DRAFT EPA/600/R-14/341
DO NOT CITE OR QUOTE November 2014
External Review Draft
c/EPA
Regional Monitoring Networks to Detect Climate Change
Effects in Stream Ecosystems
NOTICE
THIS DOCUMENT IS A DRAFT. This document is distributed solely for the
purpose of pre-dissemination peer review under applicable information quality
guidelines. It has not been formally disseminated by EPA. It does not represent
and should not be construed to represent any Agency determination or policy.
Mention of trade names or commercial products does not constitute endorsement
or recommendation for use.
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 is distributed solely for the purpose of pre-dissemination peer review under
applicable information quality guidelines. It has not been formally disseminated by EPA. It does
not represent and should not be construed to represent any Agency determination or policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use, but is for descriptive purposes only. This document does not supplant
official published methods and does not constitute an endorsement of a particular procedure or
method.
ABSTRACT
The U.S. Environmental Protection Agency (EPA) is working with its regional offices, states,
tribes, and other organizations to establish regional monitoring networks (RMNs) at which
biological, thermal, and hydrologic data will be collected from freshwater wadeable streams to
quantify and monitor changes in baseline conditions, including climate change effects. RMNs
have been established in the Northeast, Mid-Atlantic, and Southeast, and efforts are expanding
into other regions. The need for RMNs stems from the lack of long-term, contemporaneous
biological, thermal, and hydrologic data, particularly at minimally disturbed sites. Data collected
at RMNs will be used to detect temporal trends; investigate relationships between biological,
thermal, and hydrologic data; explore ecosystem responses and recovery from extreme weather
events; test hypotheses and predictive models related to climate change; and quantify natural
variability. RMN surveys build on existing bioassessment efforts, with the goal of collecting
comparable data that can be pooled efficiently at a regional level. This document describes the
development of the current RMNs for riffle-dominated, freshwater wadeable streams. It contains
information on selection of candidate sites, expectations for data collection, the rationale for
collecting these data, and provides examples of how the RMN data will be used and analyzed.
Preferred citation:
U.S. EPA (Environmental Protection Agency). (2014) Regional monitoring networks to detect climate change
effects in stream ecosystems. (EPA/600/R-14/341). Washington, DC: National Center for Environmental
Assessment, Washington. Available online at http://www.epa. gov/ncea.
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TABLE OF CONTENTS
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS viii
PREFACE ix
AUTHORS, CONTRIBUTORS, AND REVIEWERS x
EXECUTIVE SUMMARY xi
1. INTRODUCTION 1
2. METHODOLOGY 2
2.1. PROCESS FOR SETTING UP THE REGIONAL MONITORING
NETWORKS (RMNS) 2
2.2. SITE SELECTION 3
3. DATA COLLECTION 6
3.1. BIOLOGICAL INDICATORS 8
3.1.1. Macroinvertebrates 11
3.1.2. Fish 16
3.1.3. Periphyton 17
3.2. TEMPERATURE DATA 17
3.3. HYDROLOGIC DATA 20
3.4. PHYSICAL HABITAT 25
3.5. WATER CHEMISTRY 27
3.6. PHOTODOCUMENTATION 27
3.7. GEOSPATIALDATA 28
4. SUMMARIZING AND SHARING REGIONAL MONITORING NETWORK
(RMN)DATA 29
4.1. BIOLOGICAL INDICATORS 29
4.2. THERMAL STATISTICS 35
4.3. HYDROLOGIC STATISTICS 37
4.4. PHYSICAL HABITAT, WATER QUALITY, AND GEOSPATIAL DATA 43
5. DATA USAGE 44
5.1. TEMPORAL TRENDS 44
5.1.1. Basic Analytical Techniques 44
5.1.2. Trend Detection for Taxonomic versus Traits-Based Biological
Indicators 47
5.1.3. Tracking Changes in Biological Condition with Biological Condition
Gradient (BCG) Models 48
5.2. RELATIONSHIPS BETWEEN BIOLOGICAL INDICATORS AND
ENVIRONMENTAL DATA 48
5.2.1. Basic Analytical Techniques 49
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TABLE OF CONTENTS (continued)
5.2.2. Ecologically Meaningful Variables and Thresholds 51
5.2.3. Interactive Effects of Climate Change with Other Stressors 52
5.3. RESPONSE AND RECOVERY OF ORGANISMS TO EXTREME
WEATHER EVENTS 53
5.4. HYPOTHESES AND PREDICTIVE MODELS RELATED TO CLIMATE
CHANGE VULNERABILITY 54
5.4.1. Broad-Scale Vulnerability Assessments 55
5.4.2. Species Distribution Models (SDMs) 55
5.4.3. Differing Thermal Vulnerabilities 56
5.4.4. Testing the Performance of Models that Predict Effects of Climate
Change on Streamflow 57
5.5. QUANTIFYING NATURAL VARIABILITY 57
6. NEXT STEPS 58
6.1. MOST IMMEDIATE PRIORITIES 58
6.2. FUTURE STEPS 60
7. CONCLUSIONS 61
8. LITERATURE CITED 62
APPENDIX A REGIONAL WORKING GROUPS A-l
APPENDIX B CHECKLIST FOR STARTING A REGIONAL MONITORING
NETWORK (RMN) B-l
APPENDIX C PRIMARY REGIONAL MONITORING NETWORK (RMN) SITES
IN THE NORTHEAST, MID ATLANTIC, AND SOUTHEAST REGIONS C-l
APPENDIX D DISTURBANCE SCREENING PROCEDURE FOR RMN SITES D-l
APPENDIX E SECONDARY REGIONAL MONITORING NETWORK (RMN)
SITES IN THE NORTHEAST AND MID-ATLANTIC REGIONS E-l
APPENDIX F MACROINVERTEBRATE COLLECTION METHODS F-l
APPENDIX G LEVEL OF TAXONOMIC RESOLUTION G-l
APPENDIX H GUIDELINES FOR TEMPERATURE MONITORING QA/QC H-1
APPENDIX I GUIDELINES FOR HYDROLOGIC MONITORING QA/QC I-1
APPENDIX J RAPID QUALITATIVE HABITAT ASSESSMENT FORM FOR
HIGH GRADIENT STREAMS J-l
APPENDIX K DATA SHARING TEMPLATES K-l
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TABLE OF CONTENTS (continued)
APPENDIX L MACROINVERTEBRATE THERMAL INDICATOR TAX A L-1
APPENDIX M FORMULAS FOR CALCULATING PERSISTENCE AND
STABILITY M-l
APPENDIX N HYDROLOGIC SUMMARY STATISTICS AND TOOLS FOR
CALCULATING ESTIMATED STREAMFLOW STATISTICS N-l
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LIST OF TABLES
1. Main considerations when selecting primary sites for the regional
monitoring networks (RMNs) 5
2. There are four levels of rigor in the regional monitoring network (RMN)
framework, with level 1 being the lowest and level 4 being the
best/highest standard. Level 3 is the target for primary RMN sites 8
3. Recommendations on best practices for collecting biological data at
regional monitoring network (RMN) sites 10
4. Recommendations on best practices for collecting macroinvertebrate data
at regional monitoring network (RMN) sites 12
5. Recommendations on best practices for collecting temperature data at
regional monitoring network (RMN) sites 18
6. Recommendations on best practices for collecting hydrologic data at
regional monitoring network (RMN) sites 22
7. Recommendations on candidate biological indicators to summarize from
the macroinvertebrate data collected at regional monitoring network
(RMN) sites; many of these are indicators that are commonly used by
biomonitoring programs for site assessments 31
8. Recommendations for candidate thermal summary statistics to calculate
from continuous temperature data at regional monitoring network (RMN)
sites 36
9. Recommended candidate hydrologic statistics to calculate on each year of
water-level or flow data from regional monitoring network (RMN) sites 39
10. Examples of tools for estimating streamflow and/or streamflow statistics
atungaged sites 42
11. Physical habitat, water quality, and geospatial data that should be collected
at regional monitoring network (RMN) sites 43
12. Results from Hilderbrand et al. (2014) linear regression models based of
water and air temperatures from sentinel sites in the Coastal Plain,
Piedmont, and Highlands regions 56
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LIST OF FIGURES
1. States, tribes, river basin commissions (RBCs), and others in three regions
(Northeast, Mid-Atlantic, and Southeast) are working to set up regional
monitoring networks (RMNs) 2
2. Staff gage readings provide a quality check of transducer data 24
3. Photodocumentation of Big Run, WV, taken from the same location each
year 28
4. Changes in the spatial distribution of taxa can be tracked over time 34
5. Yearly trends in cold- and warm-water-preference taxa and total taxa
richness at a site on the Sheepscot River in Maine (Station 56817)
(U.S. EPA, 2012) 45
6. Effects of differences in sampling methodologies on taxonomic
composition were evident in this nonmetric multidimensional scaling
(NMDS) ordination on the Northeastern data set that was analyzed for an
EPA pilot study in 2012 47
7. Yearly trends at the Weber River site in Utah (UT 4927250) in (A)
number of cold and warm water taxa; (B) percentage cold- and warm-
water individuals; and (C) mean maximum July temperature (°C) and
mean September/October/November (SON) flow (cfs) 50
8. Connecticut Department of Energy and Environmental Protection (CT
DEEP) developed ecologically meaningful thresholds for three major
thermal classes (cold, cool, warm) 52
9. Comparison of (A) macroinvertebrate density values, (B) total taxa
richness values, and (C) Ephemeroptera, Plecoptera, and Trichoptera
(EPT) richness at 10 stream sites in Vermont before and after Tropical
Storm Irene (provided by Moore and Fiske, VT DEC, unpublished data) 54
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LIST OF ABBREVIATIONS
AMAAB Association of Mid-Atlantic Aquatic Biologists Workshop
BaSE Baseline Streamflow Estimator
BCG biological condition gradient
BIBI MD DNR's index of biotic integrity for benthic macroinvertebrates
CT DEEP Connecticut Department of Energy and Environmental Protection
E expected
ELOHA ecological limits of hydrologic alteration
EPT Ephemeroptera, Plecoptera, and Trichoptera
FIBI MD DNR' s index of biotic integrity for fish
GIS Geographic Information System
GPS Global Positioning System
MA DEP Massachusetts Department of Environmental Protection
MA SYE Massachusetts Sustainable-Yield Estimator
MD DNR Maryland Department of Natural Resources
MMI multimetric index
NARS EPA National Aquatic Resource Surveys
NC DENR North Carolina Department of Environmental and Natural Resources
NEAEB New England Association of Environmental Biologists
NLCD National Land Cover Database
NMDS nonmetric multidimensional scaling
NRSA National Rivers and Streams Assessment
NWQMC National Water Quality Monitoring Conference
O observed
OCH Odonata, Coleoptera, Hemiptera
OTU operational taxonomic units
QA/QC quality assurance/quality control
QAPP Quality Assurance Proj ect Plan
RBC river basin commission
RBP rapid bioassessment protocols
RIFLS River Instream Flow Stewards Program
RMN regional monitoring network
RIFLS River Instream Flow Stewards Program
SDM species distribution model
SON September/October/November
SWPBA Southeastern Water Pollution Biologists Association
TNC The Nature Conservancy
U.S. EPA U.S. Environmental Protection Agency
USGS U.S. Geological Survey
VT DEC Vermont Department of Environmental Conservation
WQX Water Quality Exchange
WV DEP West Virginia Department of Environmental Protection
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PREFACE
The U.S. Environmental Protection Agency (EPA) is working with states, tribes, river basin
commissions, and other organizations in different parts of the country to establish regional
monitoring networks (RMNs) to collect data that will further our understanding of biological,
thermal, and hydrologic conditions in freshwater wadeable streams and allow for detection of
changes and trends. This document describes the framework for the RMNs that have been
developed in the Northeast, Mid-Atlantic, and Southeast regions for riffle-dominated, freshwater
wadeable streams.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The National Center for Environmental Assessment, Office of Research and Development is
responsible for publishing this report. This document was prepared with the assistance of Tetra
Tech, Inc. under Contract No. EP-C-12-060, EPA Work Assignment No. 1-01. Dr. Britta
Bierwagen served as the Technical Project Officer, providing overall direction and technical
assistance.
AUTHORS
Center for Ecological Sciences, Tetra Tech, Inc., Owings Mills, MD
Jen Stamp, Anna Hamilton
U.S. EPA Region 3, Wheeling, WV
Margaret Passmore (retired)
Tennessee Department of Environment and Conservation
Debbie Arnwine
U.S. EPA, Office of Research and Development, Washington DC
Britta G. Bierwagen, Jonathan Witt
REVIEWERS
U.S. EPA Reviewers
Jennifer Fulton (R3), Ryan Hill (ORISE Fellow within ORD), Sarah Lehmann (OW)
ACKNOWLEDGMENTS
The authors would like to thank the many state and federal partners who reviewed early versions
of this report for clarity and usefulness. Their comments and input have substantially improved
this document.
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EXECUTIVE SUMMARY
The U.S. Environmental Protection Agency (EPA) has been working with its regional offices,
states, tribes, river basin commissions (RBCs), and other organizations in the Northeast, Mid-
Atlantic, and Southeast regions to establish regional monitoring networks (RMNs) at which
biological, thermal, hydrologic, physical habitat, and water chemistry data are being collected
contemporaneously from freshwater wadeable streams. RMN surveys build on existing
bioassessment efforts, with the goal of collecting comparable data that can be pooled efficiently
at a regional level. This document describes the development of RMNs in the Northeast, Mid-
Atlantic, and Southeast for riffle-dominated, freshwater wadeable streams. It contains
information on the selection process for candidate sites, describes expectations and
recommendations for data collection and quality assurance/quality control procedures, discusses
the rationale for collecting these data, and provides examples of how the RMN data will be used
and analyzed. It concludes with a discussion on how these efforts can be expanded to other
regions and water body types.
The need for RMNs stems from the lack of long-term, contemporaneous biological, thermal, and
hydrologic data, particularly at minimally disturbed stream sites. To help fill this gap, efforts are
underway to collect the following types of data from the RMN sites:
• Biological indicators: macroinvertebrates, fish, and periphyton if resources permit (fish
are considered higher priority)
• Temperature: continuous water and air temperature (30-minute intervals)
• Hydrological: continuous water-level (stage) data (15-minute intervals); converted to
streamflow via stage-discharge rating curve development if resources permit
• Habitat: qualitative visual habitat measures (e.g., EPA rapid bioassessment protocols);
quantitative measures if resources permit (e.g., EPA National Rivers and Streams
Assessment methods)
• Water chemistry: In situ, instantaneous water chemistry parameters (e.g., specific
conductivity, dissolved oxygen, pH); additional or more comprehensive water chemistry
measures if resources permit
Top priorities of the RMNs are to collect uninterrupted, long-term biological, thermal, and
hydrologic data at primary RMN sites, as well as utilize and build upon data already being
collected by states, tribes, RBCs, and other organizations. Data collected can serve many
purposes, and will be used to:
• Detect temporal trends in biological, thermal, hydrologic, habitat, and water chemistry
data;
• Investigate and resolve relationships between biological, thermal, and hydrologic data;
• Examine how organisms respond and recover from extreme weather events;
• Test hypotheses and predictive models related to climate change; and
Quantify natural variability.
The Northeast, Mid-Atlantic, and Southeast regions followed similar processes to establish their
RMNs. A regional, tribal, or state coordinator formed a working group of interested partners to
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establish regional goals to determine basic survey bounds, such as selection of a target
population (e.g., freshwater wadeable streams with abundant riffle habitat). The working groups
selected RMN sites using consistent criteria and selected appropriate data-collection protocols
and methodologies. As part of this process, working groups considered the site-selection criteria
and methods being used in the other regions and tried to use similar protocols where practical to
generate comparable data. The groups then identified logistical, training, and equipment needs
and sought resources from agencies such as EPA and the U.S. Geological Survey (USGS) to help
address high-priority goals. Concurrently, EPA held discussions with RMN members about data
collection practices (e.g., continuous temperature and flow monitoring protocols) and
infrastructure needs (e.g., data storage and sharing). Working groups have begun implementing
the RMNs in the three regions and will continue to collect status updates on sampling activities;
discuss potential changes to data-collection and processing recommendations; pursue resources
to assist with logistical, training, equipment, and data infrastructure needs; seek additional
partners; and ensure that the goals of the RMN are being met.
RMN sampling efforts revolve around a core group of "primary" sites. Primary sites are
consistent with the RMN site selection criteria and build upon data already being collected by
states, tribes, RBCs, and others. Site selection considerations include: level of anthropogenic
disturbance; length of historical sampling record for biological, thermal, or hydrological data;
environmental conditions; biological community; accessibility; potential for collaboration or
partnerships with other organizations (e.g., colocation with a USGS gage); and level of
protection from future anthropogenic disturbance. Results from a broad-scale climate change
vulnerability assessment conducted by EPA were also considered, with preference given to sites
that rated moderately or most vulnerable to one or more exposure scenarios (increasing
temperatures, increased frequency and severity of extreme precipitation events, and increased
summer low flow events). The working groups selected 2 to 15 sites per state (depending on the
size of the state and availability of resources), with the overall goal of sampling at least 30 sites
(either within or across regions) that have comparable environmental conditions and biological
communities. Analyses suggest that significant climate-related trends in regional community
composition can be detected within 10-20 years if 30 or more comparable sites are monitored
regularly.
Most primary RMN sites have minimal or low levels of upstream human-related disturbance. In
this document these types of sites are referred to as "reference" sites. Reference sites are targeted
because bioassessment programs depend on comparisons to conditions at sites that most closely
approximate natural conditions. It is critical to track changes at these sites over time to
understand how benchmarks may shift in response to environmental factors, such as climate
change. For example, streams that were once perennial could become intermittent during a late
summer or early fall sampling period, or changes in thermal and hydrologic conditions could
result in lower abundances or replacement of certain taxa, which could affect biological
condition scores. There is a higher likelihood of being able to characterize climate-related
impacts when other non-climatic stressors are absent.
Data from additional, "secondary," sites are also being considered for the RMNs. These are sites
where biological data are already being collected annually or biannually as part of other
independent monitoring efforts. In some cases, continuous temperature or hydrologic data are
being collected as well. Secondary RMN sites generally have higher levels of anthropogenic
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disturbance, and data from these non-reference sites can be used to evaluate how the effects of
climate change interact with other human-related factors like urbanization. Data from secondary
sites will also increase the sample size and range of conditions represented in the RMN data set,
which will be useful for testing predictive models and hypotheses about the vulnerability of taxa
and watersheds to climate change. In addition, secondary sites may provide information about
unique or underrepresented geographic areas, such as the New Jersey Pine Barrens or the Coastal
Plain ecoregion.
Limited resources are available to implement the RMNs, and efforts are being made to integrate
RMN data collection flexibly within existing monitoring programs. To address the challenges of
creating regionally consistent data, EPA has developed recommendations on best practices for
data collection and has established different levels of rigor for data collected at RMN sites. The
RMN framework, therefore, accommodates data collected with different sampling frequencies
and methodologies. The goal is to set up a data sharing system that allows users to see what data
are being collected at each site and the data quality (i.e., level of rigor used, as categorized in this
report) so that users can select the data that meet their needs.
This document should be reevaluated and updated periodically as data are collected and analyzed
to ensure that the objectives of the RMNs are being met. The Northeast, Mid-Atlantic, and
Southeast RMNs are the pilot studies upon which the RMN framework is based and whose data
will be used in initial evaluations and data analyses. Other regions interested in establishing an
RMN can build upon and improve these efforts. While the current focus is on states, tribes, and
RBCs, collaboration and partnerships with other organizations, such as academia and volunteer
monitoring groups, is encouraged as a way to make the networks more robust.
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1. INTRODUCTION
1 The U.S. Environmental Protection Agency (EPA) has been working with states, tribes, river
2 basin commissions (RBCs), and other organizations in different parts of the United States to
3 establish regional monitoring networks (RMNs) to collect contemporaneous biological, thermal,
4 hydrologic, physical habitat, and water chemistry data from freshwater wadeable streams. RMNs
5 have been established in the Northeast, Mid-Atlantic, and Southeast (see Figure 1), and efforts to
6 establish new networks are expanding into other regions. The concept of the RMNs stems from
7 work that began in 2006 with pilot studies that examined long-term climate-related trends in
8 macroinvertebrate data from state biomonitoring programs in Maine, North Carolina, Ohio, and
9 Utah (U.S. EPA, 2012). During these studies, a lack of long-term, contemporaneous biological,
10 thermal, and hydrologic data became apparent, particularly at minimally disturbed stream sites.
11 These data gaps have been documented elsewhere (e.g., Mazor et al., 2009; Jackson and Fureder,
12 2006; Kennen et al., 2011) and have been recognized as important gaps to fill by the National
13 Water Quality Monitoring Council (NWQMC), which endorsed the establishment of a
14 collaborative, multipurpose, multiagency national network of reference watersheds and
15 monitoring sites for freshwater streams in the United States for this purpose (NWQMC, 2011).
16 Given these needs, the top priorities of the RMNs are to collect uninterrupted, long-term
17 biological, thermal, and hydrologic data at primary RMN sites to the extent possible, and to
18 utilize and build upon data already collected. A number of states, tribes, RBCs, and others are
19 already collecting annual biological and continuous temperature data at targeted sites, and to a
20 lesser degree, hydrologic data. The goal is to supplement and integrate the RMNs surveys into
21 programs like these. Coordinating and pooling resources at the regional level is especially
22 important as program resources have become increasingly limited.
23 Data collected from RMN sites can be used to:
24 • Detect temporal trends in biological, thermal, hydrologic, habitat, and water chemistry
25 data;
26 • Investigate and resolve relationships between biological, thermal, and hydrologic data;
27 • Examine how organisms respond and recover from extreme weather events;
28 • Test hypotheses and predictive models related to climate change; and
29 • Quantify natural variability.
30 This document describes the development of RMNs in the Northeast, Mid-Atlantic, and
31 Southeast regions for riffle-dominated, freshwater wadeable streams. It contains information on
32 the selection process for candidate sites, describes expectations and recommendations for data
33 collection and quality assurance/quality control (QA/QC) procedures, discusses the rationale for
34 collecting these data, and provides examples of how the RMN data will be used and analyzed. It
35 concludes with a discussion on how these efforts can be expanded to other regions and water
36 body types in the future. New data collected and analyzed over time will begin to fulfill the
37 purpose of the RMNs.
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Northeast
Mid-atlantic
Southeast
Figure 1. States, tribes, river basin commissions (RBCs), and others in three regions
(Northeast, Mid-Atlantic, and Southeast) are working to set up regional monitoring
networks (RMNs).
2. METHODOLOGY
1 Section 2.1 contains a description of RMN development, while Section 2.2 describes site
2 selection. Appendix A contains lists of working group members in the Northeast, Mid-Atlantic,
3 and Southeast regions.
2.1. PROCESS FOR SETTING UP THE REGIONAL MONITORING NETWORKS
(RMNS)
4 The Northeast, Mid-Atlantic, and Southeast regions followed similar processes to establish their
5 RMNs. A regional, tribal, or state coordinator formed a working group of interested partners to
6 establish regional goals to determine basic survey bounds, such as selection of a target
7 population (e.g., freshwater wadeable streams with abundant riffle habitat). Working groups
8 selected RMN sites using consistent criteria (see Section 2.2), and selected appropriate
9 data-collection protocols and methodologies. As part of this process, working groups considered
10 the site selection criteria and methods being used in the other regions and tried to utilize similar
11 protocols where practical to generate comparable data. The groups then identified logistical,
12 training, and equipment needs and sought resources from agencies such as EPA and the
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1 U.S. Geological Survey (USGS) to help address high-priority goals. Concurrently, EPA held
2 discussions with RMN members about data collection practices (e.g., continuous temperature
3 and flow monitoring protocols) and infrastructure needs (e.g., data storage and sharing). Working
4 groups have begun implementing the RMNs in the three regions and will continue to collect
5 status updates on sampling activities; discuss potential changes to data collection and processing
6 recommendations; pursue resources to assist with logistical, training, equipment, and data
7 infrastructure needs; seek additional partners; and ensure the goals of the RMN are being met.
8 Appendix B includes a step-by-step checklist on the process for developing RMNs.
2.2. SITE SELECTION
9 RMN sampling efforts revolve around a core group of "primary" sites. The working groups
10 selected 2 to 15 primary RMN sites per state (depending on the size of the state and availability
11 of resources), with the overall goal of sampling at least 30 sites (either within or across regions)
12 that have comparable environmental conditions and biological communities. Analyses suggest
13 that significant climate-related trends in regional community composition can be detected within
14 10-20 years if 30 or more comparable sites are monitored regularly (Bierwagen et al., in review).
15 Appendix C lists the candidate primary RMN sites in each region.
16 Primary sites were selected to utilize and build upon data already being collected by states,
17 tribes, RBCs, and others (see Table 1). For example, where feasible, organizations colocated
18 RMN sites with existing stations like USGS gages or in established long-term monitoring
19 networks such as the sentinel networks of the Vermont Department of Environmental
20 Conservation (VT DEC), the Connecticut Department of Energy and Environmental Protection
21 (CT DEEP), Maryland Department of Natural Resources (MD DNR), West Virginia Department
22 of Environmental Protection (WV DEP), and Tennessee Department of Environment and
23 Conservation, continuous monitoring stations of the Susquehanna River Basin Commission, and
24 USGS networks, such as the Northeast Site Network and the Geospatial Attributes of Gages for
25 Evaluating Streamflow (GAGES-II) program. Some of these sites have lengthy historical
26 records, which are preferred for primary RMN sites (see Table 1). Ways to integrate these survey
27 efforts into national monitoring networks, such as the EPA National Aquatic Resource Surveys
28 (NARS) program and the NWQMC (NWQMC, 2011), have also been considered.
29 During the site selection process, efforts were made to select primary RMN sites with minimal or
30 low levels of upstream anthropogenic disturbance (see Table 1). In this document these types of
31 sites are referred to as "reference" sites. Members of the regional working groups screened the
32 initial list of sites by evaluating factors like the likelihood of impacts from land use disturbance,
33 dams, mines, and point-source pollution sites. Subsequently, we developed a standardized
34 procedure for characterizing the present-day level of anthropogenic disturbance and applied this
35 across RMNs. Sites from all states and regions were rated on a common scale (see Appendix D),
36 similar to the scale used for the Biological Condition Gradient (BCG) (Davies and Jackson,
37 2006).
38 In addition to assessing current levels of disturbance at the candidate RMN sites, EPA and the
39 regional working groups evaluated the potential for future development in the watersheds. This
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1 was done by evaluating a spatial data set provided by The Nature Conservancy (TNC)1 that
2 showed public and private lands and waters secured by a conservation agreement. In addition,
3 some RMN members contacted city planners and personnel from transportation and forestry
4 departments to obtain information about the likelihood of future urban and residential
5 development, road construction, and logging or agricultural activities. Where feasible, sites with
6 low potential for future development were selected because future alterations could limit trend
7 detection power as well as the ability to characterize climate-related impacts at RMN sites.
8 The regional working groups selected candidate RMN sites that are located in freshwater
9 wadeable streams with rocky substrates and riffle habitat (see Table 1). Existing state and
10 regional classification frameworks for macroinvertebrate assemblages were also considered. For
11 example, the Southeast working group used ecoregions during the initial site selection process
12 because they dominate the reference-site-stratification approach used by many programs for
13 assessing streams (Carter and Resh, 2013). Most of the RMN sites in the Southeast are located in
14 ecoregions with hilly or mountainous terrain (e.g., Piedmont, Blue Ridge, Central, and North
15 Central Appalachians), where streams generally have higher gradients and more riffle habitat. To
16 inform site selection, we performed a broad-scale classification analysis on macroinvertebrate
17 survey data from the EPA NARS program2 to reduce natural variability and improve our power
18 to detect long-term trends (Bierwagen et al., in review}. The data set included minimally
19 disturbed freshwater wadeable stream sites from the Northeast, Mid-Atlantic, and Southeast
20 regions. A cluster analysis was performed, and sites were grouped into three classes based on
21 similarities in taxonomic composition. We then developed a model based on environmental
22 variables to predict the probability of occurrence of the three classes in watersheds in the eastern
23 United States. The three classes are referred to as: (1) colder temperature, faster water; (2) small,
24 low gradient; and (3) warmer temperature, larger lower gradient. Using this analysis, most of the
25 primary RMN sites fell within the colder temperature, faster flow class, which is expected given
26 that sites in this class are generally located in areas with lower levels of human-related
27 disturbance. A goal of the RMNs is to sample at least 30 colder temperature, faster flow sites
28 (either within or across regions; see Table 1).
29 Because one of the RMN objectives is to detect climate change effects on macroinvertebrate
30 communities, efforts were made to select sites that we hypothesized to be vulnerable to climate
31 change. To assess potential vulnerability we considered three exposure scenarios relevant to
32 aquatic life condition: increasing temperatures, increasing frequency and magnitude of extreme
33 precipitation events, and increasing frequency of summer low flow events. Watersheds were
34 assigned a vulnerability rating (least, moderate, or most vulnerable) for each exposure scenario.
35 Sites that were assigned to the moderate or most vulnerable category for at least one of the
36 scenarios were preferred. As our understanding of climate change impacts evolves, the data
37 collected from these RMN sites will be used to test and refine regional vulnerability hypotheses
38 overtime.
39 Practical considerations were also important during the site screening process. For example,
40 organizations generally selected sites that could be sampled during a day trip and were easy to
1 Secured lands data set available at
https://www.conservationgatewav.org/ConservationBvGeographv/NorthAmerica/UnitedStates/edc/reportsdata/terres
trial/secured/Pages/defaultaspx.
2Data available at http://water.epa.gov/tvpe/rsl/monitoring/riverssurvev/index.cfm.
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1
2
3
4
5
access, which are factors that will likely increase the frequency at which sites can be visited. This
may improve the quality of data being collected (particularly the hydrologic data). Working
groups are also seeking opportunities for partnership or collaboration with outside organizations
(e.g., academia, volunteer monitoring groups) to increase the viability and robustness of the
network.
Table 1. Main considerations when selecting primary sites for the regional monitoring
networks (RMNs)
Consideration
Existing
monitoring
network
Disturbance
Potential for future
disturbance
Sampling record
Equipment
Broad-scale
classification
Sustainability
Climate change
vulnerability
Desired characteristics at primary sites
Located in established long-term monitoring networks to build upon data
already being collected by states, tribes, RBCs, and others.
Low level of anthropogenic disturbance.
Located in watersheds that are protected from future development.
Lengthy historical sampling record for biological, thermal, or hydrological
data.
Colocated with existing equipment (e.g., USGS gage, weather station).
Freshwater wadeable streams with rocky substrates and riffle habitat. At
least 30 sites (within or across regions) should fall within EPA's broad-
scale colder temperature, faster water class.
Accessible (e.g., day trip), opportunities to share the workload with
outside agencies or organizations.
Rated as moderately or most vulnerable to at least one of the exposure
scenarios: increasing temperatures, increased frequency and severity of
extreme precipitation events, and increased summer low flow events.
6
7
8
9
10
11
12
13
14
15
16
Data from additional, "secondary," sites are also being considered for the RMNs. These are sites
at which biological data are already being collected annually or biannually as part of other
independent monitoring efforts. In some cases, continuous temperature or hydrologic data are
being collected as well (if thermal and hydrologic data are not being collected, the priority is to
install the equipment at the primary RMN sites first, then at secondary RMN sites). Secondary
RMN sites generally have higher levels of anthropogenic disturbance than primary sites, and data
from these non-reference sites can be used to investigate how climate change interacts with other
human-related factors like urbanization. Data from secondary sites will also increase the sample
size and range of conditions represented in the RMN data set, which will be useful for testing
predictive models and hypotheses about the vulnerability of taxa and watersheds to climate
change. In addition, secondary sites may provide information about unique or underrepresented
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1 geographic areas, such as the New Jersey Pine Barrens or the Coastal Plain ecoregion.
2 Appendix E lists the candidate secondary RMN sites in each region.
3 In summary, the site selection process for the RMNs is a balancing act that takes into account
4 several considerations. The overall goal is to sample at least 30 comparable sites either within or
5 across regions. Reference sites are being targeted because bioassessment programs depend on
6 comparisons to conditions at sites that most closely approximate natural conditions. It is critical
7 to track changes at reference sites over time to understand how reference-condition benchmarks
8 may shift in response to environmental factors, such as climate change. For example, streams
9 that were once perennial may become intermittent during a late summer or early fall sampling
10 period, or changes in thermal and hydrologic conditions could result in lower abundances or
11 replacement of certain taxa, which could affect biological condition scores. These sites are more
12 likely to characterize climate-related impacts when other non-climatic stressors are absent.
13 Because of the limited funding for RMN implementation, RMN survey designs must be balanced
14 with practical considerations. For example, some of the primary RMN sites have higher than
15 desired levels of disturbance but have lengthy historical records, are part of existing monitoring
16 networks, or have existing equipment like a USGS gage. As part of making long-term
17 monitoring consistent and sustainable, these types of considerations play necessary and
18 important roles in site selection.
19
3. DATA COLLECTION
20 Efforts are being made to collect the following types of data from RMN sites in the Northeast,
21 Mid-Atlantic, and Southeast regions:
22 • Biological indicators: macroinvertebrates, fish, and periphyton if resources permit (fish
23 are considered higher priority)
24 • Temperature: continuous water and air temperature (30-minute intervals)
25 • Hydrological: continuous water-level data (15-minute intervals); converted to discharge
26 if resources permit
27 • Habitat: qualitative visual habitat measures [e.g., EPA rapid bioassessment protocols
28 (RBP)]; quantitative measures if resources permit [e.g., EPA National Rivers and Streams
29 Assessment (NRSA) methods].
30 • Water chemistry: In situ, instantaneous water chemistry parameters (specific
31 conductivity, dissolved oxygen, pH); additional or more comprehensive water chemistry
32 measures if resources permit
33 • Photodocumentation: photographs taken from the same locations during each site visit.
34 • Geospatial data: percentage land use and impervious cover, climate, topography, soils,
35 and geology, if resources permit.
36 To the extent possible, collecting uninterrupted, long-term biological, temperature, and
37 hydrologic data at primary RMN sites is the priority. Analyses by Bierwagen et al. (in review)
38 show that well-designed networks of 30 sites monitored consistently can detect underlying
39 changes of 1-2% per year in a variety of biological metrics within 10-20 years. However, trend
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1 detection in the thermal and hydrologic data may take longer. Stable estimates of climatic
2 conditions are typically based on 30-year averages (Stager and Thill, 2010), although some
3 researchers argue that alternate time scales may be more appropriate if climate conditions are
4 rapidly changing (e.g., Arguez and Vose, 2011). The long-term data from RMN sites will
5 substantially enhance our ability to characterize temporal trends and attribute them to climate
6 change or distinguish climate trends from other stressors. While trend detection will require
7 longer term data sets, other analyses, such as thermal and hydrologic indicator analyses and the
8 quantification of temperature and flow regimes, can be completed after only a few years of data
9 collection.
10 Limited resources are available to implement the RMNs, and efforts are being made to integrate
11 RMN data collection flexibly within existing monitoring programs. The RMN framework
12 accommodates data collected at different sampling frequencies and methodologies. For example,
13 for the Mid-Atlantic RMN, species-level identifications for macroinvertebrates for Spring and
14 Fall sampling periods have been combined with genus-level identifications generally performed
15 for these RMN sites on samples collected once a year. In some cases, RMNs can accommodate
16 differences in sampling methodologies (for macroinvertebrate data in particular) within or across
17 regions, while still providing data to generate comparable indicators. Different methodologies,
18 especially gear and subsampling procedures, affect community measures, may introduce biases
19 in analyses, and contribute to variability, which reduces the sensitivity of indicators (Bierwagen
20 et al., in review). It is important that these differences be minimized when possible so that
21 comparable data can be generated within and across regions.
22 To help minimize biases and variability in the data, we developed recommendations in
23 collaboration with the regional working groups on best practices for the collection of biological,
24 thermal, hydrologic, physical habitat, and water chemistry data at RMN sites (see Sections 3.1
25 through 3.7). Sampling methodologies are broken down into different elements, and different
26 levels of rigor are established for each element. Examples of elements include type of habitat
27 sampled, gear type, frequency of data collection, level of taxonomic resolution, level of expertise
28 of field and laboratory personnel, and QA/QC procedures. There are four levels of rigor in the
29 RMN framework, with level 1 being the lowest and level 4 being the best/highest standard (see
30 Table 2). Level 3 is the target for primary RMN sites. This framework is consistent with the EPA
31 critical elements process, in which different technical components of biological assessment
32 programs are assigned different levels of rigor (U.S. EPA, 2013a).
33 These guidelines are general. For example, one recommendation is to use kick nets for
34 macroinvertebrate collection, but there are no specifics on mesh size or frame type. It is up to the
35 regional working groups to work out these details. Appendix F (see Table F-l) describes the
36 specific protocols that were agreed upon by the regional working groups in the Northeast, Mid-
37 Atlantic, and Southeast regions. The goal is to collect comparable data that meets the desired
38 level or rigor (level 3 or 4) from at least 30 colder temperature, higher flow sites within or across
39 regions.
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Table 2. There are four levels of rigor in the regional monitoring network (RMN)
framework, with level 1 being the lowest and level 4 being the best/highest standard. Level
3 is the target for primary RMN sites
Level
Usability for RMNs
Data are usable under certain or limited circumstances. Data are not collected
and processed in accordance with methods agreed upon by the regional working
group, which severely limit the data's usefulness.
Data are usable under some, but not all circumstances. Only certain aspects of
sample collection and processing are done using the protocols that are agreed
upon by the regional working group, which limit the data's usefulness.
Data meet the desired level of rigor. They are collected in accordance with the
methods that are agreed upon by the regional working group. Where
methodological differences exist, steps have been taken to minimize biases, and
data are sufficiently similar to generate comparable indicators and meet RMN
objectives.
4 (optional)
Data exceed expectations. Data include optional high-quality data and meet or
exceed the desired level of rigor agreed upon by the regional working group.
3.1. BIOLOGICAL INDICATORS
1 At a minimum, macroinvertebrates should be collected at the primary RMN sites. Collections
2 from this assemblage are central to the RMNs because they are already collected by participating
3 states, tribes, RBCs, and other agencies for a variety of other purposes. For example,
4 macroinvertebrates are crucial for quantifying stream condition because (1) the assemblage
5 responds to a wide range of stressors, (2) they are easily and consistently identified, and (3) they
6 have limited mobility, short life cycles, and are highly diverse. Collection offish and periphyton
7 data is also encouraged, as resources permit. Fish are higher priority than periphyton because
8 they are collected more frequently, their taxonomy is better established, many species are
9 economically and socially important (e.g., trout), and there is widespread interest in predicting
10 and monitoring climate change effects on fish species (e.g., Clark et al., 2001; Flebbe et al.,
11 2006; Trumbo, 2010; Wenger et al., 2011). Guidelines for collecting macroinvertebrates, fish,
12 and periphyton can be found in Sections 3.1.1, 3.1.2, and 3.1.3, respectively.
13 Biological sampling should be conducted annually (see Table 3). Compared to less frequent
14 sampling, annual sampling can detect changes in climate-sensitive biological indicators sooner
15 (Bierwagen et al., in review). Annual data is also important for quantifying natural variability in
16 biological conditions, such as the stability and persistence of taxa, and can be used to document
17 how organisms respond to and recover from extreme weather events like heat waves, droughts,
18 and floods, which are projected to increase in frequency with climate change (Karl et al., 2009).
19 If biological data are only collected once every 5 years, which typically occurs in rotating
20 designs that focus on adequate spatial coverage, taxon- and community-level responses to key
21 events may be missed or confounded with impacts from other years.
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1 Data collection should be done by trained personnel (see Table 3) because formal training can
2 have a large impact on observer agreement and repeatability and can reduce assessment errors
3 (e.g., Herlihy et al., 2009; Haase et al., 2010). Repeatability is particularly important for RMNs
4 because data are gathered from multiple sources. Ideally, participating organizations should
5 adhere to the sample collection and processing protocols that are agreed upon by the regional
6 working group (see Appendix F, Table F-l). Some of these guidelines include QA/QC
7 procedures, which improve data quality (Stribling et al., 2008; Haase et al., 2010). Example
8 QA/QC procedures include collecting replicate samples in the field, conducting audits to ensure
9 that crews are adhering to collection and processing protocols, replicate subsampling (meaning
10 after subsampling occurs, the subsample is recombined with the original sample and subsampled
11 again), and validating taxonomic identifications at an independent laboratory.
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Table 3. Recommendations on best practices for collecting biological data at regional monitoring network (RMN) sites. The
RMN framework has four levels of rigor for biological sampling, with 4 being the best/highest and 1 being the lowest. At
primary RMN sites, RMN members should try to adhere to (at a minimum) the level 3 practices, which are in bold italicized
text
Component
1 (lowest)
4 (highest)
Sampling
frequency
Site is sampled every
5 or more years
Site is sampled every
2-4 years
Site is sampled annually
Site is sampled more than once
a year (e.g., spring and summer)
Expertise
Work is conducted by
a novice or apprentice
biologist or by
untrained personnel
Work is conducted by a
novice or apprentice
biologist under the
direction of a trained
professional
Work is conducted by a
trained biologist
Work is conducted by a trained
biologist who is experienced at
collecting aquatic macroinvertebrates
Collection
and
processing
Some but not all of
the recommended data
are collected.
Not all aspects of
sample collection and
processing use
protocols agreed upon
by the regional
working group
All of the recommended
data are being collected,
but not all aspects of
sample collection and
processing use protocols
agreed upon by the
regional working group
All of the recommended
data are being collected.
All aspects of sample
collection and processing
use protocols agreed upon
by the regional working
group
In addition to the minimum
recommended data, optional data are
also being collected. All aspects of
sample collection and processing use
protocols agreed upon by the
regional working group.
QA/QC
No QA/QC
procedures are
performed
Some but not all QA/QC
procedures agreed upon
by the regional working
group are performed
All of the QA/QC
procedures agreed upon by
the regional working group
are performed
QA/QC procedures that are more
stringent than those being used by
the regional working group are
performed
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3.1.1. Macroinvertebrates
1 Developing recommendations on macroinvertebrate sampling protocols is challenging because
2 organizations use different collection and processing protocols when they sample
3 macroinvertebrates, and each entity's biological indices are calibrated to data that are collected
4 and processed using these methods. When developing best practices at RMN sites, efforts were
5 made to accommodate differences in sampling methodologies within regions (see Appendix F)
6 while still providing data that are sufficiently similar that they can be used to generate
7 comparable indicators at the regional level. An overall goal of the RMNs is to generate data that
8 are comparable both within and across the regions.
9 At primary RMN sites, macroinvertebrate samples should be collected in reaches with abundant
10 riffle habitat (see Table 4). Cold water taxa, which are of particular interest due to their potential
11 vulnerability to climate change, typically inhabit riffles. Furthermore, riffle habitat is being
12 targeted because sample consistency is strongly associated with the type of habitats sampled
13 (Parson and Norris, 1996; Germ and Herlihy, 2006; Roy et al., 2003). Recent methods
14 comparison studies indicate that where abundant riffle habitat is present, single habitat riffle,
15 reach-wide, and multihabitat samples generally produce comparable classifications and
16 assessments, especially when fixed counts and consistent taxonomy are used (e.g., Vinson and
17 Hawkins, 1996; Hewlett, 2000; Ostermiller and Hawkins, 2004; Cao et al., 2005; Germ and
18 Herlihy, 2006; Rehn et al., 2007; Blocksom et al., 2008). While sampling at RMN sites is
19 focused primarily on riffles, other habitats are also of interest. In the Southeast region, in
20 addition to collecting quantitative samples from riffle habitat, some organizations are also
21 collecting qualitative samples from multiple habitats, keeping taxa from the different habitats
22 separate, which provides information on how changing thermal and hydrologic conditions impact
23 taxa in nonriffle habitats. For example, taxa in edge habitats may show a greater response to
24 extended summer low flow events than taxa in riffles because the edge habitats are more likely to
25 go dry.
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Table 4. Recommendations on best practices for collecting macroinvertebrate data at regional monitoring network (RMN)
sites. The RMN framework has four levels of rigor for macroinvertebrate sampling, with 4 being the best/highest and 1 being
the lowest. At primary RMN sites, RMN members should try to adhere to (at a minimum) the level 3 practices, which are in
bold italicized text
Component
1 (lowest)
4 (highest)
Habitat
No riffle habitat
Multi-habitat composite from
a sampling reach with scarce
riffle habitat
Abundant riffle habitat
Multi-habitat sample with taxa
from each habitat kept separate
Time period
Time period varies from
year to year, and
adjustments are NOT
made for temporal
variability
Time period varies from year
to year, but adjustments are
made for temporal variability
Adherence to a single time
period
Samples are collected during
more than one time period (e.g.,
spring and late summer/early
fall)
Fixed count
subsample
Presence/absence or
field estimated
categorical abundance
(e.g., rare, common,
abundant, dominant)
Fixed count with a target of
100 or 200 organisms
Fixed count with a target
of 300 organisms
Fixed count with a target of
more than 300 organisms
Processing
Organisms are sorted,
identified and counted
in the field
Samples are processed in the
laboratory by trained
individuals. Some but not all
aspects of sample processing
use methods that are agreed
upon by the regional working
group
Samples are processed in
the laboratory by trained
individuals and use
methods that are agreed
upon by the regional
working group
Samples are processed in the
laboratory by trained
individuals and use methods
that are more stringent than
those being used by the
regional working group
Sorting
efficiency
No checks on sorting
efficiency
Sorting efficiency checked
internally by a trained
individual
Sorting efficiency checked
internally by a taxonomist
Sorting efficiency checked by
an independent laboratory
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Table 4. continued...
Component
1 (lowest)
4 (highest)
Qualifications
Identifications are
done by a novice or
apprentice biologist
with no
certification
Identifications are done by an
experienced taxonomist
without certification
Identifications are done by a
trained taxonomist who has
the appropriate level of
certification
Identifications are done by a
certified taxonomist who is
recognized as an expert in
species-level taxonomy for one
or more groups
Taxonomic
resolution
Coarse resolution
(e.g., order/family)
Mix of coarse and genus-level
resolution [e.g., family-level
Chironomidae, genus-level
Ephemeroptera, Plecoptera,
and Trichoptera (EPT)]
Mix of species and genus
level. Identifications are
done to the level of
resolution specified in
Appendix G
Species level for all taxa, where
practical
Validation
No validation
Taxonomic checks are
performed internally but not
by an independent laboratory.
The entire subsample (referred
to as a "voucher sample") is
retained for each site.
Taxonomic checks are
performed internally but not
by an independent
laboratory. The entire
subsample (referred to as a
"voucher sample") is
retained for each site as well
as a reference collection with
each unique taxon
Taxonomic checks are
performed by an independent
laboratory. The entire
subsample (referred to as a
"voucher sample") is retained
for each site, as well as a
reference collection with each
unique taxon verified by an
outside expert
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1 Sampling should occur during a consistent time period to minimize the variability associated
2 with seasonal changes in the composition and abundances of stream biota and to allow for more
3 efficient trend detection (Olsen et al., 1999). At RMN sites, samples should be collected during
4 the same time period (or periods) each year, ideally within 2 weeks of a set collection date (see
5 Table 4). If flooding or high water prevents sample collection within the specified time period,
6 samples should be taken as closely to the target period as possible. In addition to taxonomic
7 consistency, samples collected during the same time period can be used to explore whether long-
8 term changes in continuous thermal and hydrologic measurements are occurring during the target
9 period.
10 States and RBCs in the Mid-Atlantic region are currently collecting samples in both spring and
11 summer, as resources permit. The spring index period is being restricted to March-April and the
12 summer index period to July-August because this range overlaps with existing state and RBC
13 index periods and reduces potential temporal variability to a 2-month window. In the future, if
14 only one collection is possible in the Mid-Atlantic region, the spring index period is preferred
15 because many of the spring-emerging organisms (e.g., Ephemeroptera and Plecoptera)
16 considered to be good cool/cold water indicators may not be present or easily collected in
17 summer index periods. In the Northeast region, sampling is taking place during a summer/early
18 fall (July-September) index period because this range overlaps with existing state index periods
19 and because environmental conditions in the spring are generally not conducive to sampling
20 (e.g., potential ice cover). In the Southeast region, macroinvertebrate samples are being collected
21 in April, with some states adding a September sample.
22 When macroinvertebrate samples from primary RMN sites are processed, subsampling should be
23 performed in a laboratory by trained personnel. Participating organizations should perform fixed
24 counts with a target of 300 (or more) organisms to reduce sample variability and ensure sample
25 comparability (see Table 4). Consistent subsampling protocols are important because sampling
26 effort and the subsampling method can affect estimates of taxonomic richness (Gotelli and
27 Graves, 1996), taxonomic composition, and relative abundance of taxa (Cao et al., 1997). The
28 300-organism target is larger than what is specified in some state, tribal, and RBC methods. The
29 purpose of using this larger fixed count is to increase the probability of collecting cold water
30 indicator taxa that are not ubiquitous and to improve the chances of detecting declines in richness
31 (Bierwagen et al., in review). If organizations normally use lower fixed targets (e.g., 100 or
32 200-count samples) for their assessments, computer software can be used to randomly subsample
33 300-count samples to those lower targets.
34 Taxa collected at primary RMN sites should be identified to the lowest practical taxonomic level
35 (see Table 4). Research has shown that finer levels of taxonomic resolution can discriminate
36 ecological signals better than coarse levels (Lenat and Resh, 2001; Waite et al., 2000; Feio et al.,
37 2006; Hawkins, 2006). If this level of resolution is not possible, efforts should be made to
38 conform to the taxonomic resolution recommendations contained in Appendix G. These call for
39 genus-level identifications (where possible) for Ephemeroptera, Plecoptera, Trichoptera,
40 Chironomidae, and Coleoptera and specify certain genera within these taxonomic groups that
41 should be taken to the species-level. These genera were selected because they are believed to be
42 good thermal indicators and have shown variability in thermal tolerances at the species level
43 (U.S. EPA, 2012). Following these recommendations will increase the chances of detecting
44 temperature-related signals at RMN sites, and will provide important information about which
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1 taxa are most sensitive to changing thermal conditions. The recommendations in Appendix G
2 should be regarded as a starting point subject to revision as better data become available in the
3 future.
4 High-quality taxonomy is a critical component of credible ecological research, and taxonomic
5 identifications for RMN samples should be done by a trained taxonomist who has the appropriate
6 level of certification (see Table 4). Analyses have shown that the magnitude of taxonomic error
7 varies among taxa, laboratories and taxonomists, and that the variability can affect interpretations
8 of macroinvertebrate data (Stribling et al., 2008). Sources of these errors include incorrect
9 interpretation of technical literature, recording errors, and vague or coarse terminology, as well
10 as differences in nomenclature, procedures, optical equipment, and handling and preparation
11 techniques (Stribling et al., 2003; Dalcin, 2004; Chapman, 2005). Experience and training can
12 prevent many of these errors (Haase et al., 2006; Stribling et al., 2008). A reference collection of
13 each unique taxon should be housed by each agency and made available for verification or
14 comparison. The entire fixed count subsample (referred to as "voucher samples") for each
15 primary RMN site should be preserved and archived. When a unique taxon is removed from a
16 voucher sample for the reference collection, it must be clearly documented. Reference
17 collections and voucher samples will be particularly important for RMN samples because
18 identifications often will be made by different taxonomists. If resources permit, a subset of
19 samples should be checked by a taxonomist from an independent laboratory to validate the
20 identifications and ensure consistency across organizations.
21 The collection of certain types of demographic or life history data could reduce the amount of
22 time needed to detect changes in biological indicators because these traits may respond to
23 climate change earlier than species richness and abundance (Sweeney et al., 1992; Hogg and
24 Williams, 1996; Harper and Peckarsky, 2006). Examples include rates of development, size
25 structure, timing of emergence, and voltinism. More importantly, the frequency and occurrence
26 of the traits themselves can be linked to environmental conditions and used to predict
27 vulnerability of other species (e.g., Townsend and Hildrew, 1994; Statzner et al., 1994;
28 Townsend et al., 1997; Richards et al., 1997; van Kleef et al., 2006; Poff et al., 2006). It is also
29 worth considering qualitative collections of adult insects to verify or assist in species
30 identification. At this time, the collection of these types of ancillary data at RMN sites is
31 optional, and any discussions of additional sampling should consider the costs and benefits of the
32 data for the states, tribes, or RBCs and RMN objectives.
33 When developing the macroinvertebrate methods for the RMNs, the intent was to balance the
34 need to generate comparable data that meets RMN objectives with generating data that has value
35 for individual RMN member's routine bioassessment programs. Without additional resources
36 and training, some organizations will not be able to attain these levels of rigor on a consistent,
37 long-term basis. For example, some organizations will not be able to follow the regional
38 protocols for the 300-organism count and species-level identifications. Instead, they will likely
39 follow their normal processing protocols, with counts of 100 or 200 organisms and genus-level
40 identifications. Although some inconsistencies are likely to occur, large differences in
41 methodologies across organizations can create substantial biases in biological metrics (see
42 Section 4.1, Table 7), which will add variability and reduce the sensitivity of indicators
43 (Bierwagen et al., in review). Reduced counts and coarser level identifications, in particular, are
44 likely to affect the richness metrics (Stamp and Gerritsen, 2009), but we currently lack the data
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1 needed to quantify exactly how much of an effect these differences would have on biological
2 measures at RMN sites.
3 If RMN members lack sufficient resources to count 300 organisms and perform species-level
4 identifications, we encourage them to collect a sample using the collection method agreed upon
5 by the regional working group and to retain this sample, in hopes that funds can eventually be
6 obtained to process the samples and perform a 300-organism count. RMN members should
7 periodically refresh these samples with preserving agent so that specimens remain in good
8 enough condition to later be identified. Regional coordinators can also seek funding to cover the
9 costs of macroinvertebrate sample processing and species-level identifications at a common
10 laboratory, at least for 1 year to establish valuable baseline information. For example, EPA
11 Region 3 was able to achieve this during the 2014 sampling season for the Mid-Atlantic RMN
12 members.
13 If the RMN protocols differ from those that are normally used by RMN members, RMN
14 members could consider conducting a methods comparison study, at least at a subset of sites.
15 There are a number of different possibilities for how to conduct comparison studies. For
16 example, RMN members can collect side-by-side samples with routine and RMN protocols.
17 After paired samples are processed with their respective methods, results can be compared and
18 differences between the methods quantified.
3.1.2. Fish
19 The collection offish at RMN sites is optional but encouraged. Fish are considered to be a higher
20 priority assemblage than periphyton at RMN sites because fish are routinely collected by
21 monitoring programs, are easily and consistently identified, and are often species of economic
22 and social importance. The public and many organizations have strong interests in protecting
23 fisheries, and numerous studies are being done to predict and monitor how fish distributions will
24 change in response to climate change (e.g., Clark et al., 2001; Flebbe et al., 2006; Trumbo, 2010;
25 Wenger et al., 2011). Best practices for fish collection at RMN sites are shown in the following
26 list.
27 • Participating organizations should follow the protocols that are agreed upon by the
28 regional working group. At this time, only the Southeast region is consistently collecting
29 fish data. Because fish sampling protocols are similar across organizations in this region,
30 the Southeast regional working group agreed to let organizations use their own standard
31 operating procedures. If organizations in other regions start to sample fish on a regular
32 basis, this topic should be revisited and the working groups should take an in-depth look
33 at the comparability offish sampling protocols within and across regions.
34 • There should be strict adherence to an index period (or periods).
35 • Species-level identifications should be done (where practical) by a trained fish
36 taxonomist.
37 • A reference collection of each unique tax on should be housed by each agency and be
38 made available for verification or comparison.
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3.1.3. Periphyton
1 The collection of periphyton at RMN sites is optional but encouraged, as periphyton are
2 important indicators of stream condition and stressors (Stevenson, 1998; McCormick and
3 Stevenson, 1998). At this time, the Southeast is the only region that has written guidelines for
4 periphyton collection. Their sampling protocols follow the Southeastern Plains instream nutrient
5 and biological response protocols (U.S. EPA, 2006) or equivalent. They strictly adhere to a
6 spring index period and have a subsampling target of 600 valves (300 cells). Species-level
7 identifications are being done (where practical) by a qualified taxonomist, and reference
8 collections of unique taxa are being retained. The protocols also recommend that the EPA rapid
9 periphyton survey field sheet or equivalent be completed (Barbour et al., 1999).
10 If organizations from other RMNs start to collect periphyton, they should follow the protocols
11 that are agreed upon by their regional working group. If standardized regional protocols are not
12 used, the methods that each entity uses should be detailed and well documented. With
13 periphyton, some programs have encountered problems with taxonomic agreement among
14 different laboratories and taxonomists, so steps should be taken to ensure consistency in
15 taxonomic identifications (e.g., send all samples to the same laboratory, photodocument taxa in
16 reference collections, conduct taxonomic checks with an independent laboratory).
3.2. TEMPERATURE DATA
17 Some states, tribes, and RBCs have been early adopters of continuous temperature sensor
18 technology and have written their own protocols for deploying these sensors. In an effort to
19 increase comparability of data collection across states and regions, EPA and collaborators
20 recently published a document on best practices for deploying inexpensive temperature sensors
21 (U.S. EPA, 2014). The best practices for collecting temperature data at RMN sites closely follow
22 these protocols.
23 At primary RMN sites, both air and water temperature sensors should be deployed (see Table 5).
24 In some cases, air temperature data are being recorded by an on-land pressure transducer (versus
25 a stand-alone temperature sensor). Readings from both temperature sensors combined can be
26 used to track responsiveness of stream temperatures to air temperatures and provide insights into
27 the factors that influence the vulnerability or buffering capacity of streams to thermal change.
28 Air temperature readings are also important for quality control (e.g., to determine when water
29 temperature sensors are dewatered) (Bilhimer and Stohr, 2009; Sowder and Steel, 2012).
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Table 5. Recommendations on best practices for collecting temperature data at regional monitoring network (RMN) sites. The
RMN framework has four levels of rigor for temperature monitoring, with 4 being the best/highest and 1 being the lowest. At
primary RMN sites, RMN members should try to adhere to (at a minimum) the level 3 practices, which are in bold italicized
text
Component
1 (lowest)
4 (highest)
Equipment
No temperature
sensors
Water temperature sensor
only
Air and water temperature
sensors
Air temperature sensor plus
multiple water temperature
sensors to measure reach-scale
variability
Period of
record
Single measurement/s
taken at time of
biological sampling
event
Continuous
measurements taken
seasonally (e.g., summer
only) at intervals of
90-minutes or less
Continuous measurements
taken year-round at
30-minute intervals
Continuous measurements taken
year-round at intervals of less
than 30 minutes
Radiation
shield
Not installed
Installed; the shield is
made using an untested
design (its effectiveness
has not been documented)
Installed; the shield is
made using a design that
has undergone some level
of testing to document its
effectiveness
Installed; the shield is made using
a design that has been tested
year-round, under a range of
canopy conditions
Pre-deployment
No accuracy checks
are performed
An accuracy check is
performed, but it does not
meet all of the
recommendations
described in Appendix H
An accuracy check is
performed in accordance
with the recommendations
described in Appendix H
An accuracy check that is more
stringent than the protocols
described in Appendix H is
performed
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Table 5. continued...
Component
1 (lowest)
4 (highest)
Mid-
deployment
No mid-deployment
checks are performed
Mid-deployment checks
are performed but the
protocols do not meet all
of the recommendations
described in Appendix H
Mid-deployment checks
are performed in
accordance with the
recommendations
described in Appendix H
Mid-deployment checks that are
more stringent than those
described in Appendix H are
performed
Post-retrieval
No post-retrieval
QA/QC procedures
are performed
Post-retrieval QA/QC
checks are performed but
the protocols do not meet
all of the
recommendations
described in Appendix H
Post-retrieval QA/QC
checks are performed in
accordance with the
recommendations
described in Appendix H
Post-retrieval QA/QC checks that
are more stringent than those
described in Appendix H are
performed
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1 Temperature measurements should be taken year-round at 30-minute intervals (see Table 5).
2 Year-round data are necessary to fully understand thermal regimes and how these regimes relate
3 to aquatic ecosystems (U.S. EPA, 2014). Radiation shields should be installed for both water and
4 air temperature sensors (see Table 5) to prevent direct solar radiation from hitting the
5 temperature sensors and biasing measurements (Dunham et al., 2005; Isaak and Horan, 2011).
6 The shields also serve as protective housings. Shield effectiveness varies by design (Holden et
7 al., 2013), so it is suggested that organizations use tested designs (see Table 5). If a new design is
8 used, organizations should test and document design performance. This can be done using
9 techniques like those described in Isaak and Horan (2011) and Holden et al. (2013).
10 To ensure that data meet quality standards, predeployment, mid-deployment and postretrieval
11 QA/QC checks should be performed in accordance with the guidelines described in Appendix H
12 (see Table 5). These checks are important because sensors may record erroneous readings during
13 deployment for a variety of reasons. For example, sensors may become dewatered or buried in
14 silt in low or high flow conditions or may malfunction because of human interference.
3.3. HYDROLOGIC DATA
15 Many of the primary RMN sites are located on smaller, minimally disturbed streams with
16 drainage areas less than 100 km2. Monitoring flow in headwater and mid-order streams is
17 important because flow is considered a master variable that effects the distribution of aquatic
18 species (Poff et al., 1997), and small streams in particular play a critical role in connecting
19 upland and riparian systems with river systems (Vannote et al., 1980). These small upland
20 streams, which are inhabited by temperature sensitive organisms, are also projected to experience
21 substantial climate change impacts (Durance and Ormerod, 2007), though some habitats within
22 these streams will likely serve as refugia from the projected extremes in temperature and flow
23 (Meyer et al., 2007).
24 The USGS has been measuring flow in streams since 1889, and currently maintains over 7,000
25 continuous gages. This network provides long-term, high quality information about our nation's
26 streams and rivers that can be used for planning and trend analysis (e.g., flood forecasting, water
27 allocation, wastewater treatment, and recreation). Efforts have been made to colocate RMN sites
28 with active USGS gages, but many gauges are located in large rivers that have multiple human
29 uses, so only a limited number meet the site selection criteria for the primary RMN sites. As
30 such, it will be necessary to collect independent hydrologic data at most RMN sites.
31 A common way to collect hydrologic data at ungaged sites is with pressure transducers, but these
32 devices can pose challenges. For one, pressure transducers are more expensive than the
33 temperature sensors, and some organizations have been unable to find funds to purchase the
34 transducers. Those that have been successful at obtaining transducers may lack the expertise and
35 staff needed to install and operate the equipment. In addition, they may lack the resources needed
36 to conduct mid-deployment and post-retrieval QA/QC checks to ensure that the data meet quality
37 standards.
38 If states, tribes, RBCs, and other participating organizations cannot deploy transducers during the
39 first several years of data collection, macroinvertebrate and temperature data should still be
40 collected. The transducers should be installed at primary RMN sites as soon as resources permit.
41 In some situations, a phased approach, in which organizations start with one transducer, may
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1 work best. Once the entity gains experience with installing and operating the transducer, it can
2 consider installing transducers at additional sites.
3 At RMN sites where pressure transducer data are being collected, efforts should be made to
4 follow the recommendations in Table 6. These closely follow the protocols described in the
5 recently published EPA best practices document on the collection of continuous hydrologic data
6 using pressure transducers (U.S. EPA, 2014).
7 If installed and maintained properly, pressure transducers will provide important information on
8 the magnitude, frequency, duration, timing, and rate of change of flows, and on the relationship
9 between hydrologic and biological variables at RMN sites. Transducer measurements should be
10 taken year-round (see Table 6). The transducers should be encased in housings to protect them
11 from currents, debris, ice, and other stressors. Staff gages should also be installed to allow for
12 instantaneous readings in the field, verification of transducer readings, and correction of
13 transducer drift (see Figure 2, Table 6). For more detailed guidance on how to install and
14 maintain pressure transducers in wadeable streams, refer to the EPA best practices document
15 (U.S. EPA, 2014).
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Table 6. Recommendations on best practices for collecting hydrologic data at regional monitoring network (RMN) sites. The
RMN framework has four levels of rigor for hydrologic monitoring, with 4 being the best/highest and 1 being the lowest. At
primary RMN sites, RMN members should try to adhere to (at a minimum) the level 3 practices, which are in bold italicized
text
Component
1 (lowest)
4 (highest)
Equipment
Pressure transducer,
water only; no staff
gage
Pressure transducer,
water and air (encased
in housings); no staff
gage
Pressure transducer, water
and air (encased in
housings); staff gage
installed
Same as level 3, plus a
precipitation gage or USGS gage
Type of data
Stage/water level
only; data are not
corrected for
barometric pressure
Stage/water level only;
data are corrected for
barometric pressure
Flow/discharge based on
stage-discharge rating
curves developed from the
full range of flow
conditions
Flow/discharge based on
stage-discharge rating curves
developed from the full range of
flow conditions; after establishing a
rating curve, discharge is measured
at least once annually, and if
possible, also after large storms or
any other potentially channel-
disturbing activities
Period of
record
Discharge
measurements taken
with flow meter at
time of biological
sampling event
Continuous
measurements taken
seasonally (e.g.,
summer only)
Continuous measurements
taken year-round
Continuous measurements taken
year-round and discharge
measurements taken with flow
meter at time of biological
sampling event
Elevation
survey
Not performed
Performed once, at
time of installation
Performed annually
Performed more than once a year,
as needed (e.g., if a storm moves
the sensor and it has to be
redeployed)
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Table 6. continued...
Component
1 (lowest)
4 (highest)
Mid-
deployment
No mid-deployment
checks
Mid-deployment
checks are performed
but the protocols do
not meet all of the
recommendations
described in
Appendix I
Mid-deployment checks are
performed in accordance
with the recommendations
described in Appendix I
Mid-deployment checks that are
more stringent than those described
Appendix I are performed
Post-retrieval
No post-retrieval
QA/QC procedures
are performed
Post-retrieval QA/QC
checks are performed
but the protocols do
not meet all of the
recommendations
described in
Appendix I
Post-retrieval QA/QC
checks are performed in
accordance with the
recommendations in
Appendix I
Post-retrieval QA/QC checks that
are more stringent than those
described in Appendix I are
performed
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Transducer data
Staft'gage leadings
Date
Figure 2. Staff gage readings provide a quality check of transducer data. In this example,
staff gage readings stopped matching transducer readings in November, indicating that the
transducer or gage may have changed elevation.
1 When the pressure transducer is installed, the elevation of the staff gage and pressure transducer
2 should be surveyed to establish a benchmark or reference point for the gage and transducer (see
3 Table 6). This benchmark allows for monitoring of changes in the location of the transducer,
4 which is important because if the transducer moves, water-level data will be affected and
5 corrections will need to be applied (see Figure 2). While water-level measurements alone yield
6 information about streamflow patterns, including the timing, frequency, and duration of high
7 flows (McMahon et al., 2003), they do not give quantitative information about the magnitude of
8 streamflows or flow volume, which makes it difficult to compare hydrologic data across streams.
9 If agencies have the resources to convert water-level measurements to streamflow (e.g., volume
10 of flow per second), the most common approach is to develop a stage-discharge rating curve. To
11 develop a rating curve, a series of discharge (streamflow) measurements are made at a variety of
12 stages, covering as wide a range of flows as possible. The EPA best practices document
13 (U.S. EPA, 2014) contains basic instructions on how to take discharge measurements in
14 wadeable streams. More detailed guidance on this topic can be found in documents like Rantz et
15 al. (1982), Shedd (2011), or Chase (2005). After establishing a rating curve, discharge should be
16 measured at least once annually, and if possible, also after large storms and other potentially
17 channel-disturbing activities. In addition, elevation surveys should be performed annually or as
18 needed to check that the sensor has not moved.
19 To ensure that data meet quality standards, mid-deployment and post-retrieval QA/QC checks
20 should be performed in accordance with the practices described in Appendix I to identify
21 erroneous readings (see Table 6). As with temperature sensors, different types of errors can occur
22 during deployment (e.g., the pressure transducers may become dewatered or buried in sediment
23 during low and high flow conditions). Participating organizations should perform the QA/QC
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1 checks when possible, but we recognize that this activity can be resource intensive, as some
2 checks require numerous site visits or are difficult to perform quickly without software aids.
3 Because the collection of high quality hydrologic data is resource-intensive, states, tribes, RBCs,
4 and other participating organizations are encouraged to explore partnerships with the USGS,
5 universities, and other organizations (e.g., volunteer watershed groups). Some states have been
6 successful at forging such partnerships. For example, the Massachusetts Department of
7 Environmental Protection (MA DEP) has formed a partnership with the Massachusetts River
8 Instream Flow Stewards (RIFLS) program. MA DEP collects macroinvertebrate and temperature
9 data from the primary RMN sites, while the RIFLS program collects the flow data. New
10 Hampshire Department of Environmental Sciences has partnered with Plymouth State
11 University, who provided pressure transducers and helped with installations at New Hampshire's
12 primary RMN sites.
13 In the future, it would be valuable to start collecting precipitation data as well at the primary
14 RMN sites. Similar to air and water temperature relationships, these data can be used to track
15 responsiveness of stream flow to precipitation. Partnerships through groups, such as the
16 Community Collaborative Rain, Hail, and Snow Network (http://www.cocorahs.org/X can help
17 in this regard. Any discussions of additional sampling should consider the costs and benefits of
18 the data for the states, tribes, or RBCs and RMN objectives.
3.4. PHYSICAL HABITAT
19 Qualitative visual habitat assessments should be performed annually at primary RMN sites in
20 conjunction with biological sampling. Many states, tribes, and RBCs have adopted EPA's RBP
21 (Barbour et al., 1999) (see Appendix J) or have a similar visual rating method (e.g., MD DNR,
22 2014). These qualitative assessments rate instream, bank, and riparian habitat parameters using
23 visual descriptions that correspond to various degrees of habitat condition (e.g., optimal,
24 suboptimal, marginal, and poor). Skilled field biologists are capable of performing comparable
25 and precise visual habitat assessments, and these data, combined with photographs, can be used
26 to qualitatively track habitat changes at RMN sites through time. Such assessments are important
27 because habitat changes associated with climate change will also contribute to shifts in biological
28 assemblage composition and structure over time.
29 The collection of quantitative habitat data (e.g., bankfull width, slope, substrate composition) is
30 optional but encouraged. If resources permit, we recommend the following basic list of
31 quantitative measurements be collected at RMN sites:
32 • Geomorphological
33 o Bankfull width (reach-wide mean or at an established transect)
34 o Bankfull depth (reach-wide mean or at an established transect)
35 o Reach-scale slope
36 • Habitat
37 o Substrate composition (pebble counts to get percentage fines, percentage sand,
38 etc.)
39 o Flow habitat types (percentage riffle, percentage pool, percentage glide,
40 percentage run)
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1 o Canopy closure (measured with spherical densitometer, mid-stream and along
2 bank)
3 There are several published methods, such as the EPA National Rivers and Streams Assessment
4 protocols (U.S. EPA, 2013b; Kaufmann et al., 1999), for making these measurements. All of the
5 methods require expertise and skill, and some can be time intensive. As such, we are not
6 recommending specific quantitative habitat methods at RMN sites. Future discussions about
7 which parameters to measure should focus on reviewing key geomorphological or quantitative
8 measures of physical habitat condition that are known to be ecologically meaningful and are
9 likely to be affected by climate change. As part of a regional classification analysis we developed
10 a predictive model for macroinvertebrate assemblages in the eastern United States. Substrate
11 (percentage sand, percentage fines, embeddedness), flow habitat (percentage pools), and reach-
12 scale slope emerged as important predictor variables in this model. Collecting these data at RMN
13 sites would improve our ability to accurately classify sites and help inform decisions on how data
14 from RMN sites could be pooled together for analyses.
15 The frequency with which quantitative habitat data should be collected from RMN sites also
16 warrants further discussion. It may not be necessary to collect these types of data on an annual
17 basis because channel forming flows that could change baseline geomorphological and instream
18 habitat features generally have 1-2 to 5 year return periods for bankfull or small flood events,
19 respectively. However, specifying an exact timeframe for these measurements is difficult
20 because channel-forming flows are hard to predict and their impacts at a given site can be highly
21 variable. To help inform this discussion, one possibility would be to conduct a pilot study in
22 which RMN members collect quantitative data on an annual basis at a subset of sites and then
23 quantify how much the measurements vary from year to year and from site to site. If this type of
24 comparison is not feasible, another option would be to take quantitative measurements less
25 frequently but then also take measurements when visible geomorphic changes are seen in the
26 photodocumenation (see Section 3.6). This topic warrants further discussion among RMN work
27 group members and outside experts.
28 Also of interest are habitat measurements that are likely to be impacted by climate change.
29 Climate change could contribute to temporally and spatially complex fluvial adjustments (Blum
30 and Tornqvist, 2000). Some of the effects will be direct (e.g., changing precipitation patterns will
31 alter hydrologic regimes, rates of erosion, and sediment yields). Other effects will be indirect,
32 such as increases in sediment yield, which may result from vegetation disturbances that stem
33 from changing thermal and hydrologic conditions (e.g., wildfire, insect/pathogen outbreak,
34 drought-related die off) (Goode et al., 2012). Modeling studies from a range of different
3 5 environments suggest that the increases in rates of erosion could be on the order of 25-50%
36 (Goudie, 2006). Changes in the frequency or magnitude of peak flows could cause significant
37 channel adjustments, especially in higher order streams (Faustini, 2000), but channel adjustments
38 will vary according to many factors. For example, channel adjustments and changes in sediment
39 transport and storage can be greatly influenced by large woody debris dams and boulders that
40 increase roughness (Faustini and Jones, 2003). Climate-related changes in riparian vegetation
41 may also occur (e.g., Iverson et al., 2008; Rustad et al., 2012), which could in turn affect the
42 structure and composition of the benthic macroinvertebrate community (Sweeney, 1993; Whiles
43 and Wallace, 1997; Foucreau et al., 2013).
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1 Monitoring the effects of climate change on physical habitat at RMN sites could be greatly
2 improved by adding carefully selected measurements of geomorphology and quantitative habitat
3 indicators. These measures could include indicators that directly or indirectly reflect changes in
4 hydrology and vertical or lateral channel adjustments (e.g., cross-sectional transects, mean
5 bankfull height throughout a study reach, bank stability, and pebble counts). Indices of relative
6 bed stability (Kaufmann et al., 2008; Kaufmann et al., 2009), measures of embeddedness, or
7 metrics derived from pebble counts (e.g., percentage fines) might be useful measures in
8 characterizing the effects of climate change if hydrological changes result in changes to rates of
9 erosion, channel geometry, slope, bank stability, or sediment supply. We believe, however, that
10 more discussion among RMN work group members and outside experts is needed before
11 recommending additional habitat measurements.
3.5. WATER CHEMISTRY
12 In situ, instantaneous water chemistry parameters (specific conductivity, dissolved oxygen, and
13 pH) should be collected when RMN sites are visited for biological sampling. The purpose of
14 collecting these data is to document whether water quality changes are occurring that could
15 potentially contribute to changes in biological assemblage composition and structure over time.
16 The collection of more complete water quality data (e.g., alkalinity, major cations, major anions,
17 trace metals, nutrients) is optional but encouraged. If additional resources are available, water
18 chemistry samples could be collected multiple times per year at primary RMN sites during
19 different flow conditions.
3.6. PHOTODOCUMENTATION
20 Digital photographs should be taken when RMN sites are visited for biological sampling.
21 Photographs are important to document any changes to the monitoring locations, show the
22 near-stream habitat where data are being collected, provide qualitative evidence of changes in
23 geomorphology (e.g., lateral and vertical channel stability), and to locate sensors during
24 subsequent visits (U.S. EPA, 2014). During each visit, the photographs should be taken from the
25 same location(s). Global Positioning System (GPS) coordinates (latitude and longitude) should
26 be recorded for the location where the photographs are taken. The coordinates should be
27 recorded in decimal degrees, using the NAD83 datum for consistency. In areas with good
28 satellite reception, field personnel should wait until there is coverage from four or more satellites
29 before recording the coordinates. The accuracy of the coordinates should later be verified in the
30 office or laboratory by using software [e.g., Google Earth or Geographic Information System
31 (GIS) software] to plot the location on a map. If GPS coordinates are not available on-site, the
32 location (or locations) should be marked on a map and the coordinates determined later.
33 At least one set of photographs should be taken from a location at mid-reach. The photos should
34 be taken looking upstream and downstream from this location, and should include specific and
35 easily identifiable objects such as large trees, large stable boulders, large woody debris, point
36 bars, established grade control, and so forth (see Figure 3). In addition, field personnel are
37 encouraged to take photos of the riffles where macroinvertebrates are collected and, for
38 hydrologic data, the location where instantaneous discharge measurements are taken. Photos of
39 the dominant substrate on point bars and of banks at established transects are also of interest to
40 document any changes in physical habitat. The photos should be archived yet easily accessible
41 for future use.
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Figure 3. Photodocumentation of Big Run, WV, taken from the same location each year.
Provided by West Virginia Department of Environmental Protection (WV DEP).
3.7. GEOSPATIAL DATA
1 If resources permit, GIS software can be used to obtain land use and land cover data for RMN
2 sites based on exact watershed delineations for each site. Percentage land use and impervious
3 cover statistics should be generated from the most recent National Land Cover Database
4 (NLCD), and changes in these statistics should be tracked over time. We recognize that other
5 land use data sets may be available in a given location. For the RMNs, the most current NLCD
6 data set is preferred because it is a standardized set of data that covers the conterminous United
7 States and can be used with a standardized disturbance screening process (see Appendix D).
8 Drainage area should also be calculated for each RMN site.
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1 Having exact watershed delineations for RMN sites makes it possible to obtain a wide range of
2 additional geospatial data (e.g., climate, topography, soils, geology), as well as generate flow and
3 temperature statistics (Carlisle et al., 2010; Carlisle et al., 2011; Hill et al., 2013). For purposes
4 of the RMNs, data that are available at a national scale from the NLCD are preferred to
5 landscape-level variables generated from sources that do not provide nationwide coverage, in
6 order to standardize disturbance screening for sites and facilitate other comparisons and analyses.
7 In addition, it would be valuable to examine aerial photographs of the RMN sites for signs of
8 past disturbance, because past land use can have lasting impacts on stream biodiversity (Harding
9 etal., 1998).
4. SUMMARIZING AND SHARING REGIONAL MONITORING NETWORK (RMN)
DATA
10 In this section, we provide recommendations on how to summarize the biological, temperature,
11 hydrologic, habitat, and water quality data that are collected at RMN sites. At a minimum,
12 certain sets of metrics or statistics should be calculated from the RMN data so that samples can
13 be characterized and compared in a consistent manner. A consistent set of summary metrics also
14 helps in sharing data across organizations. We attempted to select metrics that are:
15 • Relevant in the context of biomonitoring and to RMN members,
16 • Straightforward to calculate and interpret,
17 • Known or hypothesized to be most strongly associated with biological indicators,
18 • Known or hypothesized to respond to climate change, and
19 • Limited in redundancy.
20 These lists of metrics are intended to serve as starting points and should be reevaluated after the
21 first several years of data collection at RMN sites. Periodic literature reviews should be
22 conducted to help inform parameter selection, which is an active area of research. As such, it is
23 important that the raw data collected at RMN sites is properly archived and stored so that
24 additional metrics can be calculated in the future.
4.1. BIOLOGICAL INDICATORS
25 To facilitate the sharing of biological data among RMN members, both raw data and summary
26 metrics should be put into the templates shown in Appendix K. Because taxonomic nomenclature
27 can vary across organizations, we recommend that the USGS BioData nomenclature be used to
28 describe taxa from RMN sites. Original identifiers used by each entity will also be retained in the
29 shared file, as shown in Appendix K. The USGS nomenclature can be downloaded from this
30 website (USGS, 2014a):
31 https://my.usgs.gov/confluence/display/biodata/BioData+Taxonomy+Downloads
32 Table 7 contains a list of candidate biological indicators that should be summarized from the
33 macroinvertebrate data collected at RMN sites. When developing the list of taxonomically based
34 metrics, consideration was given to which metrics are most commonly used by biomonitoring
35 programs for site assessments. The list includes measures like total taxa richness and
36 Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness and composition (Barbour et al.,
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1 1999). Traits-based metrics related to thermal and hydrologic conditions are also included (e.g.,
2 functional feeding group, habit, thermal, and flow preference). To derive the thermal preference
3 metrics, methods described in Yuan (2006) were used to estimate the optimal temperature values
4 and ranges of occurrence (tolerances) for taxa that had a sufficient distribution and number of
5 observations to support the analysis. These data, along with supplemental data provided by states
6 and best professional judgment of regional experts, were used to derive lists of cold and warm
7 water taxa for the eastern states that are participating in the current phase of RMN work. These
8 lists, which can be found in Appendix L, are the basis of the thermal preference metrics listed in
9 Table 7. The thermal indicator lists in Appendix L should be regarded as a first step and should
10 be reevaluated as more stream temperature data become available.
11 Metrics known or hypothesized to be sensitive to changing hydrologic conditions are also
12 included in Table 7. These metrics were selected based primarily on literature review (e.g.,
13 Horrigan and Baird, 2008; Chiu and Kuo, 2012; U.S. EPA, 2012; DePhilip and Moberg, 2013a;
14 Conti et al., 2014). The list of traits-based metrics related to hydrology should be reevaluated
15 periodically and refined as more trait data becomes available and more is learned about how the
16 traits link to hydrology. Given the rapid pace of research in these fields, it is important that the
17 raw data collected at RMN sites be properly archived and stored so that additional metrics can be
18 calculated in the future.
19 Biological condition scores should also be calculated at RMN sites in accordance with each
20 entity's bioassessment methods. Biological indices often take the form of multimetric indices
21 (MMIs) or predictive models like the River Invertebrate Prediction and Classification System
22 (Wright, 2000). MMIs are generally a composite of biological metrics selected to capture
23 ecologically important structural or functional characteristics of communities, where poor MMI
24 scores represent deviations from reference condition (Karr, 1991; Barbour et al., 1995; DeShon,
25 1995; Yoder and Rankin, 1995; Sandin and Johnson, 2000; Bohmer et al., 2004; Norris and
26 Barbour, 2009). Predictive models compare which reference site taxa are expected (E) to be
27 present at a site, given a set of environmental conditions, to which taxa are actually observed (O)
28 during sampling, where low O:E community ratios represent deviation from reference condition
29 (Wright et al., 1984; Wright, 2000; Hawkins, 2006; Pond and North, 2013).
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Table 7. Recommendations on candidate biological indicators to summarize from the
macroinvertebrate data collected at regional monitoring network (RMN) sites; many of
these are indicators that are commonly used by biomonitoring programs for site
assessments
Type of
indicator
Taxonomic-
based metric
Biological indicator
Total number of taxa
(richness)
Number of EPT taxa
(Ephemeroptera
[mayflies], Plecoptera
[stoneflies], and
Trichoptera [caddisflies])
Number of Ephemeroptera
(mayfly) taxa
Number of Plecoptera
(stonefly) taxa
Number of Trichoptera
(caddisfly) taxa
Percentage EPT
individuals
Percentage Ephemeroptera
individuals
Percentage Plecoptera
individuals
Percentage Trichoptera
individuals
Number of Odonata,
Coleoptera, Hemiptera
(OCH) taxa
Percentage OCH
individuals
Expected response
Predicted to decrease
in response to
increasing
anthropogenic stress
Expected to be more
prevalent during
summer, low flow
(more pool-like)
periods
Source
Barbour et al., 1999
(compiled from DeShon,
1995; Barbour et al.,
1996; Fore et al., 1996;
Smith and Voshell,
1997); these metrics are
commonly used in
bioassessments
Bonada et al., 2007a
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Table 7. continued...
Type of
indicator
Traits-based
metric related
to temperature
Traits-based
metric related
to hydrology
Biological indicator
Number of cold water taxa
Percentage Cold water
individuals
Number of warm water
taxa
Percentage Warm water
individuals
Collector filterer
Collector gatherer
Scraper/herbivore
Shredder
Predator
Swimmer
Rheophily — deposit! onal
Rheophily — erosional
Expected response
Predicted to decrease
in response to
warming
temperatures
Predicted to increase
in response to
warming
temperatures
Predicted to decrease
during low flow
conditions
Predicted to increase
during slow velocity
conditions
Predicted to increase
during conditions of
stable flow and
habitat availability;
decrease during
drought conditions
Expected to respond
to changing thermal
and hydrologic
conditions
Predicted to increase
during low flow
conditions
Predicted to comprise
higher proportion of
assemblage during
drier, harsher climatic
conditions
Favor low flow/slow
velocity conditions
Favor high flow/fast
velocity conditions
Source
Lake, 2003; Hamilton et
al., 2010; Stamp et al.,
2010; U.S. EPA, 2012
Wills et al., 2006; Bogan
and Lytle, 2007; Walters
and Post, 2011
Heino, 2009
Richards et al., 1997;
McKay and King, 2006;
Wills et al., 2006;
Fenoglio et al., 2007;
Griswold et al., 2008;
Diaz et al., 2008
Richards et al., 1997;
Buzby and Perry, 2000;
McKay and King, 2006;
Foucreau et al., 2013
Bogan and Lytle, 2007;
Miller et al., 2007;
Walters and Post, 2011
Beche et al., 2006;
Bonada et al., 2007b;
Diaz et al., 2008
Richards et al., 1997;
Lake, 2003; Wills etal.,
2006; Poffetal., 2010;
Brooks etal., 2011
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Table 7. continued...
Type of
indicator
Biological
condition
Individual taxa
Variability
Biological indicator
Bioassessment score (e.g.,
MMI, predictive, BCG)
Presence-absence
Relative abundance
Spatial distribution
Persistence (variability in
presence/absence; see
Appendix M)
Stability (variability in
relative abundance; see
Appendix M)
Expected response
Expected to worsen
in response to
increasing
anthropogenic stress
Hypotheses have
been developed for
some individual taxa
(e.g., the cold and
warm water taxa
listed in Appendix L)
Expect lower
persistence in
disturbed or
climatically harsh
environments
Expect lower stability
in disturbed or
climatically harsh
environments
Source
Barbour et al., 1995;
DeShon, 1995; Hawkins
et al., 2000; Davies and
Jackson, 2006
Becker etal., 2010
Holling, 1973; Bradley
and Ormerod, 2001;
Milner et al., 2006;
Durance and Ormerod,
2007
Scarsbrook, 2002; Milner
et al., 2006
1 Biological condition scores should also be calculated at RMN sites, in accordance with each
2 entity's bioassessment methods. Because different organizations use different techniques for
3 calculating biological condition scores, the index scores themselves may not be comparable
4 across sites sampled by different organizations. However, the direction of trends can be tracked
5 across RMN sites, and standardized metrics, such as BCG scores, can be used to monitor
6 changes in condition levels over time (Davies and Jackson, 2006). In Section 5.1.3 we describe
7 how BCG models could be used to track changes in biological condition at RMN sites both
8 within and across regions.
9 In addition to tracking the direction of metrics and condition scores over time, changes in the
10 occurrence (i.e., presence or absence) and the relative abundance of individual taxa can be
11 evaluated at RMN sites, as is being done at MD DNR Sentinel Stream Network sites (Becker et
12 al., 2010). Data tracked across sites then can be used to monitor changes in taxa distributions
13 over time through species distribution models (SDMs) or other means (see Figure 4). These
14 modeling efforts are especially important for taxa that are expected to experience range changes
15 in response to climate change (Hawkins et al., 2013; Domisch et al., 2013; Cao et al., 2013;
16 DeWalt et al., 2013). Section 5.4.2 describes SDM modeling in more detail, and how data
17 collected at RMN sites could be used to fit and validate SDMs.
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Sweitsa
Eurytophetta
= 82
N = 163
Ablabesmyia
Stenacron
N = 296
N = 114
Figure 4. Changes in the spatial distribution of taxa can be tracked over time. At regional
monitoring network (RMN) sites, particular attention will be paid to changes in the
thermal indicator taxa (in this example, the top two plots show spatial distributions of two
of the cold water indicators; the bottom two plots show distributions of warm water
indicators).
1 Quantifying natural variation in the occurrence and the relative abundance of individual taxa
2 allows biomonitoring programs to assess how this variation affects the consistency of biological
3 condition scores and metrics, and whether variation is linked to specific environmental
4 conditions. Year-to-year variation in aquatic communities at pristine sites is poorly understood.
5 Metrics of persistence and stability can be used to quantify year-to-year variation in metrics in
6 long-term data sets (Durance and Ormerod, 2007; Milner et al., 2006), and we recommend that
7 these metrics be calculated for RMN data as well (see formulas are provided in Appendix M).
8 Persistence metrics calculate variation in community richness over time (Holling, 1973), while
9 stability measures the variability in relative abundance of taxa in a community over time
10 (Scarsbrook, 2002). Both measures can be used to assess community resilience and describe
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1 potential vulnerabilities to changing thermal and hydrologic conditions that are projected to
2 occur with climate change (Karl et al., 2009).
4.2. THERMAL STATISTICS
3 Many metrics can be calculated from year-round air and water temperature measurements taken
4 from RMN sites. These metrics capture various aspects of thermal regimes, such as timing,
5 magnitude, variability, frequency, duration, and rate of change. Summer temperature metrics are
6 typically used in analyses with biological data because summer captures a critical time period for
7 most aquatic species' survival, and have been found to predict macroinvertebrate distributions
8 better than winter and summer temperature metrics (Hawkins et al., 2013).
9 Beyond this, we have limited information on which temperature metrics are ecologically
10 meaningful in the context of biomonitoring. Thus, providing recommendations on what summary
11 thermal statistics to calculate for air and water temperature data from RMN sites is challenging.
12 Many potential metrics are also correlated, which makes teasing their effects apart in most
13 models difficult. When developing a list of potentially important temperature metrics, we sought
14 input from organizations that have been collecting and processing continuous stream temperature
15 data for years, including MD DNR and the U.S. Forest Service Rocky Mountain Research
16 Station (Isaak and Horan, 2011; Isaak et al., 2012; Isaak and Rieman, 2013). We note that other
17 unlisted metrics have promise, including the use of more complex temperature exceedance
18 metrics and moving average calculations that are related to specific biological thresholds
19 (Schwartz et al.,2008).
20 Table 8 contains a recommended list of thermal summary statistics to calculate for data from
21 RMN sites. This list of metrics should be regarded as a starting point and should be reevaluated
22 over time. It consists of basic statistics that cover daily, monthly, seasonal, and annual time
23 periods, and basic percentage exceedance metrics (e.g., percentage of days that exceed 20°C).
24 We do not recommend specific temperature thresholds for exceedance values here, as these may
25 vary by location. For example, MD DNR and CT DEEP use different threshold values.
26 Before the metrics are calculated, the data should be screened using the guidelines described in
27 Appendix H to remove questionable data. Data should be interpreted with caution if no QA/QC
28 procedures are performed during the deployment period. A variety of software packages can be
29 used to calculate thermal statistics, including Microsoft Excel and ThermoStat (Jones and
30 Schmidt, 2012). Once the calculations have been made, the metric values should be entered into
31 the template provided in Appendix K to help facilitate data sharing across RMN members. Raw
32 temperature data collected at RMN sites is properly archived and stored so that additional
33 metrics can be calculated in the future.
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Table 8. Recommendations for candidate thermal summary statistics to calculate from
continuous temperature data at regional monitoring network (RMN) sites
Timeframe
Daily
Monthly
Seasonal51
Thermal statistic
Daily mean
Daily maximum
Daily minimum
Daily difference
(maximum-minimum)
Variance of daily mean
Monthly mean
Monthly maximum
Monthly minimum
Monthly difference
(maximum-minimum)
Monthly variance
Seasonal mean
Seasonal maximum
Seasonal minimum
Seasonal difference
(maximum-minimum)
Seasonal variance
Calculation
Mean temperature for each day
Maximum temperature for each day
Minimum temperature for each day
Difference between the maximum and
minimum temperatures for each day
Standard deviation for each day
Mean of the daily means for each month
Maximum value for each month
Minimum value for each month
Difference between the maximum and
minimum temperatures for each month
Standard deviation for each month
Mean of the daily means for each season
Maximum value for each season
Minimum value for each season
Difference between the maximum and
minimum temperatures for each season
Standard deviation for each season
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Table 8. continued...
Timeframe
Annual
Thermal statistic
Annual mean
Annual maximum
Annual minimum
Mean annual difference
Maximum annual difference
Minimum annual difference
Variance of the annual mean
difference
Percentage exceedance
Calculation
Mean of the daily means for the year
(January 1 -December 31)
Maximum value for the year
(January 1 -December 31)
Minimum value for the year
(January 1 -December 31)
Mean of the daily difference
(January 1 -December 31)
Maximum of the daily difference
(January 1 -December 31)
Minimum of the daily difference
(January 1 -December 31)
Standard deviation of the daily difference
(January 1 -December 31)
([Number of measurements that exceed a
threshold13] + [total number of measurements
in a year]) x 100
"Seasons are defined as follows. Winter: December, January, February; Spring: March, April, May; Summer: June,
July, August; Fall: September, October, November.
bThresholds may vary by entity and location.
4.3. HYDROLOGIC STATISTICS
1 As with the thermal data, many different metrics can be calculated from daily hydrologic data
2 that capture different aspects of hydrologic regimes (magnitude, frequency, duration, timing, and
3 rate of change) (Olden and Poff, 2003). Again, many metrics are correlated. There has been
4 some research on which hydrologic metrics are most ecologically meaningful in the context of
5 state biomonitoring programs (e.g., Kennen et al., 2008; Chinnayakanahalli et al., 2011).
6 Table 9 contains a list of recommended hydrologic statistics to calculate for data from RMN sites
7 where water-level or flow data are being collected. This list of metrics should be regarded as a
8 starting point and should be reevaluated over time. It consists of basic statistics that cover daily,
9 monthly, seasonal, and annual time periods. Most metrics have limited redundancy and are
10 relatively easy to calculate. When developing this list, we used a combination of published
11 literature and best professional judgment to inform our recommendations, including reports from
12 TNC and several partners (states, RBCs, other federal agencies), who developed ecosystem flow
13 needs for some eastern and midwestern rivers and their tributaries (e.g., the Susquehanna, the
14 Upper Ohio, the Delaware, and the Potomac Rivers) (Cummins et al., 2010; DePhilip and
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1 Moberg, 2013a; DePhilip and Moberg, 2013b; Buchanan et al., 2013). TNC and its partners
2 utilized components of the Ecological Limits of Hydrologic Alteration (ELOHA) framework
3 (Poff et al., 2010) to make recommendations on flows to protect species, natural communities,
4 and key ecological processes within various stream and river types. For the Upper Ohio River,
5 they recommended a list of flow statistics that capture ecologically meaningful aspects of
6 hydrologic regimes (see Appendix N) (DePhilip and Moberg, 2013a). We also considered
7 research by Olden and Poff (2003) and Hawkins et al. (2013), which identifies hydrologic
8 metrics that capture critical aspects of hydrologic regimes and are ecologically meaningful in
9 different types of streams (see Appendix N).
10 The hydrologic statistics listed in Table 9 should be calculated to match periods of calculation
11 used for the annual thermal statistics (e.g., calendar year rather than water year). These include
12 both summary statistics and also measures of variability. While the hydrologic statistics listed in
13 Table 9 can be calculated after the first year of data collection, it takes many years to get stable
14 estimates of hydrologic conditions. Richter et al. (1997) and Huh et al. (2005) suggest that at
15 least 20 years of data are needed to calculate interannual variability for most parameters, and that
16 30 to 35 years of data may be needed to capture extreme high and low events (e.g., 5- and
17 20-year floods) (Olden and Poff, 2003; DePhilip and Moberg, 2013a).
18 Before the metrics are calculated, the data should be screened using the guidelines described in
19 Appendix I to remove questionable data. Data should be interpreted with caution if no QA/QC
20 procedures (e.g., staff gage readings) were performed during the deployment period, and if the
21 elevations of the staff gage and pressure transducer were not surveyed. The latter are especially
22 important, because they can determine changes in the location of the transducer. If the transducer
23 moves, stage data will be affected and corrections should be applied.
24 To make data sharing easier, the metric values should be entered into the template provided in
25 Appendix K. Raw hydrologic data collected at RMN sites should be properly archived and stored
26 so that additional metrics can be calculated in the future. Additional statistics can easily be
27 calculated from software like Indicators of Hydrologic Alteration (TNC, 2009) and Aquarius
28 (Aquatic Informatics, 2014).
29 To supplement missing field data or provide estimates of streamflow at ungaged sites, simulation
30 models have been developed in some geographic areas. For example, the Baseline Streamflow
31 Estimator (BaSE) simulates minimally altered streamflow at a daily time scale for ungaged
32 streams in Pennsylvania. This freeware is publicly available, and has a user-friendly point-and-
33 click interface (Stuckey et al., 2012). Other examples of tools used to simulate flows are listed in
34 Table 10. While these modeled data should not be regarded as a substitute for observational data,
35 we encourage participating organizations to take advantage of whatever resources are available
36 for the RMN sites that they are monitoring.
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Table 9. Recommended candidate hydrologic statistics to calculate on each year of water-level or flow data from regional
monitoring network (RMN) sites. These provide information on high, seasonal, and low flow components to maintain
ecosystem flows. These candidate metrics were derived from DePhilip and Moberg (2013) for the Upper Ohio River Basin and
Olden and Poff (2003). Work that was done by Hawkins et al. (2013) was also considered
Timeframe
Daily
Monthly
Metric
Daily mean
Daily median
Daily maximum
Daily minimum
Daily difference
(maximum-minimum)
Coefficient of variation
Monthly mean
Monthly maximum51
Monthly minimumb
Monthly difference
(maximum-minimum)
High flow magnitude
(90th percentile)
Median magnitude (50th percentile)
Low flow magnitude
(25th percentile)
Calculation
Mean stage or flow for each day
Median stage or flow for each day
Maximum stage or flow for each day
Minimum stage or flow for each day
Difference between the maximum and minimum stage or flows for each day
Standard deviation for stage or flow for each day/mean daily stage or flow
Mean stage or flow for each month
Maximum stage or flow for each month
Minimum stage or flow for each month
Difference between the maximum and minimum stage or flow values for each month
90th percentile of monthly stage or flow values; this represents high flows and is
similar to the Qio measurement used in DePhilip and Moberg (2013)
50th percentile of monthly stage or flow values; this represents the monthly median
25th percentile of monthly stage or flow values; this represents low flows in smaller
streams [drainage areas <50 mi2, per DePhilip and Moberg (2013)] and is similar to
the Q?s measurement used in DePhilip and Moberg (2013)
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Table 9. continued...
Timeframe
Monthly
(continued)
Seasonal
Metric
Low flow magnitude
(10thpercentile)
Extreme low flow magnitude
(1st percentile)
Percentage high flow and floods
Percentage low flows
Percentage typical
Percentage high flows and floods in
spring and fall
Calculation
10th percentile of monthly stage or flow values; this represents low flows in medium
to larger-sized streams [drainage areas >50 mi2 per DePhilip and Moberg (2013)] and
is similar to the Qgo measurement used in DePhilip and Moberg (2013)
1st percentile of monthly stage or flow values; this represents extreme low flows and
is similar to the Q99 measurement used in DePhilip and Moberg (2013)
Percentage of stage or flow measurements in each month that exceed the monthly
90th percentile
Percentage of stage or flow measurements in each month that are between the
monthly 25th and 1st percentiles [similar to the Q?s and Q99 measurements used in
DePhilip and Moberg (2013)]
Percentage of stage or flow measurements in each month that are between the
monthly 25th and 90th percentiles [similar to the Q?s and Qio measurements used in
DePhilip and Moberg (2013)]
Percentage of stage or flow measurements in each month that exceed the monthly
90th percentile in spring (March-May) and fall (September-November)
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Table 9. continued...
Timeframe
Annual
(January 1
December 31)
Metric
Annual mean
Annual maximum
Julian date of annual maximum
Annual minimum
Julian date of annual minimum
Mean annual difference
Maximum annual difference
Minimum annual difference
Variance of the annual mean
difference
Number of zero flow days
Calculation
Mean of the daily mean stage or flow
Maximum stage or flow
Julian date of annual maximum stage or flow
Minimum stage or flow
Julian date of annual minimum stage or flow
Mean of the daily difference
Maximum of the daily difference
Minimum of the daily difference
Standard deviation of the daily difference
Number of days having stage or flow measurements of 0
aln Olden and Poff (2003), mean maximum August flow and mean maximum October flow captured important aspects of high flow conditions.
bln Olden and Poff (2003), mean minimum April flow captured important aspects of low flow conditions.
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Table 10. Examples of tools for estimating streamflow and/or streamflow statistics at ungaged sites. A similar tool is currently
being developed for New York
Tool
Geographic area
Website
Description
USGS StreamStats
(USGS, 2014b)
Varies by state
http://water.usgs.gov/osw/streatnstats/
Available for most but not all states in the
eastern United States. The types of output
statistics that are available vary by state. These
statistics represent long-term averages and do
not capture year-to-year variability.
BaSE (Stuckey et al.,
2012)
Pennsylvania
http://pubs.usgs.gov/sir/2012/5142/
This tool simulates minimally altered
streamflow at a daily time scale for ungaged
streams in Pennsylvania using data collected
during water years 1960-2008. It is free,
publicly available, and uses a point-and-click
interface.
Massachusetts
Sustainable-Yield
Estimator (MA SYE)
(Archfield et al.,
2010)
Massachusetts
http://pubs.usgs.gov/sir/2009/5227/
The MA SYE can estimate a daily time series
of unregulated, daily mean streamflow for a
44-year period of record spanning 1960 to
2004.
West Virginia DEP
7Q10 Report Tool
(Shank, 2011)
West Virginia
http: //tagi s. dep. wv. gov/streamfl ow/
This free, publicly available tool utilizes a
point-and-click interface. Seven Qio, annual
and monthly flow estimates are generated
when you click on a location.
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4.4. PHYSICAL HABITAT, WATER QUALITY, AND GEOSPATIAL DATA
1 Table 11 contains a list of physical habitat and water quality data that should be summarized at
2 RMN sites. Some optional parameters are also included in this table. While most RMN members
3 are using EPA's RBP (Barbour et al., 1999), some have developed a visual rating method
4 customized to their streams (e.g., MD DNR, 2014). Thus, some of the qualitative physical habitat
5 data may not be directly comparable across RMN sites because of differences in methodologies.
6 Despite these potential differences, we believe that the visual habitat assessments will provide
7 sufficiently similar information on the condition of physical habitat to serve the needs of the
8 RMNs.
Table 11. Physical habitat, water quality, and geospatial data that should be collected at
regional monitoring network (RMN) sites. Optional parameters are marked with an
asterisk
Parameter
Physical
habitat
Water quality
Geospatial
Data type
Qualitative
visual
assessment
Quantitative*
In situ
Grab samples51
Land use and
impervious
cover*
Measurements
Instream, bank, and riparian habitat parameters using visual
descriptions that correspond to various degrees of habitat
condition (e.g., optimal, suboptimal, marginal, and poor)
Dominant riparian vegetation*
Bankfull width
Bankfull depth
Reach-scale slope
Substrate composition (percentage fines, percentage sand, etc.)
Flow habitat types (percentage riffle, percentage pool,
percentage glide, percentage run)
Canopy closure (mid-stream and along bank)
Specific conductivity
Dissolved oxygen
PH
Alkalinity
Nutrients
Metals
Major cations
Major anions
Percentage forest, urban, agriculture, impervious, etc. from the
2006 National Land Cover Database (Fry et al., 201 1)
"optional
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5. DATA USAGE
1 Data collected from RMN sites can serve many purposes and will be used to:
2 • Detect temporal trends in biological, thermal, hydrologic, habitat, and water chemistry
3 data;
4 • Investigate and resolve relationships between biological, thermal, and hydrologic data;
5 • Examine how organisms respond and recover from extreme weather events;
6 • Test hypotheses and predictive models related to climate change; and
7 • Quantify natural variability.
8 In this section we highlight examples of analytical techniques and applications for the biological,
9 temperature, and hydrologic data that are being collected at RMN sites. These examples were
10 selected because of their relevance to biomonitoring.
5.1. TEMPORAL TRENDS
11 One of the primary uses of RMN data will be to perform analyses to detect trends in biological,
12 thermal, and hydrologic conditions over time. In this section we provide examples of:
13 • Basic analytical techniques for conducting temporal trend analyses (see Section 5.1.1),
14 • Trend detection for taxonomic and traits-based biological indicators (see Section 5.1.2),
15 and
16 • Tracking changes in biological condition with BCG models (see Section 5.1.3).
5.1.1. Basic Analytical Techniques
17 Scatterplots, simple correlation and regression analyses, and other basic comparative tools are an
18 important first step in exploring trends or annual differences over time. A major objective of the
19 RMNs is to detect where trends are developing over time in biologic, thermal, and hydrologic
20 regimes or to map changes in biology to changing thermal or hydrologic regimes that are
21 indicative of shifting reference conditions, as well as to document natural variability. The
22 sampling recommendations (see Sections 3.1-3.3) were created to maximize this potential within
23 the context of existing monitoring efforts. Common tools for detecting trends are used in nearly
24 all monitoring programs. For example, U.S. EPA (2012) examined macroinvertebrate data from
25 state biomonitoring programs in Maine, North Carolina, Ohio, and Utah to assess whether
26 bioassessment scores, selected biological metrics, temperature, flow, and precipitation variables
27 have changed over time. Metrics at many sites in U.S. EPA (2012) exhibited considerable
28 year-to-year variability, but some showed clear patterns. For example, at the Sheepscot River in
29 Maine, total taxa richness and warm water taxa richness increased over a 20-year period of
30 continuous biological data collected during a July-September index period (see Figure 5). At a
31 site on the Weber River in Utah, the cold water metrics showed strong negative associations with
32 year, based on September-November kick-method samples collected over a 17-year period
33 (U.S. EPA, 2012).
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35
30
25
S 20
-OJ
"o
is 15
.a
z 10
5
0
-5
u " u u u 0 LJ n a a
_g . n a Q_
Total Taxa
I9S4 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 ^o^ Cold Water TaX3
Year ^x Warm Water Taxa
Figure 5. Yearly trends in cold- and warm-water-preference taxa and total taxa richness at
a site on the Sheepscot River in Maine (Station 56817) (U.S. EPA, 2012). Samples were
collected during July-September using rock baskets (Davies and Tsomides, 2002).
Historically, this site has been impacted by nonpoint source pollution.
1 The Maryland Biological Stream Survey, led by the MD DNR, Monitoring and Non-Tidal
2 Assessment Division, used similar techniques to assess annual variability in stream conditions at
3 high-quality reference streams in their sentinel site network. MD DNR also tracks changes in
4 richness and abundances of cold water macroinvertebrate and fish taxa, which were identified
5 through analyses of continuous temperature data (Becker et al., 2010). Between 2000 and 2009,
6 the percentages of cold-water-preference benthic macroinvertebrate taxa and brook trout
7 abundances at sentinel sites were negatively but not significantly correlated with year (Becker et
8 al., 2010).
9 In addition, MD DNR uses analysis of variance to determine whether MD DNR's indices of
10 biotic integrity for benthic macroinvertebrates (BIBI) and fish (FIBI) (Roth et al., 1998;
11 Southerland et al., 2005, 2007) differ between years. MD DNR runs these analyses with sites
12 grouped by geographic region. Between 2000 and 2009, MD DNR found significant differences
13 in index of biological integrity (IBI) scores in the Coastal Plain (western shore) region, but not in
14 the Piedmont, the Coastal Plain—eastern shore region, or the Highlands regions. The differences
15 in IBI scores in the Coastal Plain—western shore region may have been associated with
16 changing hydrologic conditions, because the lowest IBI scores were recorded the year after the
17 lowest flow and rainfall conditions occurred (Becker et al., 2010).
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5.1.1.1. Data Preparation
1 Before conducting analyses, data should be screened to minimize the chances of detecting false
2 trends or differences due to changes in field and laboratory protocols. In the U.S. EPA (2012)
3 pilot studies, a screening process was used to identify:
4 • Changes in taxonomic naming over time (e.g., changes in genus or higher level names,
5 changes in placement within families). This not only reveals changes in systematics over
6 time, but also changes in taxonomists and/or laboratories used to analyze samples.
7 • Changes in level of resolution over time (e.g., increasing use of species names in recent
8 years where individuals are typically left at the genus or family level in earlier samples).
9 • Changes in other types of naming conventions (e.g., changes in systematics for taxa such
10 as water mites).
11 • Changes in sampling methodology (e.g., changes in collection methods or index periods).
12 • Changes in how early instars, damaged or other unidentifiable taxa, pupae, and
13 semiaquatic taxa are treated.
14 • Changes in how richness and abundance are calculated and reported (e.g., changes or
15 errors in how subsampling was applied; whether replicates are collected, and whether
16 they are averaged, summed, or reported separately; and whether both qualitative and
17 quantitative samples are collected, and whether those data are mixed together).
18 The development of operational taxonomic units (OTUs) may be required to address changes in
19 taxonomic naming and systematics that have occurred over time. The intent of OTUs is to
20 include only distinct or unique taxa in the analyses (Cuffney et al., 2007). If possible, expert
21 taxonomists should be involved in this process to determine how to best address the changes in
22 nomenclature. In the U.S. EPA (2012) pilot studies, genus-level OTUs were generally found to
23 be most appropriate, although there were some exceptions (e.g., in the Utah database, a
24 family-level OTU had to be used for Chironomidae due to inconsistencies arising from a change
25 in taxonomy labs).
26 As part of taxonomic screening or evaluating OTUs, ordinations techniques, such as nonmetric
27 multidimensional scaling (NMDS) or principle component analysis, can be used to show how
28 closely samples cluster based on taxonomic composition. U.S. EPA (2012) used NMDS to
29 evaluate the effectiveness of the OTUs by overlaying grouping variables (e.g., year, month,
30 collection method, taxonomy lab, ecoregion, watershed) on ordinations before and after OTUs
31 were applied. The OTUs were deemed effective if distinct patterns were not evident. NMDS can
32 also be used to evaluate collection and processing protocols that can influence measures of
33 assemblage composition. This technique was used by Bierwagen et al. (in review) prior to
34 running power analyses on biomonitoring data from the Northeast. The effects of different
35 methodologies (riffle kicks vs. artificial substrates) on taxonomic composition were evident in
36 the ordination (see Figure 6).
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q
d
(Q
O
-0.4
0.6
Figure 6. Effects of differences in sampling methodologies on taxonomic composition were
evident in this nonmetric multidimensional scaling (NMDS) ordination on the Northeastern
data set that was analyzed for an EPA pilot study in 2012. Methods are represented with
different symbols and sampling devices are shown with two rings (solid [artificial
substrate] and dashed [riffle kicks] 95% confidence ellipsoids). Wadeable Streams
Assessment (WSA) and New England Wadeable Streams (NEWS) project samples are also
highlighted (dotted 95% confidence ellipsoid). Taken from Bierwagen et al. (in review).
5.1.2. Trend Detection for Taxonomic versus Traits-Based Biological Indicators
1 Data collected from RMN sites can be used to determine which biological metrics are most
2 responsive or sensitive to climate-related changes, and how long it might take for trends to
3 become evident. Bierwagen et al. (in review) performed a detailed power analysis on routine
4 biomonitoring macroinvertebrate data in the Northeast to estimate the number of years needed to
5 detect temporal trends in seven biological metrics. Three of the metrics (total taxa richness, EPT
6 richness, and relative abundance) are commonly used in bioassessments, while the other four
1 climate-sensitive metrics (richness and relative abundance of cold- and warm-water taxa) are
8 based on lists of taxa that showed strong thermal preferences (Yuan, 2006; Stamp et al., 2010).
9 Data were grouped into three stream classes that were developed for the Northeast region using
10 stream gradient and drainage area. After accounting for differences in sampling methodology,
11 results suggest that well-designed networks of 25 to 30 sites monitored consistently can detect
12 underlying changes of 1-2% per year in a variety of biological metrics within 10-20 years if
13 such trends are present. Trend detection times were longer for the thermal preference metrics
14 versus traditional metrics, such as total taxa richness and EPT richness. A potential reason for
15 this is that climate-sensitive taxa are less common in samples, so collecting enough individuals
16 to detect their presence is crucial. In support of this, Bierwagen et al. (in review) found that
11 cold-water metrics performed better in the high-gradient stream class and warm-water metrics
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1 performing better in the low-gradient class, where the richness of these contrasting groups are
2 higher.
5.1.3. Tracking Changes in Biological Condition with Biological Condition Gradient
(BCG) Models
3 Trend analyses on RMN data can be used to determine whether changes in biological condition
4 are occurring over time. As discussed in Section 4.1, different organizations often use different
5 techniques for assessing and rating biological condition, so in many cases, quantitative
6 comparisons of biological condition scores from different states are not possible. The BCG
7 model provides a possible solution for this problem. The BCG uses a standardized index with a
8 fixed number of levels that evaluates alteration to biological structure and function relative to
9 baseline of natural conditions (Davies and Jackson, 2006). It can be calibrated and applied to
10 regional and local conditions and puts biological condition on a common, quantifiable scale for
11 all states and regions.
12 BCG models are typically calibrated to six levels that reflect a continuum of quality from pristine
13 (BCG level 1) to severely degraded (BCG level 6) (Davies and Jackson, 2006). If higher levels
14 of refinement are desired, more than six BCG levels can be used. The end assessments are on a
15 single scale that can be applied nationwide. Thus, a BCG level 2 sample in one region is
16 comparable to a BCG level 2 sample in another region because both assessments are dependent
17 on comparisons to natural conditions.
18 A number of pilot projects sponsored by the EPA have been conducted for streams and rivers in
19 different regions of the United States to further develop and apply the BCG. Regional BCG
20 models that accommodate methodological differences that have been developed for cold and
21 cool streams in the Northern Forest region of the Midwest and for medium to high gradient
22 streams in parts of New England (Stamp and Gerritsen, 2009; Gerritsen and Stamp, 2012). The
23 New England model is for macroinvertebrates and is cross-calibrated for methods used by
24 biomonitoring programs in Maine, New Hampshire, Vermont, and Connecticut, as well as for
25 EPA NRSA protocols. The Northern Forest models were developed for macroinvertebrate and
26 fish assemblages for Indian Reservations and the states of Michigan, Wisconsin, and Minnesota.
27 Regional models in other parts of the country are being developed (e.g., BCG models are
28 currently being developed for macroinvertebrate and fish assemblages in Alabama and Illinois).
29 These regional BCG models can be applied to data collected from RMN sites and BCG-level
30 scores can be tracked over time across sites. In addition to BCG scores, the component metrics of
31 the BCG models, which are typically related to tolerance of individual taxa, can also be tracked
32 over time.
5.2. RELATIONSHIPS BETWEEN BIOLOGICAL INDICATORS AND
ENVIRONMENTAL DATA
33 Another primary use of RMN data will be to evaluate relationships between the biological and
34 environmental data. The paired biological, thermal, and hydrologic data from RMN sites will
35 allow us to track whether changes in biological indicators are associated with changing thermal
36 and hydrologic conditions. In this section we provide examples of how RMN data can be used
37 to:
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1 • Explore relationships between biological and environmental data (see Section 5.2.1),
2 • Derive ecologically meaningful variables and thresholds (see Section 5.2.2), and
3 • Better understand interactive effects of climate change with non-climatic stressors (see
4 Section 5.2.3).
5.2.1. Basic Analytical Techniques
5 Analytical techniques similar to those described in Section 5.1.1 can be used to explore
6 relationships between biological indicators and environmental data at RMN sites. For example,
7 MD DNR uses scatterplots and correlation analysis to evaluate relationships between biological,
8 thermal, and hydrologic data (temperature, precipitation, flow) from its sentinel sites. Between
9 2000 and 2009, MD DNR found that BIB I scores at four of six sentinel sites in the
10 Coastal—western shore region were significantly and positively correlated with summer flow
11 percentiles, with the lowest scores following extremely dry years (Becker et al., 2010).
12 U.S. EPA (2012) conducted similar types of analyses on data sets from state biomonitoring
13 programs in Maine, North Carolina, Ohio, and Utah to examine whether climate-related trends
14 were evident in long-term macroinvertebrate surveys. The analyses found that at some sites,
15 biological metrics showed patterns that were associated with changing thermal and hydrologic
16 conditions, whereas at other sites, patterns were contrary to expectation or not evident. The
17 strongest trends occurred at two Utah sites that had more than 13 years of data. At these sites,
18 richness and relative abundance metrics for cold-water taxa were negatively correlated with air
19 temperature. At one of these sites, the EPT richness metric dropped dramatically from
20 2000-2005, which corresponded to a period of higher than normal temperatures and lower than
21 normal flows (see Figure 7) (U.S. EPA, 2012).
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B
-1
22
20
18
16
14
I 12
1 10
— 8
4
2
0
-2
35
34
o
»33
| 32
- Cold Water
> - Warm Water
-••-•-••-*
Cold Water
Warm Water
• Temperature
Flow
130
120
110
100
90
SO
70
60
50
JO
30
1985
1990
1995
2000
2005
1
2
Figure 7. Yearly trends at the Weber River site in Utah (UT 4927250) in (A) number of
cold and warm water taxa; (B) percentage cold- and warm-water individuals; and (C)
mean maximum July temperature (°C) and mean September/October/November (SON)
flow (cfs). Samples were collected from riffle habitats using a Hess sampler during a
September/October index period. Trends at this site may have been influenced by nonpoint
source pollution.
To explore these differences further, U.S. EPA (2012) partitioned Utah site data into years
characterized by hotter and colder temperatures and by higher and lower flows. Results varied
across sites and regions. The strongest patterns occurred at the two Utah sites where consecutive
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1 years of hot and dry conditions occurred from 2000-2005. At both sites there were fewer total
2 taxa and EPT taxa in hot years than in cold years, and four fewer cold-water taxa in hot years
3 than in cold years. Generally, hotter and drier conditions occurred over consecutive years, and
4 these conditions correspond with declines in biological metrics (taxa richness, EPT richness, or
5 cold-water taxa richness) (U.S. EPA, 2012).
5.2.2. Ecologically Meaningful Variables and Thresholds
6 Researchers have been wrestling with the concept of ecological thresholds for many years. An
7 ecological threshold is defined as "the point at which there is an abrupt change in an ecosystem
8 quality, property or phenomenon, or where small changes in an environmental driver produce
9 large responses in the ecosystem" (Groffman et al., 2006). Setting thresholds can be challenging
10 due to factors such as nonlinear dynamics and multiple control factors that operate at diverse
11 spatial and temporal scales (Groffman et al., 2006).
12 Data from RMN sites can be used to gain a better understanding of potential ecological "tipping"
13 points related to thermal and hydrologic conditions. Furthermore, a detailed understanding of
14 temperature and flow relationships can be used to develop meaningful breakpoints in a variety of
15 studies outside of trend detection. Notably, information from the first few years of RMN data
16 collection could be used to inform the creation of break points for vulnerability assessments
17 across the RMNs, which would be used to direct future RMN work or inform management
18 decisions. For example, Beauchene et al. (2014) developed ecologically meaningful stream
19 temperature thresholds for Connecticut streams. They analyzed stream fish survey and
20 continuous water temperature data from 160 sites in perennial, 1st- to 4th-order streams across
21 Connecticut, and developed quantitative thresholds for three major thermal classes at which there
22 are discernible temperature-related changes in fish communities during summer months (see
23 Figure 8):
24 • Cold<18.29°C
25 • Cool 18.29-21.70°C
26 • Warm>21.70°C
27 Assuming that these thresholds inform on thermal tolerances, they provide easy-to-understand
28 temperature standards that can be used to protect and maintain biological communities.
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Daily Mean V
30-
25-
20-
U
if
g 15.
5,
B
a
10
5.
0
month number
Vater Temperatures (CT DEEP TITAN Class July Mean)
i
i|
11
H?
1
*
f
1
li
YY
"I
1,
| '
*
«
T*
1
i
*
I
1
|
'
2 3 4 5 6 7 8 9 10 11 12
July rne in
DEEP Mm
DCoW
| Wvm
Figure 8. Connecticut Department of Energy and Environmental Protection (CT DEEP)
developed ecologically meaningful thresholds for three major thermal classes (cold, cool,
warm). Outliers are shown with asterisks. Temperature in these three classes differ most in
the summer (figure provided by Mike Beauchene, CT DEEP).
1 Similarly, Maine is the first state in the United States to adopt statewide environmental flow and
2 lake level standards based on thresholds derived from principles of natural flow variation
3 necessary to protect aquatic life and maintain important hydrological processes (Maine DEP,
4 2007). Other states are also exploring the development of flow criteria, utilizing the ELOHA
5 framework (Poff et al., 2010). For example, TNC and several partners (states, RBCs, other
6 federal agencies) have used components of the ELOHA framework that consider flow needs for
7 sensitive species and key ecosystem processes to develop flow recommendations for some
8 eastern and midwestern rivers (e.g., the Susquehanna, the Upper Ohio, the Delaware, and the
9 Potomac Rivers) (DePhilip and Moberg, 2010; Cummins et al., 2010; DePhilip and Moberg,
10 2013a, 2013b; Buchanan et al., 2013). Because some flow recommendations are based on expert
11 elicitation and published literature, data from RMN sites can be used to greatly improve our
12 understanding of these processes to develop regionally informed standards and management
13 decisions.
5.2.3. Interactive Effects of Climate Change with Other Stressors
14 While many primary RMN sites are minimally disturbed, some primary and many secondary
15 RMN sites span larger stressor gradients. Even sites in minimally disturbed areas may be
16 impacted by more diffuse, non-climate impacts, or will be impacted over the lifetime of the
17 RMN. Here also, RMN data can provide insights into effects of anthropogenic activities on
18 thermal and hydrologic regimes, especially if there are affected and unaffected sites situated in
19 similar environmental conditions (e.g., Dunham et al., 2007; Kaushal et al., 2010). For example,
20 the temperature data from RMN sites may prove useful for addressing temperature-related
21 mandates associated with water quality standards (Birkeland, 2001; Poole et al., 2004; Todd et
22 al., 2008), while hydrologic data could provide information on how altered flows created by
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1 extraction practices, such as water withdrawals from hydraulic fracturing, affect ecosystem
2 services (e.g., Carlisle et al., 2010; Appalachian Landscape Conservation Cooperative, 2014).
3 Similarly, some RMN sites are impacted by urbanization, and data from RMN sites that span an
4 urbanization gradient will allow us to examine how climate change impacts on flow and
5 temperature interact with urban development, as well as to distinguish climate and urban
6 stressors. For example, U.S. EPA (2012) performed a case study using flow data from USGS
7 gages in the Baltimore-Washington D.C. area to examine how the hydrologic response to
8 climatic change in the Mid-Atlantic would compare with land use impacts. Results showed that
9 high flow metrics (e.g., flashiness, high-pulse-count duration, 1-day maximum flow) tend to
10 strongly reflect urbanization and swamp inputs from climate change effects. In comparison,
11 several low-flow metrics, such as 1-, 3- and 7-day minimum flows and low-pulse count, show
12 responses to climate change effects more so than to land use (U.S. EPA, 2012).
5.3. RESPONSE AND RECOVERY OF ORGANISMS TO EXTREME WEATHER
EVENTS
13 Data from RMN sites can be used to gain a better understanding of how organisms respond to
14 and recover from extreme weather events such as droughts and floods, which are projected to
15 occur with greater frequency in the future (Karl et al., 2009). These types of events can either be
16 missed or confounded with events from previous years by routine sampling that is done on a
17 rotational basis (e.g., sites visited once every 5 years) because attribution or detection of key
18 events may require sampling that closely brackets the event. For example, VT DEC (2012)
19 collected macroinvertebrate data from 10 long-term, high-quality monitoring sites after the
20 flooding from Tropical Storm Irene (August 2011) and compared them to historical records
21 collected prior to 2011. They found immediate decreases in invertebrate densities of 69% on
22 average and decreases in total taxa richness of 8% following these high-flow events, but also
23 found that most sites recovered to normal levels the following year (see Figure 9). These
24 dramatic declines and rapid recovery would have been missed if sampling had occurred at longer
25 intervals.
26 The North Carolina Department of Environment and Natural Resources (NC DENR)
27 Biomonitoring Unit has also conducted research on responses of macroinvertebrates and fish
28 communities to flooding, and assessed impacts from hurricanes (Frances, Ivan, and Jeanne,
29 which struck in September 2004) in the French and Watauga River basins (MacPherson and
30 Tracy, 2005). They found that biological condition scores for both assemblages declined after
31 flooding. In the study areas, declines in mayflies, stoneflies, and beetles likely occurred because
32 woody debris habitats were swept away in the floods. Results for the fish varied by site. NC
33 DENR also documented declines of macroinvertebrate communities in response to drought
34 conditions that occurred from 1999 to 2002 (Herring, 2004). Here, the degree of impact and
35 speed of recovery appeared to be influenced by species traits and habitat preferences. For
36 example, flow-dependent taxa, such as hydropsychids and heptageniids, were slow to recover
37 and edge species did not recover by the end of the study period.
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A
4000
*? 3000
Ł
jŁ 2000
2
1000
• Pre-Flood
• 2011
D2012
Tl
Small Streams Medium Streams Large Streams
B
Small Streams Medium Streams Large Streams
Small Streams Medium Streams Large Streams
Figure 9. Comparison of (A) macroinvertebrate density values, (B) total taxa richness
values, and (C) Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness at 10 stream
sites in Vermont before and after Tropical Storm Irene (provided by Moore and Fiske, VT
DEC, unpublished data).
5.4. HYPOTHESES AND PREDICTIVE MODELS RELATED TO CLIMATE CHANGE
VULNERABILITY
1 Data being collected at the RMN sites can be used to test predictive models and hypotheses
2 about the vulnerability of taxa and watersheds to climate change. In this section we provide
3 examples of how RMN data can be used to:
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1 • Test hypotheses from the broad-scale climate change vulnerability assessment being
2 conducted by EPA (see Section 5.4.1),
3 • Test the performance of SDMs (see Section 5.4.2),
4 • Better understand differing thermal vulnerabilities of streams (see Section 5.4.3), and
5 • Test the performance of models that predict effects of climate change on streamflow (see
6 Section 5.4.3).
5.4.1. Broad-Scale Vulnerability Assessments
7 The EPA is conducting broad-scale climate change vulnerability assessments in the Northeast,
8 Mid-Atlantic, and Southeast regions and has developed hypotheses about which watersheds will
9 be most vulnerable to projected changes in temperature and hydrologic conditions, as well as
10 which biological indicators are likely to be most responsive to these changes. Watersheds in
11 these regions are being assigned vulnerability ratings for three different scenarios: increasing
12 temperatures, increasing frequency and magnitude of peak flows, and increasing frequency of
13 summer low-flow events. RMN data can be used generally to validate specific hypotheses in the
14 assessment but more importantly can be used to refine and improve the model, as relationships
15 between biological indicators and environmental conditions are monitored over time.
5.4.2. Species Distribution Models (SDMs)
16 As discussed in Section 4.1, data tracked across RMN sites can then be used to monitor changes
17 in taxa distributions over time due to changes in thermal and hydrologic conditions, and this data
18 can be used to fit or validate SDMs. For example, using species occurrence data, Hawkins et al.
19 (2013) developed SDMs that predict how the distributions of individual macroinvertebrate taxa
20 and entire assemblages of taxa vary with stream temperature, flow, and other watershed
21 attributes in the conterminous United States. These predictive models were developed with
22 biomonitoring data from reference-quality sites that were sampled during the EPA's 2008-2009
23 NRSA. To assess potential effects of climate change on biodiversity, Hawkins et al. (2013)
24 compared SDM calculations for 2000-2010 with those for 2090-2100. Their results predicted
25 287 taxa to increase in frequency of occurrence and 252 taxa to decrease in frequency of
26 occurrence.
27 SDMs are also being developed for stonefly species in the Midwest (Cao et al., 2013; DeWalt et
28 al., 2013). A data set of 30,355 specimen records and bioclimatic variables derived from
29 downscaled modeled climate data are being used to compare the pre-European settlement and
30 future geographic distributions of 78 stonefly species with the maximum entropy (Maxent)
31 model. Based on the modeled results, approximately 70% of stonefly species and 89% of
32 stonefly families are predicted to experience large range losses, while 6% of species are
33 predicted to increase in range (DeWalt et al., 2013).
34 Similar SDMs have been developed by Domisch et al. (2013), who used an ensemble of
35 bioclimatic envelope models to model climatic suitability for 191 stream macroinvertebrate
36 species from 12 orders across Europe for two late-century (2080) scenarios. They assessed
37 relative changes in species' climatically suitable areas as well as potential geographic shifts
38 based on thermal preferences. Their models suggest that, under future scenarios, there will still
39 be climatically suitable conditions for most of the modeled stream macroinvertebrates. Suitable
40 habitat for warm-adapted species is projected to increase, while cold-adapted species are
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1 projected to lose climatically suitable area. The models showed endemic species losing
2 significantly more suitable habitat than nonendemic species (Domisch et al., 2013).
5.4.3. Differing Thermal Vulnerabilities
3 Temperature data from RMN sites can be used to investigate why streams have differing
4 vulnerabilities to thermal change. Air temperature, which is projected to increase due to climate
5 change, is known to be an important predictor of water temperature (e.g., Hill et al., 2013). The
6 relationship between air and water temperature, however, varies depending on numerous factors,
7 such as location, stream size, and groundwater contributions and the capacity of the stream to
8 absorb heat (Hill et al., 2014). In Pennsylvania, Kelleher et al. (2012) found that stream size
9 (stream order) and groundwater contribution (baseflow index) were the primary controls of the
10 sensitivity of stream temperature to air temperature. Hawkins et al. (2013) found streams in the
11 Cascades and Appalachian Mountains were most responsive to changes in air temperature,
12 compared to streams in the southeastern United States, which suggests that orography and
13 landscape variables influence rates of temperature change (Loarie et al., 2009; Isaak and Rieman,
14 2013).
15 MD DNR and collaborators performed exploratory analyses to gain a better understanding of
16 relationships between air and water temperature along with discharge at their sentinel sites
17 (Hilderbrand et al., 2014). They developed 99 linear regression models based on water and air
18 temperature sensors to evaluate air-water-temperature relationships for the Coastal Plain,
19 Piedmont, and Highlands regions for different site-years (see Table 12). They also investigated
20 the influence of streamflow on water temperatures by including discharge measurements from
21 USGS stream gages. The differences in slopes among the regions suggest that streams in the
22 Highland region may be influenced by a number of factors, such as increased baseflow and
23 increased riparian shading. Improvements in overall model fit also show that streamflow is a
24 small, but important, modifier of water temperature (see Table 12).
Table 12. Results from Hilderbrand et al. (2014) linear regression models based of water
and air temperatures from sentinel sites in the Coastal Plain, Piedmont, and Highlands
regions. Results show mean slope values for the air-water temperature relationship. Models
including discharge measurements (slopes not shown) improve overall fit in each region
Region
Coastal plain
Piedmont
Highlands
Model
Air only
Air + discharge
Air only
Air + discharge
Air only
Air + discharge
n
35
35
18
18
46
46
Mean slope
0.64
0.64
0.59
0.57
0.54
0.51
Mean R2
0.72
0.76
0.69
0.74
0.61
0.73
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5.4.4. Testing the Performance of Models that Predict Effects of Climate Change on
Streamflow
1 Hydrologic data from RMN sites can be used to investigate why streams have differing
2 vulnerabilities to hydrologic change. Because of the paucity of long-term flow data at pristine
3 locations, most explorations of climate change on hydrology have been done using models and
4 simulations; data from RMN sites can be used to improve or validate these models. For example,
5 Hawkins et al. (2013) used statistical models to predict flow responses to projected climate
6 change at specific sites in the conterminous United States, where streams were broken into
7 classes based on hydrologic characteristics. Model outputs show both potential changes in stream
8 class assignment, as well as changes in individual flow variables. On the other hand, the Variable
9 Infiltration Capacity model (Liang et al., 1994) has been used to model streamflow projections
10 for the Northeast region by Hayhoe et al. (2007). This process-based model has been applied
11 internationally and to many river basins in the United States. (Beyene et al., 2010; Livneh et al.,
12 2013) and mechanistically includes components of canopy interception, evapotranspiration,
13 runoff generation, infiltration, soil water drainage, and snow pack accumulation and melt. Many
14 other streamflow modeling efforts also exist at more regional scales that can incorporate data
15 from RMN sites (e.g., South Atlantic Landscape Conservation Cooperative).
5.5. QUANTIFYING NATURAL VARIABILITY
16 Year-to-year variation in the occurrence and relative abundance of individual taxa is not well
17 documented, particularly at pristine sites (Milner et al., 2006). Data from RMN sites can be used
18 to help quantify this, and to assess how natural variation affects the consistency of biological
19 condition scores and metrics. Natural variation can also be linked to environmental variables,
20 and an understanding of these relationships could be important for predicting vulnerability to
21 changing thermal and hydrologic conditions.
22 As part of this process, it is useful to estimate and bracket historical conditions at RMN sites
23 when possible, as a way to contextualize future changes and screen for unusual conditions. For
24 example, if conditions in a given year are abnormal, organizations may want to interpret their
25 biological condition scores with caution or consider recalibrating their index to encompass a
26 wider range of environmental conditions. Because long-term stream temperature and flow data
27 are not available for many RMN sites, RMN members are encouraged to use air temperature,
28 precipitation, and flow data from nearby weather stations and USGS gages to provide estimates
29 of past conditions. The closest active weather stations can be located, and the daily observed air
30 temperature and precipitation data for those stations can be downloaded from websites like the
31 Utah State University Climate Server:
32 http://climate.usurf.usu.edu/mapGUI/mapGUI.php
33 Streamflow data from the nearest USGS gages can be downloaded from the USGS National
34 Water Information System website:
35 http://waterdata.usgs.gov/usa/nwis/rt
36 After the first year or two of data collection at RMN sites, regression equations can be developed
37 for localized areas to allow for more accurate extrapolations of historic water temperature and
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1 hydrologic data. In addition, broader-scale information on how current conditions compare to
2 past "norms" can be obtained from the National Oceanic and Atmospheric Administration's
3 National Climatic Data Center website (http://www.ncdc.noaa.gov/sotc/) (NOAA, 2014) and the
4 USGS WaterWatch website (USGS, 2014d).
5 RMN members are also encouraged to research whether predictive stream temperature and flow
6 models are available in their geographic area. As mentioned in Sections 4.3 and 5.4.4, there are
7 many different types of predictive models, each of which have applicability at different spatial
8 scales and vary in their level of accuracy and sophistication. An example of a predictive stream
9 temperature model that could be applied at RMN sites is one developed by Hill et al. (2013). Hill
10 et al. (2013) developed spatially explicit empirical models to predict reference-condition mean
11 summer, mean winter, and mean annual stream temperatures at locations across the
12 conterminous United States that lack observational stream temperature data. The models were
13 calibrated with daily mean stream temperature data from several thousand USGS gages. Both
14 natural factors (e.g., climate, watershed area, topography) and measures of stream and watershed
15 alteration (e.g., reservoirs, urbanization, and agriculture) were considered during model
16 development. The Hill et al. (2013) model can be applied to specific sites if the proper input data
17 are available (e.g., GIS-derived geologic and climate data for the exact watershed). Other models
18 predict stream temperature for entire reaches versus specific sites. For example, Detenbeck et al.
19 (2013) used a flow-weighted spatial autocorrelation model (ver Hoef et al., 2006) to predict
20 thermal metrics for NHDPlus vl stream flowlines in New England.
6. NEXT STEPS
21 This document should be reevaluated and updated periodically as data are collected and analyzed
22 to ensure that the objectives of the RMNs are being met and recommendations remain current. In
23 this section we first discuss the most immediate priorities for the RMNs in the Northeast,
24 Mid-Atlantic, and Southeast regions and then discuss future steps, which could potentially
25 include integration of other regions, as well as other water body types.
6.1. MOST IMMEDIATE PRIORITIES
26 The most immediate priorities for the RMNs in the Northeast, Mid-Atlantic, and Southeast
27 regions are described below.
28 Formally designate a coordinator in each region to ensure sustainability. The coordinator's
29 role would include:
30 • Coordinating calls, webinars, and trainings;
31 • Obtaining and lending equipment;
32 • Obtaining periodic updates on status of activities;
33 • Potentially performing tasks related to data infrastructure [e.g., sharing data or
34 coordinating activity on EPA's Water Quality Exchange (WQX)]; and
35 • Coordinating a work group session at annual meetings [e.g., the New England
36 Association of Environmental Biologists (NEAEB) conference, the Association of Mid-
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1 Atlantic Aquatic Biologists Workshop (AMAAB), and the Southeastern Water Pollution
2 Biologists Association (SWPBA) conference]; and
3 • Keeping up on other efforts and funding opportunities that the RMNs could potentially
4 tie into.
5 It may be beneficial for the EPA Regional Monitoring and/or Biocriteria coordinator in each
6 region to fill this role or either share responsibilities or work collaboratively with the designated
7 coordinator.
8 Implementation. Efforts should be made to collect as much of the data described in
9 Section 3 as possible at the desired level or rigor. At many RMN sites, collection of
10 macroinvertebrate data and year-round stream and air temperature measurements should
11 be feasible immediately. In some cases, states, tribes, and RBCs are already collecting
12 these data, and these efforts should be expanded to include all participating organizations.
13 As described in Section 3.3, collecting the hydrologic data can pose challenges. We
14 acknowledge these challenges but also recognize the importance of obtaining a better
15 understanding of hydrologic regimes at RMN sites. Thus, we encourage pressure
16 transducer installation at primary RMN sites. Regional coordinators can assist with this
17 by:
18 • Obtaining and lending equipment;
19 • Organizing training workshops and materials on how to install and operate the
20 equipment, do elevation surveys, develop flow rating curves, or process the data
21 (Training workshops could coincide with annual regional meetings like AMAAB,
22 NEAEB and SWPBA);
23 • Finding resources and partners to help with the installations, elevation surveys,
24 and development of flow rating curves; and
25 • Managing data.
26 In some situations, a phased approach in which organizations start with one transducer
27 may work best. Once an entity gains experience with installing and operating the
28 transducer, transducers can be installed at additional sites. If high quality data can only be
29 collected at a subset of the primary RMN sites, it is better to collect higher quality
30 hydrologic data at a few sites versus collecting data of questionable quality at numerous
31 sites.
32 Taxonomic resolution. Species-level identifications for the macroinvertebrate taxa listed
33 in Appendix G is ideal for at least 1 year so that a taxonomic baseline can be established.
34 If funding permits, samples could be sent to a common laboratory. If this is not possible,
35 regional coordinators may consider taxonomic training workshops to ensure consistency
36 in identifying important indicator taxa to species. Training workshops could coincide
37 with annual regional meetings. Regional coordinators can also reach out to natural history
38 museums and other organizations for assistance in identifying important indicator species
39 in each region.
40 Data infrastructure. Sharing data is critical to the long-term sustainability of the RMNs.
41 Our current goal is to develop one system for sharing RMN data that can be accessed by
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1 RMN members as well as outside users. This system will allow users to see what data are
2 being collected at each site and provide information on data quality so that users can
3 select data that meet their needs and level of rigor. For now, participating organizations
4 should fill out the Excel templates in Appendix K to facilitate data sharing. Using the
5 Excel templates will allow participants to see what is being collected where, at what level
6 of rigor, and by which organizations, but organizations will be responsible for managing
7 the raw data in their existing databases. While the Excel templates provide a temporary
8 solution, an important next step will be to develop or utilize an existing online interface
9 to facilitate the sharing of data and to:
10 • Develop a program that assists with QA/QC checks on raw data and calculates a
11 standardized set of summary metrics,
12 • Make the online interface compatible with EPA's WQX, and
13 • Review commercially available software packages (e.g., Aquarius) and freeware
14 (e-g-, Utah State's Observations Data Model services or 52 North's Sensor
15 Observation Service) to help process the continuous data, and discuss their
16 adopti on with working group s.
17 Quality Assurance Project Plan (QAPP). At this time, a QAPP has not been developed
18 specifically for the RMNs, but we are working with regional coordinators to explore this
19 possibility. The QAPPs ensure that data meet quality standards and open up additional
20 funding opportunities. Until an umbrella QAPP for the RMNs is created, efforts will be
21 made to verify that all programs contributing to the effort have a QAPP for their methods.
6.2. FUTURE STEPS
22 Future steps for the RMNs in the Northeast, Mid-Atlantic, and Southeast regions include the
23 following items.
24 Reevaluate annually, at least for the first several years. Regional working groups should
25 consider questions like:
26 • Are we collecting the right data to meet our objectives?
27 • Is there anything else we should be collecting?
28 • Is there anything that we should stop collecting?
29 • Should we make any changes to the collection protocols, such as:
30 o Which is more appropriate: a 30- or 60-minute interval for temperature sensors?
31 o How big of a difference does 300 versus 200 versus 100 fixed counts make when
32 collecting indicator taxa?
33 • How large are the data comparability issues that result from differences in collection and
34 processing methodologies?
35 • Should samples be collected during both spring and summer/early fall index periods?
36 • Should changes be made to the list of taxa that should be identified to the species-level
37 (see Appendix G)?
38 • Which biological indicators, thermal, and hydrologic metrics are most sensitive and show
39 the greatest promise for detecting climate change effects?
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1 Conduct a methods comparison study, if different protocols are being used. Although different
2 methodologies can have large effects on community metrics (Bierwagen et al., in review), we
3 lack information on how different protocols will affect data being collected at RMN sites in
4 particular. A methods comparison study would provide that information.
5 Add-ins, as resources permit:
6 • Collect additional assemblages (fish are higher priority than periphyton).
7 • Assess the accuracy and precision of temperature sensors and pressure transducers (e.g.,
8 perhaps colocate a transducer with a USGS gage and compare results).
9 • Collect additional replicate biological samples, beyond the existing state, tribal, or RBC
10 QA/QC program requirements (e.g., collect replicate samples within index periods to see
11 whether some important indicator organisms are present in greater numbers during
12 certain dates of the index period). Existing replications sometimes include within-index
13 period replication, but are often focused on defining variability in state/tribal/RBC
14 bioassessment indices rather than variation in presence or relative abundance of specific
15 indicator taxa.
16 • Collect quantitative measures of physical habitat that are likely to be responsive to
17 climate change effects (e.g., bankfull height and width, measures of incision, measures of
18 bank stability).
19 • Deploy additional stream temperature sensors at some sites to monitor within-reach
20 variability of thermal regimes and vulnerability to increasing air temperatures.
7. CONCLUSIONS
21 The Northeast, Mid-Atlantic, and Southeast regions are pilot studies upon which the RMN
22 framework is based and whose data will be used in initial evaluations and data analyses. Other
23 regions that are interested in establishing an RMN can build upon and improve these efforts. The
24 RMN framework is flexible and is not limited to a target population of freshwater wadeable
25 riffle-dominated streams. For example, the processes outlined here can be used to integrate other
26 water body types such as estuaries, lakes, wetlands, and low gradient streams into the RMN
27 framework. While the current focus is on states, tribes, and RBCs, collaborations and
28 partnerships with other organizations, such as academia and volunteer monitoring groups, are
29 encouraged as a way to make the networks more robust. Data collected throughout the various
30 RMNs will further our understanding of biotic and abiotic processes and interactions in streams
31 in order to detect temporal trends; investigate relationships between biological, thermal, and
32 hydrologic data; explore ecosystem responses and recovery from extreme weather events; test
33 hypotheses and predictive models related to climate change; and quantify natural variability.
34 These data will be important inputs for bioassessment programs to continue to protect water
35 quality and aquatic ecosystems under a changing climate.
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APPENDIX A.
REGIONAL WORKING GROUPS
Table A-1. Northeast regional working group
Table A-2. Mid-Atlantic regional working group
Table A-3. Southeast regional working group
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 A-l DRAFT—DO NOT CITE OR QUOTE
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Table A-l. Northeast regional working group
Affiliation
Connecticut Department of Energy
and Environmental Protection (CT
DEEP)
Massachusetts Department of
Environmental Protection (MA DEP)
Massachusetts Department of Fish
and Game, River Instream Flow
Stewards Program (RIFLS)
U.S. Geological Survey (USGS),
Massachusetts Cooperative Fish and
Wildlife Research Unit
Maine Department of Environmental
Protection (ME DEP)
New Hampshire Department of
Environmental Services (NH DES)
New York Department of
Environmental Conservation (NY
DEC)
Rhode Island Department of
Environmental Management (RI
DEM)
Vermont Department of
Environmental Conservation (VT
DEC)
USGS NH-VT Science Center
U.S. Environmental Protection
Agency (U.S. EPA) Region 1
Name
Chris Belluci
Guy Hoffman
Robert Nuzzo
Laila Parker
Michelle
Craddock
Allison Roy
Leon Tsomides
David Neils
Brian Duffy
Katie DeGoosh
Steve Fiske
Aaron Moore
Jeff Deacon
Diane Switzer
Greg Hellyer
Email
Christopher.Bellucci(3)ct.gov
guv.hoffmanfoict.gov
robert.nuzzo(3)state.ma.us
laila.parker(3)state.ma.us
michelle.craddockfoistate.ma.us
arov(3)eco.umass.edu
leon.tsomides(3)maine.gov
david.neils(3)des. nh.gov
btduffy(3)gw.dec. state, nv.us
Katie. degoosh(3)dem. ri.gov
steve. fiske(a)state.vt.us
Aaron. Moore(3)state.vt.us
i rdeacon(2!usgs. gov
switzer.dianefoiepamail. epa.gov
Hellver.Greg(a)epamail. epa.gov
This document is a draft for review purposes only and does not constitute Agency policy.
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Table A-2. Mid-Atlantic regional working group
Affiliation
Delaware Department of Natural
Resources and Environmental
Control (DE DNREC)
Delaware River Basin
Commission (DRBC)
Interstate Commission on the
Potomac River Basin (ICPRB)
Maryland Department of the
Environment (MDE)
Maryland Department of Natural
Resources (MD DNR)
New Jersey Department of
Environmental Protection (NJ
DEP)
Ohio River Valley Water
Sanitation Commission
(ORSANCO)
Pennsylvania Department of
Environmental Protection (PA
DEP)
Susquehanna River Basin
Commission (SRBC)
Virginia Department of
Environmental Quality (VA
DEQ)
Western Pennsylvania
Conservancy
Name
Ellen Dickey
Robert Limbeck
John Yagecic
Claire Buchanan
Adam Griggs
John Backus
Matthew Stover
Ron Klauda
Dan Boward
Scott Stranko
Michael Kashiwagi
Dean Bryson
Jeff Thomas
Gary Walters
Dustin Shull
Heidi Biggs
Molly Pulket
Andy Gavin
Tyler Shenk
Ellyn Campbell
Jason Hill
Drew Miller
Danielle Rihel
Email
Ellen. Dickev(3) state.de. us
Robert.Limbeck(a)drbc. state. ni .us
i ohn. vasecic(3)drbc. state. ni .us
cbuchan(S)j cprb . ors
asrisss(S)jcprb . ors
JBackus(3)mde. state, md. us
mstoverfoimde. state, md. us
RKLAUDA(o>dnr. state.md.us
DBOWARD(o>dnr. state.md.us
SSTRANKO(o>dnr.state.md.us
mkashiwasi(3)dnr. state.md.us
Dean. Brvson(3)dep. state. ni .us
ithomas(3)orsanco.ors
sawalters(3)pa. sov
dushull(a!pa.sov
hbisss(3)pa.sov
mpulket(3)pa.sov
asavin(2!srbc.net
TShenk(3)srbc.net
ecampbell(3)srbc.net
Jason. Hill(3)deq.virRinia. gov
Richard.Miller(a!deq.virRinia.sov
drihel(a)paconserve.ors
This document is a draft for review purposes only and does not constitute Agency policy.
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Table A-2. continued...
Affiliation
West Virginia Department of
Environmental Protection (WV
DEP)
National Park Service (NFS)
U.S. EPA Region 2
U.S. EPA Region 3
Name
Jeff Bailey
Nick Murray
Michael Whitman
John Wilts
Jalyn Cummings
Caleb Tzilkowski
Matt Marshall
Jim Kurtenbach
Jennifer Fulton
Bill Richardson
Christine Mazzarella
Matt Nicholson
Email
Jeffrey. E.Bailev(3);wv.sov
Nick. S .Murray® wv. sov
michael.i .whitman(3);wv.sov
John.C . Wirts(3);wv. sov
ialvn cumminss(a)nps.sov
caleb tzilkowski(3)nps.sov
matt marshall(a)nps.sov
kurtenbach.iamesfoiepa.sov
Fulton. Jennifer(3)epa.sov
Richardson. William (Siepa. sov
Mazzarella.Christine(a)epamail.epa.sov
Nicholson. Matt(a)epamail.epa.sov
This document is a draft for review purposes only and does not constitute Agency policy.
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Table A-3. Southeast regional working group
Affiliation
Alabama Department of
Environmental Management
(ADEM)
Georgia Department of
Natural Resources (GA
DNR)
Kentucky Department for
Environmental Protection
(KY DEP)
North Carolina Department
of Environment and Natural
Resources (NC DENR)
South Carolina Department
of Health and Environmental
Control (SC DHEC)
Tennessee Department of
Environment and
Conservation (TN DEC)
Tennessee Valley Authority
(TVA)
USGS Tennessee Water
Science Center
Department of Interior (DOT)
Southeast Climate Science
Center
National Park Service (NFS)
Southeast Aquatics
South Atlantic Landscape
Conservation Commission
(LCC)
Name
Lisa Huff
Michele Brossett
Cody Jones
Jeremy Smith
Ryan Evans
Eric Fleek
Jim Glover
David Eargle
Scott Castleberry
Debbie Arnwine
Terry Shannon O'Quinn
Jon Mollish
Tyler Baker
Anne Choquette
Cari Furiness
Matt Kulp
Mary Davis
Rua Mordecia
Email
ESH(3)adem . state, al .us
Michele Brossett(3)dnr. state. sa.us
codv.i ones(3)dnr. state, sa.us
Jeremy. Smithfoidnr. state, sa.us
Rvan.Evans(3)kv. sov
eric.fleek(3)ncdenr.sov
sloverib(3)dhec.sc.sov
David.Earsle(3)dhec.sc.sov
cast! ews(3)dhec.sc. sov
Debbie. Arnwine(3Hn.gov
tsoquinn(3Hva. sov
i mmolli sh(a)tva. sov
tfbaker(3Hva. sov
achoq(3)usss.sov
cari furinessfolncsu.edu
Matt Kulp(3),NPS.sov
marv(3)southeastaquatics.net
rua(a), southatl anti cl cc . ors
This document is a draft for review purposes only and does not constitute Agency policy.
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Table A-3. continued.
Affiliation
U.S. EPA Region 4
Name
Chris Decker
David Melgaard
Jim Harrison
Lisa Perras Gordon
Email
Decker. Chris(a)epa.sov
melsaard.david(3)epa.sov
Harri son. Jimfoiepamail . epa. sov
Gordon. lisa-perras(3)epa.sov
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX B.
CHECKLIST FOR STARTING A
REGIONAL MONITORING
NETWORK (RMN)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 1. Establish the regional working group.
2
3 • Coordinator (e.g., from a U.S. EPA Region or a state) volunteers to lead the regional
4 working group.
5 • The coordinator creates a contact list (see template in Appendix A).
6 • The coordinator holds a kick-off webinar with EPA to brief the regional working group
7 on the process that will be followed and the timeline (it is ok to include contacts that are
8 interested but not fully committed).
9
10 2. The coordinator requests candidate sites from each entity. Considerations include:
11
12 • Level of anthropogenic disturbance;
13 • Length of historical record for biological, thermal, and hydrologic data;
14 • Level of protection from future anthropogenic disturbance;
15 • Colocation with existing equipment (e.g., USGS gage);
16 • Accessibility;
17 • Environmental conditions and biological potential/classification; and
18 • Vulnerability to climate change (as available).
19
20 3. The regional coordinator compiles information on data collection protocols being used by each
21 regional working group member (see template in Appendix F). The regional working group
22 discusses appropriate data collection protocols for the RMN. During this process, the working
23 group will consider site selection criteria and methods being used in the other regions and will
24 try to use similar protocols where practical. The goal is to generate data that are comparable
25 across the regions. When the regional working group is deciding on protocols, the working
26 group should consider the objectives of the RMN, how different sampling approaches meet or
27 do not meet those objectives, and factors such as:
28
29 • What types of habitats are being targeted?
30 • What collection gear is being used (e.g., artificial substrate vs. kick nets)?
31 • How big are the differences in sampling protocols across entities?
32 • What effects will these differences have on the RMN indicators?
33 • How long have data been collected at candidate RMN sites with different sampling
34 methods?
35
36 4. EPA has been conducting research on screening, classification, and vulnerability analyses for
37 several pilot RMNs. Additional documentation to conduct these steps are available from EPA.
38 Pending availability and funding, EPA may be able to assist with the following steps:
39
40 • Screening the candidate sites by running them through a disturbance screening process
41 similar to what is described in Appendix D. This may include developing criteria for
42 "reference" sites in urban and agricultural areas. Disturbance ratings will be assigned to
43 the candidate sites.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 • Gathering information from the regional working group on existing classification
2 schemes in the region and performing analyses to explore regional classification. Sites
3 will be assigned to classification groups.
4 • Gathering information from the regional working group on existing climate change
5 vulnerability assessments and performing broad-scale analyses similar to what was done
6 in the eastern United States to rate vulnerability of the candidate RMN sites to climate
7 change.
8
9 5. The regional working group evaluates results of these analyses and designates primary and
10 secondary RMN sites.
11
12 6. The regional coordinator works with regional working group members to help find resources
13 for implementation. High priority items include obtaining equipment and finding funds to
14 process macroinvertebrate samples.
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX C.
PRIMARY REGIONAL
MONITORING NETWORK (RMN)
SITES IN THE NORTHEAST,
MID-ATLANTIC, AND SOUTHEAST
REGIONS
Table C-l. Northeast primary sites—site information
Table C-2. Northeast primary sites—equipment
Table C-3. Mid-Atlantic primary sites—site information
Table C-4. Mid-Atlantic primary sites—equipment
Table C-5. Southeast primary sites—site information
Table C-6. Southeast primary sites—equipment
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 C-l DRAFT—DO NOT CITE OR QUOTE
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Table C-l. Site information for primary RMN sites in the Northeast (4/2/2014). Drainage area, slope, and elevation are estimates based on
NHDPlus vla local catchment data. Percent forest is derived from the NLCD 2001b data layer and is based on the total watershed
Longitude
-73.27990
-71.83424
-72.83917
-72.16196
-73.03027
-72.96731
-72.04780
-72.38454
-69.64424
-71.35110
-71.24924
-71.36166
-71.29306
-71.87633
-73.54621
-74.26626
-71.61201
-71.63562
-72.88583
Latitude
41.92670
41.47482
41.94639
42.03448
42.66697
42.06555
42.39431
42.46471
44.95675
43.14410
44.21896
44.35426
43.89639
44.10563
41.49457
42.01954
41.83760
41.76482
43.87167
State
CT
CT
CT
MA
MA
MA
MA
MA
ME
NH
NH
NH
NH
NH
NY
NY
RI
RI
VT
Entity
CT DEEP
CT DEEP
CT DEEP
MADEP
MADEP
MADEP
MADEP
MADEP
MEDEP
NHDES
NHDES
NHDES
NHDES
NHDES
NY DEC
NY DEC
RIDEM
RIDEM
VTDEC
Station ID
CTDEP_2342
CTDEP_1748
CTDEP_1433
MADEP_Browns
MADEP_Cold
MADEP_B0215
MADEP_Parkers
MADEP_WBrSwift
MEDEP_57229
NHDES_99M-44
USGS_0 1064300
NHDES_19-ISR
NHDES_98S-44
NHDES_WildAmmo
NYDEC_HAVI_01
NYDEC_LBEA_01
RIDEM_RMR03a
RIDEM_SCI01
VTDEC_135404000013
Water body name
Brown Brook
Pendleton Hill
West Branch Salmon
Browns
Cold River
Hubbard
Parkers Brook
West Branch Swift
East Branch Wesserunsett
Stream— Station 486
Bear
Ellis
Israel
Paugus
Wild Ammo
Haviland Hollow
Little Beaver Kill
Rush
Wilbur Hollow
Bingo
Drainage
area
(km2)
14.7
10.4
34.5
14.7
17.7
30.0
13.8
9.8
126.0
25.7
28.2
16.6
31.5
96.2
24.9
42.7
12.2
11.2
29.2
Slope
(unitless)
0.026
0.006
0.021
0.023
0.026
0.029
0.011
0.011
0.008
0.005
0.031
0.023
0.008
0.010
0.011
0.008
0.017
0.008
0.017
Elevation
(m)
286.4
55.2
169.35
253.5
592.4
359.8
244.9
209.9
207.2
138.9
686.7
544.7
264.2
481.0
202.9
393.3
118.2
124.3
458.5
% Forest
90.2
71.7
81.6
87.3
89.3
86.5
79.5
91.5
83.4
81.5
88.6
92.5
97.8
96.7
85.7
90.3
72.6
74.5
97.3
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-l. continued...
Longitude
-72.66250
-71.78528
-72.53705
-72.93194
Latitude
42.76389
44.58417
44.43400
43.13833
State
VT
VT
VT
VT
Entity
VTDEC
VTDEC
VTDEC
VTDEC
Station ID
VTDEC_670000000166
VTDEC_2 1 1200000268
VTDEC_495400000161
VTDEC_033500000081
Water body name
Green
Moose
North Branch Winooski
Winhall
Drainage
area
(km2)
67.8
59.0
29.1
43.8
Slope
(unitless)
0.010
0.015
0.014
0.017
Elevation
(m)
293.3
532.7
327.1
587.7
% Forest
89.9
97.5
95.3
95.0
ahttp://www.horizon-sy stems.com/nhdplus/nhdplusvl_home.php
bhttp://www.mrlc.gov/nlcd01_data.php
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-2. Equipment installed at primary RMN sites in the Northeast (4/2/2014)
State
CT
CT
CT
MA
MA
MA
MA
MA
ME
NH
NH
NH
Entity
CT DEEP
CT DEEP
CT DEEP
MADEP
MADEP
MADEP
MADEP
MADEP
MEDEP
NHDES
NHDES
NHDES
Station ID
CTDEP_2342
CTDEP_1748
CTDEP_1433
MADEP_Browns
MADEP_Cold
MADEP_B0215
MADEP_Parkers
MADEP_WBrSwift
MEDEP_57229
NHDES_99M-44
USGS_01064300
NHDES_19-ISR
Water body name
Brown Brook
Pendleton Hill
West Branch Salmon
Browns
Cold River
Hubbard
Parkers Brook
West Branch Swift
East Branch Wesserunsett
Stream - Station 486
Bear
Ellis
Israel
Temperature
water
water and air
water
water and air
water and air
water and air
water and air
water and air
water*
water
water
water
Hydrologic
equipment
none
USGS gage
(01118300)
none
pressure
transducer
pressure
transducer
USGS gage
(01187300)
pressure
transducer
USGS gage
(01174565)
USGS gage
(01048220)
pressure
transducer
pressure
transducer
pressure
transducer
Hydrologic
data type
none
discharge
none
stage
stage
discharge
stage
discharge
discharge
stage
stage
stage
Notes
gage located at biological
sampling site
gage is downstream of
site but location looks
representative of stream
conditions
gage is downstream of
site but location looks
representative of stream
conditions
gage located at biological
sampling site
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-2. continued...
State
NH
NH
NY
NY
RI
RI
VT
VT
VT
VT
VT
Entity
NHDES
NHDES
NY DEC
NY DEC
RIDEM
RIDEM
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
Station ID
NHDES_98S-44
NHDES_WildAmmo
NYDEC_HAVI_01
NYDEC_LBEA_01
RIDEM_RMR03a
RIDEM_SCI01
VTDEC_135404000013
VTDEC_670000000 166
VTDEC_2 11200000268
VTDEC_495400000 16 1
VTDEC_033500000081
Water body name
Paugus
Wild Ammo
Haviland Hollow
Little Beaver Kill
Rush
Wilbur Hollow
Bingo
Green
Moose
North Branch Winooski
Winhall
Temperature
water
water
water and air
water and air
water
water
water*
water
water and air
water
water*
Hydrologic equipment
pressure transducer
pressure transducer
none
USGS gage (01362497)
USGS gage (01 115 114)
USGS gage (01 115297)
none
USGS gage (01 170 100)
none
none
none
Hydrologic
data type
stage
stage
none
discharge
discharge
discharge
none
discharge
none
none
none
Notes
gage located at
biological sampling site
gage located at
biological sampling site
gage located at
biological sampling site
gage is downstream of
site but location looks
representative of stream
conditions
planning to install a
transducer in 20 14
*not deployed year-round
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-3. Site information for primary RMN sites in the Mid-Atlantic (4/2/2014). Most drainage area, slope, and elevation measurements
are estimates based on NHDPlus vla local catchment data. Percent forest is based on total watershed and is mostly derived from the
NLCD 2001b data layer. Better data were used, where available (e.g., MD DNR was able to provide information based on exact watershed
delineations and the NLCD 2006C data layer)
Longitude
-75.74869
-75.75587
-79.27980
-79.15566
-77.43406
-78.90556
-79.06689
-75.12664
-74.43437
-74.52972
-77.45100
-77.01929
-79.23750
-77.77068
-79.57152
-79.44821
-78.32446
-80.57420
Latitude
39.74567
39.72995
39.64252
39.50363
39.60929
39.54581
39.59930
40.97143
41.10693
40.76500
39.89700
41.42653
40.00333
41.49970
41.69451
37.53920
38.74832
37.37265
State
DE
DE
MD
MD
MD
MD
MD
NJ
NJ
NJ
PA
PA
PA
PA
PA
VA
VA
VA
Entity
DNREC
DNREC
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
NJ DEP/EPA
R2
NJDEP
NJDEP
PADEP
SRBC
PADEP
SRBC
PADEP
VDEQ
ShenNP
VDEQ
Station ID
105212
105213
YOUG-432-S
SAVA-204-S
UMON-288-S
PRLN-626-S
SAVA-225-S
AN0012
AN0260
USGS_01378780
PADEP_Carbaugh
SRBC_Grays
WQN_734
SRBCJCettle
WQN_873
2-HUO005.87
1BJER009.67
9-LRY006.90
Water body name
Tributary of White Clay
Tributary of White Clay
Bear Creek
Crabtree Creek
High Run
Mill Run
Savage River
Dunnfield Creek
Mossmans Brook
Primrose
Carbaugh Run
Grays Run
Jones Mill Run
Kettle
West Branch of Caldwell Creek
Hunting Creek
Jeremys Run (upper)
Little Stony Creek
Drainage
area
(km2)
2
2.2
22.7
43.9
3.3
2.0
138.3
9.5
10.0
0.01
15.5
51.2
12.8
210.3
50.7
10
2.0
48.0
Slope
(unitless)
0.023
0.018
0.011
0.041
0.075
0.108
0.018
0.048
0.009
0.014
0.022
0.014
0.019
0.000
0.005
0.047
0.030
0.061
Elevation
(m)
84.4
69.5
805.9
620.0
310.7
522.0
682.7
358.4
343.9
123.6
435.3
429.8
710.1
418.8
453.7
581.1
479.1
968.1
% Forest
57.9
61.8
65.9
84.3
100.0
100.0
83.6
96.8
80.9
91.0
93.2
93.1
84.8
82.0
90.6
83.6
97.4
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
C-6 DRAFT—DO NOT CITE OR QUOTE
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Table C-3. continued...
Longitude
-78.26867
-79.34634
-80.30465
-81.75611
-79.60111
-79.56808
-79.67617
-79.48686
-80.30063
Latitude
38.70296
38.32267
36.81065
36.62583
38.74322
38.62673
38.61844
38.84942
38.23512
State
VA
VA
VA
VA
WV
WV
WV
WV
WV
Entity
ShenNP
VDGIF
VDEQ
TVA
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
Station ID
3-PIY003.27
2-RAM007.29
4ARCC006.89
TVA_Whitetop
3593
6112
2571
8756
2039
Water body name
Piney River
Ramseys Draft
Rock Castle Creek
Whitetop Laurel Creek
Big Run
Big Run
East Fork/Greenbrier River
Seneca Creek
South Fork/Cranberry River
Drainage
area
(km2)
10.0
20.0
20.6
145.3
10.4
36.0
28.0
42.5
36.3
Slope
(unitless)
0.047
0.020
0.020
0.012
0.031
0.027
0.011
0.024
0.004
Elevation
(m)
578.8
868.7
562.5
790.0
1099.0
930.9
1078.6
873.8
1143.6
% Forest
96.1
94.0
90.0
91.1
98.3
96.3
93.5
98.3
97.5
ahttp://www.horizon-sy stems. com/nhdplus/nhdplusvl_home.php
bhttp://www.mrlc.gov/nlcd01_data.php
°http://www.mrlc.gov/nlcd2006.php
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-4. Equipment installed at primary RMN sites in the Mid-Atlantic (4/2/2014)
State
DE
DE
MD
MD
MD
MD
MD
NJ
NJ
NJ
PA
Entity
DNREC
DNREC
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
NJ DEP/
EPAR2
NJDEP
NJDEP
PA DEP
Station ID
105212
105213
YOUG-432-S
SAVA-204-S
UMON-288-S
PRLN-626-S
SAVA-225-S
AN0012
AN0260
Water body name
Trib White Clay
Trib White Clay
Bear Creek
Crabtree Creek
High Run
Mill Run
Savage River
Dunnfield Creek
Mossmans Brook
Primrose
Carbaugh Run
Temperature
water and air
water and air
water and air
water and air
water and air
Hydrologic
equipment
USGS gage
(01597000)
USGS gage
(01596500)
USGS staff
gage
(01378780)
Hydrologic
data type
discharge
discharge
occasional
stage
Notes
planning to install water and air
temperature sensors and pressure
transducers in 2014
USGS gage (03076600) downstream
of site; about nine tributaries
(including a major one) enter between
gage and site
gage is downstream of site but
location looks representative of stream
conditions
planning to install a water and air
temperature sensor in 2014; applied
for a grant to get a USGS gage here
planning to install a water and air
temperature sensor in 2014
planning to install a water and air
temperature sensor in 2014; applied
for a grant to get a USGS gage here
planning to install a water and air
temperature sensor and possibly a
pressure transducer in 2014
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
C-8 DRAFT—DO NOT CITE OR QUOTE
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Table C-4. continued...
State
PA
PA
PA
PA
VA
VA
VA
VA
VA
VA
VA
WV
WV
Entity
SRBC
PADEP
SRBC
PADEP
VDEQ
ShenNP
VDEQ
ShenNP
VDGIF
VDEQ
TVA
WVDEP
WVDEP
Station ID
SRBC_Grays
WQN_734
SRBCJCettle
WQN_873
2-HUO005.87
1BJER009.67
9-LRY006.90
3-PIY003.27
2-RAM007.29
4ARCC006.89
TVA_Whitetop
3593
6112
Water body name
Grays Run
Jones Mill Run
Kettle
West Branch of Caldwell
Creek
Hunting Creek
Jeremys Run (upper)
Little Stony Creek
Piney River
Ramseys Draft
Rock Castle Creek
Whitetop Laurel Creek
Big Run
Big Run
Temperature
water
water
water and air
water and air
water and air
Hydrologic
equipment
pressure
transducer
pressure
transducer
Unconfirmed
gage
pressure
transducer
pressure
transducer
Hydrologic
data type
stage
stage
stage
stage
Notes
planning to install an air temperature
sensor in 20 14
planning to install a water and air
temperature sensor in 2014
planning to install an air temperature
sensor in 20 14
planning to install a water and air
temperature sensor in 2014
planning to install a water and air
temperature sensor in 2014
gage nearby in another drainage,
possibly on North Fork Dry Run
planning to install a water and air
temperature sensor in 2014
planning to install a water and air
temperature sensor in 2014
planning to install a water and air
temperature sensor in 2014
planning to install a water and air
temperature sensor in 2014
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
C-9 DRAFT—DO NOT CITE OR QUOTE
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Table C-4. continued...
State
WV
wv
WV
Entity
WVDEP
WVDEP
WVDEP
Station ID
2571
8756
2039
Water body name
East Fork/Greenbrier River
Seneca Creek
South Fork/Cranberry River
Temperature
water and air
water and air
water and air
Hydrologic
equipment
Hydrologic
data type
Notes
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-5. Site information for primary RMN sites in the Southeast (4/2/2014). Most drainage areas are estimates based on NHDPlus vla
local catchment data. Where available, data from exact watershed delineations were used. Slope and elevation are estimated based on
NHDPlus vl local catchment data. Percent forest is derived from the NLCD 2001b data layer and is based on the total watershed
Longitude
-87.2862
-86.1330
-87.3991
-83.5716
-83.5166
-84.3851
-84.1512
-83.9039
-83.1924
-82.9940
-82.7916
-82.1014
-83.0728
-82.8089
-80.0303
-81.5672
-79.9906
-83.8552
Latitude
34.3307
34.9180
34.2856
34.9590
34.9520
34.9851
34.6020
37.4550
38.1311
37.0774
37.0666
35.7347
35.6672
35.2281
35.3792
35.5906
36.5355
35.3094
State
AL
AL
AL
GA
GA
GA
GA
KY
KY
KY
KY
NC
NC
NC
NC
NC
NC
NC
Entity
ALDEM
ALDEM
ALDEM
GADNR
GADNR
TVA
GADNR
KYDEP
KYDEP
KYDEP
KYDEP
NCDENR
NC DENR/TVA
NC/DENR/TVA
NCDENR
NCDENR
NCDENR
TVA
Station ID
BRSL-3
HURR-2
SF-1
66d-WRD768
66d-44-2
3890-1
66g-WRD773
DOW04036022
DOW06013017
DOW04055002
DOW02046004
CB6
EB320
EB372
QB283
CB192
NB28
10605-2
Water body name
Brushy Creek
Hurricane Creek
Sipsey Fork
Charlies Creek
Coleman River
Fightingtown Creek
Jones Creek
Hughes Fork
Laurel Creek
Line Fork UT
Presley House Branch
Buck Creek
Cataloochee Creek
Cedar Rock Creek
Dutchmans Creek
Jacob Fork
Mayo River
Snowbird Creek
Drainage
area (km2)
23.6
102.6
231.8
7.2
13.6
182.9
9.1
3.5
37.8
0.6
3.0
37.5
127.0
3.1
9.1
66.5
626.8
108.8
Slope
(unitless)
0.002
0.000
0.000
0.040
0.033
0.003
0.011
0.019
0.002
NA
0.093
0.011
0.010
0.042
0.014
0.001
0.010
0.007
Elevation
(m)
240.8
297.07
204.6
927.0
866.9
468.8
586.0
359.1
294.3
335.6
736.6
529.7
939.2
985.9
177.5
380.1
254.9
677.8
% Forest
96.9
93.5
95.5
99.0
96.8
86.8
98.4
86.6
72.9
100.0
97.0
96.6
99.0
98.6
92.2
89.4
73.4
97.1
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-5. continued...
Longitude
-83.0793
-82.6477
-82.5739
-82.2515
-87.5355
-84.1182
-82.5291
-84.0597
-85.9921
-85.9111
-82.9381
-87.7614
-84.6981
-83.5773
-84.9827
-84.4803
-84.6122
-83.8917
-82.9456
Latitude
34.9235
35.0642
35.1254
35.1831
35.4217
35.4548
36.1508
36.2136
35.9286
35.1155
36.5001
35.9806
36.5161
35.6533
36.1299
35.0539
35.0031
36.3436
35.9224
State
SC
sc
SC
sc
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
Entity
SC DHEC
SC DHEC
SC DHEC
SC DHEC
TNDEC
TVA
TNDEC
TNDEC
TNDEC
TNDEC
TNDEC/TVA
TNDEC
TNDEC
TNDEC
TN DEC/TVA
TNDEC
TNDEC
TNDEC
TVA
Station ID
SV-684
S-086
S-076
B-099-7
ECO71F19
CITICOll.OMO
ECO66E09
ECO67F06
ECO71H17
ECO68C20
ECO6702
ECO71F29
ECO68A03
ECO66G05
MYATT005.1CU
ECO66G20
ECO66G12
ECO67F13
12358-1
Water body name
Crane Creek
Matthews Creek
Middle Saluda River
Vaughn Creek
Brush Creek
Citico Creek
Clark Creek
Clear Creek
Clear Fork Creek
Crow Creek
Fisher Creek
Hurricane Creek
Laurel Fork Station
Camp Creek
Little River
Myatt Creek
Rough Creek
Sheeds Creek
White Creek
Wolf Creek
Drainage
area (km2)
4.0
25.8
16.0
12.0
33.3
118.1
23.8
7.2
38.1
47.7
30.0
177.6
15.3
81.2
12.4
15.5
14.8
8.0
28.5
Slope
(unitless)
0.078
0.003
0.042
0.008
0.004
0.010
0.017
0.014
0.005
0.006
0.003
0.003
0.014
0.029
0.016
0.020
0.031
0.009
0.014
Elevation
(m)
623.6
360.2
582.3
368.4
245.1
399.0
596.6
337.1
262.9
311.5
429.7
156.3
392.9
879.5
525.1
520.6
436.6
379.8
429.9
% Forest
97.0
96.3
96.6
95.6
75.8
97.2
95.1
87.9
88.8
84.5
82.0
81.0
97.2
99.8
78.8
98.9
98.8
90.9
96.0
ahttp://www.horizon-systems.com/nhdplus/nhdplusvl_home.php
bhttp://www.mrlc.gov/nlcd01_data.php
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-6. Equipment installed at primary RMN sites in the Southeast (4/2/2014). EPA R4 is planning to install equipment at the sites in
North and South Carolina as resources permit
State
AL
AL
AL
GA
GA
GA
GA
KY
KY
KY
KY
NC
NC
NC
NC
NC
NC
Entity
ALDEM
ALDEM
ALDEM
GADNR
GADNR
TVA
GADNR
KYDEP
KYDEP
KYDEP
KYDEP
NCDENR
TVA
NCDENR
NCDENR
NCDENR
NCDENR
Station ID
BRSL-3
HURR-2
SF-1
66d-WRD768
66d-44-2
3890-1
66g-WRD773
DOW04036022
DOW06013017
DOW04055002
DOW02046004
CB6
EB320
EB372
QB283
CB192
NB28
Water body name
Brushy Creek
Hurricane Creek
Sipsey Fork
Charlies Creek
Coleman River
Fightingtown Creek
Jones Creek
Hughes Fork
Laurel Creek
Line Fork UT
Presley House Branch
Buck Creek
Cataloochee Creek
Cedar Rock Creek
Dutchmans Creek
Jacob Fork
Mayo River
Temperature
water and air
water and air
water
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
none
water
none
none
none
none
Hydrologic
equipment
pressure transducer
pressure transducer
USGS gage
(02450250)
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
none
USGS gage
(03460000)
none
none
USGS gage
(02143040)
USGS gage
(02070500)
Hydrologic
data type
stage
stage
discharge
stage
stage
stage
stage
stage
stage
stage
stage
none
discharge
none
none
discharge
discharge
Notes
water temperature is being
measured at the USGS gage
Inactive USGS gage
(03560000)
water temperature is being
measured at the USGS gage
USGS gage downstream on
Catheys Creek (03440000)
inactive USGS gage (02123567)
precip is being measured at the
USGS gage
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table C-6. continued...
State
NC
sc
sc
sc
sc
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
TN
Entity
TVA
SC DHEC
SC DHEC
SC DHEC
SC DHEC
TNDEC
TVA
TNDEC
TNDEC
TNDEC
TNDEC
TN DEC/TVA
TNDEC
TNDEC
TNDEC
TN DEC/TVA
TNDEC
Station ID
10605-2
SV-684
S-086
S-076
B-099-7
ECO71F19
CITICOll.OMO
ECO66E09
ECO67F06
ECO71H17
ECO68C20
ECO6702
ECO71F29
ECO68A03
ECO66G05
MYATT005.1CU
ECO66G20
Water body name
Snowbird Creek
Crane Creek
Matthews Creek
Middle Saluda River
Vaughn Creek
Brush Creek
Citico Creek
Clark Creek
Clear Creek
Clear Fork Creek
Crow Creek
Fisher Creek
Hurricane Creek
Laurel Fork Station
Camp Creek
Little River
Myatt Creek
Rough Creek
Temperature
water and air
none
none
none
none
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
water and air
Hydrologic
equipment
pressure transducer
none
none
none
none
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
pressure transducer
Hydrologic
data type
stage
none
none
none
none
stage
stage
stage
stage
stage
stage
stage
stage
stage
stage
stage
stage
Notes
inactive USGS gage (03516000)
USGS gage (02162350)
downstream of site but unsure
whether it is representative
(some major tributaries enter
between site and gage); EPA R4
will install equipment as
resources permit
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
C-14 DRAFT—DO NOT CITE OR QUOTE
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Table C-6. continued...
State
TN
TN
TN
Entity
TNDEC
TNDEC
TVA
Station ID
ECO66G12
ECO67F13
12358-1
Water body name
Sheeds Creek
White Creek
Wolf Creek
Temperature
water and air
water and air
water and air
Hydrologic
equipment
pressure transducer
pressure transducer
pressure transducer
Hydrologic
data type
stage
stage
stage
Notes
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
C-15 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX D.
DISTURBANCE SCREENING
PROCEDURE FOR RMN SITES
Section D-l. Background
Section D-2. Methodology
• Land use disturbance
• Likelihood of impacts from dams, mines, and point-source pollution sites
• Likelihood of impact from other non-climatic stressors (roads, atmospheric
deposition, coal mining, shale gas drilling, future urban development, and
water withdrawals)
SectionD-3. References
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-l DRAFT—DO NOT CITE OR QUOTE
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D.I. BACKGROUND
1 We performed a screening exercise on the preliminary regional monitoring network (RMN) sites
2 to determine where the sites fall along a standardized disturbance gradient, using data that are
3 available for the entire study area and that are derived using common data sources and
4 methodologies. This allows us to apply this framework within and across regions. We will be
5 using a similar framework for the resiliency component of our climate change vulnerability
6 assessment.
7
8 Our screening process has limitations. For one, it is relatively coarse. As an example, we did not
9 do exact watershed delineations when deriving the land use data. Instead, the land cover
10 screenings are estimates based on data associated with the National Hydrography Dataset Plus
11 Version 1 (NHDPlusVl) catchments where the sites are located (U.S. EPA and USGS, 2006).
12 While this approach generally provides a good approximation, sometimes there are
13 discrepancies, which are described in Section D.2.1. Thus, we are soliciting feedback from
14 experts in each state to help provide "ground truth" for our data and identify sites where our
15 results seem inaccurate.
16
17 Some sites have higher levels of disturbance than others. This is not necessarily grounds for
18 exclusion from the "core" group of sites that we are considering for the RMNs. In fact,
19 depending on how sites fall out along this gradient, we may be interested in targeting sites with
20 certain types of disturbance. That being said, we do want to make sure we have sufficient
21 representation of minimally disturbed sites in the RMNs. This is because:
22
23 • Minimally disturbed sites are the standard against which other sites are compared; thus, it
24 is critical to track changes at these sites over time.
25 • There is a better chance of distinguishing climate-related impacts at these sites versus
26 those being impacted by other stressors.
27 • A lack of long-term biological, thermal, and hydrologic data has been documented at
28 these types of sites (e.g., U.S. EPA, 2012; Mazor et al., 2009; Jackson and Fureder, 2006;
29 Kennenetal., 2011).
30
D.2. METHODOLOGY
31 We used Geographic Information System software (ArcGIS 10.0) to spatially join the
32 preliminary RMN sites with NHDPlusVl catchments (U.S. EPA and USGS, 2006). Each
33 NHDPlusVl catchment has a unique identifier called a COMID. Many data were linked to sites
34 via this COMID.
35
36 We performed three different types of disturbance screenings:
37
38 1. Land use (see Section D.2.1);
39 2. Likelihood of impact from dams, mines, and point-source pollution sites (see
40 Section D.2.2); and
41 3. Likelihood of impact by the following other non-climatic stressors:
42 • Roads (see Section D.2.3.1),
43 • Atmospheric deposition (see Section D.2.3.2),
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-2 DRAFT—DO NOT CITE OR QUOTE
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1 • Coal (see Section D.2.3.3),
2 • Shale gas (see Section D.2.3.4),
3 • Future urban development (see Section D.2.3.5), and/or
4 • Water withdrawals (see Section D.2.3.6).
5
6 We selected our data with the following considerations in mind:
7
8 • Are they meaningful for assessing biological habitat?
9 • Do they have sufficient spatial coverage?
10 • Were they derived using consistent methods and procedures?
11 • Are they representative of conditions in the past 10 years?
12 • Are they of sufficient spatial resolution to allow for valid comparisons across
13 catchments?
14
15 These considerations are in keeping with the recent work performed by Michigan State
16 University (MSU) on the National Fish Habitat Action Plan (NFHAP) (DFW MSU et al., 2011;
17 Esselman et al., 201 la. That work included the development of the cumulative disturbance index
18 (DFW MSU et al., 2011; Esselman et al., 201 Ib).
19
D.2.1. Land use disturbance
20 Our first set of screening was done on land use and impervious cover data from the 2001
21 National Land Cover Database (NLCD) version 1 data set (Homer et al., 2007). The land use
22 disturbance screening was conducted at both the local catchment and total watershed scales
23 [important note: for purposes of this exercise, we will refer to the total watershed scale as the
24 "network" scale, in keepingwith the work done by DFW MSU et al. (2011)}. Local catchments
25 are defined as the land area draining directly to a reach, and network catchments are defined by
26 all upstream contributing catchments to the reach's outlet, including the reach's own local
27 catchment (see Figure D-l). GIS shapefiles with delineations of the local catchments were
28 downloaded from the Horizon-Systems website: http://www.horizon-
29 systems.com/NHDPlus/NHDPlusVl_data.php. The network-scale data were generated (and
30 graciously shared) by MSU.
31
This document is a draft for review purposes only and does not constitute Agency policy.
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A Local Catchments for
Reaches 20. 21, and 22
B Total Upstream Watershed
for Reach 20
Figure D-l. Land use data were evaluated at both the (A) local catchment and (B) total
watershed scales, using NHDPlusVl delineations (U.S. EPA and USGS, 2006).
1 While these data generally provide good approximations of land use, they have limitations. For
2 one, there are biases and accuracy issues associated with the NLCD data set (e.g., Novak and
3 Greenfield, 2010; Wickham et al., 2013). Another limitation is that we lack information on
4 whether landscape disturbance mitigation measures are being applied in a given catchment, and
5 if so, how effective those measures are. Thus, we have to assume that the impacts associated
6 with each land use type are equal.
7
8 Another limitation of our preliminary land use screening is that the data are not based on exact
9 watershed delineations. Rather the data are associated with the entire catchment where the site is
10 located, regardless of where the site falls within the catchment. We would have preferred to use
11 data based on exact watershed delineations for our initial screening, but we lacked the resources
12 needed to do exact watershed delineations for all of the candidate sites. The estimates that we
13 used were readily available for all of the sites and generally provide a good approximation
14 (especially when sites are located at the downstream end of the catchment). However, sometimes
15 inaccuracies occur. An example is illustrated in Figure D-2. Maryland site UMON-288-S is
16 located about halfway up the catchment flowline. Urban and agricultural land uses are located
17 within this catchment, but are all downstream of the site. Because these land uses are in the
18 catchment, they are included in the land cover output for this site. An accurate output for that site
19 would only include forested land cover. Thus, we are checking with each entity to verify that our
20 data match with expectations.
21
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-4 DRAFT—DO NOT CITE OR QUOTE
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NHDPlusVl local
catchment delineation
Site UMON-288-S
Even though the
urban and agricultural
land use (color-coded
in pink, red, yellow
and brown) are
located downstream
from this site, they
are included in the
land cover output
associated with this
site; thus, we are
performing visual
checks and soliciting
feedback from
entities to ensure that
these land cover
estimates match with
expectations.
Figure D-2. Example of a situation in which the land use output for a site is inaccurate.
1 We assessed land use disturbance at both the local catchment and network scales. This was done
2 for the following four parameters (source: NLCD 2001 version 1 data setl):
3
4 1. Percentage impervious cover
5 2. Percentage urban (this includes low, medium, and high intensity developed—NLCD
6 codes 22 + 23 + 24)
7 3. Percentage cultivated crops (NLCD code 82)
8 4. Percentage pasture/hay (NLCD code 81)
9
10 We developed a land use disturbance scale with six levels. Thresholds for each parameter are
11 listed in Table D-l. It should be noted that these thresholds are arbitrary, although some research
12 provides guidelines for these levels (e.g., King and Baker, 2010; Carlisle et al., 2008). When
13 rating a site, we first assessed each parameter separately. If the parameter values at the local
14 catchment and network scales differed, we applied the thresholds to the maximum value. For
15 example, if a site has 2% urban land cover at the local catchment scale and 1% urban land cover
16 at the network scale, we applied the threshold to the maximum value (in this case, 2% or level 3
17 for urban land use). This was done for each parameter. Then, sites were assigned an overall
1http://www.mrlc.gov/nlcd01 data.php
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-5 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
disturbance level. This was based on the highest disturbance level assigned across parameters.
For example, if a site was level 3 for impervious, level 2 for urban, level 1 for crops, and level 2
for pasture/hay, it was assigned to disturbance level 3. As a final step, we are checking with each
entity to verify that our disturbance level assignments match with expectations.
Table D-l. The thresholds used when assigning sites to the six levels of land use
disturbance. Each of the four parameters (impervious, urban, crops, pasture/hay) were
assessed separately. Then, sites were assigned an overall disturbance level based on the
highest level of disturbance across parameters
Level of land use disturbance
1
2
3
4
5
6
% Impervious
<0.1
<1
<2
<5
<10
>10
% Urban
0
<1
<3
<5
<10
>10
% Crops
0
<1
<5
<15
<25
>25
% Pasture/hay
0
<5
<15
<25
<35
>35
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
D.2.2. Likelihood of impacts from dams, mines, and point-source pollution sites
In our second set of screening, we flagged sites that had a high likelihood of being impacted by
dams, mines, National Pollutant Discharge Elimination System (NPDES) major discharges
and/or Superfund National Priorities List (SNPL) sites. We considered both the proximity of
these stressors to the sites as well as the attribute data associated with each stressor. The attribute
data are important because there are many site-specific factors, such as dam size and storage
capacity, that can greatly affect the degree of impact. Table D-2 contains a list of data that were
assessed, along with the sources of those data.
We used the following screening procedures:
1. We gathered the data listed in Table D-2.
2. Using GIS software (ArcGIS 10.0), we created a 1-km buffer around the preliminary
RMN sites (this included both the upstream and downstream areas).
3. Using GIS software (ArcGIS 10.0), we performed a procedure to identify whether any
dams, mines, NPDES major discharges or SNPL sites were located within the 1-km
buffer.
4. If so, we flagged those sites and assessed the likelihood of impact based on the following
considerations:
a. Location in relation to the site, assessed via a desktop screening with GIS software
(ArcGIS 10.0) and Google Earth.
b. Attributes of the stressors (e.g., dam size, storage capacity, size of NPDES major
discharge).
This document is a draft for review purposes only and does not constitute Agency policy.
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1 We used best professional judgment to assign the flagged sites to one of three impact categories:
2
3 • Unlikely impacted
4 • Likely impacted
5 • Unsure
6
7 Some examples of situations in which sites were assigned to the "unlikely impacted" category
8 are:
9
10 • The site was flagged for an NPDES major discharge, but the discharge was relatively
11 small and was located hundreds of meters downstream from the site.
12 • The site was flagged for a dam, but the dam was located on a different stream.
13
14 Some examples of situations in which sites were assigned to the "likely impacted" category are:
15
16 • The site was flagged for a NPDES major discharge. It was a large discharge occurring
17 about 100 m upstream from the site.
18 • The site was flagged for a dam. It was a large dam located on the same stream, just
19 upstream from the site.
20
21 Some examples of situations in which sites were assigned to the "unsure" category are:
22
23 • The site was flagged for a NPDES major discharge, but the site was located near a
24 confluence and it was difficult to determine which stream contained the discharge.
25 • The stressor was small- or medium-sized and was located 500 m or more from the site.
26
27 We performed one additional check to assess the potential for flow alteration at the sites. We
28 examined the type of NHDPlusVl flowline (FTYPE) located on the site (e.g., stream/river,
29 artificial pathway, canal/ditch, pipeline, connector) (U.S. EPA and USGS, 2006). If the site was
30 located on a flowline designated as something other than a stream/river, the site was flagged.
31
32 As a final step, we checked with each entity to verify that our assessments match with the
33 expectations.
34
D.2.3. Likelihood of impact from other non-climatic stressors
35 In our third set of screening, we flagged sites that had a high likelihood of being impacted by:
36
37 • Roads,
38 • Atmospheric deposition,
39 • Coal mining,
40 • Shale gas drilling,
41 • Future urban development, and/or
42 • Water withdrawals.
43
This document is a draft for review purposes only and does not constitute Agency policy.
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Table D-2. These data were assessed when screening for the likelihood of impacts from flow
alteration, mines, National Pollutant Discharge Elimination System (NPDES) major
discharges, and/or Superfund National Priorities List (SNPL) sites
Stressor
Source
Dams
National Atlas of the United States. 2006. Major Dams of the
United States: National Atlas of the United States, Reston,
VA. Available online:
http://nationalatlas.gov/atlasftp.htmltfdamsOOx
Mines
U.S. Geological Survey (USGS). 2005. Active mines and
mineral processing plants in the United States in 2003.
http://tin.er.usgs.gov/metadata/mineplant.faq.html
Pennsylvania industrial mine permits—Pennsylvania Spatial
Data Access (PASDA). 2013. Data Download—Mine and
refuse permits. Available online: http://www.pasda.psu.edu
National Pollutant Discharge
Elimination System (NPDES)
major discharges from the
Permit Compliance System
Superfund National Priorities
List (SNPL) from the
Compensation and Liability
Information System
U.S. Environmental Protection Agency. Geospatial data
download service—Geospatial information for all publicly
available FRS facilities that have latitude/longitude data [file
geodatabase]. Accessed August 27, 2013. Available online:
http://www.epa.gov/enviro/geo data.html
1 Table D-3 contains a list of data that were gathered and assessed, along with the sources of those
2 data. There are a lot of site-specific factors that can greatly affect the degree of impact from these
3 stressors, which makes it difficult to set thresholds. For example, a site could be exposed to high
4 concentrations of atmospheric deposition but may not be impacted by acidity because of
5 site-specific mediating factors like calcareous geology. Another example is permit activity
6 associated with coal mining. Just because mining permits have been issued in an area does not
7 mean that mining activities are actually taking place. And even if mining activities are taking
8 place, impacts can vary greatly depending on site-specific factors such as the size and type of
9 mine.
10
11 Because of these factors, we decided to assess the likelihood of impact based on a relative scale
12 instead of by setting firm thresholds. The relative scales were based on values found in
13 NHDPlusVl catchments across the entire study area. If a site rated on the high end of the risk
14 scale, we flagged it for further evaluation. We then checked with entities to find out their
15 thoughts on the degree of impact and inquired about the availability of more detailed data to help
16 us better assess the potential degree of impact [e.g., is mining actually taking place? What are the
17 pH and acid neutralizing capacity (ANC) values at sites flagged for atmospheric deposition?].
18 The specific screening procedures that were followed for each stressor are described below.
This document is a draft for review purposes only and does not constitute Agency policy.
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Table D-3. These data were assessed when screening for the likelihood of impacts from
roads, atmospheric deposition, coal mining, shale gas drilling, future urban development,
and water withdrawals
Stressor
Parameters/description
Source
Roads
Length of roads, local catchment, and network
scales
U.S. Census Bureau (2000)
from DFW MSU et al. (2011)
Number of road crossings, local catchment,
and network scales
Atmospheric
deposition
s and SO4 concentrations, based on 2011
deposition grids
NADPa(2013)
The Nature Conservancy (TNC) geology class
Olivero and Anderson (2008)
Coal mining
Potential for development, based on:
• whether the site is located in a coal
field and/or the mountaintop removal
(MTR) region
• coal production by state
Coal fields (USGS, Eastern
Energy Team, 2001)
MTR region [unknown source;
GIS layer was provided by
Christine Mazzarella
(U.S. EPA)]
Coal production by State [see
Table 6 in U.S. EIA, (2012)]
Permit activity, based on number of permits
issued within 1 km of the site. Data type and
availability varied by state.
Alabama:
• Number of active coal mine permits
Pennsylvania:
• Anthracite permits
• Anthracite refuse
• Bituminous permits
• Bituminous refuse
West Virginia:
• WV_permitboundary
• WV_refuse
• WV_valleyfill
• WV_all_mining
Virginia:
• Surface mine permit boundaries
Alabama (Alabama Surface
Mining Commission, 2013)
Pennsylvania (PA SDA, 2013)
West Virginia (WV DEP
TAGIS, 2013; WV GES, 2014)
Virginia (VA DEQ-DMLR,
2013)
This document is a draft for review purposes only and does not constitute Agency policy.
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Table D-3. continued...
Stressor
Shale gas
drilling
Future urban
development
Water
withdrawals
(county-level)
Parameters/description
Potential for development, based on whether
the site is located in the shale play region
Permit activity, based on the number of
unconventional permits issued within 1 km of
the site. These data were available for
Pennsylvania (file name:
PA UncPermits 05092013) and West Virginia
(file name: WV_Perm_05132013).
Potential for future urban development based
on projected change in percentage
imperviousness by 2050
Irrigation, total withdrawals, fresh (Mgal/day)
Total withdrawals, fresh (Mgal/day)
Total withdrawals, total (fresh + saline)
(Mgal/day)
Source
U.S. EIA (2013)
Frac Tracker (20 13)
U.S. EPA (2011);
work performed by Angie
Murdukhayeva (U.S. EPA)
USGS (2010)
ahttp://nadp.sws.uiuc.edu/NTN/annualmapsbvvear.aspx
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
D.2.3.1. Roads
We assessed two aspects of potential road impacts:
• Length of roads and
• Number of road crossings
First we gathered the roads data listed in Table D-3 for both the local catchment and network
scales.
Next, to assess the likelihood of impact from length of roads, we used the following formulas to
normalize the data:
Local catchment scale = Length of roads in the local catchment (m) + Area of the local
catchment (km2)
Network scale = Length of roads in the network (m) + Area of the network (km2)
Then, we used the following formula to convert these values to a scoring scale ranging from 0
(no roads) to 100 (highest length of roads per area) (note: the minimum and maximum values
used in this formula are based on the range of values found across the entire study area):
This document is a draft for review purposes only and does not constitute Agency policy.
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1 100 x (Value - Minimum) + (Maximum - Minimum)
2
3 If the parameter values at the local catchment and network scales differed, we used the maximum
4 score for our assessment. For example, if the local catchment score was 80 and the network score
5 was 50, we used the higher score of 80 for our assessment.
6
7 We flagged sites for further evaluation if they received a score of >75%.
8
9 The same procedure was followed when assessing the likelihood of impact from road crossings.
10
11 As a final step, we consulted with entities for input on the degree of impact at flagged sites. This
12 is important because entities have local knowledge about these sites. Also, our data are not based
13 on exact watershed delineations. Rather, the data are associated with the entire catchment in
14 which the site is located, regardless of where a site falls within the catchment. While this
15 generally provides a good approximation, sometimes inaccuracies occur, as described in
16 Section D.2.1 and Figure D-2.
17
D.2.3.2. Atmospheric deposition
18 We assessed two aspects of atmospheric deposition:
19
20 • Concentrations of NCb
21 • Concentrations of SO4
22
23 In addition, we considered TNC geology class (Olivero and Anderson, 2008) as a potential
24 mediating factor. First we gathered the data listed in Table C-3. Using GIS software (ArcGIS
25 10.0), we linked the NCb and SO4 deposition grid data (1-km resolution) to the sites. Next, we
26 took the average of NCb and SO4. Then, we used the following formula to convert these values
27 to a scoring scale ranging from 0 (no nitrogen and sulfate deposition) to 100 (highest average
28 concentration of NOs and SO4) (note: the minimum and maximum values used in this formula
29 are based on the range of values found across the entire study area):
30
31 100 x (Value - Minimum) + (Maximum - Minimum)
32
33 We flagged sites for further evaluation if they received a score of >75%.
34
35 Geology can potentially mediate some of the effects of atmospheric deposition. To assess this
36 potential, we used GIS software (ArcGIS 10.0) to link the TNC geology class (Olivero and
37 Anderson, 2008) to the sites (note: at this time the TNC geology class data are only available for
38 Northeast and Mid-Atlantic regions).
39
40 Sites were scored as follows:
41
42 • Sites located in areas designated as "low buffered, acidic" received a score of 100.
43 • Sites located in areas designated as "moderately buffered, neutral" or "assume
44 moderately buffered (Size 3+ rivers)" received a score of 50.
45 • Sites located in areas designated as "highly buffered, calcareous" received a score of 0.
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1 • Sites located in areas that lacked data or were designated as "unknown buffering/missing
2 geology" were not assessed.
3 We flagged sites if they received a score of 100%.
4
5 As a final step, we consulted with entities to discuss the degree of impact at flagged sites. This is
6 important because entities have local knowledge about these sites. Also, they may have more
7 detailed data, such as pH and ANC measurements, to help us better assess the potential degree of
8 impact.
9
D.2.3.3. Coalmining
10 We assessed two aspects of coal mining:
11
12 • Potential for mining
13 • Permit activity
14
15 First we gathered the data listed in Table D-3.
16
17 To assess the potential for coal mining, we considered the following:
18
19 • Whether the site is located in an area that has been designated as a mountaintop removal
20 (MTR) area and/or a coal field (USGS, Eastern Energy Team, 2001).
21 o If the site is located in a coal field, is it designated as "potentially minable" or is it
22 tagged for "other uses"?
23 • What the total coal production is for the state where the site is located [source: Table 6 in
24 the 2011 Annual Coal Report (U.S. EIA, 2012)].
25
26 We performed the following steps when assessing a site for mining potential:
27
28 1. First we assigned a coal field score, as follows:
29 • Using GIS software (ArcGIS 10.0), we linked the coal field and MTR GIS layers to
30 the sites.
31 • If the site is located in a catchment that has been designated as a "potentially
32 minable" coal field (USGS, Eastern Energy Team, 2001) and/or a mountaintop
33 removal (MTR) area, we assigned it a score of 1.
34 • If the site is located in a catchment that has been designated as a coal field with "other
35 uses" (USGS, Eastern Energy Team, 2001), we assigned it a score of 0.5.
36 • If the site is located in a catchment that is not part of a coal field or MTR area, it
37 received a score of 0.
38 2. Then we assigned a coal production score, as follows:
39 • Total coal production values for each state were taken from Table 6 in the 2011
40 Annual Coal Report (U.S. EIA, 2012).
41 • Those values were converted to a scale of 0 to 100 using this formula (note: the
42 minimum and maximum values used in this formula are based on the range of values
43 found in the states in our study area):
44
This document is a draft for review purposes only and does not constitute Agency policy.
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1 100 x (Value - Minimum) + (Maximum - Minimum)
2
3 • Sites were assigned scores based on what state they were located in. For example,
4 West Virginia had the highest total coal production of all of the states in the study
5 area, so any sites located in West Virginia received a coal production score of 100.
6 3. To get the final score for mining potential, we multiplied the coal field score by the coal
7 production score. Scores ranged from 0 (no mining potential) to 100 (highest potential for
8 mining).
9
10 We flagged sites for further evaluation if they received a score of >75%.
11
12 Permit data were not available for all the states, and where those data were available, data type
13 and quality varied, as did the attribute data. Therefore, we assessed permit activity on a
14 state-by-state basis. If sites were located in states where permit data were available, we
15 performed the following steps to assess the intensity of permit activity:
16
17 1. We gathered the permit data listed in Table D-3.
18 2. Using GIS software (ArcGIS 10.0), we created a 1-km buffer around the preliminary
19 RMN sites (this included both the upstream and downstream areas).
20 3. Using GIS software (ArcGIS 10.0), we performed a procedure to determine how many
21 mining permits had been issued within the 1-km buffer.
22 4. The following formula was used to convert those values to a scale of 0 to 100 (note: since
23 the type of data available for each state varied, the minimum and maximum values used
24 in this formula were based on the range of data found in each state):
25
26 100 x (Value - Minimum) + (Maximum - Minimum)
27
28 We flagged sites for further evaluation if they received a score of >0.
29
30 As a final step, we checked with entities to find out their thoughts on the degree of impact at
31 flagged sites. This is important because entities have local knowledge about these sites and may
32 have access to more detailed data. Just because mining permits have been issued in an area does
33 not mean that mining activities are actually taking place. And even if mining activities are taking
34 place, impacts can vary greatly depending on site-specific factors such as the size and type of
35 mine.
36
D.2.3.4. Shale gas drilling
37 We assessed two aspects of shall gas drilling:
38
39 • Potential for drilling
40 • Permit activity
41
42 First we gathered the data listed in Table D-3.
43
44 To assess the potential for shale gas drilling, we performed the following screening procedure:
45
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1 • Using GIS software (ArcGIS 10.0), we linked the shale play GIS layer (see Table D-3) to
2 the sites.
3 • If the site is located in a shale play region, we assigned it a score of 100 and flagged it for
4 further evaluation.
5
6 Permit data were only available for the states of West Virginia and Pennsylvania. We performed
7 the following steps to assess the intensity of permit activity at sites in those sites:
8
9 1. We gathered the permit data listed in Table D-3.
10 2. Using GIS software (ArcGIS 10.0), we created a 1-km buffer around the preliminary
11 RMN sites (this included both the upstream and downstream areas).
12 3. Using GIS software (ArcGIS 10.0), we performed a procedure to determine how many
13 unconventional permits had been issued within the 1-km buffer.
14 4. The following formula was used to convert those values to a scale of 0 to 100 (note: since
15 the type of data available for each state varied, the minimum and maximum values used
16 in this formula were based on the range of data found in each state):
17
18 100 x (Value - Minimum) + (Maximum - Minimum)
19
20 We flagged sites for further evaluation if they received a score of >0%.
21
22 As a final step, we checked with entities to find out their thoughts on the degree of impact at
23 flagged sites. This is important because entities have local knowledge about these sites and may
24 have access to more detailed data. Just because drilling permits have been issued in an area does
25 not mean that drilling activities are actually taking place. And even if drilling activities are taking
26 place, impacts can vary greatly depending on site-specific factors.
27
D.2.3.5. Potential for future urban development
28 We used EPA's ICLUS tools and data sets (Version 1.3 and 1.3.1) (U.S. EPA, 2011) to assess
29 the potential that a site will experience future urban development. We used the ICLUS Tools to
30 project the percentage change in imperviousness in each NHDPlusVl local catchment by 2050
31 based on high (A2) and low (Bl) emissions scenarios (note: the ICLUS data have a resolution of
32 1-km).
33
34 First we used GIS software (ArcGIS 10.0) to link sites with NHDPlusVl local catchments. Sites
35 were flagged for further evaluation if the following conditions occurred:
36
37 • The percentage impervious value in the NHDPlusVl local catchment where the site is
38 located is currently <10% (based on values derived from the 2001 NLCD version 1 data
39 set), and
40 • The future projection is for a positive value >0.5% [this is based on an average of the
41 high (a2) and low (bl) emissions scenarios].
42
43 As a final step, we checked with entities to find out their thoughts on the potential for future
44 development at flagged sites. This is important because entities have local knowledge about
This document is a draft for review purposes only and does not constitute Agency policy.
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1 these sites and may have access to more detailed information on the potential for future
2 development in areas near the sites.
3
D.2.3.6. Water withdrawals
4 We assessed three aspects of water use:
5
6 • Irrigation, total withdrawals, fresh;
7 • Total withdrawals, fresh only; and
8 • Total withdrawals, total.
9
10 First we gathered the data listed in Table D-3. These data are based on 2005 water use and are
11 only available at the county-level (USGS, 2010). Then we used GIS software (ArcGIS 10.0) to
12 associate the county-level data with NHDPlusVl local catchments. Next we linked sites with
13 NHDPlusVl local catchments. For each parameter, we used the following formula to convert the
14 values to a scoring scale ranging from 0 (no withdrawals) to 100 (highest withdrawals) (note: the
15 minimum and maximum values used in this formula are based on the range of values found
16 across the entire study area):
17
18 100 x (Value - Minimum) + (Maximum - Minimum)
19
20 We flagged sites for further evaluation if they received a score of >50% for any of the three
21 parameters.
22
23 As a final step, we consulted with entities to discuss the potential for impacts from water
24 withdrawals at the flagged sites. This is important because entities have local knowledge about
25 these sites and may have access to more detailed information on water use in areas near the sites.
This document is a draft for review purposes only and does not constitute Agency policy.
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D.3. REFERENCES
Alabama Surface Mining Commission. (2013) Alabama coal mine geospatial data - permit
boundaries (1983-present). Jasper, AL: State of Alabama http^/surface-
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assessments of Appalachian streams based on predictive models for fish,
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DFW MSU (Department of Fisheries and Wildlife, Michigan State University), Esselman, PC;
Infante, DM; Wang, L; Taylor, WW; Daniel, WM; Tingley, R; Fenner, J; Cooper, A;
Wieferich, D; Thornbrugh, D; Ross, J. (2011) National Fish Habitat Action Plan
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Esselman, PC; Infante, DM; Wang, L; Taylor, WW; Daniel, WM; Tingley, R; Fenner, J; Cooper,
C; Wieferich, D; Thornbrugh, D; Ross, R, (201 la) A landscape assessment offish habitat
conditions in United States rivers and their watersheds. Denver, Co: National Fish
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Esselman, PC; Infante, DM; Wang, L; Wu, D; Cooper, AR; Taylor, WW. (201 Ib) An index of
cumulative disturbance to river fish habitats of the conterminous United States from
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Jackson, JK; Fureder, L. (2006) Long-term studies of freshwater macroinvertebrates - a review
of the frequency, duration, and ecological significance. FreshwBiol 51:591-603.
Kennen, JG; Sullivan, DJ; May, JT; Bell, AH; Beaulieu, KM; Rice, DE. (2011) Temporal
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King, RS; Baker, ME. (2010) Considerations for identifying and interpreting ecological
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Mazor, RD; Purcell, AH; Resh, VH. (2009) Long-term variability in benthic macroinvertebrate
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Nowak, DJ; Greenfield, EJ. (2010) Evaluating the National Land Cover Database tree canopy
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Olivero, AP; Anderson, MG. (2008) Northeast aquatic habitat classification. Denver, CO: The
Nature Conservancy. Available from http ://rcngrants. org/content/northeastern-aquatic-
habitatclassification-proj ect
PASDA (Pennsylvania Spatial Data Access). (2013) Data download -mine and refuse permits.
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U.S. Bureau of the Census. (2000) Census 2000 TIGER/Line data.
www.esri.com/data/download/census2000-tigerline/index.html
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U.S. EIA (Energy Information Administration). (2013) Maps: exploration, resources, reserves,
and production - United States shale gas maps - Lower 48 states shale plays.
http://www.eia.gov/pub/oil gas/natural gas/analysis_publications/maps/maps.htm
U.S. EIA (Energy Information Administration). (2012) Annual coal report 2011.
http: //www. ei a. gov/coal/annual/
U.S. EPA/USGSE (Environmental Protection Agency/U.S. Geological Survey). (2005). National
hydrography dataset plus, NHDPlus Version 1.0. ((NHDPlusVl)
http://www.fws.gov/r5gomp/gom/nhd-gom/metadata.pdf
U.S. EPA (Environmental Protection Agency). (2011) ICLUS tools and datasets (Version 1.3 &
1.3.1). [EPA/600/R-09/143F]. Washington, DC: U.S. Environmental Protection Agency.
http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=205305
U.S. EPA (Environmental Protection Agency). (2012) Implications of climate change for
bioassessment programs and approaches to account for effects. [EPA/600/R-11/036F].
Washington, DC: Global Change Research Program, National Center for Environmental
Assessment. http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=239585
U.S. EPA (U.S. Environmental Protection Agency). (2013) Geospatial data download service -
Geospatial information for all publicly available FRS facilities that have
latitude/longitude data [file geodatabase]. Accessed August 27, 2013.
http://www.epa.gov/enviro/geo data.html
USGS (U.S. Geological Survey). (2001) Coal fields of the United States: National Atlas of the
United States. Reston, VA: USGS. Available online:
http://nationalatlas.gov/atlasftp.htmltfcoalfdp
USGS (U.S. Geological Survey). (2005) Active mines and mineral processing plants in the
United States in 2003. Reston, VA: USGS.
http://tin.er.usgs.gov/metadata/mineplant.faq.html
USGS (U.S. Geological Survey) (2010). Water use in the United States: Estimated use of water
in the United States, 2005. Reston, VA: USGS. http://water.usgs.gov/watuse/
USGS (U.S. Geological Survey). (2014) Major dams of the United States: National Atlas of the
United States. Reston, VA: United States Geological Survey. Available online:
http: //nati onal atl as. gov/atl asftp. html#dam sOOx
VDEQ (Virginia Department of Environmental Quality. (2013) Surface mine permit boundaries.
Richmond, VA: Division of Mined Land Reclamation.
ftp://ftp.dmme.virginia.gov/DMLR/downloads/permits/shape files/nad83/
Wickham, JD; Stehman, SV; Gass, L; Dewitz, J; Fry, JA; Wade, TG. (2013) Accuracy
assessment of NLCD 2006 land cover and impervious surface. Rem Sens Environ 130:
294-304.
WVDEP (West Virginia Department of Environmental Protection). (2013) Data download -
mining permit shapefiles. Charleston, WV: Technical Applications and GIS Unit
(TAGIS). http://tagis.dep.wv.gov/home/Downloads
WVGES (West Virginia Geological Ecomic Survey (WGES). (2014). West Virginia coal bed
mapping [online mapper]. Accessed September 11, 2014.
http://www.wvgs.wvnet.edu/www/coal/cbmp/coalims.html
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-17 DRAFT—DO NOT CITE OR QUOTE
-------
Special thanks to:
Kyle Herreman and Dana Infante from MSU Department of Fisheries and Wildlife
Phil Morefield and Angie Murdukhayeva (U.S. EPANCEA)
Christine Mazzarella and Jennifer Fulton (U.S. EPA R3)
Arlene Olivero (TNC)
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 D-18 DRAFT—DO NOT CITE OR QUOTE
-------
APPENDIX E.
SECONDARY REGIONAL
MONITORING NETWORK (RMN)
SITES IN THE NORTHEAST AND
MID-ATLANTIC REGIONS
Table E-1. Northeast secondary sites
Table E-2. Mid-Atlantic secondary sites
At this time there are no secondary sites in the Southeast region
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 E-l DRAFT—DO NOT CITE OR QUOTE
-------
Table E-l. Secondary RMN sites in the Northeast (4/2/2014). At all of the VT DEC and CT DEEP sentinel sites,
macroinvertebrates are collected annually and water temperature sensors are deployed year-round
Longitude
-72.7439
-72.7464
-72.8952
-72.9458
-72.1542
-72.1614
-71.6356
-71.6356
-72.7819
-73.2336
-73.2292
-72.7472
-72.9384
-72.3289
-72.3343
Latitude
43.7667
43.7708
43.8714
43.8556
43.9917
44.4911
44.7522
44.7550
44.5036
44.2486
44.2483
42.7469
42.0356
41.4100
41.4603
State
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
VT
CT
CT
CT
Entity
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
VTDEC
CT DEEP
CT DEEP
CT DEEP
Station ID
130000000319
130000000324
135404000018
135411000013
170000000026
211109100032
280000000002
280000000003
493238200015
530000000035
530000000037
660600000117
1156
1236
1239
Water body name
White River
White River
Bingo Brook
Smith Brook
Waits River
Pope Brook
Nulhegan River
Nulhegan River
Ranch Brook
Lewis Creek
Lewis Creek
East Branch North
River
Hubbard Brook
Beaver Brook
Burnhams Brook
Notes
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site; USGS gage
(01135150)
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site; USGS gage
(04288230)
VT DEC sentinel site
VT DEC sentinel site
VT DEC sentinel site
CT DEEP sentinel site; colocated with USGS
gage (01 1873 00)
CT DEEP sentinel site
CT DEEP sentinel site
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-2 DRAFT—DO NOT CITE OR QUOTE
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Table E-l. continued...
Longitude
-72.82146
-73.2155
-72.5365
-72.4226
-73.1214
-72.4338
-73.3200
-73.1679
-72.1509
-73.3678
-73.1745
-72.9630
-72.4640
-72.1256
Latitude
41.93717
41.5575
41.6615
41.4283
41.9328
41.5623
41.9459
41.8646
41.7812
41.2931
41.5783
41.7807
41.8272
41.9199
State
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
CT
Entity
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
CT DEEP
Station ID
359
1468
2295
2297
2299
2304
2309
2312
2331
2346
2676
2711
345
2532
Water body name
West Branch Salmon
Weekepeemee River
Mott Hill Brook
Hemlock Valley
Brook
Rugg Brook
Day Pond Brook
Flat Brook
Jakes Brook
Stonehouse Brook
Little River
Nonewaug River
Bunnell Brook
Tankerhoosen River
Branch
Notes
CT DEEP sentinel site
CT DEEP sentinel site; colocated with
USGS gage (01203805)
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site
CT DEEP sentinel site; USGS gage
(01203600)
CT DEEP sentinel site; USGS gage
(01188000)
CT DEEP sentinel site
initially selected as a primary RMN site but
not being sampled annually for benthic
macroinvertebrates
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-3 DRAFT—DO NOT CITE OR QUOTE
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Table E-l. continued...
Longitude
-72.3372
-69.0440
-70.3620
-70.6035
-69.5933
-69.5313
-68.2346
Latitude
41.4671
44.3143
44.8553
44.6826
44.2232
44.3679
44.3934
State
CT
ME
ME
ME
ME
ME
ME
Entity
CT DEEP
MEDEP
MEDEP
MEDEP
MEDEP
MEDEP
MEDEP
Station ID
1092
MEDEP 5736
8
MEDEP 5676
0
MEDEP 5708
9
MEDEP 5681
7
MEDEP 5701
1
MEDEP 5706
5
Water body name
Eightmile
Ducktrap
River— Station 626
Sandy River — Station
17
Swift River — Station
346
Sheepscot
River — Station 74
West Branch
Sheepscot
River— Station 268
Duck Brook — Station
322
Notes
initially selected as a primary RMN site but
not being sampled annually for benthic
macroinvertebrates
initially selected as a primary RMN site but
not being sampled annually for benthic
macroinvertebrates; USGS gage
(01037380) — air and water temperature,
discharge
initially selected as a primary RMN site but
not being sampled annually for benthic
macroinvertebrates; USGS gage
(01047200)— discharge, but too far away to
be representative?
initially selected as a primary RMN site but
not being sampled annually for benthic
macroinvertebrates; USGS gage
(01055000) — discharge, air temperature
ME DEP long-term monitoring site; USGS
gage (01038000)— water and air
temperature, discharge
ME DEP long-term biological monitoring
site
ME DEP long-term biological monitoring
site
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-4 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. Secondary RMN sites in the Mid-Atlantic (4/2/2014). At all of the MD DNR sentinel sites, macroinvertebrates are
collected annually and water and air temperature sensors are deployed year-round. At the WV DEP sites, macroinvertebrates
are collected annually and water temperature sensors may be deployed. At the SRBC continuous monitoring sites,
macroinvertebrates are collected annually and water temperature sensors are deployed year-round; stage and precipitation
data are also being collected at some sites (see Notes field). At the NFS—ERMN sites (National Park Service sites that are in
the Eastern Rivers and Mountains Network), macroinvertebrates are collected every other year and efforts will be made to
install temperature sensors at high priority sites
Longitude
-79.21349
-78.45571
-77.54528
-77.48935
-76.97198
-76.86417
-76.71875
-76.69843
-76.69829
-76.04611
-75.46182
Latitude
39.54119
39.68672
39.65833
39.58739
39.16949
39.44055
39.42925
39.43951
39.48052
39.61055
38.26359
State
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
Entity
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
Station ID
SAVA-276-S
FIMI-207-S
ANTI-101-S
UMON-119-S
RKGR-119-S
LIBE-102-S
JONE-315-S
JONE-109-S
LOCH-120-S
FURN-101-S
NASS-302-S-
2012
Water body name
Double Lick Run
Fifteen Mile Creek
Unnamed tributary to
Edgemont Reservoir
Buzzard Branch
Unnamed tributary to Patuxent
River
Timber Run
North Branch of Jones Falls
Unnamed tributary to Dipping
Pond Run
Baisman Run
Unnamed tributary to Principio
Creek
Nassawango Creek
Notes
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site — Highlands
MD DNR sentinel site— Coastal
Plain
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-5 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-75.49247
-75.59259
-75.78362
-75.96062
-76.09499
-76.21896
-76.73717
-76.76012
-76.90348
-77.02912
-77.08594
-77.09766
Latitude
38.24950
38.41408
39.28768
38.72408
39.08754
39.19352
38.36662
38.56392
38.49936
38.51108
38.48386
38.58225
State
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
MD
Entity
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
MDDNR
Station ID
NASS-108-S-
2012
WIRH-220-S-
2012
UPCR-208-S-
2012
UPCK-113-S-
2012
CORS-102-S-
2012
LOCR-102-S-
2012
STCL-051-S-
2012
PAXL-294-S-
2012
ZEKI-012-S-
2012
PTOB-002-S-
2012
NANJ-331-S-
2012
MATT-033-S-
2012
Water body name
Millville Creek
Leonard Pond Run
Cypress Branch
Unnamed tributary to Skeleton
Creek
Unnamed tributary to Emory
Creek
Swan Creek
Unnamed tributary to St.
Clements Creek
Swanson Creek
Unnamed tributary to Zekiah
Swamp Run
Hoghole Run
Mill Run
Mattawoman Creek
Notes
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
MD DNR sentinel site— Coastal
Plain
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-6 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-74.88980
-74.84479
-74.50486
-75.12652
-75.10517
-74.94059
-74.98445
-74.96505
-74.94123
-74.92372
-74.79550
-75.00528
-74.50528
-77.73670
-77.37918
-76.92222
Latitude
40.77471
40.75211
40.95164
40.97400
40.98337
41.08567
41.06470
41.07109
41.09062
41.09674
41.29461
41.03179
39.88500
42.31903
42.07520
42.10278
State
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NY
NY
NY
Entity
EPAR2
EPAR2
EPAR2
NPS— ERMN
NPS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
USGS
SRBC
SRBC
SRBC
Station ID
1
2
17
DEWA.3005
DEWA.3033
DEWA.3026
DEWA.3025
DEWA.3014
DEWA.3038
DEWA.3010
DEWA.3028
DEWA.3030
USGS
01466500
CANA
Tuscarora
SING 0.9
Water body name
Unnamed tributary to
Musconetcong River
Teetertown Brook
Hibernia Brook
Dunnfield Creek 03
Dunnfield Creek 26
Unnamed tributary
Vancampens Brook 05
Vancampens Brook 22
Vancampens Brook 43
Vancampens Brook 76
Vancampens Brook 95
White Brook 15
Yards Creek 07
McDonalds Branch
Canacadea Creek
Tuscarora Creek
Sing Sing Creek
Notes
long-term monitoring site — Jim
Kurtenbach (U.S. EPA R2)
long-term monitoring site — Jim
Kurtenbach (U.S. EPA R2)
long-term monitoring site — Jim
Kurtenbach (U.S. EPA R2)
NFS— ERMN high priority
USGS gage in Byrne State Forest
(Pine Barrens)
precip gage
pressure transducer (real-time)
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-7 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-76.72019
-76.47508
-76.15029
-76.10589
-76.05357
-76.00931
-75.50220
-74.79921
-75.323216
-74.86975
-74.87711
-74.88168
-74.89043
-78.45247
-78.51846
-75.14398
-74.90309
-74.87464
-74.89481
Latitude
42.04209
42.20472
42.06312
42.59277
42.20426
42.01582
42.77596
42.70639
41.73465
41.24147
41.24882
41.25185
41.25780
40.41597
40.43269
40.97139
41.19744
41.22245
41.23067
State
NY
NY
NY
NY
NY
NY
NY
NY
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Entity
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
DRBC
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
Station ID
Baldwin
Catatonk
Apal
Trout Brook
Nanticoke
CHOC
Sangerfield
Cherry
MB_Dyberry
DEWA.3027
DEWA.3011
DEWA.3039
DEWA.3012
DEWA.3001
DEWA.3003
DEWA.3004
DEWA.3031
Water body name
Baldwin Creek
Catatonk Creek
Apalachin Creek
Trout Brook
Nanticoke Creek
Choconut Creek
Sangerfield River
Cherry Valley Creek
Middle Branch Dyberry Creek
Adams Creek 03
Adams Creek 14
Adams Creek 21
Adams Creek 33
Blair Gap Run — Foot of Ten
Blair Gap Run — Muleshoe
Caledonia Creek 13
Deckers Creek 03
Dingmans Creek 05
Dingmans Creek 30
Notes
precip gage
pressure transducer (stand-alone)
precip gage
precip gage
pressure transducer (stand-alone)
NFS— ERMN high priority
NFS— ERMN high priority
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-8 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-74.90343
-74.91831
-79.92348
-79.58149
-74.89987
-79.93024
-80.97161
-75.00533
-74.92431
-74.92673
-78.48373
-81.02055
-74.84545
-75.01434
-74.90598
-74.95645
-74.95916
-74.96252
-74.96279
Latitude
41.23052
41.23772
39.78393
39.81449
41.19356
39.78248
37.58466
41.09383
41.15917
41.16889
40.41876
37.53483
41.29520
41.08235
41.17560
41.12711
41.12946
41.13729
41.14150
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Entity
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
NPS— ERMN
Station ID
DEWA.3015
DEWA.3008
DEWA.3035
DEWA.3013
DEWA.3036
DEWA.3020
DEWA.3032
DEWA.3029
DEWA.3007
DEWA.3034
DEWA.3018
DEWA.3006
DEWA.3022
Water body name
Dingmans Creek 39
Dingmans Creek 57
Dublin Run
Great Meadows Run
Hornbecks Creek 15
Ice Pond Run
Little Bluestone River
Little Bushkill Creek 01
Mill Creek 12
Mill Creek 25
Milll stone Run
Mountain Creek
Raymondskill Creek 13
Sand Hill Creek 08
Spackmans Creek 08
Toms Creek 03
Toms Creek 07
Toms Creek 20
Toms Creek 25
Notes
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-9 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-74.88573
-79.59970
-74.98444
-76.91134
-78.80331
-78.64757
-78.59258
-78.46158
-78.40722
-78.36118
-78.27484
-78.27008
-78.25348
-78.22029
-78.17458
Latitude
41.23542
39.81014
41.11381
41.32519
40.69289
40.63052
40.26388
41.04564
40.97000
41.07359
41.49444
41.52649
41.36235
41.51169
41.45256
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Entity
NPS— ERMN
NPS— ERMN
NPS— ERMN
PADEP
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
Station ID
DEWA.3023
DEWA.3002
WQN_408
WB SUS
CHEST
BOBS
PA Moose
LCLFO.l
TROT
West
Driftwood
Hicks
Portage
Hunts
Water body name
Unnamed tributary Dingmans
Creek 07
Unnamed tributary (Scotts
Run)
Van Campen Creek 12
Loyal sock Creek
West Branch Susquehanna
River
Chest Creek
Bobs Creek
Moose Creek
Little Clearfield Creek
Trout Run
West Creek
Driftwood Branch
Sinnemahoning Creek
Hicks Run
Portage Creek
Hunts Run
Notes
long-term data, EV (protected)
pressure transducer (stand-alone)
pressure transducer (real-time) and
precip gage
pressure transducer (real-time) and
precip gage
pressure transducer (real-time)
precip gage
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-10 DRAFT—DO NOT CITE OR QUOTE
-------
Table E-2. continued...
Longitude
-77.91244
-77.76387
-77.76123
-77.68520
-77.66985
-77.60997
-77.60667
-77.58154
-77.55928
-77.45056
-77.41333
-77.36278
-77.29313
-77.23044
-77.18943
-76.92300
-76.91416
-76.91233
Latitude
41.57467
41.79146
41.79011
41.40016
41.72483
41.06022
41.24694
41.73642
41.76142
41.64694
41.76306
41.31000
41.85752
41.47393
41.32739
41.49143
41.70931
41.99164
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Entity
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
Station ID
East Fork
Ninemile
Upper Pine
Young
WPIN
MARS
BAKRO.l
ELKR
Long
Pine
Blackwell
Marsh Tioga
LPIN0.2
CROK
BLOC
LARR
Pies
TIOG
HAMM
Water body name
East Fork First Fork
Sinnemahoning Creek
Ninemile Run
Pine Creek
Young Woman's Creek
West Branch Pine Creek
Marsh Creek
Baker Run
Elk Run
Long Run
Pine Creek
Marsh Creek
Little Pine Creek
Crooked Creek
Blockhouse Creek
Larrys Creek
Pleasant Stream
Tioga River
Hammond Creek
Notes
precip gage
pressure transducer (real-time)
pressure transducer (real-time) and
precip gage
precip gage
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-11 DRAFT—DO NOT CITE OR QUOTE
-------
Table E-2. continued...
Longitude
-76.76835
-76.76011
-76.64148
-76.60723
-76.34434
-76.33104
-76.28083
-76.27436
-76.24282
-76.07111
-76.06980
-76.02756
-75.98474
-75.84137
-75.77788
-75.52351
-75.47324
-81.08737
Latitude
41.78974
41.65262
41.19353
41.78132
41.32261
41.45880
41.96661
41.62644
41.23366
41.78832
41.58154
41.42725
41.61164
41.92994
41.55783
41.95946
41.68331
37.96331
State
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
WV
Entity
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
SRBC
NPS— ERMN
Station ID
SUGR
TOWA
LMUN
TOMJ
EBFC
LYSK5.0
WAPP
Sugar Run
Kitchen
EBWC
LMEHOOP
BOWN
MESH
SNAK
SBTK
STAR
LACK
NERI.3038
Water body name
Sugar Creek
Towanda Creek
Little Muncy Creek
Tomjack Creek
East Branch Fishing Creek
Loyal sock Creek
Wappasening Creek
Sugar Run
Kitchen Creek
East Branch Wyalusing Creek
Little Mehoopany Creek
Bowman Creek
Meshoppen Creek
Snake Creek
South Branch Tunkhannock
Creek
Starrucca Creek
Lackawanna River
Arbuckle Creek 2
Notes
pressure transducer (real-time) and
precip gage
pressure transducer (real-time)
pressure transducer (stand-alone)
pressure transducer (stand-alone)
pressure transducer (real-time)
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-12 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-81.09031
-81.10399
-80.90375
-80.90266
-80.95156
-81.01278
-81.02195
-81.04551
-81.05947
-81.03647
-81.01287
-80.93452
-80.93170
-81.06012
-81.05947
-81.02453
-81.02102
-81.01693
Latitude
37.96421
37.84261
37.71400
37.71391
37.87324
37.91956
37.91346
37.87994
37.88203
37.87402
37.96168
37.74875
37.74969
38.06032
38.06101
37.94417
38.03256
38.03013
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
Entity
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
Station ID
NERI.3054
NERI.3064
NERI.3024
NERI.3072
NERI.3042
NERI.3005
NERI.3069
NERI.3013
NERI.3077
NERI.3025
NERI.3050
NERI.3100
NERI.3052
NERI.3035
NERI.3099
NERI.3021
NERI.3018
NERI.3082
Water body name
Arbuckle Creek 5
BatoffCreek7
Big Branch 10
Big Branch 9
Bucklick Branch 3
Buffalo Creek 16
Buffalo Creek 4
Dowdy Creek 16
Dowdy Creek 2
Dowdy Creek 30
Ephraim Creek 8
Fall Branch 10
Fall Branch 7
Fern Creek 1 1
Fern Creek 12
Fire Creek 17
Keeney Creek 10
Keeney Creek 1 5
Notes
NFS— ERMN high priority; WV
DEP reference site
NFS— ERMN high priority; WV
DEP reference site
NFS— ERMN high priority; WV
DEP reference site
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-13 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-81.00490
-80.98218
-81.03925
-80.97903
-80.91077
-80.89788
-80.88025
-81.09167
-81.09510
-81.01654
-80.95197
-81.04918
-81.04749
-80.92717
-81.05316
-81.05710
-81.02849
-81.02305
-81.02506
Latitude
37.85802
37.86476
37.85120
37.85864
37.81927
37.83271
37.83799
37.94410
37.94727
37.78795
37.86122
37.82895
37.82782
37.80196
37.83172
37.82369
37.89156
37.88808
37.98267
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
Entity
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
Station ID
NERI.3037
NERI.3085
NERI.3044
NERI.3026
NERI.3011
NERI.3043
NERI.3059
NERI.3001
NERI.3065
NERI.3040
NERI.3058
NERI.3016
NERI.3032
NERI.3047
NERI.3080
NERI.3048
NERI.3053
NERI.3009
NERI.3034
Water body name
Laurel Creek 47
Laurel Creek 61
Laurel Creek 8
Little Laurel Creek 6
Meadow Creek 17
Meadow Creek 39
Meadow Creek 58
Meadow Fork 1
Meadow Fork 6
Polls Branch 14
Richlick Branch 17
River Branch 4
River Branch 6
Sewell Branch 2
Slate Fork— Mill Creek 1
Slate Fork— Mill Creek 12
Slater Creek 13
Slater Creek 20
Unnamed tributary 21 New
River 1
Notes
W V DEP reference site
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-14 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-81.01080
-80.94296
-81.01984
-81.08293
-81.08257
-79.61147
-79.69583
-79.39594
-80.37117
-80.32127
-81.14683
-81.93119
-80.86781
-81.09958
Latitude
37.91417
37.74477
37.85830
38.04904
38.04763
39.04225
38.73825
38.97394
38.33544
38.25981
37.50275
38.38489
38.88133
39.22211
State
WV
wv
WV
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
wv
Entity
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
NFS— ERMN
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
WVDEP
Station ID
NERI.3049
NERI.3036
NERI.3041
NERI.3029
NERI.3093
8357
12455
8255
9315
2046
2359
8482
12689
12690
Water body name
Unnamed tributary Buffalo
Creek 6
Unnamed tributary Fall Branch
2
Unnamed tributary Laurel
Creek 3
Wolf Creek 30
Wolf Creek 32
Otter Creek
Laurel Fork/Dry Fork
Red Creek
Middle Fork/Williams River
North Fork/Cranberry River
Mash Fork
Sams Fork
Long Lick Run
Unnamed tributary/North Fork
river mile 22.26/Hughes River
Notes
long-term monitoring site impacted
by acid rain
long-term monitoring site impacted
by acid rain
long-term monitoring site impacted
by acid rain
long-term monitoring site impacted
by acid rain
long-term monitoring site impacted
by acid rain
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-15 DRAFT—DO NOT CITE OR QUOTE
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Table E-2. continued...
Longitude
-82.12353
-82.28014
Latitude
38.48514
38.06845
State
WV
wv
Entity
WVDEP
WVDEP
Station ID
11897
4513
Water body name
Unnamed tributary/Left Fork
river mile 1.69/Mill Creek
Little Laurel Creek
Notes
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
E-16 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX F.
MACROINVERTEBRATE
COLLECTION METHODS
Table F-l. Macroinvertebrate collection methods agreed upon by the Northeast, Mid-
Atlantic, and Southeast regional working groups
Table F-2. Macroinvertebrate collection methods used in the Northeast region for routine
monitoring in riffle habitat
Table F-3. Macroinvertebrate collection methods used in the Mid-Atlantic region for routine
monitoring in riffle habitat
Table F-4. Macroinvertebrate collection methods used in the Southeast region for routine
monitoring in riffle habitat
Table F-5. Macroinvertebrate collection methods used in national surveys conducted by U.S.
EPA and USGS
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 F-l DRAFT—DO NOT CITE OR QUOTE
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Table F-l. Macroinvertebrate collection methods for medium-high gradient freshwater wadeable streams with abundant riffle
habitat and rocky substrate, as agreed upon by the Northeast, Mid-Atlantic, and Southeast regional working groups
Regional
network
Northeast
Mid-
Atlantic
Effort
Kick samples are taken
from riffle habitats in 4
different locations in the
sampling reach. At each
location the substrate is
disturbed for
approximately
30 seconds, for a total
active sampling effort of
2 minutes.
Data should be collected
with existing state or RBC
methods, or in such a way
that the data can be
rendered comparable to
historical state methods.
A minimum of 1 m2 is
collected using a
minimum of 4 separate
kicks in riffle habitats
throughout the 100-m
reach.
Reach length
150m
100m
Gear
D -frame net
(46 cm wide *
30 cm high)
with 500-um
mesh
Varies by
entity (either
square frame
kick nets or
d-frame nets,
with mesh
size ranging
from 450-
600 urn)
Habitat
Riffles
Abundant
riffles
Sampling area
Approximately
1m2
Minimum of
1m2
Index
period
September-
mid-
October
Spring
(March-
April) and
summer
(July-
August)
Target #
organisms
300
300
Taxonomic
resolution
Lowest
practical
(species
whenever
possible)
Lowest
practical
(species
whenever
possible)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-2 DRAFT—DO NOT CITE OR QUOTE
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Table F-l. continued...
Regional
network
Southeast
Effort
Semiquantitative: riffle
kick samples are taken
from 2 riffles or upper or
lower end of a large riffle
and composited; in
smaller streams, multiple
riffles may need to be
collected to achieve the
desired area
Qualitative: 3 "jabs" will
be collected from all
available habitats; taxa
from each habitat will be
kept in separate
containers (separate
species lists will be
generated for each
habitat)
Reach length
100m
100m
Gear
Kick-net with
500-um mesh
Dip-net with
500-um mesh
Habitat
Riffles
Multihabitat
Sampling area
Approximately
2m2
NA
(qualitative)
Index
period
April 20 13.
Subsequent
samples will
be collected
annually
within
2 weeks of
the original
collection
Target #
organisms
300± 10%
NA
(qualitative)
Taxonomic
resolution
Lowest
practical
(species
whenever
possible)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-3 DRAFT—DO NOT CITE OR QUOTE
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Table F-2. Macroinvertebrate collection methods used by Northeastern states when sampling medium-high gradient
freshwater wadeable streams with riffle habitat and rocky substrate
Entity
CT
DEEP
VT
DEC
ME
DEP
Project or
stream type
Streams with
riffle habitat
Moderate to
high gradient
streams with
riffle habitat
Streams with
riffle and run
habitat
Effort
12 kick samples
are taken
throughout riffle
habitats within
the sampling
reach
Kick samples are
taken from riffle
habitats in 4
different
locations in the
sampling reach.
At each location
the substrate is
disturbed for
approximately
30 seconds, for a
total active
sampling effort
of 2 minutes.
3 cylindrical
rock-filled wire
baskets are
placed in
locations with
similar habitat
characteristics for
28 ± 4 days.
Gear
Rectangular
net (46 cm x
46 cm x 25
cm) with
800-900-um
mesh
D-frame net
(46 cm wide x
30 cm high)
with 500-um
mesh
Contents are
washed into a
sieve bucket
with 600-um
mesh
Habitat
Riffles
Riffles
Riffle/run is
the
preferred
habitat.
Sampling area
Approximately
2m2
Approximately
1m2
Approximately
0.3 m2 per
basket
Index
period
October 1-
November
30
September-
mid-
October
July 1-
September
30
Target #
organisms
200
300
Entire samples
are processed
and identified,
with
exceptions
Taxonomic
resolution
Lowest practical
(species whenever
possible)
Lowest practical
(species whenever
possible)
Lowest practical
(species whenever
possible)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-4 DRAFT—DO NOT CITE OR QUOTE
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Table F-2. continued...
Entity
NH
DBS
RIDEM
NY
DEC
Project or
stream type
Streams with
riffle and run
habitat
Routine
monitoring
in streams
with riffle
habitat
Routine
monitoring
in streams
with riffle
habitat
Effort
3 cylindrical
rock-filled wire
baskets are
placed in riffle
habitats or at the
base of riffles at
depths that cover
the artificial
substrate by at
least 5 inches for
6 to 8 weeks.
Kick samples are
taken from riffle
habitats along
100-m reach
representative of
the stream
sampled timed
for a total active
sampling effort
of 3 minutes.
Substrate is
dislodged by
foot, upstream of
the net for
5 minutes and a
distance of 5 m.
The preferred
line of sampling
is a diagonal
transect of the
stream
Gear
Contents are
washed into a
sieve bucket
with 600-um
mesh
D -frame net
(30-cm
width) with
500-um
mesh
Rectangular
net (23 cm x
46 cm) with
800-900-um
mesh
Habitat
Riffle/run
is the
preferred
habitat.
Riffle
Riffle
Sampling area
Approximately
0.3 m2 per
basket
Within reach
(100 linear
meters)
2.5m2
Index
period
late July-
September
August-
September
July-
September
Target #
organisms
100
100
100
Taxonomic
resolution
Genus, except
Chironomidae
(family-level)
Mostly
genus -level.
Chironomidae are
identified to the
subfamily or
tribe-level
Lowest practical
[mostly genus- or
species-level,
some family-level
(e.g., Gastropoda
and Pelecypoda)]
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-5 DRAFT—DO NOT CITE OR QUOTE
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Table F-2. continued...
Entity
MA
DEP
Project or
stream type
Routine
monitoring
in streams
with riffle
habitat
Effort
10 kick -samples
are taken in riffle
habitats within
the sampling
reach and
composited
Gear
Kick-net,
46-cm wide
opening,
500-nm
mesh
Habitat
Riffle/run
is the
preferred
habitat
Sampling area
Approximately
2m2
Index
period
July 1-
September
30
Target #
organisms
100
Taxonomic
resolution
Lowest practical
level
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-6 DRAFT—DO NOT CITE OR QUOTE
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Table F-3. Macroinvertebrate collection methods used by Mid-Atlantic states and RBCs when sampling medium-high gradient
freshwater wadeable streams with riffle habitat and rocky substrate
Entity
NJDEP
DE
DNREC
PADEP
Project or
stream type
Riffle/run
Piedmont
Smaller
freestone
riffle -run
streams
(<25-50 mi2)
Limestone
spring
streams
Effort
10-20 kicks are
taken from riffle/run
areas and
composited
2 kicks composited
6 kicks are taken
from riffle areas and
composited
2 kicks are taken
from riffle-run areas
(1 fast, 1 slow) and
composited
Gear
D-frame net
(30 cm) with
800 x
900-um
mesh
Kick-net
(1-m2 area)
with 600 um
mesh
D-frame net
(30 cm wide
x 20cm
high) with
500-um
mesh
D-frame net
(30 cm wide
20 cm high)
with 500-um
mesh
Habitat
Riffle/run
Riffle
Riffle
Riffle-run (1 fast,
1 slow)
Sampling
area
10-20 net
dimensions
2m2
6m2
2m2
Index
period
April-
November
October-
November
Year-
round
January-
May
Target #
organisms
100 ± 10%
200 ± 20%
200 ± 20%
300 ± 20%
Taxonomic
resolution
Genus
Genus or lowest
practical
Genus, except
Chironomidae, snails,
clams, mussels
(family); Nematoda,
Nemertea, Bryozoa
(phylum); Turbellaria,
Hirudenia,
Oligochaeta (class);
water mites (artificial)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-7 DRAFT—DO NOT CITE OR QUOTE
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Table F-3. continued...
Entity
MDDNR
WVDEP
Project or
stream type
Maryland
Biological
Stream
Survey
(MBSS)
Wadeable
streams
(WVSCI)
Effort
Approximately
20 kicks/jabs/swee
ps/rubs from
multiple habitats
(sampled in
proportion to
availability in
reach) are
composited
4 kicks
composited
Gear
D-frame net
(about
30 cm wide)
with
450-um
mesh
Rectangular
kick net
(50 cm wide
Habitat
Multi-habitat (in
order of
preference)
riffles, root wads,
root mats/woody
debris/snag, leaf
packs,
SAV/associated
habitat, undercut
banks; less
preferred =
gravel, broken
peat, clay lumps,
detrital/sand
areas in runs;
moving water
preferred to still
water; sampled in
proportion to
availability in
reach, ensuring
all potentially
productive
habitats are
represented in
sample
riffle -run
Sampling
area
About 2 m2
1m2
Index period
March-April
April 15-
October 15
Target #
organisms
100 ±20%
200 ± 20%
Taxonomic
resolution
Genus (or lowest
practical);
crayfish and
mussels identified
to species
(sometime
subspecies?) in
the field along
with fish, reptiles,
amphibians, and
some invasive
plants
Family (all
insects)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-8 DRAFT—DO NOT CITE OR QUOTE
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Table F-3. continued...
Entity
VADEQ
Project or
stream type
Wadeable
streams
(GLIMPSS)
— Mountain
and Plateau
Noncoastal
Plain (VSCI)
Effort
6 kicks from riffle
habitat (unless
absent, then multi-
habitat) are
composited
Gear
x 30 cm
high x
50 cm deep)
with
600-um net
mesh
(5 95 -urn
sieve);
D-frame net
(30cm
wide) can be
used for
smaller
streams
D-frame net
(50 cm wide
x 30 cm
high x
50 cm deep)
with 500 um
net mesh
Habitat
Riffle, unless
absent, then
multi-habitat
Sampling
area
1m2
2m2
Index period
Winter
(December-
mid-
February),
spring
(March-
May) —
Plateau only,
summer
(June-mid-
October)
Spring
(March-May)
and fall
(September-
November)
Target #
organisms
200 ± 20%
110± 10%
Taxonomic
resolution
Genus (all insects
minus
Collembola)
Family (working
toward developing
a genus-level
index)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-9 DRAFT—DO NOT CITE OR QUOTE
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Table F-3. continued...
Entity
SRBC
Project or
stream type
Aquatic
Resource
Surveys
Sub-basin
Survey, Year
1 /Interstate
Streams
Remote
Water
Quality
Monitoring
Network
Effort
6 kicks
composited or
5 minutes for a
distance of 5 m
(PA or NY)
2 kicks
composited
6 kicks
composited
Gear
D-frame
net/aquatic
net [30 cm x
20 to 23 cm
x 46 cm (PA
or NY)];
500-um;
800 (im x
900 (im
(depending
on PA or
NY)
Kick-net
(1 m2) with
600-um
mesh
D-Frame
Net (46 cm
x 20 cm)
with 500-
um mesh
Habitat
Riffle-run
Sampling
area
6 m2or
distance of
5 m (PA or
NY)
2m2
6 m2
Index period
Typically late
April into
May, late June
into July, and
October
Year 1—
historically
spring-fall,
now spring-
May 30.
Interstate —
May (Group
3) or August
(Group 1 and
2); varies
depending on
site
classification
October
Target #
organisms
PADEP or
NYSDEC
protocol
200 ± 20%
200 ± 20%
Taxonomic
resolution
Genus, except
Chironomidae,
snails, clams
mussels (family);
Nematoda,
Nemertea,
Bryozoa
(phylum);
Turbellaria,
Hirudenia,
Oligochaeta
(class); water
mites (artificial)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-10 DRAFT—DO NOT CITE OR QUOTE
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Table F-3. continued...
Entity
NFS
Project or
stream type
Eastern
Rivers and
Mountains
Network
Effort
A semiquantitative
sample consisting
of 5 discrete
collections from
the richest targeted
habitat (typically
riffle, main-
channel, coarse-
grained substrate
habitat type) are
processed and
combined into a
single composited
sample.
Gear
Slack
sampler,
500-um nets
and sieves
Habitat
Riffle
Sampling
area
Each
discrete
sample =
0.25 m2
area; total
area
sampled =
1.25m2
Index period
April-early
June
Target #
organisms
300
Taxonomic
resolution
Genus, except
Chironomidae,
snails, clams
mussels (family);
Nematoda,
Nemertea,
Bryozoa
(phylum);
Turbellaria,
Hirudenia,
Oligochaeta
(class); water
mites (artificial)
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
F-l 1 DRAFT—DO NOT CITE OR QUOTE
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Table F-4. Macroinvertebrate collection methods used by Southeast states when sampling medium-high gradient freshwater
wadeable streams with riffle habitat and rocky substrate
Entity
ALDEM
GADNR
Stream
type
WMB-I
protocols
High
(riffle/run)
gradient
Effort
Several samples
are collected at a
site by stream
habitat type;
each sample is
processed
separately; the
taxa lists are
recombined
after
standardizing
individual
counts to density
units
20 jabs from
multiple habitats
are composited
Gear
Kick net, 2
A-frame nets, 2
#30 sieve
buckets, 2 #30
sieves, plastic
elutriation treys,
100% denatured
ethanol, and
plastic sample
containers
D -frame net
(30-cm width)
with 500-um
net mesh
Habitat
Riffle, rock-log,
Rootbank, CPOM,
sand, and
macrophytes
(macrophytes not
always available
and excluded from
index)
Multi-habitat —
riffles, woody
debris/snags,
undercut
banks/rootwads,
leafpacks, soft
sediment/sandy
substrate, and
submerged
macrophytes (when
present)
Sampling
area
Approximately
4 m2
20 jabs, each
for a linear
distance of
1m
Index
period
Late April-
early July
Mid-
September-
February
Target #
organisms
100
organisms
per habitat
200 ± 20%
Taxonomic
resolution
Genus or lowest
possible level
Lowest practical
level (generally
genus or
species)
11/26/14
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Table F-4. continued...
Entity
KYDEP
NC
DENR
Stream type
Wadeable,
moderate/hig
h gradient
streams
Headwater,
moderate/hig
h gradient
streams
Standard
qualitative
method for
wadeable
flowing
streams and
rivers
Effort
Combination of
quantitative
(composite of 4
riffle kicks) and
qualitative
(multi-habitat)
samples
Composite of 2
kicks, 3 sweeps,
1 leaf pack
sample, 2 fine
mesh rock and/or
log wash
samples, 1 sand
sample and visual
collections from
habitats and
substrate types
missed or
under-sampled
by the other
collection
techniques
Gear
Quantitative
— kick net
(600-um
mesh);
qualitative —
dip net, mesh
bucket,
forceps
r
600-um mesh
Multiple gear
types [kick
net with 600-
um mesh;
triangular
sweep net;
fine -mesh
samplers
(300-um
mesh); sieve
bucket]
Habitat
Quantitative
samples are taken
from riffles;
qualitative are
taken from
multiple habitats
(undercut
banks/roots, wood,
vegetation, leaf
packs, soft and
rocky substrates)
Multi-habitat
(riffles, bank areas,
macrophyte beds,
woody debris, leaf
packs, sand, etc.)
Sampling
area
1m2
(quantitative)
NA
(qualitative
only)
Index
period
Summer
(June-
September)
Spring
index period
(February-
May)
Year-round
Target #
organisms
300
Organisms
are field
picked
roughly in
proportion
to their
abundance.
Abundance
data are
recorded as
rare (1-2
specimens),
common
(3-9
specimens)
or abundant
specimens)
Taxonomic
resolution
Lowest practical
level (generally
genus or
species)
All of the
field-picked
organisms are
identified in the
laboratory to the
lowest practical
level (generally
genus or
species)
11/26/14
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Table F-4. continued...
Entity
SC
DHEC
TNDEC
Stream type
Ambient
monitoring
Streams with
riffles
Effort
Gear
Habitat
Sampling
area
Same as NC DENR
Single habitat,
semiquantitative;
composite of 2
riffle kicks
Kick net
(1-m2,
500-um
mesh)
Riffle
2m2
Index
period
Feb 1 to
March 15:
Middle
Atlantic
Coastal
Plain
Ecoregion
(U.S. EPA
Level III
63); June 15
to Sept 1:
Statewide,
minus EPA
Level III
Ecoregion
63
Year-round
Target # Taxonomic
organisms resolution
Same as NC DENR
200 ± 20% Genus level
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table F-5. Macroinvertebrate collection methods used in national surveys conducted by U.S. EPA and USGS
Entity
U.S. EPA
National
Aquatic
Resource
Surveys
USGS
Project or
stream type
WSAand
NRSA
NAWQA
Effort
A 0.1 -m2 area was
sampled for
30 seconds at a
randomly selected
location at each of
the 1 1 transects. The
samples were
composited into one
sample per site.
A semiquantitative
sample consisting of
5 discrete collections
from the richest
targeted habitat
(typically riffle,
main -channel,
coarse-grained
substrate habitat
type) are processed
and combined into a
single composited
sample.
Gear
Modified
D-frame
net (30 cm
wide) with
500-um
mesh
Slack
sampler,
500-um
nets and
sieves
Habitat
Multi-
habitat
Composite
Riffle
Sampling area
Approximately
1m2
Each discrete
sample =
0.25-m2 area;
total area
sampled =
1.25m2
Index
period
June-
September
Late June-
mid-
October
Target #
organisms
500
300
Taxonomic
resolution
Genus level
Lowest
practical
level
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX G.
LEVEL OF TAXONOMIC
RESOLUTION
Table G-l. Recommendations on levels of taxonomic resolution for specific taxa
Table G-2. List of taxa that were considered for inclusion in Table G-l
This document is a draft for review purposes only and does not constitute Agency policy.
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1 When possible, all taxa should be taken to the lowest practical taxonomic level (ideally species
2 level). If this is not possible, efforts should be made to identify the taxa listed in Table G-l to the
3 level of resolution described in the table. Ephemeroptera, Plecoptera, Trichoptera, and
4 Chironomidae that are not listed in Table G-l should be identified to at least the genus level,
5 where possible.
6
7 The taxa in Table G-l were selected based on differences in thermal tolerances that were evident
8 in analyses (U.S. EPA, 2012; unpublished Northeast pilot study) and from best professional
9 judgment. The list in Table G-l should be regarded as a starting point and should be updated as
10 better data become available in the future. Table G-2 contains a list of taxa that were considered
11 for inclusion in Table G-l but for various reasons, were not selected.
12
This document is a draft for review purposes only and does not constitute Agency policy.
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Table G-l. At RMN sites, we recommend that the taxa listed below be taken to the specified
level of resolution, where practical
Order
Coleoptera
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Plecoptera
Family
Elmidae
Chironomidae
Chironomidae
Chironomidae
Simuliidae
Baetidae
Ephemerellidae
Perlidae
Genus
Promoresia
Eukiefferiella
Polypedilum
Tvetenia
Baetis
Ephemerella
Acroneuria
Level of
resolution
adults to
species
species
species
species
group
genus
species
species (as
maturity
allows)
species
Notes
Potential variability in thermal
preferences of P. tardella (cold)
and P. elegans (warm).
Potential variability in thermal
preferences of E. brevicalar, E.
brehmi, and E. tirolemis (cold);
and E. claripennis and E.
devonica (warm).
P. aviceps is generally regarded
as a cold water taxon.
T. vi trades is warm water
oriented in the Northeast.
General agreement that
Prosimilium is a cold water
indicator but there is potential
for variability within this genus
(e.g., P. mixtum vs. P. vernale),
and species-level systematics are
not well developed at this time.
Potential variability in thermal
preferences (e.g., B.
tricaudatus — cold; B.
intercalaris and B.
flavistriga — warm) .
Potential variability in thermal
preferences (e.g., E.
subvaria — colder); need mature
individuals (early instars are
difficult to speciate).
Potential variability in thermal
preferences of A. abnormis
(warmer) and A. carolinensis
(cooler).
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table G-l. continued...
Order
Plecoptera
Plecoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Family
Perlidae
Pteronarcyidae
Brachycentridae
Hydropsychidae
Rhyacophilidae
Uenoidae
Genus
Paragnetina
Pteronarcys
Brachycentrus
Ceratopsyche
Rhyacophila
Neophylax
Level of
resolution
species
species
species
species
species
species
Notes
Potential variability in thermal
preferences of P. immarginata
(cold) and P. media and P.
kansanensis.
P. dorsata may be warmer water
oriented.
Potential variability in thermal
preferences in the Northeast.
Potential variability in thermal
preferences.
Most species are cold water, but
some variability has been
documented in the Northeast
(U.S. EPA, 2012, unpublished
data).
Some variability was noted in a
pilot study in North Carolina
(U.S. EPA, 2012).
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table G-2. Taxa that were considered for inclusion in Table G-l
Order
Coleoptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Family
Elmidae
Chironomidae
Ceratopogonidae
Ephemerellidae
Ephemerellidae
Heptageniidae
Heptageniidae
Genus
Oulimnius
Micropsectra
Drunella
Eurylophella
Epeorus
Stenacron
Level of
resolution
species
species
species
species
species
species
species
Notes
O. latiusculus is regarded as a
cold-water taxon in Vermont, but
species-level IDs may not be
necessary for the larger region
because most of the taxa are O.
latiusculus.
General agreement that there is
variability in thermal preferences,
but the taxonomy for this genus
needs to be further developed.
General agreement that there is
variability in thermal preferences,
but the taxonomy for this family
needs to be further developed.
Variability in thermal tolerances
within this genus was noted in the
Utah pilot study, but in the
Eastern states, species are
believed to be all cold/cool water.
Some variability was noted in a
pilot study in North Carolina
(U.S. EPA, 20 12); could be
seasonal phenology vs. thermal
preference.
Some variability was noted in a
pilot study in Utah (U.S. EPA,
2012); can be difficult to speciate.
In the Mid-Atlantic region, some
regard S. interpunctatum as a
warm-water taxon and the others
as cooler/some cold. Taxonomy
may be tricky.
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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Table G-2. continued...
Order
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Oligochaeta
Family
Goeridae
Hydropsychidae
Leptoceridae
Philopotamidae
Genus
Goera
Hydropsyche
Oecetis
Chimarra
Level of
resolution
species
species
species
species
family
Notes
Some variability was noted in a
pilot study in North Carolina
(U.S. EPA, 2012). The two species
found in Kentucky are associated
with cold water. In New Jersey,
this genus is found as often in the
coastal plain as in northern high
gradient streams and is currently
not taken to the species level.
Some variability was noted in a
pilot study in New England
(U.S. EPA, 2012, unpublished
data) but is generally considered to
be eurythermal (not sure which
species would be regarded as cold
water taxa).
Some variability was noted in a
pilot study in North Carolina
(U.S. EPA, 2012). The species
found in Kentucky are associated
with warm water. In New Jersey,
this genus is typically found in low
gradient coastal plain streams.
Some variability was noted in a
pilot study in New England
(U.S. EPA, 2012, unpublished
data) but most species were warm-
water oriented. C. obscura and C.
atterima predominate, but tend to
co-occur.
Enchytraeidae is regarded as a
cold-water family in Vermont. In
the Mid- Atlantic region, it is found
mostly in small streams. In New
Jersey, it is found throughout the
state.
11/26/14
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Table G-2. continued...
Order
Amphipoda
Amphipoda
Isopoda
Neoophora
Neoophora
Family
Gammaridae
Hyalellidae
Asellidae
Planariidae
Dugesiidae
Genus
Gammarus
Hyallela
Caecidotea
Dugesia
Cura
Level of
resolution
species
species
species
species
species
Notes
G. pseudolimnaeus is regarded as a
cold- or cool-water taxon in
Vermont (and is tolerant of
nutrients). Gammarus (assumed to
be pseudolimnaeus) is also
regarded as a cold-water indicator
in Minnesota (Gerritsen and
Stamp, 2012).
H. azteca is regarded as a cold/cool
water taxon in Vermont. In
Kentucky, Hyallela it is believed to
be a completely warm-water genus.
C. brevicauda has been noted as a
potential cold-water indicator in
the Midwest (Gerritsen and Stamp,
2012).
D. tigrina is regarded as a
warm-water taxon in Vermont, as
well as in New Jersey. Can be
difficult to speciate in speciose
regions.
C.formanii is regarded as a
cold-water taxon in Vermont. Can
be difficult to speciate in speciose
regions.
11/26/14
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G.I. LITERATURE CITED:
Gerritsen, J; Stamp, J. (2012) Calibration of the biological condition gradient (BCG) in cold and
cool waters of the upper Midwest for fish and benthic macroinvertebrate assemblages
[Final Report]. Prepared by Tetra Tech, Inc. for the USEPA Office of Water and USEPA
Region 5. Owings Mills, MD: Tetra Tech http://www.uwsp.edu/cnr-
ap/biomonitoring/Documents/pdf/USEP A-BCG-Report-Final-2012.pdf
1 U.S. EPA (Environmental Protection Agency). (2012) Implications of climate change for
2 bioassessment programs and approaches to account for effects. [EPA/600/R-11/036F].
3 Washington, DC: Global Change Research Program, National Center for Environmental
4 Assessment. http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=239585
5
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX H.
GUIDELINES FOR TEMPERATURE
MONITORING QA/QC
Section H-l. Predeployment
Section H-2. Field checks
SectionH-3. Postretrieval
Section H-4. Summarizing data
SectionH-5. References
This document is a draft for review purposes only and does not constitute Agency policy.
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1 These recommendations are intended to make data processing and screening easier and more
2 efficient.
3
4 H.l. PREDEPLOYMENT
5
6 • Set the sensors up so that they start recording at the top of the hour (xx:00) or on the
7 half hour (xx:30)
8 • Set the air and water temperature sensors up so that they record at the same time.
9 • Consider using military time (if this is an option) to avoid potential confusion with
10 AM/PM.
11 • Consider using standard time (e.g., UTC-5 for sites in the Eastern Time zone) instead of
12 daylight savings time. Regardless of which one you choose, make sure that any discrete
13 measurements that are taken for accuracy checks are consistent with this setting.
14 • Conduct a predeployment accuracy check.
15
16 o Use either an ice bath technique, like the one described in MD DNR's quality
17 assurance document
18 (http://www.dnr.state.md.us/streams/pdfs/QA_TemperatureMonitoring.pdf) or a
19 multipoint technique, like the one described in U.S. EPA (2014).
20 o The measurement from the sensor should not exceed the accuracy quoted by
21 the manufacturer. Sensors that have anomalous readings should be returned to
22 the manufacturer for replacement.
23
24 H.2. FIELD CHECKS
25
26 • It is essential to take good field notes! Sample field forms can be found in the
27 appendices of U.S. EPA (2014). If you have existing field forms already [and they are
28 comparable or more detailed than the ones in U.S. EPA (2014)], it is fine to use those
29 instead.
30 • Be sure to record the exact times of deployment (in proper position) and recovery
31 This information is needed for trimming data after retrieval.
32 • During your field checks, note things that could affect the quality of your data, such
33 as:
34
35 o Signs of physical damage, vandalism, or disturbance;
36 o Signs of the sensor being buried in sediment;
37 o Signs of the sensor being out of the water; and
38 o Potential fouling from debris, aquatic vegetation, algae.
39
40 • Conduct middeployment accuracy checks, as described in US EPA (2014) (optional
41 but encouraged). To minimize the chance of a faulty measurement:
42
43 o Take the instantaneous measurement with a National Institute of Standards and
44 Technology (NIST)—certified field thermometer,
45 o Take the measurement as close as possible to the sensor,
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-2 DRAFT—DO NOT CITE OR QUOTE
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1 o Take the measurement as close as possible to the time that the sensor is
2 recording a measurement. Note whether the time is standard or daylight
3 savings time, and
4 o Make sure that sufficient time has passed to allow the temperature reading to
5 stabilize.
6
7 • Conduct a biofouling check (optional). To do this, remove the sensor and gently clean it
8 (per manufacturer's instructions) to remove any biofilm or sediment, then replace it. Note
9 on your field form the time at which the "precleaning" measurement was made as well as
10 the time of the first "postcleaning" measurement. Compare the readings.
11
12 H.3. POSTRETRIEVAL
13 H.3.1. Record keeping and data storage
14 Make sure you set up a good record keeping and data storage system Large amounts of data
15 will accumulate quickly, so a central temperature database should be developed and maintained
16 from the initial stages of monitoring. Also, all field and accuracy check forms should be
17 organized, easily accessible, and archived in a way that allows for safe, long-term storage.
18
19 Original raw data files should be retained for all sites, and should be kept separate from files
20 in which data have been manipulated. The data should be accessible because someone may want
21 to go back and calculate different metrics in the future.
22
23 H.3.2. Postdeployment accuracy check
24 Conduct a postdeployment accuracy check using the same technique that was used for the
25 predeployment accuracy check.
26
27 H.3.3. Data evaluation
28 Conduct QA/QC checks. Carefully document these steps as well as any changes that you
29 make to the data. The checks can be conducted using a number of different software packages
30 (e.g., Microsoft Excel, Hoboware, Aquarius). Recommended steps for evaluating data include:
31
32 1. Save the file that you are manipulating with a different file name so that you do not
33 confuse it with the original raw data file.
34
35 2. Format the data so that it is easy to analyze. An example of how the data could
36 potentially be formatted in Excel is shown in Table H-l. Tips on formatting data in
37 Excel are available upon request (email: Jen.Stamp@tetratech.com).
38
39 3. Trim data (as necessary) to remove measurements taken before and after the sensor is
40 correctly positioned.
41
42 4 Plot all of the measurements and visually check the data Look for missing data and
43 abnormalities. Consider doing the following, as data permits:
44
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-3 DRAFT—DO NOT CITE OR QUOTE
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Table H-l. Potential format for water and air temperature data if MS Excel software is used. Information on formatting data
in Excel is available upon request (email: Jen.Stamp@tetratech.com).
Water
serial
number
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
Air serial
number
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
10229571
Station ID
EC066G12
EC066G12
EC066G12
ECO66G12
ECO66G12
EC066G12
EC066G12
ECO66G12
ECO66G12
ECO66G12
EC066G12
EC066G12
ECO66G12
Year
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
Month
7
7
7
7
7
7
7
7
7
7
7
7
7
Season
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
Day
25
25
25
25
25
25
25
25
25
25
25
25
25
Julian
date
206
206
206
206
206
206
206
206
206
206
206
206
206
Date
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
Time
10:30:00
11:00:00
11:30:00
12:00:00
12:30:00
1:00:00
1:30:00
2:00:00
2:30:00
3:00:00
3:30:00
4:00:00
4:30:00
AM
/
PM
AM
AM
AM
PM
PM
PM
PM
PM
PM
PM
PM
PM
PM
Date time,
GMT— 04:00
07/25/13
10:30:00 AM
07/25/13
11:00:00 AM
07/25/13
11:30:00 AM
07/25/13
12:00:00 PM
07/25/13
12:30:00 PM
07/25/13
01:00:00 PM
07/25/13
01:30:00 PM
07/25/13
02:00:00 PM
07/25/13
02:30:00 PM
07/25/13
03:00:00 PM
07/25/13
03:30:00 PM
07/25/13
04:00:00 PM
07/25/13
04:30:00 PM
#
1
2
3
4
5
6
7
8
9
10
11
12
13
Water
temp-
erature,
°C
20.14
20.04
20.33
20.71
21.09
21.28
21.47
21.76
21.95
22.24
22.33
22.43
22.53
Water
temp-
erature
grade
good
good
good
good
good
good
good
good
good
good
good
good
good
Water
temp-
erature
QC
notes
Air
temp-
erature,
°C
21.76
22.24
22.43
23.00
23.68
24.35
24.74
25.22
25.51
25.81
25.90
25.61
25.51
Air
temp-
erature
grade
good
good
good
good
good
good
good
good
good
good
good
good
good
Air
temp-
erature
QC
notes
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
H-4 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
• Plot air and stream temperature data on the same graph, as shown in Figure H-l.
• Plot stream temperature data with stage data
UMON-288-S-2009
IS
•
25-
10
5-
1 789 1577 2365 3153 3941 4729 5517 6305 7093 7881 8669 9457 10245110331182112609
YOUG-121-A-2009
'•
''
r
I 15-
5-
1 800 1599 2398 3197 3996 4795 5594 6393 7192 7991 8790 9580 10388111871198612785
Figure H-l. Examples of how air and stream temperature data can be plotted together to
visually screen continuous temperature data. At the site shown in the bottom graph,
dewatering occurred, evidenced by the close correspondence between water and air
temperature. These graphs were provided by Michael Kashiwagi, MD DNR.
Specific things to watch for:
• Missing data
• A close correspondence between water and air temperature—this indicates that the
stream sensor may have been out of the water.
• Diel fluxes with flat tops—this indicates that the sensor may have been buried in
sediment.
Optional:
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-5 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
• Graphically compare data across sites.
• Graphically compare data across years; when data from one year are dramatically
different, there may be errors.
• Graphically compare with data from the nearest active weather station, if
appropriate. The closest active weather stations can be located and the daily observed air
temperature data for those stations can be downloaded from websites like the Utah State
University Climate server: http://climate.usurf.usu.edu/mapGUI/mapGUI.php.
Additional checks (optional):
• If using MS Excel, use pivot tables to check for missing data, as shown in Figure H-2.
If a 30-minute interval is used, there should be 48 measurements per day. If there are
fewer (or more) than 48, check the original data and your field notes and try to determine
what might have caused this to occur.
• Flag data points for potential errors if they:
o Exceed a thermal maximum of 25°C*
o Exceed a thermal minimum of -1°C*
o Exceed a daily change of 10°C*
o Exceed the upper 5th percentile of the overall distribution
o Fall below the lower 5th percentile of the overall distribution
* These values should be adjusted to thermal limits appropriate for each location.
= 9 o
lirtdi Item i. hNrtOuft
r«« -,.
A. . - /. do. KM)
' i«~ M
Cl^Z^
5 7/26/2011 Jfi
6 7/27/2013
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9 7/30/2013
10 7/31/2013
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Figure H-2. Example of how pivot tables in MS Excel can be used to identify missing data.
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-6 DRAFT—DO NOT CITE OR QUOTE
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1 H.3.4. Application of data corrections
2 Errors should be addressed on a case-by case-basis. In general, there are three possible actions:
O
4 1) Leave data as is,
5 2) Apply correction factor, or
6 3) Remove data.
7
8 If you are inexperienced at addressing errors with continuous temperature data, consider seeking
9 guidance from someone with more experience and consult references like Wagner et al. (2006)
10 (see Section H.5). Table H-2 provides a general summary of different types of problems that can
11 occur (e.g., missing data, failed accuracy check) and recommended actions for addressing them.
12 Corrections should not be made unless the cause(s) of error(s) can be validated or explained in
13 the field notes or by comparison with information from nearby stations. Accurate field notes and
14 accuracy check logs are essential in the data correction process. Any discrepancies should be
15 documented in your data file and any actions you take should be carefully documented.
16
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-7 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
Table H-2. General summary of different types of problems that can occur with continuous
temperature data and recommended actions for addressing them
Problem
Recommended action
Missing data
Leave blank
Water temperature sensor was
dewatered or buried in sediment
for part of the deployment period.
Use the plot to determine the period during which the
problem occurred. Exclude these data when calculating the
summary statistics.
Recorded values are off by a
constant, known amount (e.g.,
due to a calibration error).
Adjust each recorded value by a single, constant value within
the correction period.
There is a large amount of drift
and there is no way to tell when
and how much the sensor was
"off by. (When drift occurs, the
difference between discrete
measurements and sensor
readings increases over time.)
The data should be removed.
Discrepancy between sensor
reading and discrete measurement
taken during an accuracy or
fouling check
General rules:
If the errors are smaller than the sensor accuracy
quoted by the manufacturer and cannot be easily
corrected (e.g., they are not off by a constant amount),
leave the data as is, and include the data in the
summary statistics calculations.
If the sensor fails a mid-deployment accuracy check,
review field notes to see if any signs of disturbance or
fouling were noted, and also look for notes about the
quality of the QC measurement (e.g., was the
thermometer NIST-certified? Did environmental
conditions prevent the measurement from being taken
next to the sensor?). Also check whether the same
time setting was used for both the sensor and discrete
measurements (daylight savings time vs. standard
time). Based on this information, use your best
judgment to decide which action (leave as is, apply
correction, or remove) is most appropriate.
If a sensor fails a postretrieval accuracy check, repeat
the procedure. If it fails a second time, use your best
judgment to decide which action (leave as is, apply
correction, or remove) is most appropriate.
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-8 DRAFT—DO NOT CITE OR QUOTE
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1 H.4. SUMMARIZING THE DATA
2 Recommendations on thermal summary statistics to calculate from continuous temperature data
3 at RMN sites can be found in Section 4.2 of the RMN report. Annual statistics should be
4 calculated based on calendar year (January 1 through December 31). For years with incomplete
5 data (e.g., in the example in Table H-l, the sensor installation was done on July 25, 2013),
6 calculate daily, monthly, and seasonal statistics as data permit. Instructions on how the summary
7 statistics should be formatted to facilitate data sharing can be found in Appendix K. Tips on how
8 to calculate summary statistics with pivot tables in MS Excel are available upon request (email:
9 Jen. Stamp@tetratech.com). Free software programs like ThermoStat can also be used to
10 calculate some of the summary statistics (Jones and Schmidt, 2013).
11
H.5. REFERENCES
12 Jones, NE; Schmidt, B. (2013). ThermoStat 3.1: Tools for analyzing thermal regimes. Ontario,
13 Canada: Ontario Ministry of Natural Resources, Aquatic Research and Development.
14 http://people.trentu.ca/nicholasjones/ThermoStat31 Manual.pdf
15 U.S. EPA (U.S. Environmental Protection Agency). (2014) Best practices for continuous
16 monitoring of temperature and flow in wadeable streams (External review draft).
17 (EPA/6--/R-13/170). Washington, DC; National Center for Environmental Assessment.
18 http ://cfpub. epa.gov/ncea/global/recordisplay. cfm?deid=261911
19 Wagner, RJ; Boulger, RW, Jr; Oblinger, CJ; Smith, BA. (2006) Guidelines and standard
20 procedures for continuous water-quality monitors—Station operation, record
21 computation, and data reporting. (USGS Techniques and Methods 1-D3, 51p).
22 Washington, DC: U.S. Department of the Interior, U.S. Geological Survey.
23 http://pubs.usgs.gov/tm/2006/tmlD3/
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 H-9 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX I.
GUIDELINES FOR HYDROLOGIC
MONITORING QA/QC
Section I-1. Predeployment
Section 1-2. Field checks
Section 1-3. Postretrieval
Section 1-4. Summarizing data
Section 1-5. References
This document is a draft for review purposes only and does not constitute Agency policy.
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1 If the site is colocated with a USGS gage, the stage or discharge data can be downloaded from
2 the USGS National Water Information System (NWIS) website available at
3 http://waterdata.usgs.gov/usa/nwis/rt. The USGS data are put through a rigorous QA/QC process
4 before they are posted, so the summary statistics can be calculated directly from those data.
5
6 If you are working with pressure transducer data, these recommendations are intended to make
7 data processing and screening easier and more efficient.
8
9 1.1. PREDEPLOYMENT
10
11 • Set the sensors up so that they start recording at the top of the hour (xx:00), half hour
12 (xx:30), or quarter after/of the hour (xx:15 or xx:45).
13 • If you are using unvented pressure transducers, set both transducers up so that they
14 record at the same time.
15 • Consider using military time (if this is an option) to avoid potential confusion with
16 AM/PM.
17 • Consider using standard time (e.g., UTC-5 for sites in the Eastern Time zone) instead of
18 daylight savings time. Regardless of which one you choose, make sure that any discrete
19 measurements that are taken for accuracy checks are consistent with this setting.
20
21 1.2. FIELD CHECKS
22
23 • It is essential to take good field notes! Sample field forms can be found in the
24 appendices of U.S. EPA (2014). If you have existing field forms already [and they are
25 comparable or more detailed than the ones in U.S. EPA (2014)], it is fine to use those
26 instead.
27 • Be sure to record the exact times of deployment (in proper position) and recovery
28 This information is needed for trimming data after retrieval.
29 • During your field checks, note things that could affect the quality of your data, such
30 as:
31
32 o Signs of physical damage, vandalism, or disturbance;
33 o Signs of the stream pressure transducer being buried in sediment;
34 o Signs of the stream pressure transducer being out of the water; and
35 o Potential fouling from debris, aquatic vegetation, algae.
36
37 • Take staff gage readings or measure the depth of water over the transducer with a
38 stadia rod or other measuring device (as frequently as resources permit) to check the
39 accuracy of the transducer data (U.S. EPA, 2014). Data should be compared over a
40 variety of water depths to ensure the transducer is accurate over the full range of depths.
41 To minimize the chance of a faulty measurement:
42
43 o Take the measurement as close as possible to the time that the pressure
44 transducer is recording a measurement, and
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
o Get as stable a reading as possible. If flows are fluctuating rapidly at the time of
the measurement, note this on your field form and do the best you can to record
the depth accurately.
When the pressure transducer is installed, the elevation of the staff gage and pressure
transducer should be surveyed to establish a benchmark or reference point for the gage
and transducer. This allows for monitoring of changes in the location of the
transducer, which is important because if the transducer moves, stage data will be
affected and corrections will need to be applied (see Figure 1-1).
Conduct a biofouling check (optional). To do this, remove the transducer and gently
clean it (per manufacturer's instructions) to remove any biofilm or sediment, then replace
it. Note on your field form the time at which the "precleaning" measurement was made as
well as the time of the first "postcleaning" measurement. Compare the readings.
Transducer data
Staff gage readings
Date
Figure 1-1. Staff gage readings provide a quality check of transducer data. In this example,
staff gage readings stopped matching transducer readings in November, indicating that the
transducer or gage may have changed elevation.
1.3. POSTRETRIEVAL
1.3.1. Record keeping and data storage
Make sure you set up a good record keeping and data storage system. Large amounts of data
will accumulate quickly, so a central hydrologic database should be developed and maintained
from the initial stages of monitoring. Also, all field and accuracy check forms should be
organized, easily accessible, and archived in a way that allows for safe, long-term storage.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Original raw data files should be retained for all sites, and should be kept separate from files
2 in which data have been manipulated. The data should be accessible because someone may want
3 to go back and calculate different metrics in the future.
4
5 1.3.2. Data evaluation
6 Conduct QA/QC checks Carefully document these steps as well as any changes that you
7 make to the data. The checks can be conducted using a number of different software packages
8 (e.g., Microsoft Excel, Hoboware, Aquarius). Recommended steps for evaluating data include:
9
10 1. Save the file that you are manipulating with a different file name so that you do not
11 confuse it with the original raw data file.
12 2. Format the data so that it is easy to analyze. An example of how the data could
13 potentially be formatted in Excel is shown in Table 1-1. Tips on formatting data in Excel
14 are available upon request (email: Jen.Stamp@tetratech.com).
15 3. Trim data (as necessary) to remove measurements taken before and after the sensor is
16 correctly positioned.
17 4 Plot all of the stage measurements and visually check the data (see Figure 1-2) Look
18 for missing data and abnormalities
19
20 Specific things to watch for:
21
22 • Missing data
23 • Values of 0—this could mean that the pressure transducer was dewatered. Another
24 possibility (with vented transducers) is that moisture got into the cable and caused
25 readings of zero water depth.
26 • Values flat-lining at 0°C/32°F—the stream pressure transducer is likely encased in ice.
27 • Negative values—if unvented transducers are being used, this may indicate that the
28 barometric pressure correction is off. This could occur for a number of reasons, such as:
29
30 o The land-based transducer is not close enough to the stream pressure transducer to
31 accurately capture barometric pressure.
32 o If the land-based transducer is housed in PVC pipe that has a solid bottom,
33 condensation and laterally blown rain and snow can penetrate through the drilled
34 holes and collect in the bottom, filling the pipe to a depth sufficient to inundate
35 the ports through which the barometric pressure is compensated. Thereafter,
36 "barometric pressure" is actual barometric pressure plus a small amount of
37 pressure due to this accumulated water. (A hole should be drilled in the bottom of
38 the PVC pipe to prevent this from happening.)
39
40 • Outliers or rapidly fluctuating values—the stream pressure transducer may have
41 moved (e.g., due to a high flow event or vandalism).
42
This document is a draft for review purposes only and does not constitute Agency policy.
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Table 1-1. Potential format for stage water temperature data if MS Excel software is used. Information on formatting data in
Excel is available upon request (email: Jen.Stamp@tetratech.com).
Water
sensor
serial
number
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
10229557
Station ID
EC066G12
EC066G12
EC066G12
ECO66G12
ECO66G12
EC066G12
EC066G12
ECO66G12
ECO66G12
ECO66G12
EC066G12
EC066G12
ECO66G12
Year
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
Month
7
7
7
7
7
7
7
7
7
7
7
7
7
Season
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
summer
Day
25
25
25
25
25
25
25
25
25
25
25
25
25
Julian
date
206
206
206
206
206
206
206
206
206
206
206
206
206
Date
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
7/25/2013
Time
10:30:00
11:00:00
11:30:00
12:00:00
12:30:00
1:00:00
1:30:00
2:00:00
2:30:00
3:00:00
3:30:00
4:00:00
4:30:00
AM/
PM
AM
AM
AM
PM
PM
PM
PM
PM
PM
PM
PM
PM
PM
Date time,
GMT— 04:00
07/25/13
10:30:00 AM
07/25/13
11:00:00 AM
07/25/13
11:30:00 AM
07/25/13
12:00:00 PM
07/25/13
12:30:00 PM
07/25/13
01:00:OOPM
07/25/13
01:30:00 PM
07/25/13
02:00:00 PM
07/25/13
02:30:00 PM
07/25/13
03:00:00 PM
07/25/13
03:30:00 PM
07/25/13
04:00:00 PM
07/25/13
04:30:00 PM
#
1
2
3
4
5
6
7
8
9
10
11
12
13
Sensor
depth,
feet
0.574
0.577
0.578
0.579
0.579
0.572
0.579
0.581
0.579
0.578
0.577
0.572
0.569
Stage
grade
good
good
good
good
good
good
good
good
good
good
good
good
good
Stage
QC
notes
Water
temp-
erature,
°C
20.14
20.04
20.33
20.71
21.09
21.28
21.47
21.76
21.95
22.24
22.33
22.43
22.53
Water
temp-
erature
grade
good
good
good
good
good
good
good
good
good
good
good
good
good
Water
temp-
erature
QC
notes
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Measurement number
Figure 1-2. Examples of how stage data can be plotted to visually screen the data.
In addition, consider doing the following, as data permits:
• Plot stage and temperature data on the same graph. Watch for the following signals in
the temperature data:
o Diel fluxes with flat tops—this indicates that the pressure transducer may have been
buried in sediment.
o A close correspondence between water and air temperature (if air temperature
data are available)—this indicates that the pressure transducer may have been out of
the water.
Optional:
• Graphically compare data across years; when data from one year are dramatically
different, there may be errors.
• Graphically compare with precipitation data from the nearest active weather station,
if appropriate. The closest active weather stations can be located and the daily observed
precipitation data for those stations can be downloaded from websites like the Utah State
University Climate server: http://climate.usurf.usu.edu/mapGUI/mapGUI.php.
• Graphically compare with data from the nearest USGS stream gage, if appropriate.
The closest active USGS gage can be located and the daily flow data for those gages can
be downloaded from the USGS National Water Information System (NWIS) website:
http://waterdata.usgs.gov/usa/nwis/rt.
Additional checks (optional):
• If using MS Excel, use pivot tables to check for missing data, as shown in Figure 1-3.
In this example, a 30-minute interval is used. If a 15-minute interval is used [as
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 1-6 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
recommended in U.S. EPA (2014)], there should be 96 measurements per day. If there
are fewer (or more) than 96, check the original data and your field notes and try to
determine what might have caused this to occur.
99 a|MBe«
-------
1 3) Remove data.
2
3 If you are inexperienced at addressing errors with continuous stage data, consider seeking
4 guidance from someone with more experience and consult references like Wagner et al. (2006)
5 and Shedd and Springer (2012) (see Section 1.5). Corrections should not be made unless the
6 cause(s) of error(s) can be validated or explained in the field notes or by comparison with
7 information from nearby stations. Accurate field notes and accuracy check logs are essential in
8 the data correction process. Any discrepancies should be documented in your data file and any
9 actions you take should be carefully documented.
10
11 The types of errors that can occur and how they manifest themselves will vary, which makes it
12 difficult to develop specific guidelines for applying data corrections. Moreover, the discrepancies
13 with the stage data can be more difficult to understand and interpret than problems that arise with
14 temperature data, which tend to show more consistent signals (e.g., close correspondence with
15 air temperature if the sensor becomes dewatered). Your ability to apply corrections to stage data
16 may also be limited by the software you are using. If you do not have access to software like
17 Aquarius, which has built-in functions that facilitate data correction, you may have to remove
18 more data unless simple corrections can be made. You may also be limited by the number of
19 gage readings you were able to make. Frequent gage readings facilitate error screening and early
20 detection and correction of transducer problems that help minimize data loss, but can be resource
21 intensive.
22
23 Table 1-2 provides a general summary of different types of problems that can occur (e.g., missing
24 data, failed accuracy check) and recommended actions for addressing them. Any discrepancies
25 should be documented in your data file and any actions you take should be carefully
26 documented.
27
28 1.4. SUMMARIZING THE DATA
29 Recommendations on hydrologic summary statistics to calculate from continuous stage or
30 discharge data at RMN sites can be found in Section 4.3 of the RMN report. Annual statistics
31 should be calculated based on calendar year (January 1 through December 31) (this is consistent
32 with how the annual temperature statistics are calculated). For years with incomplete data (e.g.,
33 the transducer installation was done mid-year), calculate daily, monthly, and seasonal statistics
34 as data permit. Instructions on how the summary statistics should be formatted to facilitate data
35 sharing can be found in Appendix K. Tips on how to calculate the summary statistics in MS
36 Excel are available upon request (email: Jen.Stamp@tetratech.com). Free software programs like
37 Indicators of Hydrologic Analysis (MA) (TNC, 2009) can also be used to calculate some of the
38 summary statistics.
39
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 1-8 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
Table 1-2. General summary of different types of problems that can occur with pressure
transducer data and recommended actions for addressing them.
Problem
Recommended action
Missing data
Leave blank
Stream pressure transducer was
dewatered or buried in sediment for
part of the deployment period.
Use the plot (and temperature data, if available) to
determine the period during which the problem occurred.
Exclude these data when calculating the summary
statistics.
Recorded values are off by a
constant, known amount (e.g., due to
a calibration error).
Adjust each recorded value by a single, constant value
within the correction period.
There is a large amount of drift and
there is no way to tell when and how
much the sensor was "off by. (When
drift occurs, the difference between
staff gage or depth readings and
transducer readings increases over
time.)
The data should be removed.
Discrepancy between pressure
transducer reading and discrete
measurement taken during a staff
gage or depth check.
General rules:
If the errors are smaller than the accuracy quoted
by the manufacturer and cannot be easily
corrected (e.g., they are not off by a constant
amount), leave the data as is, and include the data
in the summary statistics calculations.
If the transducer fails a staff gage or depth
accuracy check, review field notes to see if any
signs of disturbance or fouling were noted, and
also look for notes about the quality of the gage
measurement (e.g., if flows were fluctuating
rapidly at the time of the measurement). Also
check whether the same time setting was used for
both the transducer and gage or depth
measurements (daylight savings time vs. standard
time). Based on this information, use your best
judgment to decide which action (leave as is,
apply correction, or remove) is most appropriate.
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 1-9 DRAFT—DO NOT CITE OR QUOTE
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Table 1-2. General summary of different types of problems that can occur with pressure
transducer data and recommended actions for addressing them, (continued)
Problem
Recommended action
A shift is detected and an elevation
survey reveals that the stream
pressure transducer has moved.
Stage readings can be adjusted by adding or subtracting
the difference in elevation. If the exact date of the
elevation change is unknown, compare gage data to
transducer data to observe any shifts. If there are no gage
data for the time period, transducer data should be
examined for any sudden shifts in stage. Changes in the
elevation typically occur during high flows, so closely
examine all data during these time periods.
The sensitivity of the transducer
changes with stage (e.g., the
transducer is less sensitive or
accurate at high stages).
Sensitivity drift may be detected by graphing the
difference between transducer and staff gage readings
against the gage height and plotting a linear trend line
through it. A strong correlation between the data sets and
a positive or negative trend line as stage increases or
decreases may indicate a sensitivity shift. Based on this
information, use your best judgment to decide which
action (leave as is, apply correction, or remove) is most
appropriate.
1.5. REFERENCES
2 Shedd, JR; Springer, C. (2012). Standard operating procedures for correction of continuous stage
3 records subject to instrument drift, analysis of instrument drift, and calculation of
4 potential error in continuous stage records. (EAP No. EAP082). Olympia, WA:
5 Washington State Department of Ecology, Environmental Assessment Program.
6 http://www.ecy.wa.gov/programs/eap/qa/docs/ECY EAP SOP CorrectionOfContinuous
7 StageRecords_vl_OEAP082.pdf
8 TNC (The Nature Conservancy). (2009). Indicators of hydrologic alteration version 7.1 user's
9 manual. Harrisburg, PA: The Nature Conservancy.
10 https://www. conservationgateway.org/Documents/IHA V7.pdf.
11 U.S. EPA (U.S. Environmental Protection Agency). (2014) Best practices for continuous
12 monitoring of temperature and flow in wadeable streams (External review draft). (EPA/6-
13 -/R-13/170). Washington, DC; National Center for Environmental Assessment.
14 http ://cfpub. epa.gov/ncea/global/recordisplay. cfm?deid=261911
15 Wagner, RJ; Boulger, RW, Jr; Oblinger, CJ; Smith, BA. (2006) Guidelines and standard
16 procedures for continuous water-quality monitors—Station operation, record
17 computation, and data reporting. (USGS Techniques and Methods 1-D3, 51p).
18 Washington, DC: U.S. Department of the Interior, U.S. Geological Survey.
19 http://pubs.usgs.gov/tm/2006/tmlD3/
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX J.
RAPID QUALITATIVE HABITAT
ASSESSMENT SURVEY FORM FOR
HIGH-GRADIENT STREAMS
(BARBOUR ET AL., 1999)
This document is a draft for review purposes only and does not constitute Agency policy.
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HABITAT ASSESSMENT FIELD DATA SHEET—HIGH-GRADIENT STREAMS (FRONT)
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
LOCATION
STREAM
RIVER BASIN
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE REASON FOR SURVEY
TIME AM PM
1
ex
c
U
c
i
•a
Ł
Ł
O
1
Oi
Oi
1
Habitat
parameter
1. Epifaunal
Substrate/
Available
Cover
SCORE
2. Embeddedness
SCORE
3. Velocity/Depth
Regime
SCORE
4. Sediment
Deposition
SCORE
5. Channel Flow
Status
SCORE
Condition category
Optimal
Greater than 70% of
substrate favorable for
epifaunal colonization and
fish cover; mix of snags,
submerged logs, undercut
banks, cobble or other
stable habitat and at stage
to allow full colonization
potential (i.e., logs/snags
that are not new fall and
not transient.
20 19 18 17 16
Gravel, cobble, and
boulder particles are 0-
25% surrounded by fine
sediment. Layering of
cobble provides diversity
of niche space.
20 19 18 17 16
All four velocity /depth
regimes present (slow-
deep, slow-shallow, fast-
deep, fast-shallow).
(Slow is <0.3 m/s, deep
is >0.5 m.)
20 19 18 17 16
Little or no
enlargement of islands
or point bars and less
than 5% of the bottom
affected by sediment
deposition.
20 19 18 17 16
Water reaches base
of both lower banks,
and minimal amount
of channel substrate
is exposed.
20 19 18 17 16
Suboptimal
40-70% mix of stable
habitat; well suited for
full colonization
potential; adequate
habitat for maintenance
of populations; presence
of additional substrate
in the form of newfall,
but not yet prepared for
colonization (may rate
at high end of scale).
15 14 13 12 11
Gravel, cobble, and
boulder particles are
25-50% surrounded by
fine sediment.
15 14 13 12 11
Only 3 of the 4 regimes
present (if fast-shallow is
missing, score lower than
if missing other regimes).
15 14 13 12 11
Some new increase in bar
formation, mostly from
gravel, sand or fine
sediment; 5-30% of the
bottom affected; slight
deposition in pools.
15 14 13 12 11
Water fills >75% of
the available channel;
or <25% of channel
substrate is exposed.
15 14 13 12 11
Marginal
20-40% mix of stable
habitat; habitat
availability less than
desirable; substrate
frequently disturbed
or removed.
10 987 6
Gravel, cobble, and
boulder particles are
50-75% surrounded by
fine sediment.
10 987 6
Only 2 of the 4 habitat
regimes present (if fast-
shallow or slow-shallow
are missing, score low).
10 987 6
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bars; 30-50% of the
bottom affected; sediment
deposits at obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
10 987 6
Water fills 25-75% of the
available channel, and/or
riffle substrates are mostly
exposed.
10 987 6
Poor
Less than 20% stable
habitat; lack of habitat
is obvious; substrate
unstable or lacking.
5 43210
Gravel, cobble, and
boulder particles are more
than 75% surrounded by
fine sediment.
5 43210
Dominated by 1 velocity/
depth regime (usually
slow-deep).
5 43210
Heavy deposits of fine
material, increased bar
development; more than
50% of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.
5 43210
Very little water in
channel and mostly
present as standing pools.
5 43210
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
J-2 DRAFT—DO NOT CITE OR QUOTE
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HABITAT ASSESSMENT FIELD DATA SHEET—HIGH GRADIENT STREAMS (BACK)
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Habitat
parameter
6 Channel
Alteration
SCORE
7. Frequency of
Riffles (or bends)
SCORE
8 Bank Stability
(score each bank)
Note: determine
left or right side by
facing downstream.
SCORE (LB)
SCORE (RB)
9. Vegetative
Protection
(score each
bank)
SCORE (LB)
SCORE (RB)
1ft Riparian
Vegetative Zone
Width (score each
bank riparian zone)
SCORE (LB)
SCORE (RB)
Condition category
Optimal
Channelization or
dredging absent or
minimal; stream with
normal pattern.
20 19 18 17 16
Occurrence of riffles
relatively frequent; ratio of
distance between riffles
divided by width of the
stream <7:1 (generally 5 to
7); variety of habitat is key.
In streams where riffles are
continuous, placement of
boulders or other large,
natural obstruction is
important.
20 19 18 17 16
Banks stable; evidence of
erosion or bank failure
absent or minimal; little
potential for future problems.
<5% of bank affected.
Left Bank 10 9
Right Bank 10 9
More than 90% of the
streambank surfaces and
immediate riparian zone
covered by native
vegetation, including trees,
understory shrubs, or non-
woody macrophytes;
vegetative disruption
through grazing or
mowing minimal or not
evident; almost all plants
allowed to grow naturally.
Left Bank 10 9
Right Bank 10 9
Width of riparian zone >18
meters; human activities
(i.e., parking lots, roadbeds,
clear-cuts, lawns, or crops)
have not impacted zone.
Left Bank 10 9
Right Bank 10 9
Suboptimal
Some channelization
present, usually in
areas of bridge
abutments; evidence
of past
channelization, i.e.,
dredging, (greater
than past 20 yr) may
be present, but recent
channelization is not
present.
15 14 13 12 11
Occurrence of riffles
infrequent; distance
between riffles divided
by the width of the
stream is between 7 and
15.
15 14 13 12 11
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5— 30% of bank in
reach has areas of
erosion.
8 76
8 76
70-90% of the
streambank surfaces
covered by native
vegetation, but one
class of plants is not
well- represented;
disruption evident but
not affecting full plant
growth potential to any
great extent; more man
one-half of the potential
plant stubble height
remaining.
8 76
8 76
Width of riparian zone
12—18 meters; human
activities have impacted
zone only minimally.
8 76
8 76
Marginal
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
10 987 6
Occasional riffle or bend;
bottom contours provide
some habitat; distance
between riffles divided by
the width of the stream is
between 15 and 25.
10 987 6
Moderately unstable;
30—60% of bank in reach
has areas of erosion; high
erosion potential during
floods .
5 43
5 43
50-70% of the
streambank surfaces
covered by vegetation;
disruption obvious; patches
of bare soil or closely
cropped vegetation
common; less than one-
half of the potential plant
stubble height remaining.
5 43
5 43
Width of riparian zone
6—12 meters; human
activities have impacted
zone a great deal.
5 43
5 43
Poor
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
5 43210
Generally all flat water or
shallow riffles; poor
habitat; distance between
riffles divided by the
width of the stream is a
ratio of >25.
5 43210
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60-100% ofbank has
erosional scars.
2 1 0
2 1 0
Less than 50% of the
streambank surfaces
covered by vegetation;
disruption of streambank
vegetation is very high;
vegetation has been
removed to
5 centimeters or less in
average stubble height.
2 1 0
2 1 0
Width of riparian zone
<6 meters : little or no
riparian vegetation due
to human activities.
2 1 0
2 1 0
Total Score
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
J-3 DRAFT—DO NOT CITE OR QUOTE
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J.I. REFERENCES
Barbour, MT; Gerritsen, J; Snyder, BD; Stribling, JB. (1999) Rapid bioassessment protocols for
use in streams and wadeable rivers: Periphyton, benthic macroinvertebrates and fish,
Second Edition. [EPA 841-B-99-002]. Washington, D.C: U.S. Environmental Protection
Agency, Office of Water. Available online:
http://water.epa.gov/scitech/monitoring/rsl/bioassessment/index.cfm
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 J-4 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX K.
DATA SHARING TEMPLATES
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 K-l DRAFT—DO NOT CITE OR QUOTE
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1 The templates in the Excel worksheets that accompany this Appendix (see Excel file titled
2 "Appendix_K_Excel") are intended to facilitate the sharing of data across entities. Data are
3 organized into different worksheets as follows:
Worksheet name
Bugs MasterTaxa
Bugs_Raw
Bugs_Metrics
WT_Daily
WT_Month
WT Seasonal
WT Annual
AT_Daily
AT_Month
AT Seasonal
AT Annual
Stage Daily
Stage_Monthly
Stage Season
Stage_Annual
Flow Daily
Flow Monthly
Flow Season
Flow Annual
Habitat
WaterQual
Sitelnfo
DisturbScreen
CCVuln
Description
Taxa attributes used in the bug metric calculations (e.g., thermal
preference, FFG, habit)
Raw macroinvertebrate data for each sampling event (list of taxa and
number of individuals)
Macroinvertebrate metrics (taxonomic-based, traits-based related to
temperature and hydrology, persistence and stability)
Daily water temperature summary statistics
Monthly water temperature summary statistics
Seasonal water temperature summary statistics
Annual water temperature summary statistics
Daily air temperature summary statistics
Monthly air temperature summary statistics
Seasonal air temperature summary statistics
Annual air temperature summary statistics
Daily stage summary statistics
Monthly stage summary statistics
Seasonal stage summary statistics
Annual stage summary statistics
Daily discharge summary statistics
Monthly discharge summary statistics
Seasonal discharge summary statistics
Annual discharge summary statistics
Qualitative [per RBP high gradient field form; Barbour et al. (1999)]
some optional quantitative measures
plus
in situ measurements (pH, DOa, specific conductance)
Site information (e.g., latitude, longitude, drainage area), ecoregion,
NLCD land use
Land use rating, likelihood of impacts from dams, mines, point-source
pollution sites
Climate change vulnerability ratings and classification (eastern United
States)
""Dissolved oxygen
This document is a draft for review purposes only and does not constitute Agency policy.
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1 The tables below show the list of parameters that are included in each worksheet, along with
2 descriptions of these parameters. Not all parameters will be collected at every RMN site (e.g.,
3 some sites may only have water temperature and macroinvertebrate data, while others may have
4 macroinvertebrate, water and air temperature, and stage data).
5
6 Each regional working group should decide on a process for compiling the data across entities
7 (e.g., perhaps the data from each entity will be sent to the regional coordinator, and the
8 coordinator will then compile the data and distribute it to the regional working group).
9
10 There are a number of different techniques that can be used to combine data from different
11 worksheets, so that will be left to the discretion of the user (e.g., one technique would be to
12 upload the worksheets into MS Access, link the tables via Station ID and collection date (or
13 month, season, or year), and write and run queries to get the desired outputs).
14
15 These Excel worksheets are intended to serve as a temporary solution for sharing data. Ideally,
16 an online interface will be developed that will make it easier to share and use data from RMN
17 sites.
18
19 The tables below show the list of parameters that are included in each worksheet, along with
20 descriptions of these parameters.
21
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 K-3 DRAFT—DO NOT CITE OR QUOTE
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Worksheet name
Bugs_MasterTaxa
Type of data
Taxa
attributes
used for bug
metric
calculations
Variable
ITIS_TSN
BiodataTaxonName
orig FinallD
Phylum
Class
Order
Family
Tribe
Genus
Species
FFG
Habit
Thermal
Rheo
Description
TSN number (unique identifier) in
www.itis.sov
Taxon name based on the USGS
BioData nomenclature (version 4.7)
Taxon name based on the
nomenclature of the entity that
collected the sample
Taxonomy
Taxonomy
Taxonomy
Taxonomy
Taxonomy
Taxonomy
Taxonomy
Primary functional feeding group
Primary habit
Thermal preference (cold, warm)
Rheophily (depositional, erosional,
both)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet
name
Bugs Raw
Type of data
Raw
macroinvertebrate
data
Variable
StationID
Waterbody Name
CollMeth
SampID
Year
Month
CollDate
ITIS_TSN
BiodataTaxonName
orig FinallD
Numlnd
Totallnd
RA
Description
Unique station identifier
Name of water body
Collection method
Unique identifier for the sample
(unique station-date-method
combination)
Year of the sampling event
Month of the sampling event
Date of the sampling event
TSN number (unique identifier) in
www.itis.sov
Taxon name based on the USGS
BioData nomenclature (version 4.7)
Taxon name based on the
nomenclature of the entity that
collected the sample
Number of individuals
Total number of individuals in the
sample
Relative abundance; number of
individuals of each taxon/total number
of individuals in the sample
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Bugs_Metrics
Type of data
Taxonomic-
based metric
Traits-based
metric related
to
temperature
Traits-based
metric related
to hydrology
Variable
ntjotal
nt_EPT
nt Ephem
nt_Plecop
nt_Trichop
pi_EPT
pi Ephem
pi Plecop
pi_Trichop
nt_OCH
pi_OCH
nt_cold
pt_cold
pi_cold
nt warm
pt warm
pi warm
nt_CollFilt
nt_CollGath
nt_Scraper
nt_Shred
nt_Pred
nt Swim
nt_RheoDepo
nt RheoEros
pi_CollFilt
pi_CollGath
pi Scraper
pi Shred
Description
Total number of taxa (richness)
Number of EPT taxa (Ephem eroptera
[mayflies], Plecoptera [stoneflies], and
Trichoptera [caddisflies])
Number of Ephem eroptera (mayfly) taxa
Number of Plecoptera(stonefly) taxa
Number of Trichoptera (caddisfly) taxa
Percentage EPT individuals
Percentage Ephemeroptera individuals
Percentage Plecoptera individuals
Percentage Trichoptera individuals
Number of Odonata/Coleoptera/Hemiptera
(OCH) taxa
Percentage Odonata/Coleoptera/Hemiptera
(OCH) individuals
Number of cold water taxa
Percentage cold water taxa
Percentage cold water individuals
Number of warm water taxa
Percentage warm water taxa
Percentage warm water individuals
Number of collector filterer taxa
Number of collector gatherer taxa
Number of scraper/herbivore taxa
Number of shredder taxa
Number of predator taxa
Number of swimmer taxa
Number of rheophily — deposit! onal taxa
Number of rheophily — erosional taxa
Percentage collector filterer individuals
Percentage collector gatherer individuals
Percentage scraper/herbivore individuals
Percentage shredder individuals
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 K-6 DRAFT—DO NOT CITE OR QUOTE
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Worksheet name
Type of data
Year-to-year
variability
Variable
pi_Pred
pi Swim
pi RheoDepo
pi RheoEros
Persist
Stab
Description
Percentage predator individuals
Percentage swimmer individuals
Percentage Rheophily — deposit! onal
individuals
Percentage Rheophily — erosional individuals
Persistence (variability in presence/absence
from year to year; see Appendix L)
Stability (variability in relative abundance
from year to year; see Appendix L)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet
name
WT_Daily
WT_Month
WT_Seasonal
WT Annual
Type of
statistics
Daily water
temperature
Monthly water
temperature
Seasonal water
temperature
Annual water
temperature
(January
1 -December
31)
Variable
WT_DMean
WT_DMax
WT_DMin
WT_DDif
WT_DVar
WT_MMean
WT_MMax
WT_MMin
WT_MDif
WT_MVar
WT_SMean
WT_SMax
WT_SMin
WT_SDif
WT_SVar
WT_AMean
WT_AMax
WT_AMin
WT_ADifMean
WT_ADifMax
WT_ADifMin
WT_AVar
Description
Daily mean (°C)
Daily maximum (°C)
Daily minimum (°C)
Daily difference (maximum-minimum)
(°C)
Standard deviation for each day (°C)
Monthly mean (°C)
Monthly maximum (°C)
Monthly minimum (°C)
Monthly difference (maximum-minimum)
(°C)
Standard deviation for each month (°C)
Seasonal mean (°C)
Seasonal maximum (°C)
Seasonal minimum (°C)
Seasonal difference (maximum-minimum)
(°C)
Standard deviation for each season (°C)
Annual mean (°C)
Annual maximum (°C)
Annual minimum (°C)
Mean annual difference (°C)
Maximum annual difference (°C)
Minimum annual difference (°C)
Standard deviation of the annual mean
difference (°C)
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 K-8 DRAFT—DO NOT CITE OR QUOTE
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Worksheet name
AT_Daily
AT_Month
AT_Seasonal
AT Annual
Type of
statistics
Daily air
temperature
Monthly air
temperature
Seasonal air
temperature
Annual air
temperature
(January
1 -December
31)
Variable
AT_DMean
AT_DMax
AT_DMin
AT_DDif
AT_DVar
AT_MMean
AT_MMax
AT_MMin
AT_MDif
AT_MVar
AT_SMean
AT_SMax
AT_SMin
AT_SDif
AT_SVar
AT AMean
AT_AMax
AT_AMin
AT_ADifMean
AT_ADifMax
AT_ADifMin
AT_AVar
Description
Daily mean (°C)
Daily maximum (°C)
Daily minimum (°C)
Daily difference
(maximum-minimum)(°C)
Standard deviation for each day (°C)
Monthly mean (°C)
Monthly maximum (°C)
Monthly minimum (°C)
Monthly difference
(maximum-minimum) (°C)
Standard deviation for each month (°C)
Seasonal mean (°C)
Seasonal maximum (°C)
Seasonal minimum (°C)
Seasonal difference
(maximum-minimum) (°C)
Standard deviation for each season (°C)
Annual mean (°C)
Annual maximum (°C)
Annual minimum (°C)
Mean annual difference (°C)
Maximum annual difference (°C)
Minimum annual difference (°C)
Standard deviation of the annual mean
difference (°C)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet
name
Stage Daily
Stage_Monthly
Type of
statistics
Daily stage
Monthly
stage
Variable
Stage DMean
Stage_DMed
Stage_DMax
Stage_DMin
Stage_DDif
Stage_DVar
Stage_MMean
Stage_MMax
Stage_MMin
Stage_MDif
Stage_MMag90
Stage_MMag50
Stage_MMag25
Stage_MMaglO
Stage_MMagl
Stage_Mp90
Stage_Mpl_25
Description
Mean stage for each day (ft)
Median stage for each day (ft)
Maximum stage for each day (ft)
Minimum stage for each day (ft)
Difference between the maximum and
minimum stage for each day (ft)
Standard deviation for stage for each day (ft)
Mean stage for each month (ft)
Maximum stage for each month (ft)
Minimum stage for each month (ft)
Difference between the maximum and
minimum stage values for each month (ft)
High flow magnitude (90th percentile of
monthly stage values) (ft)
Median magnitude (50th percentile of
monthly stage values) (ft)
Low flow magnitude (ft) (25th percentile of
monthly stage values); this represents low
flows in smaller streams [drainage areas <50
mi2, per DePhilip and Moberg (2013)]
Low flow magnitude (ft) (10th percentile of
monthly stage values); this represents low
flows in medium to larger-sized streams
[drainage areas >50 mi2 per DePhilip and
Moberg (20 13)]
Extreme low flow magnitude (ft) (1st
percentile of monthly stage values); this
represents extreme low flows
Percentage high flow and floods (%)
(percentage of stage values in each month
that exceed the monthly 90th percentile)
Percentage low flows (%); percentage of
stage values in each month that are between
the monthly 25th and 1st percentiles
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet
name
Stage Season
or
Flow_Season
Stage Annual
Type of
statistics
Seasonal
stage
Annual
stage
Variable
Stage_Mp25_90
Stage_Sp90
Stage AMean
Stage_AMax
Stage_ADateMax
Stage_AMin
Stage_ADateMin
Stage_ADifMean
Stage_ADifMax
Stage_ADifMin
Stage_AVar
Stage_AZero
Description
Percentage typical (%); percentage of stage
values in each month that are between the
monthly 25th and 90st percentiles
Percentage high flows and floods in spring
and fall (%); percentage of stage values in
each month that exceed the monthly 90th
percentile in spring (March-May) and fall
( S eptemb er-Novemb er)
Annual mean stage (ft)
Annual maximum stage (ft)
Julian date of annual maximum stage
(number)
Annual minimum stage (ft)
Julian date of annual minimum stage
(number)
Mean annual difference in stage (ft)
Maximum of the daily difference in stage (ft)
Minimum of the daily difference in stage (ft)
Standard deviation of the daily difference in
stage (ft)
Number of days having stage values of 0
(number)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Flow Daily
Flow Monthly
Type of
statistics
Daily
discharge
Monthly
discharge
Variable
Flow DMean
Flow_DMed
Flow DMax
Flow DMin
Flow_DDif
Flow DVar
Flow MMean
Flow_MMax
Flow_MMin
Flow_MDif
Flow_MMag90
Flow MMagSO
Flow_MMag25
Flow_MMaglO
Flow MMagl
Flow_Mp90
Description
Mean flow for each day (ftVsec)
Median flow for each day (ft3/sec)
Maximum flow for each day (ft3/sec)
Minimum flow for each day (ft3/sec)
Difference between the maximum and
minimum flow for each day (ft3/sec)
Standard deviation for flow for each day
(ft3/sec)
Mean flow for each month (ft3/sec)
Maximum flow for each month (ft3/sec)
Minimum flow for each month (ft3/sec)
Difference between the maximum and
minimum flow values for each month
(ft3/sec)
High flow magnitude (90th percentile of
monthly flow values) (ft3/sec)
Median flow magnitude (50th percentile
of monthly flow values) (ft3/sec)
Low flow magnitude (ft3/sec) (25th
percentile of monthly flow values); this
represents low flows in smaller streams
[drainage areas <50 mi2, per DePhilip
andMoberg(2013)]
Low flow magnitude (ft3/sec) (10th
percentile of monthly flow values); this
represents low flows in medium to
larger-sized streams [drainage areas
>50 mi2 per DePhilip and Moberg
(2013)]
Extreme low flow magnitude (ft3/sec) (1st
percentile of monthly flow values); this
represents extreme low flows
Percentage high flow and floods (%)
(percentage of flow values in each month
that exceed the monthly 90th percentile)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Flow Season
Flow Annual
Type of
statistics
Seasonal
discharge
Annual
discharge
Variable
Flow_Mpl_25
Flow_Mp25_90
Flow_Sp90
Flow AMean
Flow AMax
Flow_ADateMax
Flow AMin
Flow ADateMin
Flow_ADifMean
Flow_ADifMax
Flow ADifMin
Flow_AVar
Flow AZero
Description
Percentage low flows (%); percentage of
flow values in each month that are
between the monthly 25th and 1st
percentiles
Percentage typical (%); percentage of
flow values in each month that are
between the monthly 25th and 90st
percentiles
Percentage high flows and floods in
spring and fall (%); percentage of flow
values in each month that exceed the
monthly 90th percentile in spring
(March-May) and fall
(Septemb er-Novemb er)
Annual mean flow (ft3/sec)
Annual maximum flow (ft3/sec)
Julian date of annual maximum flow
(number)
Annual minimum flow (ft3/sec)
Julian date of annual minimum flow
(number)
Mean annual difference in flow (ft3/sec)
Maximum of the daily difference in flow
(ft3/sec)
Minimum of the daily difference in flow
(ft3/sec)
Standard deviation of the daily difference
in flow (ft3/sec)
Number of days having flow values of 0
(number)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Habitat
Type of
statistics
Qualitative
(per RBP
high gradient
field form)
Quantitative
(optional)
Variable
Epif Cover
Embed
VeloDepth
SedDepo
ChanFlow
ChanAlt
FreqRiff
BankStab_LB
BankStab_RB
VegProt_LB
VegProt_RB
RipWidth_LB
RipWidth_RB
BFwidth
BFdepth
Slope
Canopy mid
Canopy_bank
pRiffle
pRun
Description
Rating of epifaunal substrate/available
cover, from 0 (worst) to 20 (best)
Rating of embeddedness, from 0 (worst)
to 20 (best)
Rating of velocity/depth regime, from 0
(worst) to 20 (best)
Rating of sediment deposition, from 0
(worst) to 20 (best)
Rating of channel flow status, from 0
(worst) to 20 (best)
Rating of channel alteration, from 0
(worst) to 20 (best)
Rating of frequency of riffles, from 0
(worst) to 20 (best)
Rating of bank stability on left bank,
from 0 (worst) to 10 (best)
Rating of bank stability on right bank,
from 0 (worst) to 10 (best)
Rating of vegetative protection on left
bank, from 0 (worst) to 10 (best)
Rating of vegetative protection on right
bank, from 0 (worst) to 10 (best)
Rating of riparian vegetative zone width
on left bank, from 0 (worst) to 10 (best)
Rating of riparian vegetative zone width
on right bank, from 0 (worst) to 10 (best)
Bankfull width (m)
Bankful depth (m)
Reach-scale slope (unitless)
Canopy closure (mid-stream)
Canopy closure (along bank)
Percentage riffle habitat in biological
sampling reach
Percentage run habitat in biological
sampling reach
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Type of
statistics
Variable
pPool
pGlide
pFine
pSand
pGravel
pCobble
pBoulder
pBedrock
Description
Percentage pool habitat in biological
sampling reach
Percentage glide habitat in biological
sampling reach
Percentage fine substrate in biological
sampling reach
Percentage sand substrate in biological
sampling reach
Percentage gravel substrate in biological
sampling reach
Percentage cobble substrate in biological
sampling reach
Percentage boulder substrate in biological
sampling reach
Percentage bedrock substrate in
biological sampling reach
Worksheet name
WaterQual
Type of
statistics
in situ
Variable
SpCond
DO
PH
Description
Specific conductivity (|iS/cm)
Dissolved oxygen (%)
PH
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Sitelnfo
Type of
statistics
Site
information
Ecoregion
NLCD total
watershed
Variable
StationID
Waterbody Name
Long
Lat
State
DrArea km2
SLOPE
Elev m
BFI
US_L4CODE
US_L4NAME
US_L3CODE
US_L3NAME
IMPERV
LU_11
LU_12
LU_21
LU_22
LU_23
Description
Unique station identifier
Name of water body
Longitude, decimal degrees, NAD83
Latitude, decimal degrees, NAD83
State that the site is located in
Drainage area (km2)
Slope of flowline (unitless) (source:
NHDPlus)
Elevation of site (m)
Baseflow index (Wolock, 2003)
U.S. EPA level 4 ecoregion (code) that
the site is located in
U.S. EPA level 4 ecoregion (name) that
the site is located in
U.S. EPA level 3 ecoregion (code) that
the site is located in
U.S. EPA level 3 ecoregion (name) that
the site is located in
Percentage of total watershed defined as
impervious (source: most recent NLCD)
Percentage of total watershed defined as
open water (source: most recent NLCD)
Percentage of total watershed defined as
perennial ice/snow (source: most recent
NLCD)
Percentage of total watershed defined as
developed, open space (source: most
recent NLCD)
Percentage of total watershed defined as
developed, low intensity (source: most
recent NLCD)
Percentage of total watershed defined as
developed, medium intensity (source:
most recent NLCD)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Type of
statistics
Variable
LU_24
LU_31
LU_41
LU_42
LU_43
LU_52
LU_71
LU_81
LU_82
LU_90
LU_95
Description
Percentage of total watershed defined as
developed, high intensity (source: most
recent NLCD)
Percentage of total watershed defined as
barren land (Rock/Sand/Clay) (source:
most recent NLCD)
Percentage of total watershed defined as
deciduous forest (source: most recent
NLCD)
Percentage of total watershed defined as
evergreen forest (source: most recent
NLCD)
Percentage of total watershed defined as
mixed forest (source: most recent NLCD)
Percentage of total watershed defined as
shrub/scrub (source: most recent NLCD)
Percentage of total watershed defined as
grassland/herbaceous (source: most
recent NLCD)
Percentage of total watershed defined as
pasture/hay (source: most recent NLCD)
Percentage of total watershed defined as
cultivated crops (source: most recent
NLCD)
Percentage of total watershed defined as
woody wetlands (source: most recent
NLCD)
Percentage of total watershed defined as
emergent herbaceous wetlands (source:
most recent NLCD)
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Type of
statistics
Variable
Description
Disturbance
screening
Land use
Overall
Overall land use disturbance level; see
Appendix C—Table C-l
Imperv
Impervious disturbance level; see
Appendix C—Table C-l
Urban
Urban disturbance level; see Appendix
C—Table C-l
Crops
Crops disturbance level; see Appendix
C—Table C-l
Hay
Hay disturbance level; see Appendix
C—Table C-l
Impacts
from dams,
mines and
point-source
pollution
sites
Flag_FTYPE
1 = flagged; 0 = not flagged. NHDPlus
vl1 flowline (FTYPE) the site is located
on (e.g., stream/river, artificial pathway,
canal/ditch, pipeline, connector). If the
site was located on a flowline designated
as something other than a stream/river,
the site was flagged.
Flag_Dams
1 = flagged; 0 = not flagged. Sites are
flagged if dams are present within 1 km
of the site.
Dam Assess
Likelihood of impact (unlikely, likely,
unsure) from dams at the flagged sites;
for more information see Appendix
C—Section C2.2
Flag_Mines
1 = flagged; 0 = not flagged. Sites are
flagged if mines are present within 1 km
of the site.
Mines Assess
Likelihood of impact (unlikely, likely,
unsure) from mines at the flagged sites;
for more information see Appendix
C—Section C.2.2
Flag_NPDES
1 = flagged; 0 = not flagged. Sites are
flagged if NPDES major discharge
permits have been issued within 1 km of
the site.
1http://www.horizon-svstems.com/nhdplus/nhdplusvl home.php
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Type of
statistics
Variable
Description
NPDES Assess
Likelihood of impact (unlikely, likely,
unsure) from NPDES major discharges at
the flagged sites; for more information
see Appendix C—Section C.2.2
Flag_SNPL
1 = flagged; 0 = not flagged. Sites are
flagged if Superfund National Priorities
List (SNPL) sites are present within 1 km
of the site.
SNPL Assess
Likelihood of impact (unlikely, likely,
unsure) from SNPL sites at the flagged
sites; for more information see Appendix
C—Section C.2.2
Impact from
other
nonclimatic
stressors
Flag_Roads
1 = flagged; 0 = not flagged. Sites are
flagged if road score is >75%; for more
information see Appendix C—Section
C.2.3
Roads Assess
Likelihood of impact (unlikely, likely,
unsure) from roads at the flagged sites;
for more information see Appendix
C—Section C.2.3
Flag_AtmosDep
1 = flagged; 0 = not flagged. Sites are
flagged if atmospheric deposition score is
>75%; for more information see
Appendix C—Section C.2.3
AtmosDep_Assess
Likelihood of impact (unlikely, likely,
unsure) from atmospheric deposition at
the flagged sites
Flag_Coal
1 = flagged; 0 = not flagged. Sites are
flagged if the coal mining potential score
is >75% and/or the permit activity score
(if available) is >0; for more information
see Appendix C—Section C.2.3
Coal Assess
Likelihood of impact (unlikely, likely,
unsure) from coal mining at the flagged
sites
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet name
Type of
statistics
Variable
Description
Flag_ShaleGas
1 = flagged; 0 = not flagged. Sites are
flagged if the shale gas drilling potential
score is 100% and/or the permit activity
score (if available) is >0; for more
information see Appendix C—Section
C.2.3
ShaleGas Assess
Likelihood of impact (unlikely, likely,
unsure) from shale gas drilling at the
flagged sites
Flag_FutureUrb
1 = flagged; 0 = not flagged. Sites are
flagged if they currently have a local
catchment-scale percentage impervious
value <10% and the average projected
future change (by 2050) is >0.5%; for
more information see Appendix
C—Section C.2.3
FutureUrb Assess
Likelihood of impact (unlikely, likely,
unsure) from future urban development at
the flagged sites
Flag_WaterUse
1 = flagged; 0 = not flagged. Sites are
flagged if they received a score of >50%
for any of the 3 water use parameters
listed below; for more information see
Appendix C—Section C.2.3
WaterUse Assess
Likelihood of impact (unlikely, likely,
unsure) from water withdrawals at the
flagged sites
This document is a draft for review purposes only and does not constitute Agency policy.
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Worksheet
name
Climate
change
vulnerability
Type of
statistics
Classificatio
n
Vulnerability
rating
Variable
Class Bug
Prob_Gl
Prob_G3
Prob_G4
Vuln_Scl
Vuln_Sc2
Vuln_Sc3
Vuln Overall
Description
Bug classification group — eastern United States,
based on the maximum probability value (e.g., if
a site received a Group 1 membership value of
0.7 and a Group 4 membership value of 0.3, it
was assigned to Group 1).
Probability of membership in classification Group
1; scores range from 0 to 1; higher values indicate
higher probability of membership
Probability of membership in classification Group
3; scores range from 0 to 1; higher values indicate
higher probability of membership
Probability of membership in classification Group
4; scores range from 0 to 1; higher values indicate
higher probability of membership
Vulnerability rating (least, moderate, most) for
scenario 1 (increasing temperatures)
Vulnerability rating (least, moderate, most) for
scenario 2 (increase in frequency and severity of
peak flows)
Vulnerability rating (least, moderate, most) for
scenario 3 (increased frequency of summer low
flow events)
Overall vulnerability rating (least, moderate,
most) (lowest rating across scenarios)
This document is a draft for review purposes only and does not constitute Agency policy.
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K.l. REFERENCES
Barbour, MT; Gerritsen, J; Snyder, BD; Stribling, JB. (1999) Rapid bioassessment protocols for
use in streams and wadeable rivers: Periphyton, benthic macroinvertebrates and fish,
Second Edition. [EPA 841-B-99-002]. Washington, D.C: U.S. Environmental Protection
Agency, Office of Water. Available online:
http://water.epa.gov/scitech/monitoring/rsl/bioassessment/index.cfm
DePhilip, M; Moberg, T. (2013) Ecosystem flow recommendations for the Upper Ohio River
basin in western Pennsylvania. Report to the Pennsylvania Department of Environmental
Protection. Harrisburg, PA: The Nature Conservancy.
http://www.nature.org/media/pa/ecosvstem-flow-recommendations-upper-ohio-river-pa-
2013.pdf
Wolock, DM. (2003) Base-flow index grid for the conterminous United States. (USGS open-file
report 03-263). Lawrence, KS: US Department of the Interior, US Geological Survey.
http://water.usgs.gov/lookup/getspatial7bfi48grd
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX L.
MACROINVERTEBRATE
THERMAL INDICATOR TAXA
Table L-l. Taxa that were the basis of the thermal preference metrics used in the regional
classification analyses
Table L-2. Thermal indicator taxa in New England and New York
Table L-3. Thermal indicator taxa that have been identified by VT DEC
Table L-4. Taxa that have been identified as cold or cool water indicators in the Mid-Atlantic
region
Table L-5. Thermal indicator taxa in the Southeast region
1
This document is a draft for review purposes only and does not constitute Agency policy.
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1 This appendix contains lists of macroinvertebrate taxa that are believed to have strong thermal
2 preferences based on analyses conducted by EPA (U.S. EPA, 2012; unpublished Northeast pilot
3 study) and state biomonitoring programs (MD DNR, PA DEP, VT DEC). Best professional
4 judgment from regional taxonomists was also considered.
5
6 Table L-l contains the list of taxa that were the basis of the thermal preference metrics used in
7 the regional classification analyses (unpublished data). There are 51 cold/cool water taxa and 39
8 warm water taxa on this regional list. The taxonomic resolution is genus level or higher to match
9 with the taxonomic resolution of the NRSA/WSA data. Please note:
10
11 • The list in Table L-l only includes taxa that occur in the NRSA/WSA data set analyzed
12 for the regional classification analysis.
13 • Initially we tried to distinguish between cold and cool water taxa but later decided that
14 additional data and further analyses are necessary to better refine those designations (if
15 such designations can be made).
16
17 Table L-2 contains a list of thermal indicator taxa identified based on thermal tolerance analyses
18 (per Yuan, 2006) conducted on data from New England and New York (unpublished U. S. EPA
19 Northeast pilot study), and Table L-3 contains lists of taxa that have been identified as thermal
20 indicators by VT DEC (Steve Fiske and Aaron Moore, unpublished).
21
22 Table L-4 contains the list of taxa that have been identified as cold water taxa by Maryland DNR
23 (Becker et al., 2010) and also contains information that was provided by Pennsylvania DEP
24 (Amy Williams and Dustin Shull, unpublished data).
25
26 Table L-5 contains a list of thermal indicator taxa identified based on thermal tolerance analyses
27 (per Yuan, 2006) conducted on data from North Carolina (U.S. EPA, 2012), and also contains
28 information that was provided by Debbie Arnwine from Tennessee DEC.
29
30 All of these lists are intended to be starting points. They should be revised as better data become
31 available and may need to be further customized by region. It may be appropriate to have a list
32 that spans the three regions, plus customized lists for each region. If so, Table L-l could
33 potentially serve as the "three-region" list, Tables L-2 and L-3 could potentially serve as the
34 starter list for the Northeast region, Table L-4 could potentially serve as the starter list for the
35 Mid-Atlantic region, and Table L-5 could potentially serve as the starter list for the Southeast
36 region.
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-l. Taxa that were the basis of the thermal preference metrics used in the regional
classification analyses (unpublished data, U.S. EPA, 2012). This list only includes taxa that
occur in the NRSA/WSA data set analyzed. We primarily received reviewer feedback from
biologists in the Mid-Atlantic region. Final identifications at the genus level are italicized in
the Final ID column
Order
Trichoptera
Plecoptera
Ephemeroptera
Plecoptera
Trichoptera
Trichoptera
Diptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Ephemeroptera
Ephemeroptera
Plecoptera
Trichoptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Trichoptera
Plecoptera
Trichoptera
Plecoptera
Plecoptera
Coleoptera
Trichoptera
Final ID
Agapetus
Alloperla
Ameletus
Amphinemura
Apatania
Arctopsyche
Brillia
Capniidae
Allocapnia
Paracapnia
Sweltsa
Cinygmula
Diphetor
Diploperla
Dolophilodes
Drunella
Ephemerella
Eurylophella
Glossosoma
Isoperla
Lepidostoma
Malirekus
Nemouridae
Oulimnius
Parapsyche
Type
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Reviewer feedback
Agree
Agree
Agree
Mixed
Mixed
Agree
Mixed
Agree
Agree
Agree
Agree
Agree
Agree
Unsure
Agree
Agree
Agree
Mixed
Mixed
Mixed
Mixed
Agree
Mixed
Mixed
Agree
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-l. continued...
Order
Plecoptera
Plecoptera
Trichoptera
Plecoptera
Plecoptera
Plecoptera
Trichoptera
Plecoptera
Diptera
Diptera
Trichoptera
Ephemeroptera
Ephemeroptera
Odonata
Diptera
Coleoptera
Ephemeroptera
Diptera
Lumbriculida
Diptera
Megaloptera
Diptera
Diptera
Trichoptera
Trichoptera
Diptera
Odonata
Hemiptera
Final ID
Peltoperla
Pteronarcys
Rhyacophila
Taenionema
Taeniopteryx
Tallaperla
Wormaldia
Zapada
Antocha
Atherix
Diplectrona
Epeorus
Habrophlebia
Lanthus
Pagastia
Promoresia
Rhithrogena
Diamesa
Lumbriculidae
Micropsectra
Nigronia
Orthocladius
Parametriocnemus
Polycentropus
Psilotreta
Ablabesmyia
Argia
Belostoma
Type
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Cold/cool
Warm
Warm
Warm
Reviewer Feedback
Agree
Mixed
Agree
Agree
Mixed
Agree
Agree
Agree
Disagree
Mixed
Agree
Agree
Agree
Agree
Mixed
Agree
Agree
Unsure
Disagree
Disagree
Disagree
Disagree
Disagree
Disagree
Agree
Agree
Agree
Unsure
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-l. continued...
Order
Coleoptera
Isopoda
Ephemeroptera
Diptera
Trichoptera
Diptera
Unionoida
Ephemeroptera
Arhynchobdellida
Arhynchobdellida
Amphipoda
Diptera
Rhynchobdellida
Odonata
Odonata
Neotaenioglossa
Trichoptera
Odonata
Ephemeroptera
Coleoptera
Odonata
Trichoptera
Trichoptera
Odonata
Diptera
Diptera
Trichoptera
Diptera
Final ID
Berosus
Caecidotea
Caenis
Cardiocladius
Chimarra
Dicrotendipes
Elliptic
Ephoron
Erpobdella
Mooreobdella
Gammarus
Glyptotendipes
Helobdella
Helocordulia
Hetaerina
Hydrobiidae
Hydroptila
Ischnura
Leucrocuta
Lioporeus
Macromia
Macrostemum
Neureclipsis
Neurocordulia
Nilotanypus
Nilothauma
Oecetis
Pentaneura
Type
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Reviewer Feedback
Agree
Unsure
Agree
Agree
Agree
Agree
Unsure
Agree
Agree
Agree
Unsure
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Unsure
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
Agree
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-l. continued.
Order
Basommatophora
Veneroida
Ephemeroptera
Coleoptera
Diptera
Diptera
Ephemeroptera
Final ID
Physella
Sphaerium
Stenacron
Stenelmis
Stenochironomus
Tanytarsus
Tricorythodes
Turbellariaa
Type
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Warm
Reviewer Feedback
Agree
Agree
Mixed
Agree
Agree
Agree
Agree
Agree
Tinal ID is a Class
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-2. Thermal indicator taxa in New England and New York, based on thermal
tolerance analyses (per Yuan, 2006) conducted on state biomonitoring data from New
England and New York (unpublished U.S. EPA Northeast pilot study). Results are based
on relative ranks from: (1) the generalized additive model (GAM) only and (2) multiple
models. Final identifications at the genus level are italicized in the Final ID column
Order
Basommatophora
Coleoptera
Coleoptera
Coleoptera
Coleoptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Family
Ancylidae
Dryopidae
Elmidae
Hydrophilidae
Psephenidae
Ceratopogonidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Dixidae
Psychodidae
Simuliidae
Tipulidae
Tipulidae
Tipulidae
Tipulidae
Tipulidae
Tipulidae
Ameletidae
Regional final ID
Nematomorphaa
Laevapex
Helichus
Oulimnius
Tropisternus
Ectopria
Ceratopogonidae
Brillia
Brundiniella
Diplocladius
Heleniella
Parachaetocladius
Paraphaenocladius
Stilocladius
Dixa
Pericoma
Prosimulium
Dicranota
Hexatoma
Limnophila
Molophilus
Pseudolimnophila
Tipula
Ameletus
Thermal
preference
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
GAM
only
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-2. continued...
Order
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Odonata
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Family
Ephemerellidae
Ephemerellidae
Heptageniidae
Leptophlebiidae
Gomphidae
Capniidae
Chloroperlidae
Leuctridae
Nemouridae
Peltoperlidae
Perlodidae
Perlodidae
Perlodidae
Pteronarcyidae
Taeni opterygi dae
Taeniopterygidae
Apataniidae
Glossosomatidae
Hydropsychidae
Hydropsychidae
Hydropsychidae
Hydroptilidae
Lepidostomatidae
Limnephilidae
Philopotamidae
Philopotamidae
Regional final ID
Ephemerella
Eurylophella
Rhithrogena
Leptophlebiidae
Lanthus
Capniidae
Chloroperlidae
Leuctridae
Nemouridae
Peltoperla
Isogenoides
Isoperla
Malirekus
Pteronarcys
Taenionema
Taeniopteryx
Apatania
Glossosoma
Arctopsyche
Diplectrona
Parapsyche
Palaeagapetus
Lepidostoma
Hydatophylax
Dolophilodes
Wormaldia
Thermal
preference
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
GAM
only
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-2. continued...
Order
Trichoptera
Tricladida
Trombidiformes
Trombidiformes
Trombidiformes
Trombidiformes
Trombidiformes
Basommatophora
Coleoptera
Coleoptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Haplotaxida
Lumbriculida
Megaloptera
Odonata
Trichoptera
Trichoptera
Aeolosomatida
Family
Rhyacophilidae
Dugesiidae
Hydrachnidae
Hydryphantidae
Hygrobatidae
Sperchonidae
Torrenticolidae
Ancylidae
Elmidae
Elmidae
Athericidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Enchytraeidae
Lumbriculidae
Corydalidae
Aeshnidae
Odontoceridae
Polycentropodidae
Aeolosomatidae
Regional final ID
Rhyacophila
Cura
Hydrachnidae
Hydryphantidae
Hygrobates
Sperchon
Torrenticolidae
Ferrissia
Optioservus
Promoresia
Atherix
Diamesa
Micropsectra
Orthocladius
Pagastia
Parametriocnemus
Rheocricotopus
Subletted
Enchytraeidae
Lumbriculidae
Nigronia
Boyeria
Psilotreta
Polycentropus
Turbellariab
Aeolosomatidae
Thermal
preference
cold
cold
cold
cold
cold
cold
cold
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
cold/cool
warm
warm
GAM
only
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 L-9 DRAFT—DO NOT CITE OR QUOTE
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Table L-2. continued...
Order
Amphipoda
Amphipoda
Amphipoda
Basommatophora
Basommatophora
Coleoptera
Coleoptera
Coleoptera
Coleoptera
Coleoptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Family
Crangonyctidae
Gammaridae
Hyalellidae
Physidae
Planorbidae
Elmidae
Gyrinidae
Gyrinidae
Haliplidae
Hydrophilidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Regional final ID
Synurella
Gammarus
Hyalella
Physella
Planorbella
Stenelmis
Dineutus
Gyrinus
Haliplus
Berosus
Ablabesmyia
Cardiocladius
Chironomus
Cryptotendipes
Dicrotendipes
Endochironomus
Glyptotendipes
Helopelopia
Labrundinia
Nilotanypus
Parachironomus
Paratany tarsus
Paratendipes
Pentaneura
Phaenopsectra
Pseudochironomu
s
Thermal
preference
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
GAM
only
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
yes
yes
yes
yes
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-2. continued...
Order
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Isopoda
Lepidoptera
Neotaenioglossa
Neotaenioglossa
Neotaenioglossa
Odonata
Odonata
Odonata
Odonata
Odonata
Plecoptera
Plecoptera
Family
Chironomidae
Chironomidae
Chironomidae
Chironomidae
Baetidae
Baetidae
Baetidae
Caenidae
Ephemerellidae
Heptageniidae
Heptageniidae
Leptohyphidae
Polymitarcyidae
Potamanthidae
Asellidae
Pyralidae
Hydrobiidae
Pleuroceridae
Bithyniidae
Coenagrionidae
Coenagrionidae
Coenagrionidae
Corduliidae
Gomphidae
Perlidae
Perlidae
Regional final ID
Rheopelopia
Tanytarsus
Tribelos
Xenochironomus
Centroptilum
Procloeon
Pseudocloeon
Caenis
Attenella
Leucrocuta
Stenacron
Tricorythodes
Ephoron
Anthopotamus
Caecidotea
Pyralidae
Hydrobiidae
Pleuroceridae
Bithyniidae
Argia
Enallagma
Ischnura
Corduliidae
Hagenius
Attaneuria
Perlesta
Thermal
preference
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
GAM
only
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
yes
yes
yes
yes
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-2. continued...
Order
Rhynchobdellida
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Tricladida
Tubificida
Tubificida
Veneroida
Veneroida
Family
Glossiphoniidae
Hydropsychidae
Hydroptilidae
Leptoceridae
Leptoceridae
Leptoceridae
Polycentropodidae
Polycentropodidae
Planariidae
Naididae
Naididae
Pisidiidae
Pisidiidae
Regional final ID
Placobdella
Macrostemum
Hydroptila
Ceraclea
Nectopsyche
Oecetis
Cernotina
Neureclipsis
Planariidae
Chaetogaster
Dero
Musculium
Sphaerium
Thermal
preference
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
GAM
only
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Multiple
models
yes
yes
yes
yes
yes
Tinal identification is a Phylum.
bFinal identification is a Class
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-3. Thermal indicator taxa that have been identified by VT DEC (Steve Fiske,
Aaron Moore and Jim Kellogg, unpublished data)
Order
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Plecoptera
Plecoptera
Plecoptera
Decapoda
Trichoptera
Diptera
Coleoptera
Coleoptera
Amphipoda
Amphipoda
Neoophora
Diptera
Diptera
Diptera
Genus
Polypedilum
Neostempellina
Tvetenia
Rhithrogena
Ameletus
Arctopsyche
Arctopsyche
Rhyacophila
Rhyacophila
Rhyacophila
Rhyacophila
Rhyacophila
Peltoperla
Tallaperla
Taenionema
Cambarus
Palaeagapetus
Eukiefferella
Oulimnius
Promoresia
Gammarus
Hyallela
Cura
Eukiefferella
Polypedilum
Tvetenia
Species
aviceps
reissi
bavarica grp
sp
sp
sp
ladogensis
Carolina
torva
nigrita
invaria
acutiloba
sp
sp
sp
bartoni
sp
brevicalar, brehmi, and tirolensis
latiusculus
tardella
pseudolimnaeus
azteca
formanii
claripennis
flavum
discoloripes, vitracies
Indicator
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold/cool
cold/cool
cold
warm
warm
warm
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-3. continued...
Order
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Plecoptera
Plecoptera
Coleoptera
Neoophora
Genus
Leucotrichia
Rhyacophila
Rhyacophila
Rhyacophila
Neoperla
Taeniopteryx
Promoresia
Dugesia
Species
sp
mainensis
manistee
minora
sp
sp
elegans
tigrina
Indicator
warm
warm
warm
warm
warm
warm
warm
warm
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-4. Taxa that have been identified as cold or cool water indicators by MD DNR
(Becker et al., 2010) and/or PA DEP (Amy Williams and Dustin Shull, unpublished data)
Type
cold
cold
cold
cold
cold
cold
cold
cold
cold (MD)/cool (PA)
cold
cold
cold
cold (MD)/cool (PA)
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold
Order
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Genus
Bittacomorpha
Dixa
Heleniella
Prodiamesa
Ameletus
Cinygmula
Diphetor
Drunella
Epeorus
Ephemera
Ephemerella
Eurylophella
Habrophlebia
Paraleptophlebia
Alloperla
Amphinemura
Diploperla
Haploperla
Isoperla
Leuctra
Malirekus
Peltoperla
Pteronarcys
Remenus
Sweltsa
MD
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
PA
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Occurrence in
PA DEP data set
common
common
common
common
common
common
common
rare
common
common
rare
rare
common
rare
rare
rare
rare
common
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-4. continued...
Type
cold
cold
cold
cold
Order
Plecoptera
Plecoptera
Trichoptera
Trichoptera
Genus
Tallaperla
Yugus
Diplectrona
Wormaldia
MD
yes
yes
yes
PA
yes
yes
yes
Occurrence in
PA DEP data set
common
rare
common
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-5. Taxa that have been identified as cold, cool, or warm water indicators based on
thermal tolerance analyses (per Yuan, 2006) conducted on data from North Carolina
(U.S. EPA, 2012) and/or based on unpublished data provided by Debbie Arnwine from TN
DEC
Type
cold (NC)/
cool (TN)
cold (NC)/
cool (TN)
cold (NC)/
cool (TN)
cold
cold
cold
cold
cold
cold (NC)/
cool (TN)
cold
cold
cold
cold
cold (NC)/
cool (TN)
cold (NC)/
cool (TN)
cold
cold (NC)/
cool (TN)
cold (NC)/
cool (TN)
cold
Order
Coleoptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Ephemeroptera
Odonata
Plecoptera
Genus
Promoresia
Antocha
Atherix
Cardiocladius
Diamesa
Dicranota
Eukiefferiella
Heleniella
Pagastia
Potthastia
Rheopelopia
Acentrella
Cinygmula
Drunella
Epeorus
Nixe
Rhithrogena
Lanthus
Amphinemura
NC
(U.S. EPA,
2012)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
TN
yes
yes
yes
yes
yes
yes
yes
yes
Notes— TN
This document is a draft for review purposes only and does not constitute Agency policy.
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Table L-5. continued...
Type
cold
cold
cold
cold
cold
cold
cold
cold
cold
cold (NC)/
cool (TN)
cold
cold
cold
cold
cold
cold/cool
cold/cool
cool
cool
Order
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Plecoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Ephemeroptera
Trichoptera
Ephemeroptera
Plecoptera
Taxon
Clioperla
Cultus
Diploperla
Isoperla
Malirekus
Peltoperla
Pteronarcys
Tallaperla
Zapada
Agapetus
Apatania
Arctopsyche
Dolophilodes
Glossosoma
Parapsyche
Ameletus
Lepidostoma
Habrophlebia
Alloperla
NC
(U.S. EPA,
2012)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
TN
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Notes— TN
uncommon in
TN data set
uncommon in
TN data set
uncommon in
TN data set
uncommon in
TN data set
uncommon in
TN data set
mostly cool or
cold
mostly cool or
cold
uncommon in
TN data set
uncommon in
TN data set
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 L-18 DRAFT—DO NOT CITE OR QUOTE
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Table L-5. continued...
Type
cool
cool
cool
cool
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
warm
Order
Plecoptera
Plecoptera
Trichoptera
Trichoptera
Basommatophora
Coleoptera
Coleoptera
Decapoda
Diptera
Diptera
Diptera
Diptera
Diptera
Ephemeroptera
Ephemeroptera
Hemiptera
Isopoda
Odonata
Odonata
Odonata
Odonata
Odonata
Odonata
Taxon
Sweltsa
Taenionema
Diplectrona
Wormaldia
Physella
Berosus
Lioporeus
Palaemonetes
Nilothauma
Parachironomus
Pentaneura
Procladius
Stenochironomus
Diphetor
Tricorythodes
Belostoma
Caecidotea
Epicordulia
Helocordulia
Hetaerina
Ischnura
Macromia
Neurocordulia
NC
(U.S. EPA,
2012)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
TN
yes
yes
yes
yes
yes
Notes— TN
warm and
cold but
mostly cool
uncommon in
TN data set
warm and
cold — more
common in
cool or cold
11/26/14
This document is a draft for review purposes only and does not constitute Agency policy.
L-19 DRAFT—DO NOT CITE OR QUOTE
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Table L-5. continued...
Type
warm
warm
warm
warm
warm
warm
warm
warm
warm
Order
Odonata
Rhynchobdellida
Rhynchobdellida
Trichoptera
Trichoptera
Trichoptera
Trichoptera
Unionoida
Taxon
Tetragoneuria
Helobdella
Placobdella
Chimarra
Macrostemum
Neureclipsis
Phylocentropus
Elliptic
Erpobdella/Mooreobdella
NC
(U.S. EPA,
2012)
yes
yes
yes
yes
yes
yes
yes
yes
yes
TN
Notes— TN
This document is a draft for review purposes only and does not constitute Agency policy.
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L.I. REFERENCES
Becker, A.J., Stranko, S.A., Klauda, R.J., Prochaska, A.P., Schuster, J.D., Kashiwagi, M.T. and
P.H. Graves. 2010. Maryland Biological Stream Survey's Sentinel Site Network: A
Multi-purpose Monitoring Program. Prepared by the Maryland Department of Natural
Resources Monitoring and Non-tidal Assessment Division. Prepared for the Maryland
Department of Natural Resources Natural Heritage Program.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Implications of climate change for
bioassessment programs and approaches to account for effects. Global Change Research
Program, National Center for Environmental Assessment, Washington, DC; EPA/600/R-
11/036A. Available from the National Technical Information Service, Springfield, VA,
and online at http://www.epa.gov/ncea.
Yuan, Lester. 2006. Estimation and Application of Macroinvertebrate Tolerance Values. Report
No. EPA/600/P-04/116F. National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Washington, D.C.
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 L-21 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX M.
FORMULAS FOR CALCULATING
PERSISTENCE AND STABILITY
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
Persistence between samples can be calculated using Jaccard's similarity coefficient (J):
J(AB) =
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
a + b - j
Herey is the number of taxa common to both years (or sites) A and B, while a and b are the
number of taxa in year (or site) A and B, respectively. It is interpreted as the proportion of taxa
common to both samples, such that values close to zero and one have low and high persistence,
respectively.
Stability, on the other hand, can be calculated using Bray-Curtis similarity (BC) (Bray and
Curtis, 1957):
BC(AB)=\ -
1 \nAi -nm\
NA+NB
Here riAi and TIBI are the number of individuals of taxa /' in year (or site) A and B, and NA and NB
are the total number of individuals in year (or site) A and B, respectively. It is interpreted as the
proportion of individuals (rather than taxa) common to both samples, such that values close to
zero and one have low and high stability, respectively.
As an example, we calculate persistence and stability using Jaccard and Bray-Curtis similarities
with the data in Table M-l:
BC(AB) = 1 -
|0-35| + |5-5|
9 + 35 + 0
23 + 73
5 + 1 50
Table M-l. Sample data for calculating persistence and stability
Samples
Sample year (or site) A
Sample year (or site) B
Taxa V
10
19
TaxaW
0
35
TaxaX
5
5
TaxaY
8
13
TaxaZ
0
1
Sum
23
73
28 High persistence and stability are thought to occur where environmental conditions are similar or
29 relatively constant, or where change occurs incrementally. For additional background and an
30 example of these techniques applied to long running surveys in Alaskan streams, see Milner et
31 al. (2006). At their sites, mean persistence and stability between study years ranged from 0.49 to
32 0.70 and from 0.29 to 0.44, respectively, which suggests that even among the most persistent
33 sites there can exist substantial year-to-year shifts in relative abundances.
This document is a draft for review purposes only and does not constitute Agency policy.
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M.l. REFERENCE:
Bray J.R. and J.T. Curtis. 1957. An ordination of the upland forest communities of southern
Wisconsin. Ecological Monographs 27: 325-349.
Milner, AM; Conn, SC; Brown, LE. (2006) Persistence and stability of macroinvertebrate
communities in streams of Denali National Park, Alaska: implications for biological
monitoring. FreshwBiol 51:373-387.
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 M-3 DRAFT—DO NOT CITE OR QUOTE
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APPENDIX N.
HYDROLOGIC SUMMARY
STATISTICS AND TOOLS FOR
CALCULATING ESTIMATED
STREAMFLOW STATISTICS
Table N-l. Flow statistics that were selected to track changes to high, seasonal, and low flow
components in the Upper Ohio River Basin
Table N-2. 34 hydrologic flow statistics that effectively capture different aspects of the flow
regime in all stream types and have limited redundancy (Olden and Poff, 2003)
Table N-3. 16 streamflow variables hypothesized to be important to stream biota (Hawkins et
al., 2013)
This document is a draft for review purposes only and does not constitute Agency policy.
11/26/14 N-l DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
The Nature Conservancy (TNC) and several partners (states, RBCs, other federal agencies) have
developed ecosystem flow needs for some Eastern and Midwestern rivers and their tributaries
(e.g., the Susquehanna, the Upper Ohio, and the Potomac Rivers) (DePhilip and Moberg, 2010;
Cummins et al., 2010; DePhilip and Moberg, 2013; Buchanan et al., 2013). Table N-l contains
the lists of 10 flow statistics that were chosen to represent the high, seasonal, and low flow
components in the Upper Ohio River basin (DePhilip and Moberg, 2013). These statistics were
selected because they are easy to calculate, commonly used, and integrate several aspects of the
flow regime, including frequency, duration, and magnitude (DePhilip and Moberg, 2013).
Diagrams like the one shown in Figure N-l can be generated for data from RMN sites.
Table N-l. Flow statistics that were selected to track changes to high, seasonal, and low
flow components in the Upper Ohio River basin. These are flow exceedance values. For
example, Qio equals the 10% exceedance probability (Qio), which represents a high flow
that has been exceeded only 10% of all days in the flow period. This is a reproduction of
Table 3.2 in DePhilip and Moberg (2013)
Flow component
High flows
Annual/interannual (>bankfull)
Large flood
Small flood
Bankfull
High flow pulses ( monthly Qio in spring and fall
Monthly Qio
Monthly median
Area under monthly flow duration curve between Q?s
Qio (or some part of this range)
and
Area under monthly flow duration curve between Q?s
Q99
and
Monthly Q?s
Monthly Qgo
17
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
LOMO
H. «
•^ ic
i
i
2000
Figure N-l. In the Upper Ohio River basin, monthly flow exceedance values (Qex) were
plotted against daily discharges to highlight specific portions of the hydrograph and
facilitate discussions about the ecological importance of each portion (from DePhilip and
Moberg, 2013).
Olden and Poff (2003) did a comprehensive review of 171 hydrologic metrics, including
Indicators of Hydrologic Alteration (IHA). They provided recommendations on a reduced set of
metrics that capture critical aspects of the hydrologic regime, are not overly redundant, and are
ecologically meaningful in different types of streams. Table N-2 contains a list of 34 metrics that,
based on their analyses, effectively capture different aspects of flow regimes in all stream types
and have limited redundancy.
This document is a draft for review purposes only and does not constitute Agency policy.
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Table N-2. Based on analyses done by Olden and Poff (2003), these 34 hydrologic flow statistics effectively capture different
aspects of the flow regime in all stream types and have limited redundancy. This is a reproduction of Table 3 (all streams) in
Olden and Poff (2003)
Category
Magnitude — average
flow conditions
Magnitude — low
flow conditions
Magnitude — high
flow conditions
Metric
Skewness in daily flows
Mean annual runoff
Variability in daily flows 1
Spreads in daily flows
Baseflow index 1
Mean minimum April flow
Variability across annual
minimum flows
Variability in baseflow index
1
High flow discharge
Mean maximum August flow
Mean maximum October
flow
Median of annual maximum
flows
Description
Mean daily flows divided by median daily flows
Mean annual flow divided by catchment area
Coefficient of variation in daily flows
Ranges in daily flows (25th/75th percentiles) divided by median
daily flows
7-day minimum flow divided by mean annual daily flows
averaged across all years
Mean minimum monthly flow in April
Coefficient of variation in annual minimum flows averaged
across all years
Coefficient of variation in baseflow index (Ml 17)
Mean of the 10th percentile from the flow duration curve
divided by median daily flow across all years
Mean maximum monthly flow in August
Mean maximum monthly flow in October
Median of the highest annual daily flow divided by the median
annual daily flow averaged across all years
Abbreviated
metric
Ma5
Ma41
Ma3
Mall
M117
M14
M121
M118
Mhl6
Mh8
MhlO
Mhl4
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This document is a draft for review purposes only and does not constitute Agency policy.
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Table N-2. continued...
Category
Frequency of
flow
events — low
flow
conditions
Frequency of
flow
events — high
flow
conditions
Metric
Frequency of low flow spells
Variability in low flow pulse
count
Low flow pulse count
High flood pulse count 2
Flood frequency
Flood frequency
Variability in high flood pulse
count
Description
Total number of low flow spells (threshold equal to 5% of mean
daily flow) divided by record length in years
Coefficient of variation in Fll
Number of annual occurrences during which the magnitude of flow
remains below a lower threshold. Hydrologic pulses are defined as
those periods within a year in which the flow drops below the 25th
percentile (low pulse) of all daily values for the time period.
Number of annual occurrences during which the magnitude of flow
remains above an upper threshold. Hydrologic pulses are defined as
those periods within a year in which the flow goes above 3 times the
median daily flow and the value is an average instead of a tabulated
count.
Mean number of high flow events per year using an upper threshold
of 3 times median flow over all years
Mean number of high flow events per year using an upper threshold
of 7 times median flow over all years
Coefficient of variation in high pulse count (defined as 75th
percentile)
Abbreviated
metric
F13
F12
Fll
Fh3
Fh6
Fh7
Fh2
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This document is a draft for review purposes only and does not constitute Agency policy.
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Table N-2. continued...
Category
Duration
Timing of
flow events
Metric
Number of zero flow days
Variability in low flow pulse
duration
Low flow pulse duration
Means of 30-day minimum
daily discharge
Means of 30-day maximum
daily discharge
Variability in high flow pulse
duration
High flow duration
High flow pulse duration
Constancy
Seasonal predictability of
nonflooding
Variability in Julian date of
annual minimum
Description
Mean annual number of days having 0 daily flow
Coefficient of variation in low flow pulse duration
Mean duration of Fll
Mean annual 30-day minimum divided by median flow
Mean annual 30-day maximum divided by median flow
Coefficient of variation in Fhl
Upper threshold is defined as the 75th percentile of median flows
Mean duration of Fhl
See Colwell( 1974)
Maximum proportion of the year (number of days/365) during which
no floods have ever occurred over the period of record
Coefficient of variation in Til
Abbreviated
metric
D118
D117
D116
D113
Dhl3
Dhl6
Dh20
Dhl5
Tal
Th3
T12
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This document is a draft for review purposes only and does not constitute Agency policy.
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Table N-2. continued...
Category
Rate of
change
Metric
Variability in reversals
Reversals
Change of flow
No day rises
Description
Coefficient of variation in Ra8
Number of negative and positive changes in water conditions from
1 day to the next
Median of difference between natural logarithm of flows between 2
consecutive days with increasing/decreasing flow
Ratio of days where flow is higher than the previous day
Abbreviated
metric
Ra9
Ra8
Ra6
Ra5
10/16/14
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Hawkins et al. (2013) used an iterative process to identify 16 streamflow variables that, in their
2 judgment, could characterize those general aspects of streamflow regimes relevant to stream
3 ecosystem structure and function. These variables are listed in Table N-3.
4
5 Table N-3. These 16 streamflow variables were selected by Hawkins et al. (2013) to
6 quantify aspects of hydrologic regimes believed to be important to stream biota
7
Metrics
Extended low flow index (ELFI); this equals BFI—ZDF, where BFI is the baseflow index (ratio
of the minimum daily flow in any year to the mean annual flow) and ZDF is the zero day
fraction
CV of daily flows (DAYCV)
Contingency (M)
Number of low flow events (LFE)
Number of zero flow events (ZFE)
Mean 7-day minimum flow (Qmin7)
Mean daily discharge (QMEAN)
Mean bankfull flow (Q167)
Mean 7-day maximum flow (Qmax7)
Flow reversals (R)
Flood duration (FLDDUR)
Number of high flow events (FIFE)
Day of year of 50% of flow (T50)
Day of year of peak flow (Tp)
Predictability (P)
Constancy (C)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 N.I. REFERENCES
2 Buchanan, C; Moltz, HLN; Haywood, HC; Palmer, JB; Griggs, AN. (2013) A test of The
3 Ecological Limits of Hydrologic Alteration (ELOHA) method for determining
4 environmental flows in the Potomac River basin, U.S.A. Freshw Biol 58(12):2632-2647.
5 Cummins, J; Buchanan, C; Haywood, HC; Moltz, H; Griggs, A; Jones, C; Kraus, R; Hitt, NP; R.
6 Bumgardner, R. (2010). Potomac large river ecologically sustainable water management
7 report. [ICPRB Report 10-3]. Interstate Commission on the Potomac River Basin for The
8 Nature Conservancy, www.potomacriver.org/pubs.
9 DePhilip, M; Moberg, T. (2010) Ecosystem flow recommendations for the Susquehanna River
10 Basin. Harrisburg, PA. Available online:
11 http://www.srbc.net/policies/docs/TNCFinalSusquehannaRiverEcosystemFlowsStudvRe
12 port NovIO 20120327 fs135148v1.PDF
13 DePhilip, M; Moberg, T. (2013). Ecosystem flow recommendations for the Upper Ohio River
14 basin in western Pennsylvania. Harrisburg, PA: The Nature Conservancy.
15 Hawkins, CP; Tarboton, DG; Jin, J. (2013) Consequences of global climate change for stream
16 biodiversity and implications for the application and interpretation of biological
17 indicators of aquatic ecosystem condition. Final Report. [EPA Agreement Number:
18 RD834186]. Logan Utah: Utah State University.
19 http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/9
20 063
21 Olden, JD; Poff, NL. (2003) Redundancy and the choice of hydrologic indices for characterizing
22 streamflow regimes. River Res Appl 19:101-121.
This document is a draft for review purposes only and does not constitute Agency policy.
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