to 1994 at 250 river sites with
drainages greater than 1000 square kilometers.
Total index score is the sum of scores for a variety of individual measures,
also called indicators or metrics. The metrics used to develop the Macro-
invertebrate Index for the WSA covered six different characteristics of
macroinvertebrate assemblages that are commonly used to evaluate
biological condition: taxonomic richness, taxonomic composition,
taxonomic diversity, feeding groups, habits, and pollution tolerance. Each
metric was scored and then combined to create an overall
Macroinvertebrate Index for each region, with values ranging from 0 to
100.*
Literature Source
[See Appendix A for full
citation]
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
Lettenmaier et al., 2008.
USEPA, 2006b.
Duplicate
186
Page B-60
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
461**
462
463X
464
465
466
467
468
Indicator
Macroinvertebrate
Observed/Expected (O/E)
Ratio of Taxa Loss
Coastal Benthic
Communities
Alien or Invasive Plant
Species
Native Plant Species &
Genera
Plant Species Diversity
Percent Similarity of Plant
Species Composition to
Reference Standard
Threatened &
Endangered Plant Species
Graminoid Taxa
Definition
The Macroinvertebrate O/E Ratio of Taxa Loss (henceforth referred to as
O/E Taxa Loss) measures a specific aspect of biological health: taxa that
have been lost at a site. The taxa expected (E) at individual sites are
predicted from a model developed from data collected at least-disturbed
reference sites; thus, the model allows a precise matching of sampled
taxa with those that should occur under specific, natural environmental
conditions. By comparing the list of taxa observed (O) at a site with those
expected to occur, the proportion of expected taxa that have been lost
can be quantified as the ratio of O/E.
This indicator is based on a multi-metric benthic communities index that
reflects overall species diversity in estuarine areas throughout the
contiguous United States (adjusted for salinity, if necessary) and, for
some regions, the presence of pollution-tolerant and pollution-sensitive
species. The benthic community condition at each sample site is given a
high score if the index exceeds a particular threshold (e.g., has high
diversity or populations of many pollution-sensitive species), a low score
if it falls below the threshold conditions, and a moderate score if it falls
within the threshold range.
N/A
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2006b.
USEPA, 2008b.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
569, 32
143
22
Page B-61
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
469
470
471
472
473
474
475
476
477
478
479
Indicator
Dominant Plant Taxa
Dicot and Monocot
Species
Woody Species
Seedless Vascular Plants
(Ferns & Fern Allies)
Nonvascular
Plants/Bryophytes
Overall Floristic Quality -
Floristic Quality
Assessment Indices (FQAI)
and Coefficients of
Conservatism (CC)
Vegetation Indices of
Biotic Integrity (IBI)
Other Indices of
Vegetation Condition
PFG Composition -
Nutrient Functional
Groups
PFG Composition -
Sediment Functional
Groups
PFG Composition - Growth
Habit/Canopy
Architecture
Definition
N/A
N/A
N/A
N/A
N/A
FQAI use species-specific CCs that reflect the tendency of a species to occur
in pristine vs. disturbed habitats. Intact native plant communities in
relatively undisturbed sites will have high FQAI scores.
IBI use relationships of plant species, plant communities, plant guilds,
vegetation structure, etc. to anthropogenic disturbance and stress to
describe wetland condition. Typically, the highest IBI values represent
reference standards or least-disturbed ecological conditions.
N/A
Composition of plant functional groups (PFG) based on nutrient properties.
Composition of PFG based on sediment properties.
Composition of PFG based on growth habit (tap-rooted, stoloniferous,
rhizomatous, matrix interstitial, etc.).
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
Page B-62
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
480
481
482
483
484
485
486
487
488
489
490
491
Indicator
PFG Composition -
Sensitive Species
PFG Composition -
Tolerant Species
PFG Composition -
Hydrophyte Status
PFG Composition - Life
Span
PFG Composition - Aquatic
Plant Guilds
PFG Composition -
Pioneer Species or
Opportunistic Native or
Alien Species
PFG Composition - Light
Requirements/Shade
Tolerance
Community Type
Patchiness (Interspersion)
of Vegetation
Distribution of Plant
Communities
Zonation or Vegetation
Distribution Typical of
Wetland Class
Total Absolute Cover
Definition
Composition of PFG based on species with high coefficients of
conservatism.
Composition of PFG based on species with low coefficients of conservatism.
Composition of PFG based on hydrophyte status.
Composition of PFG based on life span (annual, perennial, .etc.).
Composition of PFG based on aquatic plants.
Composition of PFG based on pioneer status or opportunism.
Composition of PFG based on light requirements or shade tolerance.
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
US EPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
Page B-63
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
492
493
494
495
496
497
498
499
500
501
502
503
504
Indicator
Distribution and Cover of
Growth Forms
Vertical Strata Height and
Volume
Local Microtopography
Generated By Structure of
Non-woody Plants
Leaf Area Index
Shrub or Small Tree Stem
Density
Evidence or Quantity of
Regeneration of Key Plant
Species
Litter Cover
Below Ground Biomass
Wrack Extent/Cover
Allochthonous Inputs
Depth of Submerged Plant
Cover
Distribution of Woody
Debris
Percent Leaf and Root
Tissue Carbon/Nitrogen
Definition
Floating/submerged plants, emergent/terrestrial herbs, shrubs, and trees.
Herbaceous ground layer, shrub layer(s), tree canopy layer(s).
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
Page B-64
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
Indicator
Root and Leaf Tissue
Carbon/Phosphorus
Photosynthesis/Respiratio
n (P/R) Ratio
Plant
Health/Stress/Herbivory
Stable Nitrogen Isotopes
in Leaf Tissue
Genetic Diversity
Palynology
Paleoecology
USDA-NRCSHydricSoil
Field Indicators
Soil Profile Description
Soil Bulk Density
Thickness of Organic Soil
Layers
Soil Texture
SoilpH
Major Plant Nutrients in
Soil
Soil Organic Matter
Soil Electrical Conductivity
Soil Nitrogen
Definition
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Presence of hydric soil indicators.
N/A
The mass of soil particles divided by the total volume they occupy.
N/A
Percent sand, silt, and clay particles.
N/A
Presence of P, K, Ca, Mg.
Percent organic matter or carbon.
A measure of how well soil units conduct an electric current.
Presence of all N in soil.
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
Page B-65
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
522
523
524
525
526
527
528
529
530
531
532X
533
534
535
536
537
538
539
Indicator
Soil Micronutrients
Soil Enzyme Analysis
Soil Sulfur
GeomorphicSoil
Disturbance
Soil Surface Disturbance
Soil Channels
Sedimentation Rate
Bare Soil
Presence of Plow Layer
Soil Subsidence
Paleoecology
Depth Measurements
Water Sources
Hydrogeomorphic Unit
Vegetation Hydrologic
Guild
Landscape Characteristics
Water Chemistry
Season of Flooding
Definition
Presence of Cu, Mn, Zn, Fe.
N/A
Presence of S.
Presence of, e.g., aggregation/degradation.
N/A
Presence of incisions, channels, ditches, etc.
Rate of accretion or erosion.
Presence of bare soil features.
N/A
N/A
N/A
Surface and groundwater depth (depth of standing water, depth of water
in soil excavation pit, etc.).
N/A
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
567
Page B-66
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
540
541
542
543
544
545
546
547
548
549X
550
551
552
553
554
555
556
Indicator
Hydrologic
Complexity/Microhabitats
Water Velocity
Stream Bedforms
Hydrologic Regimes
COE Primary Indicators of
Inundation
COE Secondary Indicators
of Inundation
Extent of Inundation
Tidal Range
Spatial Pattern of Flooding
Stream Discharge
Direct Observation of
Streams
Stream Gauge Data
Ditch Spacing & Depth
Dams or Weirs
Levees
Drain Spacing and Depth
Irrigation
Definition
N/A
N/A
N/A
N/A
N/A
N/A
Percent of assessment area inundated, as well as spatial pattern and
average depth of inundation.
N/A
N/A
N/A
Information from landowner interviews.
N/A
N/A
Presence of dams or weirs.
Presence of levees.
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
285
Page B-67
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
557
558
559
560
561
562
563
564
565
566
567
568
569X
570
571
572
Indicator
Sediment Load
Snag Density
Fetch
Bird Species Diversity
Sensitive/Tolerant Bird
Species
Alien Bird Species
Dominant Bird Species
Bird Guild Composition -
Foraging
Bird Guild Composition -
Dietary
Bird Guild Composition -
Nesting Strategy
Bird Abundance
Bird Habitat Evaluation
Macroinvertebrate
Species Diversity
Sensitive/Tolerant
Macroinvertebrate
Species
Alien Macroinvertebrate
Species
Dominant
Definition
N/A
Snag density is a sign of impoundment.
Length of water surface over which wind blows in generating waves.
N/A
N/A
N/A
N/A
Composition of bird guild based on foraging technique.
Composition of bird guild based on dietary habits (omnivores, granivores,
insectivores, etc.).
Composition of bird guild based on nesting strategy (platform, ground,
cavity, etc.).
Number, status, and types of nests.
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
532
461
Page B-68
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
573
574
575
576
577
578
579X
580
581
582
583
584
585
586
587
588
Indicator
Macroinvertebrate
Species
Macroinvertebrate Guild
Compositions
Macroinvertebrate
Habitat Evaluation
Fish Species Diversity
Sensitive/Tolerant Fish
Species
Alien Fish Species
Dominant Fish Species
Fish Tissue Contaminants
Fish Guilds
Fish Habitat Evaluation
Fish Deformities
Algae Species Diversity
Sensitive/Tolerant Algae
Species
Alien Algae Species
Dominant Algae Species
Algal Productivity
Algae Habitat Evaluation
Definition
N/A
N/A
N/A
N/A
N/A
N/A
Presence of toxicants in fish tissue.
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Measure of algal chlorophyll and biovolume.
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
48, 58, 99
Page B-69
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603X
Indicator
Hydrologic Modification
Vegetative Alteration
Buffer Alteration/Buffer
Characteristics
Onsite Human
Disturbance
Invasive Species
Sedimentation
Landscape
Composition/Land
use/Land cover
Substrate Alterations
Local Surface Disturbance
Point Source Stormwater
Input
Eutrophication and
Nutrient Enrichment
Water Quality
Trash, Dredge, and Fill
Shoreline
Hardening/Barriers to
Landward Migration
Salinity and/or
Definition
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
240, 84
Page B-70
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
604
605
606
607
608X
609
610
611
612
613
614
615
616
Indicator
Conductivity
Acidity
Alteration of Natural
Disturbance Regime
Toxic Contaminants
(Pharmaceuticals)
Habitat Modification
Recreational Use
Turbidity
Pipelines, Wells, Oil Rigs,
Sewer Lines
Contaminants
Thermal Stress
Alteration of Natural
Turbidity
Pathogens
Mercury Body Burdens in
Sentinel Species
Thermal Alterations
Definition
Change in pH.
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Literature Source
[See Appendix A for full
citation]
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
USEPA, 2008a.
Duplicate
621, 151
220
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Indicator
ID#
Indicator
Definition
Literature Source
[See Appendix A for full
citation]
Duplicate
617
Carbon Storage
This indicator reports the overall amount of carbon gained or lost over time
by major ecosystem types; the change in the amount of carbon stored per
unit area of land or water (carbon density), by major ecosystem type; and
the recent trends in the concentrations of major carbon-containing gases -
carbon dioxide and methane - in the atmosphere compared to average
preiundustrial concentrations.
Heinz Center, 2008.
35,36
618
Established Non-native
Species
This indicator reports for plants, animals, and plant and animal pathogens
across all ecosystem types: (a) the number of new non-native species that
become established over time, by decade; (b) the area with different
numbers of established non-native species; (c) the area with different
proportions of established non-native species, as a percentage of total
species.
Heinz Center, 2008.
144
619
Pattern in Coastal Area
This indicator describes the intermingling of "natural" and "non-natural"
landscape (or seascape) features in coastal areas. The interplay between
various types of coastal habitats (such as wetlands and open waters), as
well as between these habitat types and human development in the coastal
zone (such as built structures and dredged areas) provide a general
description of the structural pattern of coastal areas. The structural pattern
of these areas can be linked to how well they function ecologically and to
the amount and type of ecosystem services humans receive from them.
Heinz Center, 2008.
620
Instream Connectivity
This indicator reports on the proportion of watersheds with different
levels of instream connectivity measured as the distance downstream
from the "pour point" (where the streams leaves the watershed) to the
nearest dam or diversion, such as a pumping station. This indicator also
reports on the proportion of watersheds that contain streams with
unobstructed flow to their natural endpoint, typically the ocean or a large
lake. This indicator focueses on the loss of connectivity over and above
any natural discontinuities in aquatic systems.
Heinz Center, 2008.
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Indicator
ID#
621*
622
623**
Indicator
Freshwater Acidity
Total withdrawal
information by source &
type of use
Water Availability: Net
Streamflow per Capita
(Vulnerability of
Domestic Water Uses)
Definition
This indicator reports: (a) the amount of nitrogen and sulfate deposited
from the atmosphere to watersheds each year; (b) the percentage of
stream miles and area of lakes and ponds with different levels of acid-
neutralizing capacity, a measure of sensitivity of acidification.
N/A
A measure estimating per capita water availability using net withdrawals
(QW) from streamflow. Method of calculation: (QS-QW)/pop.
Literature Source
[See Appendix A for full
citation]
Heinz Center, 2008.
Miller, personal
communication
Hurd et al., 1998
Duplicate
604, 151
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The following appendix describes the data sources obtained for 32 of the 53 vulnerability
indicators. These data sources were used, when possible, to create one or more maps for each
indicator. Supporting information for each data source include: the names of the data sets used;
how to obtain the data; website, when available; spatial and temporal resolution; coverage of
data set (i.e. national, state, or local); type of data source; format of data relevant to mapping
(e.g., ArcGIS, Excel, Access, etc.); and metadata (e.g., definitions of variables in data set,
method of data collection, etc.). The Additional Data Characteristics sections for each indicator
provide additional detail not captured in the preceding sections.
In addition, this appendix contains technical notes for the 32 vulnerability indicators for which
data sources were readily available. The issues presented below may affect the interpretation of
an indicator map (if the indicator was mappable) or may provide guidance for future mapping
efforts (if the indicator was non-mappable or had an incomplete map).
One indicator (marked with *) had an incomplete map. Five indicators (marked with **) were
not mappable given project resources and technical difficulties encountered, as noted in the main
report and in the technical notes in this appendix. The remaining 25 indicators were mapped. The
mapping methodology for the 25 mapped indicators is presented in Appendix D, and maps for
these indicators are presented in Appendix E (displayed using 4-digit hydrologic units) and
Appendix F (displayed using ecoregions).
#1 Acid Neutralizing Capacity (ANC)
Literature Source (see Appendix A for full citation):
USEPA, 2006b.
Data Sets Used:
USEPA - Wadeable Streams Assessment (WSA): Water Chemistry Data.
How To Obtain Data:
Download online
URL to Data (if any):
http://www.epa.gov/owow/streamsurvey/web_data.html
Spatial Resolution:
Small streams
Temporal Resolution (period and frequency of collection):
2004-2005; every 5 years (first year of round of data collection was 2004-2005)
Extent/Coverage of Data Set:
National
Type of Data Source:
Survey
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Format of Data:
Comma separated
Metadata:
Definitions and data descriptions as . txt files.
USEPA. 2008. Wadeable Streams Assessment - Definitions of Variables. Available at:
http://www.epa.gov/owow/streamsurvey/web_data.html. Accessed July 21, 2009.
Additional Data Characteristics:
The WSA Water Chemistry data set contains water chemistry data for small streams, including
pH data. Data files are associated with companion text files (using EPA's WSA Definitions of
Variables in metadata) that list data set labels and give individual descriptions for each variable.
The original literature source, EPA's 2006 WSA report (USEPA, 2006b; see Appendix A for full
citation), provides an explanation of how wadeable streams were selected for this study and how
data were collected from various sites.
Technical Notes
Non-uniform Spatial Distribution of Data: The ANC sample point data are not distributed
homogeneously, which results in dissimilar amounts of ANC data within a HUC-4
boundary. In cases where there are few sample points within a HUC-4 boundary, individual
sites may have a large influence on the metric (percentage of "at risk" sites) that is calculated
for the reporting unit.
#22 At-Risk Freshwater Plant Communities
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
NatureServe - Explorer (customized dataset)
How To Obtain Data:
Data were obtained from Jason McNees at NatureServe, 1101 Wilson Blvd., 15th Floor
Arlington, VA 22201 via email on July 31, 2009.
URL to Data (if any):
N/A
Spatial Resolution:
State
Temporal Resolution (period and frequency of collection):
2006; not specified
Extent/Coverage of Data Set:
National
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Type of Data Source:
Census
Format of Data:
Excel
Metadata:
Details of the National Vegetation Classification System (NVCS).
Federal Geographic Data Committee. International Classification of Ecological Communities
- Terrestrial Vegetation of the United States. Volume I: The National Vegetation
Classification System: Development, Status, and Applications. Available online at:
http://www.NatureServe.org/library/vol 1 .pdf. Accessed July 21, 2009.
Explanation of Conservation Status Ranks.
NatureServe and Natural Heritage. 2009. Conservation Status Ranks. Available online at:
http://www.NatureServe.org/explorer/ranking.htm. Accessed July 21, 2009.
Explanation of the Terrestrial Ecological Classification System.
NatureServe. 2008. Terrestrial Ecological Classification System. Available online at:
http://www.NatureServe.org/publications/usEcologicalsystems.jsp.
Additional Data Characteristics:
NatureServe data, customized for EPA, were used to inform this indicator. Data include the
percent of plant species at risk of extinction in each state. In each state, species considered to be
at a risk of extinction were ones classified by NatureServe as critically imperiled, imperiled, or
vulnerable (using NatureServe and Natural Heritage's Conservation Status Ranks in metadata).
In addition, plant species were classified into plant community types based on their physiognomy
(using FGDC's NVCS) or based on their landscape settings, biological dynamics, and
environmental features (using NatureServe's Terrestrial Ecological Classification System).
Technical Notes
State-level Data: The map for this indicator is based on data calculated on a state-by-state
basis. Transforming the data from a state-based representation to a HUC-4 representation
requires an assumption that the distribution of at-risk plants is uniform within each state. This
assumption allows for area-weighted percentages to be calculated for HUC-4 units that
intersect more than one state. Although this is an accepted transformation method because
the spatial extent of the HUC-4 units and the state boundaries are identical, the assumption of
uniform distribution may result in errors in the metric calculated for each HUC-4 unit.
#24 At-Risk Native Freshwater Species
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
NatureServe - Explorer (customized dataset).
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How To Obtain Data:
Data were obtained from Jason McNees at NatureServe, 1101 Wilson Blvd., 15th Floor
Arlington, VA 22201 via email on July 31, 2009.
URL to Data (if any):
N/A
Spatial Resolution:
State
Temporal Resolution (period and frequency of collection):
2006; not specified
Extent/Coverage of Data Set:
National
Type of Data Source:
Census
Format of Data:
Excel
Metadata:
Explanation of Conservation Status Ranks.
NatureServe and Natural Heritage. 2009. Conservation Status Ranks. Available online at:
http://www.NatureServe.org/explorer/ranking.htm. Accessed July 21, 2009.
Additional Data Characteristics:
NatureServe data, customized for EPA, were used to inform this indicator. Data include the
percent of all species at risk of extinction in each state. In each state, species considered to be at
a risk of extinction were ones classified by NatureServe as critically imperiled, imperiled, or
vulnerable (using NatureServe and Natural Heritage's Conservation Status Ranks in metadata).
Note: The data set used to inform this indicator was the same as that used for the indicator #326
(Wetland and Freshwater Species at Risk (number of species)).
Technical Notes
State-level Data. The map for this indicator is based on data calculated on a state-by-state
basis. Transforming the data from a state-based representation to a HUC-4 representation
requires an assumption that the percentage of at-risk species is uniform within each state.
This assumption allows for area-weighted percentages to be calculated for HUC-4 units that
intersect more than one state. Although this is an accepted transformation method because
the spatial extent of the HUC-4 units and the state boundaries are identical, the assumption of
uniform distribution may result in errors in the metric calculated for each HUC-4 unit.
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#51 Coastal Vulnerability Index (CVI)
Literature Source (see Appendix A for full citation):
Day etal., 2005.
Data Sets Used:
USGS - A Preliminary Database for the U.S. Atlantic, Pacific and Gulf of Mexico Coasts (U.S.
Geological Survey Digital Data Series - 68)
How To Obtain Data:
Download online
URL to Data (if any):
http://pubs.usgs.gov/dds/dds68/htmldocs/data.htm
Spatial Resolution:
1:2,000,000 shoreline at 3-minute resolution
Temporal Resolution (period and frequency of collection):
Time periods vary for the 3 different regions of United States: Atlantic Coast (2000); Pacific
Coast (2001); and Gulf Coast (2001). Variable frequency (as data on each of the six variables on
which the CVI depends are collected for different time periods and at a different frequencies).
Extent/Coverage of Data Set:
All U.S. coastline.
Type of Data Source:
Database
Format of Data:
ARC/INFO or ASCII
Metadata:
Calculation of the CVI.
Thieler, R.R., Hammar-Klose, E.S., 2001. National Assessment of Coastal Vulnerability to
Sea-Level Rise: Preliminary Results for the U.S. Gulf of Mexico Coast. U.S. Geological
Survey Open-File Report 00-179. Available online at: http://pubs.usgs.gov/of/2000/ofOO-
179/index.html. Accessed July 21, 2009.
Additional Data Characteristics:
The calculation of the value of this indicator is based on six independent variables (described in
Thieler and Hammar-Klose, 2001 in metadata). The original data were housed in the Carbon
Dioxide Information Analysis Center's (CDAIC) Coastal Hazards Database(CHD) for 3
geographic regions - East Coast, West Coast, and Gulf Coast. The attributes in this dataset are
based on A Coastal Hazards Database for the U.S. Gulf Coast (Gornitz, V. and White, T. W.
1992. ORNL/CDIAC-60, NDP-043B. Oak Ridge National Laboratory, Oak Ridge, Tennessee)
updated with data from more recent sources.
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Technical Notes
Spatial Distribution of Data (special case: coastal indicator): The spatial extent of the data
for this indicator is for coastal and nearshore areas only. This is inherently disconnected from
the HUC-4 boundaries, which extend inland and do not consistently include coastal and
nearshore regions. This makes aggregation for the purposes of reporting at the HUC-4 scale
problematic. To address this issue, a special reporting unit was developed for this indicator.
Each unit extends approximately 20 miles inland and includes approximately 150 miles of
coastline.
Local Variation: CVI values vary considerably at local scales. By averaging the CVI values
within the special coastal reporting units, localized vulnerability is masked.
#55 Commercially Important Fish Stocks**
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
NOAA National Marine Fisheries Service (NMFS)
How To Obtain Data:
Request directly from NOAA NMFS Research Centers
URL to Data (if any):
N/A
Spatial Resolution:
Regional (e.g., Pacific Coast, Southeast, Northeast, etc.)
Temporal Resolution (period and frequency of collection):
1981-2005; at least 10 years of annual data per stock
Extent/Coverage of Data Set:
National (coastal)
Type of Data Source:
Census
Format of Data:
Unknown
Metadata:
N/A
Additional Data Characteristics:
The NOAA NMFS data set includes spawning stock biomass and total exploitable stock biomass
data on 109 fish stocks over a 10-year period. These data do not include near-shore stocks (i.e.,
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those in state waters within 3 miles of the shore), many of which are under state management
jurisdiction, and anadromous salmon stocks from the Pacific Northwest. The Heinz Center
calculated stock trends for 109 stocks based on the estimated weight or biomass of the entire
stock using linear regression analysis to establish which stocks were increasing or decreasing by
more than 25%.
Technical Notes
Spatial Distribution of Data (special case: marine indicator): This indicator does not provide
vulnerability information for any land or nearshore areas. This is a marine indicator that only
evaluates fish stocks located at least 3 miles from the coast and is inherently disconnected
from the HUC-4 boundaries which extend inland. No other data source for commercially
important fish stocks in inland waters or nearshore areas was identified.
Low-Re solution Data: The data set for this indicator only provides three data values at a very
coarse spatial scale: NE Atlantic, SE Atlantic, and Pacific. These three data points do not
provide the variation necessary to assess vulnerability.
#95 Fish and Bottom-Dwelling Animals**
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
(a) USEPA - Wadeable Streams Assessment (WSA)
(b) USEPA - Storage and Retrieval System (STORET)
How To Obtain Data:
(a) Download online
(b) Download online (if file size is large, can get via e-mail).
URL to Data (if any):
(a) http://www.epa.gov/owow/streamsurvey/web data.html
(b) http://iaspub.epa.gov/storpubl/DW_home
Spatial Resolution:
(a) N/A
(b) HUC-8 watershed level
Temporal Resolution (period and frequency of collection):
(a) 2004-2005, every 5 years (first year of round of data collection was 2004-2005)
(b) 1900-2009; daily
Extent/Coverage of Data Set:
(a) National
(b) National
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Type of Data Source:
(a) Survey
(b) Database
Format of Data:
(a) Comma separated
(b) Excel
Metadata:
Definitions and data descriptions as txt files.
USEPA. 2008a. Wadeable Streams Assessment - Definitions of Variables. Available at:
http://www.epa.gov/owow/streamsurvey/web_data.html. Accessed July 21, 2009.
Additional Data Characteristics:
To assess the condition offish and bottom-dwelling animals, the Macroinvertebrate Index of
Biotic Condition was calculated (using methods in EPA's 2006 WSA report; USEPA 2006b).
This index is based on multiple metrics, such as: taxa richness, evenness of species across taxa,
the relative abundance of different taxa, the feeding strategy of taxa, the habitat preference of
taxa, and the tolerance of taxa to stressors. Data on these are available in EPA's WSA data set
and in EPA's STORET data set. Sites with index scores 75-95% lower than the reference streams
were identified as 'moderate,' whereas 'degraded' sites were those with index scores lower than
95% of the reference streams.
Technical Notes
Duplicate indicator. Upon further analysis, this indicator was determined to be a duplicate of
the indicator Macroinvertebrate Index of Biotic Condition (#460). Although these indicators
were cited by two distinct literature sources (Heinz Center, 2008 and USEPA, 2006b
respectively), their underlying data source i.e. EPA's Wadeable Streams Assessment is the
same and the same calculation is involved in obtaining the value of the indicator.
#725 Groundwater Reliance
Literature Source (see Appendix A for full citation):
Kurd et al., 1998; Kurd et al.,1999.
Data Sets Used:
United States Geological Survey (USGS) - National Water-Use Dataset.
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/watuse/
Spatial Resolution:
HUC-8 watershed
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Temporal Resolution (period and frequency of collection):
1985-2000; every 5 years
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
ASCII
Metadata:
Description of water use parameters.
USGS. Estimated Use of Water in the United States. Available online at:
http://water.usgs.gov/watuse/. Accessed December 15, 2010.
Additional Data Characteristics:
The USGS National Water-Use Dataset provides data on the total annual withdrawals from
groundwater and surface water and total annual withdrawals from only groundwater for the year
1990.
Technical Notes
There are no technical notes for this indicator.
#165 Meteorological Drought Indices
Literature Source (see Appendix A for full citation):
National Assessment Synthesis Team, 2000a.
Data Sets Used:
NOAA National Climatic Data Center (NCDC) - Divisional Data on the Palmer Drought
Severity Index (PSDI).
How To Obtain Data:
Download online
URL to Data (if any):
http://www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.isp
Spatial Resolution:
344 "climate divisions," with varying number of divisions per state
Temporal Resolution (period and frequency of collection):
1895 to present; monthly
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Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
ASCII
Metadata:
Description of the PSDI.
Karl, T.R., R.W. Knight, D.R. Easterling, and R.G. Quayle. 1996. Indices of climatic change
for the United States. Bulletin of the American Meteorological Society. 77 (2): 279-292.
Data dictionary for PSDI data.
National Oceanic and Atmospheric Administration (NOAA). 2007. Time-bias Corrected
Divisional Temperature-Precipitation-Drought Index. Prepared by the National Climatic Data
Center (NCDC). Available online at: http://www7.ncdc.noaa.gov/CDO/DIV DESC.txt.
Accessed July 21,2009.
Definitions of climate divisions.
National Oceanic and Atmospheric Administration (NOAA). 2009. Definitions of Climate
Divisions. Prepared by the National Climatic Data Center (NCDC). Available online at:
http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll7WWDI~getstate~US. Accessed July 21,
2009.
Additional Data Characteristics:
The NOAA NCDC Divisional Data on the PSDI includes the calculated value of the PSDI for
each of 344 climate divisions in the U.S. The description of the index (from Karl et al., 1996 in
metadata) and other supporting information (from NOAA's data dictionary for PSDI data and
definitions of climate divisions in metadata) are also available.
Technical Notes
There are no technical notes for this indicator.
#190 Number of Dry Periods in Grassland/Shrubland Streams and Rivers
(Percent of streams with dry periods over time) *
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
(a) USGS - Hydro Climatic Data Network (HDCN)
(b) USGS - Stream Gauge Data
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How To Obtain Data:
Data (integrated data set of (a) and (b) above) were obtained from Anne Marsh, Ph. D., Director
of Observation and Understanding Programs, The H. John Heinz III Center for Science,
Economics, and the Environment, 900 17th Street, NW, Suite 700, Washington, D. C. 20006 via
email on April 28, 2009.
URL to Data (if any):
(a) N/A
(b) N/A
Spatial Resolution:
(a) HUC-8 watershed level
(b) HUC-8 watershed level
Temporal Resolution (period and frequency of collection):
(a) 1874-1988 (inclusive); daily, monthly, and annual
(b) 1899-2007; daily, monthly, annual
Extent/Coverage of Data Set:
(a) National
(b) National
Type of Data Source:
(a) Database
(b) Database
Format of Data:
(a) ASCII;
(b) .rrd or .img or .xml or .sgml
Metadata:
Ecoregion definitions.
Bailey, R. G. 1995. Description of the Ecoregions of the United States. Available online at:
http://www.fs.fed.us/land/ecosysmgmt/. Accessed July 21, 2009.
National Land Cover Dataset (NLCD) - Landcover categories.
USEPA. Undated. National Land Cover Dataset (NLCD) - Landcover categories. Available
online at: http://www.mrlc.gov/index.php. Accessed July 21, 2009.
Additional Data Characteristics:
Data were obtained from the two different datasets: daily streamflow data at study sites from
USGS' Stream Gauge Data data set, and daily streamflow data for reference sites in relatively
undisturbed locations from USGS' HDCN data set. Data were integrated into a single data set by
the Heinz Center; this integrated data set was ultimately used for mapping. In addition, ecoregion
definitions (from Bailey, 1995 in metadata) and land cover region definitions (from USEPA's
NLCD Landcover Categories in metadata) were used to define watersheds in grasslands and
shrublands.
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Technical Notes
Data Gaps in National Coverage: This indicator is based on a 2009 study by the H. John
Heinz HI Center for Science, Economics, and the Environment (Heinz Center) that evaluated
the number of "zero-flow" periods in streams that flow through watersheds dominated by
grassland and/or shrubland land cover types. The authors of the study delineated watersheds
for USGS stream gages, calculated the land use within those watersheds, and then analyzed
streamflow records for watersheds dominated by grassland and shrubland cover. They
determined the average annual percent of streams in Western ecoregions that experienced dry
periods during a rolling 5-year period. The Heinz Center did not conduct this analysis for
Eastern ecoregions. Although the reasons for this are unclear, it is likely that this is because
few, if any, stream sites in Eastern ecoregions satisfied their definition of a "grassland" or
"shrubland" stream.
Non-uniform Spatial Distribution of Data: In the Western ecoregions, the number of
grassland sites within each HUC-4 unit varies widely. In cases where there are few grassland
sites within a HUC-4 boundary, individual sites may have a large influence on the average
number of dry periods that is calculated for that areas. The resulting spatial heterogeneity
may be a reflection of this sensitivity to data availability.
#209 Population (human) Susceptible to Flood Risk**
Literature Source (see Appendix A for full citation):
Kurd et al., 1998; Kurd et al.,1999.
Data Sets Used:
(a) FEMA Q3 Flood Data
(b) ESRI ArcUSA - US Census tract data.
How To Obtain Data:
(a) 26 CDs (for all USA) available for a fee, online or on phone.
(b) Available on ArcGIS.
URL to Data (if any):
(a) http://www.fema.gov/hazard/map/q3.shtmtfO
(b) N/A
Spatial Resolution:
(a) Individual street addresses
(b) 2 scales - 1:2,000,000 and 1:25,000,000
Temporal Resolution (period and frequency of collection):
(a) N/A
(b) N/A
Extent/Coverage of Data Set:
(a) National
(b) National
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Type of Data Source:
(a) Collection of flood maps
(b) Census
Format of Data:
(a) Digital Line Graph (DLG), ARC/INFO, Maplnfo
(b) ARC/INFO (ArcView and ArcGIS compatible)
Metadata:
N/A
Additional Data Characteristics:
500-year floodplain data from FEMA's Q3 Digital Flood Data were overlaid with U.S. Census
Tract Data from ArcGIS.
Technical Notes
Data Gaps in National Coverage: This indicator evaluates the human population currently
residing within the 500-year floodplain. This would be calculated by overlaying estimates of
the 500-year floodplain from the Federal Emergency Management Agency (FEMA) with
population data from the U.S. Census Bureau. According to FEMA's Map Service Center,
GIS-compatible digital flood plain data are only available for certain parts of the country, as
shown in the map below. Without the digital flood plain data, significant effort is required to
create a national map for this indicator.
- - *? \'- V
Availability of Digital Flood Insurance Rate Maps (DFIRMs). The Federal Emergency Management Agency
provides GIS-compatible flood plain maps for all areas in purple. Source: Federal Emergency Management Agency
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#218 Ratio of Snow to Total Precipitation
Literature Source (see Appendix A for full citation):
Lettenmaier et al., 2008.
Data Sets Used:
Monthly Climate Data and Observation Station Locations from National Climatic Data Center.
How To Obtain Data:
Download online (free for .gov and .edu domains).
URL to Data (if any):
http://gis.ncdc.noaa.gov/snowfallmo/
Spatial Resolution:
18,116 stations
Temporal Resolution (period and frequency of collection):
1867 to present; annual
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Tabular, Delimited, Time Series, NWS Condensed B (suitable for SWMM), Worksheet, dBase,
Binary, NCDC
Metadata:
N/A
Additional Data Characteristics:
NOAA's Monthyl Climate Data and Observation Locations data set contains climate data (rain,
snow, evaporation, temperature, degree days). Based on available station-level time-series data,
the unweighted numeric average for each HUC-4 can be calculated using stations within that
HUC watershed.
Technical Notes
There are no technical notes for this indicator.
#219 Ratio of Withdrawals to Streamflow
Literature Source (see Appendix A for full citation):
Kurd etal., 1999.
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Data Sets Used:
(a) Oregon State University - PRISM Climate Modeling System: Mean Annual Precipitation
data
(b) Oregon State University - PRISM Climate Modeling System: Mean Daily Maximum
Temperature data
(c) USGS - National Water-Use Dataset.
How To Obtain Data:
Download online
URL to Data (if any):
(a) http://www.prism.oregonstate.edu/products/matrix.phtml
(b) http://www.prism.oregonstate.edu/products/matrix.phtml
(c) http://water.usgs.gov/watuse/
Spatial Resolution:
(a) 30 arc-second (800 meters)
(b) 30 arc-second (800 meters)
(c) HUC-8 watershed
Temporal Resolution (period and frequency of collection):
(a) 1971 - 2000; monthly
(b) 1971-2000; monthly
(c) 1985 - 2000; every 5 years
Extent/Coverage of Data Set:
(a) National
(b) National
Type of Data Source:
(a) Interpolated grid
(b) Interpolated grid
(c) Database
Format of Data:
ASCII
Metadata:
Calculation of stream/low.
Vogel, R.M., I. Wilson, and C. Daly. 1999. Regional Regression Models of Annual
Streamflow for the United States." Journal of Irrigation and Drainage Engineering. 125 (3):
148-157.
Metadata for PRISM U.S. average monthly or annual precipitation data.
http://www.prism.oregonstate.edu/docs/meta/ppt_30s_meta.htm
Metadata for PRISM U.S. average monthly temperature data.
http://www.climatesource.com/us/fact sheets/meta tmin us 71b.html
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Description of water use parameters.
USGS. Estimated Use of Water in the United States. Available online at:
http://water.usgs.gov/watuse/. Accessed December 15, 2010.
Additional Data Characteristics:
As described in the metadata for (b), mean annual temperature was calculated as the average of
the mean maximum and mean minum temperature for a given location.
Technical Notes
There are no technical notes for this indicator.
#284 Stream Habitat Quality
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
USEPA - Wadeable Streams Assessment (WSA).
How To Obtain Data:
Download online
URL to Data (if any):
http://www.epa.gov/owow/streamsurvey/web_data.html
Spatial Resolution:
Small streams
Temporal Resolution (period and frequency of collection):
2004-2005; every 5 years (first year of round of data collection was 2004-2005)
Extent/Coverage of Data Set:
National
Type of Data Source:
Survey
Format of Data:
Comma separated
Metadata:
Definitions and data descriptions as . txt files.
USEPA. 2008. Wadeable Streams Assessment - Definitions of Variables. Available at:
http://www.epa.gov/owow/streamsurvey/web data.html. Accessed July 21, 2009.
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Additional Data Characteristics:
Data on the biological condition of small streams, water quality, and biological data on various
stream types were used to inform this indicator.
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for EPA's Wadeable Streams
Assessment (WSA) varies across HUC-4 units. In cases where there are few sample points
within a HUC-4 boundary, individual sites may have a large influence on the average rapid
assessment score that is calculated for that area. The map for this indicator shows a
heterogeneous distribution of vulnerability, which may be a reflection of this sensitivity to
sampling effort.
Local Variation: Habitat quality varies at local scales and is affected by local factors. By
calculating an average assessment score for each HUC-4 unit, localized vulnerability within
the HUC-4 unit is masked.
#322 Waterborne Human Disease Outbreaks (events)**
Literature Source (see Appendix A for full citation):
Heinz Center, 2008.
Data Sets Used:
Centers for Disease Control and Prevention (CDC) - Waterborne Disease and Outbreak
Surveillance System (WBDOSS).
How To Obtain Data:
Download online
URL to Data (if any):
http://www.cdc.gov/healthywater/statistics/wbdoss/surveillance.html
Spatial Resolution:
State
Temporal Resolution (period and frequency of collection):
1985-2004, yearly or every 2 to 3 years
Extent/Coverage of Data Set:
National
Type of Data Source:
Data summaries
Format of Data:
PDF and web page
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Metadata:
N/A
Additional Data Characteristics:
Data from the CDC's WBDOSS on the number of waterborne human disease outbreaks in both
drinking and recreational water, listed by state, by etiologic agent and type of water system, by
deficiency/type of exposure and type of water system, are used to inform this indicator.
Technical Notes
Data Collection and Reporting Discontinued: The most recent data from the Waterborne
Disease and Outbreak Surveillance System (WBDOSS) appear to be from 2006. According
to the Heinz Center (2008), data are no longer reported.
Quality of Source Data: The WBDOSS relies on voluntary reporting from public health
departments within the Unites States. The data collection methods used for the WBDOSS
raise data quality concerns due to:
o Variable Resources for Reporting: Some states, territories, or local public health
departments may have more available resources for reporting WBDOs and may be more
able or inclined to report such cases than other states. Therefore, a map of nationwide
WBDOs reported may represent the distribution of actual outbreaks.
o Voluntary Data Reporting: The Heinz Center (2008) study, which defined this indicator,
states that inconsistencies in reporting or an altogether lack of reporting may occur as it is
not mandatory for states, territories, and local public health departments to report
WBDOs.
o Skewed Representation of Large vs. Small Public Water Supplies: There may be under-
reporting of WBDOs from public water supplies serving small communities and over-
reporting due to larger outbreaks from larger PWSs as the latter is more likely to receive
widespread attention from authorities and the media.
#325 Wetland Loss**
Literature Source (see Appendix A for full citation):
MEA, 2005b.
Data Sets Used:
(a) USFWS - National Wetlands Inventory (NWI)
(b) NatureServe Explorer
How To Obtain Data:
(a) Download online
(b) Download online
URL to Data (if any):
(a) http://www.fws.gov/wetlands/Data/DataDownload.html
(b) http://www.NatureServe.org/explorer/
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Spatial Resolution:
(a) Lower 48 states (USGS 1:24,000 or 1:100,000 topographic quadrangle), Alaska (USGS
1:63,000 topographic quadrangle), Hawaii (County and USGS topographic quadrangle),
Puerto Rico and USVI (County and USGS topographic quadrangle), Pacific Trust Territories
(County/Island), Data are in decimal degrees on the North American Datum of 1983
(b) State or by NLCD 2001 map-zones
Temporal Resolution (period and frequency of collection):
(a) N/A
(b) N/A
Extent/Coverage of Data Set:
(a) National
(b) National
Type of Data Source:
(a) Database
(b) Census
Format of Data:
(a) NWI - digital wetlands polygon, .sgml and .xml
(b) .xls or .xml
Metadata:
National Vegetation Classification System (NVCS).
Federal Geographic Data Committee. 2009. National Vegetation Classification System.
Available online at: http://biology.usgs.gov/npsveg/nvcs.html. Accessed on November 19,
2009.
Terrestrial Ecological Classification System.
Nature Serve/Natural Heritage. 2009. Terrestrial Ecological Classification System. Available
online at: http://www.natureserve.org/explorer/classeco.htm. Accessed on November 19,
2009.
Additional Data Characteristics:
Data from two datasets were used to inform this indicator: data on wetland vegetation and
hydrologic properties of wetlands from NWI, and plant species vulnerability rankings and data
on their abundance in U.S. states from NatureServe Explorer. Vulnerability rankings (from
Natural Heritage) were used to estimate relative susceptibility to extinction. In addition, plant
species were classified into plant community types based on their physiognomy (using NVCS) or
based on their landscape settings, biological dynamics, and environmental features (using
Terrestrial Ecological Classification System).
Technical Notes
Significant Data Processing Required: National Wetlands Inventory (NWI) data can only be
downloaded at the 7.5 minute (1:24K) or 15 minute (1:100K) scales. In the lower 48 states,
the USGS has designated approximately 56,500 l:24K-scale quadrangles. It would require
substantial effort to download data for each of these quadrangles and assemble them into a
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national dataset. It may be possible to acquire a national wetlands dataset from the U. S. Fish
& Wildlife Service, although the FWS web site currently states that "Due to limited
resources, custom wetland data extractions by the Wetlands Team are no longer available."
Data Gaps in National Coverage: There are data gaps in the national coverage that would
diminish the usefulness of this national map. According to the FWS, the wetlands in 13 states
are either unmapped or are recorded on hardcopy maps only.
Inadequate Historical Data: The indicator describes wetland loss, which suggests that a
second data source must be used to calculate a change in the area of wetlands over time. A
data source that delineates historical wetland extent at the national scale has not been
identified.
#326 Wetland and Freshwater Species at Risk (number of species)
Literature Source (see Appendix A for full citation):
Hurdetal., 1998.
Data Sets Used:
NatureServe - Explorer (customized dataset).
How To Obtain Data:
Data were obtained from Jason McNees at NatureServe, 1101 Wilson Blvd., 15th Floor
Arlington, VA 22201 via email on July 31, 2009.
URL to Data (if any):
N/A
Spatial Resolution:
State
Temporal Resolution (period and frequency of collection):
2006; not specified
Extent/Coverage of Data Set:
National
Type of Data Source:
Census
Format of Data:
Excel
Metadata:
Explanation of Conservation Status Ranks.
NatureServe and Natural Heritage. 2009. Conservation Status Ranks. Available online at:
http://www.NatureServe.org/explorer/ranking.htm. Accessed July 21, 2009.
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Additional Data Characteristics:
Data on the number of at-risk water-dependent species were originally compiled by the Natural
Heritage Data Centers and The Nature Conservancy for the EPA's Index of Watershed Indicators
(IWI), a study published in 1997. The IWI project was discontinued due to lack of adequate
funding. Therefore, NatureServe data, customized for EPA, were used to inform this indicator.
Data include the percent of all species at risk of extinction in each state. In each state, species
considered to be at a risk of extinction were ones classified by NatureServe as critically
imperiled, imperiled, or vulnerable (using NatureServe and Natural Heritage's Conservation
Status Ranks in metadata).
Note: The data set used to inform this indicator was identical to that used for the indicator #24
(At-Risk Native Freshwater Species).
Technical Notes
State-level Data: The map for this indicator is based on data calculated on a state-by-state
basis. Transforming the data from a state-based representation to a HUC-4 representation
requires an assumption that the distribution of at-risk species is uniform within each state.
This assumption allows for area-weighted totals to be calculated for HUC-4 units that
intersect more than one state. Although this is an accepted transformation method because
the spatial extent of the HUC-4 units and the state boundaries are identical, the assumption of
uniform distribution may result in errors in the metric calculated for each HUC-4 unit.
#348 Erosion Rate
Literature Source (see Appendix A for full citation):
Murdoch et al., 2000.
Data Sets Used:
Soil erosion rates estimated with the Revised Universal Soil Loss Equation (RUSLE)
How To Obtain Data:
Request directly from:
Dawen Yang, PhD, Professor
Department of Hydraulic Engineering
Tsinghua University
Beijing 100084, China
Tel: +86-10-62796976
Fax: +86-10-62796971
E-mail: yangdw@tsinghua.edu.cn
URL to Data (if any):
N/A
Spatial Resolution:
0.5 degree grid
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Temporal Resolution (period and frequency of collection):
1980; not applicable (modeled data)
Extent/Coverage of Data Set:
Global
Type of Data Source:
Model output
Format of Data:
ASCII grid
Metadata:
Description ofdataset development.
Yang, D. W., S. Kanae, T. Oki, T. Koike, and K. Musiake. 2003. Global potential soil
erosion with reference to land use and climate changes. HydrologicalProcesses. 17:2913-
2928.
Additional Data Characteristics:
Erosion Rate was estimated with the Revised Universal Soil Loss Equation (RUSLE). RUSLE
does not account for deposition of eroded soil, so this indicator is more precisely defined as soil
movement. The literature source also describes variations of this dataset that incorporate
simulations of future climatic conditions (see Yang et al. (2003) for details). The study also
includes future soil erosion predictions based on changes in precipitation, temperature, and land
cover.
Technical Notes
Limited Temporal Resolution of Source Data: The source data for this map are based on land
cover patterns in 1980. Changes in land cover since then may affect the spatial distribution of
soil erosion.
#351 Instream Use/Total Streamflow
Literature Source (see Appendix A for full citation):
Meyer etal., 1999.
Data Sets Used:
(a) USGS - National Water-Use Dataset
(b) USGS - Mean annual runoff data
(c) WRC (U.S. Water Resources Council) - Groundwater Recharge. From: WRC. 1978. The
Nation's Water Resources: 1975-2000 (Vol. 2). U.S. Government Printing Office, Washington
D.C.
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How To Obtain Data:
(a) Download online
(b) Download online
(c) Hardcopy book (obtained from library).
URL to Data (if any):
(a) http://water.usgs.gov/watuse/
(b) http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
(c) N/A
Spatial Resolution:
(a) HUC-8 watershed
(b) 1:5,000,000 (runoff of tributary streams)
(c) HUC-2 regions
Temporal Resolution (period and frequency of collection):
(a) 1985-2000; every 5 years
(b) 1951-1980; one-time effort
(c) 1975; one-time effort
Extent/Coverage of Data Set:
(a) National
(b) National
(c) National
Type of Data Source:
(a) Database
(b) Modeled dataset
(c) Published report
Format of Data:
(a) ASCII
(b) ArcGIS file (.eOO)
(c) Tabular (hard copy)
Metadata:
Description of water use parameters.
USGS. Estimated Use of Water in the United States. Available online at:
http://water.usgs.gov/watuse/. Accessed December 15, 2010.
Additional Data Characteristics:
Data for this indicator were derived from three sources: data on total groundwater withdrawals
for the year 1995 from USGS' National Water-Use Dataset, modeled mean annual runoff
estimates based on annual streamflow data from USGS' mean annual runoff dataset, and
groundwater recharge estimates from WRC, 1978. The ratio of instream use to total streamflow
was then calculated as described in the WRC report (1978).
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Technical Notes
Missing Data for Input Variables: Estimates of runoff (streamflow) and groundwater
overdraft (the amount of groundwater withdrawals that exceeds the long-term average annual
recharge rate) are used to calculate this indicator. For some regions, groundwater recharge
estimates were not available from the original source based on 1975 data. In these areas,
withdrawal (as measured in 1975) was assumed to equal recharge. These recharge estimates
were then compared to 1995 groundwater withdrawals to calculate updated estimates of
groundwater overdraft.
#352 Total Use/Total Streamflow
Literature Source (see Appendix A for full citation):
Meyer et al., 1999.
Data Sets Used:
(a) USGS - National Water-Use Dataset
(b) USGS - Mean annual runoff data
(c) WRC (U.S. Water Resources Council) - Groundwater Recharge. From: WRC. 1978. The
Nation's Water Resources: 1975-2000 (Vol. 2). U.S. Government Printing Office,
Washington D.C.
How To Obtain Data:
(a) Download online
(b) Download online
(c) Hardcopy book (obtained from library).
URL to Data (if any):
(a) http://water.usgs.gov/watuse/
(b) http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
(c) N/A
Spatial Resolution:
(a) HUC-8 watershed
(b) 1:5,000,000 (runoff of tributary streams)
(c) HUC-2 regions
Temporal Resolution (period and frequency of collection):
(a) 1985-2000; every 5 years
(b) 1951-1980; 1 time effort
(c) 1975; one-time effort
Extent/Coverage of Data Set:
(a) National
(b) National
(c) National
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Type of Data Source:
(a) Database
(b) Modeled dataset
(c) Published report
Format of Data:
(a) ASCII
(b) ArcGIS file (.eOO)
(c) Tabular (hard copy)
Metadata:
Description of water use parameters.
USGS. Estimated Use of Water in the United States. Available online at:
http://water.usgs.gov/watuse/. Accessed December 15, 2010.
Additional Data Characteristics:
Data for this indicator were derived from three sources: data on total groundwater withdrawals
and consumptive use for the year 1995 from USGS' National Water-Use Dataset, modeled mean
annual runoff estimates based on annual streamflow data from USGS' mean annual runoff
dataset, and groundwater recharge estimates from WRC, 1978. The ratio of total use to total
streamflow was then calculated as described in the WRC report (1978).
Technical Notes
Missing Data for Input Variables: Estimates of runoff (streamflow) and groundwater
overdraft (the amount of groundwater withdrawals that exceeds the long-term average annual
recharge rate) are used to calculate this indicator. For some regions, groundwater recharge
estimates were not available from the original source based on 1975 data. In these areas,
withdrawal (as measured in 1975) was assumed to equal recharge. These recharge estimates
were then compared to 1995 groundwater withdrawals to calculate updated estimates of
groundwater overdraft. In addition, this indicator assesses water consumption as a
component of total use. The consumptive use data from USGS does not distinguish between
water consumed from groundwater and surface water sources, and it is unclear in the original
data source whether this distinction is relevant. Additional investigation into the role of
consumptive use in this indicator and the original source of water consumed is
recommended.
Low-Resolution Data: Estimates of groundwater recharge were developed at the 2-digit HUC
scale and applied evenly to all sub-regions (4-digit HUCs) within the region.
#364 Pesticide Toxicity Index (PTI)
Literature Source (see Appendix A for full citation):
Gilliom et al., 2006.
Data Sets Used:
USGS - NAWQA.
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How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/
Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/study_units.html. Accessed July 21, 2009.
Daphnia EC50 Values.
USEPA. 2009. ECOTOX Database. Available online at: http://cfpub.epa.gov/ecotox/.
Accessed September 11, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 76 pesticides (including
herbicides and insecticides) and 7 pesticide by-products in streams and shallow groundwater
(100ft or less below ground level) at 20 USGS study sites in 1991, 1994, and 1997, was used to
inform this indicator. Descriptions of study sites and their year of assessment were also available
(from USGS's About NAWQA webpage in metadata). The Pesticide Toxicity Index (PTI)
accounts for multiple pesticides in a sample, including pesticides without established
benchmarks for aquatic life. The PTI combines information on measured concentrations of
pesticides in stream water with toxicity estimates (i.e. toxicity quotients calculated based on
laboratory toxicity studies, e.g. EC50 values for Daphnia from EPA's ECOTOX database) to
produce a relative index value for a sample or stream. The PTI value is computed for each
sample of stream water by summing the toxicity quotients for all pesticides detected in the
sample.
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Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for the USGS National Water
Quality Assessment (NAWQA) Program varies across HUC-4 units. In cases where there
are few sample points within a HUC-4 boundary, individual sites may have a large influence
on the average pesticide concentration that is calculated for that area. The map for this
indicator shows a heterogeneous distribution of vulnerability, which may be a reflection of
this sensitivity to sampling effort. In addition, there are numerous HUC-4 units where no data
are available.
Local Variation: Pesticide concentrations in streams vary at local scales and are affected by
local factors. By calculating an average concentration for each HUC-4 unit, localized
vulnerability within the HUC-4 unit is masked.
Relative Rather than Absolute Toxicity: The Pesticide Toxicity Index (PTI) accounts for
multiple pesticides in a sample, including pesticides without established benchmarks for
aquatic life. The PTI combines information on exposure of aquatic biota to pesticides
(measured concentrations of pesticides in stream water) with toxicity estimates (results from
laboratory toxicity studies) to produce a relative index value for a sample or stream. The PTI
value is computed for each sample of stream water by summing the toxicity quotients for all
pesticides detected in the sample. The toxicity quotient is the measured concentration of a
pesticide divided by its toxicity concentration from bioassays (such as an LC50 or EC50).
This approach follows the Concentration Addition Model of toxicity. Although simple
addition is unlikely to strictly apply for complex mixtures of pesticides from different classes
and with different effects and modes of action, the PTI is still useful as a relative index.
While the PTI does not indicate whether water in a sample is toxic, its value can be used to
rank or compare the relative potential toxicity of different samples or different streams.
#367 Herbicide Concentrations in Streams
Literature Source (see Appendix A for full citation):
USGS, 1999.
Data Sets Used:
USGS - NAWQA.
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/
Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
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Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/study_units.html. Accessed July 21, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 76 pesticides (including
herbicides and insecticides) and 7 pesticide by-products in streams and shallow groundwater
(100ft or less below ground level) at 20 USGS study sites in 1991, 1994, and 1997, was used to
inform this indicator. Descriptions of study sites and their year of assessment were also available
(from USGS's About NAWQA webpage in metadata).
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for the USGS National Water
Quality Assessment (NAWQA) Program varies across HUC-4 units. In cases where there
are few sample points within a HUC-4 boundary, individual sites may have a large influence
on the average herbicide concentration that is calculated for that area. The map for this
indicator shows a heterogeneous distribution of vulnerability, which may be a reflection of
this sensitivity to sampling effort. In addition, there are numerous HUC-4 units where no data
are available.
Local Variation: Herbicide concentrations in streams vary at local scales and are affected by
local factors. By calculating an average concentration for each HUC-4 unit, localized
vulnerability within the HUC-4 unit is masked.
#369 Insecticide Concentrations in Streams
Literature Source (see Appendix A for full citation):
USGS, 1999.
Data Sets Used:
USGS - NAWQA.
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs. gov/nawq a/pnsp/pub s/circ 1291 /appendix6/
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Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/studv units.html. Accessed July 21, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 76 pesticides (including
herbicides and insecticides) and 7 pesticide by-products in streams and shallow groundwater
(100ft or less below ground level) at 20 USGS study sites in 1991, 1994, and 1997, was used to
inform this indicator. Descriptions of study sites and their year of assessment were also available
(from USGS's About NAWQA webpage in metadata).
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for the USGS National Water
Quality Assessment (NAWQA) Program varies across HUC-4 units. In cases where there are
few sample points within a HUC-4 boundary, individual sites may have a large influence on
the average insecticide concentration that is calculated for that area. The map for this
indicator shows a heterogeneous distribution of vulnerability, which may be a reflection of
this sensitivity to sampling effort. In addition, there are numerous HUC-4 units where no data
are available.
Local Variation: Insecticide concentrations vary at local scales and are affected by local
factors. By calculating an average concentration for each HUC-4 unit, localized vulnerability
within the HUC-4 unit is masked.
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#371 Organochlorines in Bed Sediment
Literature Source (see Appendix A for full citation):
USGS, 1999.
Data Sets Used:
USGS - National Water-Quality Assessment (NAWQA).
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/
Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/studv units.html. Accessed July 21, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 32 compounds (8
individual parent compounds, 1 individual breakdown product, and 7 groups of parent
compounds, plus related breakdown products or chemical impurities in the manufactured
product) in bed sediment at 20 USGS study sites in 1991, 1994, and 1997, was used to inform
this indicator. Descriptions of study sites and their year of assessment were also available (from
USGS's About NAWQA webpage in metadata).
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for the USGS National Water
Quality Assessment (NAWQA) Program varies across HUC-4 units. In cases where there
are few sample points within a HUC-4 boundary, individual sites may have a large influence
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on the average concentration that is calculated for that area. The map for this indicator
shows a heterogeneous distribution of vulnerability, which may be a reflection of this
sensitivity to sampling effort. In addition, there are numerous HUC-4 units where no data
are available.
Local Variation: Organochlorine concentrations vary at local scales and are affected by local
factors. By calculating an average concentration for each HUC-4 unit, localized vulnerability
within the HUC-4 unit is masked.
#373 Herbicides in Groundwater
Literature Source (see Appendix A for full citation):
USGS, 1999.
Data Sets Used:
USGS - NAWQA.
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/
Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/studv units.html. Accessed July 21, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 76 pesticides (including
herbicides and insecticides) and 7 pesticide by-products in streams and shallow groundwater
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(100ft or less below ground level) at 20 USGS study sites in 1991, 1994, and 1997, was used to
inform this indicator. Descriptions of study sites and their year of assessment were also available
(from USGS's About NAWQA webpage in metadata).
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for the USGS National Water
Quality Assessment (NAWQA) Program varies across HUC-4 units. In cases where there are
few sample points within a HUC-4 boundary, individual sites may have a large influence on
the average concentration that is calculated for that area. The map for this indicator shows a
heterogeneous distribution of vulnerability, which may be a reflection of this sensitivity to
sampling effort. In addition, there are numerous HUC-4 units where no data are available.
Local Variation: Herbicide concentrations vary at local scales and are affected by local
factors. By calculating an average concentration for each HUC-4 unit, localized vulnerability
within the HUC-4 unit is masked.
#374 Insecticides in Groundwater
Literature Source (see Appendix A for full citation):
USGS, 1999.
Data Sets Used:
USGS - NAWQA.
How To Obtain Data:
Download online
URL to Data (if any):
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/
Spatial Resolution:
51 study units
Temporal Resolution (period and frequency of collection):
1991 (20 study units); 1994 (16 study units); 1997 (15 study units); Variable frequency (from
one-time collection to daily depending on purpose and collection site)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Comma separated or Excel
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Metadata:
NA WQA Study Description.
USGS. 2006. About NAWQA Study Units. Available online at:
http://water.usgs.gov/nawqa/studies/study_units.html. Accessed July 21, 2009.
Additional Data Characteristics:
The USGS NAWQA data set, which contained data on the occurrence of 76 pesticides (including
herbicides and insecticides) and 7 pesticide by-products in streams and shallow groundwater
(100ft or less below ground level) at 20 USGS study sites in 1991, 1994, and 1997, was used to
inform this indicator. Descriptions of study sites and their year of assessment were also available
(from USGS's About NAWQA webpage in metadata).
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for EPA's Wadeable Streams
Assessment (WSA) varies across HUC-4 units. In cases where there are few sample points
within a HUC-4 boundary, individual sites may have a large influence on the average
concentration that is calculated for that area. The map for this indicator shows a
heterogeneous distribution of vulnerability, which may be a reflection of this sensitivity to
sampling effort. In addition, there are numerous HUC-4 units where no data are available.
Local Variation: Insecticide concentrations vary at local scales and are affected by local
factors. By calculating an average concentration for each HUC-4 unit, localized vulnerability
within the HUC-4 unit is masked.
#437 Precipitation Elasticity of Stream/low
Literature Source (see Appendix A for full citation):
Sankarasubramanian et al., 2001.
Data Sets Used:
(a) USGS - Hydroclimatic Data Network (HDCN): Streamflow data
(b) Oregon State University - PRISM Climate Modeling System: Mean Annual Precipitation data
How To Obtain Data:
(a) Download online
(b) Download online
URL to Data (if any):
(a) http://pubs.usgs.gov/of/1992/ofr92-129/hcdn92/hcdn/ascii/
(b) http://www.prism.oregonstate.edu/
Spatial Resolution:
(a) HUC-8
(b) 2.5 arc-min
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Temporal Resolution (period and frequency of collection):
(a) 1874-1988; annual
(b) 1990 - present; monthly
Extent/Coverage of Data Set:
(a) National
(b) National
Type of Data Source:
(a) Database
(b) Database
Format of Data:
(a) ASCII
(b) ASCII
Metadata:
Description of HDCN data.
Landwehr, J. M., and J. R. Slack, 1992. Hydro-Climatic Data Network: A U.S. Geological
Survey Streamflow Data Set for the United States for the Study of Climate Variations, 1874-
1988. U.S. Geological Survey Open-File Report 92-129. Available online at:
http://pubs.er.usgs.gov/usgspubs/ofr/ofr92129. Accessed July 21, 2009.
Description of PRISM modeling.
Daly, C., R. P. Neilson, and D. L. Phillips. 1994. "A Statistical-Topographic Model for
Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied
Meterology. 33: 140-158.
Calculation of precipitation elasticity ofstreamflow.
Sankarasubramanian, A., R. M. Vogel, and J. F. Limbrunner. 2001. Climate Elasticity of
Streamflow in the United States. Water Resources Research. 37 (6): 1771-1781.
Additional Data Characteristics:
Data on Streamflow were obtained from USGS's HDCN data set. Data on precipitation were
obtained from Oregon State University's PRISM data set. Explanations for how data in HDCN
and PRISM were collected and/or modeled are also available (from Landwehr and Slack, 1992
and Daly et al., 1994 in metadata). For the purposes of mapping this indicator with relative ease,
Figure 4 in the original literature source, Sankarasubramanian et al., 2001 (see Appendix A for
full reference) was used. However, the complete original data set could also be recalculated for
mapping purposes using the data sources listed here.
Technical Notes
Map Derived from Figure in Source Literature: The original data used to calculate this
indicator were not available and suitable alternatives would require significant effort to
assemble. Therefore, the map for this indicator was derived from isopleths that are presented
in the source literature (Figure 4 in Sankarasubramanian et al. 2001). The original map was
based on data collected from 1951-1988. Streamflow and precipitation data available from
the U.S. Geological Survey and Oregon State University's PRISM Group, respectively, could
be used to reproduce an updated version of this map.
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#449 Ratio of Reservoir Storage to Mean Annual Runoff
Literature Source (see Appendix A for full citation):
Lettenmaier et al., 2008.
Data Sets Used:
(a) US Army Corps of Engineers (USAGE) - National Inventory of Dams (NID)
(b) USGS - Mean annual runoff data
How To Obtain Data:
(a) Download online (need to register for free)
(b) Download online
URL to Data (if any):
(a) https://nid.usace.army.mil (Note: You must accept the security certificate in order to view
database) OR http://www.nationalatlas.gov/mld/damsOOx.html
(b) http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
Spatial Resolution:
(a) 80,000 dams (latitude and longitude specifications included only major dams available at
National Atlas site)
(b) 1:5,000,000 (runoff of tributary streams)
Temporal Resolution (period and frequency of collection):
(a) 1972-2006; unclear how often it is updated
(b) 1951-1980; 1 time effort
Extent/Coverage of Data Set:
(a) National
(b) National
Type of Data Source:
(a) Database
(b) Modeled dataset
Format of Data:
(a) Tabular (need to copy-paste into Excel)
(b) ArcGIS file (.eOO)
Metadata:
Calculation of ratio of reservoir storage to mean annual runoff.
Graf, W.L., 1999: Dam nation: A geographic census of American dams and their large-scale
hydrologic impacts, Water Resources Research, 35 (4): 1305-1311.
Additional Data Characteristics:
Data for this indicator were derived from two sources: dam reservoir storage data from the
USACE's NID data set and modeled mean annual runoff estimates based on annual streamflow
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data from USGS' mean annual runoff dataset. The storage to runoff ratio can then be calculated
(as specified in Graf, 1999 in metadata).
Technical Notes
Quality of Source Data: This indicator uses the National Inventory of Dams to evaluate
reservoir storage. This database contains a wide variety of water control structures, some of
which may not be relevant for this indicator. An effort to remove irrelevant water control
structures (e.g. coastal flood gates, navigational locks) was made, although the accuracy of
the dam record attributes used to apply these filters is unclear.
#453 Runoff Variability
Literature Source (see Appendix A for full citation):
Lettenmaier et al., 2008.
Data Sets Used:
University of Washington (Land Surface Hydrology Research Group) - Variable Infiltration
Capacity (VIC) Land Surface Data Set.
How To Obtain Data:
Download online (need to register for free).
URL to Data (if any):
http://www.hydro.washington.edu/SurfaceWaterGroup/Data/VIC retrospective/index.html (file
is:
ftp://ftp.hydro. Washington.edu/pub/CE/HYDRO/nijssen/vic_global/calibrated/runoff calibrated.
monthly. 1980_1993 .nc.gz)
Spatial Resolution:
2 degrees
Temporal Resolution (period and frequency of collection):
1950-2000; 3-hourly
Extent/Coverage of Data Set:
National
Type of Data Source:
Modeled dataset
Format of Data:
NetCDF
Metadata:
Explanation of VIC model.
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Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002. A long-term
hydrologically-based data set of land surface fluxes and states for the conterminous United
States, Journal of Climate, 15: 3237-3251.
VIC Global Hydrologic Simulations - Variable descriptions.
University of Washington (Land Surface Hydrology Research Group). Undated. VIC Global
Hydrologic Simulations. Available online at:
http://www.hydro.Washington.edu/SurfaceWaterGroup/Data/vic global.html. Accessed July
21, 2009.
Additional Data Characteristics:
Data on runoff are available from the output of the University of Washington's VIC model
which simulates the global land surface hydrological cycle. The model outputs include five
model-derived variables: evapotranspiration, snow-water equivalent, soil moisture storage, total
storage, and runoff (which is the variable of interest). Detailed descriptions of input variables and
output parameters of this model are also availabled (from Maurer et al., 2002 in metadata).
Technical Notes
Low-Re solution Data: This indicator uses simulations of the global land surface hydrological
cycle from the Variable Infiltration Capacity (VIC) Model to predict runoff. The native
spatial resolution of the model output is a 2° x 2° grid and the model output is stored in
NetCDF format. A more recent series of VIC model predictions at a finer spatial resolution
may be available from the same source, but the NetCDF file available online appears to be
corrupt.
#460 Macroinvertebrate Index ofBiotic Condition
Literature Source (see Appendix A for full citation):
USEPA, 2006b.
Data Sets Used:
USEPA - Wadeable Streams Assesment (WSA) (Stream Water Benthic Macroinvertebrate
Metrics).
How To Obtain Data:
Download online
URL to Data (if any):
http://www.epa.gov/owow/streamsurvey/web data.html (file is: wsa_benmet300_ts_final.csv)
Spatial Resolution:
Small streams
Temporal Resolution (period and frequency of collection):
2004-2005; every 5 years (first year of round of data collection was 2004-2005)
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Extent/Coverage of Data Set:
National
Type of Data Source:
Survey
Format of Data:
Comma separated
Metadata:
Definitions and data descriptions as . txt files.
USEPA. 2008. Wadeable Streams Assessment - Definitions of Variables. Available at:
http://www.epa.gov/owow/streamsurvey/web_data.html. Accessed July 21, 2009.
Site information for WSA sites.
USEPA 2008. Wadeable Streams Assessment - Post-Sampling Site Info and Survey Design.
Available at: http://www.epa.gov/owow/streamsurvey/web_data.html. Accessed July 21,
2009. (file is: wsa_siteinfo_ts_fmal.csv).
Additional Data Characteristics:
Data on counts of individual benthic macroinvertebrates at various stages of their life cycle were
obtained from EPA's WSA Benthic Macroinvertebrate Metrics data set. Data files are associated
with companion text files (using EPA's WSA Definitions of Variables in metadata) that list data
set labels and give individual descriptions for each variable. The original literature source, EPA's
2006 WSA report (USEPA, 2006b; see Appendix A for full citation), provides an explanation of
how wadeable streams were selected for this study and how data were collected from various
sites.
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for EPA's Wadeable Streams
Assessment (WSA) varies across HUC-4 units. In cases where there are few sample points
within a HUC-4 boundary, individual sites may have a large influence on the average
Macroinvertebrate Index of Biotic Condition value that is calculated for that area. The map
for this indicator shows a heterogeneous distribution of vulnerability, which may be a
reflection of this sensitivity to sampling effort.
Local Variation: Macroinvertebrate community condition varies at local scales and is
affected by local factors. By calculating an average value for the Macroinvertebrate Index of
Biotic Condition for each HUC-4 unit, localized vulnerability within the HUC-4 unit is
masked.
#461 Macroinvertebrate Observed/Expected (O/E) Ratio ofTaxa Loss
Literature Source (see Appendix A for full citation):
USEPA, 2006b.
Data Sets Used:
USEPA - Wadeable Streams Assessment (Stream Water Benthic Macroinvertebrate Metrics).
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How To Obtain Data:
Download online
URL to Data (if any):
http://www.epa.gov/owow/streamsurvey/web_data.html (file is: wsa_benmet300_ts_fmal.csv)
Spatial Resolution:
Small streams
Temporal Resolution (period and frequency of collection):
2004-2005; every 5 years (first year of round of data collection was 2004-2005)
Extent/Coverage of Data Set:
National
Type of Data Source:
Survey
Format of Data:
Comma separated
Metadata:
Definitions and data descriptions as . txt files.
USEPA. 2008. Wadeable Streams Assessment - Definitions of Variables. Available at:
http://www.epa.gov/owow/streamsurvey/web data.html. Accessed July 21, 2009.
Additional Data Characteristics:
Data on percent diversity of benthic macroinvertebrates were obtained from EPA's WSA
Benthic Macroinvertebrate Metrics data set. Data files are associated with companion text files
(using EPA's WSA Definitions of Variables in metadata) that list data set labels and give
individual descriptions for each variable. The original literature source, EPA's 2006 WSA report
(USEPA, 2006b; see Appendix A for full citation), provides an explanation of how wadeable
streams were selected for this study and how data were collected from various sites.
Technical Notes
Non-uniform Spatial Distribution of Data: The sampling effort for EPA's Wadeable Streams
Assessment (WSA) varies across HUC-4 units. In cases where there are few sample points
within a HUC-4 boundary, individual sites may have a large influence on the average
Macroinvertebrate O/E value that is calculated for that area. The map for this indicator
shows a heterogeneous distribution of vulnerability, which may be a reflection of this
sensitivity to sampling effort.
Local Variation: Macroinvertebrate community condition varies at local scales and is
affected by local factors. By calculating an average value for the Macroinvertebrate O/E
Ratio, localized vulnerability is masked.
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#462 CoastalBenthic Communities**
Literature Source (see Appendix A for full citation):
USEPA, 2008b.
Data Sets Used:
Underlying sampling data in USEPA's National Coastal Assessment (NCA) database.
How To Obtain Data:
Download online
URL to Data (if any):
http://www.epa.gov/emap/nca/html/data/index.html
Spatial Resolution:
Not clear (EPA's website states that there are "thousands" of sampling sites, but no specific
number; data contains latitude/longitude specs.)
Temporal Resolution (period and frequency of collection):
1990-2002; no defined frequency (1 datum/site for only year listed)
Extent/Coverage of Data Set:
National
Type of Data Source:
Database
Format of Data:
Excel
Metadata:
Definition and calculation ofbenthic index.
USEPA. 2005. National Coastal Condition Report (NCCR). EPA-620/R-03/002. Available
online at: http://www.epa.gov/owow/oceans/nccr/2005/index.html. Accessed July 21, 2009.
Additional Data Characteristics:
The coastal benthic communities index used in this indicator is based on multiple independent
variables (described in EPA's NCCR in metadata). Data for these independent variables,
(including total count of taxa, total abundance, mean abundance, mean biomass, total biomass,
and diversity index) can be obtained from the underlying sampling data in EPA's NCA dataset.
Technical Notes
Inconsistencies in Reporting Data: Data provided on the National Coastal Assessment
(NCA) web site were collected by multiple agencies. The methods used to calculate an index
to describe the status ofbenthic communities vary between agencies. These differences make
comparisons between states and regions problematic.
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Data Gaps in National Coverage: Data are not currently available for many of the nation's
coastal areas.
#623 Water Availability: Net Streamflow per Capita
Literature Source (see Appendix A for full citation):
Kurd etal., 1999.
Data Sets Used:
(a) Oregon State University - PRISM Climate Modeling System: Mean Annual Precipitation
data
(b) Oregon State University - PRISM Climate Modeling System: Mean Daily Maximum
Temperature data
(c) USGS - National Water-Use Dataset.
How To Obtain Data:
Download online
URL to Data (if any):
(a) http://www.prism.oregonstate.edu/products/matrix.phtml
(b) http://www.prism.oregonstate.edu/products/matrix.phtml
(c) http://water.usgs.gov/watuse/
Spatial Resolution:
(a) 30 arc-second (800 meters)
(b) 30 arc-second (800 meters)
(c) HUC-8 watershed
Temporal Resolution (period and frequency of collection):
(a) 1971 - 2000; monthly
(b) 1971-2000; monthly
(c) 1985 - 2000; every 5 years
Extent/Coverage of Data Set:
(a) National
(b) National
(c) National
Type of Data Source:
(a) Interpolated grid
(b) Interpolated grid
(c) Database
Format of Data:
ASCII
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Metadata:
Calculation of stream/low.
Vogel, R.M., I. Wilson, and C. Daly. 1999. "Regional Regression Models of Annual
Streamflow for the United States." Journal of Irrigation and Drainage Engineering. 125(3):
148-157.
Metadata for PRISM U.S. average monthly or annual precipitation data.
http://www.prism.oregonstate.edu/docs/meta/ppt_30s_meta.htm
Metadata for PRISM U.S. average monthly temperature data.
http://www.climatesource.com/us/fact sheets/meta tmin us 7Ib.html
Description of water use parameters.
USGS. Estimated Use of Water in the United States. Available online at:
http://water.usgs.gov/watuse/. Accessed December 15, 2010.
Additional Data Characteristics:
As described in the metadata for (b), mean annual temperature was calculated as the average of
the mean maximum and mean minum temperature for a given location.
Technical Notes
There are no technical notes for this indicator.
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This appendix provides details of the procedures used for mapping each of 26 indicators
(including the indicator marked with a * that had an incomplete map.) Maps were created using
ArcMap 9.2. Prior to mapping, data were prepared (including aggregation to the appropriate
scale, when necessary) using Microsoft Excel or Microsoft Access.
Data sources listed in this appendix and their technical notes are described in greater detail in
Appendix C, as are technical issues related to creating or interpreting maps for these indicators.
Maps of the 25 indicators are presented in Appendix E (displayed using 4-digit hydrologic units)
and in Appendix F (displayed using ecoregions).
#1 Acid Neutralizing Capacity (ANC)
Data Sources (see Appendix C for more details):
Stream Water Chemistry (Filename: waterchemistry.csv): EPA Wadeable Streams
Assessment. Available online at: http://www.epa.gov/owow/streamsurvey/web_data.html
Stream Site Info: EPA Wadeable Streams Assessment (Filename:
wsa_siteinfo_ts_final.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Water chemistry data were downloaded and opened as a comma-delimited text file in
Microsoft Excel. The columns containing "SitelD" and "ANC" were saved as a new .dbf
file.
(2) The .dbf file with water chemistry data, the hydrologic units, and Site Info were opened
in ArcMap 9.2. The water chemistry table was joined to the Site Info table using the
SitelD field.
(3) An event theme was mapped for all sites with corresponding water chemistry data, using
the LON_DD and LAT_DD fields (North American Datum of 1983).This event theme
was exported as a shapefile.
(4) The "AtRisk" field was added to the exported shapefile. If the value in the "ANC" field
was < 100, then the AtRisk field value was calculated as 1. All other records were
assigned a value of 0.
(5) The sites were aggregated with the 4-digit HUCs using a spatial join. If a HUC contained
more than one site, the total (sum) for the numeric fields was calculated. As a result of
the spatial join, a new shapefile was created. The "Pct_AtRisk" field was added to the
new shapefile, and field values were calculated by using:
Percent at Risk = At Risk Sites within HUC
Total Sites within HUC
(6) Data were mapped using the Pct_AtRisk field to indicate low, medium, and high
vulnerability categories.
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#22 Percent ofAt-Risk Freshwater Plant Communities
Data Sources (see Appendix C for more details):
Freshwater Plants Status: The H. John Heinz III Center for Science, Economics, and the
Environment (Heinz Center). 2009. Email message to Cadmus. April 17, 2009.
(Filename: G1-G5 wetlands by state.xls).
State Boundaries: Environmental Systems Research Institute (ESRI) Data & Maps. -
Projected to Albers map projection)
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The Microsoft Excel spreadsheet containing the percentages of at-risk plants was opened
in ArcMap. These percentages were reported by state.
(2) State boundaries and hydrologic units were also opened in ArcMap.
(3) The percentages were joined to the state boundaries shapefile using a table join and the
State Name attribute. The resulting shapefile was intersected with the 4-digit hydrologic
units.
(4) Using the area of each hydrologic unit, the area of the intersected shapes, and the
percentage of at-risk plant species, an area-weighted percentage value was calculated for
each intersected area.
(5) The shapefile was dissolved by the 4-digit HUC code to re-aggregate the 4-digit HUCs.
The area-weighted percentages were summed.
(6) The final map was created using the summed area-weighted percentages in the
HUC4_AtRiskFWPlants.shp shapefile to indicate low, medium, and high vulnerability
categories.
#24 Percent ofAt-Risk Freshwater Species
Data Sources (see Appendix C for more details):
Freshwater Species Status: The H. John Heinz III Center for Science, Economics, and
the Environment (Heinz Center). 2009. Email message to Cadmus. April 17, 2009.
(Filename: AtRiskFWanimalSPby state.xls)
State Boundaries: Environmental Systems Research Institute (ESRI) Data & Maps. -
Projected to Albers map projection)
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The Microsoft Excel spreadsheet containing the percentages of at-risk species was
opened in ArcMap. These percentages were reported by state.
(2) State boundaries and hydrologic units were also opened in ArcMap.
(3) The percentages were joined to the state boundaries shapefile using a table join and the
State Name attribute. The resulting shapefile was intersected with the 4-digit hydrologic
units.
(4) Using the area of each hydrologic unit, the area of the intersected shapes, and the
percentage of at-risk species, an area-weighted percentage value was calculated for each
intersected area.
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(5) The shapefile was dissolved by the 4-digit HUC code to reaggregate the 4-digit HUCs.
The area-weighted percentages were summed.
(6) The final map was created using the summed area-weighted percentages in the
HUC4_AtRiskFWSpecies.shp shapefile to indicate low, medium, and high vulnerability
categories.
#57 Coastal Vulnerability Index
Data Sources (see Appendix C for more details):
USGS - A Preliminary Database for the U. S. Atlantic, Pacific and Gulf of Mexico Coasts
(U.S. Geological Survey Digital Data Series - 68; Three data sets: Gulf Coast, East
Coast, and West Coast).) Available online at:
http://pubs.usgs.gov/dds/dds68/htmldocs/data.htm
Processing Steps:
(1) ArcGIS shapefiles, containing attributes for the raw CVI variables, CVI, and risk
categories associated with CVI values, were opened in ArcMap 9.2.
(2) The Coastal Vulnerability Index (CVI) uses 6 variables, which appear as attributes in the
shapefiles:
Mean Wave Height - - mean elevation of all nonnegative 5' by 5' grid cells within a
given 0.25° grid cell; values in meters. WIS hindcast nearshore mean wave height
1976-1995.
Mean Tide Range - average of the mean tide range for all the gauge stations that
occur within a given 0.25° grid cell (mean tide range is the difference in height
between mean high water and mean low water in 1988); values in meters.
Regional Coastal Slope (%) - Acquired from ETOPO5 and NGDC elevation data.
Erosion and Accretion rates (m/yr) - the local subsidence trend.
Relative Sea-Level Rise (mm/yr) - Acquired from NOS tide stations.
Geomorphology Risk - ordinal value indicative of the type and susceptibility of the
landforms within a given 0.25° grid cell to inundation and erosion.
(3) The values for each variables are grouped into risk categories, and these risk categories
are used to calculate the CVI value using the following formula:
CVI = (a*b*c*d*e*f*g)/6] A !/2
The calculated CVI values are then grouped into risk categories (4 = very high risk, 3 =
high risk, 2 = medium risk, 1 = low risk).
(4) Hydrologic units are not a suitable reporting unit for this indicator, so a new coastal unit
was produced by creating a 20 mile inland buffer of the shoreline. The buffer was
divided into 150-mile long segments to show variation along the coast.
(5) The vertices within the linear geometry of each shapefile feature were converted to
points. Each point inherited the attributes, including the CVI risk categories (4 = very
high risk, 3 = high risk, 2 = medium risk, 1 = low risk), of the original linear feature. All
points within the new coastal units were averaged using a spatial join.
(6) The final map of coastal units was created using the average CVI risk values to indicate
low, medium, and high vulnerability categories.
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#125 Groundwater Reliance
Data Sources (see Appendix C for more details):
United States Geological Survey (USGS). Water Usage, 1995
http://water.usgs.gov/watuse/spread95/ush895.txt
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Data from USGS were downloaded and imported into ArcMap 9.2.
(2) The water usage data table was joined to the attribute table for the hydrologic units
shapefile, using the "HUC_CODE" and "HUCSCode" fields.
(3) The 8-digit HUC regions were aggregated into 4-digit HUCs with the 'dissolve' function
in ArcMap. The "SUB" attribute was used as the basis for the dissolve process. During
the dissolve operation, summary statistics were calculated for Total Groundwater
Withdrawals (TO_WGWTo) and Total Withdrawals (TO_WTotl) attributes.
(4) After the 4-digit HUC shapefile was produced with the dissolve operation, a new field for
Groundwater Reliance (GWRel_95) was added to store groundwater reliance values.
(5) Next, the indicator values were calculated using:
Groundwater Reliance (GWRel_95) = Total Groundwater Withdrawals (TO WGWTo)
Total Withdrawals (TOJWTotl)
(6) Finally, the Groundwater Reliance data were displayed on the map with symbology to
indicate low, medium, and high vulnerability categories.
#165 Meteorological Drought Indices
Data Sources (see Appendix C for more details):
Palmer Drought Severity Index: National Climatic Data Center. Available online at:
http://www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp#
NCDC Climate Division Boundaries. Available online at:
ftp://ftp.ncdc.noaa.gov/pub/data/divboundaries/gis/
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The drought index data were downloaded for the climate divisions within each of the
continental U.S. states as comma-delimited text for the years 2003-2007. The data for
each state were imported into a Microsoft Access database and compiled into a single
table.
(2) A select query was used to compute average Palmer Drought Severity Index values for
each climate division over the 2003-2007 time period using values in the PDSI column.
The query results were exported as a .dbf.
(3) The NCDC Climate Division Boundaries shapefile was opened in ArcMap 9.2, along
with the hydrologic units and the .dbf containing drought severity values.
(4) The DIVISION_ID attribute was used to join the climate boundaries to the drought index
data.
(5) The joined shapefile was intersected with the hydrologic units.
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(6) Using the area of the original climate boundaries, the area of the intersected shapes, and
the PDSI values, an area-weighted Palmer Drought Severity Index value was calculated
for each intersected area.
(7) The shapefile was dissolved by the 4-digit HUC code to reaggregate the 4-digit HUCs.
The area-weighted percentages were summed during the 'dissolve' operation.
(8) The final map was created using summed area-weighted percentages in the dissolved
shapefile to indicate low, medium, and high vulnerability categories.
#190 Number of Dry Periods in Grassland/Shrubland Streams and Rivers*
Data Sources (see Appendix C for more details):
Grassland Stream Sites and Flow Data, 2002-2006: The H. John Heinz III Center for
Science, Economics, and the Environment (Heinz Center). 2009. Email message to
Cadmus. April 28, 2009. (Filename: GSdry periods_Cadmus.xls).
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The Microsoft Excel spreadsheet containing site and zero-flow data from the Heinz
Center was opened in ArcMap, along with the hydrologic units, and Site data.
(2) Grassland sites within the zero-flow table were joined to the site information table using
the Site Number field.
(3) An event theme was created for the joined records, using the LON_DD and LAT_DD
fields (North American Datum 1983). The event theme was exported as a shapefile.
(4) The sites were aggregated within the 4-digit HUCs using a spatial join. If a HUC
contained more than one site, the numeric attributes within the site data were summarized
with the "SUM" option. As a result of the spatial join, a new shapefile was created
(Ind 190_HUC4_Sites. shp).
(5) To determine the average annual percentage of streams with zero-flow period, the
proportion of streams with zero flow within each 4-digit HUC was computed for each
year, and then the mean of five years (2002-2006) was computed for each HUC.
(6) The final map was created using the Mean_Pct attribute to indicate low, medium, and
high vulnerability categories. In 4-digit HUCs where no site data were available, the
HUC was assigned to a 'No Data' category.
#218 Ratio of Snow to Total Precipitation
Data Sources (see Appendix C for more details):
Monthly Climate Data and Observation Station Locations from National Climatic Data
Center. Available online at: http://gis.ncdc.noaa.gov/snowfallmo/ (The web site provides
access to multiple parameters, including snowfall totals. Data can be downloaded for
free from .edu and .gov domains.)
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
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Processing Steps:
(1) Climate data for all U.S. observation stations were downloaded and imported into
Microsoft Access. (Data were downloaded in two batches due to NCDC file size limits).
(2) The ratio of average annual snowfall (Element Code = 'TSNW') to average annual
precipitation (Element Code = 'TPCP') was calculated using a series of queries that
summed the monthly snow and precipitation totals for each year, calculated an annual
ratio, then averaged the annual ratios across the 1998-2007 time period. The output of the
queries was saved as a .dbf and imported into ArcMap 9.2.
(3) Observation station site data were also downloaded and opened as a fixed-width file in
Excel. Within this file, latitude and longitude coordinates for the observation stations
were in the format of Degrees: Minutes. These values were converted in Microsoft Excel
to Decimal Degrees using the formula:
Decimal Degrees = Degrees + (Minutes/60)
(4) An event theme was created in ArcMap for the Observation Stations using the adjusted
Lat/Long coordinates. This theme was joined to the .dbf containing heat sensitivity data
using the "COOPID" attribute. Not all Observation Stations had corresponding heat
sensitivity data; sites with heat sensitivity data were exported to shapefile and mapped.
(5) The stations (points) were aggregated within each 4-digit HUC (polygon) using a spatial
join. If a HUC contained more than one site, the average value across sites was
calculated.
(6) The final map was produced using the Avg_RATIO field to indicate low, medium, and
high vulnerability categories.
#219 Ratio of Withdrawals to Stream/low
Data Sources (see Appendix C for more details):
Mean Annual Precipitation 1971-2000: PRISM Climate Group (Oregon State
University). Available online at:
http://www.prism.oregonstate.edu/products/matrix.phtml
Mean Daily Maximum Temperature 1971-2000: PRISM Climate Group (Oregon State
University). Available online at:
http://www.prism.oregonstate.edu/products/matrix.phtml
United States Geological Survey (USGS). Water Usage, 1995
http://water.usgs.gov/watuse/spread95/ush895.txt
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) PRISM data were downloaded and converted to ESRI© GRID format using the ASCII
Text to Grid tool in ArcMap 9.2.
(2) Mean daily temperature was calculated with the raster calculator using the following
formula as described by Vogel, 1999: (Max Temp + Min Temp) / 2.
(3) Mean precipitation and mean daily temperature within each 4-digit HUC were calculated
using the Zonal Statistics function.
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(4) A new attribute for mean annual streamflow was calculated with the field calculator in
ArcMap for each HUC-4 unit using the regression equations in Vogel, 1999 (Table 2).
The HUC-2 code was used to associate each HUC-4 with the appropriate regional
regression equation.
(5) The water usage data table was joined to the attribute table for the hydrologic units
shapefile, using the "HUC_CODE" and "HUCSCode" fields.
(6) The 8-digit HUC regions were aggregated into 4-digit HUCs with the 'dissolve' function
in ArcMap. The "SUB" attribute was used as the basis for the dissolve process. During
the dissolve operation, summary statistics were calculated for Total Withdrawals.
(7) Streamflow and withdrawals were adjusted for some HUC-4 units to account for
withdrawals and streamflow that occur upstream.
(8) A new attribute for the ratio of withdrawals to streamflow was calculated with the field
calculator. The units for streamflow and withdrawals were converted as needed.
(9) The final map was produced indicating low, medium, and high vulnerability categories.
#284 Stream Habitat Quality
Data Sources (see Appendix C for more details):
Stream Rapid Assessment Metrics: EPA Wadeable Streams Assessment. (Filename:
rapidhabmetrics.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Stream Site Info: EPA Wadeable Streams Assessment. (Filename:
wsa_siteinfo_ts_final.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The rapid assessment data were downloaded and opened as a comma-delimited text file
in ArcMap, along with the stream site data, and the hydrologic units.
(2) This Rapid Assessment and Site Info tables were joined using the SitelD field.
(3) An event theme was mapped for the joined records, using the LON_DD and LAT_DD
fields (North American Datum 1983). Only sites with corresponding Rapid Assessment
scores were mapped. This event theme was exported to shapefile.
(4) The sites were aggregated within the 4-digit HUCs using a spatial join. If a HUC
contained more than one site, the average value for the Rapid Assessment score
(RH_SUM) was calculated. As a result of the spatial join, a new shapefile was created
(HUC4_RapidAssessment.shp).
(5) The final map was created using the Avg_RH_SUM field to indicate low, medium, and
high vulnerability categories. In 4-digit HUCs where no sampling occurred, the HUC
was assigned a 'No Data' category.
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#348 Erosion Rate
Data Sources (see Appendix C for more details):
Revised Universal Soil Loss Equation (RUSLE): RUSLE_1980.asc grid file of soil
erosion rates estimated for entire globe at 0.5 deg resolution with the RUSLE. Data were
obtained from: Dawen YANG, PhD, Professor, Department of Hydraulic Engineering
Tsinghua University, Beijing 100084, China; Tel: +86-10-62796976; Fax: +86-10-
62796971; E-mail: yangdw@tsinghua.edu.cn
State Boundaries: Environmental Systems Research Institute (ESRI) Data & Maps. -
Projected to Albers map projection)
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The ASCII file containing RUSLE data was converted to raster grid using ASCII to
Raster tool in ArcMap.
(2) Using the raster calculator, the grid values were multiplied by 1,000,000 to facilitate
conversion to polygons. The raster type was changed to integer.
(3) The raster was converted to a polygon layer using the Raster to Polygon tool in ArcMap.
(4) A new field called RUSLE = grid/1,000,000 was created in the polygon layer.
(5) The Intersect tool was used to combine the HUC4 layer with the RUSLE polygon layer to
create a new layer called HUC4_RUSLE_Intersect.
(6) A new field called AREAXRUSLE = AREA RUSLE was created.
(7) Summarized HUC4_RUSLE_Intersect layer on the HUC ID field (SUB), and calculating
the sum of AREAXRUSLE
(8) The summarized data table was joined to the HUC4 polygon layer and exported as a new
layer called HUC4_RUSLE.
(9) In HUC4_RUSLE, a new field RUSLE = AREAXRUSLE / AREA was created.
(10) The final map was created using the "RUSLE" attribute in the HUC4_RUSLE.shp
shapefile to indicate low, medium, and high vulnerability categories.
#351 Instream Use / Total Stream/low
Data Sources (see Appendix C for more details):
United States Geological Survey (USGS). Water Usage, 1995
http://water.usgs.gov/watuse/spread95/ush895.txt
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Mean Annual Runoff: U.S. Geological Survey. Available online at:
http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
Groundwater Recharge - WRC (U.S. Water Resources Council), 1978. The Nation's
Water Resources: 1975-2000 (Vol. 2). U.S. Government Printing Office, Washington
D.C.
Processing Steps:
(1) The water usage data table was joined to the attribute table for the hydrologic units
shapefile, using the "HUC_CODE" and "HUCSCode" fields.
(2) The 8-digit HUC regions were aggregated into 4-digit HUCs with the 'dissolve' function
in ArcMap. The "SUB" attribute was used as the basis for the dissolve process. During
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the dissolve operation, summary statistics were calculated for the Total Groundwater
Withdrawals attribute.
(3) After the 4-digit HUC shapefile was produced with the dissolve operation, surface water
withdrawal values were converted from megagallons per day to gallons per year.
(4) The isopleths in the mean annual runoff dataset were opened in ArcMap. The Spatial
Analyst extension was used to interpolate continuous runoff values across the country.
(5) Mean runoff values within each 4-digit HUC were calculated using the Zonal Statistics
function.
(6) An attribute for groundwater recharge rates was added and groundwater overdraft values
were calculated based on the definition in the WRC (1978) report: (Groundwater
Recharge - Groundwater Withdrawals)
(7) An attribute for instream use was added and calculated based on the definition in the
WRC (1978) report: (Streamflow * 0.6)
(8) Indicator values were calculated using the formula described in WRC (1978).
Streamflow is assumed to be equal to runoff:
Instream use / (Streamflow - Groundwater overdraft)
(9) Finally, the indicator values were displayed on the map with symbology to indicate low,
medium, and high vulnerability categories.
#352 Total Use / Total Streamflow
Data Sources (see Appendix C for more details):
United States Geological Survey (USGS). Water Usage, 1995
http://water.usgs.gov/watuse/spread95/ush895.txt
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Mean Annual Runoff: U.S. Geological Survey. Available online at:
http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
Groundwater Recharge - WRC (U.S. Water Resources Council), 1978. The Nation's
Water Resources: 1975-2000 (Vol. 2). U.S. Government Printing Office, Washington
D.C.
Processing Steps:
(1) The water usage data table was joined to the attribute table for the hydrologic units
shapefile, using the "HUC_CODE" and "HUCSCode" fields.
(2) The 8-digit HUC regions were aggregated into 4-digit HUCs with the 'dissolve' function
in ArcMap. The "SUB" attribute was used as the basis for the dissolve process. During
the dissolve operation, summary statistics were calculated for the Total Consumptive Use
and Total Groundwater Withdrawals attributes.
(3) After the 4-digit HUC shapefile was produced with the dissolve operation, surface water
withdrawal values were converted from megagallons per day to gallons per year.
(4) The isopleths in the mean annual runoff dataset were opened in ArcMap. The Spatial
Analyst extension was used to interpolate continuous runoff values across the country.
(5) Mean runoff values within each 4-digit HUC were calculated using the Zonal Statistics
function.
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(6) An attribute for groundwater recharge rates was added and groundwater overdraft values
were calculated based on the definition in the WRC (1978) report: (Groundwater
Recharge - Groundwater Withdrawals)
(7) An attribute for instream use was added and calculated based on the definition in the
WRC (1978) report: (Streamflow * 0.6)
(8) Indicator values were calculated using the formula described in WRC (1978).
Streamflow is assumed to be equal to runoff:
(Instream use + Total Consumptive Use) / (Streamflow - Groundwater overdraft)
(9) Finally, the indicator values were displayed on the map with symbology to indicate low,
medium, and high vulnerability categories.
#364 Pesticide Toxicity Index (PTI)
Data Sources (see Appendix C for more details):
Pesticide (herbicides and insecticide) Concentrations: USGS NAWQA Program. "The
Quality of Our Nation's Waters Pesticides in the Nation's Streams and Ground Water,
1992-2001" (USGS Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6a.txt
USEPA. 2009. ECOTOX Database. Available online at: http://cfpub.epa.gov/ecotox/.
Accessed September 11, 2009.
NAWQA Sites: USGS. NAWQA Data Warehouse: SiteFile Master.
shttp://infotrek.er.usgs.gov/nawqa queries/sitemaster/index.jsp
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) EC50 values for all Daphnia species for pesticides measured by NAWQA were
downloaded from EPA's ECOTOX database. For each pesticide, the mean Daphnia
EC50 value was calculated.
(2) Pesticide concentration data were downloaded and imported into Microsoft Excel.
(3) In Excel, the concentration of each pesticide at each sample event was divided by its
mean Daphnia EC50 value to calculate its toxicity quotient.
(4) The toxicity quotients for all pesticides were summed for each sampling event to
calculate that event's PTI. The toxicity quotients for constituents that were not measured
or were below detection levels were assumed to be zero.
(5) The PTIs for all sampling events at each site were summed to calculate a site PTI. This
table was imported into ArcMap.
(6) The PTI table was joined to the NAWQA Sites using the "STAID" attribute. In some
cases, the STAID value required minor edits to correctly join the tables. A total of 187
sites with pesticide concentration data were successfully joined to the NAWQA spatial
data.
(7) NAWQA spatial data were displayed as an event theme, using the Latitude and
Longitude variables (North American Datum of 1983).
(8) Next, the hydrologic units (4-digit HUCs) were added to the ArcMap project and joined
using a spatial join to the NAWQA points. If more than one site with pesticide data was
located within a 4-digit HUC, the PTI values were averaged across sites. In some cases,
there were no sites with pesticide data within a 4-digit HUC.
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(9) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Pesticide concentrations in agricultural areas, urban areas, and mixed land use areas
were combined for this indicator, although the USGS reports these land use types separately.
#367 Herbicide Concentrations in Streams
Data Sources (see Appendix C for more details):
Herbicide Concentrations: USGS NAWQA Program. "The Quality of Our Nation's
Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001" (USGS
Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6a.txt
NAWQA Sites: USGS. NAWQA Data Warehouse: SiteFile Master.
shttp://infotrek.er.usgs.gov/nawqa_queries/sitemaster/index.jsp
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Herbicide data were downloaded and imported into Microsoft Excel.
(2) In Excel, measured values for herbicides, herbicide degradates, and fungicides were
identified and summed for each sampling event. Constituents that were not measured or
were below detection levels were assumed to be zero.
(3) The herbicide concentration table was imported into ArcMap. For each sampling site, the
average of these total concentrations was calculated for all sampling events that occurred
at that site using the "Summarize" function.
(4) The summarized herbicide table and was joined to the NAWQA Sites using the "STAID"
attribute. In some cases, the STAID value required minor edits to correctly join the
tables. A total of 182 sites with herbicide concentration data were successfully joined to
the NAWQA spatial data.
(5) NAWQA spatial data were displayed as an event theme, using the Latitude and
Longitude variables (North American Datum of 1983).
(6) Next, the hydrologic units (4-digit HUCs) were added to the ArcMap project and joined
using a spatial join to the NAWQA points. If more than one site with herbicide data was
located within a 4-digitt HUC, the concentration values were averaged across sites. In
some cases, there were no sites with herbicide data within a 4-digit HUC.
(7) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Herbicide concentrations in agricultural areas, urban areas, and mixed land use areas
were combined for this indicator, although the USGS reports these land use types separately.
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#369 Insecticide Concentrations in Streams
Data Sources (see Appendix C for more details):
Insecticide Concentrations: USGS NAWQA Program. "The Quality of Our Nation's
Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001" (USGS
Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6a.txt
NAWQA Sites: USGS. NAWQA Data Warehouse: SiteFile Master. Available online at:
shttp://infotrek.er.usgs.gov/nawqa_queries/sitemaster/index.jsp
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Data were downloaded (see data sources) and imported into Microsoft Excel.
(2) In Excel, measured values for insecticides, insecticide degradates, and acaricides were
identified and summed for each sampling event. Constituents that were not measured or
were below detection levels were assumed to be zero.
(3) The insecticide concentration table was imported into ArcMap. For each sampling site,
the average of these total concentrations was calculated for all sampling events that
occurred at that site using the "Summarize" function.
(4) The summarized insecticide table and was joined to the NAWQA Sites using the
"STAID" attribute. In some cases, the STAID value required minor edits to correctly
join the tables. A total of 182 sites with insecticide concentration data were successfully
joined to the NAWQA spatial data.
(5) NAWQA spatial data were displayed as an event theme, using the Latitude and
Longitude variables (North American Datum of 1983).
(6) Next, the hydrologic units (4-digit HUCs) were added to the ArcMap project and joined
using a spatial join to the NAWQA points. If more than one site with insecticide data
was located within a 4-digitt HUC, the concentration values were averaged across sites.
In some cases, there were no sites with insecticide data within a 4-digit HUC.
(7) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Insecticide concentrations in agricultural areas, urban areas, and mixed land use areas
were combined for this indicator, although the USGS reports these land use types separately.
#371 Organochlorines in Bed Sediment
Data Sources (see Appendix C for more details):
Organochlorine Concentrations: USGS NAWQA Program. "The Quality of Our Nation's
Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001" (USGS
Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6c.txt
NAWQA Sites: USGS. NAWQA Data Warehouse: SiteFile Master. Available online at:
shttp://infotrek.er.usgs.gov/nawqa queries/sitemaster/index.jsp
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
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Processing Steps:
(1) Organochlorine concentration data were downloaded and imported into Microsoft Excel.
(2) In Excel, measured values for all parameters except P49271 (organic carbon in sediment)
were summed for each site. Only one sampling event occurred at each site, so
aggregation at the site level was not conducted. Constituents that were not measured or
were below detection levels were assumed to be zero.
(3) The organochlorine occurrence data were imported into ArcMap, and joined to the
NAWQA Sites using the "STAID" attribute. A total of 1,015 sites with organochlorine
concentration data were successfully joined to the NAWQA spatial data.
(4) NAWQA spatial data were displayed as an event theme, using the Latitude and
Longitude variables (North American Datum of 1983).
(5) The hydrologic units (4-digit HUCs) were opened in ArcMap and joined to the NAWQA
points using a spatial join. If more than one site with organochlorine data was located
within a 4-digit HUC, the total concentration values were averaged across sites. In some
cases, there were no sites with organochlorine data within a 4-digit HUC.
(6) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Organochlorine concentrations in agricultural areas, urban areas, and mixed land use
areas were combined for this indicator, although the USGS reports these land use types
separately.
#373 Herbicide Concentrations in Groundwater
Data Sources (see Appendix C for more details):
Herbicide Concentrations: USGS NAWQA Program. "The Quality of Our Nation's
Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001" (USGS
Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6b.txt
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Herbicide concentration data were downloaded and imported into Microsoft Excel.
(2) In Excel, measured values for herbicides, herbicide degradates, and acaricides were
identified and summed for each sampling event. Constituents that were not measured or
were below detection levels were assumed to be zero.
(3) These herbicide occurrence data were imported into ArcMap and displayed as an event
theme, using the Latitude and Longitude variables (North American Datum of 1983).
(4) Next, the hydrologic units (4-digit HUCs) were added to the ArcMap project and joined
using a spatial join to the sampling event points. If more than one sampling event with
herbicide data occurred within a 4-digit HUC, the concentration values were averaged
across events. In some cases, no herbicide collection events occurred within a 4-digit
HUC.
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(5) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Herbicide concentrations in agricultural and urban areas were combined for this
indicator, although the USGS reports these land use types separately.
#374 Insecticide Concentrations in Groundwater
Data Sources (see Appendix C for more details):
Insecticide Concentrations: USGS NAWQA Program. "The Quality of Our Nation's
Waters Pesticides in the Nation's Streams and Ground Water, 1992-2001" (USGS
Circular 1291). Available online at:
http://water.usgs.gov/nawqa/pnsp/pubs/circl291/appendix6/appendix6b.txt
Hydrologic Units: http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Insecticide concentration data were downloaded and imported into Microsoft Excel.
(2) In Excel, measured values for insecticides, insecticide degradates, and acaricides were
identified and summed for each sampling event. Constituents that were not measured or
were below detection levels were assumed to be zero.
(3) These insecticide concentration data were imported into ArcMap and displayed as an
event theme, using the Latitude and Longitude variables (North American Datum of
1983).
(4) Next, the hydrologic units (4-digit HUCs) were added to the ArcMap project and joined
using a spatial join to the sampling event points. If more than one sampling event with
insecticide data occurred within a 4-digit HUC, the concentration values were averaged
across events. In some cases, no insecticide collection events occurred within a 4-digit
HUC.
(5) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC. In 4-digit HUCs where no sampling
occurred, the HUC was assigned a 'No Data' category.
Note: Insecticide concentrations in agricultural and urban areas were combined for this
indicator, although the USGS reports these land use types separately.
#437 Precipitation Elasticity of Stream/low
Data Sources (see Appendix C for more details):
Precipitation Elasticity of Streamflow: Adapted from Figure 4 in Sankarasubramanian et
al. (2001). Water Resources Research 37(6).
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) An image of Figure 4 was imported into ArcMap and georeferenced.
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(2) Using the Spatial Analyst extension, the isopleths in Figure 4 were digitized and used to
interpolate continuous elasticity values across the country.
(3) Mean elasticity values within each 4-digit HUC were calculated using the Zonal Statistics
function.
(4) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC.
Note: For the purposes of mapping this indicator with relative ease, Figure 4 in the original
literature source, Sankarasubramanian et al., 2001 (see Appendix A for full reference) was used.
However, the complete original data set could also be recalculated for mapping purposes using
the data sources listed in Appendix C.
#449 Ratio of Storage to Runoff
Data Sources (see Appendix C for more details):
Reservoir Storage: National Inventory of Dams (from the National Atlas). Available
online at: http://www.nationalatlas.gov/mld/damsOOx.html
Mean Annual Runoff: U.S. Geological Survey. Available online at:
http://water.usgs.gov/GIS/metadata/usgswrd/XML/runoff.xml
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) The isopleths in the mean annual runoff dataset were opened in ArcMap. The Spatial
Analyst extension was used to interpolate continuous runoff values across the country.
(2) Mean runoff values within each 4-digit HUC were calculated using the Zonal Statistics
function.
(3) The shapefile containing data for dams was opened in ArcMap. The total maximum
reservoir storage capacity was calculated by joining the 4-digit HUCs to the dam data
using a spatial join. If more than one reservoir occurred within a 4-digit HUC, the
storage capacity was summed.
(4) The ratio of storage capacity to mean annual runoff was calculated within each 4-digit
HUC and saved within a new attribute.
(5) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC.
#453 Runoff Variability
Data Sources (see Appendix C for more details):
Global Runoff, 1980-1993. Available online at:
http: //www. hydro. Washington. edu/SurfaceWaterGroup/Data/vi c_gl ob al. html
Specific data file:
ftp: //ftp. hydro. Washington. edu/pub/CE/HYDRO/nij s sen/vi c_gl ob al/calibrated/runoff. cali
brated.monthly. 1980_1993 .nc.gz
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
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Processing Steps:
(1) The modeled runoff data were downloaded and decompressed.
(2) The NetCDF file, containing monthly runoff data, was imported into ArcMap. Monthly
grids were exported from the NetCDF format for all months during the 1984-1993 time
period (120 months).
(3) Annual runoff was calculated by aggregating the monthly values for each year.
(4) The mean and standard deviation of the annual runoff within each 2° x 2° grid cell was
calculated using the 10 annual values.
(5) The coefficient of variation was calculated by dividing mean annual runoff by the
standard deviation for each grid cell.
(6) The mean coefficient of variation within the 4-digit HUCs was calculated using the Zonal
Statistics function.
(7) The final map was created using symbology to indicate low, medium, and high
vulnerability categories for each 4-digit HUC.
#460 Macroinvertebrate Index ofBiotic Condition
Data Sources (see Appendix C for more details):
Stream Water Benthic Macroinvertebrate Metrics: EPA Wadeable Streams Assessment.
(Filename: wsa_benmet300_ts_fmal.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Stream Site Info: EPA Wadeable Streams Assessment (Filename:
wsa_siteinfo_ts_final.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Benthic macroinvertebrate data were downloaded and opened as a comma-delimited text
file in Microsoft Excel. The columns containing the SitelD and Macroinvertebrate Index
(MMI_WSA) attributes were saved as a new comma-delimited text file.
(2) The text file with macroinvertebrate data, the hydrologic units, and Site Info were opened
in ArcMap 9.2. The macroinvertebrate and Site Info tables were joined using the SitelD
attribute.
(3) An event theme was mapped for the joined records with corresponding macroinvertebrate
data, using the LON_DD and LAT_DD fields (North American Datum 1983). This event
theme was exported as a shapefile.
(4) The sites were aggregated within the 4-digit HUCs using a spatial join. If a HUC
contained more than one site, the average value for the Macroinvertebrate Index was
calculated.
(5) The final map was created using the Avg_MMI_WS field to indicate low, medium, and
high vulnerability categories. In 4-digit HUCs where no sampling occurred, the HUC was
mapped assigned a 'No Data' category.
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#461 Macroinvertebrate Observed / Expected Ratio ofTaxa Loss
Data Sources (see Appendix C for more details):
Stream Water Benthic Macroinvertebrate Metrics: EPA Wadeable Streams Assessment.
(Filename: wsa_benmet300_ts_final.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Stream Site Info: EPA Wadeable Streams Assessment. (Filename:
wsa_siteinfo_ts_final.csv). Available online at:
http://www.epa.gov/owow/streamsurvey/web_data.html
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) Benthic macroinvertebrate data were downloaded and opened as a comma-delimited text
file in Microsoft Excel. The columns containing the SitelD and Macroinvertebrate O/E
Ratio (OE_5_3REG) attributes were saved as a new comma-delimited text file.
(2) The text file with macroinvertebrate data, the hydrologic units, and Site Info were opened
in ArcMap 9.2. The macroinvertebrate and Site Info tables were joined using the SitelD
attribute.
(3) An event theme was mapped for the joined records with corresponding macroinvertebrate
data, using the LON_DD and LAT_DD fields (North American Datum 1983). This event
theme was exported as a shapefile.
(4) The sites were aggregated within the 4-digit HUCs using a spatial join. If a HUC
contained more than one site, the average value for the O/E Ratio was calculated.
(5) The final map was created using the Avg_OE_5_3 field to indicate low, medium, and
high vulnerability categories. In 4-digit HUCs where no sampling occurred, the HUC was
mapped assigned a 'No Data' category.
#623 Water Availability: Net Streamflow per Capita
Data Sources (see Appendix C for more details):
Mean Annual Precipitation 1971-2000: PRISM Climate Group (Oregon State
University). Available online at:
http://www.prism.oregonstate.edu/products/matrix.phtml
Mean Daily Maximum Temperature 1971-2000: PRISM Climate Group (Oregon State
University). Available online at:
http://www.prism.oregonstate.edu/products/matrix.phtml
United States Geological Survey (USGS). Water Usage, 1995
http://water.usgs.gov/watuse/spread95/ush895.txt
This data set includes population within each HUC-8 unit.
Hydrologic Units - http://water.usgs.gov/GIS/metadata/usgswrd/XML/huc250k.xml
Processing Steps:
(1) PRISM data were downloaded and converted to ESRI© GRID format using the ASCII
Text to Grid tool in ArcMap 9.2.
(2) Mean daily temperature was calculated with the raster calculator using the following
formula as described by Vogel, 1999: (Max Temp + Min Temp) / 2.
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(3) Mean precipitation and mean daily temperature within each 4-digit HUC were calculated
using the Zonal Statistics function.
(4) A new attribute for mean annual streamflow was calculated with the field calculator in
ArcMap for each HUC-4 unit using the regression equations in Vogel, 1999 (Table 2).
The HUC-2 code was used to associate each HUC-4 with the appropriate regional
regression equation.
(5) The water usage data table was joined to the attribute table for the hydrologic units
shapefile, using the "HUC_CODE" and "HUCSCode" fields.
(6) The 8-digit HUC regions were aggregated into 4-digit HUCs with the 'dissolve' function
in ArcMap. The "SUB" attribute was used as the basis for the dissolve process. During
the dissolve operation, summary statistics were calculated for total population and total
withdrawals.
(7) Streamflow and withdrawals were adjusted for some HUC-4 units to account for
streamflow and withdrawals upstream of the HUC-4 unit.
(8) A new attribute for net per capita streamflow was calculated with the field calculator,
using the following equation:
(Streamflow - Withdrawals) / Population
The units for streamflow and population were converted as needed.
(9) The final map was produced indicating low, medium, and high vulnerability categories.
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Appendix E. Example Maps for Indicators of Water Quality and Aquatic Ecosystem Vulnerability,
Displayed Using 4-digit Hydrologic Units
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This appendix contains example maps (and map descriptions) for 25 vulnerability indicators displayed using 4-digit hydrologic units.
These maps are intended to illustrate the methodological challenges of creating indicator maps, and rely on simplifying assumptions
(such as the use of quantiles instead of thresholds). Therefore, they are not appropriate for drawing conclusions regarding the
vulnerability of water quality and aquatic ecosystems in any particular state or region. Descriptions of U.S. geographic regions are
based on definitions provided by the U.S. Census Bureau. Subregion descriptions were based on U.S. Census definitions, but modified
slightly for clarity.
1. Northeast
i.
ii.
iii.
iv.
V.
vi.
i.
ii.
iii.
2. Midwest
i.
ii.
iii.
iv.
V.
i.
ii.
iii.
iv.
V.
vi.
a. New England
Connecticut
Maine
Massachussetts
New Hampshire
Rhode Island
Vermont
b. Middle Atlantic
New Jersey
New York
Pennsylvania
a. Great Lakes
Indiana
Illinois
Michigan
Ohio
Wisconsin
b. Western Midwest
Iowa
Kansas
Minnesota
Missouri
Nebraska
North Dakota
vii.
3. South
i.
ii.
iii.
iv.
V.
vi.
vii.
i.
ii.
iii.
iv.
V.
vi.
i.
ii.
iii.
iv.
4. West
i.
ii.
South Dakota
a. South Atlantic
Delaware
District of Columbia
Maryland
North Carolina
South Carolina
Virginia
West Virginia
b. Southeast
Florida
Georgia
Kentucky
Alabama
Mississippi
Tennessee
c. Central South
Texas
Oklahoma
Arkansas
Louisiana
a. Mountain West
Arizona
Colorado
iii. Idaho
iv. New Mexico
v. Montana
vi. Utah
vii. Nevada
viii. Wyoming
b. Pacific West
i. California
ii. Oregon
iii. Washington
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#1 Acid Neutralizing Capacity, 2000-2004
Percent of Sites with ANC < 100 millieq/L
o%
0.0001%- 10.00%
10.01%-17.39%
17.40%-25.00%
25.01%-100.0%
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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#1 Acid Neutralizing Capacity
This continental U.S. indicator map shows the percentage of sites with Acid Neutralizing Capacity (ANC) less than 100
millieq/L in each HUC-4 area. Data were available for the vast majority of lower-48 watersheds. The majority of watersheds are at
0%. Most of the watersheds with less ANC are a narrow band which spans from the Southeast to the Northeast. Only six watersheds
are in the lowest category of ANC (25.01 - 100% of sites <100 millieq/L).
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#22 A t-Risk Freshwater Plant Communities, 2OO6
Percent of At-Risk Freshwater Plant
Communities
8.708-38.91
38.92-48.02
48.03-52.24
52.25-56.50
56.51 - 100.0
States
0 100 200 300 400 500 Miles
I I I I I I
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#22 At-Risk Freshwater Plant Communities
The continental U.S. map for this indicator shows the percentage of freshwater plant communities that are considered at-risk in
each HUC-4 area. Data were available for all lower-48 watersheds. The regions with the highest percentages of freshwater plant
communities at risk (56.51 - 100.0% at risk) for this indicator occur in the South Atlantic, Southeast, Northwest, large parts of Kansas,
Missouri, Indiana, Ohio, and Louisiana. Relatively high percentages of plant communities at risk (52.25 - 56.50%) occur in Texas and
in parts of the Mountain West and Midwest, extending eastward from Wyoming to Ohio.
Moderate percentages of plant communities at risk (48.03 - 52.24%) occur in a contiguous band in the Southwest, and in large
parts of Montana, South Dakota, and Arkansas. Relatively lower percentages of communities at risk (38.92 - 48.02%) occur in two
vertical bands in the northern Midwest region, and a horizontal band in the Southwest, including parts of Arizona, New Mexico, and
small parts of Texas and Oklahoma. The Northeast and parts of Minnesota, Iowa, and Arizona have the lowest percentages (8.708 -
38.91%) of at-risk freshwater plant communities.
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Final Report, August 2011
#24 At-Risk Native Freshwater Species, 2OO9
Percent of At-Risk Native Freshwater
Species
2.135%-4.032%
4.033%-6.314%
6.315%-9.839%
9.840%-12.22%
12.23%-25.25%
States
0 100 200 300 400 500 Miles
I I I I I I
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#24 At-Risk Native Freshwater Species
This continental U.S. indicator map shows the percentage of at-risk native freshwater species in each HUC-4 area. Data were
available for all lower-48 watersheds. This map displays a very clear pattern. Homogenous blocks of high percentages of risk (12.23 -
25.25%) are found in the Southwest, East Texas, and the Southeast. With very few exceptions, risk is a steady gradation from these
areas to New England and the central U.S., which are at very low percentages of risk (2.135 - 4.032%). The Chesapeake Bay is also an
area with low percentage of species at risk.
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#51 Coastal Vulnerability Index, 2OO1
Coastal Vulnerability Index
1.00-1.77
1.78-2.41
2.42-2.83
2.84-3.18
3.19-3.97
States
0 100 200 300 400 500 Miles
I I I I I I
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#51 Coastal Vulnerability Index- CVI
This continental U.S. indicator map shows the Coastal Vulnerability Index (CVI) for coastline areas. Data were available for
all lower-48 coastlines. Areas of high vulnerability (3.19 - 3.97) include parts of the California, Texas, and North Carolina, as well as
the entire Mississippi Delta coastline and the Chesapeake Bay. Areas of moderate (2.84 - 3.18), medium (2.42 - 2.83), and low (1.78 -
2.41) vulnerability are interspersed along the coastline. Very low (1.00 - 1.77) vulnerability occurs mostly on the Northeast coastline.
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#125 Groundwater Reliance, 1995
Percent of Water Withdrawals from
Groundwater
0.080% - 4.284%
4.285%-12.53%
12.54%-26.80%
26.81%-54.94%
54.95% - 99.94%
States
0 100 200 300 400 500 Miles
I I I I I I
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#125 Groundwater Reliance
This continental U.S. indicator map shows the percentage of groundwater reliance in each HUC-4 area. Data were available for
all lower-48 watersheds. A high level (54.95 - 99.94%) of groundwater reliance is mainly observed in a vertical band in the Midwest,
stretching from parts of North Dakota to much of West Texas, as well as in two clusters in the Southwest and along the Mississippi
River. Moderate to low (4.285 - 54.94%) groundwater reliance is observed scattered across the nation. The main area with almost no
groundwater reliance (0.080 - 4.284%) is in the Mountain West, and stretches from Montana to the Four Corners. Other watersheds
with almost no groundwater reliance are scattered across the nation, most notably in central Texas; which is in direct contrast with
adjacent watersheds with high groundwater reliance.
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Final Report, August 2011
#165 Meteorological Drought Indices, 2OO3-2OO7
Average Palmer Drought Severity Index
1.39-15.0
0.308-1.38
-0.214-0.307
-0.931 --0.215
-7.33--0.932
States
0 100 200 300 400 500 Miles
I I I I I I
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#165 Meteorological Drought Indices
This continental U.S. indicator map shows the average Palmer Drought Severity Index (PDSI) in each HUC-4 area. Data were
available for all lower-48 watersheds. A high PDSI values (1.39 to 15) are observed mainly in the Northeast. Moderate and low PDSI
values (0.308 to 1.38, and -0.214 to 0.307) are observed in the central Midwestern states. Very low and extremely low PDSI values (-
0.931 to -0.215, and -7.33 to -0.932) are observed mainly in the Northwest, southern Mountain West, Central South, and the
Southeast.
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#218 Ratio of Snow to Total Precipitation, 1998-2OO7
Ratio of Total Snowfall to
Total Precipitation
0 - 0.0040
0.0041 - 0.036
0.037-0.11
0.12-0.19
0.20 - 0.47
States
0 100 200 300 400 500 Miles
I I I I I I
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#218 Ratio of Snow to Total Precipitation
This continental U.S. indicator map shows the ratio of total snowfall to total precipitation in each HUC-4 area. Data were
available for all lower-48 watersheds. Unsurprisingly, this map shows a strong north-south trend, with the highest ratios (0.20 - 0.47)
in the northen and mountainous regions, including the West, Great Lakes region, and parts of New England. These high ratios are
surrounded by graded bands of moderate (0.12 - 0.19), low (0.037 -0.11), and very low (0.0041 - 0.036) snowfall to total precipitation
ratios. Parts of California and Arizona have a ratio of zero, as does the Gulf Region.
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Final Report, August 2011
#219 Ratio of Water Withdrawls to Annual Streamflow,
1995
Total Withdrawls /
Annual Streamflow
0.00068-0.055
0.056-0.16
0.17-0.53
0.54-1.5
1.6-59
States
0 100 200 300 400 500 Miles
I I I I I I
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#219 Ratio of Water Withdrawals to Annual Stream/low
This continental U.S. indicator map shows the ratio of water withdrawals to annual streamflow in each HUC-4 area. Data were
available for all lower-48 watersheds. High ratios (1.6 - 59) are almost exclusively found in the West (with small exceptions in the
Lower Peninsula of Michigan and the Buffalo region). Moderate ratios (0.54 - 1.5) are found largely in the West, along the Middle
Atlantic Corridor, in Florida, and in the Great Lakes region. Low (0.17 - 0.53) and very low (0.056 - 0.16) ratios are scattered
throughout the country, but with higher prevalence in the East. Ratios of almost zero (0.00068 - 0.055) are found largely near the
Mississippi River or tributaries, in the Pacific Northwest, and in New England.
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Final Report, August 2011
#284 Stream Habitat Quality, 2OOO-2OO4
Average Rapid
Bioassessment Protocol Score
40.0-109.2
109.3- 125.0
125.1 -135.6
135.7- 147.0
147.1 -190.0
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#284 Stream Habitat Quality
This continental U.S. indicator map shows stream habitat quality, as defined by the average rapid bioassessment protocol
score, in each HUC-4 area. Data were available for the vast majority of lower-48 watersheds. The highest scores (147.1 - 190.0 and
135-.7-147.0) are scattered throughout the country, with clusters found in the South Atlantic and Northeast, the northern Mountain
West, and the Pacific Northwest. Moderate scores (125.1 - 135.6 and 109.3 - 125.0) are also found throughout the country, with
clusters in the Northwest and Great Lakes Region. The lowest scores (40.0 - 109.2) are found in Georgia and a vertical band in the
Midwest.
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Final Report, August 2011
#326 Wetland and Freshwater Species A t-Risk, 2OO6
Number of Wetland and Freshwater
Species At-Risk
0-5
6-10
11 -15
16-28
29-161
States
0 100 200 300 400 500 Miles
I I I I I I
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#326 Wetland and Freshwater Species at Risk
This continental U.S. indicator map shows the number of wetland and freshwater species that are at risk in each HUC-4 area.
Data were available for all lower-48 watersheds. A large number (29 - 161) of species are at risk in most of the watersheds in the
Southeast, and in a few watersheds in the Northeast and West. Watersheds with a moderate (16 - 28) number of species at risk are
largely found near watersheds with a high number of species at risk. Watersheds with a low (11-15) and very low (6 - 10) number of
species at risk are found everywhere but the Southeast. Watersheds with almost no (0 - 5) species at risk are mostly found in the
northern Mountain West and Western Midwest
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Final Report, August 2011
Soil Loss (tons/ha/year)
0.5391 -2.888
2.889-4.205
4.206-5.861
5.862-9.594
9.595-25.57
States
#348 Erosion Rate, 198O
0 100 200 300 400 500 Miles
I I I I I I
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#348 Erosion Rate
This continental U.S. indicator map shows the average erosion rate in each HUC-4 area. Data were available for all lower-48
watersheds. High (9.595 - 25.57 tons/ha/year) and moderate (5.862 - 9.594 tons/ha/year) soil loss is found principally in the West,
Middle Atlantic, and parts of the Southeast. Lower (0.5391 - 5.861 tons/ha/year) soil loss rates are found scattered about the country
and in a vertical band from Montana to Texas.
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Final Report, August 2011
#351 In-Stream Use / Total Streamflow
In-Stream Use / Total Streamflow
0.60-1.00
1.01 -1.09
States
0 100 200 300 400 500 Miles
I I I I I I
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#351 Instream Use/Total Stream/low
This continental U.S. indicator map shows the ratio of in stream use to total streamflow in each HUC-4 area. Data were
available for all lower-48 watersheds. All watersheds but one fall in the 0.60 to 1.00 category, with one watershed in Oklahoma and
Kansas in the 1.01 to 1.09 category.
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Final Report, August 2011
#352 Total Use / Total Streamflow
Total Use / Total Streamflow
0.60-1.00
1.01 -17.82
States
0 100 200 300 400 500 Miles
I I I I I I
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#352 Total Use / Total Streamflow
This continental U.S. indicator map shows the ratio of total use to total streamflow in each HUC-4 area. Data were available
for all lower-48 watersheds. Most watersheds fall in the 0.60 to 1.00 category. However there are a few in the Southwest and southern
Midwest in the 1.01 to 17.82 category.
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Final Report, August 2011
#364 Pesticide Toxicity Index, 1992-2OO1
Pesticide Toxicity Index
for Daphnia species
0.0000020 - 0.00028
0.00029-0.0017
0.0018-0.0072
0.0073-0.017
0.018-0.16
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#364 Pesticide Toxicity Index
This continental U.S. indicator map shows the Daphnia species pesticide toxicity index in each HUC-4 area. Data were not
available for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#367 Herbicide Concentrations in Streams, 1993-2OO1
Herbicide Concentrations (ug/L)
0.00975-0.0837
0.0838 - 0.243
0.244-0.857
0.858-2.66
2.67-18.4
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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#367 Herbicide Concentrations in Streams
This continental U.S. indicator map shows herbicide concentrations in streams in each HUC-4 area. Data were not available
for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#369 Insecticide Concentrations in Streams, 1993-2O01
Insecticide Concentrations (ug/L)
0.000-0.007
0.008-0.020
0.021 - 0.048
0.049-0.115
0.116-0.681
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#369 Insecticide Concentrations in Streams
This continental U.S. indicator map shows insecticide concentrations in streams in each HUC-4 area. Data were not available
for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns.
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Final Report, August 2011
#371 Organochlorines in Bed Sediment, 1991-1997
Organochlorines in
Streambed Sediment
(ug/kg dry weight)
0.000- 7.834
7.835-32.00
32.01 - 75.82
75.83- 175.2
175.3-453.0
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#371 Organochlorines in Bed Sediment
This continental U.S. indicator map shows organochlorine concentrations in streambed sediment in each HUC-4 area. Data
were not available for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns.
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Final Report, August 2011
#373 Herbicides in Groundwater, 1992-2OO3
Herbicides in Groundwater (ug/L)
0.00 - 0.0029
0.0030-0.016
0.017-0.063
0.064-0.20
0.21 -2.3
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#373 Herbicides in Groundwater
This continental U.S. indicator map shows herbicide concentrations in groundwater in each HUC-4 area. Data were available
for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#374 Insecticides in Groundwater, 1992-2OO3
Insecticides in Groundwater (ug/L)
o.oo
0.0000010-0.00026
0.00027 - 0.00074
0.00075-0.0031
0.0032 - 0.28
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#374 Insecticides in Groundwater
This continental U.S. indicator map shows insecticide concentrations in groundwater in each HUC-4 area. Data were not
available for many of the lower-48 watersheds. Available data is insufficient to infer geographic patterns, but does indicate the
possibility of higher concentrations in the Middle Atlantic Corridor.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#437 Precipitation Elasticity of Stream flow, 1951-1988
Precipitation Elasticity of Streamflow
0.0-1.0
1.1 -3.0
States
0 100 200 300 400 500 Miles
I I I I I I
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#437 Precipitation Elasticity of Stream/low
This continental U.S. indicator map shows the precipitation elasticity of streamflow in each HUC-4 area. Data were available
for all lower-48 watersheds. Every watershed has elasticity in the higher range (1.1 - 3.0) except for a few scattered throughout the
Midwest and one in Texas, which have elasticity in the lower range (0.0 - 1.0).
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#449 Ratio of Reservoir Storage to Mean Annual Runoff
Reservoir Storage (acre-feet) /
Mean Annual Runoff (in.)
1,408,421 -73,371,814
394,810-1,408,420
133,419-394,809
53,513-133,418
0-53,512
States
0 100 200 300 400 500 Miles
I I I I I I
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Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#449 Ratio of Reservoir Storage to Mean Annual Runoff
This continental U.S. indicator map shows the ratio of reservoir storage to mean annual runoff in each HUC-4 area. Data were
available for all lower-48 watersheds. High (1,408,421 - 73,371,814 acre-feet/inch) ratios are largely found in the vertical band
between North Dakota and Texas. Moderate ratios (394,810 - 1,408,420 acre-feet/inch) are largely found in the Midwest and the West.
Low (133,419 - 394,809 acre-feet/inch), very low (53,513 - 133,418 acre-feet/inch), and extremely low (0 - 53,512 acre-feet/inch)
ratios are largely found in coastal and Great Lakes watersheds.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#453 Runoff Variability, 1984-1993
Coefficient of Variation
of Annual Runoff
0.170-0.250
0.251 - 0.293
0.294-0.335
0.336 - 0.426
0.427- 1.111
States
0 100 200 300 400 500 Miles
I I I I I I
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#453 Runoff Variability
This continental U.S. indicator map shows the coefficient of variation of annual runoff in each HUC-4 area. Data were
available for all lower-48 watersheds. A high (0.427 - 1.111) coefficient is observed in clusters covering much of the West, an area in
the Midwest centered on Iowa, and part of Texas. Watersheds with a moderate (0.336 - 0.426) ratio are observed adjacent to those
clusters as well as in Maine and the Chesapeake region. Watersheds with a low (0.294 - 0.335) and very low (0.251 - 0.293) ratio are
observed across the country. The watersheds with the lowest ratio (0.170 - 0.250) are south of the Great Lakes, in New England, and
the lower Mississippi Basin.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#46O Macroinvertebrate Index of Biotic Condition,
2OOO-2OO4
Average Macroinvertebrate Index
51.80-77.94
44.98-51.79
37.54-44.97
29.50-37.53
4.20-29.49
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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#460 Macroinvertebrate Index ofBiotic Condition
This continental U.S. indicator map shows the macroinvertebrate index of biotic condition in each HUC-4 area. Data were
available for the vast majority of lower-48 watersheds. There is no discernable geographic pattern to the distribution of categories of
watersheds.
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Aquatic Ecosystems, Water Quality, and Global Change: Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report, August 2011
#461 Macroinvertebrate O/E Ratio of Taxa Loss,
2OOO-2OO4
Average Observed/
Expected Ratio of Taxa Loss
^B 71.12%-80.95%
^^| 80.96% - 87.45%
87.46% - 96.87%
96.88%-127%
No Data
States
0 100 200 300 400 500 Miles
I I I I I I
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#461 Macroinvertebrate Observed/Expected (O/E) Ratio ofTaxa Loss
This continental U.S. indicator map shows the observed taxa as a percentage of the expected macroinvertebrate taxa in each
HUC-4 area. Data were available for the vast majority of lower-48 watersheds. This map shows a certain amount of spatial
heterogeneity. The highest ratios (96.88% - 127%) occur mostly along the West Coast, in the Pacific Northwest, the Midwest, and
New England. Moderate ratios (87.46% - 96.87%) are found in large parts of California, parts of the Northwest, Great Lakes, and
South and Middle Atlantic regions. The remaining ratio categories (20.19% - 71.11%, 71.12% - 80.95%, and 80.96% - 87.45%) have
no discernable geographic distribution.
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Final Report, August 2011
#623 Water Availability: Net Stream f/ow per Capita, 1995
[Flow (gpd) - Withdrawals (gpd)]/
Population
24,220- 1,779,536
7,465-24,219
2,438 - 7,464
1 - 2,437
0
States
0 100 200 300 400 500 Miles
I I I I I I
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#623 Water Availability: Net Stream/low per Capita
This continental U.S. indicator map shows the net streamflow per capita in each HUC-4 area. Data were available for all
lower-48 watersheds. High flow per capita (24,220 - 1,779,536 gpd/capita) watersheds are found in the Pacific Northwest, Colorado
and Utah, the Mississippi Basin, and Maine. Moderate streamflow per capita (2,438 - 7,464 gpd/capita) watersheds are found mostly
around high streamflow per capita watersheds. Very low streamflow per capita (1 - 2,437 gpd/capita) watersheds are found in the
Great Lakes region, the Middle Atlantic Corridor, Florida, and the West. Zero net streamflow per capita watersheds are found in the
Great Lakes Region, and throughout the West.
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Appendix F. Example Maps for Indicators of Water Quality and Aquatic Ecosystem Vulnerability,
Displayed Using Ecoregions
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August 2011
This appendix contains example maps (and map descriptions) for the 25 vulnerability indicators displayed using Omernik's Level 3
ecoregions (Omernik, 19871). As with the maps in the previous appendix, these maps are intended to illustrate the methodological
challenges of creating indicator maps, and rely on simplifying assumptions (such as the use of quantiles instead of thresholds).
Therefore, they are not appropriate for drawing conclusions regarding the vulnerability of water quality and aquatic ecosystems in any
particular state or region. Descriptions of U.S. geographic regions are based on definitions provided by the U.S. Census Bureau.
Subregion descriptions were based on U.S. Census definitions, but modified slightly for clarity.
1. Northeast
a. New England
i.
ii.
iii
iv
v.
vi
2.
Connecticut
Maine
Massachussetts
New Hampshire
Rhode Island
Vermont
b. Middle Atlantic
i. New Jersey
ii. New York
iii. Pennsylvania
Midwest
a. Great Lakes
i. Indiana
ii. Illinois
iii. Michigan
iv. Ohio
v. Wisconsin
b. Western Midwest
i. Iowa
ii. Kansas
3.
iii. Minnesota
iv. Missouri
v. Nebraska
vi. North Dakota
vii. South Dakota
South
a. South Atlantic
i.
ii.
iii.
iv.
Texas
Oklahoma
Arkansas
Louisiana
4.
i.
ii.
iii.
iv.
v.
Delaware
District of Columbia
Maryland
North Carolina
South Carolina
vi. Virginia
vii. West Virginia
Southeast
i.
ii.
iii.
iv.
v.
vi.
Florida
Georgia
Kentucky
Alabama
Mississippi
Tennessee
West
a. Mountain West
i. Arizona
ii. Colorado
iii. Idaho
iv. New Mexico
v. Montana
vi. Utah
vii. Nevada
viii. Wyoming
b. Pacific West
i. California
ii. Oregon
iii. Washington
c. Central South
Omernik, J.M. 1987. Ecoregions of the conterminous United States. Map (scale 1:7,500,000). Annals ofthe Association of American Geographers. 77 (1): 118-
125.
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Aquatic Econtieaa. ffatrr Qualifr. and Giobai Change: CkaUmges of Conducting Main Strtasor Global Change l'ulnmtt>\lit\ AxmxmmO
final Ktport August :0l I
#1 Acid Neutralizing Capacity, 2000-2004
Percent of Sites with ANC < 100 millieq/L
| | 0%
J 0.01%- 4.17%
H 4.18%-11.11%
|^| 11 12%-27.27%
^f 27 28% - 66 67%
No Data
1 States
0 100 200 300 400 500 Mites
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
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#1 Acid Neutralizing Capacity
This continental U.S. indicator map shows the percentage of sites with Acid Neutralizing Capacity (ANC) less than 100
millieq/L in each ecoregion. Data were available for the vast majority of lower-48 ecoregions. The greater part of ecoregions in the
West, Midwest, and Central South have 0% of sites with ANC less than 100 millieq/L. Several ecoregions in the South East and South
Atlantic have a moderate (4.18 - 11.11%) or high (11.12 - 27.27%) percentage of sites with ANC less than 100 millieq/L. Ecoregions
with the highest percentage of sites (27.28 - 66.67%) with ANC less than 100 millieq/L cover a majority of Florida, parts of the
Northeast, and a smaller ecoregion spanning parts of Arkansas and Oklahoma.
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Aquatic Ecotyttemt. tfatrr Quatift. and GloM Change. Ckaltmgcs of Conducting Uuln Slrtssor Global Change Pu/n*ra4t/ifV Aatammo
Final Apart August Ml I
#22 At-Risk Freshwater Plant Communities, 2006
Percent of At-Risk Freshwater Plant Communities
| | 0.0%-31.3%
| 31.4% -45.7%
^H 45.8%-51.8%
^| 51 9%-55.5%
^B 55.6% -71.1%
| [States
0 100 200 300 400 500 Mite*
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
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#22 At-Risk Freshwater Plant Communities
This continental U.S. indicator map shows the percentage of freshwater plant communities that are considered at-risk in each
ecoregion. Data were available for all lower-48 ecoregions. The ecoregions with the highest percentages of freshwater plant
communities at risk (55.6 - 71.1%) for this indicator occur largely in the Southeast and the Pacific Northwest. Relatively high
percentages of plant communities at risk (51.9 - 55.5%) occur in ecoregions extending northward from Texas to the Midwest, as well
as in ecoregions scattered in the Mountain West.
Moderate percentages of plant communities at risk (45.8 - 51.8%) occur predominantly in the Southwest and Western Midwest
regions. Relatively lower percentages of communities at risk (31.4 - 45.7%) occur in New Mexico, the Great Lakes region, and parts
of the Middle-Atlantic. The northern Middle Atlantic and Northeast ecoregions have the lowest percentages (0-31.3%) of at-risk
freshwater plant communities.
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Aquatic Ecotyitma. Waltr Quality, and Global Change Ckaltmges of Conducting Multi Siressor Global Changr
Final Report. August 2011
#24 At-Risk Native Freshwater Species, 2006
Percentage of Freshwater Species At-Risk
| | 0.5%-4.6%
] 4.7% - 9.3%
|^| 9.4%-10.6%
Hi 10.7%. 15.3%
^B15 4% -22 7%
| | States
0 100 200 300 400 500 Mites
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
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#24 At-Risk Native Freshwater Species
This continental U.S. indicator map shows the percentage of at-risk native freshwater species in each ecoregion. Data were
available for all lower-48 ecoregions. Ecoregions with high percentages of freshwater species at risk (15.4 - 22.7%) are found in the
Southwest and in the Southeast. These high risk areas are surrounded by bands of moderate (10.7 - 15.3%) percentages of freshwater
species at risk. With very few exceptions, risk is a steady gradation from high risk areas in the southern U.S. to low risk areas (0.5 -
4.6%) in New England and the north-central U.S.
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Aquatic EfonHemi Watrr Quality; and Global Cltange: C'kailmgti of Conducting Main Strrssor Global Changt
Final Krport. August 2011
#125 Groundwater Reliance, 1995
Percent of Water Withdrawals from Groundwater
1.7%-4.8%
4.9%-13.4%
^B 13.5% -23.7%
JH 23.8%* 42.2%
IB 42.3% -78.7%
| | States
0 100 200 300 400 500 Mites
i I i i I I
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#125 Groundwater Reliance
This continental U.S. indicator map shows the percentage of groundwater reliance in each ecoregion. Data were available for
all lower-48 ecoregions. A high level (42.3 - 78.7%) of groundwater reliance is mainly observed in the ecoregions that stretch from
parts of North Dakota through parts of the western Midwest and Mountain West to western Texas. Other areas with high groundwater
reliance are found in the ecoregions along the U.S.-Mexico border, in ecoregions along the lower Mississippi River, and in parts of
California and Nevada. Moderate to low (4.9 - 42.2%) groundwater reliance is observed scattered across the nation. The main
ecoregions with almost no groundwater reliance (1.7 - 4.8%) stretch from Montana to the Four Corners and also occur in parts of the
Southeast, South, and Middle Atlantic regions.
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Aquatic Ecanttrmi. WssUr Qusdav. and Global Change: Ckalt«mg*s afCondwUng Mnlti Sfreaor Global Ckangr I'ulnfrabtlm AnrixmenO
Ftmtl Rrport. Attpal 2011
#165 Meteorological Drought Indices, 2003-2007
Average Palmer Drought Severity Index
| | 1.29 -3.07
0.52-1.28
-0.16-0.51
^| -2.79 - -0.42
| | States
0 100 200 300 400 500 Miles
i I i i i I
Pogtb
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#165 Meteorological Drought Indices
This continental U.S. indicator map shows the average Palmer Drought Severity Index (PDSI) in each ecoregion. Data were
available for all lower-48 ecoregions. Negative values of the PDSI indicate drought, positive values indicate excess rainfall, while 0
represents normal conditions for a given region. A very distinctive pattern emerges on this map. Ecoregions having the lowest PDSI
values (-2.79 to -0.42) occur predominantly in the West, but also in along the Great Lakes, and parts of the Central South. Low (-0.41
to -0.17) and moderate (-0.16 to 0.51) PDSI values are observed in the central states. High (0.52 to 1.28) and very high (1.29 to 3.07)
PDSI values observed mainly in parts of the Western Midwest, Great Lakes, South Atlantic, and Northeast.
Page F-14
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Aquatic Econiinni WiWrr Quahty. and GJoAo/ Changr. CkaUmgvi of Conducing Uulti Stfeaor Global Chang*
Find Rrport. August 2011
#218 Ratio of Snow to Total Precipitation, 1998-2007
Ratio of Total Snow to Total Precipitation
0.000 - 0.003
0.004 - 0 031
0.032-0.112
0.113-0.174
0.175-0821
No Data
States
0 100 200 300 400 500 Mites
i I I I I I
Pagef
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#218 Ratio of Snow to Total Precipitation
This continental U.S. indicator map shows the ratio of total snowfall to total precipitation in each ecoregion. Data were
available for all lower-48 ecoregions. Unsurprisingly, this map shows a strong north-south trend, with the ecoregions with the highest
ratios (0.175 - 0.821) occurring in the northern and mountainous regions including the northern West, Great Lakes, and parts of the
Northeast. These high ratios are surrounded by graded bands of moderate (0.113 - 0.174), low (0.032 - 0.112) and very low (0.004 -
0.031) snowfall to total precipitation ratios. Parts of California, Arizona, the Central South, and the Southeast have a ratio of zero,
indicating no snowfall.
Page F-16
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Aquatic Efonitrmi. Water Quality, and Giokal Change CMItnget of Conducing Sfitlti Siressor (jioba) Change Vulnerability AaeurnenO
Final Report. August ?0ll
#219 Ratio of Water Withdrawals to
Annual Streamflow, 1995
Total Withdrawals / Annual Streamflow
0.00 - 0.03
004-0.18
0.19-0.43
0.44 - 0.48
0.49-4.25
States
0 100 200 300 400 500 Mites
i I i i I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#219 Ratio of Water Withdrawals to Annual Stream/low
This continental U.S. indicator map shows the ratio of water withdrawals to annual streamflow in each ecoregion. Data were
available for all lower-48 ecoregions. High ratios (0.49 - 4.25) are almost exclusively found in the West. Ecoregions with moderate
ratios (0.44 - 0.48) cover parts of Oregon, Nevada, Idaho, Montana, Wyoming, Nebraska, Kansas, Oklahoma, and Texas. Low (0.19 -
0.43) and very low (0.04 - 0.18) ratios are scattered throughout the country, but with higher prevalence in the Midwest and the East.
Ratios near zero (0.00 - 0.03) are found largely near the Mississippi River or tributaries, in the Pacific Northwest, southern Wisconsin,
and Maine.
Page F-18
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Aquatic £rorviima. WtOtr Quality, and Global Change. Ckallmgts of Conducting Uulti Strtaor Global Cluing* I'ulnrraMin Auramrnit
Final Ktpoft. August :01I
#284 Stream Habitat Quality, 2000-2004
Average Rapid Bioassessment Protocol Score
) | 78.5 -114.4
] 114.5-126.8
|^| 1269-1358
^B 1359-1460
I | No Data
States
0 100 200 300 400 500 Mites
I I i I I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#284 Stream Habitat Quality
This continental U.S. indicator map shows stream habitat quality based on the rapid bioassessment protocol score in each
ecoregion. Data were available for the vast majority of lower-48 ecoregions. The ecoregions with the highest bioassessment protocol
scores (146.1 -182.8 and 135.9 - 146.0) include the northern Great Lakes region, an area which extends from parts of the Southeast to
the Northeast and other areas scattered throughout the country. Moderate scores (126.9 - 135.8) are found primarily in the upper
Midwest and Mountain West. The lowest scores (114.5 - 126.8 and 78.5 - 114.4) are found scattered in parts of the Southeast,
Midwest, Mountain West, and Pacific Northwest.
Page F-20
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Aquatic Eros\strmj (Tatar Quality, ami Global Changf: Ckallmgtt of Conducting Multi Strttoor Global Change Vulnfrabtlin Aatismeno
f-tnal Rrport. August :0ll
#326 Wetland and Freshwater Species At-Risk, 2006
Number of Wetland and Freshwater Specie* At-RI«k
0 12
Jtetol
0 100 200 300 400 500 Miles
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#326 Wetland and Freshwater Species at Risk
This continental U.S. indicator map shows the number of wetland and freshwater species that are at risk in each ecoregion.
Data were available for all lower-48 ecoregions. Ecoregions with the largest number (64 - 572) of species at risk are found primarily
in the Southeast and South Atlantic regions. Ecoregions with a moderate (34 - 63) number of species at risk are largely found near
ecoregions with a high number of species at risk in the Southeast, as well in ecoregions in the West and Midwest. Ecoregions with a
low (20 - 33) and very low (13 - 19) number of species at risk are found everywhere but the Southeast. Ecoregions with almost no (0 -
12) species at risk are mostly found primarily in the northern Midwest and Mountain West.
Page F-22
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Aqnafic Eronitrms. tPaler Qualift: ami Giobai Chaagt: Ckallntg*i of Conducting Main Sfrruor Global Chang* I'uinfrainlin AxnummtU
Final Rrpon. August 2011
#348 Erosion Rate, 1980
Soil Loss (tons/ha/year)
| 0.00-202
] 2.03-3.18
^| 939-3841
| | States
0 100 200 300 400 500 Miles
i I i I I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#348 Erosion Rate
This continental U.S. indicator map shows the average erosion rate in each ecoregion. Data were available for all lower-48
ecoregions. Ecoregions with high (9.39 - 38.41 tons/ha/year) and moderate (5.45 - 9.38 tons/ha/year) soil loss are found principally in
the West but also in the central Midwest and an area extending from parts of the Southeast to the Northeast. Lower (0.00 - 2.02
tons/ha/year) soil loss rates are found scattered throughout the country, including in a vertical band from North Dakota to Texas, the
Great Lakes region, and the Eastern seaboard.
Page F-24
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Aquatic Econstrnu. Wirtrr Quahty; and Global Change ('fallengts of Conducting Ualti Sft-rssor Giobal Change Vulnfrabtlity An u JUMiilI
Tina; Rrport. August 2011
#351 Instream Use / Total Streamflow
Instream Use / Total Streamflow
0.6-1.0
0 100 200 300 400 500 Miles
i I i i i i
States
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#351 Instream Use/Total Stream/low
This continental U.S. indicator map shows the ratio of instream use to total streamflow in each ecoregion. Data were available
for all lower-48 ecoregions. All ecoregions have values within the range of 0.6 - 1.0.
Page F-26
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Aqttotte Eaayitrms Vfatrr Quahry. anil Global Change CtutltmgfS ofCanducnitg 14ulti Streaor Global Cttangt I'ulnrrabthtY Aaramtntj
Ftna) Report. August :0l I
#352 Total Use / Total Streamflow
Total Use / Total Streamflow
0.601 -1.000
1.001-4.187
] States
0 100 200 300 400 500 Miles
i I I l I I
Pag* F 27
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#352 Total Use / Total Streamflow
This continental U.S. indicator map shows the ratio of total use to total streamflow in each ecoregion. Data were available for
all lower-48 ecoregions. Most ecoregions have values within the 0.601 - 1.000 range. However, there are a few ecoregions in the West
and in an area extending from Texas into the Central Midwest, that fall in the 1.001 to 4.187 category.
Page F-28
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Aqtutie Ecatyjirm*. Waitr Quality; and Global Change Ckalttnget of Conducting Ualti Strmaor Global Changr Vulnerability Atm
Final Report August ?0l 1
#364 Pesticide Toxicity Index, 1992-2001
Pesticide Toxicity Index for Daphnia Species
0.0000 - 0.0001
0.0002-0.0022
Hi ° °°23 - ° °°78
Bl 0.0079-0.0142
Bi 0.0143-0.0926
No Data
| | States
0 100 200 300 400 500 Miles
i I i I I I
/'.^r t ">
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#364 Pesticide Toxicity Index
This continental U.S. indicator map shows the Pesticide Toxicity Index (PTI) for Daphnia species in each ecoregion. Data
were not available for many of the lower-48 ecoregions. Available data is insufficient to infer geographic patterns, but does indicate
the possibility of higher pesticide toxicity index values in the central states, Southwest, and Southeast.
Page F-30
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.lOTtanc Efanrtrma. Waitr Quality; and Giobal Change Ckaltfugft o/Conducting Main Strusor Global Chang* Vulnrrabilm Auramfrtti
final Report. August 2011
#367 Herbicide Concentrations in Streams, 1993-2001
Herbicide Concentration (ug/L)
| ! 0.00 - 0.08
0.09-0.30
I [ No Data
1 States
0 100 200 300 400 500 Mite*
i I i l I l
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#367 Herbicide Concentrations in Streams
This continental U.S. indicator map shows herbicide concentrations in streams in each ecoregion. Data were not available for
many of the lower-48 ecoregions. Ecoregions with the highest (2.43 - 16.06 |ig/L) herbicide concentrations in streams occur
predominantly in the Midwest and the lower Mississippi Basin. Ecoregions with high (1.23 - 2.42 |ig/L) and moderate (0.31 - 1.22
|ig/L) herbicide concentrations in streams are scattered in the East, while ecoregions with low (0.09 - 0.30 |ig/L) and very low (0.00 -
0.08 |ig/L) herbicide concentrations in streams cover most of the Mountain West and parts of the Northeast.
Page F-32
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Aquatic Econitrms. Wttirr Qualm, anil Giobai Change CkattmgB of Conducting Uuiti Straxor Global Chang* VuintmlnlHY Aa vumtHU
f-tnal Rrport. August 2011
#369 Insecticide Concentrations in Streams, 1993-2001
Insecticide Concentration (ug/L)
| 0.000 - 0.006
0.007 - 0.020
B 0-021 - 0.044
B 0.045-0.107
BJ 0108-0.751
| No Data
] States
0 100 200 300 400 500 Mite*
i I I I I I
Pog*F3i
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#369 Insecticide Concentrations in Streams
This continental U.S. indicator map shows insecticide concentrations in streams in each ecoregion. Data were not available for
many of the lower-48 ecoregions. Available data is insufficient to infer geographic patterns, but does indicate the possibility of higher
concentrations of insecticides in streams in the central states, Southwest, and the lower Mississippi basin.
Page F-34
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Aquatic Econitraa. Wattr Quality; and Global Change OiaHmga of Conducting Main Srrnsor Global Chang* Vulntnibilny AsmsmenO
Final Report. August :0lI
#371 Organochlorines in Streambed Sediment, 1991-1997
Organochlorlrws in Streambed Sediment (ug/Kg dry weight
0.14-1.33
1.34-345
3.46-820
8^1 -17.77
17.78-13622
No Data
0 100 200 300 400 500 Mites
I I i l I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#371 Organochlorines in Bed Sediment
This continental U.S. indicator map shows organochlorine concentrations in streambed sediment in each ecoregion. Data were
not available for many of the lower-48 ecoregions. Available data is insufficient to infer geographic patterns, but does indicate the
possibility of higher concentrations of organochlorines in streams in the East and the Southwest.
Page F-36
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Aquatic Ecotyjtema. Wirtrr Quality, and Global Change Ckalttmges of Conducting Main Strmssor Global Chang* Vulnerability Atmimrnb
Final Report. August Mil
Herbicides in Ground wafer, 1992-2003
Herbicides in Groundwater (ug'L)
0000-0003
0004-0015
0.016-0.059
0.060-0.168
0169-2 162
No Data
States
0 100 200 300 400 500 Mites
I I I l I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#373 Herbicides in Groundwater
This continental U.S. indicator map shows herbicide concentrations in groundwater in each ecoregion. Data were not available
for many of the lower-48 ecoregions. Ecoregions with the highest (0.169 - 2.162 |ig/L and 0.060 - 0.168 |ig/L) concentrations of
herbicides occur predominantly in the Southeast and in the central Midwestern states. Ecoregions with moderate (0.016 - 0.059 |ig/L)
and low (0.004 - 0.015 |ig/L) concentrations of herbicides in groundwater are scattered throughout the country, while ecoregions with
the lowest (0.000 - 0.003 |ig/L) concentrations occur primarily in the Northwest.
Page F-38
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Aquatic Ecanitrms. Wirtrr Quality, and Global Ckange Ckaltmges of Conducting Ualti Strasor Global Cfcangr t'ulnrrnbiliH AarirmenO
Final Report. August :0l I
#374 Insecticides in Ground wafer, 1992-2003
Insecticides in Groundwater (ug/L)
0.0000 - 0.0002
0.0003 * 0.0006
0.0007 - 0.0010
0.0011 -0.0058
0.0059-0.1183
No Data
States
0 100 200 300 400 500 Mite*
i I I I I I
Pag*F39
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#374 Insecticides in Groundwater
This continental U.S. indicator map shows insecticide concentrations in groundwater in each ecoregion. Data were available
for only a fraction of lower-48 ecoregions. Ecoregions with the highest (0.0059 - 0.1183 |ig/L and 0.0011 - 0.0058 |ig/L)
concentrations of insecticides in groundwater occur predominantly in the Southeast and South Atlantic regions. Ecoregions with
moderate (0.0007 - 0.0010 |ig/L) and low (0.0003 - 0.0006 |ig/L) concentrations of insecticides in groundwater are scattered
throughout the country, while ecoregions with the lowest (0.0000 - 0.0002 |ig/L) concentrations occur mostly in the West.
Page F-40
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4yMflC Eeutfttmu. Wattr Quality, and Global Change: Ckallmgts of Conducting \tulti Slressor Global Change Vulnrrabilin Atteummti
Final Kffort August 2011
#437 Precipitation Elasticity of Streamflow, 1951-1988
Precipitation Elasticity of Streamflow
072-1.00
^H 101-315
States
0 100 200 300 400 500 Mites
i I l l I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#437 Precipitation Elasticity of Stream/low
This continental U.S. indicator map shows the precipitation elasticity of streamflow in each ecoregion. Data were available for
all lower-48 ecoregions. Every ecoregion has elasticity in the higher range (1.01 -3.15) except for one ecoregion in Nebraska, which
has elasticity in the 0.72 - 1.00 range.
Page F-42
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Aquanc Ecaiyttmi Watrr Qualm, ami G/oAo/ Change Ckaltmgrs of Conducting Main Sirtxtor Giobal Change I'ulntraMin
Final Ktport August :0l I
#449 Ratio of Reservoir Storage to Mean Annual Runoff
Reservoir Storage (acre-feet) / Annual Runoff (in.)
8.330.000-161.000.000
850,000 - 8.320.000
^B 202.000 - 849.000
|^| 109.000-201.000
H 0- 108.000
| [States
0 100 200 300 400 500 Miles
i I l I I I
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#449 Ratio of Reservoir Storage to Mean Annual Runoff
This continental U.S. indicator map shows the ratio of reservoir storage to mean annual runoff in each ecoregion. Data were
available for all lower-48 ecoregions. Ecoregions with the lowest (0 - 108,000 acre-feet/inch) ratios occur in the Pacific Northwest,
the lower Mississippi basin, and in Michigan. Ecoregions with low (109,000 - 201,000 acre-feet/inch) and moderate ratios (202,000 -
849,000 acre-feet/inch) are found primarily in the Northeast, but are also scattered throughout the country. Ecoregions with high
(850,000 - 8,320,000 acre-feet/inch) and very high (8,330,000 - 161,000,000 acre-feet/inch) ratios cover most of the country with the
highest ratios occurring in Western Midwest and Mountain West regions.
Page F-44
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/ffMOfir Ecvnitemu. Waltr Quality; and Global Change Ckallmga of Conducting Haiti Strasor Gtobal Changt Vulnrrabilin Aarumenti
Final Report. August 2011
#453 Runoff Variability, 1984-1993
Coefficient of Variation of Annual Runoff
0.183-0.267
0 268 - 0.276
0.277 - 0 323
0.324 - 0.352
0.353-1.193
States
0 100 200 300 400 500 Miles
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#453 Runoff Variability
This continental U.S. indicator map shows the coefficient of variation of annual runoff in each ecoregion. Data were available
for all lower-48 ecoregions. A very distinctive pattern emerges on this map, with ecoregions with the highest (0.353 - 1.193 and 0.324
- 0.352) coefficients covering almost the entire western half of the country. Ecoregions with moderate (0.277 - 0.323) ratios are
observed largely in the Southeast, while ecoregions with low (0.268 - 0.276) and very low (0.183 - 0.267) ratios are observed in the
lower Mississippi basin, parts of the Great Lakes region, the Northeast, and Florida.
Page F-46
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ilmi Water Qualify, and Global Change ChttOmtgtt of Conducting Uulti Srreaor Global Change t'ulaerabilm AmamrnO
Final Report. August :OI 1
#460 Macroinvertebrate Index of Biotic Condition,
2000-2004
Average Macroinvertebrate Index
55.6 - 66.3
44.0 - 55.5
H 40.4 43 9
No Data
States
0 100 200 300 400 500 Miles
i I l I I I
Pug**'*7
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
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#460 Macroinvertebrate Index ofBiotic Condition
This continental U.S. indicator map shows the macroinvertebrate index of biotic condition in each ecoregion. Data were
available for the vast majority of lower-48 ecoregions. Ecoregions with the lowest (2.4 - 30.4) macroinvertebrate index values occur in
the central Midwest and along the Gulf Coast, while ecoregions with the highest (44.0 - 55.5 and 55.6 - 66.3) index values occur
primarily in the Northwest. The remaining ecoregions with macroinvertebrate index values in the moderate range (30.5 - 40.3 and 40.4
- 43.9) are scattered throughout the country.
Page F-48
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AqmMe £ron Jlrmj (Fairr Qualm, and Global < kange: Ckattmgt* of Conducting Mulfi Sireaor Viokni Changr I'uintrabtlm
Final Rrport. August 2011
#461 Macroinvertebrate O/E Ratio of
2000-2004
Average Observed/Expected Ratio
94 76% - 149.07%
88 66%-94.75%
80.71%-88.65%
68.31%-80.70%
20 19%-68.30%
No Data
States
0 100 200 300 400 500 Miles
i I l I I I
PagrHO
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
#461 Macroinvertebrate Observed/Expected (O/E) Ratio ofTaxa Loss
This continental U.S. indicator map shows the observed taxa as a percentage of the expected macroinvertebrate taxa in each
ecoregion. Data were available for the vast majority of lower-48 ecoregions. This map shows no discernable pattern, but does indicate
the possibility of higher ratio of taxa loss in the lower Mississippi basin and parts of the Southwest.
Page F-50
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Aquatic Ecanitraa. Waitr Quatit\. and Global Change: Chtttlmtgts of Conducting Mulei Strrssor Global Change Vulmrabilih Aarammtj
Final Report August 2011
#623 Water Availability: Net Stream Flow per capita
[Flow (gpd) - Withdrawals (gpd)] / Population
24.000 - 275,000
5.800 - 24,000
2.800 - 5.700
1.000-2.700
0.0 - 1.000
States
0 100 200 300 400 500 Mites
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
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#623 Water Availability: Net Stream/low per Capita
This continental U.S. indicator map shows the net streamflow per capita in each ecoregion. Data were available for all lower-
48 ecoregions. Ecoregions with the lowest (0.0 - 1,000 gallons/day/capita and 1,000 - 2,700 gallons/day/capita) water availability
occur predominantly in the Southwest and parts of the central Mountain West and Midwest. Ecoregions with moderate (2,800-5,700)
water availability occur in the northern Mountain West, the Northeast, and in Florida. Ecoregions with the highest (5,800 - 24,000
gallons/day/capita and 24,000 - 275,000 gallons/day/capita) water availability occur in parts of the Midwest, Southeast, the
Northwest, and Main
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Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
The following matrix displays the data for 25 mapped indicators for each of the 204 HUC-4 watersheds in the continental United
States. (Note that one mapped indicator, the Coastal Vulnerability Index (#51), is not included here because a different spatial unit was
used to map it). Values for each indicator are represented both by colors and numbers: No data (white, 0); Lowest (light gray, 1); Low
(medium gray, 2); Medium (dark gray, 3); High (darker gray, 4), and Highest (black, 5). The shades of black, white, and gray in this
matrix match those on the maps in Appendix E and Appendix F.
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
0101
0102
0103
0104
0105
0106
0107
0108
0109
0110
0111
0201
0202
0203
0204
0205
0206
0207
0208
0301
0302
0303
0304
0305
0306
0307
INDICATOR
Acid Neutralizing Capacity [#1]
1
4
1
5
1
1
5
1
1
1
1
1
4
1
2
2
3
1
1
1
1
1
1
4
1
1
At-Risk Freshwater Plant Communities
[#22]
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
4
5
5
5
5
5
5
5
At-Risk Native Freshwater Species [#24]
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
3
1
3
4
4
4
4
4
4
5
5
Groundwater Reliance [#125]
4
3
4
4
4
3
4
2
2
1
5
4
3
2
2
3
1
2
1
2
4
2
4
1
3
3
Meteorological Drought Indices [#165]
2
2
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
3
3
4
3
4
Ratio of Snow to Precipitation [#218]
5
4
4
4
4
4
4
4
3
3
5
5
4
3
3
3
2
3
2
2
2
2
2
2
1
1
Stream Habitat Quality [#284]
5
5
5
5
5
4
5
5
2
3
5
3
4
4
3
5
3
4
5
5
1
5
1
5
1
1
Wetland and Freshwater Species at Risk
[#326]
2
2
1
1
1
2
3
5
4
4
1
3
4
3
5
5
4
5
5
5
5
4
5
5
5
5
Herbicide Concentrations in Streams
[#367]
0
0
0
0
0
0
0
0
1
1
0
0
2
3
2
4
4
4
0
0
3
0
0
3
0
2
Insecticide Concentrations in Streams
[#369]
0
0
0
0
0
0
0
0
4
5
0
0
3
5
3
3
2
4
0
0
2
0
0
3
0
4
Organochlorines in Bed Sediment [#371]
0
0
1
1
0
0
2
2
4
2
0
0
2
2
3
3
0
1
0
2
1
0
0
1
1
2
Herbicides in Groundwater [#373]
0
0
2
2
0
1
2
5
2
4
0
0
3
2
5
5
4
4
5
5
3
0
0
5
1
2
Insecticides in Groundwater [#374]
0
0
1
2
0
2
2
5
3
4
0
0
4
3
5
5
4
3
5
4
5
0
0
5
1
1
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
5
3
3
5
4
5
4
3
5
4
5
5
4
4
3
3
5
4
3
3
4
4
5
2
1
2
Runoff Variability [#453]
1
3
3
1
4
1
1
2
1
3
1
1
3
3
3
1
3
2
4
4
2
2
3
3
3
3
Macroinvertebrate Index of Biotic
Condition [#460]
1
1
1
1
1
1
2
1
2
1
1
3
4
1
3
2
3
3
2
4
5
2
5
1
5
5
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
5
3
4
4
4
3
2
5
2
5
5
3
2
5
2
4
3
4
4
2
1
3
3
4
1
2
Ratio of Water Withdrawals to Annual
Streamflow [#219]
1
1
1
1
1
3
2
2
4
4
1
1
2
4
4
3
4
3
4
3
1
3
2
3
3
3
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
0308
0309
0310
0311
0312
0313
0314
0315
0316
0317
0318
0401
0402
0403
0404
0405
0406
0407
0408
0409
0410
0411
0412
0413
0414
0415
INDICATOR
Acid Neutralizing Capacity [#1]
1
0
0
1
0
1
5
2
1
4
5
1
1
1
0
1
3
0
1
0
1
1
1
1
1
4
At-Risk Freshwater Plant Communities
[#22]
5
5
5
5
5
5
5
5
4
2
3
1
1
2
3
2
1
1
1
1
4
4
1
1
1
1
At-Risk Native Freshwater Species [#24]
5
5
5
5
5
5
5
5
5
4
4
2
2
2
3
3
2
2
2
2
3
3
2
2
2
2
Groundwater Reliance [#125]
3
4
3
5
4
4
4
2
2
4
4
1
2
2
2
3
3
2
2
1
1
3
1
1
1
4
Meteorological Drought Indices [#165]
3
5
3
4
3
4
3
4
1
2
2
4
5
4
3
2
3
3
2
2
1
1
2
2
1
1
Ratio of Snow to Precipitation [#218]
1
1
1
1
1
1
1
1
1
1
1
5
5
4
3
4
5
5
4
4
3
3
4
4
4
5
Stream Habitat Quality [#284]
2
0
0
3
0
1
3
4
5
2
3
4
5
3
0
4
5
0
4
0
1
3
1
2
1
5
Wetland and Freshwater Species at Risk
[#326]
5
5
4
5
3
5
5
5
5
5
4
2
2
4
1
3
3
2
2
1
4
2
1
1
2
2
Herbicide Concentrations in Streams
[#367]
0
4
0
1
2
2
0
2
5
0
0
0
0
5
3
0
0
0
0
5
5
3
0
0
0
0
Insecticide Concentrations in Streams
[#369]
0
3
0
1
4
3
0
3
3
0
0
0
0
1
1
0
0
0
0
2
2
4
0
0
0
0
Organochlorines in Bed Sediment [#371]
3
3
2
2
2
1
0
1
2
0
0
0
0
1
2
0
0
0
0
2
1
2
1
0
0
0
Herbicides in Groundwater [#373]
4
5
4
5
1
4
0
3
1
0
1
0
0
5
3
0
0
0
0
1
2
0
0
0
0
0
Insecticides in Groundwater [#374]
1
3
1
5
4
5
0
4
1
0
1
0
0
3
3
0
0
0
0
3
1
0
0
0
0
0
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
4
1
3
5
5
3
5
3
3
3
4
3
4
2
5
5
5
1
5
5
5
5
5
4
4
4
Runoff Variability [#453]
1
3
3
3
1
2
1
3
3
2
2
2
3
3
1
1
2
2
1
1
1
1
1
1
2
2
Macroinvertebrate Index of Biotic
Condition [#460]
4
3
0
5
0
4
3
2
2
2
1
2
2
2
0
5
1
0
4
0
3
3
5
5
5
4
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
1
3
0
1
0
1
2
4
3
4
1
2
3
4
0
2
5
0
5
0
5
2
1
1
1
1
Ratio of Water Withdrawals to Annual
Streamflow [#219]
4
4
4
1
2
2
2
2
2
1
1
2
2
3
5
3
4
2
3
5
3
2
5
4
3
1
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
0501
0502
0503
0504
0505
0506
0507
0508
0509
0510
0511
0512
0513
0514
0601
0602
0603
0604
0701
0702
0703
0704
0705
0706
0707
0708
INDICATOR
Acid Neutralizing Capacity [#1]
4
3
1
1
4
1
1
1
4
1
1
1
3
1
4
5
5
1
1
1
1
4
1
1
1
1
At-Risk Freshwater Plant Communities
[#22]
1
1
1
5
1
5
1
5
3
2
2
5
3
3
5
5
4
4
1
1
1
1
2
2
2
2
At-Risk Native Freshwater Species [#24]
3
3
3
3
3
3
4
3
3
5
5
3
5
4
5
5
5
5
2
2
2
2
2
2
2
2
Groundwater Reliance [#125]
2
1
1
3
2
4
3
5
2
2
1
3
1
1
1
4
1
4
3
4
2
3
5
3
4
3
Meteorological Drought Indices [#165]
1
1
1
1
1
1
1
1
1
2
2
1
2
1
1
3
4
3
2
1
4
2
4
2
2
2
Ratio of Snow to Precipitation [#218]
4
3
3
3
3
3
2
2
2
2
2
3
2
2
2
1
1
2
4
4
4
3
4
3
4
3
Stream Habitat Quality [#284]
5
4
3
1
5
3
2
1
5
4
3
2
2
1
3
2
5
2
2
2
5
4
4
3
3
2
Wetland and Freshwater Species at Risk
[#326]
4
3
4
4
5
3
4
3
4
5
5
5
5
5
5
4
5
5
2
2
2
2
2
2
3
4
Herbicide Concentrations in Streams
[#367]
2
0
0
0
2
0
0
4
0
0
0
5
0
0
2
0
4
0
3
3
0
0
0
0
0
4
Insecticide Concentrations in Streams
[#369]
2
0
0
0
1
0
0
4
0
0
0
3
0
0
2
0
2
0
3
2
0
0
0
0
0
2
Organochlorines in Bed Sediment [#371]
1
2
0
0
1
0
0
1
1
0
0
1
0
0
1
1
2
1
2
2
1
1
0
0
0
1
Herbicides in Groundwater [#373]
2
3
0
0
2
0
0
3
2
0
0
3
0
0
5
3
5
4
4
1
5
5
0
0
5
5
Insecticides in Groundwater [#374]
3
4
0
0
5
0
0
1
1
0
0
3
0
0
2
1
5
3
5
5
4
4
0
0
1
4
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
4
4
3
3
4
4
5
3
3
4
3
3
2
3
2
3
4
3
2
3
5
1
3
3
3
3
Runoff Variability [#453]
1
2
1
1
2
1
2
1
2
2
2
1
2
2
3
3
3
3
4
4
4
4
3
4
3
4
Macroinvertebrate Index of Biotic
Condition [#460]
3
2
4
4
2
2
4
2
3
4
5
3
4
4
2
2
3
2
3
3
1
4
4
5
4
2
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
4
3
1
2
2
4
1
4
2
1
2
5
1
1
2
3
2
4
3
5
5
3
2
3
2
5
Ratio of Water Withdrawals to Annual
Streamflow [#219]
2
3
3
3
2
2
1
2
1
1
2
3
3
1
3
2
2
1
3
2
2
1
1
1
2
1
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
0709
0710
0711
0712
0713
0714
0801
0802
0803
0804
0805
0806
0807
0808
0809
0901
0902
0903
1001
1002
1003
1004
1005
1006
1007
1008
INDICATOR
Acid Neutralizing Capacity [#1]
1
1
1
1
1
1
0
1
1
3
1
1
1
2
5
1
1
0
0
1
1
1
1
1
1
2
At-Risk Freshwater Plant Communities
[#22]
3
1
5
4
4
4
4
3
2
3
5
3
5
5
1
2
1
1
3
3
3
3
3
3
4
4
At-Risk Native Freshwater Species [#24]
3
2
3
3
3
3
5
4
4
4
3
4
3
3
2
1
1
2
1
1
1
1
1
1
1
2
Groundwater Reliance [#125]
5
4
3
1
2
2
5
5
5
4
5
5
2
4
1
5
4
2
1
1
1
1
1
2
1
1
Meteorological Drought Indices [#165]
2
1
4
2
5
3
2
5
2
5
4
2
4
4
4
2
1
4
3
5
5
5
4
4
5
5
Ratio of Snow to Precipitation [#218]
3
3
2
3
3
2
1
1
1
1
1
1
1
1
1
5
4
4
5
5
5
4
4
4
5
5
Stream Habitat Quality [#284]
1
1
4
1
2
2
0
1
1
4
1
4
3
3
1
4
2
0
0
5
5
5
3
5
4
2
Wetland and Freshwater Species at Risk
[#326]
3
3
3
4
5
4
5
5
5
5
2
4
3
4
3
1
3
1
1
1
1
1
1
1
1
2
Herbicide Concentrations in Streams
[#367]
0
0
0
4
5
0
5
5
5
0
5
0
5
4
3
0
3
0
0
0
0
0
0
0
1
1
Insecticide Concentrations in Streams
[#369]
0
0
0
4
3
0
5
2
4
0
5
0
5
4
1
0
2
0
0
0
0
0
0
0
1
1
Organochlorines in Bed Sediment [#371]
0
0
0
3
1
1
1
1
3
0
4
0
2
1
1
0
1
0
0
0
0
0
0
0
1
1
Herbicides in Groundwater [#373]
0
0
0
3
3
1
5
3
4
1
5
0
1
2
1
1
4
0
0
0
0
0
0
0
4
3
Insecticides in Groundwater [#374]
0
0
0
2
2
1
5
1
2
1
1
0
3
2
1
1
2
0
0
0
0
0
0
0
3
2
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
5
5
5
5
5
5
5
1
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
4
2
3
5
4
2
5
4
2
2
5
4
5
5
5
2
1
2
5
3
2
1
2
5
5
2
Runoff Variability [#453]
2
5
4
1
2
2
3
2
3
1
2
2
1
1
1
2
2
2
2
2
2
2
3
4
2
3
Macroinvertebrate Index of Biotic
Condition [#460]
5
2
3
2
2
4
0
5
5
3
5
1
2
4
5
5
3
0
0
1
1
2
4
5
1
2
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
2
4
5
4
5
4
0
1
3
2
1
4
2
1
1
5
5
0
0
5
4
5
3
1
5
3
Ratio of Water Withdrawals to Annual
Streamflow [#219]
2
1
1
4
3
1
1
1
1
1
3
1
1
2
1
3
2
1
1
4
5
5
5
4
4
5
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1101
1102
1103
1104
INDICATOR
Acid Neutralizing Capacity [#1]
1
1
1
1
1
1
1
1
1
1
2
1
0
1
1
1
1
1
1
1
1
1
1
1
1
0
At-Risk Freshwater Plant Communities
[#22]
4
3
2
4
2
3
2
3
2
4
4
2
2
2
1
2
4
5
4
4
5
5
3
4
5
4
At-Risk Native Freshwater Species [#24]
2
1
1
1
1
1
1
1
1
2
1
1
1
1
2
2
1
1
1
3
3
3
4
1
2
2
Groundwater Reliance [#125]
1
1
3
4
1
4
5
5
5
3
4
5
5
5
2
2
5
5
5
1
2
2
5
2
5
5
Meteorological Drought Indices [#165]
5
4
4
5
5
4
4
1
1
5
3
3
3
2
2
3
5
4
3
3
4
4
5
4
1
2
Ratio of Snow to Precipitation [#218]
5
4
5
5
4
4
4
4
4
5
5
3
4
3
3
3
3
3
3
3
2
2
2
5
3
4
Stream Habitat Quality [#284]
2
5
3
4
3
3
2
1
1
3
4
1
0
1
1
1
1
1
2
1
4
3
4
3
2
0
Wetland and Freshwater Species at Risk
[#326]
1
1
1
2
2
1
1
1
1
3
2
1
2
1
2
3
2
2
2
3
4
3
5
2
2
2
Herbicide Concentrations in Streams
[#367]
0
1
0
0
0
0
0
0
0
0
5
5
0
5
0
0
0
0
0
0
1
0
1
0
0
0
Insecticide Concentrations in Streams
[#369]
0
1
0
0
0
0
0
0
0
0
5
4
0
2
0
0
0
0
0
0
2
0
1
0
0
0
Organochlorines in Bed Sediment [#371]
1
1
0
0
0
0
0
0
0
0
2
1
1
1
0
0
0
0
0
0
1
0
1
0
0
0
Herbicides in Groundwater [#373]
0
0
0
0
0
0
2
0
1
5
5
5
1
1
0
0
3
5
3
0
3
0
2
0
5
5
Insecticides in Groundwater [#374]
0
0
0
0
0
0
1
0
1
1
5
2
1
1
0
0
2
1
3
0
1
0
3
0
3
4
Precipitation Elasticity of Streamflow
[#437]
5
1
5
5
5
5
5
5
5
5
5
5
1
5
5
5
1
5
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
3
5
5
1
1
1
4
2
1
1
1
3
3
5
4
4
1
1
2
3
2
5
2
1
3
4
Runoff Variability [#453]
4
4
4
4
4
4
4
4
5
3
1
4
4
5
5
5
4
4
4
5
4
4
2
1
4
3
Macroinvertebrate Index of Biotic
Condition [#460]
4
5
3
4
3
3
4
5
5
2
2
3
0
5
4
3
4
5
4
5
3
3
3
5
4
4
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
3
4
3
5
5
3
2
5
4
4
4
3
0
1
2
2
2
5
2
2
3
5
4
3
5
1
Ratio of Water Withdrawals to Annual
Streamflow [#219]
5
5
2
3
3
1
4
2
1
5
5
5
4
4
3
3
5
3
4
3
3
2
2
5
4
5
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1301
1302
1303
1304
1305
INDICATOR
Acid Neutralizing Capacity [#1]
1
1
1
1
1
1
1
1
1
2
1
1
1
1
0
1
1
0
1
1
0
1
1
0
0
0
At-Risk Freshwater Plant Communities
[#22]
1
3
4
2
2
2
2
3
2
3
5
4
4
3
4
4
4
4
4
4
4
4
2
2
4
3
At-Risk Native Freshwater Species [#24]
3
2
2
4
4
3
4
4
3
3
4
5
5
4
4
5
5
4
5
4
4
2
4
4
4
4
Groundwater Reliance [#125]
5
5
2
3
5
5
2
5
5
1
4
3
1
3
5
1
4
5
3
4
4
3
4
4
3
5
Meteorological Drought Indices [#165]
2
2
2
3
2
2
5
2
2
5
3
3
3
3
2
3
3
2
2
3
1
5
3
3
2
3
Ratio of Snow to Precipitation [#218]
2
2
2
5
2
3
2
2
2
1
1
1
1
1
2
2
1
2
1
1
1
5
4
2
1
3
Stream Habitat Quality [#284]
1
5
3
5
2
1
3
4
1
5
3
4
4
1
0
4
2
0
5
2
0
3
5
0
0
0
Wetland and Freshwater Species at Risk
[#326]
2
2
4
2
3
4
5
3
4
5
2
2
3
1
2
3
2
3
4
3
4
1
4
2
3
3
Herbicide Concentrations in Streams
[#367]
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
3
0
1
0
0
0
0
Insecticide Concentrations in Streams
[#369]
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
4
0
1
0
0
0
0
Organochlorines in Bed Sediment [#371]
0
0
1
0
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
1
1
1
1
1
0
0
Herbicides in Groundwater [#373]
0
1
3
0
2
3
4
4
1
0
1
0
2
4
3
0
1
2
1
3
2
2
3
4
0
0
Insecticides in Groundwater [#374]
0
4
4
0
5
2
3
1
4
0
1
0
5
1
4
0
1
2
1
4
2
2
3
4
0
0
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
4
2
2
1
1
2
2
1
1
1
1
2
1
4
1
1
2
1
1
2
1
4
1
1
3
4
Runoff Variability [#453]
3
3
3
4
3
3
1
4
2
1
1
3
2
4
5
3
4
5
5
5
5
1
1
2
4
3
Macroinvertebrate Index of Biotic
Condition [#460]
4
3
4
3
5
2
4
0
5
5
3
3
5
5
0
1
1
0
4
3
0
2
4
0
0
5
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
1
5
4
3
2
5
3
0
2
1
4
3
1
1
0
2
5
0
2
1
0
3
1
0
0
4
Ratio of Water Withdrawals to Annual
Streamflow [#219]
2
2
1
4
4
5
2
5
3
2
3
2
4
4
5
5
3
5
4
4
5
5
5
5
5
5
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
1306
1307
1308
1309
1401
1402
1403
1404
1405
1406
1407
1408
1501
1502
1503
1504
1505
1506
1507
1508
1601
1602
1603
1604
1605
1606
INDICATOR
Acid Neutralizing Capacity [#1]
0
1
0
0
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
1
1
1
1
1
1
At-Risk Freshwater Plant Communities
[#22]
2
4
4
4
4
4
3
5
4
3
2
2
2
2
2
2
1
1
1
1
4
3
3
3
3
3
At-Risk Native Freshwater Species [#24]
4
4
5
4
1
1
3
2
2
3
3
3
5
4
5
4
4
4
4
3
3
4
3
5
5
5
Groundwater Reliance [#125]
5
5
2
1
1
1
1
1
1
1
2
1
5
5
3
5
4
4
4
5
3
3
3
4
3
5
Meteorological Drought Indices [#165]
3
2
2
3
4
4
4
5
4
4
3
5
5
5
5
5
5
5
5
4
4
5
4
5
4
3
Ratio of Snow to Precipitation [#218]
3
2
1
1
5
5
5
5
5
5
4
5
3
4
2
2
2
3
1
1
5
5
5
4
5
4
Stream Habitat Quality [#284]
0
1
0
0
5
4
4
2
2
3
3
2
3
1
3
2
0
3
4
0
3
5
1
2
3
2
Wetland and Freshwater Species at Risk
[#326]
4
3
1
1
1
1
1
2
1
3
3
4
5
4
3
3
3
3
3
1
1
4
3
5
4
5
Herbicide Concentrations in Streams
[#367]
0
0
0
0
2
2
0
0
0
0
0
0
4
0
0
0
0
0
4
0
1
2
0
0
0
0
Insecticide Concentrations in Streams
[#369]
0
0
0
0
4
1
0
0
0
0
0
0
5
0
0
0
0
0
5
0
2
4
0
0
0
0
Organochlorines in Bed Sediment [#371]
0
0
0
0
2
1
0
0
0
0
0
0
1
0
0
0
2
3
5
0
1
1
0
0
1
0
Herbicides in Groundwater [#373]
0
0
1
0
1
2
0
0
0
0
0
0
2
0
0
0
2
2
5
0
3
4
0
0
4
0
Insecticides in Groundwater [#374]
0
0
1
0
1
1
0
0
0
0
0
0
1
0
0
0
1
2
4
0
1
2
0
0
3
0
Precipitation Elasticity of Streamflow
[#437]
5
5
5
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
1
1
1
4
3
4
3
2
4
2
1
2
1
2
1
2
1
2
1
5
5
2
2
2
2
1
Runoff Variability [#453]
4
4
4
4
1
1
5
4
3
5
5
5
5
5
5
3
4
5
5
4
5
5
5
4
5
4
Macroinvertebrate Index of Biotic
Condition [#460]
0
5
0
0
3
3
2
1
1
2
5
4
4
5
5
4
0
5
4
0
3
3
4
2
1
4
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
0
1
0
0
5
4
4
3
2
3
1
3
2
1
1
2
0
4
5
0
3
3
1
3
3
1
Ratio of Water Withdrawals to Annual
Streamflow [#219]
5
5
5
5
4
4
3
4
3
3
1
4
2
3
4
4
5
5
5
4
5
5
5
5
5
4
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
SUB
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
INDICATOR
Acid Neutralizing Capacity [#1]
2
2
1
1
1
1
1
3
1
1
3
1
1
1
1
1
1
1
1
1
1
1
At-Risk Freshwater Plant Communities
[#22]
4
5
5
5
5
5
5
5
5
5
3
5
3
3
3
3
2
3
3
3
3
3
At-Risk Native Freshwater Species [#24]
2
2
2
2
3
3
4
3
4
3
2
4
5
5
5
5
5
5
5
5
5
5
Groundwater Reliance [#125]
2
3
2
3
2
2
3
2
3
2
4
3
3
4
4
4
3
3
3
5
5
2
Meteorological Drought Indices [#165]
5
5
4
5
5
5
5
4
5
3
5
5
5
4
3
3
3
3
4
4
4
4
Ratio of Snow to Precipitation [#218]
5
5
4
5
4
5
4
2
2
2
2
4
3
3
3
3
1
1
2
5
3
2
Stream Habitat Quality [#284]
5
1
1
3
2
2
2
2
3
2
2
3
4
4
5
4
2
4
3
2
5
4
Wetland and Freshwater Species at Risk
[#326]
2
3
1
4
4
4
4
1
2
4
2
3
4
5
4
4
1
3
3
1
5
4
Herbicide Concentrations in Streams
[#367]
1
3
1
1
1
3
0
0
4
0
2
0
0
4
0
3
0
0
2
0
0
0
Insecticide Concentrations in Streams
[#369]
1
5
5
1
1
2
0
0
5
0
3
0
0
5
0
5
0
0
4
0
0
0
Organochlorines in Bed Sediment [#371]
1
2
0
1
1
2
0
3
2
0
1
0
0
1
1
3
0
0
2
0
0
0
Herbicides in Groundwater [#373]
1
4
0
4
0
2
0
0
4
0
2
0
0
5
4
3
0
0
3
0
0
0
Insecticides in Groundwater [#374]
3
5
0
4
0
1
0
0
3
0
5
0
0
5
5
3
0
0
2
0
0
0
Precipitation Elasticity of Streamflow
[#437]
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Ratio of Reservoir Storage to Mean Annual
Runoff [#449]
2
2
4
2
2
3
2
4
4
5
4
4
3
1
2
1
5
4
3
5
3
5
Runoff Variability [#453]
1
2
3
5
5
4
5
2
2
4
1
5
5
5
5
5
5
5
5
5
5
5
Macroinvertebrate Index of Biotic
Condition [#460]
2
1
1
2
2
1
1
1
1
1
1
3
1
1
2
1
3
1
2
3
1
2
Macroinvertebrate Observed/Expected
(O/E) Ratio of Taxa Loss [#461]
4
5
5
2
3
5
5
4
4
2
5
2
4
4
3
2
1
5
4
1
5
4
Ratio of Water Withdrawals to Annual
Streamflow [#219]
2
2
4
4
4
2
1
1
2
1
1
4
2
4
5
5
3
5
5
3
4
5
00
^-
*
-------
Aquatic Ecosystems, Water Quality, and Global Change: Final Report
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments August 2011
Appendix H. Evaluation and Potential Modification of Vulnerability Indicators
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
This appendix provides an evaluation of each of the 25 mappable indicators within the framework of the five questions presented in
the flowchart in Figure 13 (Indicator Evaluation Process) of the report. Each indicator is evaluated to determine how well it represents
vulnerability of water quality or aquatic ecosystems, and, when appropriate, how it might be modified to improve its representation of
vulnerability (Table 1). In addition, the indicators are also evaluated to examine the extent to which objective functional thresholds
may apply to them.
Table 1. Indicator Selection
Indicators are evaluated for the extent to which they represent vulnerability. The indicators can be further evaluated to determine how
to modify them to improve their representation of vulnerability. An indicator that accounts for or could account for is then sifted
through the Indicator Display (Table 2). An indicator that neither accounts for vulnerability nor can be modified to represent
vulnerability is considered inappropriate for mapping with objective breakpoints.
Indicator
ID#
Indicator
Does the indicator describe
vulnerability?
Can the indicator be modified to describe vulnerability?
Acid Neutralizing
Capacity (ANC)
Does not directly account for exposure to
acidification.
If possible, develop model to predict changes in acidity of precipitation, and the
resulting change in stream pH, given ANC.
22
At-Risk
Freshwater Plant
Communities
Does not directly account for exposure to
additional stress from climate change.
Identify plant communities that would be most susceptible to changes in
temperature or precipitation. Overlay with predicted climate changes.
24
At-Risk Native
Freshwater
Species
Does not directly account for exposure to
additional stress from climate change.
Identify species that would be most susceptible to changes in temperature or
precipitation. Overlay with predicted climate changes.
51
Coastal
Vulnerability
Index (CVI)
Yes
N/A
125
Groundwater
Reliance
Does not put groundwater reliance into
context of groundwater availability or
availability of other water sources.
Changes in groundwater availability per capita could be simulated by coupling
population projections with a groundwater model. However, these estimates would
be more meaningful if they were integrated into a model of overall water availability
that also included surface water.
PageH-3
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
Indicator
Does the indicator describe
vulnerability?
Can the indicator be modified to describe vulnerability?
165
Meteorological
Drought Indices
Does not directly account for exposure to
additional stress from climate change.
A stochastic climate model could be used to predict change in drought frequency.
218
Ratio of Snow to
Total
Precipitation
(S/P)
Does not directly account for exposure to
additional stress from climate change.
This indicator could be improved by identifying areas where the ratio of snow to
precipitation is most sensitive to a unit change in temperature. It could also be
improved by accounting for the reliance of streamflow and human water use on
snowmelt.
219
Ratio of
Withdrawals to
Streamflow
Does not account for water shortage risk
associated with temporal variability in
streamflow and does not directly
account for exposure to additional stress
from climate change or growth in water
demand.
This indicator could be considered one factor in an integrated climatic-hydrologic
model (e.g., Wilby, R. L, P. G. Whitehead, A. J. Wade, D. Butterfield, R. J. Davis, and
G. Watts. 2006. Integrated modelling of climate change impacts on water resources
and quality in a lowland catchment: River Kennet, UK. Journal of Hydrology 330:204-
220.)
284
Stream Habitat
Quality
Does not directly account for exposure to
additional stress from climate change.
Predictions from a climate model could be used to forecast changes in streamflow,
which could be linked to stream channel stability (one component of habitat quality)
with a hydraulic model.
326
Wetland and
Freshwater
Species At Risk
Does not directly account for exposure to
additional stress from climate change.
Identify species that would be most susceptible to changes in temperature or
precipitation. Overlay with predicted climate changes.
348
Erosion Rate
Does not account for exposure to
precipitation changes.
Yang et al. (2003) provide projections of the change in erosion rate that would result
from climate change. These projections would account for both sensitivity and
exposure. However, the model for this indicator does not account for deposition of
eroded sediment and therefore cannot be solely relied upon to estimate sediment
delivery to aquatic ecosystems.
351
Instream
Use/Total
Streamflow
Does not directly account for exposure to
additional stress from climate change.
The USGS used the information in this indicator and other information to calculate
the ratio of consumptive use to renewable water supply. This indicator is a more
holistic view of water sustainability. Forecasts of the effects of climate change and
population growth on this indicator would integrate sensitivity and exposure.
PageH-4
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Aquatic Ecosystems, Water Quality, and Global Change:
Challenges of Conducting Multi-Stressor Global Change Vulnerability Assessments
Final Report
August 2011
Indicator
ID#
Indicator
Does the indicator describe
vulnerability?
Can the indicator be modified to describe vulnerability?
352
Total Use/Total
Streamflow
Does not directly account for exposure to
additional stress from climate change.
The USGS used the information in this indicator and other information to calculate
the ratio of consumptive use to renewable water supply. This indicator is a more
holistic view of water sustainability. Forecasts of the effects of climate change and
population growth on this indicator would integrate sensitivity and exposure.
364
Pesticide
Toxicity Index
(PTI)
Does not directly account for exposure to
additional stress from climate change.
USGS is developing predictive models for individual pesticides (e.g., atrazine;
http://infotrek.er.usgs.gov/warp/). Some of these models contain precipitation
variables whose values could be adjusted to simulate the effect of climate change on
pesticide concentrations. These individual predictions could be combined to calculate
the change in PTI that would be caused by climate change.
367
Herbicide
Concentrations
in Streams
Does not directly account for exposure to
additional stress from climate change.
The pesticide indicators could be improved by: 1. comparing the concentration of an
individual pesticide to its health-based regulatory threshold (e.g., atrazine), or 2.
calculating the pesticide toxicity index.
369
Insecticide
Concentrations
in Streams
Does not directly account for exposure to
additional stress from climate change.
The pesticide indicators could be improved by: 1. comparing the concentration of an
individual pesticide to its health-based regulatory threshold (e.g., atrazine), or 2.
calculating the pesticide toxicity index.
371
Organochlorines
in Bed Sediment
Does not directly account for exposure to
additional stress from climate change.
EPA's National Sediment Quality Survey reports and maps human health and aquatic
life risk due to contaminated sediment
(http://www.epa.gov/waterscience/cs/report/1997/). Risk is based on all sediment
contaminants, so this would be a different indicator.
373
Herbicides in
Groundwater
Does not directly account for exposure to
additional stress from climate change.
The pesticide indicators could be improved by: 1. comparing the concentration of an
individual pesticide to its health-based regulatory threshold (e.g., atrazine), or 2.
calculating the pesticide toxicity index.
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August 2011
Indicator
ID#
Indicator
Does the indicator describe
vulnerability?
Can the indicator be modified to describe vulnerability?
374
Insecticides in
Groundwater
Does not directly account for exposure to
additional stress from climate change.
The pesticide indicators could be improved by: 1. comparing the concentration of an
individual pesticide to its health-based regulatory threshold (e.g., atrazine), or 2.
calculating the pesticide toxicity index.
437
Precipitation
Elasticity of
Streamflow
Does not account for exposure to
precipitation changes.
This indicator could be combined with predicted changes in precipitation to predict
changes in streamflow.
449
Ratio of
Reservoir
Storage to Mean
Annual Runoff
Does not directly account for exposure to
additional stress from climate change.
This indicator could be considered one factor in an integrated climatic-hydrologic
model (e.g., Wilby, R. L, P. G. Whitehead, A. J. Wade, D. Butterfield, R. J. Davis, and
G. Watts. 2006. Integrated modelling of climate change impacts on water resources
and quality in a lowland catchment: River Kennet, UK. Journal of Hydrology 330:204-
220.)
453
Runoff
Variability
Does not directly account for exposure to
additional stress from climate change.
This indicator could be considered one factor in an integrated climatic-hydrologic
model (e.g., Wilby, R. L, P. G. Whitehead, A. J. Wade, D. Butterfield, R. J. Davis, and
G. Watts. 2006. Integrated modelling of climate change impacts on water resources
and quality in a lowland catchment: River Kennet, UK. Journal of Hydrology 330:204-
220.)
460
Macroinvertebra
te Index of Biotic
Condition
The stress-response curve may be
improperly characterized, and spatial
variation in exposure to future stress is
not accounted for.
Indexes of biotic condition respond linearly to stress, so vulnerability to further
degradation should be relatively constant.
461
Macroinvertebra
te
Observed/Expec
ted (O/E) Ratio
of Taxa Loss
The stress-response curve may be
improperly characterized, and spatial
variation in exposure to future stress is
not accounted for.
The scale of vulnerability for this indicator should be reversed. The first taxa that are
lost are sensitive to small amounts of stress.
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August 2011
Indicator
ID#
623
Indicator
Water
Availability: Net
Streamflow per
Capita
Does the indicator describe
vulnerability?
Does not account for water shortage risk
associated with temporal variability in
streamflow and does not directly
account for exposure to additional stress
from climate change or growth in water
demand.
Can the indicator be modified to describe vulnerability?
It may be more appropriate to consider net streamflow in the context of instream
flow requirements. This indicator could be considered one factor in an integrated
climatic-hydrologic model (e.g., Wilby, R. L, P. G. Whitehead, A. J. Wade, D.
Butterfield, R. J. Davis, and G. Watts. 2006. Integrated modelling of climate change
impacts on water resources and quality in a lowland catchment: River Kennet, UK.
Journal of Hydrology 330:204-220.)
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Table 2. Indicator Display
The numerical thresholds used for indicator example maps were determined based on the information available in the literature or by
using a continuous grayscale color ramp. Indicators that already reflect vulnerability or could be modified to do so (based on Table
1) can be further evaluated to determine whether objective, functional breakpoints can be used in displaying their values. If so,
attributes necessary for determining such functional breakpoints can be identified through a review of relevant literature or through
new data collection and analysis efforts. Finally, the validity of the breakpoints when data are aggregated to the appropriate spatial
unit can be analyzed to assess the accuracy of the resultant map.
An indicator for which objective breakpoints exist, or for which objective breakpoints can be identified, and for which breakpoints
remain valid even when data are aggregated, is considered mappable with objective thresholds. An indicator for which objective
breakpoints cannot be identified or for which breakpoints are not valid after data are aggregated is considered mappable along a
continuous gradient.
Indicator
ID#
1
22
24
Indicator
Acid Neutralizing
Capacity (ANC)
At-Risk Freshwater
Plant Communities
At-Risk Native
Freshwater Species
Are objective breakpoints in the range of
vulnerability documented?
Yes; when ANC values fall below zero, the
water is considered acidic and can be either
directly or indirectly toxic to biota (i.e., by
mobilizing toxic metals, such as aluminum).
When ANC is between 0 and 25
milliequilivents, the water is considered
sensitive to episodic acidification during
rainfall events. These threshold values were
determined based on values derived from
the National Acid Precipitation Assessment
Program (USEPA 2006).
Yes; risk levels for individual communities
are semi-quantitatively defined.
Yes; risk levels for individual species are
semi-quantitatively defined.
Can objective breakpoints be identified?
N/A
N/A
N/A
Are the breakpoints valid
when the data are
aggregated?
No; indicator mapped as
percentage of sites.
No; indicator mapped as
percentage of sites.
No; indicator mapped as
percentage of sites.
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Indicator
ID#
Indicator
Are objective breakpoints in the range of
vulnerability documented?
Can objective breakpoints be identified?
Are the breakpoints valid
when the data are
aggregated?
51
Coastal Vulnerability
Index (CVI)
No; it indicates relative risk.
Ideally, the CVI would be calibrated to the
occurrence of actual physical effects. This
stress response relationship could then
be divided into vulnerability categories
with natural breaks, or with subjective
evaluations of acceptable risk.
Yes
125
Groundwater Reliance
No, because this indicator is not a good
measure of vulnerability.
The ratio of per capita water availability
to water use has a natural threshold: 1.
Other thresholds would be somewhat
arbitrary.
Yes, but only with suggested
modifications.
165
Meteorological
Drought Indices
The thresholds that were used are
somewhat objective because a PDSI of 0
indicates neutral conditions, and negative
numbers indicate drought. Because the
medium category is centered around zero,
it appropriately separates areas that have
experienced recent drought from those
that have not.
While there is an objective breakpoint for
separating drought from non-drought, an
objective measure of what constitutes a
critical drought frequency was not
identified.
Yes
218
Ratio of Snow to Total
Precipitation (S/P)
No
A general model of water availability that
included snowmelt could be used to
simulate changes in water availability
relative to water demand. A ratio of 1
would be an objective threshold.
Yes, but only with suggested
modifications.
219
Ratio of Withdrawals
to Annual StreamFlow
Yes, a value of 1 indicates that there is no
room for further water withdrawals.
N/A
Yes
284
Stream Habitat Quality
No, breakpoints are arbitrary.
No
N/A
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Indicator
ID#
Indicator
Are objective breakpoints in the range of
vulnerability documented?
Can objective breakpoints be identified?
Are the breakpoints valid
when the data are
aggregated?
326
Wetland and
Freshwater Species At
Risk
Yes; risk levels for individual species are
semi-quantitatively defined.
N/A
No; indicator mapped as
percentage of sites.
348
Erosion Rate
No, tolerable erosion rates vary among
ecosystems and are not documented at the
national scale.
With the suggested modification, three
objective categories of vulnerability
would be less erosion, no change, and
more erosion with predicted climate
change.
Yes, but only with suggested
modifications.
351
Instream Use/Total
Streamflow
Yes, a value of 1 indicates that there is no
room for further water withdrawals,
assuming that there is no consumptive use.
The same breakpoint also applies to the
suggested modification of the indicator.
Yes, because the HUC is the
original scale of
measurement.
352
Total Use/Total
Streamflow
Yes, a value of 1 indicates that there is no
room for further water withdrawals.
The same breakpoint also applies to the
suggested modification of the indicator.
Yes, because the HUC is the
original scale of
measurement.
364
Pesticide Toxicity
Index (PTI)
Pesticide toxicity index values have a built-
in threshold (1) that indicates probable
cumulative effects equivalent to an LC50 or
EC50 assuming that the additive toxicity
model is appropriate. However, even these
standard measures of toxicity are based on
a somewhat arbitrary standard of what
constitutes a serious health effect (affects
50% of test organisms).
What constitutes a critical level of risk is a
subjective choice.
There is no basis for
identifying a critical value for
the average PTI in a HUC.
Averages also obscure
variance among the values at
individual sites.
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August 2011
Indicator
ID#
Indicator
Are objective breakpoints in the range of
vulnerability documented?
Can objective breakpoints be identified?
Are the breakpoints valid
when the data are
aggregated?
367
Herbicide
Concentrations in
Streams
No, not for mixtures of pesticides.
What constitutes a critical level of risk is a
subjective choice. Pesticide toxicity index
values have a built-in threshold that
indicates probable cumulative effects
equivalent to an LC50 or EC50 assuming
that the additive toxicity model is
appropriate. However, even these
standard measures of toxicity are based
on a somewhat arbitrary standard of
what constitutes a serious health effect
(affects 50% of test organisms).
There is no basis for
identifying a critical value for
the average probability of
exceeding a health-based
threshold or the average PTI
in a HUC. Averages also
obscure variance among the
values at individual sites.
369
Insecticide
Concentrations in
Streams
No, not for mixtures of pesticides.
What constitutes a critical level of risk is a
subjective choice. Pesticide toxicity index
values have a built-in threshold that
indicates probable cumulative effects
equivalent to an LC50 or EC50 assuming
that the additive toxicity model is
appropriate. However, even these
standard measures of toxicity are based
on a somewhat arbitrary standard of
what constitutes a serious health effect
(affects 50% of test organisms).
There is no basis for
identifying a critical value for
the average probability of
exceeding a health-based
threshold or the average PTI
in a HUC. Averages also
obscure variance among the
values at individual sites.
371
Organochlorines in
Bed Sediment
Yes, but only with the suggested
modification.
N/A
No, data would have to be
mapped as percentages for
which thresholds are
arbitrary, or averages, which
obscure variance.
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Indicator
ID#
Indicator
Are objective breakpoints in the range of
vulnerability documented?
Can objective breakpoints be identified?
Are the breakpoints valid
when the data are
aggregated?
373
Herbicides in
Groundwater
No, not for mixtures of pesticides.
What constitutes a critical level of risk is a
subjective choice. Pesticide toxicity index
values have a built-in threshold that
indicates probable cumulative effects
equivalent to an LC50 or EC50 assuming
that the additive toxicity model is
appropriate. However, even these
standard measures of toxicity are based
on a somewhat arbitrary standard of
what constitutes a serious health effect
(affects 50% of test organisms).
There is no basis for
identifying a critical value for
the average probability of
exceeding a health-based
threshold or the average PTI
in a HUC. Averages also
obscure variance among the
values at individual sites.
374
Insecticides in
Groundwater
No, not for mixtures of pesticides.
What constitutes a critical level of risk is a
subjective choice. Pesticide toxicity index
values have a built-in threshold that
indicates probable cumulative effects
equivalent to an LC50 or EC50 assuming
that the additive toxicity model is
appropriate. However, even these
standard measures of toxicity are based
on a somewhat arbitrary standard of
what constitutes a serious health effect
(affects 50% of test organisms).
There is no basis for
identifying a critical value for
the average probability of
exceeding a health-based
threshold or the average PTI
in a HUC. Averages also
obscure variance among the
values at individual sites.
437
Precipitation Elasticity
of Streamflow
The thresholds that were used are
somewhat objective because a value of 1
separates areas where a given percentage
change in precipitation results in a lower
percentage change in streamflow from
areas where a that same percentage
change in precipitation results in a higher
percentage change in streamflow.
Changes in streamflow could be
evaluated against instream flow
requirements for aquatic life.
Instream flow requirements
tend to stream-specific, and
therefore, cannot be
generalized to all streams in
a HUC.
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August 2011
Indicator
ID#
Indicator
Are objective breakpoints in the range of
vulnerability documented?
Can objective breakpoints be identified?
Are the breakpoints valid
when the data are
aggregated?
449
Ratio of Reservoir
Storage to Mean
Annual Runoff
No
Stochastic model output from an
integrated climatic-hydrologic model
could be evaluated to identify areas
where reservoir storage is expected to
drop to zero more often than a specified
frequency.
Yes, but only with suggested
modifications.
453
Runoff Variability
No
Stochastic model output from an
integrated climatic-hydrologic model
could be evaluated to identify areas
where reservoir storage is expected to
drop to zero more often than a specified
frequency.
Yes, but only with suggested
modifications.
460
Macroinvertebrate
Index of Biotic
Condition
No, breakpoints are arbitrary.
No
N/A
461
Macroinvertebrate
Observed/Expected
(O/E) Ratio of Taxa
Loss
No, breakpoints are arbitrary.
No
N/A
623
Water Availability: Net
Streamflow per Capita
Regional differences in water-using
activities mean that the sufficiency of
available water supplies varies
geographically. No documented thresholds
were found.
Possibly, although Indicator #351
(Instream Use/Total Streamflow)
describes the same concept and has an
objective threshold (1).
Yes
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