EPA/600/R-15/280 | FebruaryFebruary 2016 | www.epa.gov/research

Regional Monitoring Networks (RMNs) 

to in 

Freshwater Wadeable Streams
EPA/600/R-15/280 

February 2016 

Final Report

Regional Monitoring Networks (RMNs) to Detect Changing 

Baselines in Freshwater Wadeable Streams

National Center for Environmental Assessment 

Office of Research and Development

U.S. Environmental Protection Agency 

Washington, DC 20460 
ii

DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

ABSTRACT

The United States Environmental Protection Agency (U.S. EPA) is working with its 

regional offices, states, tribes, river basin commissions and other entities to establish Regional 

Monitoring Networks (RMNs) for freshwater wadeable streams. RMNs have been established in 

the Northeast, Mid-Atlantic, and Southeast, and efforts are expanding into other regions. 

Long-term biological, thermal, hydrologic, physical habitat and water chemistry data are being 

collected at RMN sites to document current conditions and detect long-term changes. Consistent 

methods are being used to increase the comparability of data, minimize biases and variability, 

and ensure that the data meet data quality objectives. RMN surveys build on existing state and 

tribal bioassessment efforts, with the goal of collecting comparable data at a limited number of 

sites that can be pooled at a regional level. Pooling data enables more robust regional analyses 

and improves the ability to detect trends over shorter time periods. This document describes the 

development and implementation of the RMNs. It includes information on selection of sites, 

expectations for data collection, the rationale for collecting these data, data infrastructure and 

provides examples of how the RMN data will be used and analyzed. The report concludes with a 

discussion on the status of monitoring activities and next steps. 

Preferred citation:

U.S. EPA (Environmental Protection Agency). (2016) Regional Monitoring Networks (RMNs) to detect changing 

baselines in freshwater wadeable stream. (EPA/600/R-15/280). Washington, DC: Office of Research and 

Development, Washington. Available online at http://www.epa.gov/ncea. 
iii

TABLE OF CONTENTS

LIST OF TABLES ...........................................................................................................................v

LIST OF FIGURES ....................................................................................................................... vi

LIST OF ABBREVIATIONS ....................................................................................................... vii

PREFACE viii

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................. ix

EXECUTIVE SUMMARY .............................................................................................................x

1. INTRODUCTION ......................................................................................... 1-1

2. PROCESS FOR SETTING UP THE REGIONAL MONITORING

NETWORKS (RMNS) .................................................................................. 2-1

3. REGIONAL MONITORING NETWORK (RMN) DESIGN ....................... 3-1

3.1. SITE SELECTION .................................................................................................... 3-1

3.2. METHODS FOR DATA COLLECTION ................................................................. 3-4

3.2.1. BIOLOGICAL INDICATORS ...................................................................... 3-6

3.2.2. TEMPERATURE DATA ............................................................................ 3-16

3.2.3. HYDROLOGIC DATA ............................................................................... 3-18

3.2.4. PHYSICAL HABITAT ............................................................................... 3-23

3.2.5. WATER CHEMISTRY ............................................................................... 3-25

3.2.6. PHOTODOCUMENTATION ..................................................................... 3-25

3.2.7. GEOSPATIAL DATA ................................................................................. 3-27

4. SUMMARIZING AND SHARING REGIONAL MONITORING

NETWORK (RMN) DATA ........................................................................... 4-1

4.1. BIOLOGICAL INDICATORS .................................................................................. 4-1

4.2. THERMAL STATISTICS ......................................................................................... 4-4

4.3. HYDROLOGIC STATISTICS .................................................................................. 4-5

5. DATA USAGE .............................................................................................. 5-1

5.1. APPLICATIONS IN A 1−5 YEAR TIMEFRAME .................................................. 5-1

5.2. APPLICATIONS IN A 5−10 YEAR TIMEFRAME ................................................ 5-8

5.3. APPLICATIONS IN A 10+ YEAR TIMEFRAME .................................................. 5-9

6. DATA MANAGEMENT............................................................................... 6-1

7. IMPLEMENTATION AND NEXT STEPS .................................................. 7-1

8. LITERATURE CITED .................................................................................. 8-1
iv

TABLE OF CONTENTS (continued)

APPENDIX A POWER ANALYSIS .................................................................................... A-1 

APPENDIX B CHECKLIST FOR STARTING A REGIONAL MONITORING 

NETWORK (RMN) .......................................................................................B-1 

APPENDIX C PRIMARY REGIONAL MONITORING NETWORK (RMN) SITES 

IN THE NORTHEAST, MID-ATLANTIC, AND SOUTHEAST RMN 

REGIONS ......................................................................................................C-1 

APPENDIX D DISTURBANCE SCREENING PROCEDURE FOR RMN SITES ............ D-1 

APPENDIX E SECONDARY REGIONAL MONITORING NETWORK (RMN) 

SITES IN THE NORTHEAST AND MID-ATLANTIC REGIONS ............ E-1 

APPENDIX F MACROINVERTEBRATE COLLECTION METHODS ............................ F-1

APPENDIX G LEVEL OF TAXONOMIC RESOLUTION ................................................ G-1

APPENDIX H SUMMARIZING MACROINVERTEBRATE DATA ................................ H-1 

APPENDIX I MACROINVERTEBRATE THERMAL INDICATOR TAXA .................... I-1 

APPENDIX J THERMAL SUMMARY STATISTICS ........................................................ J-1 

APPENDIX K HYDROLOGIC SUMMARY STATISTICS AND TOOLS FOR 

CALCULATING ESTIMATED STREAMFLOW STATISTICS ............... K-1
v 

LIST OF TABLES

3-1. Main considerations when selecting primary sites for the regional 

monitoring networks (RMNs) .............................................................................. 3-2

3-2. 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 ............................................................................................ 3-6

3-3. Recommendations on best practices for collecting biological data at 

regional monitoring network (RMN) sites ........................................................... 3-7

3-4. Recommendations on best practices for collecting macroinvertebrate data 

at Northeast, Mid-Atlantic and Southeast regional monitoring network 

(RMN) sites .......................................................................................................... 3-9

3-5. Recommendations on best practices for collecting temperature data at 

regional monitoring network (RMN) sites ......................................................... 3-17

3-6. Recommendations on best practices for collecting hydrologic data at 

regional monitoring network (RMN) sites ......................................................... 3-21
vi 

LIST OF FIGURES

1-1. States, tribes, river basin commissions (RBCs), and others in three RMN 

regions (Northeast, Mid-Atlantic, and Southeast) have established regional 

monitoring networks (RMNs). ............................................................................. 1-2

3-1. Seasonal differences (spring vs. summer) in percentage cold water taxa 

and individuals in the Mid-Atlantic 2014 data. ................................................. 3-12

3-2. Species accumulation curve based on the Mid-Atlantic 2014 data ................... 3-13

3-3. Staff gage readings provide a quality check of transducer data......................... 3-20

3-4. Photodocumentation of Big Run, WV, taken from the same location each 

year. .................................................................................................................... 3-27

4-1. Spatial distributions of macroinvertebrate taxa, based on the National 

Aquatic Resource Survey (NARS) data ............................................................... 4-3

5-1. RMN data can be used for multiple purposes, over short and long-term 

timeframes............................................................................................................ 5-2

5-2. Proportion of cold/cool indicator taxa at RMN sites, based on preliminary 

data from a subset of sites .................................................................................... 5-4

5-3. The thermal tolerances of Sweltsa and Tallaperla match very closely with 

brook trout ............................................................................................................ 5-4

5-4. Connecticut Department of Energy and Environmental Protection (CT 

DEEP) developed ecologically meaningful thresholds for three major 

thermal classes (cold, cool, warm) ....................................................................... 5-5

5-5. Salmon life cycle plotted in relation to yearly flow cycle (Ricupero, 2009). ...... 5-6

5-6. RMN data will help us gain a better understanding of natural variability in 

hydrologic conditions in small least disturbed streams, and will allow us to 

investigate relationships between biological, thermal, and hydrologic 

conditions. ............................................................................................................ 5-7

5-7. EPA and partners are conducting a broad-scale climate change 

vulnerability assessment on streams in the eastern United States, based on 

a scenario in which stream temperatures warm and the frequency and 

duration of summer low flow events increases .................................................. 5-10

5-8. Modeling results predict declines in species richness across much of the 

Northeast by mid-century (2040−2069). ........................................................... 5-11

5-9. Comparison of macroinvertebrate density values at 10 stream sites in 

Vermont before and after Tropical Storm Irene. ............................................... 5-12

7-1. Sampling has been underway at the Northeast, Mid-Atlantic and Southeast 

RMNs for several years. RMNs are currently being developed in the 

Midwest................................................................................................................ 7-1
vii

LIST OF ABBREVIATIONS

BCG biological condition gradient

CT DEEP Connecticut Department of Energy and Environmental Protection

CWA Clean Water Act

E expected

ELOHA ecological limits of hydrologic alteration

EPT Ephemeroptera, Plecoptera, and Trichoptera

GIS geographic information system

GPS global positioning system

MA DEP Massachusetts Department of Environmental Protection

MD DNR Maryland Department of Natural Resources

MMI multimetric index

NARS EPA National Aquatic Resource Surveys

NLCD National Land Cover Database

NMDS nonmetric multidimensional scaling

NRSA National Rivers and Streams Assessment

NWQMC National Water Quality Monitoring Conference

O observed

QA/QC quality assurance/quality control

QAPP Quality Assurance Project Plan

RBC river basin commission

RIFLS River Instream Flow Stewards Program

RMN regional monitoring network

SDM species distribution model

SOP standard operating procedure

SSN Sentinel Sites Network

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
viii

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.
ix

AUTHORS 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 Assignments

No.1-01 and 2-01. 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

Fairfax County Stormwater Planning Division, VA

Jonathan Witt

REVIEWERS

U.S. EPA Reviewers

Jennifer Fulton (R3), Ryan Hill, Ph.D. (ORISE Fellow within ORD), Sarah Lehmann (OW)

External Peer Reviewers

Lucinda B. Johnson, Ph.D. (University of Minnesota), Kent W. Thornton, Ph.D. (FTN), Chris O. 

Yoder, Ph.D. (Midwest Biodiversity Institute)

ACKNOWLEDGMENTS

The authors would like to thank the many partners who reviewed early versions of this 

report for clarity and usefulness. Their comments substantially improved this document. Special 

thanks to K. Herreman and D. Infante (Michigan State University), P. Morefield, C. Mazzarella, 

and J. Fulton (U.S. EPA), former ORISE participant A. Murdukhayeva, and A. Olivero (The 

Nature Conservancy) for their contributions to the disturbance screening process described in 

Appendix D.
x

EXECUTIVE SUMMARY

The United States Environmental Protection Agency (EPA) is working with its regional 

offices, states, tribes, river basin commissions and other entities to establish Regional Monitoring 

Networks (RMNs) for freshwater wadeable streams. RMNs have been established in the 

Northeast, Mid-Atlantic, and Southeast, and efforts are expanding into other regions. Long-term 

biological, thermal, hydrologic, physical habitat and water chemistry data are being collected at 

RMN sites to document current conditions and detect long-term changes. Consistent methods are 

being used to increase the comparability of data, minimize biases and variability, and ensure that 

the data meet data quality objectives. RMN surveys build on existing state and tribal 

bioassessment efforts, with the goal of collecting comparable data at a limited number of sites 

that can be pooled at a regional level. Pooling data enables more robust regional analyses and 

improves the ability to detect trends over shorter time periods.

The goal of the RMNs is to provide data that can be used by biomonitoring programs for 

multiple purposes, spanning short and long-term timeframes. Uses include:

• Monitoring the condition of minimally and least disturbed streams

• Detecting trends attributable to climate change

• Supplementing Clean Water Act (CWA) programs and initiatives under Sections 303 and 

305(b)

− Defining natural conditions/quantifying natural variability

− Informing criteria refinement or development

− Developing biological indicators for protection planning

• Gaining a better understanding of relationships between biological, thermal, and 

hydrologic data

• Gaining a better understanding of ecosystem responses and recovery from extreme 

weather events

• Gaining insights into effects of regional phenomena such as drought, pollutant/nutrient 

deposition and riparian forest infestations on aquatic ecosystems and bioassessment 

programs

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

− Optional: fish and periphyton, if resources permit (fish are higher priority)
xi

• Temperature: continuous water and air temperature (30-minute intervals)

• Hydrological: continuous water-level data (15-minute intervals); converted to discharge

if resources permit

• Habitat: parameters agreed upon by regional working group

• Water chemistry: In situ, instantaneous water chemistry parameters (specific 

conductivity, dissolved oxygen, pH), plus additional or more comprehensive water 

chemistry measures agreed upon by regional working group

• Photodocumentation

• Geospatial data

The RMNs are designed to detect potentially small trends in biological, thermal, 

hydrologic, physical habitat and water chemistry data at high quality sites in a decision-relevant 

timeframe (e.g., within 5 years to inform criteria development; in 10−20 years to inform 

changing baselines). The RMN design calls for sampling at least 30 sites with similar 

environmental and biological characteristics in each region on an annual basis for 10 or more 

years, using comparable methods. To help inform this design, EPA and partners performed 

power analyses on an aggregated biomonitoring data set from a 2012 Northeast pilot study. The 

power analyses suggest that significant trends in regional community composition can be 

detected within 10−20 years if 30 or more comparable sites are monitored regularly. EPA and 

partners also used literature and standard operating procedures (SOPs) from participating 

organizations to help inform design decisions.

A generic Quality Assurance Project Plan (QAPP)

1 has been developed for the RMNs 

that details the core requirements for participation in the network, and outlines best practices for 

the collection of biological, thermal, hydrologic, physical habitat, and water chemistry data at 

RMN sites. The QAPP was written in a way that should be transferable across regions, with 

region-specific protocols included as addendums. The QAPP is intended to increase the 

comparability of data being collected at RMN sites, improve the ability to detect long-term 

trends by minimizing biases and variability, and to ensure that the data are of sufficient quality to 

meet data quality objectives. The ability of some participants to use the regional RMN methods 

has been limited by resource constraints, so in some situations, there have been differing levels 

of effort and differing methods across sites and organizations. While this is not ideal, the data 

can still be used, just in more limited ways. The data management system that EPA and partners 

are developing will contain metadata that will enable users to select data that meet their needs 

(e.g., collected using certain methods and at certain levels of rigor).

Sampling efforts at the RMNs are concentrated at a core group of sites called “primary”

sites, where efforts are being made to collect the full suite of biological, thermal, hydrologic, 

 

1

The QAPP (U.S. EPA, 2016. Generic Quality Assurance Project Plan for monitoring networks for tracking longterm conditions and changes in high quality wadeable streams) is available online at 

http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=295758&inclCol=eco#tab-3.
xii

physical habitat and water chemistry data. Efforts were made to select sites that had as many of 

the following characteristics as possible:

• Part of established, long-term monitoring networks (e.g., U.S. Geological Survey 

[USGS], sentinel)

• Low level of anthropogenic disturbance

• Exhibit similar environmental and biological characteristics

• Longevity (e.g., accessible [day trip], opportunities to share the workload with outside 

agencies or organizations)

• Located in watersheds protected from future development

• Lengthy historical sampling record for biological, thermal, or hydrological data

Most primary RMN sites are minimally or least disturbed sites (per Stoddard et al.,

2006). High quality waters are being targeted because they are the standard against which other 

bioassessment sites are compared. It is critical to document current conditions at high quality 

sites and to track changes at these sites over time to understand how benchmarks may be shifting 

in response to changing environmental and climatic conditions. Data from additional, 

“secondary,” sites are also being considered for the RMNs. These are sites at which a subset of 

parameters are already being collected in accordance with RMN protocols as part of other 

independent monitoring efforts. Data from secondary sites will increase the sample size and 

range of conditions represented in the RMN data set, and may provide information about unique 

or underrepresented geographic areas.

Data collection has been underway in the Northeast, Mid-Atlantic, and Southeast RMNs 

for several years. This report describes the development and implementation of these pilot

RMNs. It includes information on selection of sites, expectations for data collection, the rationale 

for collecting these data, and data infrastructure. The report also provides examples of how the 

RMN data will be used and analyzed, and concludes with a discussion on the status of 

monitoring activities. Currently, EPA and partners continue to build capacity and refine 

protocols, indicator lists, analytical techniques and data management systems for the RMNs, 

including working with regions to establish RMNs. Long-term data from RMNs can support 

CWA programs, fill data gaps, and help detect trends attributable to climate change. The RMN 

framework is flexible and allows for expansion to new regions, as well as to new stream classes 

and waterbody types. The monitoring data being collected from these regional efforts will 

provide important inputs for bioassessment programs as they strive to protect water quality and 

aquatic ecosystems under a changing climate.
1-1 

1. INTRODUCTION

The U.S. Environmental Protection Agency (EPA) has been working with states, tribes,

river basin commissions (RBCs), and other organizations in different parts of the United States

to establish regional monitoring networks (RMNs) to collect contemporaneous biological, 

thermal, hydrologic, physical habitat, and water chemistry data from freshwater wadeable 

streams. RMNs have been established in the Northeast, Mid-Atlantic, and Southeast2 (see 

Figure 1-1), and efforts to establish new networks are expanding into other regions. The concept 

of the RMNs stems from work that began in 2006 with pilot studies that examined long-term 

climate-related trends in macroinvertebrate data from state biomonitoring programs in Maine, 

North Carolina, Ohio, and Utah (U.S. EPA, 2012a). During these studies, a lack of long-term, 

contemporaneous biological, thermal, and hydrologic data became apparent, particularly at 

minimally disturbed (Stoddard et al., 2006) stream sites. These data gaps have been documented 

elsewhere (e.g., Mazor et al., 2009; Jackson and Fureder, 2006; Kennen et al., 2011) and have 

been recognized as important gaps to fill by the National Water Quality Monitoring Council 

(NWQMC) (NWQMC, 2011). 

The goal of the RMNs is to provide data that can be used by biomonitoring programs for 

multiple purposes, spanning short and long-term timeframes. Uses include:

• Monitoring the condition of minimally and least disturbed streams

• Detecting trends attributable to climate change

• Supplementing Clean Water Act (CWA) programs and initiatives 

− Defining natural conditions/quantifying natural variability to support Section 305(b) 

programs 

− Informing criteria refinement or development under Section 303 

− Developing biological indicators for protection planning for Section 303(d) programs

• Gaining a better understanding of relationships between biological, thermal, and 

hydrologic data

• Gaining a better understanding of ecosystem responses and recovery from extreme 

weather events

• Gaining insights into effects of regional phenomena such as drought, pollutant/nutrient 

deposition and riparian forest infestations on aquatic ecosystems and bioassessment 

programs 

 

2

RMN regions are largely (but not exactly) based on EPA regions to help facilitate coordination and sharing of 

resources. Differences include: New York (EPA Region 2), which joined the EPA Region 1 states in the Northeast 

RMN; New Jersey (EPA Region 2), which joined the EPA Region 3 states in the Mid-Atlantic RMN; and 

Mississippi and Florida, which did not join EPA Region 4 states in the Southeast RMN because they lack the 

targeted habitat (medium to high gradient, cold, riffle-dominated streams).
1-2 

Figure 1-1. States, tribes, river basin commissions (RBCs), and others in 

three RMN regions (Northeast, Mid-Atlantic, and Southeast) have 

established regional monitoring networks (RMNs).

The RMNs are designed to detect potentially small trends in biological, thermal, 

hydrologic, physical habitat and water chemistry data at high quality sites in a decision-relevant 

timeframe (e.g., 10−20 years to be relevant to climate change). Several states, tribes, RBCs, and 

others are already collecting annual biological and continuous temperature data at targeted sites, 

and to a lesser degree, hydrologic data. The goal is to supplement existing efforts like these, and 

to collect comparable data at a limited number of sites to pool at a regional level. Pooling data 

enables more robust regional analyses, improves the ability to detect trends over shorter time 

periods and can inform on changes at a spatial scale similar to climatic changes.

The RMN design calls for sampling at least 30 sites with similar environmental and 

biological characteristics in each region on an annual basis for 10 or more years, using 

comparable methods. To help inform this design, EPA and partners performed power analyses 

on an aggregated biomonitoring data set from the 2012 Northeast pilot study. The power 

analyses suggest that significant trends in regional community composition can be detected 

within 10−20 years if 30 or more comparable sites are monitored regularly. A detailed account of 

these analyses can be found in Appendix A. Design decisions were also informed by literature 

and standard operating procedures (SOPs) being used by the RMN participants. Efforts are being 
1-3

made to use consistent methods at RMN sites to increase the comparability of data and minimize 

biases and variability. A Quality Assurance Project Plan (QAPP) has been developed for the 

RMNs to ensure that participating entities understand the requirements and meet the data quality 

objectives. Scientific considerations are balanced with practical considerations by participating 

entities. The RMN framework needs to be flexible enough to tie into existing state and tribal 

bioassessment efforts and must stay within the resource constraints of its participants.

Data collection in the Northeast, Mid-Atlantic, and Southeast RMNs has been underway 

for several years, and EPA and partners are starting to use these data in initial evaluations and 

data analyses. This report describes the development and implementation of these RMNs. It 

includes information on selection of sites, expectations for data collection, the rationale for 

collecting these data, and data infrastructure. The report also provides examples of how the RMN 

data will be used and analyzed. It concludes with a discussion on the status of monitoring 

activities and next steps.
2-1

2. PROCESS FOR SETTING UP THE REGIONAL MONITORING 

NETWORKS (RMNS)

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 establish regional goals to determine basic survey bounds, such as selection 

of a target population (e.g., freshwater wadeable streams with abundant riffle habitat). Working 

groups selected RMN sites using consistent criteria (see Section 3.1), and selected appropriate 

data-collection protocols and methodologies (see Section 3.2). As part of this process, working 

groups considered the site selection criteria and methods being used in the other regions and tried 

to utilize 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. The regional 

working groups began implementation several years ago and are starting to use the RMN data in 

initial evaluations and data analyses. EPA and partners recently developed a generic RMN QAPP

that details the core requirements for participation in the network, and outlines best practices for 

the collection of biological, thermal, hydrologic, physical habitat, and water chemistry data at 

RMN sites. The regional working groups are in the process of reviewing and approving the 

QAPP. The EPA and partners are also developing a data management system that will allow 

participating organizations and outside users to access data and metadata that are being collected 

at RMN sites (see Section 6). Appendix B includes a step-by-step checklist on the process for 

developing and implementing RMNs.
3-1 

3. REGIONAL MONITORING NETWORK (RMN) DESIGN

The RMN design calls for sampling at least 30 sites with similar environmental and 

biological characteristics in each region on an annual basis for 10 or more years, using 

comparable methods. In 2011−2012, EPA collaborated with seven states in the northeastern 

United States on a pilot study that helped lay the groundwork for the RMNs. The goal of the 

pilot was to design a monitoring network that could detect potentially small trends in biological, 

thermal, hydrologic, physical habitat and water chemistry data at high quality sites in a 

decision-relevant timeframe (e.g., 10−20 years to be relevant to climate change). EPA and 

partners performed power analyses on an aggregated biomonitoring data set from the Northeast 

to explore questions such as: How long will it take to detect trends in biological metrics? How 

much of an effect does sampling frequency and classification scheme have on trend detection 

time? The results suggest that detection times of 10−20 years (at 80% power) are possible for 

some biological metrics if 30 or more sites with comparable environmental conditions and 

biological communities are monitored regularly. These results are consistent with a study by

Larsen et al. (2004), which found that well-designed networks of 30−50 sites monitored 

consistently can detect underlying changes of 1−2% per year in a variety of metrics within 

10−20 years, or sooner, if such trends are present. The Northeast power analyses are described in 

detail in Appendix A.

3.1. SITE SELECTION

Sampling efforts at the RMNs are concentrated at a core group of sites called “primary” 

sites, where efforts are being made to collect the full suite of biological, thermal, hydrologic, 

physical habitat and water chemistry data (see Section 3.2). The working groups selected 2 to 

15 primary sites per state (depending on the size of the state and availability of resources), with 

the overall goal of sampling at least 30 primary sites in each RMN region. The site selection 

process takes into account numerous considerations, which are summarized in Table 3-1. Efforts 

were made to select sites that had as many of the desired characteristics listed in Table 3-1 as 

possible. Appendix C lists the primary RMN sites in each region as of September 2015. 
3-2

Table 3-1. Main considerations when selecting primary sites for the regional 

monitoring networks (RMNs)

Consideration Desired characteristics at primary sites

Existing monitoring network Located in established long-term monitoring networks to 

build upon data already being collected by states, tribes, 

RBCs, and others.

Disturbance Low level of anthropogenic disturbance. 

Equipment Colocated with existing hydrologic equipment (e.g., USGS 

gage, weather station).

Classification Sites exhibit similar environmental and biological 

characteristics, which minimizes natural variability across 

sites, improves power for detecting long-term trends and 

allows for pooling of data within and across regions.

Longevity Accessible (e.g., day trip), opportunities to share the 

workload with outside agencies or organizations.

Sampling record Lengthy historical sampling record for biological, thermal, 

or hydrological data.

Potential for future disturbance Located in watersheds that are protected from future 

development.

Where feasible, organizations colocated RMN sites with existing stations like USGS 

gages or in established long-term monitoring networks such as the sentinel networks of the 

Vermont Department of Environmental Conservation (VT DEC), the Connecticut Department of 

Energy and Environmental Protection (CT DEEP), Maryland Department of Natural Resources 

(MD DNR), West Virginia Department of Environmental Protection (WV DEP), and Tennessee 

Department of Environment and Conservation, continuous monitoring stations of the 

Susquehanna River Basin Commission, and USGS networks, such as the Northeast Site Network 

and the Geospatial Attributes of Gages for Evaluating Streamflow (GAGES-II) program. Some 

of these sites have lengthy historical records, which are preferred for primary RMN sites.

Efforts were made to select minimally disturbed or least disturbed sites (per Stoddard 

et al. 2006). High quality waters are being targeted because they are the standard against which 

other bioassessment sites are compared. It is critical to document current conditions at high 

quality sites and to track changes at these sites over time to understand how benchmarks may be

shifting in response to changing environmental and climatic conditions. EPA and partners 

developed a standardized procedure for characterizing the present-day level of anthropogenic 

disturbance and applied this across RMNs so that sites from all states and regions are rated on a 

common scale (see Appendix D). Sites are screened for likelihood of impacts from land use 

disturbance, dams, mines, point-source pollution and other factors. 
3-3

The selection criteria also prioritize sites that exhibit similar environmental and 

biological characteristics, as this helps reduce natural variability across sites (which improves 

power for detecting long-term trends) and allows for pooling of data within and potentially 

across regions. The Southeast working group used ecoregions during the initial site selection 

process because ecoregions dominate the reference-site-stratification approach used by many 

programs for assessing streams (Carter and Resh, 2013). Most of the RMN sites in the Southeast 

are located in ecoregions with hilly or mountainous terrain (e.g., Piedmont, Blue Ridge, Central, 

and North Central Appalachians), where streams generally have higher gradients and more riffle 

habitat. In the Northeast and Mid-Atlantic regions, size and gradient were key classification 

variables (see Appendix A).

To further inform stream classification, EPA performed a broad-scale analysis on 

macroinvertebrate survey data from the EPA National Aquatic Resource Surveys (NARS) 

program.3 The data set included minimally disturbed freshwater wadeable stream sites from the 

Northeast, Mid-Atlantic, and Southeast regions. A cluster analysis was performed, and sites were 

grouped into three classes based on similarities in taxonomic composition. EPA then developed a 

model based on environmental variables to predict the probability of occurrence of the three 

classes in watersheds in the eastern United States. The three classes are referred to as: (1) small 

to medium size, medium to high gradient, colder temperature; (2) small, low gradient; and 

(3) warmer temperature, larger size, lower gradient. Most of the primary RMN sites that were

selected fall within the small to medium size, medium to high gradient, colder temperature

stream class. On average, sites in this stream class have higher numbers of cold water taxa,

which improves the likelihood of detecting temperature-related trends in this thermal indicator

metric over shorter time periods (see Appendix A).

There were several additional site selection considerations. Where feasible, sites with low 

potential for future development were selected because future alterations could limit trend 

detection power as well as the ability to characterize climate-related impacts at RMN sites. 

Participants utilized what they felt were the best, most current data to assess potential for future 

development. The Northeast utilized a spatial data set provided by The Nature Conservancy 

(TNC)4

 that showed public and private lands and waters secured by a conservation agreement. 

Other RMN members contacted city planners and personnel from transportation and forestry 

departments to obtain information about the likelihood of future urban and residential 

development, road construction, and logging or agricultural activities.

Practical considerations were also important during the site screening process. For 

example, organizations generally selected sites that could be sampled during a day trip and were 

easy to access, which are factors that will likely increase the frequency at which sites can be 

visited. More sites visits may improve the quality of data being collected (particularly the 

3

Data available at http://water.epa.gov/type/rsl/monitoring/riverssurvey/index.cfm. 4

Secured lands data set available at 

https://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/terre

strial/secured/Pages/default.aspx.
3-4

hydrologic data). Sites were prioritized if they were colocated with existing equipment, such as 

USGS gages, or if there were opportunities to share the workload with outside agencies or 

organizations. Efforts have been made to partner with national monitoring programs, such as the 

EPA NARS, Long Term Ecological Research Network and the National Ecological Observatory 

Network. For various reasons (e.g., sites are not revisited annually, sites are not located in same 

stream class), sites that are being sampled for these programs have not been selected as primary 

RMN sites, but EPA and partners are continuing to seek opportunities for collaboration with 

these and other potential partners. 

Data from additional, “secondary,” sites are also being considered for the RMNs. These 

are sites at which a subset of parameters are already being collected in accordance with RMN 

protocols as part of other independent monitoring efforts. Data from secondary sites will increase 

the sample size and range of conditions represented in the RMN data set, and may provide 

information about unique or underrepresented geographic areas, such as the New Jersey Pine 

Barrens or the Coastal Plain ecoregion. Appendix E lists the candidate secondary RMN sites in 

each region as of September 2015. 

3.2. METHODS FOR DATA COLLECTION

Efforts are being made to collect the following types of data (consistent with existing 

programs and scientific literature) from RMN sites:

• Biological indicators: macroinvertebrates

− Optional: fish and periphyton, if resources permit (fish are higher priority)

• Temperature: continuous water and air temperature (30-minute intervals)

• Hydrological: continuous water-level data (15-minute intervals); converted to discharge

if resources permit

• Habitat: parameters agreed upon by regional working group

• Water chemistry: in situ, instantaneous water chemistry parameters (specific

conductivity, dissolved oxygen, pH), plus additional or more comprehensive water

chemistry measures agreed upon by regional working group

• Photodocumentation: photographs taken from the same locations during each site visit

• Geospatial data: percentage land use and impervious cover, climate, topography, soils,

and geology, if resources permit

The goal is to use methods that will maximize the likelihood of detecting subtle changes 

over as short a time period as possible, while staying within the resource constraints of 

participating organizations. EPA and partners used results from the Northeast power analyses 

(see Appendix A), literature and SOPs from participating organizations to help inform methods 

decisions. Efforts are being made to use as consistent and comparable methods as possible since 
3-5

different methodologies may introduce biases in analyses and contribute to variability, which 

reduces the sensitivity of indicators and increases trend detection times.

During the initial phases of RMN development, the regional working groups agreed upon 

methods to use at primary RMN sites. These methods are summarized in Appendix F. EPA and 

partners recently developed a generic RMN QAPP that details the core requirements for 

participation in the network, and outlines best practices for the collection of biological, thermal, 

hydrologic, physical habitat, and water chemistry data at RMN sites. The QAPP is available 

online at http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=295758&inclCol=eco#tab-3. The 

regional working groups are in the process of reviewing and approving the QAPP, and are 

customizing it for their regions via addendums. 

The ability of some participants to use the regional RMN methods has been limited by 

resource constraints, so there have been differing levels of effort and in some situations, differing 

methods across sites and organizations. While this is not ideal, the data can still be used, just in 

more limited ways. The data management system that EPA and partners are developing (see 

Section 6) will contain metadata that will enable users to select data that meet their needs (e.g., 

collected using certain methods and at certain levels of rigor). To account for the differing levels 

of effort across sites and organizations, EPA and partners have broken the sampling 

methodologies down into different elements, and different levels of rigor are established for each 

element. Examples of elements include type of habitat sampled, gear type, frequency of data 

collection, level of taxonomic resolution, level of expertise of field and laboratory personnel, and 

quality assurance/quality control (QA/QC) procedures. There are four levels of rigor in the RMN 

framework, with Level 1 being the lowest and Level 4 being the best/highest standard (see 

Table 3-2). Level 3 is the target for primary RMN sites. These elements and levels of rigor are 

covered in more detail in Sections 3.2.1 through 3.2.7. 
3-6

Table 3-2. 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

1 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.

2 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.

3 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. 

3.2.1. BIOLOGICAL INDICATORS

Collection of multiple assemblages (macroinvertebrates, fish, periphyton) at RMN sites is 

encouraged. At a minimum, macroinvertebrates should be collected at the primary RMN sites. 

Collections from this assemblage are central to the RMNs because macroinvertebrates are 

already collected by participating states, tribes, RBCs, and other agencies for a variety of other 

purposes. For example, macroinvertebrates are crucial for quantifying stream condition because 

(1) the assemblage responds to a wide range of stressors, (2) many (not all) are easily and

consistently identified, and (3) they have limited mobility, short life cycles, and are highly

diverse. Guidelines for collecting macroinvertebrates, fish, and periphyton can be found in

Sections 3.2.1.1, 3.2.1.2, and 3.2.1.3, respectively.

Data collection should be done by trained personnel (see Table 3-3) because formal 

training can have a large impact on observer agreement and repeatability and can reduce 

assessment errors (e.g., Herlihy et al., 2009; Haase et al., 2010). Repeatability is particularly 

important for RMNs because data are gathered from multiple sources. Ideally, participating 

organizations should adhere to the sample collection and processing protocols that are agreed 

upon by the regional working group. Some of these guidelines include QA/QC procedures, 

which improve data quality (Stribling et al., 2008; Haase et al., 2010). Example QA/QC 

procedures include collecting replicate samples in the field, conducting audits to ensure that 

crews are adhering to collection and processing protocols, replicate subsampling (meaning after 

subsampling occurs, the subsample is recombined with the original sample and subsampled 

again), and validating taxonomic identifications at an independent laboratory. 
3-7

Table 3-3. 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) 2 3 4 (highest)

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 who has 

some prior experience 

collecting the assemblage 

of interest

Work is conducted by a trained

biologist who has multiple years of 

experience collecting the assemblage 

of interest

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
3-8

3.2.1.1. Macroinvertebrates

Developing recommendations on macroinvertebrate sampling protocols is challenging 

because organizations use different collection and processing protocols when they sample 

macroinvertebrates, and each entity’s biological indices are calibrated to data that are collected 

and processed using these methods. When developing best practices at RMN sites, efforts were 

made to accommodate differences in sampling methodologies within regions (see Appendix F) 

while still providing data that are sufficiently similar that they can be used to generate 

comparable indicators at the regional level, and to minimize variability where possible. 

At primary RMN sites, macroinvertebrate sampling should be conducted at least once 

annually (see Table 3-4). The Northeast power analyses showed that sampling frequency (1 vs. 2 

vs. 5-year intervals) had a significant effect on trend detection time. Sampling 

macroinvertebrates on an annual basis improves trend detection times, particularly if trends are 

subtle (see Appendix A). Annual data are also important for quantifying temporal variability. As 

discussed in Section 5, the data will help us to better understand how natural variability affects 

the consistency of biological condition scores and metrics from year to year, and how this relates 

to changing thermal and hydrologic conditions.

In the Northeast, Mid-Atlantic and Southeast RMNs, macroinvertebrate samples are 

being collected in reaches with abundant riffle habitat (see Table 3-4). Cold water taxa, which 

are of particular interest due to their potential vulnerability to climate change, typically inhabit 

riffles. Furthermore, riffle habitat is being targeted because sample consistency is strongly 

associated with the type of habitats sampled (Parson and Norris, 1996; Gerth and Herlihy, 2006; 

Roy et al., 2003). Recent methods comparison studies indicate that where abundant riffle habitat 

is present, single habitat riffle, reach-wide, and multihabitat samples generally produce 

comparable classifications and assessments, especially when fixed counts and consistent 

taxonomy are used (e.g., Vinson and Hawkins, 1996; Hewlett, 2000; Ostermiller and Hawkins, 

2004; Cao et al., 2005; Gerth and Herlihy, 2006; Rehn et al., 2007; Blocksom et al., 2008). 

While sampling at RMN sites is focused primarily on riffles, other habitats are also of interest. In 

the Southeast region, in addition to collecting quantitative samples from riffle habitat, some 

organizations are also collecting qualitative samples from multiple habitats. They are keeping 

taxa from the different habitats separate, which provides information on how changing thermal 

and hydrologic conditions impact taxa in nonriffle habitats. For example, taxa in edge habitats 

may show a greater response to extended summer low flow events than taxa in riffles because the 

edge habitats are more likely to go dry. 
3-9

Table 3-4. Recommendations on best practices for collecting macroinvertebrate data at Northeast, Mid-Atlantic 

and Southeast 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) 2 3 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)

Habitat No riffle habitat Multihabitat composite from 

a sampling reach with scarce 

riffle habitat

Abundant riffle habitat Multihabitat 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 
3-10

Table 3-4. continued...

Component 1 (lowest) 2 3 4 (highest)

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 

Qualifications Identifications are 

done by a novice or 

apprentice biologist 

with no 

certification

Identifications are done by 

experienced taxonomist 

without certification

an 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 

practical

for all taxa, where 

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
3-11

At primary RMN sites, macroinvertebrate sampling should occur during a consistent time 

period to minimize the variability associated with seasonal changes in the composition and 

abundances of stream biota and to allow for more efficient trend detection (Olsen et al., 1999). 

At RMN sites, samples should be collected during the same time period (or periods) each year, 

ideally within 2 weeks of a set collection date (see Table 3-4). If flooding or high water prevents 

sample collection within the specified time period, samples should be taken as closely to the 

target period as possible. In addition to taxonomic consistency, samples collected during the 

same time period can be used to explore whether long-term changes in continuous thermal and 

hydrologic measurements are occurring during the target period. For example, streams that were 

once perennial may 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. 

In the Northeast RMN, sampling is taking place during a summer/early fall 

(July−September) index period because this range overlaps with existing state index periods and 

because environmental conditions in the spring are generally not conducive to sampling (e.g., 

potential ice cover). In the Southeast RMN, macroinvertebrate samples are being collected in 

April, with some states adding a September sample. States and RBCs in the Mid-Atlantic RMN

are currently collecting samples in both spring and summer, as resources permit. The spring 

index period is being restricted to March−April and the summer index period to July−August 

because this range overlaps with existing state and RBC index periods and reduces potential 

temporal variability to a 2-month window. In the future, if only one collection is possible in the 

Mid-Atlantic RMN, the spring index period is preferred because preliminary data suggest that on 

average, assemblages are comprised of slightly higher proportions of cold water taxa and 

individuals in the spring (see Figure 3-1).
3-12

Figure 3-1. Seasonal differences (spring vs. summer) in percentage cold 

water taxa and individuals in the Mid-Atlantic 2014 data. 

When macroinvertebrate samples from primary RMN sites are processed, subsampling 

should be performed in a laboratory by trained personnel. Participating organizations should 

perform fixed counts with a target of 300 (or more) organisms to reduce sample variability and 

ensure sample comparability (see Table 3-4). Consistent subsampling protocols are important

because sampling effort and the subsampling method can affect estimates of taxonomic richness 

(Gotelli and Graves, 1996), taxonomic composition, and relative abundance of taxa (Cao et al., 

1997). The 300-organism target is larger than what is specified in some state, tribal, and RBC 

methods. The purpose of using this larger fixed count is to increase the probability of collecting 

cold water indicator taxa that are rarer and to improve the chances of detecting trends in richness 

metrics over shorter time periods, as suggested in the Northeast pilot study (see Appendix A). 

Having a 300-organism or higher target is further supported by the species accumulation curve 

shown in Figure 3-2. The curve, which is based on preliminary 2014 data from the Mid-Atlantic 

RMN, shows that the larger the subsample size, the higher the richness of the thermal indicator 

taxa. If organizations normally use lower fixed targets (e.g., 100- or 200-count samples) for their 

assessments, computer software can be used to randomly subsample 300- or higher-count 

samples to those lower targets. 
3-13

Figure 3-2. Species accumulation curve based on the Mid-Atlantic 2014 data. 

The larger the subsample size, the higher the richness of thermal indicator taxa.

Taxa collected at primary RMN sites should be identified to the lowest practical 

taxonomic level (see Table 3-4). Research has shown that finer levels of taxonomic resolution 

can discriminate ecological signals better than coarse levels (Lenat and Resh, 2001; Waite et al., 

2000; Feio et al., 2006; Hawkins, 2006). If this level of resolution is not possible, efforts should 

be made to conform to the taxonomic resolution recommendations contained in Appendix G. 

These call for genus-level identifications (where possible) for Ephemeroptera, Plecoptera, 

Trichoptera, Chironomidae, and Coleoptera and specify certain genera within these taxonomic 

groups that should be taken to the species level. These genera were selected because they are 

believed to be good thermal indicators and have shown variability in thermal tolerances at the 

species level (U.S. EPA, 2012a). Following these recommendations will increase the chances of 

detecting temperature-related signals over shorter time periods at RMN sites, and will provide 

important information about which taxa are most sensitive to changing thermal conditions. The 

recommendations in Appendix G should be regarded as a starting point subject to revision as 

better data become available in the future.
3-14

High-quality taxonomy is a critical component of credible ecological research, and 

taxonomic identifications for RMN samples should be done by a trained taxonomist who has the 

appropriate level of certification (see Table 3-4). Analyses have shown that the magnitude of 

taxonomic error varies among taxa, laboratories and taxonomists, and that the variability can 

affect interpretations of macroinvertebrate data (Stribling et al., 2008). Sources of these errors 

include incorrect interpretation of technical literature, recording errors, and vague or coarse 

terminology, as well as differences in nomenclature, procedures, optical equipment, and handling 

and preparation techniques (Stribling et al., 2003; Dalcin, 2004; Chapman, 2005). Experience 

and training can prevent many of these errors (Haase et al., 2006; Stribling et al., 2008). A 

reference collection of each unique taxon should be housed by each agency and made available 

for verification or comparison. The entire fixed count subsample (referred to as “voucher 

samples”) for each primary RMN site should be preserved and archived. When a unique taxon is 

removed from a voucher sample for the reference collection, it must be clearly documented. 

Reference collections and voucher samples will be particularly important for RMN samples 

because identifications often will be made by different taxonomists. If resources permit, a subset 

of samples should be checked by a taxonomist from an independent laboratory to validate the 

identifications and ensure consistency across organizations. 

The collection of certain types of demographic or life history data could reduce the 

amount of time needed to detect changes in biological indicators because these traits may 

respond to climate change earlier than species richness and abundance (Sweeney et al., 1992; 

Hogg and Williams, 1996; Harper and Peckarsky, 2006). Examples include rates of 

development, size structure, timing of emergence, and voltinism. More importantly, the 

frequency and occurrence of the traits themselves can be linked to environmental conditions and 

used to predict vulnerability of other species (e.g., Townsend and Hildrew, 1994; Statzner et al., 

1994; Townsend et al., 1997; Richards et al., 1997; van Kleef et al., 2006; Poff et al., 2006). It is 

also worth considering qualitative collections of adult insects to verify or assist in species 

identification. At this time, the collection of these types of ancillary data at RMN sites is 

optional, and any discussions of additional sampling should consider the costs and benefits of the 

data for the states, tribes, or RBCs and RMN objectives. 

When developing the macroinvertebrate methods for the RMNs, the intent was to balance 

the need to generate comparable data that meets RMN objectives with generating data that has 

value for individual RMN member’s routine bioassessment programs. Without additional 

resources and training, some organizations will not be able to attain these levels of rigor on a 

consistent, long-term basis. For example, some organizations will not be able to follow the 

regional protocols for the 300-organism count and species-level identifications. Instead, they will 

likely follow their normal processing protocols, with counts of 100 or 200 organisms and 

genus-level identifications. Reduced counts and coarser level identifications, in particular, are 

likely to affect the richness metrics (Stamp and Gerritsen, 2009; also see Figure 3-2). 

RMN members should collect each sample using the method agreed upon by the regional 

working group and retain this sample, even if the organization lacks sufficient resources to count 
3-15

300 organisms and perform species-level identifications at this time, since funds may become 

available at a future date to process samples in accordance with the RMN protocols. RMN 

members should periodically refresh these samples with preserving agent so that specimens 

remain in good enough condition to later be identified. In some cases, regional coordinators may 

be able to obtain funding to cover the costs of macroinvertebrate sample processing and 

species-level identifications at a common laboratory. For example, EPA Region 3 was able to 

achieve this during the 2014 sampling season for the Mid-Atlantic RMN members. Even if this 

can only be done for one year, it serves to establish valuable baseline information.

If the RMN protocols differ from those that are normally used by RMN members, EPA 

and partners are exploring the possibility of conducting methods comparison studies at a subset 

of sites. This could involve the collection of side-by-side samples using the different methods. 

After the paired samples are processed using the respective methods, results would be compared 

and differences between the methods could be quantified. 

3.2.1.2. Fish

The collection of fish at RMN sites is optional but encouraged. Fish are considered to be 

a higher priority assemblage than periphyton at RMN sites because fish are routinely collected 

by monitoring programs, are easily and consistently identified, and are often species of economic 

and social importance. Further, the data can be obtained without a significant amount of further 

sample processing, making this assemblage a cost-effective group to analyze, and the behavioral 

and physiologic traits can be linked to environmental conditions. Many organizations have 

strong interests in protecting fisheries, and numerous studies are being done to predict and 

monitor how fish distributions will change in response to climate change (e.g., Clark et al., 2001; 

Flebbe et al., 2006; Trumbo, 2010; Wenger et al., 2011). Best practices for fish collection at 

RMN sites are shown in the following list. 

• Participating organizations should follow the protocols that are agreed upon by the

regional working group. At this time, only the Southeast region is consistently collecting

fish data. Because fish sampling protocols are similar across organizations in this region,

the Southeast regional working group agreed to let organizations use their own standard

operating procedures. If organizations in other regions start to sample fish on a regular

basis, this topic should be revisited and the working groups should take an in-depth look

at the comparability of fish sampling protocols within and across regions.

• There should be strict adherence to an index period (or periods).

• Species-level identifications should be done (where practical) by a trained fish

taxonomist.

• A reference collection of each unique taxon should be housed by each agency and be

made available for verification or comparison.
3-16

3.2.1.3. Periphyton

The collection of periphyton at RMN sites is optional but encouraged, as periphyton are 

important indicators of stream condition and stressors (Stevenson, 1998; McCormick and 

Stevenson, 1998). At this time, the Southeast is the only region that has written guidelines for 

periphyton collection. Their sampling protocols follow the Southeastern Plains instream nutrient 

and biological response protocols (U.S. EPA, 2006) or equivalent. They strictly adhere to a 

spring index period and have a subsampling target of 600 valves (300 cells). Species-level 

identifications are being done (where practical) by a qualified taxonomist, and reference 

collections of unique taxa are being retained. The protocols also recommend that the EPA rapid 

periphyton survey field sheet or equivalent be completed (Barbour et al., 1999). 

If organizations from other RMNs start to collect periphyton, they should follow the 

protocols that are agreed upon by their regional working group. If standardized regional 

protocols are not used, the methods that each entity uses should be detailed and well 

documented. With periphyton, some programs have encountered problems with taxonomic 

agreement among different laboratories and taxonomists, so steps should be taken to ensure 

consistency in taxonomic identifications (e.g., send all samples to the same laboratory, 

photodocument taxa in reference collections, conduct taxonomic checks with an independent 

laboratory). 

3.2.2. TEMPERATURE DATA

Some states, tribes, and RBCs have been early adopters of continuous temperature sensor 

technology and have written their own protocols for deploying these sensors. In an effort to 

increase comparability of data collection across states and regions, EPA and collaborators 

published a document on best practices for deploying inexpensive temperature sensors 

(U.S. EPA, 2014). The best practices for collecting temperature data at RMN sites closely follow 

these protocols. 

At primary RMN sites, both air and water temperature sensors should be deployed (see 

Table 3-5). Together, the air and water temperature readings can be used to gain a better 

understanding of the responsiveness of stream temperatures to air temperatures (also referred to 

as thermal sensitivity), and provide insights into the factors that influence the vulnerability of 

streams to thermal change (see Section 5). Air temperature readings are also used for quality 

control (e.g., to determine when water temperature sensors are dewatered; Bilhimer and Stohr, 

2009; Sowder and Steel, 2012). 
3-17

Table 3-5. 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. 

a

Component 1 (lowest) 2 3 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 

QA/QC―sensor 

accuracy

No accuracy checks 

are performed

No accuracy

performed

checks are Predeployment accuracy 

check is performed, along 

with any other QA/QC 

checks that are agreed 

upon by the regional 

working group

In addition to the predeployment 

accuracy check, the following 

checks are also performed: initial 

deployment, mid-deployment, 

biofouling, and postdeploymenta

 

For more details, see the QAPP (U.S. EPA, 2016. Generic Quality Assurance Project Plan for monitoring networks for tracking long-term conditions and 

changes in high quality wadeable streams), which is available online at http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=295758&inclCol=eco#tab-3.
3-18

Temperature measurements at RMN sites should be taken year-round at 30-minute 

intervals (see Table 3-5). Year-round data are necessary to fully understand thermal regimes and 

how these regimes relate to aquatic ecosystems (U.S. EPA, 2014). Radiation shields should be 

installed for both water and air temperature sensors (see Table 3-5) to prevent direct solar 

radiation from hitting the temperature sensors and biasing measurements (Dunham et al., 2005; 

Isaak and Horan, 2011). The shields also serve as protective housings. Shield effectiveness varies 

by design (Holden et al., 2013), so it is suggested that organizations use tested designs (see 

Table 3-5). If a new design is used, organizations should test and document design performance. 

This can be done using techniques like those described in Isaak and Horan (2011) and Holden 

et al. (2013). 

To ensure that data meet quality standards, at a minimum, predeployment accuracy

checks should be performed. In addition, participants are encouraged to perform initial 

deployment, mid-deployment, biofouling and postdeployment checks. These types of QA/QC 

checks are important because sensors may record erroneous readings during deployment for a 

variety of reasons, such as being dewatered or buried in silt. The QA/QC checks improve data 

quality and allow for data to be corrected (if needed). The QAPP contains more detailed 

information on these checks.

3.2.3. HYDROLOGIC DATA

Many of the primary RMN sites are located on smaller, minimally disturbed streams with 

drainage areas less than 100 km2. Monitoring flow in headwater and mid-order streams is 

important because flow is considered a master variable that effects the distribution of aquatic 

species (Poff et al., 1997), and small streams in particular play a critical role in connecting 

upland and riparian systems with river systems (Vannote et al., 1980). These small upland 

streams, which are inhabited by temperature sensitive organisms, are also projected to experience 

substantial climate change impacts (Durance and Ormerod, 2007), though some habitats within 

these streams will likely serve as refugia from the projected extremes in temperature and flow 

(Meyer et al., 2007). 

The USGS has been measuring flow in streams since 1889, and currently maintains over 

7,000 continuous gages. This network provides long-term, high quality information about our 

nation’s streams and rivers that can be used for planning and trend analysis (e.g., flood 

forecasting, water allocation, wastewater treatment, and recreation). Efforts have been made to 

colocate RMN sites with active USGS gages, but many gages are located in large rivers that have 

multiple human uses, so only a limited number meet the site selection criteria for the primary 

RMN sites. As such, it is necessary to collect independent hydrologic data at most RMN sites. 

A common way to collect hydrologic data at ungaged sites is with pressure transducers. If 

installed and maintained properly, pressure transducers will provide important information on the 

magnitude, frequency, duration, timing, and rate of change of flows. These devices can pose 

challenges. For one, pressure transducers are more expensive than the temperature sensors, 

which makes it more difficult for RMN participants to purchase the equipment. Then, if 
3-19

participants are successful at obtaining the transducers, they need the expertise to install and 

operate the equipment, and also need resources to conduct QA/QC checks to ensure that the data 

meet quality standards. Because of these challenges, some participating organizations have 

adopted a “phased” approach, in which they start by installing pressure transducers at one or two

RMN sites (instead of all sites at once), and add more as they gain experience and as resources 

permit.

When pressure transducers are installed at RMN sites, efforts should be made to follow 

the recommendations in Table 3-6. These closely follow the protocols described in the recently 

published EPA best practices document on the collection of continuous hydrologic data using 

pressure transducers (U.S. EPA, 2014). Transducer measurements should be taken year-round5

(see Table 3-6). The transducers should be encased in housings to protect them from currents, 

debris, ice, and other stressors. Staff gages should also be installed to allow for instantaneous 

readings in the field, verification of transducer readings, and correction of transducer drift (see 

Figure 3-3, Table 3-6).

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 (see Table 3-6). This benchmark allows for monitoring of changes in the location of 

the transducer, which is important because if the transducer moves, water-level data will be 

affected and corrections will need to be applied (see Figure 3-3). While water-level 

measurements alone yield information about streamflow patterns, including the timing, 

frequency, and duration of high flows (McMahon et al., 2003), they do not give quantitative 

information about the magnitude of streamflows or flow volume, which makes it difficult to 

compare hydrologic data across streams.

If agencies have the resources to convert water-level measurements to streamflow (e.g., 

volume of flow per second), the most common approach is to develop a stage-discharge rating 

curve. To develop a rating curve, a series of discharge (streamflow) measurements are made at a 

variety of stages, covering as wide a range of flows as possible. The EPA best practices 

document (U.S. EPA, 2014) contains basic instructions on how to take discharge measurements 

in wadeable streams. More detailed guidance on this topic can be found in documents like Rantz 

et al. (1982), Shedd (2011), or Chase (2005). After establishing a rating curve, discharge should 

be measured quarterly. If resources don’t permit quarterly measurements, discharge should be 

measured at least once annually, and if possible, also after large storms and other potentially 

channel-disturbing activities. In addition, elevation surveys should be performed annually or as 

needed to check that the sensor has not moved. 

5

In places where streams become completely frozen during the winter, pressure transducers may be removed during 

winter months if freezing will result in damage to the equipment.
3-20

Figure 3-3. 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.
3-21

Table 3-6. 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) 2 3 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

Stage/water level corrected for 

barometric pressure. In 

addition, a minimum of 

5−10 discharge measurements 

are taken at a variety of flow 

conditions to develop a 

stage-discharge rating curve. 

The stage-discharge rating 

curve is used to convert water 

level to flow/discharge

Stage/water level corrected for 

barometric pressure. In addition, 

10 or more discharge 

measurements that capture the 

full range of flow conditions are 

taken to develop a 

stage-discharge rating curve. The 

stage-discharge rating curve is 

used to convert water level to 

flow/discharge.

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-rounda

Continuous measurements taken 

year-rounda

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)
3-22

Table 3-6. continued...

Component 1 (lowest) 2 3 4 (highest)

QA/QC―sensor 

accuracy

No accuracy checks 

are performed

No accuracy checks 

are performed

At least once annually, field 

crews take a staff gage reading 

or water depth measurement 

over the transducer with a 

stadia rod or other measuring 

device and compare this to the 

sensor reading

Multiple times per year, field 

crews take a staff gage reading 

or water depth measurement 

over the transducer with a stadia 

rod or other measuring device 

and compare this to the sensor 

reading

QA/QC―stagedischarge rating 

curve

After the rating curve 

is established, no 

checks are performed 

to verify the 

stage-discharge rating 

curve 

After the rating curve 

is established, no 

checks are performed 

to verify the 

stage-discharge 

rating curve

After the rating curve is 

established, discharge is 

measured at least once 

annually to verify the 

stage-discharge rating curve, 

and if possible, also after large 

storms or any other potentially 

channel-disturbing activities

After the rating curve is 

established, discharge 

measurements are taken 

quarterly to verify the 

stage-discharge rating curve, 

and if possible, also after large 

storms or any other potentially 

channel-disturbing activities

QA/QC―

discharge

No discharge checks 

are performed

No discharge checks 

are performed

Periodically, duplicate 

discharge measurements are 

taken, ideally by different 

peopleb

Periodically, duplicate discharge 

measurements are taken, ideally 

by different people. Discharge 

measurements are also 

periodically compared to a 

standard, such as a real-time 

USGS gage, or to measurements 

obtained by an experienced 

hydrographer from the USGS or 

another agency.

a

In places where streams become completely frozen during the winter, pressure transducers may be removed during winter months if freezing will result in 

damage to the equipment. b

For more details, see the QAPP (U.S. EPA, 2016. Generic Quality Assurance Project Plan for monitoring networks for tracking long-term conditions and 

changes in high quality wadeable streams), which is available online at http://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=295758&inclCol=eco#tab-3.
3-23

As with temperature sensors, different types of errors can occur during deployment (e.g., 

the pressure transducers may become dewatered, buried in sediment, or fouled by algae). 

Participants are encouraged to perform QA/QC checks to improve data quality and allow for data 

to be corrected (if needed). For example, during site visits, field crews should take a staff gage 

reading or water depth measurement over the transducer with a stadia rod or other measuring 

device and compare this to the sensor reading. Periodically, the transducer measurements can 

also be compared to a standard, such as a real-time USGS gage, or to measurements obtained by 

an experienced hydrographer from the USGS or another agency. Additional information on 

QA/QC checks for the hydrologic data can be found in the QAPP.

Because the collection of high quality hydrologic data is resource-intensive, states, tribes, 

RBCs, and other participating organizations are encouraged to explore partnerships with the 

USGS, universities, and other organizations (e.g., volunteer watershed groups). Some states have 

been successful at forging such partnerships. For example, the Massachusetts Department of 

Environmental Protection (MA DEP) has formed a partnership with the Massachusetts River 

Instream Flow Stewards (RIFLS) program. MA DEP collects macroinvertebrate and temperature 

data from the primary RMN sites, while the RIFLS program collects the flow data. New 

Hampshire Department of Environmental Sciences has partnered with Plymouth State 

University, who provided pressure transducers and helped with installations at New Hampshire’s 

primary RMN sites.

In the future, it would be valuable to start collecting precipitation data as well at the 

primary RMN sites. Similar to air and water temperature relationships, these data can be used to 

track responsiveness of stream flow to precipitation. Partnerships through groups, such as the 

Community Collaborative Rain, Hail, and Snow Network (http://www.cocorahs.org/), can help 

in this regard. Any discussions of additional sampling should consider the costs and benefits of 

the data for the states, tribes, or RBCs and RMN objectives.

3.2.4. PHYSICAL HABITAT

During the first several years of data collection, EPA and partners considered the 

biological, thermal and hydrologic data to be higher priority than the habitat and chemistry data, 

so not all participants have been collecting habitat data. Of the entities that have been collecting 

habitat data, most have been using qualitative assessments like EPA’s Rapid Bioassessment 

Protocol (Barbour et al., 1999). These qualitative assessments rate instream, bank, and riparian 

habitat parameters using visual descriptions that correspond to various degrees of habitat 

condition (e.g., optimal, suboptimal, marginal, and poor). With the proper training, skilled field 

biologists can perform comparable and precise visual habitat assessments, and these data, 

combined with photographs, can be used to qualitatively track habitat changes at RMN sites 

through time.

The regional working groups are starting to reevaluate the habitat protocols. Many RMN 

participants feel that quantitative measurements would be better suited for trend detection. 

Quantitative habitat data would also be helpful for stream classification. EPA performed a 
3-24

broad-scale classification analysis on macroinvertebrate survey data in the eastern United States 

and found that substrate (percentage sand, percentage fines, embeddedness), flow habitat 

(percentage pools), and reach-scale slope were important predictor variables (see Appendix A). 

Collecting these types of quantitative habitat data at RMN sites would improve the ability to 

accurately classify sites and help inform decisions on how data from RMN sites could be pooled 

together for analyses.

At this time, EPA and partners are encouraging RMN participants to collect the following 

types of quantitative habitat data at RMN sites:

• Geomorphological

− Bankfull width (reach-wide mean or at an established transect)

− Bankfull depth (reach-wide mean or at an established transect)

− Reach-scale slope

• Habitat

− Substrate composition (pebble counts to get percentage fines, percentage sand, etc.)

− Flow habitat types (percentage riffle, percentage pool, percentage glide, percentage

run)

− Canopy closure (measured with spherical densitometer, mid-stream and along bank)

There are several published methods, such as the EPA National Rivers and Streams 

Assessment protocols (U.S. EPA, 2013a; Kaufmann et al., 1999), for making these 

measurements. All of the methods require expertise and skill, and some can be time intensive. As 

such, the regional working groups will decide which specific quantitative habitat methods to use 

at RMN sites. The frequency with which quantitative habitat data should be collected from RMN 

sites also warrants further discussion. It may not be necessary to collect these types of data on an 

annual basis because channel forming flows that could change baseline geomorphological and 

instream habitat features generally have 1−2 year return periods for bankfull events or 1−5 year 

return periods for small flood events. However, specifying an exact timeframe for these 

measurements is difficult because channel-forming flows are hard to predict and their impacts at 

a given site can be highly variable. To help inform this discussion, one possibility would be to 

conduct a pilot study in which RMN members collect quantitative data on an annual basis at a 

subset of sites and then quantify how much the measurements vary from year to year and from 

site to site. If this type of comparison is not feasible, another option would be to take quantitative 

measurements less frequently but then also take measurements when visible geomorphic changes 

are seen in the photodocumentation (see Section 3.2.6). This topic warrants further discussion 

among RMN work group members and outside experts.

Also of interest are habitat measurements that are likely to be impacted by climate 

change. Climate change could contribute to temporally and spatially complex fluvial adjustments 

(Blum and Törnqvist, 2000). Some of the effects will be direct (e.g., changing precipitation 
3-25

patterns will alter hydrologic regimes, rates of erosion, and sediment yields). Other effects will 

be indirect, such as increases in sediment yield, which may result from vegetation disturbances 

that stem from changing thermal and hydrologic conditions (e.g., wildfire, insect/pathogen 

outbreak, drought-related die off; Goode et al., 2012). Modeling studies from a range of different 

environments suggest that the increases in rates of erosion could be on the order of 25−50% 

(Goudie, 2006). Changes in the frequency or magnitude of peak flows could cause significant 

channel adjustments, especially in higher order streams (Faustini, 2000), but channel adjustments 

will vary according to many factors. For example, channel adjustments and changes in sediment 

transport and storage can be greatly influenced by large woody debris dams and boulders that 

increase roughness (Faustini and Jones, 2003). Climate-related changes in riparian vegetation

may also occur (e.g., Iverson et al., 2008; Rustad et al., 2012), which could in turn affect the 

structure and composition of the benthic macroinvertebrate community (Sweeney, 1993; Whiles 

and Wallace, 1997; Foucreau et al., 2013).

Monitoring the effects of climate change on physical habitat at RMN sites could be 

greatly improved by adding carefully selected measurements of geomorphology and quantitative 

habitat indicators. These measures could include indicators that directly or indirectly reflect 

changes in hydrology and vertical or lateral channel adjustments (e.g., cross-sectional transects, 

mean bankfull height throughout a study reach, bank stability, and pebble counts). Indices of 

relative bed stability (Kaufmann et al., 2008; Kaufmann et al., 2009), measures of 

embeddedness, or metrics derived from pebble counts (e.g., percentage fines) might be useful 

measures in characterizing the effects of climate change if hydrological changes result in 

changes to rates of erosion, channel geometry, slope, bank stability, or sediment supply. 

However, more discussion among RMN work group members and outside experts is needed 

before recommending additional habitat measurements.

3.2.5. WATER CHEMISTRY

In situ, instantaneous water chemistry parameters (specific conductivity, dissolved 

oxygen, and pH) should be collected when RMN sites are visited for biological sampling. Some 

participating organizations have also been collecting more complete water quality data (e.g., 

alkalinity, major cations, major anions, trace metals, nutrients). The regional working groups are 

considering whether to require that a subset of these additional water quality parameters be 

collected at primary RMN sites. If sufficient resources are available, these water chemistry 

samples could potentially be collected multiple times per year during different flow conditions.

The purpose of collecting these data is to document whether water quality changes are occurring 

that could potentially contribute to changes in biological assemblage composition and structure 

over time.

3.2.6. PHOTODOCUMENTATION

Digital photographs should be taken when RMN sites are visited for biological sampling. 

Photographs are important to document any changes to the monitoring locations, show the 
3-26

near-stream habitat where data are being collected, provide qualitative evidence of changes in 

geomorphology (e.g., lateral and vertical channel stability), and to locate sensors during 

subsequent visits (U.S. EPA, 2014). During each visit, the photographs should be taken from the 

same location(s). Global positioning system (GPS) coordinates (latitude and longitude) should be 

recorded for the location where the photographs are taken, as well as cardinal direction. The 

coordinates should be recorded in decimal degrees, using the NAD83 datum for consistency. In 

areas with good satellite reception, field personnel should wait until there is coverage from four 

or more satellites before recording the coordinates. The accuracy of the coordinates should later 

be verified in the office or laboratory by using software (e.g., Google Earth or geographic 

information system [GIS] software) to plot the location on a map. If GPS coordinates are not 

available on-site, the location (or locations) should be marked on a map and the coordinates 

At least one set of photographs should be taken from a location at mid-reach. The photos 

should be taken looking upstream and downstream from this location, and should include 

specific and easily identifiable objects such as large trees, large stable boulders, large woody 

debris, point bars, established grade control, and so forth (see Figure 3-4). In addition, field 

personnel are encouraged to take photos of the riffles where macroinvertebrates are collected 

and, for hydrologic data, the location where instantaneous discharge measurements are taken. 

Photos of point bars (dominant substrate, extent and type of vegetation) and of banks at 

established transects are also of interest to document any changes in physical habitat. The photos 

should be archived yet easily accessible for future use.
3-27 

Figure 3-4. Photodocumentation of Big Run, WV, taken from the same 

location each year. 

Source: Provided by West Virginia Department of Environmental Protection (WV DEP). 

3.2.7. GEOSPATIAL DATA

If resources permit, GIS software can be used to obtain land use and land cover data for 

RMN sites based on exact watershed delineations for each site. Percentage land use and 

impervious cover statistics should be generated from the most recent National Land Cover 

Database (NLCD), and changes in these statistics should be tracked over time. For the RMNs, 

the most current NLCD data set is preferred over other land use data sets because it is a
3-28

standardized set of data that covers the conterminous United States and can be used with a 

standardized disturbance screening process (see Appendix D). Drainage area should also be 

calculated for each RMN site.

Having exact watershed delineations for RMN sites makes it possible to obtain a wide 

range of additional geospatial data (e.g., climate, topography, soils, geology), and can also be 

used to generate flow and temperature statistics (Carlisle et al., 2010; Carlisle et al., 2011; Hill 

et al., 2013). For purposes of the RMNs, data that are available at a national scale from the

NLCD are preferred to landscape-level variables generated from sources that do not provide 

nationwide coverage, in order to standardize disturbance screening for sites and facilitate other 

comparisons and analyses. In addition, it would be valuable to examine aerial photographs of the 

RMN sites for signs of past disturbance, because past land use can have lasting impacts on 

stream biodiversity (Harding et al., 1998). The use of high resolution Light Detection and 

Ranging data is also encouraged (if available) to delineate geomorphic features.
4-1

4. SUMMARIZING AND SHARING REGIONAL MONITORING NETWORK (RMN)

DATA

This section contains recommendations on how to summarize the biological, thermal and

hydrologic data that are being collected at RMN sites.

6 At a minimum, certain sets of metrics or 

statistics should be calculated from the RMN data so that samples can be characterized and 

compared in a consistent manner. A consistent set of summary metrics also helps in sharing data

across organizations. Metrics were selected that are:

• Relevant in the context of biomonitoring and to RMN members

• Straightforward to calculate and interpret

• Known or hypothesized to be most strongly associated with biological indicators

• Known or hypothesized to respond to climate change, and

• Limited in redundancy

These lists of metrics are intended to serve as starting points and should be reevaluated 

after the first several years of data collection at RMN sites. Periodic literature reviews should 

also be conducted to help inform parameter selection. Given the rapid pace of research, it is 

important that the raw data collected at RMN sites be properly archived and stored so that 

additional metrics can be calculated in the future.

4.1. BIOLOGICAL INDICATORS

Hundreds of different metrics could potentially be calculated from the biological data 

being collected at RMN sites. When developing the list of recommended summary metrics for 

the macroinvertebrate data, EPA and partners used a combination of published literature and best 

professional judgment to narrow down the list. The list, which can be found in Appendix H, 

contains both taxonomic and traits-based metrics. The list of taxonomic-based metrics includes 

measures like total taxa richness and Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness 

and composition (Barbour et al., 1999), which are commonly used by biomonitoring programs 

for site assessments. Traits-based metrics related to thermal and hydrologic conditions are also 

included (e.g., functional feeding group, habit, thermal, and flow preference). Trait assignments

were obtained from the Freshwater Traits database7 (U.S. EPA, 2012b).

To derive the thermal preference metrics, methods described in Yuan (2006) were used to 

estimate the optimal temperature values and ranges of occurrence (tolerances) for taxa that had a 

sufficient distribution and number of observations to support the analysis. These data, along with 

 

6

The management and sharing of habitat and water chemistry data are discussed in Section 6 (Data Management). 7

If time and resources permit, regional experts will review and edit the trait assignments and fill in data gaps where 

possible.
4-2 

supplemental data provided by states and best professional judgment of regional experts, were 

used to derive lists of cold and warm water taxa for the eastern states that are participating in the 

current phase of RMN work (see Appendix I). Metrics known or hypothesized to be sensitive to 

changing hydrologic conditions are also included in Appendix H. These metrics were selected 

based primarily on literature review (e.g., Horrigan and Baird, 2008; Chiu and Kuo, 2012; 

U.S. EPA, 2012a; DePhilip and Moberg, 2013b; Conti et al., 2014). These thermal and 

hydrologic traits-based metrics should be reevaluated periodically and refined as more data 

become available and more is learned about relationships between biological, thermal and 

hydrologic data.

In addition to the taxonomic and traits-based metrics, metrics of persistence and stability

are also being recommended. Persistence is a measure of variation in community richness over 

time (Holling, 1973), while stability measures the variability in relative abundance of taxa in a 

community over time (Scarsbrook, 2002; for formulas, see Appendix H). The persistence and 

stability metrics can be used to quantify year-to-year variation in long-term data sets (Durance 

and Ormerod, 2007; Milner et al., 2006). Quantifying natural variation in the occurrence and the 

relative abundance of individual taxa allows biomonitoring programs to assess how this variation 

affects the consistency of biological condition scores and metrics, and whether variation is linked 

to specific environmental conditions. In addition, changes in the occurrence (i.e., presence or 

absence) and the relative abundance of individual taxa should also be evaluated at RMN sites. 

Spatial distribution maps like the one shown in Figure 4-1 can be created periodically (e.g., every 

5 years) to track changes in species distributions over time.

Biological condition scores should also be calculated at RMN sites. Biological indices 

often take the form of multimetric indices (MMIs) or predictive models like the River 

Invertebrate Prediction and Classification System (Wright, 2000). MMIs are generally a 

composite of biological metrics selected to capture ecologically important structural or 

functional characteristics of communities, where poor MMI scores represent deviations from 

reference condition (Karr, 1991; Barbour et al., 1995; DeShon, 1995; Yoder and Rankin, 1995; 

Sandin and Johnson, 2000; Böhmer et al., 2004; Norris and Barbour, 2009). Predictive models 

compare which reference site taxa are expected (E) to be present at a site, given a set of 

environmental conditions, to which taxa are actually observed (O) during sampling, where low 

O:E community ratios represent deviation from reference condition (Wright et al., 1984; Wright, 

2000; Hawkins, 2006; Pond and North, 2013). 
4-3 

Figure 4-1. Spatial distributions of macroinvertebrate taxa, based on the 

National Aquatic Resource Survey (NARS) data. These types of maps could be 

created periodically to track shifts in spatial distributions of taxa 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). 

At this time, there are no plans to develop regional MMIs or O:E predictive models. 

Rather the biological condition scores should be calculated in accordance with each entity’s 

bioassessment methods. Because different organizations use different techniques for calculating 

biological condition scores, the index scores themselves may not be comparable across sites 

sampled by different organizations. However, as discussed in Section 5 (Data Usage) the 

direction of trends can be tracked over time. In some locations, it may be possible to obtain 

comparable biological condition scores from regional Biological Condition Gradient (BCG) 
4-4

models. Section 5 (Data Usage) contains more detailed information on how the BCG could 

potentially be used to summarize RMN data.

The biological data from the RMN sites will need to undergo some level of review, 

formatting and standardization before it can be summarized and shared. For example, there will 

be differences in nomenclature across entities that need to be resolved, as well as differences in 

levels of taxonomic resolution. Users may need to develop operational taxonomic units (OTUs) 

to address changes in taxonomic naming and systematics that have occurred over time (Cuffney 

et al., 2007). EPA and partners are currently working on guidance, procedures and R scripts (R 

Core Team, 2015) that will help facilitate the sharing of the biological data. As discussed in 

Section 6, the biological data will be eventually be uploaded to a national water data system such 

as the Water Quality Exchange (WQX). Users will need to be able to access metadata from the 

data management system so that they can select data that meet their needs (e.g., collected using 

certain methods and at certain levels of rigor). The raw biological data collected at RMN sites 

should be properly archived and stored so that additional metrics can be calculated in the future.

4.2. THERMAL STATISTICS

Many metrics can be calculated from year-round air and water temperature 

measurements. Summer temperature metrics are typically used in analyses with biological data 

because summer captures a critical time period for most aquatic species’ survival, and have been 

found to predict macroinvertebrate distributions better than winter and summer temperature 

metrics (Hawkins et al., 2013). Beyond this, there is currently limited information on which 

temperature metrics are most ecologically meaningful in the context of biomonitoring.

When developing a list of potentially important temperature metrics for the RMN data, 

EPA and partners sought input from organizations that have been collecting and processing 

continuous stream temperature data for years, including MD DNR and the U.S. Forest Service 

Rocky Mountain Research Station (Isaak and Horan, 2011; Isaak et al., 2012; Isaak and Rieman, 

2013). They recommended a list of basic statistics that cover daily, monthly, seasonal, and 

annual time periods, and basic percentage exceedance metrics (e.g., percentage of days that 

exceed 20°C). The metrics are easy to calculate and capture various aspects of thermal regimes, 

such as magnitude, frequency, duration, and variability. The list, which can be found in 

Appendix J, should be regarded as a starting point. Other unlisted metrics also have promise, 

including the use of more complex temperature exceedance metrics and moving average 

calculations that are related to specific biological thresholds. Some studies have found that 

moving average metrics such as 7-day mean and maximum are useful descriptors of thermal 

regimes and often associate well with stream fish distribution patterns (Wehrly et al., 2003; 

Nelitz et al., 2007). Other studies (e.g., Butryn et al., 2013) have found that additional metrics are 

needed to sufficiently capture the variation caused by irregular and extreme events. As such, it is 

important that the raw data collected at RMN sites is properly archived and stored so that 

additional metrics can be calculated in the future.
4-5

Before the temperature metrics are calculated, the continuous data should be run through 

a series of quality assurance checks, which are described in the QAPP. EPA and partners are in 

the process of developing R scripts and procedures to facilitate completion of quality control 

activities, summarizing and sharing the temperature data (see Section 6). Other options are also 

available for generating summary statistics, such as the ThermoStat software package (Jones and 

Schmidt, 2012) and the StreamThermal Version 1.0 R code package that was recently developed 

by Tsang et al. (in review). In addition to calculating the summary statistics, the metadata for 

each site should also be reviewed. Data should be interpreted with caution if no accuracy checks 

were performed during the deployment period. For more information on data management, see 

Section 6.

4.3. HYDROLOGIC STATISTICS

As with the thermal data, many different metrics can be calculated from daily hydrologic

data. Researchers have investigated which hydrologic metrics are most ecologically meaningful 

in the context of state biomonitoring programs (e.g., Kennen et al., 2008; Chinnayakanahalli 

et al., 2011) but a detailed understanding of how flow affects ecological conditions remains 

elusive, in part because observed hydrologic data are unavailable for many biological sampling 

sites. Also, due to the highly variable nature of hydrologic data, it takes a long period of record 

to characterize hydrologic regimes. Richter et al. (1997) and Huh et al. (2005) suggest that at 

least 20 years of data are needed to calculate interannual variability for most hydrologic

parameters, and that 30 to 35 years of data may be needed to capture extreme high and low 

events (e.g., 5- and 20-year floods; Olden and Poff, 2003; DePhilip and Moberg, 2013b).

When developing the list of recommended hydrologic metrics for the RMN data, EPA 

and partners used a combination of published literature and best professional judgment. The 

literature included reports from TNC and several partners (states, RBCs, other federal agencies), 

who developed ecosystem flow needs for some eastern and midwestern rivers and their 

tributaries (e.g., the Susquehanna, the Upper Ohio, the Delaware, and the Potomac Rivers; 

Cummins et al., 2010; DePhilip and Moberg, 2013a, 2013b; Buchanan et al., 2013). TNC and its 

partners utilized components of the Ecological Limits of Hydrologic Alteration (ELOHA) 

framework (Poff et al., 2010) to make recommendations on flows to protect species, natural 

communities, and key ecological processes within various stream and river types. For the Upper 

Ohio River, they recommended a list of flow statistics that capture ecologically meaningful 

aspects of hydrologic regimes (see Appendix K; DePhilip and Moberg, 2013b). Research by 

Olden and Poff (2003) and Hawkins et al. (2013; see Appendix K), which identifies hydrologic 

metrics that capture critical aspects of hydrologic regimes and are ecologically meaningful in 

different types of streams, also informed the list of metrics.

Appendix K contains the list of recommended hydrologic statistics to calculate for data 

from RMN sites where water-level or flow data are being collected. The metrics are relatively 

easy to calculate and include both summary statistics and measures of variability. As with the 

thermal metrics, this list should be regarded as a starting point and should be reevaluated over 
4-6

time. The raw hydrologic data collected at RMN sites should be properly archived and stored so 

that additional metrics can be calculated in the future.

Before the hydrologic metrics are calculated, the data should be run through a series of 

quality assurance checks, which are described in the QAPP. EPA and partners are in the process 

of developing R scripts and procedures to facilitate completion of quality control activities, 

summarizing and sharing the hydrologic data (see Section 6). Other options may be available as 

well, such as software like Indicators of Hydrologic Alteration (TNC, 2009) and Aquarius.

8 In 

addition to calculating the summary statistics, the metadata for each site should also be reviewed.

Data should be interpreted with caution if no accuracy checks (e.g., staff gage readings) were 

performed during the deployment period, and if the elevations of the staff gage and pressure 

transducer were not surveyed. The latter are especially important, because they can determine 

changes in the location of the transducer. If the transducer moves, stage data will be affected and

corrections should be applied.

To supplement missing field data or provide estimates of streamflow at ungaged sites, 

simulation models have been developed in some geographic areas. For example, the Baseline 

Streamflow Estimator simulates minimally altered streamflow at a daily time scale for ungaged 

streams in Pennsylvania. This freeware is publicly available, and has a user-friendly point‐and‐

click interface (Stuckey et al., 2012). Other examples of tools used to simulate flows are listed in 

Appendix K. While these modeled data should not be regarded as a substitute for observational 

data, participating organizations may want to take advantage of these additional resources to 

supplement their monitoring efforts.

 

8

http://aquaticinformatics.com/
5-1 

5. DATA USAGE

Biomonitoring programs can use RMN data for multiple purposes, spanning time periods 

of 1−5, 5−10 and 10+ years (see Figure 5-1). Uses include:

• Monitoring the condition of minimally and least disturbed streams

• Detecting trends attributable to climate change

• Supplementing CWA programs and initiatives

− Defining natural conditions/quantifying natural variability to support Section 305(b) 

programs 

− Informing criteria refinement or development under Section 303 

− Developing biological indicators for protection planning for Section 303(d) programs

• Gaining a better understanding of relationships between biological, thermal, and 

hydrologic data

• Gaining a better understanding of ecosystem responses and recovery from extreme 

weather events

• Gaining insights into effects of regional phenomena such as drought, pollutant/nutrient 

deposition and riparian forest infestations on aquatic ecosystems and bioassessment 

programs 

5.1. APPLICATIONS IN A 1−5 YEAR TIMEFRAME

Many of the RMN sites are located on minimally or least disturbed streams (per Stoddard 

et al. 2006), which are the standard against which other bioassessment sites are compared. It is 

critical to document current conditions at these sites and to monitor how conditions change over 

time, as this has implications for CWA programs. Monitoring high quality waters fits in with the 

long-term vision and goals for a number of CWA programs, such as the Section 303(d) Program. 

Historically, the 303(d) program has focused on the assessment and identification of waters that 

are not meeting State water quality standards and on the development of Total Maximum Daily 

Loads to inform restoration of those waters, but starting in 2016, protection planning priorities 

that target high quality sites will also be incorporated into the reporting cycle (U.S. EPA, 2013b). 

Monitoring high quality waters also ties into EPA’s Healthy Watershed Initiative, in which State 

and other partners identify high quality watersheds and develop and implement watershed 

protection plans to maintain the integrity of those waters (U.S. EPA, 2011). 
5-2 

Figure 5-1. RMN data can be used for multiple purposes, over short- and 

long-term timeframes.

To characterize current conditions at the RMN sites, the data, metrics, and summary 

statistics described in Section 4 and Appendices H, J, and K will be compiled for each site and 

sent to regional coordinators and EPA. Before this happens, the interim data infrastructure 

systems described in Section 6 will be put into place. The procedures and R scripts (R Core 

Team, 2015) that EPA and partners are currently working on will help facilitate these outputs. 

For the macroinvertebrate data, the output will include metrics that are commonly used by 

biomonitoring programs for site assessments (e.g., EPT metrics), as well as traits-based metrics 

related to thermal and hydrologic conditions. At this time, there are no plans to develop regional 

MMIs or O:E predictive models. Rather, assessments of overall biological condition will be 

based on biological condition scores that are calculated in accordance with each entity’s 

bioassessment methods. In many cases, the index scores will not be comparable across sites 

sampled by different organizations, but valuable information can be gleaned by monitoring the 

direction of trends in biocondition scores across RMN sites, in addition to changes in the 

biological metrics. Moreover, some programs may be able to use the biological data from RMN 

sites to help calibrate or refine biological indices specific to their programs.

In some places, it may be possible to obtain comparable biological condition scores from 

regional BCG models. The BCG is a conceptual, narrative model that describes how biological 
5-3

structure and function of aquatic ecosystems change along a gradient of increasing anthropogenic 

stress (Davies and Jackson, 2006). The BCG model can be calibrated and applied to regional and 

local conditions and puts biological condition on a common, quantifiable scale that can be 

applied nationwide. BCG models are typically calibrated to six levels that reflect a continuum of 

quality from pristine (BCG Level 1) to severely degraded (BCG Level 6; Davies and Jackson, 

2006). Thus, a BCG Level 2 sample in one region is comparable to a BCG Level 2 sample in 

another region because both assessments are dependent on comparisons to natural conditions. At 

this time regional BCG models for macroinvertebrate or fish assemblages have been developed 

for cold and cool streams in the Northern Forest region of the Midwest (Stamp and Gerritsen, 

2009) and medium to high gradient streams in parts of New England (Stamp and Gerritsen, 

2009). In addition, BCG models for fish and macroinvertebrate assemblages have been calibrated 

for northern Piedmont streams of Maryland (Stamp et al., 2014), and are currently being 

calibrated in Alabama, Illinois, and Indiana. These models can be applied to data collected from 

RMN sites and BCG-level scores, as well as the component metrics of the BCG models (which 

are typically related to tolerance of individual taxa), can be used to characterize biological 

condition and track changes at sites over time.

Once the first several years of biological data become available, taxonomic composition 

at the RMN sites will also be evaluated, along with the broad-scale macroinvertebrate 

classification model developed by EPA and partners (see Section 2.2). Based on the broad-scale 

model, which was developed using National Rivers and Streams Assessment (NRSA) data, most 

of the primary RMN sites fall within the small to medium-size, colder temperature, faster water 

stream class. EPA and partners will use nonmetric multidimensional scaling (NMDS) to evaluate 

similarities and differences in taxonomic composition across RMN sites and to test the 

performance of the classification model. Results will help inform if and how macroinvertebrate 

data can be pooled for regional analyses. The ability to pool data could be particularly valuable 

for biomonitoring programs that are trying to calibrate biocondition indices and develop numeric 

biocriteria but only have limited numbers of high quality sites.

EPA and partners will also assess the number of cold/cool thermal indicator taxa at RMN 

sites. Having higher numbers of cold/cool taxa at RMN sites will improve trend detection ability 

(see Appendix A). Based on preliminary analyses, RMN sites have relatively high proportions of 

cold/cool taxa (see Figure 5-2). The biological and continuous stream temperature data from 

RMN sites can be used to refine the regional list of cold/cool and warm water macroinvertebrate 

taxa, which were developed based on instantaneous stream temperature measurements (see 

Appendix I). Biological indicator lists can also be used for protection planning. For example, 

regulatory agencies in Maryland are currently assessing the accuracy of their current use 

designations for cold water streams as part of their protection planning process. As part of these 

efforts, MD DNR used continuous temperature data from its Sentinel Sites Network (SSN)9 to 

develop a thermal indicator organism list for macroinvertebrates. Two stoneflies, Sweltsa and 

 

9

MD DNR has been collecting biological data and continuous temperature data from 27 high quality sites since 2000 

as part of it SSN. Five of the SSN sites are primary RMN sites. For more information, see Becker et al. (2010).
5-4 

Tallaperla, meet obligate cold taxa requirements for Maryland streams and are being used in 

combination with trout to help identify and protect cold water streams (see Figure 5-3).

Figure 5-2. Proportion of cold/cool indicator taxa at RMN sites, based on 

preliminary data from a subset of sites. More details on the cold/cool taxa list 

can be found in Appendix I. 

Figure 5-3. The thermal tolerances of Sweltsa and Tallaperla match very 

closely with brook trout. These two macroinvertebrate taxa are being used in 

combination with trout to help identify and protect cold water streams in 

Maryland. This figure was provided by the Maryland Department of Natural 

Resources.
5-5 

The biological and stream temperature data from RMN sites can also be used to inform 

criteria refinement or development and to help identify ecologically meaningful thresholds. For 

example, some regulatory agencies are in the process of assessing whether their current 

temperature criteria are adequately protecting designated uses related to cold water fisheries. In 

Connecticut, Beauchene et al. (2014) used year-round temperature data and fish data to develop 

quantitative thresholds for three major thermal classes at which there are discernible 

temperature-related changes in stream fish communities (see Figure 5-4). This type of 

information is very useful for fisheries management and can be used to help make criteria more 

biologically meaningful and defensible. 

Figure 5-4. 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). 

The hydrologic data being collected at RMN sites can be used for similar types of 

analyses and applications. For example, Maine used biological and hydrologic data to develop 

statewide environmental flow and lake level standards. The standards are based on thresholds 

derived from principles of natural flow variation necessary to protect aquatic life and maintain 

important hydrological processes (MDEP, 2007; see Figure 5-5; Ricupero, 2009). Other states 

are also exploring the development of flow criteria, utilizing the ELOHA framework (Poff et al., 

2010). TNC and several partners (states, RBCs, other federal agencies) have used components of 

the ELOHA framework that consider flow needs for sensitive species and key ecosystem 
5-6 

processes to develop flow recommendations for some eastern and midwestern rivers (e.g., the 

Susquehanna, the Upper Ohio, the Delaware, and the Potomac Rivers; DePhilip and Moberg, 

2010; Cummins et al., 2010; DePhilip and Moberg, 2013a, 2013b; Buchanan et al., 2013). Data 

from RMN sites can be used in similar ways to improve our understanding of these processes 

and to help develop regionally informed standards and management strategies. 

Figure 5-5. Salmon life cycle plotted in relation to yearly flow cycle 

(Ricupero, 2009).

At many RMN sites, the year-round thermal and hydrologic regimes are poorly 

documented, so the first several years of continuous thermal and hydrologic data will be used to 

start characterizing these regimes. The continuous data will provide robust data sets that capture 

natural temporal patterns, episodic events and spatial variability, which may be missed by 

limited numbers of discrete measurements (see Figure 5-6). The collection of these types of data 

will also build capacity of biomonitoring programs that have limited experience with continuous 

sensors and management of continuous data. 
5-7 

Figure 5-6. RMN data will help us gain a better understanding of natural 

variability in hydrologic conditions in small least disturbed streams, and will 

allow us to investigate relationships between biological, thermal, and 

hydrologic conditions.

The continuous RMN data could also be used to help further the development of 

ecologically relevant classifications of thermal and hydrologic regimes. For example, Maheu 

et al. (2015) recently developed a thermal classification scheme for streams in the conterminous 

United States based on magnitude and variability (amplitude and timing), with six classes: highly 

variable cool, variable cold, variable cool, variable warm, stable cool, and stable cold. In another 

study, Dhungel (2014) developed an empirical model and hydrologic classification scheme for 

streams in the conterminous United States based on magnitude, timing, predictability, and 

intermittency of flows, with eight classes: small steady perennial, large steady perennial, steady 

intermittent, early intermittent, late intermittent, early flashy perennial, small flashy perennial, 

and large flashy perennial. Efforts are also underway to develop a hierarchical classification for 

natural flowing stream and river systems in the Appalachians (Olivero et al. 2015). These 

existing models could be used to classify RMN sites based on thermal and hydrologic data. 

Once the RMN sites have been classified, the continuous thermal and hydrologic data can 

be used to help characterize baseline or reference conditions for the different stream classes. 

Characterization (and quantification) of these regimes is important because some water quality 

standards are based on comparisons with natural conditions. Thermal and hydrologic data from 

stream segments altered by anthropogenic activities such as dams and land use changes could 

potentially be compared to data from high quality RMN sites. Another potential application of 

the RMN data is to improve or validate models and simulations that predict stream temperature 

and flow. Several stream temperature models now exist for the conterminous United States, 

including empirical models developed by Hill et al. (2013) and Segura et al. (2015). 
5-8

Advancements in flow models, such as the Variable Infiltration Capacity model (Liang et al., 

1994), continue to be made as well. These models could potentially be applied to RMN sites, and 

the simulated stream temperatures and flows could then be compared to the observed values. If 

the models provide good approximations, the models could potentially be used to predict climate 

change effects on thermal and hydrologic regimes at RMN sites.

Another way in which RMN data can be used is to assess the “thermal sensitivity” of 

each site. The “thermal sensitivity” of a stream is often quantified as the slope of the regression 

line between air and stream temperature (Kelleher et al. 2012). Air temperature, which is 

projected to increase due to climate change (Melillo et al. 2014), is known to be an important 

predictor of water temperature (Kelleher et al., 2012; Hill et al., 2013). The relationship between 

air and water temperature, however, varies depending on numerous modifying factors, such as 

geographic location, stream size, and groundwater contributions (Kelleher et al., 2012; Hill et al., 

2013). Hildebrand et al. (2014) used the paired continuous air-water temperature data from MD

DNR’s sentinel sites to explore thermal sensitivities in different regions of Maryland. They 

found baseflow and riparian shading to be important modifying factors, along with discharge. In 

Pennsylvania, Kelleher et al. (2012) found that stream size and groundwater contribution were 

the primary controls of the sensitivity of stream temperature to air temperature. Relationships 

between the air and stream temperature data can also be used to characterize groundwater 

influence at local scales (Kanno et al., 2014; Snyder et al., 2015). The first several years of 

paired air-stream temperature data can be used to assess the sensitivity of each RMN site to 

rising air temperatures, and to gain a better understanding of factors that make some sites more 

vulnerable to climate change than others. This information could be very useful for management 

and conservation planning.

5.2. APPLICATIONS IN A 5−10 YEAR TIMEFRAME

Over the 5−10 year time period, RMN data will continue to be used to characterize 

current conditions against which future climate influences can be assessed, to support CWA 

programs in ways similar to those described in Section 5.1 and to evaluate and refine the lists of 

recommended metrics and indicators provided in Section 4 and Appendices H, J, and K. In 

addition, a variety of analyses can be performed to look for trends and patterns in the biological, 

thermal, hydrologic, habitat, and water chemistry data. Scatterplots, simple correlation and 

regression analyses, analysis of variance, NMDS ordinations, and other analytical tools will be 

used to explore differences or trends over time, as well as relationships between the different 

types of data. The analyses will be similar to those described in MD DNR’s SSN report (Becker 

et al., 2010) and the 2012 EPA pilot study in which long-term state biomonitoring data in Maine, 

North Carolina, Ohio, and Utah were evaluated for climate-related trends (U.S. EPA, 2012a). In 

both studies, there were examples of shifts in biological indicators occurring in association with 

changing thermal or hydrologic conditions. In the Maryland study, the lowest Index of 

Biological Integrity scores in the Coastal Plain―western shore region were recorded the year 

after the lowest flow and rainfall conditions occurred (Becker et al., 2010), and in the EPA pilot 
5-9

study, a strong decline in EPT richness corresponded with a period of higher than normal 

temperatures and lower than normal flows at one of the Utah sites (U.S. EPA, 2012a). RMN data 

for the 5−10 year time period can also be used to assess temporal (year to year) variability, which 

is not well documented at high quality sites (Milner et al., 2006). The RMN data facilitates a 

better understanding of how natural variability affects the consistency of biological condition 

scores and metrics from year to year, and how this relates to changing thermal and hydrologic 

conditions.

10

5.3. APPLICATIONS IN A 10+ YEAR TIMEFRAME

For the 10+ year timeframe, the data will continue to be used to characterize conditions 

and temporal variability, support CWA programs, evaluate and refine the lists of recommended 

metrics and indicators and perform trend analyses. In addition, climate change effects may start 

to become evident. A number of climate projections are relevant to aquatic life condition, 

including increasing temperatures, increasing frequency and magnitude of extreme precipitation 

events, and increasing frequency of summer low flow events (Melillo et al. 2014). The long-term 

data from high quality RMN sites will substantially enhance the ability to detect and characterize 

trends attributable to climate change.

Many organizations are performing vulnerability assessments and developing hypotheses 

about which organisms, community types, watersheds or stream classes are likely to be most 

vulnerable to climate change. For example, the EPA and partners are conducting a broad-scale 

climate change vulnerability assessment on streams in the eastern United States. They are 

assigning vulnerability ratings to each watershed11 based on a scenario in which stream 

temperatures warm and the frequency and duration of summer low flow events increases (see 

Figure 5-7). The RMN data can be used to help test these types of hypotheses and to track 

whether certain types of streams are showing greater resiliency to climate change effects than 

others. This type of information can inform adaptation strategies and conservation planning.

 

10In order to be able to attribute variability in the biological data to ‘natural’ vs. other factors, anthropogenic factors 

(such as changes in land use) also need to be tracked at each site over time.

11Watershed delineations are based on the NHDPlus v2 local catchment layer: http://www.horizonsystems.com/NHDPlus/NHDPlusV2_data.php.
5-10 

Figure 5-7. EPA and partners are conducting a broad-scale climate change 

vulnerability assessment on streams in the eastern United States, based on a 

scenario in which stream temperatures warm and the frequency and 

duration of summer low flow events increases. Vulnerability ratings (least, 

moderate or most) are being assigned to each watershed.

The RMN data can also be used to track whether shifts in the distributions of biological 

indicators (such as cold water taxa) are occurring as the climate changes, which would have 

implications for bioassessment programs. In some locations, species distribution models (SDMs) 

have been developed. For example, Zheng et al.

12 (2014) generated models to predict how 

species occurrence will change by mid-century in the Northeast under conditions of rising air 

temperatures and changing precipitation patterns.

13 Results suggest an overall decline in species 

richness across much of the region (see Figure 5-8). SDM models have been generated for other 

regions as well. For example, Hawkins et al. (2013) used biomonitoring data from the EPA’s 

2008−2009 NRSA to develop SDMs that predict how the distributions of individual 

macroinvertebrate taxa and entire assemblages of taxa vary with stream temperature, flow, and 

 

12Zheng, L., Stamp, J., Hamilton, A., Bierwagen, B. and J. Witt. 2014. Species Distribution Modeling in the 

Northeast US - Impact of Climate Change and Taxa Vulnerability. Poster. Joint Aquatic Sciences Meeting. Portland, 

OR.

13Mid-century (2040−2069) projections for air temperature, precipitation and moisture surplus were based on 

average values from an ensemble of 15 GCMs, using the a2 (high) emissions scenario. Data were obtained from the 

Climate Wizard website and are based on the WCRP CMIP3 multimodel data set.
5-11 

other watershed attributes in the conterminous United States, for baseline (2000−2010) versus 

late century (2090−2100) time periods. SDMs are also being developed for stonefly species in 

the Midwest (Cao et al., 2013; DeWalt et al., 2013). If applied to RMN sites, SDMs could serve 

as valuable tools for conservation planning. 

Figure 5-8. Modeling results predict declines in species richness across much 

of the Northeast by mid-century (2040−2069).14

Models have also been developed to predict climate change effects on stream temperature 

and flow. For example, at the national scale, Hill et al. (2013) developed a stream temperature 

model for the conterminous United States based on air temperature and watershed feature data 

(e.g., watershed area and slope) from reference-condition USGS sampling sites, and applied the 

model to simulate the effects of climate change on mean summer stream temperature (Hill et al. 

2014). The model predicts a mean warming of 2.2°C for stream temperatures in the 

 

14Zheng, L., Stamp, J., Hamilton, A., Bierwagen, B. and J. Witt. 2014. Species Distribution Modeling in the 

Northeast US—Impact of Climate Change and Taxa Vulnerability. Poster. Joint Aquatic Sciences Meeting. 

Portland, OR.
5-12 

conterminous United States by late century (2090−2099) relative to a 2001−2010 baseline period, 

with values at individual sites ranging from 0°C to +6.2°C. In another study, Dunghel (2014) 

developed statistical models to predict flow responses to projected changes in precipitation and 

temperature. Results suggest that changes in flow attributes will be most evident in rain-fed 

small perennial streams and intermittent streams in the central and eastern United States. These 

models could potentially be applied to RMN sites, and the performance of the models could be 

tracked over time.

RMN data may also provide insights on how organisms respond to and recover from 

extreme weather events such as droughts and floods, which are projected to occur with greater 

frequency in the future (Melillo et al., 2014). Impacts can be evaluated through comparative 

analyses on the pre- and postevent data. For example, VT DEC performed these types of 

analyses on macroinvertebrate data collected before and after flooding from Tropical Storm 

Irene. Using data from 10 high-quality sites, VT DEC documented immediate decreases in 

invertebrate densities of 69% on average, but also found that most sites recovered to normal 

levels the following year (see Figure 5-9).15 The substantial decline in density and the rapid 

recovery would have been missed if sampling had occurred at longer intervals, such as on a 

5-year rotational sampling schedule. Moreover, the collection of continuous thermal and 

hydrologic data will allow the magnitude, frequency and duration of the event to be documented. 

Whether or not the RMN data can fully capture biological responses to extreme weather events 

will depend on the timing of the event in relation to the RMN sampling period. 

Figure 5-9. Comparison of macroinvertebrate density values at 10 stream 

sites in Vermont before and after Tropical Storm Irene.

16

 

15Moore, A. and S. Fiske. (2012) What’s Left in Vermont’s Streams after Tropical Storm Irene―Monitoring Results 

From Long Term Reference Sites. Presentation. New England Association of Environmental Biologists Annual 

Conference. Falmouth, MA.

16Ibid.
6-1

6. DATA MANAGEMENT

The EPA and partners are developing a data management system that will allow 

participating organizations and outside users to access data and metadata that are being collected 

at RMN sites. The biological, habitat, and water chemistry data will be uploaded to a national 

water data system such as the WQX. At this time not all RMN partners have the capacity to 

upload these data into WQX, so the EPA and partners are working on an interim solution. Until 

the interim system is in place, the individual organizations will be custodians and owners of 

these data, and all data files will be backed up and stored in a centralized, secure location. 

Because WQX cannot accommodate continuous data, the thermal and hydrologic data being 

collected by RMN partners will be uploaded into a separate data management system. A 

multiagency effort is underway to develop a data management system for the continuous data. In 

the interim, EPA and partners are developing guidance for storing and managing the continuous 

RMN data files. This includes the development of R scripts (R Core Team, 2015) and procedures 

for performing QC on the data and for deriving standardized sets of summary outputs for desired 

time periods. Until the permanent data management system is in place, the individual 

organizations will be the custodians and owners of the continuous thermal and hydrologic 

monitoring data. The EPA and partners are also developing procedures for joining the different 

sets of data and facilitating the types of data analyses described in Section 5.
7-1 

7. IMPLEMENTATION AND NEXT STEPS 

Implementation of the RMNs is underway in the Northeast, Mid-Atlantic, and Southeast 

regions. Sampling efforts began in the Northeast in 2012, followed by the Southeast in 2013 and 

the Mid-Atlantic in 2014. Currently there are 25 primary RMN sites in the Northeast, 27 in the 

Mid-Atlantic and 38 in the Southeast (see Appendix C). More sites will be added in all regions as 

resources permit. Efforts are also underway to develop RMNs in the Midwest, where sampling is 

expected to start in 2016 (see Figure 7-1). A number of organizations in the western United 

States have also expressed interest in setting up RMNs. 

Figure 7-1. Sampling has been underway at the Northeast, Mid-Atlantic and 

Southeast RMNs for several years. RMNs are currently being developed in the 

Midwest.

Efforts are being made to integrate RMN data collection flexibly within existing 

monitoring programs to maximize available resources. Some RMN partners have taken a phased 

approach to implementation, particularly with collecting hydrologic data at ungaged sites. For 

example, some participating organizations have installed pressure transducers at one or two sites, 

and are planning to add more as they gain experience and as resources permit. In many states, the 

collection of macroinvertebrate data and year-round temperature measurements have been 

feasible during the first year of implementation. However, it has been difficult for some 

participating organizations to find resources to process the macroinvertebrates samples in 

accordance with the regional methods. When this occurs, the preserved samples are being 

retained so that they can be processed at a later time, when resources become available.
7-2

To help build capacity and improve data quality, EPA and partners have held several 

training workshops on field protocols for continuous temperature sensors and pressure 

transducers, and wrote the EPA best practices document (U.S. EPA, 2014). In addition, the 

regional coordinator in the Mid-Atlantic is planning to hold a training workshop on species-level 

identifications for high priority taxa (see Appendix G). There are also plans to hold hands-on, 

interactive workshops on data management at annual meetings (e.g., the New England 

Association of Environmental Biologists conference, the Association of Mid-Atlantic Aquatic 

Biologists Workshop, the Southeastern Water Pollution Biologists Association conference).

As discussed in Section 3, EPA has also developed a QAPP for the RMNs to address the 

challenges of creating regionally consistent data sets. It is a generic QAPP that details the core 

requirements for participation in the network, and outlines best practices for the collection of 

biological, thermal, hydrologic, physical habitat, and water chemistry data at RMN sites. The 

QAPP was written in a way that should be transferable to other regions, with region-specific 

protocols included as addendums. The QAPP is intended to increase the comparability of data 

being collected at RMN sites, improve the ability to detect long-term trends by minimizing 

biases and variability, and to ensure that the data are of sufficient quality to meet data quality 

objectives. Efforts will be made to finalize the QAPPs for the Northeast, Mid-Atlantic, Southeast 

and Midwestern regions by 2016. The protocols in the QAPP will be reevaluated periodically 

and updated as needed. EPA and partners are also exploring the possibility of conducting 

methods comparison studies in regions where macroinvertebrate collection and processing 

protocols differ across participating organizations. This could involve the collection of 

side-by-side samples using the different methods. After the paired samples are processed using 

the respective methods, results would be compared and differences between the methods could 

be quantified.

In coming years, EPA and partners will continue to build capacity and refine protocols, 

indicator lists, analytical techniques and data management systems for the Northeast, 

Mid-Atlantic, and Southeast RMNs. Other regions have expressed interest in establishing RMNs 

as well. They recognize how these types of long-term data can support CWA programs, fill data 

gaps, and help detect trends attributable to climate change. The RMN framework is flexible and 

allows for expansion to new regions, as well as to new stream classes and waterbody types. For 

example, some parts of the Midwest lack the higher gradient, riffle-dominated cold water 

streams that are being targeted in the Northeast, Mid-Atlantic, and Southeast RMN pilot studies. 

Instead, these regions may focus their sampling efforts on low gradient, sandy-bottom, warm 

water streams. A Midwestern working group has also been formed to explore the possibility of

setting up a RMN for inland lakes. The monitoring data being collected from these regional 

efforts will provide important inputs for bioassessment programs as they strive to protect water 

quality and aquatic ecosystems under a changing climate.
8-1

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A-1 

APPENDIX A. _________________________ 

POWER ANALYSIS
A-2 

A.1. BACKGROUND

In 2011−2012, EPA collaborated with seven states in the northeastern United States on a 

pilot study that laid the groundwork for the Northeast Regional Monitoring Network (RMN). 

The intent was to design a monitoring network that could detect potentially small trends in 

biological, thermal, hydrologic, physical habitat, and water chemistry data at high quality sites in 

a decision-relevant timeframe (e.g., 10−20 years to be relevant to climate change). The design 

had to achieve a balance between scientific and practical considerations. It had to build on 

existing state and tribal bioassessment efforts and not exceed resource limitations of the 

biomonitoring programs. 

To help inform the network design, EPA and partners performed a series of analyses on 

an aggregated data set to explore the following questions: 

1) How long will it take to detect trends in biological metrics? 

2) How much of an effect do different design decisions, such as sampling frequency and 

classification scheme, have on trend detection times? 

A.2. METHODS

A.2.1. Data Preparation

EPA performed a series of analyses on a regional data set comprised of benthic 

macroinvertebrate, habitat, and water quality data from participating state biomonitoring 

programs in New York, Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and 

Vermont. In addition, macroinvertebrate data sets covering the same northeastern study area 

were obtained from the U.S. EPA Wadeable Streams Study (WSA; U.S. EPA, 2006), the New 

England Wadeable Streams project (NEWS; Snook et al., 2007), and the U.S. Geological Survey 

(USGS) National Water Quality Assessment (NAWQA). For purposes of the analyses, the data

set was limited to reference sites only, which were defined as locations with the least amount of 

anthropogenic disturbance (Hughes et al., 1986). Table A-1 lists the reference criteria and

Figure A-1 shows the locations of the reference sites. The data set was further limited to include 

samples collected from June−September1

 and samples with 80 or more total individuals2. 

Samples that had only family-level or coarser identifications were also removed from the data

set. 

The final data set was comprised of 1,398 samples from 953 reference sites, with sample 

years ranging from 1981 to 2010. Ten different methods were represented (see Table A-2). The 

 

1

This encompasses the general timeframe during which participating organizations collected samples for routine 

assessments.

2

We selected this threshold because it is 20% smaller than the smallest subsample target size among sampling 

entities, and a target of ±20% is in keeping with data quality objectives for most subsampling routines (Barbour et 

al., 1999). While some samples could have low densities due to natural conditions, others reasons, such as quality 

assurance issues, may account for the low count.
A-3

methods differ to varying degrees in sampling effort, sampling gear, habitats sampled, index 

periods, subsampling/sample processing, and/or level of taxonomic resolution (see Table A-3). 

Approximately 70% of the samples were collected using kick nets. The other sampling gear 

consisted of artificial substrates or Surber samplers. Most samples were collected from riffle 

habitats. Approximately 7% were collected from multiple habitats.

Table A-1. Reference site criteria

Variable Reference criterion

Percentage Natural land covera (NLCD 2001 upstream) >85%

Landscape Disturbance Index (LDI)b <1.5 index units

Percentage Imperviousness (2001 upstream) <1%

Percentage Imperviousness (2006 1-km radius)c <1%

NPDES major discharges >500 m from the site

Dams >500 m from the site

Dissolved oxygen >6 mg/L

Conductivity <200 μS/cm

a

Land cover screenings were estimates based on data associated with the National Hydrography 

Dataset Plus Version 1 (NHDPlusV1) catchments in which the sites are located 

(http://www.horizon-systems.com/NHDPlus). Land use data were based on the 2001 National 

Land Cover Database (NLCD; Homer et al., 2007) (http://www.mrlc.gov/nlcd01_data.php). For 

this exercise, natural land cover included open water, forest, wetlands, barren, and 

grassland/herbaceous. Data were accumulated for the entire upstream catchment.

b

The LDI (Brown and Vivas, 2005) was calculated by associating land uses with a scale of 

disturbance intensity and weighting the index score by percentage coverage of the 2001 land uses 

in the upstream catchment of each site.

c

Percentage impervious data for the 1-km radius were based on the 2006 National Land Cover 

Database (NLCD; Fry et al., 2011) (http://www.mrlc.gov/nlcd06_data.php).
A-4

Figure A-1. Reference site locations throughout New England and New York. 
A-5

Table A-2. Distribution of samples across states, sampling agencies and 

methods

State Method # Samples

CT CT DEEP Kick Riffle 53

NEWS Kick Multihabitat 6

MA CT DEEP Kick Riffle 1

MA DEP Kick Riffle 49

NEWS Kick Multihabitat 1

USGS Surber Riffle 14

WSA Kick Multihabitat 1

ME ME DEP Rock Basket 129

NEWS Kick Multihabitat 30

WSA Kick Multihabitat 7

NH NEWS Kick Multihabitat 25

NH DES Rock Basket 85

USGS Surber Riffle 1

WSA Kick Multihabitat 5

NY NY DEC Kick Riffle 497

USGS Surber Riffle 12

WSA Kick Multihabitat 13

RI NEWS Kick Multihabitat 4

RIDEM Kick Riffle 21

VT NEWS Kick Multihabitat 12

USGS Surber Riffle 1

VT DEC Kick Riffle 430

WSA Kick Multihabitat 1

Total 1,398
A-6

Table A-3. Description of collection and processing methods included in the analytical data set

Entity Collection method Gear Habitat

Sample 

area Subsampling

Index 

period

CT 

DEEP

12 kick samples are taken 

throughout riffle habitats within 

the sampling reach

Rectangular 

net (18" wide 

× 9 " high ), 

500 μm mesh

Riffle ~2 m2 200-organism minimum

count, randomly selected

from a 56 grid (5 cm × 5 cm

grids) subsampling tray

Sep 

15−Nov 

30 

VT 

DEC

Kick samples taken from riffle 

habitat in 4 different locations in 

the sampling reach. Substrate 

disturbed at each for ~ 

30 seconds, total active sampling 

effort 2 minutes. 

D-frame net

(18" wide ×

12" high)

with 500 μm

mesh

Riffle ~1 m2 1/4 of the sample, with a 

minimum of 300 organisms 

(if less than 300 organisms 

are found, 1 grid at a time is 

picked until the target is 

reached or the whole sample 

is picked)

Sep− mid

Oct

ME 

DEP

3 cylindrical rock-filled wire 

baskets, placed in locations with 

similar habitat characteristics for 

28 ± 4 days.

Contents 

washed into 

sieve bucket 

with 600 μm 

mesh

Riffle/run 

preferred.

~0.3 m2

per 

basket

Subsampling rules are 

difficult to briefly summarize 

(see Davies and Tsomides, 

2002). For this project, the 

entire samples were 

processed and identified.

Jul−Sep 

30 

NH 

DES

3 cylindrical rock-filled wire 

baskets in riffle habitat or at base 

of riffles at depths that cover the 

baskets by at least 5 inches, for 6 

to 8 weeks.

Contents 

washed into 

sieve bucket 

with 600 μm 

mesh

Riffle/run 

preferred.

~0.3 m2

per 

basket

Quarter of the sample with a 

minimum of 100 organisms 

(if less than 100 organisms 

are found, then the entire 

sample is processed)

late 

Jul−Sep

NEWS A 0.2-m2 quadrat randomly 

tossed in a particular mesohabitat

of stream reach; sampled for 1 

minute. 20 total quadrats 

collected per site in proportion to 

existing habitat in reach.

1/5 meter 

square 

quadrat. Dframe net 

with 500 μm 

mesh.

Multihabitat 

Composite 

~4 m2 200-organism minimum

count, randomly selected

from a Caton grid

Jul−Sep
A-7 

Table A-3. continued...

Entity Collection method Gear Habitat

Sample 

area Subsampling

Index 

period

WSA A 1 square foot area was sampled 

for 30 seconds at a randomly 

selected location at each of the 11 

transects. The samples were 

composited into one sample per 

site.

Modified 

D-frame net 

(12" wide) 

with 500 μm 

mesh

Multihabitat 

Composite 

~1 m2 500-organism minimum 

count, randomly selected 

from a Caton grid

Jun− mid

Octa 

MA 

DEP

10 kick-samples are taken in riffle 

habitats within the sampling reach 

and composited 

kick-net, 

46 cm wide 

opening, 

500 μm mesh

Riffle/run 

preferred

~2 m2 Count-based, 100-organism 

randomized pick 

Jul 1−Sep 

30 

RI 

DEM

Kick samples are taken from riffle 

habitats along 100 meter reach 

representative of the stream 

sampled timed for a total active 

sampling effort of 3 minutes.

D-frame net 

(0.3 m width) 

with 500 μm 

mesh

Riffle 100 

liner 

meters

100 organism minimum 

count, grids randomly 

selected from a 16-grid tray 

until minimum is picked

Aug−Sep

NY 

DEC

Substrate is dislodged by foot, 

upstream of the net for 5 minutes 

and a distance of 5 meters. The 

preferred line of sampling is a 

diagonal transect of the stream

Rectangular 

net (9” × 

18”) with 

800−900 μm 

mesh

Riffle 2.5 m 100 Jul−Sep

USGS Semiquantitative sample, 

composite of 5 discrete 

collections from the richest 

targeted habitat (typically riffle, 

main-channel, coarse-grained 

substrate habitat type). 

Slack 

sampler, 

500-μm nets 

and sieves

Riffle 1.25 m2 300-organism target Late 

Jun−mid

Oct

a

The WSA index period was supposed to end in September but some samples were collected in October
A-8 

The analysis consisted of 7 different biological metrics. Three of the metrics (total taxa 

richness and EPT richness and percentage composition) are commonly used in bioassessments, 

and the other 4 (richness and percentage composition of cold and warm water taxa) are believed 

to be climate-sensitive. The list of cold and warm water taxa, which can be found in Appendix I, 

were derived from: (1) generalized additive modeling analyses (GAM; Yuan, 2006) on the 

Northeast data set; (2) supplemental data provided by participating organizations in the 

Northeast, Mid-Atlantic and Southeast regions; and (3) best professional judgment of regional 

experts. Samples with larger numbers of individuals are likely to have greater numbers of taxa, 

necessitating adjustments to account for differences in subsampling procedures before 

calculating richness metrics (Gotelli and Colwell, 2001). Samples were randomly subsampled to 

120 organisms, which is the upper end of the 20% target used for most subsampling routines 

(Barbour et al., 1999). The subsampling routine was repeated 1,000 times, and metric values 

were averaged across these 1,000 runs. 

A.2.2. Classification

This study explored three different classification frameworks for the Northeast. The first 

one is based on an analysis of the regional macroinvertebrate data set (for details, see the 

supplemental data at the end of this appendix). The classification scheme is comprised of four

broad stream classes based on slope (NHDPlus flowline slope; unitless) and size (NHDPlus 

cumulative drainage area; km2

). The classes are broad enough to be represented in most states 

(see Figure A-2) and similar enough biologically to justify combining the macroinvertebrate data 

across the region. These analyses assessed the following 3 classes: high gradient, less than 

100 km2 (HGL); moderate gradient, less than 100 km2 (MGL); and low gradient and/or greater 

than 100 km2 (LGG). The fourth class (‘other,’ which are streams that are low gradient [<0.005] 

with drainage areas <10 km2 or high gradient [>0.02] with drainage areas >100 km2

) was 

excluded from the analysis because it occurs infrequently in the data set. 
A-9 

Figure A-2. Spatial distribution of NHDPlus local catchments grouped by 

size/slope class.

The other two stream classifications were: EPA Level 2 ecoregions (Omernik, 1995) and 

the Northeast Aquatic Habitat Classification (NAHC) developed by The Nature Conservancy 

(TNC; Olivero and Anderson, 2008). Ecoregions are delineated based on similarities in 

characteristics such as geology, physiography, vegetation, climate, soils, land use, wildlife, and 

hydrology, while the NAHC represents natural flowing-water aquatic habitat types using stream 

size (seven classes; headwaters to great rivers), NHDPlus flowline slope (six classes; very low to 

very high gradient), temperature (four classes: cold to warm water) and geology (three classes: 

buffered to acidic). These analyses evaluated two of the EPA Level 2 ecoregions (the Atlantic 

Highlands and Mixed Wood Plains, which encompass the majority of the study area) and two

groupings of the NAHC classes (Creek―Moderate to High Gradient―Moderately 

Buffered―Cold [TNC 1]; and Creek―Moderate to High Gradient―Acidic―Cold [TNC 2]).

Table A-4 summarizes the number of samples in each classification group. 
A-10

Table A-4. Summary of the data sets on which the power analyses are based

Data set Class Description

Number 

of 

Samples

Years 

covered

Slope/size HGL High gradient (>0.02), drainage area <100 km2 515 1986− 

2010

MGL Moderate gradient (0.005 to 0.02), drainage 

area <100 km2

362 1987− 

2010

LGG Low gradient (<0.005) and/or drainage area 

>100 km2 EXCEPT low gradient (<0.005),

drainage area <10 km2

 and high gradient

(>0.02), drainage area >100 km2

500 1981− 

2010 

EPA Level 2 

Ecoregion 

ER 5.3 Atlantic Highlands (5.3) 1,108 1983− 

2010

ER 8.1 Mixed Wood Plains (8.1) 278 1981− 

2009

Northeast 

Aquatic 

Habitat 

Classification 

(Olivero and 

Anderson 

2008)

TNC 1 Creek (10−100 km2), moderate or high 

gradient (≥0.005 and <0.05), cold, moderately 

buffered or neutral

325 1986− 

2010 

TNC 2 Creek (10−100 km2), moderate or high 

gradient (≥0.005 and <0.05), cold, low 

buffered or acidic 

159 1988− 

2010 

A.2.3. Analytical techniques

Estimations of variance components and power analyses simulations can help assess 

differences in trend detection ability between: 

• climate-sensitive and traditional metrics

• sampling frequency (1 vs. 2 vs. 5-year)

• effect size3 (0.5, 1, and 2%)

• classification schemes (size/slope; Level 2 ecoregion; NAHC)

3

Effect size refers to the annual rate of change (e.g., the 0.5% effect size represents the smallest rate of change while 

the 2% effect size represents the largest rate of change).
A-11 

Power analyses are well-suited for experimental design because one can experiment with 

different settings, such as power4 (e.g., 80% or higher), sample size (e.g., 25 vs. 50), and effect 

size (0.5, 1, or 2%), to get a sense of how long it will take to detect a change of a given size at a 

certain level of confidence. Results can also be used to provide information on design decisions 

that minimize variability and increase the power to detect trends over shorter time periods. 

The variance components analysis focused on three major components of variation: 

among methods, among subbasins (using eight digit hydrologic units, or HUC8s), and residual 

variation. Data were aggregated at the subbasin level instead of the site level because very few 

sites had a significant number of revisits. Variance among methods was included because 

methods strongly influence community structure (see Figure A-S1 in the supplemental material 

at end of this appendix). Variance components were estimated for each metric and stream class 

using mixed effects models of the form yyiiiiii = ββ0 + ββ1 ∗ xxiiiiii + εεiiiiii, where xxiiiiii is the year (k) 

the sample was collected with a specific method (i) and within a specific subbasin (j), and where 

the error term is partitioned into three components, εεiiiiii = bb0,ii + bb0, + ηηiiiiii. The models produce 

linear equations of change in each metric over time, and estimate the variability among 

collection methods, bb0,ii~NN(0, σσCCCCCCCCCCCCtth), among subbasins, bb0, ~NN(0, σσHHHH 8), and unaccounted 

for interannual variation, ηηiiiiii~NN(0, σσrr rr rrrrrr ). In each simulation run, samples were assigned to 

an artificial subbasin and one of ten methods (3 samples per method). Each simulation was run 

1,000 times, for a fixed number of years, ranging from 2 to 131 years, where power is the 

percentage of runs with a significant slope effect (ββ1, p < 0.05). Variance components for each 

model were estimated with REML using the LME4 package in R (Bates et al., 2011). 

Power analyses were conducted for each combination of stream class (using the 7 classes 

listed in Table A-4), invertebrate metric (7 metrics), effect size (0.5, 1.0, or 2.0%), and sampling 

frequency (1 vs. 2 vs. 5-year) as categorical main effects. For each metric and stream class 

combination, the power analyses calculated the number of years needed to reach 80% power by 

creating simulated data sets using the above estimated error components at different effect sizes

(2.0, 1.0, or 0.5% annual rate of change) and sampling intervals (annual, biannual, or every five 

years). It was assumed that 30 sites were sampled at each sampling frequency5

. To summarize 

these results, the number of years needed to exceed 80% power was analyzed using a linear 

model that included the stream class, invertebrate metric, effect size, and sampling frequency as 

categorical factors. Since it was expected that specific metrics would perform well in specific 

stream classes (e.g., that cold-water taxa would have higher detection probabilities in classes that 

included higher gradient, upland streams), a metric-class interaction was added into the model. 

Similarly, because it was expected that higher sampling frequency would be more important for 

low effect sizes, an effect size-sampling frequency interaction was also included in the model. To 

 

4

Power is the likelihood or probability of correctly detecting an outcome of a given size; for example, 80% power 

means that there is an 80% probability that an outcome of a given size is correctly detected.

5

EPA and partners performed an exploratory analysis to evaluate how much of a difference it would make if 30 vs. 

50 sites were sampled. They found that trend detection times were very similar (detection times differed by 

1−2 years, depending on the effect size; the smaller the effect size, the greater the difference; unpublished data).
A-12

meet model assumptions, exceedances were log transformed before analysis. When applicable, 

follow-up multiple comparisons were conducted using a Bonferroni correction for multiple 

testing. 

A.3. RESULTS

Trend detection time was significantly (P < 0.0001) influenced by frequency of sampling, 

type of metric, and stream classification (see Table A-5). The interactions included in the model 

were significant as well, indicating that detection times associated with each metric depended on 

the stream class, and that the relationship between sampling frequency and detection time 

depended on the effect size (P < 0.001; see Table A-5). 

Table A-5. Power analysis model output table, assuming 80% power and a 

30-site sample size

Factor DF SS MS F P

Biological metric 6 92.32 15.39 7,120.07 <0.0001

Stream class 6 0.92 0.15 70.63 <0.0001

Biological metric × stream class 36 3.95 0.11 50.75 <0.0001

Effect size 2 62.8 31.4 14,529.82 <0.0001

Sampling frequency 2 15.87 7.94 3,672.31 <0.0001

Effect size × sampling frequency 4 0.04 0.01 4.12 0.0003

Residual 384 0.83 0.002

Results suggest that trends in biological indicators can be detected within 10−20 years (at 

80% power) if 30 or more sites that have comparable environmental conditions and biological 

communities are monitored regularly (see Figure A-3). As shown in Figure A-3, the ‘traditional’ 

metrics (total taxa richness and EPT richness and percentage composition) had shorter trend 

detection times than the climate-sensitive metrics, and the climate-sensitive richness metrics had 

shorter trend detection times than the climate-sensitive percentage composition metrics (see 

Figure A-3). Table A-6 contains a complete list of the number of years needed to exceed 80% 

power for each metric and stream class combination with different effect sizes (0.5, 1, and 2%) 

and sampling frequencies (1, 2 and 5-year). Overall, the total taxa richness metric had the 

shortest trend detection time (13−15 years, depending on classification scheme), while the 

warm-water percentage individual metric had the longest trend detection time (30+ years; see 

Table A-6). As expected, the total taxa and EPT richness metrics had higher mean values than 

the climate-sensitive richness metrics. As shown in Figure A-4, mean richness metric values are
A-13

inversely related to the number of years needed to detect a trend with 80% power. Put more 

simply, richness metrics that have higher mean values have shorter trend detection times. 

There were differences in performance across the different stream classes (see 

Figure A-3, Table A-6). The differences were most evident in the climate-sensitive metrics.

Overall, the Mixed Wood Plains (8.1) EPA Level 2 ecoregion generally had the longest trend 

detection times, particularly with the traditional metrics (see Figure A-3, Table A-6). The NAHC 

classes and 2 of the size/slope classes (high gradient/less than 100 km2 [HGL] and moderate 

gradient/less than 100 km2 [MGL]) generally had the shortest trend detection times for the 

traditional metrics and the cold water metrics, while the low gradient and/or greater than 100 km2

(LGG) class had the shortest trend detection times for the warm water metrics (see Figure A-3, 

Table A-6). Generally speaking, the classifications that were built to separate out small to 

medium-sized, moderate to high gradient, cold water streams versus large, low gradient warm

water streams tended to detect trends in the climate-sensitive metrics in the shortest time periods.

Figure A-3. Mean (±SD) years to detect trends with 80% power for metric 

and stream class combinations. Results were averaged across the 3 effect 

sizes (0.5, 1, and 2%) and 3 sampling frequencies (1, 2 and 5-year; 

9 estimates per data point). This analysis assumes a 30-site sample size. If the 

classes (top) share the same letter, this means that the mean number of years 

to detect trends with 80% power across those classes (for a given metric) are 

not significantly different (p < 0.001). If the biological metrics (bottom) share 

the same number, this means that the mean number of years to detect trends 

with 80% power across those metrics (for a given class) are not significantly 

different (p < 0.001). 
A-14

Figure A-4. Relationship between mean metric richness values and number 

of years to detect trends. Each dot represents the number of years needed to 

exceed 80% power based on various combinations of the 4 richness metrics, 

7 stream classes, 3 effect sizes, and 3 sampling frequencies. Richness metrics 

are color-coded (cold water richness = black; EPT richness = red; total 

richness = green; warm water richness = blue). This analysis assumes a 

30-site sample size.

Sampling frequency (1 vs. 2 vs. 5-year) also had a significant effect on trend detection 

times. The weaker the trend (and lower the effect size), the more of a difference the sampling 

frequency made (see Figure A-5). Annual sampling had the shortest trend detection time across 

all effect sizes. Annual sampling at 1.0 and 0.5% effect sizes was equivalent to sampling every 

five years at 2.0 and 1.0% effect sizes, respectively (see Figure A-5).
A-15 

Figure A-5. Mean (±SD) years to detect trends with 80% power for effect size 

and sampling frequency combinations. Results were averaged across the 

7 stream classes and 7 biological metrics (49 estimates per data point). If 

results share the same letter, this means that the mean number of years to 

detect trends with 80% power across those effect sizes (for a given sampling 

frequency) are not significantly different (p < 0.001). This analysis assumes a 

30-site sample size.
A-16 

Table A-6. Number of years to detect trends with 80% power for metric and 

stream class combinations with different effect sizes (0.5, 1, and 2%) and 

sampling frequencies (1, 2 and 5-year). This assumes a 30-site sample size. 

Asterisks (a) indicate that trend detection times exceed 40 years

Metric Class

2.0% 1.0% 0.5%

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

Number of

Total taxa

Atlantic Highlands (ER 5.3) 7 9 11 11 13 16 17 21 26

Mixed Wood Plains (ER 8.1) 7 9 11 11 13 16 18 23 26

High gradient, <100 km2

(HGL) 7 7 11 11 13 16 16 21 26 

Low gradient and/or 

>100 km2a (LGG) 7 9 11 11 13 16 17 21 26 

Moderate gradient, <100 km2

(MGL) 7 7 11 10 11 16 15 19 26 

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1)

6 7 11 10 11 16 15 19 26 

Creek, mod/high gradient, 

cold, low buffer/acidic (TNC 

2)

6 7 11 9 11 16 15 19 21 

Number of

EPT taxa

Atlantic Highlands (ER 5.3) 9 11 11 13 17 21 21 25 31

Mixed Wood Plains (ER 8.1) 9 11 16 14 17 21 23 29 36

High gradient, <100 km2

(HGL) 8 9 11 13 15 21 20 25 31 

Low gradient and/or 

>100 km2a (LGG) 9 11 11 13 17 21 21 25 36 

Moderate gradient, <100 km2

(MGL) 9 11 11 14 17 21 21 27 36 

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1)

8 9 11 12 15 21 19 23 31 

Creek, mod/high gradient, 

cold, low buffer/acidic 

(TNC 2)

7 9 11 11 13 16 17 21 26 
A-17 

Table A-6. continued...

Metric Class

2.0% 1.0% 0.5%

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

Percentage

EPT 

individuals 

Atlantic Highlands (ER 5.3) 9 11 16 14 17 21 22 29 36

Mixed Wood Plains (ER 8.1) 11 13 16 18 21 26 28 33 a

High gradient, <100 km2

(HGL) 9 11 16 15 17 26 23 29 36 

Low gradient and/or 

>100 km2a (LGG) 10 11 16 15 19 26 24 31 a

Moderate gradient, <100 km2

(MGL) 10 11 16 15 19 26 23 29 a

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1)

9 11 11 13 15 21 20 25 31 

Creek, mod/high gradient, 

cold, low buffer/acidic 

(TNC 2)

8 9 11 13 15 21 20 25 31 

Number of 

Cold water 

taxa

Atlantic Highlands (ER 5.3) 13 15 21 21 25 31 32 39 a

Mixed Wood Plains (ER 8.1) 13 15 21 21 25 31 32 a a

High gradient, <100 km2

(HGL) 11 15 16 18 21 26 29 35 a

Low gradient and/or 

>100 km2a (LGG) 13 17 21 21 25 36 33 a a

Moderate gradient, <100 km2

(MGL) 11 13 16 18 21 26 28 35 a

Creek, mod/high gradient,

cold, mod buffer/neutral 

(TNC 1)

10 13 16 16 21 26 26 31 a

Creek, mod/high gradient, 

cold, low buffer/acidic (TNC 

2)

11 13 16 18 21 26 28 35 a
A-18 

Table A-6. continued...

Metric Class

2.0% 1.0% 0.5%

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

1

yr

2

yr

5

yr

Percentage

Cold water 

individuals 

Atlantic Highlands (ER 5.3) 18 23 31 28 35 a a a a

Mixed Wood Plains (ER 8.1) 17 21 26 26 33 a a a a

High gradient, <100 km2

(HGL) 14 17 21 22 29 36 35 a a

Low gradient and/or 

>100 km2a (LGG) 21 25 31 32 a a a a a

Moderate gradient, <100 km2

(MGL) 17 21 26 26 33 a 40 a a

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1)

14 17 21 22 27 36 34 a a

Creek, mod/high gradient, 

cold, low buffer/acidic 

(TNC 2)

14 17 21 21 25 36 33 a a

Number of

Warm water 

taxa

Atlantic Highlands (ER 5.3) 17 21 26 27 33 a a a a

Mixed Wood Plains (ER 8.1) 17 21 26 26 33 a a a a

High gradient, <100 km2

(HGL) 17 21 26 28 33 a a a a

Low gradient and/or 

>100 km2a (LGG) 14 17 21 23 27 36 34 a a

Moderate gradient, <100 km2

(MGL) 16 21 26 26 33 a a a a

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1)

18 23 31 29 37 a a a a

Creek, mod/high gradient, 

cold, low buffer/acidic 

(TNC 2)

15 19 26 24 29 a 38 a a
A-19 

Table A-6. continued...

2.0% 1.0% 0.5%

1 2 5 1 2 5 1 2 5 

Metric Class yr yr yr yr yr yr yr yr yr

Percentage

Warm water Atlantic Highlands (ER 5.3) 25 31 a 39 a a a a a

individuals 

Mixed Wood Plains (ER 8.1) 26 31 a 40 a a a a a

High gradient, <100 km2

(HGL) 25 31 a 39 a a a a a

Low gradient and/or 

>100 km2a (LGG) 22 27 36 34 a a a a a

Moderate 

(MGL)

gradient, <100 km2

29 37 a a a a a a a

Creek, mod/high gradient, 

cold, mod buffer/neutral 

(TNC 1) 

31 a a a a a a a a

Creek, mod/high gradient, 

cold, low buffer/acidic 

(TNC 2) 

32 39 a a a a a a a

a

Except low gradient (<0.005), drainage area <10 km2 and high gradient (>0.02), drainage area >100 km2

Table A-7 contains 3 sets of results from the variance components analysis: (1) overall 

variability (estimates are averaged across all metric and stream class combinations); (2) mean 

variability for each biological metric (estimates are averaged across the stream classes); and 

(3) mean variability for each stream class (estimates are averaged across the biological metrics). 

Overall, the residual variation (which represents variability that cannot be attributed to the trend 

over time, the collection method, and HUC8) was largest (7.02), followed by collection method 

(4.9) and HUC8 subbasin (3.3; see Table A-7). Percentage individual metrics had higher mean 

variability than the richness metrics, particularly the percentage EPT and percentage cold water

individuals metric (see Table A-7). The mean variability estimates were fairly similar across 

stream classes.
A-20

Table A-7. Results from the variance components analysis. For the overall 

average, estimates are averaged across all metric and stream class 

combinations. For the biological metrics, estimates are averaged across the 

stream classes and for the stream classes, estimates are averaged across the 

biological metrics.

Set of results Mean CollMeth Mean HUC8 Mean Residual

Overall average 4.9 3.3 7.0

Biological metrics

Total taxa richness 5.7 2.6 5.1

EPT richness 2.0 1.8 3.5

Cold water taxa richness 1.8 1.1 2.3

Warm water taxa richness 0.8 0.7 1.3

Percentage EPT individuals 14.1 6.8 16.5

Percentage Cold water individuals 7.0 6.7 13.0

Percentage Warm water individuals 3.0 3.3 7.4

Stream classes

Atlantic Highlands (5.3) 5.0 3.0 7.5

Mixed Wood Plains (8.1) 5.3 3.2 7.7

High gradient, <100 km2 (HGL) 5.1 2.7 7.0

Moderate gradient, <100 km2 (MGL) 4.8 3.0 7.1

Low gradient and/or >100 km2* (LGG) 4.9 3.8 7.0

Creek, mod/high gradient, cold, mod 

buffer/neutral (TNC 1)

4.0 3.6 6.5

Creek, mod/high gradient, cold, low 

buffer/acidic (TNC 2)

5.5 4.0 6.3

A.4. CONCLUSIONS

The analyses suggest that detection times of 10−20 years (at 80% power) are possible for 

several of the biological metrics if 30 or more sites with comparable environmental conditions 

and biological communities are monitored regularly. These results are consistent with other 

research. For example, Larsen et al. (2004) found that well-designed networks of 30−50 sites 

monitored consistently can detect underlying changes of 1−2% per year in a variety of metrics 

within 10−20 years, or sooner, if such trends are present. Larsen et al. (2004) also emphasized 

the importance of the duration of the survey, citing that trend detection capability increases 

substantially with time. 
A-21 

The analyses also show that richness metrics have lower variability than percentage 

individual metrics, which means that there is a greater likelihood of detecting trends in the 

richness metrics over shorter time periods. Because richness values increase with subsampling 

effort, it is important to identify an adequate number of organisms, particularly for metrics with 

low representation (e.g., cold-water taxa). Identifying a larger subsample of organisms at RMN 

sites (e.g., 300 or more organisms) will improve the power to detect trends. While this may 

increase costs in the short-term, the resulting increase in power lowers detection times, thereby 

reducing overall costs of the long-term monitoring effort. Moreover, the earlier detection may 

lead to management actions that could alleviate additional, costlier impacts. 

The classification scheme also influences trend detection times. Thus, the Northeast 

should be partitioned to minimize environmental variability and gradients. Size and gradient are 

key variables to capture. Schemes that consider additional variables like thermal class and 

geology (like TNC’s NAHC) may further improve performance, but those variables are generally 

not as readily available (or dependable), and in this study, did not make a large difference in 

trend detection times. Classification is also an important consideration during site selection. 

Since one of the objectives of the RMNs is to detect trends attributable to climate change, 

tracking changes in the thermal indicator taxa (particularly cold water taxa) is of interest. As 

discussed above, the more cold water taxa present at the RMN sites, the greater the chance of 

detecting trends in the cold water richness metric over shorter time periods. Based on 

unpublished analyses on the Northeast data set, sites in small to medium-sized, medium to high 

gradient stream classes (MGL and HGL) have higher numbers of cold-water taxa, so selecting 

sites in these stream classes will improve the chances of detecting climate-related trends over

shorter time periods. 

Sampling frequency is another important consideration. The analyses show that sampling 

macroinvertebrates on an annual basis improves trend detection times, particularly if trends are 

subtle. If sites in the Northeast RMN are sampled annually over a long time period (e.g., 

10+ years), this will improve the chances of detecting trends. Other design recommendations,

which are described in Section 3.1.1, are based on literature and Standard Operating Procedures 

(SOPs) commonly used by RMN partners. These include recommendations on time periods for 

sample collection (also referred to as ‘index periods’), collection protocols, type(s) of habitats 

sampled, and level of taxonomic identification. The use of consistent methods will increase the 

comparability of data, minimize biases and variability, and ensure that the data meet data quality 

objectives of the Northeast RMN. These recommendations also can be applied to other regions 

interested in climate change detection in streams.
A-22 

A.5. SUPPLEMENTAL MATERIAL

A.5.1. Derivation of the Size/Slope Classification Scheme

Prior to running the classification analysis on the aggregated Northeast data set, the data

set was reduced by randomly selecting one sample per site so that sites with multiple years of 

data would not receive unequal weight (sample size = 689). The 689 sites were distributed across 

the whole study region. For site classification, nonmetric multidimensional scaling (NMDS) 

ordination was used to explore predominant types of reference samples based on taxa presence 

and absence. The ecodist package in R was used to calculate Bray-Curtis similarity measures 

(Goslee and Urban, 2007; R Core Development Team, 2012) to perform NMDS. Prior to running 

the analysis, guidelines recommended in Cuffney et al. (2007) were used to develop Operational 

Taxonomic Units (OTUs) as necessary to achieve an acceptable level of taxonomic consistency 

across the data set. For interpretability, the NMDS was limited to four axes (Stress = 0.20, 

r2

 = 0.4). 

Initial NMDS ordination using all data showed that sites group by method (see 

Figure A-S1), indicating that method is an important determinant of taxonomic structure. These 

differences appeared along all four axes, but were most pronounced in the first three. For 

example, samples from Maine and New Hampshire (artificial substrate ellipsoid, see 

Figure A-S1) overlapped and were largely distinct from New York and Vermont samples (riffle 

D-net ellipsoid, see Figure A-S1). The strongest correlations (|r| > 0.3) between taxa and 

environmental variables included some combination of slope and drainage size in all four axes, 

but also included longitude on axis 2, and elevation and maximum temperature on axis four (see 

Table A-S1, left). Axis 1 can be interpreted as shifts in the macroinvertebrate community that 

occurred as measures of slope increased and drainage area decreased (particularly slope and log10

drainage area), while axes two and three separated the two. Axis two was most strongly related 

to mean slope (and longitude) and axis three to log10 drainage area. Axis four is less easily 

interpreted.
A-23 

-0.4 -0.2 0.0 0.2 0.4 0.6

-0.6 -0.4 -0.2 0.0 0.2 0.4

Axis 2

Axis 1

CT DEEP

MA DEP

ME DEP

NEWS

NH DES

NY DEC

RI DEM

USGS

VT DEC

WSA

MultiHabitat

Riffle

Artificial

Substrate

Figure A-S1. Axes 1 and 2 from first NMDS ordination, using data from all 

methods. All four axis correlations are confounded by method. Methods are 

represented with different symbols and sampling devices are shown with two 

rings (solid and dashed 95% confidence ellipsoids). WSA and NEWS samples 

are also highlighted (dotted 95% confidence ellipsoid).
A-24

Table A-S1. Correlations between environmental variables and NMDS axes 

for all sites (left) and NEWS and WSA sites (right). Both analyses show 

consistent ties between taxonomic axes and aspects of slope and drainage 

area. |r2| > 0.3 are bolded. 

Environmental All Sites NEWS and WSA sites

Variable X1 X2 X3 X4 X1 X2 X3 X4

Latitude −0.157 0.033 0.056 −0.178 0.062 −0.123 −0.241 0.121

Longitude −0.173 0.405 0.024 −0.244 0.270 −0.130 0.037 −0.001

Sinuosity −0.039 0.080 −0.002 0.068 0.128 −0.041 0.112 −0.223

Slope 0.405 −0.112 −0.093 −0.462 −0.205 0.174 −0.506 −0.320

Mean Slope 0.259 −0.327 0.219 −0.313 −0.064 0.028 −0.602 −0.237

Drainage Area −0.245 0.086 0.016 0.150 0.317 −0.257 0.154 0.136

Log10(Dr Area) −0.403 −0.117 0.369 0.366 0.403 −0.339 0.023 0.116

Elevation 0.215 −0.247 −0.073 −0.308 −0.203 0.278 −0.351 −0.095

Stream Aspect −0.070 0.015 −0.003 0.053 −0.256 0.112 0.060 0.094

Max Temp 0.041 0.165 −0.049 0.305 0.031 −0.004 0.416 −0.004

Min Temp 0.027 0.057 0.013 0.263 −0.086 −0.064 −0.013 0.253

Length 0.082 −0.118 0.143 −0.115 0.102 −0.002 −0.309 −0.324

To eliminate the confounding effects of method, a second NMDS ordination used data 

only from agencies that used similar methods and sampled across the region. This included 64 

sites from the NEWS and WSA studies (multihabitat ellipsoid, see Figure A-S1). The resulting 

ordination also was limited to four axes for interpretability (Stress = 0.18, r2

 = 0.53). This 

ordination showed high overlap between the two studies on all four axes. Likewise, the strongest 

correlations (|r| > 0.3) between taxa axes and environmental variables included some 

combination of slope or mean slope and drainage area or log10(drainage area) in all four axes, but 

also included elevation and maximum temperature on axis 3, and NHDPlus flowline length on 

axes 3 and 4 (see Table A-S1, right). Axes 1 and 2 reflected shifts in the macroinvertebrate 

community that occur as measures of drainage area change, while axes 3 and 4 showed shifts 

related to measures of slope.

Given the consistent appearance of slope and drainage area variables in both NMDS 

results, these two variables were chosen as a simple way to identify stream classes. Thresholds 

for both slope and drainage were selected to be consistent with the TNC’s Northeast Aquatic 

Habitat Classification (NAHC; Olivero and Anderson, 2008). The distributions of the biological 

metrics used in this study were compared in multiple categories of slope and catchment area. In 

streams with catchments <100 km2

, slope was a dominant effect. Streams with similar slopes but 
A-25 

differing catchment size (<10 or >10 km2) had similar metric distributions. In small rivers with 

catchments >100 km2, slope was less of a factor and metric distributions resembled those in 

lower gradient smaller streams.

The end result was the following four broad stream classes based on slope (NHDPlus 

flowline slope; unitless) and size (NHDPlus cumulative drainage area; km2): 

• High gradient, less than 100 km2 (HGL) 

• Moderate gradient, less than 100 km2 (MGL) 

• Low gradient and/or greater than 100 km2 (LGG)

• ‘Other’―low gradient (<0.005), drainage area <10 km2

 and high gradient (>0.02), 

drainage area >100 km2
A-26

A.6. LITERATURE CITED

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 

Bates, D; Maechler, M; Bolker, B. (2011) lme4: Linear mixed-effects models using S4 classes. R package version 

0.999375-42. Available online at http://CRAN.R-project.org/package=lme4. 

Brown, MT; Vivas, MB. (2005) Landscape development intensity index. Environmental Monitoring and 

Assessment 101(289-309). 

Cuffney, TF; Bilger, MD; Haigler, AM. (2007) Ambiguous taxa: effects on the characterization and interpretation of 

invertebrate assemblages. J N Am Benthol Soc 26(2):286−307.

Davies, SP; Tsomides, L. (2002). Methods for biological sampling and analysis of Maine’s rivers and streams. (DEP 

LW0387-B2002). August, Maine: Maine Department of Environmental Protection. 

http://www.maine.gov/dep/water/monitoring/biomonitoring/materials/finlmeth1.pdf

Fry, J; Xian, G; Jin, S; J.Dewitz, C.Homer, L.Yang, C.Barnes, N.Herold, and J. Wickham. (2011) Completion of the 

2006 national land cover database for the conterminous United States. PE&RS 77(9): 859−864. Available 

online at www.mrlc.gov/downloadfile2.php?file=September2011PERS.pdf

Goslee, S; Urban, D. (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Soft 

22:1−19.

Gotelli, NJ; Colwell, RK. (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and 

comparison of species richness. Ecol Lett 4:379−391.

Homer, C; Dewitz, J; Fry, J; Coan, M; Hossain, N; Larson, C; Herold, N; McKerrow, A; VanDriel, JN; Wickham, J. 

(2007) Completion of the 2001 National Land Cover Database for the Conterminous United States. PE&RS 

73(4):337−341. Available online at 

http://www.asprs.org/a/publications/pers/2007journal/april/highlight.pdf

Hughes, RM; Larsen, DP; Omernik, JM. (1986) Regional reference sites: a method for assessing stream potentials. 

Environmental Management 10(5):629-635.

Larsen, D; Kaufmann, P; Kincaid, T; Urquhart, N. (2004) Detecting persistent change in the habitat of salmonbearing streams in the Pacific Northwest. Can J Fish Aqua Sci 61:283−291.

Olivero, AP; Anderson, MG. (2008) Northeast aquatic habitat classification. Denver, CO: The Nature Conservancy. 

Available online at http://rcngrants.org/content/northeastern-aquatic-habitat-classification-project

Omernik, JM. (1995) Ecoregions: a spatial framework for environmental management. In: Davis, W; Simon, T; Eds 

Biological assessment and criteria: tools for water resource planning and decision making. [pp 49−62]. 

Boca Raton, FL: Lewis Publishers.

R Core Development Team. (2012) R: A language and environment for statistical computing. Vienna, Austria.

Snook, H; Davies, S; Gerritsen, J; Jessup, B; Langdon, R; Neils, D;Pizutto, E. (2007) The New England Wadeable 

Stream Survey (NEWS): development of common assessments in the framework of the biological 

condition gradient. Prepared for USEPA Office of Science and Technology and Office of Wetlands, 

Oceans and Watersheds, Washington, DC.

U.S. EPA (Environmental Protection Agency). (2006) Wadeable Streams Assessment: a collaborative survey of the 

Nation's streams. Office of Water, Washington, DC; EPA 841/B-06/002.
A-27

Yuan, LL. (2006) Estimation and application of macroinvertebrate tolerance values. [EPA/60/P-04/116F]. 

Washington, DC National Center for Environmental Assessment. U.S. Environmental Protection Agency, 

Washington, DC.
B-1 

APPENDIX B. ___________________ 

CHECKLIST FOR 

STARTING A REGIONAL 

MONITORING NETWORK 

(RMN) 
B-2

1. Establish the regional working group.

• Coordinator (e.g., from an U.S. EPA Region or a state) volunteers to lead the regional

working group.

• The coordinator works with EPA and partners to create a contact list.

• EPA and the coordinator hold a kick-off webinar to brief potential partners on current

RMN efforts, describe the RMN framework and development process, and discuss a

potential timeline for implementation.

2. The coordinator requests candidate sites from each entity. During the site selection process,

the working group considers site selection criteria being used in other RMN regions and tries

to use similar criteria where practical. Desired site characteristics include:

• Part of established, long-term monitoring networks

• Low level of anthropogenic disturbance

• Colocated with existing equipment (e.g., USGS gage, weather station)

• Exhibit similar environmental and biological characteristics

• Longevity (e.g., accessible [day trip], opportunities to share the workload with outside

agencies or organizations)

• In watersheds that are protected from future development

• Lengthy historical sampling record for biological, thermal or hydrological data

3. The regional coordinator compiles information on data collection and processing protocols

being used by each regional working group member, and EPA distributes the generic RMN

QAPP1

. The regional working group reviews the generic QAPP and discusses appropriate

protocols for their region. The group attempts to use similar protocols to other RMNs. The

draft (region-specific) protocols are written up in an addendum to the QAPP.

4. EPA has been conducting research on screening, classification, and vulnerability analyses for

several pilot RMNs (additional documentation to conduct these steps are available from

EPA). Pending availability and funding, EPA may be able to assist with the following steps:

• Screening the candidate sites by running them through a disturbance screening process

similar to what is described in Appendix D. This may include developing criteria for

1

U.S. EPA. 2015. Draft generic Quality Assurance Project Plan for monitoring networks for tracking long-term 

conditions and changes in high quality wadeable streams. This document is available from EPA upon request. 

Contact Britta Bierwagen (bierwagen.britta@epa.gov). 
B-3 

“reference” sites in urban and agricultural areas. Disturbance ratings will be assigned to 

the candidate sites.

• Gathering information from the regional working group on existing classification 

schemes in the region and performing analyses to explore regional classification. Sites 

will be assigned to classification groups. 

• Gathering information on existing climate change vulnerability assessments and 

performing broad-scale analyses to assess the vulnerability of the candidate RMN sites to 

climate change.

5. The regional coordinator works with EPA and regional working group members to finalize 

site selection and protocols. These are included as addendums to the QAPP. 

6. The group identifies and seeks resources for implementation. High priority start-up items 

typically include obtaining equipment and finding funds to process macroinvertebrate 

samples.
C-1 

APPENDIX C. __________________________ 

PRIMARY REGIONAL 

MONITORING NETWORK 

(RMN) SITES IN THE 

NORTHEAST, 

MID-ATLANTIC, AND 

SOUTHEAST RMN 

REGIONS
C-2

Table C-1. Site information for primary RMN sites in the Northeast (3/2015). Drainage area, slope, and 

elevation are estimates based on NHDPlus v1a local catchment data. Percentage forest is derived from the NLCD 

2001b data layer and is based on the total watershed

Longitude Latitude State Entity Station ID Water body name

Drainage area 

(km2)

Slope 

(unitless)

Elevatio

n (m)

Percentage

Forest (%)

−73.27990 41.92670 CT CT DEEP CTDEP_2342 Brown Brook 14.7 0.026 286.4 90.2

−71.83424 41.47482 CT CT DEEP CTDEP_1748 Pendleton Hill 10.4 0.006 55.2 71.7

−72.83917 41.94639 CT CT DEEP CTDEP_1433 West Branch Salmon 34.5 0.021 169.35 81.6

−72.16196 42.03448 MA MA DEP MADEP_BB01 Browns 14.7 0.023 253.5 87.3

−73.03027 42.66697 MA MA DEP MADEP_CR01AA Cold River 16.8 0.026 592.4 89.3

−72.96731 42.06555 MA MA DEP MADEP_HRCC Hubbard 30.0 0.029 359.8 86.5

−72.04780 42.39431 MA MA DEP MADEP_PBCC Parkers Brook 13.8 0.011 244.9 79.5

−72.38454 42.46471 MA MA DEP MADEP_WSR01 West Branch Swift 9.8 0.011 209.9 91.5

−69.64424 44.95675 ME ME DEP MEDEP_57229 East Branch Wesserunsett 

Stream―Station 486

126.0 0.008 207.2 83.4

−71.35110 43.14410 NH NH DES NHDES_99M-44 Bear 25.7 0.005 138.9 81.5

−71.24924 44.21896 NH NH DES USGS_01064300 Ellis 28.2 0.031 686.7 88.6

−71.36166 44.35426 NH NH DES NHDES_19-ISR Israel 16.6 0.023 544.7 92.5

−71.29306 43.89639 NH NH DES NHDES_98S-44 Paugus 31.5 0.008 264.2 97.8

−71.87633 44.10563 NH NH DES NHDES_WildAmmo Wild Ammo 96.2 0.010 481.0 96.7

−73.54621 41.49457 NY NY DEC NYDEC_HAVI_01 Haviland Hollow 24.9 0.011 202.9 85.7

−74.26626 42.01954 NY NY DEC NYDEC_LBEA_01 Little Beaver Kill 42.7 0.008 393.3 90.3

−71.61201 41.83760 RI RI DEM RIDEM_RMR03a Rush 12.2 0.017 118.2 72.6

−71.63562 41.76482 RI RI DEM RIDEM_SCI01 Wilbur Hollow 11.2 0.008 124.3 74.5

−72.88583 43.87167 VT VT DEC VTDEC_135404000013 Bingo 29.2 0.017 458.5 97.3

−72.66250 42.76389 VT VT DEC VTDEC_670000000166 Green 67.8 0.010 293.3 89.9
C-3

Table C-1. continued...

Longitude Latitude State Entity Station ID Water body name

Drainage area 

(km2)

Slope 

(unitless)

Elevatio

n (m)

Percentage 

Forest (%)

−71.78528 44.58417 VT VT DEC VTDEC_211200000268 Moose 59.0 0.015 532.7 97.5

−72.53705 44.43400 VT VT DEC VTDEC_495400000161 North Branch Winooski 29.1 0.014 327.1 95.3

−72.93194 43.13833 VT VT DEC VTDEC_033500000081 Winhall 43.8 0.017 587.7 95.0

a

http://www.horizon-systems.com/nhdplus/nhdplusv1_home.php b

http://www.mrlc.gov/nlcd01_data.php
C-4

Table C-2. Equipment installed at primary RMN sites in the Northeast (3/2015) 

State Entity Station ID Water body name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

CT CT DEEP CTDEP_2342 Brown Brook water none none

CT CT DEEP CTDEP_1748 Pendleton Hill water and air USGS gage 

(01118300)

discharge gage located at biological 

sampling site

CT CT DEEP CTDEP_1433 West Branch Salmon water none none

MA MA DEP MADEP_BB01 Browns water and air pressure 

transducer

stage and 

discharge

flow rating curve 

developed by MA RIFLS

MA MA DEP MADEP_CR01AA Cold River water and air pressure 

transducer/USGS 

gage

stage and 

discharge

flow rating curve 

developed by MA 

RIFLS; USGS gage is 

now being installed at 

this site 

MA MA DEP MADEP_ HRCC Hubbard water and air USGS gage 

(01187300)

discharge gage is downstream of 

site but location looks 

representative of stream 

conditions

MA MA DEP MADEP_PBCC Parkers Brook water and air pressure 

transducer

stage and 

discharge

flow rating curve 

developed by MA RIFLS

MA MA DEP MADEP_WSR01 West Branch Swift water and air USGS gage 

(01174565)

discharge gage is downstream of 

site but location looks 

representative of stream 

conditions

ME ME DEP MEDEP_57229 East Branch Wesserunsett 

Stream―Station 486

watera USGS gage 

(01048220)

discharge gage located at biological 

sampling site

NH NH DES NHDES_99M-44 Bear water and air pressure 

transducer

stage

NH NH DES USGS_01064300 Ellis water and air pressure 

transducer

stage
C-5

Table C-2. continued...

State Entity Station ID Water body name Temperature Hydrologic equipment

Hydrologic

data type Notes

NH NH DES NHDES_19-ISR Israel water and air pressure transducer stage

NH NH DES NHDES_98S-44 Paugus water and air pressure transducer stage

NH NH DES NHDES_WildAmmo Wild Ammo water and air pressure transducer stage

NY NY DEC NYDEC_HAVI_01 Haviland Hollow water and air none none

NY NY DEC NYDEC_LBEA_01 Little Beaver Kill water and air USGS gage (01362497) discharge gage located at 

biological sampling site

RI RI DEM RIDEM_RMR03a Rush Water USGS gage (01115114) discharge gage located at 

biological sampling site

RI RI DEM RIDEM_SCI01 Wilbur Hollow Water USGS gage (01115297) discharge gage located at 

biological sampling site

VT VT DEC VTDEC_135404000013 Bingo watera none none

VT VT DEC VTDEC_670000000166 Green Water USGS gage (01170100) discharge gage is downstream of 

site but location looks 

representative of stream 

conditions

VT VT DEC VTDEC_211200000268 Moose water and air 

(Wx station)

pressure transducer stage working on flow rating 

curve

VT VT DEC VTDEC_495400000161 North Branch Winooski water none none

VT VT DEC VTDEC_033500000081 Winhall watera none none

a

not deployed year-round
C-6

Table C-3. Site information for primary RMN sites in the Mid-Atlantic (3/2015). Most drainage area, slope, and 

elevation measurements are estimates based on NHDPlus v1a local catchment data. Percentage 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 Latitude State Entity Station ID Water body name

Drainage

area 

(km2)

Slope 

(unitless)

Elevatio

n (m)

Percentage

Forest (%)

−75.74869 39.74567 DE DNREC 105212 Tributary of White Clay 2 0.023 84.4 57.9

−75.75587 39.72995 DE DNREC 105213 Tributary of White Clay 2.2 0.018 69.5 61.8

−79.27980 39.64252 MD MD DNR YOUG-432-S Bear Creek 22.7 0.011 805.9 65.9

−79.15566 39.50363 MD MD DNR SAVA-204-S Crabtree Creek 43.9 0.041 620.0 84.3

−77.43406 39.60929 MD MD DNR UMON-288-S High Run 3.3 0.075 310.7 100.0

−78.90556 39.54581 MD MD DNR PRLN-626-S Mill Run 2.0 0.108 522.0 100.0

−79.06689 39.59930 MD MD DNR SAVA-225-S Savage River 138.3 0.018 682.7 83.6

−75.12664 40.97143 NJ NJ DEP/EPA R2 AN0012 Dunnfield Creek 9.5 0.048 358.4 96.8

−74.43437 41.10693 NJ NJ DEP AN0260 Mossmans Brook 10.0 0.009 343.9 80.9

−74.52972 40.76500 NJ NJ DEP AN0215A Primrose 0.01 0.014 123.6

−77.45100 39.89700 PA PA DEP CR Carbaugh Run 15.5 0.022 435.3 91.0

−77.01929 41.42653 PA SRBC SRBC_Grays Grays Run 51.2 0.014 429.8 93.2

−79.23750 40.00333 PA PA DEP JMR/WQN_734 Jones Mill Run 12.8 0.019 710.1 93.1

−77.77068 41.49970 PA SRBC SRBC_Kettle Kettle 210.3 0.000 418.8 84.8

−79.57152 41.69451 PA PA DEP WBC/WQN_873 West Branch of Caldwell Creek 50.7 0.005 453.7 82.0

−79.44821 37.53920 VA VDEQ 2-HUO005.87 Hunting Creek 10 0.047 581.1 90.6

−78.40670074 38.48708676 VA Shen NP 3-RAP088.21 Rapidan River (upper) 12.7 0.030 928 96

−80.57352 37.37393 VA VDEQ 9-LRY007.02 Little Stony Creek 48.0 0.061 968.1 97.4

−78.26867 38.70296 VA Shen NP 3-PIY003.27 Piney River 10.0 0.047 578.8 96.1
C-7

Table C-3. continued...

Longitude Latitude State Entity Station ID Water body name

Drainage 

area 

(km2)

Slope 

(unitless)

Elevatio

n (m)

Percentage 

Fores (%)t

−79.34634 38.32267 VA VDGIF 2-RAM007.29 Ramseys Draft 20.0 0.020 868.7 94.0

−80.33483 36.80553 VA VDEQ 4ARCC008.86 Rock Castle Creek 20.6 0.020 562.5 90.0

−81.75611 36.62583 VA TVA TVA_Whitetop Whitetop Laurel Creek 145.3 0.012 790.0 91.1

−79.60111 38.74322 WV WV DEP 3593 Big Run 10.4 0.031 1,099.0 98.3

−79.56808 38.62673 WV WV DEP 6112 Big Run 36.0 0.027 930.9 96.3

−79.67617 38.61844 WV WV DEP 2571 East Fork/Greenbrier River 28.0 0.011 1,078.6 93.5

−79.48686 38.84942 WV WV DEP 8756 Seneca Creek 42.5 0.024 873.8 98.3

−80.30063 38.23512 WV WV DEP 2039 South Fork/Cranberry River 36.3 0.004 1,143.6 97.5

a

http://www.horizon-systems.com/nhdplus/nhdplusv1_home.php b

http://www.mrlc.gov/nlcd01_data.php

c

http://www.mrlc.gov/nlcd2006.php
C-8

Table C-4. Equipment installed at primary RMN sites in the Mid-Atlantic (3/2015) 

State Entity Station ID

Water body 

name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

DE DNREC 105212 Trib White Clay planning to install water and air temperature 

sensors and pressure transducers in 2015 DE DNREC 105213 Trib White Clay

MD MD DNR YOUG-432-S Bear Creek water and air USGS gage (03076600) downstream of site; about 

nine tributaries (including a major one) enter 

between gage and site

MD MD DNR SAVA-204-S Crabtree Creek water and air USGS gage 

(01597000)

discharge

MD MD DNR UMON-288-S High Run water and air stage

MD MD DNR PRLN-626-S Mill Run water and air stage

MD MD DNR SAVA-225-S Savage River water and air USGS gage 

(01596500)

discharge gage is downstream of site but location looks 

representative of stream conditions

NJ NJ DEP/

EPA R2

AN0012 Dunnfield Creek water and air applied for a grant to get a USGS gage installed 

here

NJ NJ DEP AN0260 Mossmans Brook water and air

NJ NJ DEP AN0215A Primrose water and air USGS staff gage 

(01378780)

occasional 

stage

applied for a grant to get a USGS gage installed 

here

PA PA DEP CR Carbaugh Run water and air Will try to install pressure transducer in 2015

PA SRBC SRBC_Grays Grays Run water and air pressure 

transducer

stage Working on flow rating curve

PA PA DEP JMR/WQN_734 Jones Mill Run water and air

PA SRBC SRBC_Kettle Kettle water and air pressure 

transducer

stage Working on flow rating curve

PA PA DEP WBC/WQN_873 West Branch of 

Caldwell Creek

water and air

VA VDEQ 2-HUO005.87 Hunting Creek water and air
C-9

Table C-4. continued...

State Entity Station ID

Water body 

name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

VA Shen NP 3-RAP088.21 Rapidan River 

(upper)

water and air Flow nearby though in another drainage (gage in 

Staunton R)

VA VDEQ 9-LRY007.02 Little Stony 

Creek

water and air

VA Shen NP 3-PIY003.27 Piney River water and air gage

VA VDGIF 2-RAM007.29 Ramseys Draft water and air

VA VDEQ 4ARCC008.86 Rock Castle 

Creek

water and air

VA TVA TVA_Whitetop Whitetop Laurel 

Creek

water and air pressure 

transducer

stage

WV WV DEP 3593 Big Run water and air pressure 

transducer

stage

WV WV DEP 6112 Big Run water and air pressure 

transducer

stage

WV WV DEP 2571 East 

Fork/Greenbrier 

River

water and air planning to install pressure transducer in 2015

WV WV DEP 8756 Seneca Creek water and air planning to install pressure transducer in 2015

WV WV DEP 2039 South 

Fork/Cranberry 

River

water and air planning to install pressure transducer in 2015
C-10

Table C-5. Site information for primary RMN sites in the Southeast (3/2015). Most drainage areas are estimates 

based on NHDPlus v1a local catchment data. Where available, data from exact watershed delineations were 

used. Slope and elevation are estimated based on NHDPlus v1 local catchment data. Percentage forest is derived 

from the NLCD 2001b data layer and is based on the total watershed 

Longitude Latitude State Entity Station ID Water body name

Drainage

area (km2)

Slope 

(unitless)

Elevation

(m)

Percentage 

Forest (%)

−87.2862 34.3307 AL AL DEM BRSL-3 Brushy Creek 23.6 0.002 240.8 96.9

−86.1330 34.9180 AL AL DEM HURR-2 Hurricane Creek 102.6 0.000 297.07 93.5

−87.3991 34.2856 AL AL DEM SF-1 Sipsey Fork 231.8 0.000 204.6 95.5

−83.5716 34.9590 GA GA DNR 66d-WRD768 Charlies Creek 7.2 0.040 927.0 99.0

−83.5166 34.9520 GA GA DNR 66d-44-2 Coleman River 13.6 0.033 866.9 96.8

−84.3851 34.9851 GA TVA 3890-1 Fightingtown Creek 182.9 0.003 468.8 86.8

−84.1512 34.6020 GA GA DNR 66g-WRD773 Jones Creek 9.1 0.011 586.0 98.4

−83.9039 37.4550 KY KY DEP DOW04036022 Hughes Fork 3.5 0.019 359.1 86.6

−83.1924 38.1311 KY KY DEP DOW06013017 Laurel Creek 37.8 0.002 294.3 72.9

−82.9940 37.0774 KY KY DEP DOW04055002 Line Fork UT 0.6 NA 335.6 100.0

−82.7916 37.0666 KY KY DEP DOW02046004 Presley House Branch 3.0 0.093 736.6 97.0

−82.1014 35.7347 NC NC DENR CB6 Buck Creek 37.5 0.011 529.7 96.6

−83.0728 35.6672 NC NC DENR/TVA EB320 Cataloochee Creek 127.0 0.010 939.2 99.0

−82.8089 35.2281 NC NC/DENR/TVA EB372 Cedar Rock Creek 3.1 0.042 985.9 98.6

−80.0303 35.3792 NC NC DENR QB283 Dutchmans Creek 9.1 0.014 177.5 92.2

−81.5672 35.5906 NC NC DENR CB192 Jacob Fork 66.5 0.001 380.1 89.4

−79.9906 36.5355 NC NC DENR NB28 Mayo River 626.8 0.010 254.9 73.4

−83.8552 35.3094 NC TVA 10605-2 Snowbird Creek 108.8 0.007 677.8 97.1

−83.0793 34.9235 SC SC DHEC SV-684 Crane Creek 4.0 0.078 623.6 97.0

−82.6477 35.0642 SC SC DHEC S-086 Matthews Creek 25.8 0.003 360.2 96.3
C-11

Table C-5. continued...

Longitude Latitude State Entity Station ID Water body name

Drainage

area (km2)

Slope 

(unitless)

Elevation

(m)

Percentage

Forest (%)

−82.5739 35.1254 SC SC DHEC S-076 Middle Saluda River 16.0 0.042 582.3 96.6

−82.2515 35.1831 SC SC DHEC B-099-7 Vaughn Creek 12.0 0.008 368.4 95.6

−87.5355 35.4217 TN TN DEC ECO71F19 Brush Creek 33.3 0.004 245.1 75.8

−84.1182 35.4548 TN TVA CITIC011.0MO Citico Creek 118.1 0.010 399.0 97.2

−82.5291 36.1508 TN TN DEC ECO66E09 Clark Creek 23.8 0.017 596.6 95.1

−84.0597 36.2136 TN TN DEC ECO67F06 Clear Creek 7.2 0.014 337.1 87.9

−85.9921 35.9286 TN TN DEC ECO71H17 Clear Fork Creek 38.1 0.005 262.9 88.8

−85.9111 35.1155 TN TN DEC ECO68C20 Crow Creek 47.7 0.006 311.5 84.5

−82.9381 36.5001 TN TN DEC/TVA ECO6702 Fisher Creek 30.0 0.003 429.7 82.0

−87.7614 35.9806 TN TN DEC ECO71F29 Hurricane Creek 177.6 0.003 156.3 81.0

−84.6981 36.5161 TN TN DEC ECO68A03 Laurel Fork Station 

Camp Creek

15.3 0.014 392.9 97.2

−83.5773 35.6533 TN TN DEC ECO66G05 Little River 81.2 0.029 879.5 99.8

−84.9827 36.1299 TN TN DEC/TVA MYATT005.1CU Myatt Creek 12.4 0.016 525.1 78.8

−84.4803 35.0539 TN TN DEC ECO66G20 Rough Creek 15.5 0.020 520.6 98.9

−84.6122 35.0031 TN TN DEC ECO66G12 Sheeds Creek 14.8 0.031 436.6 98.8

−83.8917 36.3436 TN TN DEC ECO67F13 White Creek 8.0 0.009 379.8 90.9

−82.9456 35.9224 TN TVA 12358-1 Wolf Creek 28.5 0.014 429.9 96.0

a

http://www.horizon-systems.com/nhdplus/nhdplusv1_home.php b

http://www.mrlc.gov/nlcd01_data.php
C-12

Table C-6. Equipment installed at primary RMN sites in the Southeast (3/2015). EPA R4 is planning to install 

equipment at the sites in North and South Carolina as resources permit

State Entity Station ID Water body name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

AL AL DEM BRSL-3 Brushy Creek water and air pressure transducer stage

AL AL DEM HURR-2 Hurricane Creek water and air pressure transducer stage

AL AL DEM SF-1 Sipsey Fork water USGS gage 

(02450250)

discharge water temperature is being measured at the 

USGS gage

GA GA DNR 66d-WRD768 Charlies Creek water and air pressure transducer stage

GA GA DNR 66d-44-2 Coleman River water and air pressure transducer stage

GA TVA 3890-1 Fightingtown Creek water and air pressure transducer stage Inactive USGS gage (03560000)

GA GA DNR 66g-WRD773 Jones Creek water and air pressure transducer stage

KY KY DEP DOW04036022 Hughes Fork water and air pressure transducer stage

KY KY DEP DOW06013017 Laurel Creek water and air pressure transducer stage

KY KY DEP DOW04055002 Line Fork UT water and air pressure transducer stage

KY KY DEP DOW02046004 Presley House 

Branch

water and air pressure transducer stage

NC NC DENR CB6 Buck Creek none none none

NC TVA EB320 Cataloochee Creek water USGS gage 

(03460000)

discharge water temperature is being measured at the 

USGS gage

NC NC DENR EB372 Cedar Rock Creek none none none USGS gage downstream on Catheys Creek 

(03440000)

NC NC DENR QB283 Dutchmans Creek none none none inactive USGS gage (02123567)

NC NC DENR CB192 Jacob Fork none USGS gage 

(02143040)

discharge precip is being measured at the USGS gage

NC NC DENR NB28 Mayo River none USGS gage 

(02070500)

discharge
C-13

Table C-6. continued...

State Entity Station ID Water body name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

NC TVA 10605-2 Snowbird Creek water and air pressure 

transducer

stage inactive USGS gage (03516000)

SC SC DHEC SV-684 Crane Creek none none none

SC SC DHEC S-086 Matthews Creek none none none

SC SC DHEC S-076 Middle Saluda 

River

none none none 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

SC SC DHEC B-099-7 Vaughn Creek none none none

TN TN DEC ECO71F19 Brush Creek water and air pressure 

transducer

stage

TN TVA CITIC011.0MO Citico Creek water and air pressure 

transducer

stage

TN TN DEC ECO66E09 Clark Creek water and air pressure 

transducer

stage

TN TN DEC ECO67F06 Clear Creek water and air pressure 

transducer

stage

TN TN DEC ECO71H17 Clear Fork Creek water and air pressure 

transducer

stage

TN TN DEC ECO68C20 Crow Creek water and air pressure 

transducer

stage

TN TN 

DEC/TVA

ECO6702 Fisher Creek water and air pressure 

transducer

stage

TN TN DEC ECO71F29 Hurricane Creek water and air pressure 

transducer

stage

TN TN DEC ECO68A03 Laurel Fork Station 

Camp Creek

water and air pressure 

transducer

stage
C-14

Table C-6. continued...

State Entity Station ID Water body name Temperature

Hydrologic

equipment

Hydrologic

data type Notes

TN TN DEC ECO66G05 Little River water and air pressure 

transducer

stage

TN TN 

DEC/TVA

MYATT005.1CU Myatt Creek water and air pressure 

transducer

stage

TN TN DEC ECO66G20 Rough Creek water and air pressure 

transducer

stage

TN TN DEC ECO66G12 Sheeds Creek water and air pressure 

transducer

stage

TN TN DEC ECO67F13 White Creek water and air pressure 

transducer

stage

TN TVA 12358-1 Wolf Creek water and air pressure 

transducer

stage
D-1 

APPENDIX D. __________________________ 

DISTURBANCE SCREENING 

PROCEDURE FOR RMN SITES
D-2 

D.1. BACKGROUND

This project developed a screening procedure for candidate regional monitoring network 

(RMN) sites to determine where the sites fall along a standardized disturbance gradient, using 

data that are available nationwide and that are derived using common data sources and 

methodologies. The first iteration of the screening process was developed during the Northeast 

RMN pilot study in 2012, when over 900 sites were screened. Additional screening 

considerations related to coal mining, shale gas drilling, atmospheric deposition and other 

stressors were added during the Mid-Atlantic RMN site selection process. 

The screening processes that were performed during the development of the pilot RMNs 

have limitations. For one, there were insufficient resources to do exact watershed delineations for 

all of the candidate sites. Instead, the land use data for many of the candidate sites were based on 

data associated with the National Hydrography Dataset Plus Version 1 (NHDPlusV1) catchments 

in which the sites are located (U.S. EPA, 2005. While this approach generally provides a good 

approximation, sometimes there are discrepancies, which are described in Section D.2.1. To 

address this issue, additional checks were performed to verify the accuracy of the results before 

finalizing site selection (e.g., local experts who were familiar with the sites verified that results 

were in keeping with their expectations). In the future, as resources permit, the RMN site 

screening process will be further refined, and exact watershed delineations will be done for all of 

the RMN sites. 

D.2. METHODOLOGY

Candidate RMN sites were spatially joined with NHDPlusV1 catchments (U.S. EPA and 

USGS, 2005) using Geographic Information System software (ArcGIS 10.0). Each NHDPlusV1 

catchment has a unique identifier called a COMID. Many data were linked to sites via this 

COMID.

Three different types of disturbance screenings were performed: 

1. Land use (see Section D.2.1); 

2. Likelihood of impact from dams, mines, and point-source pollution sites (see 

Section D.2.2); and

3. Likelihood of impact by the following other nonclimatic stressors: 

• Roads (see Section D.2.3.1), 

• Atmospheric deposition (see Section D.2.3.2), 

• Coal (see Section D.2.3.3), 

• Shale gas (see Section D.2.3.4), 

• Future urban development (see Section D.2.3.5), and/or 

• Water withdrawals (see Section D.2.3.6). 
D-3 

These considerations are consistent with recent work performed by Michigan State 

University (MSU) on the National Fish Habitat Action Plan (NFHAP; DFW MSU et al., 2011; 

Esselman et al., 2011a). That work included the development of the cumulative disturbance 

index (DFW MSU et al., 2011; Esselman et al., 2011b).

D.2.1. Land Use Disturbance

The first set of screening was done on land use and impervious cover data from the 2001 

National Land Cover Database (NLCD) version 1 data set (Homer et al., 2007). The land use 

disturbance screening was conducted at both the local catchment and total watershed scales 

[important note: for purposes of this exercise, the total watershed scale will be referred to as the 

“network” scale, in keeping with the work done by DFW MSU et al. (2011)]. Local catchments 

are defined as the land area draining directly to a reach, and network catchments are defined by 

all upstream contributing catchments to the reach's outlet, including the reach's own local 

catchment (see Figure D-1). GIS shapefiles with delineations of the local catchments were 

downloaded from the Horizon-Systems website: http://www.horizonsystems.com/NHDPlus/NHDPlusV1_data.php. The network-scale data were generated (and 

graciously shared) by MSU. 

Figure D-1. Land use data were evaluated at both the (A) local catchment 

and (B) total watershed scales, using NHDPlusV1 delineations (U.S. EPA and 

USGS, 2005).

While these data generally provide good approximations of land use, they have 

limitations. For one, there are biases and accuracy issues associated with the NLCD data set 

(e.g., Novak and Greenfield, 2010; Wickham et al., 2013). Another limitation is that there is no

information on whether landscape disturbance mitigation measures are being applied in a given 
D-4 

catchment, and if so, how effective those measures are. Thus, we had to assume that the impacts 

associated with each land use type are equal. 

As mentioned earlier, the preliminary land use screening was not based on exact 

watershed delineations. Rather the data were associated with the entire catchment in which the 

site is located, regardless of where the site falls within the catchment (it would have been 

preferable to use data based on exact watershed delineations, but we lacked the resources needed 

to do exact watershed delineations for all of the candidate sites). The estimates that were used 

were readily available for all of the sites and generally provide a good approximation (especially 

when sites are located at the downstream end of the catchment). However, sometimes 

inaccuracies occur. An example is illustrated in Figure D-2. Maryland site UMON-288-S is 

located about halfway up the catchment flowline. Urban and agricultural land uses are located 

within this catchment, but are all downstream of the site. Because these land uses are in the 

catchment, they are included in the land cover output for this site. An accurate output for that site 

would only include forested land cover. To catch errors like these, an additional series of checks 

were performed on the top candidate sites (e.g., we asked local experts to verify that results were 

in keeping with their expectations and also performed additional desktop screening using aerial 

photos from Google Earth). 
D-5 

Figure D-2. Example of a situation in which the land use output for a site is 

inaccurate. 

Land use disturbance was assessed at both the local catchment and network scales. This 

was done for the following four parameters (source: NLCD 2001 version 1 data set1

):

1. Percentage impervious cover 

2. Percentage urban (this includes low, medium, and high intensity developed―NLCD 

codes 22 + 23 + 24) 

3. Percentage cultivated crops (NLCD code 82) 

4. Percentage pasture/hay (NLCD code 81) 

The project developed a land use disturbance scale with six levels. Thresholds for each 

parameter, which are guided by literature where available (e.g., King and Baker, 2010; Carlisle 

et al., 2008), are listed in Table D-1. When rating a site, each parameter was first assessed 

 

1

http://www.mrlc.gov/nlcd01_data.php
D-6

separately. If the parameter values at the local catchment and network scales differed, the 

thresholds were applied to the maximum value. For example, if a site had 2% urban land cover at 

the local catchment scale and 1% urban land cover at the network scale, the threshold was 

applied to the maximum value (in this case, 2% or level 3 for urban land use). This was done for 

each parameter. Then, sites were assigned an overall 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, local biologists familiar with the sites were consulted to find 

out their thoughts on the degree of land use impact and also to inquire about the availability of 

more detailed land use data to help better assess the potential degree of impact.

Table D-1. 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

Percentage

Impervious (%)

Percentage

Urban (%)

Percentage

Crops (%)

Percentage

Pasture/hay (%)

1 <0.1 0 0 0

2 ≤1 ≤1 ≤1 ≤5

3 ≤2 ≤3 ≤5 ≤15

4 ≤5 ≤5 ≤15 ≤25

5 ≤10 ≤10 ≤25 ≤35

6 >10 >10 >25 >35

D.2.2. Likelihood of Impacts from Dams, Mines, and Point-source Pollution Sites

In the second set of screening, sites were flagged if they 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. Both the proximity of these 

stressors to the sites as well as the attribute data associated with each stressor were considered. 

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. 

The following screening procedures were performed: 

1. The data listed in Table D-2 were gathered.
D-7 

2. Using GIS software (ArcGIS 10.0), a 1-km buffer was created around the preliminary 

RMN sites (this included both the upstream and downstream areas). 

3. Using GIS software (ArcGIS 10.0), a procedure was performed to identify whether any 

dams, mines, NPDES major discharges or SNPL sites were located within the 1-km 

buffer.

4. If so, those sites were flagged and the likelihood of impact based on the following 

considerations was assessed: 

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).

Best professional judgment was used to assign the flagged sites to one of three impact 

categories: 

• Unlikely impacted

• Likely impacted

• Unsure 

Some examples of situations in which sites were assigned to the “unlikely impacted” 

category are: 

• The site was flagged for an NPDES major discharge, but the discharge was relatively 

small and was located hundreds of meters downstream from the site. 

• The site was flagged for a dam, but the dam was located on a different stream. 

Some examples of situations in which sites were assigned to the “likely impacted” 

category are: 

• The site was flagged for a NPDES major discharge. It was a large discharge occurring 

about 100 m upstream from the site.

• The site was flagged for a dam. It was a large dam located on the same stream, just 

upstream from the site.
D-8 

Some examples of situations in which sites were assigned to the “unsure” category are: 

• The site was flagged for a NPDES major discharge, but the site was located near a 

confluence and it was difficult to determine which stream contained the discharge. 

• The stressor was small- or medium-sized and was located 500 m or more from the site. 

One additional check was performed to assess the potential for flow alteration at the sites. 

We examined the type of NHDPlusV1 flowline (FTYPE) located on the site (e.g., stream/river, 

artificial pathway, canal/ditch, pipeline, connector; U.S. EPA and USGS, 2005). If the site was 

located on a flowline designated as something other than a stream/river, the site was flagged. 

As a final step, local biologists familiar with the sites were consulted to find out their 

thoughts on the degree of impact and also to inquire about the availability of more detailed data 

to help better assess the potential degree of impact. 

D.2.3. Likelihood of impact from other nonclimatic stressors

In the third set of screening, sites were flagged if they had a high likelihood of being 

impacted by:

• Roads, 

• Atmospheric deposition, 

• Coal mining,

• Shale gas drilling,

• Future urban development, and/or 

• Water withdrawals.
D-9 

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 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 at : 

http://nationalatlas.gov/atlasftp.html#dams00x

Mines USGS (U.S. Geological Survey). (2005) Active 

mines and mineral processing plants in the United 

States in 2003. Reston, VA: USGS. Available online 

at http://tin.er.usgs.gov/metadata/mineplant.faq.html

PASDA (Pennsylvania Spatial Data Access). (2013) 

Data download –mine and refuse permits. Available 

online at www.pasda.psu.edu.

National Pollutant Discharge Elimination 

System (NPDES) major discharges from 

the Permit Compliance System

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]. Available online at 

http://www.epa.gov/enviro/geo_data.html

Superfund National Priorities List 

(SNPL) from the Compensation and 

Liability Information System

Table D-3 contains a list of data that were gathered and assessed, along with the sources 

of those data. There are a lot of site-specific factors that can greatly affect the degree of impact 

from these stressors, which makes it difficult to set thresholds. For example, a site could be 

exposed to high concentrations of atmospheric deposition but may not be impacted by acidity 

because of site-specific mediating factors like calcareous geology. Another example is permit 

activity associated with coal mining. Just because mining permits have been issued in an area 

does not mean that mining activities are actually taking place. And even if mining activities are 

taking place, impacts can vary greatly depending on site-specific factors such as the size and type 

of mine. 
D-10

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. These data were 

converted to relative measures, as described in the text.

Stressor Parameters/description Source

Roads Length of roads, local catchment, and network 

scales/catchment area

U.S. Census Bureau (2000)

from DFW MSU et al. (2011) 

Number of road crossings, local catchment,

and network scales/catchment area

Atmospheric 

deposition 

NO3 and SO4 concentrations, based on 2011 

deposition grids

NADPa

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, 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 (PASDA, 2013) 

West Virginia (WVDEP, 2013; 

WVGES, 2014) 

Virginia (VDEQ, 2013) 
D-11

Table D-3. continued...

Stressor Parameters/description Source

Shale gas 

drilling

Potential for development, based on whether

the site is located in the shale play region

U.S. EIA (2013)

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).

Frac Tracker (2013)

Future urban 

development 

Potential for future urban development based 

on projected change in percentage

imperviousness by 2050

U.S. EPA (2011); 

work performed by Angie 

Murdukhayeva (U.S. EPA)

Water 

withdrawals 

(county-level)

Irrigation, total withdrawals, fresh (Mgal/day) USGS (2010)

Total withdrawals, fresh (Mgal/day)

Total withdrawals, total (fresh + saline; 

Mgal/day)

a

http://nadp.sws.uiuc.edu/NTN/annualmapsbyyear.aspx

Because of these factors, a relative scale (vs. firm thresholds) was used to assess the 

likelihood of impact. The relative scales were based on values found in NHDPlusV1 catchments 

across the entire study area. If a site had an elevated risk score (e.g., >75th percentile), it was 

flagged for further evaluation (the thresholds [e.g., 50%, 75%] vary depending on the 

distribution of data in each data set and best professional judgment). These thresholds should be 

regarded as a starting point, and should be further refined in future iterations of the screening 

procedure. Local biologists familiar with the sites were then consulted to find out their thoughts 

on the degree of impact and also to inquire about the availability of more detailed data to help 

better assess the potential degree of impact [e.g., is mining actually taking place? What are the 

pH and acid neutralizing capacity (ANC) values at sites flagged for atmospheric deposition?]. 

The specific screening procedures that were followed for each stressor are described below.

D.2.3.1. Roads

Two aspects of potential road impacts were assessed: 

• Length of roads and

• Number of road crossings
D-12 

First the roads data listed in Table D-3 were gathered for both the local catchment and 

network scales.

Next, to assess the likelihood of impact from length of roads, the following formulas 

were used 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, the following formula was used 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): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

If the parameter values at the local catchment and network scales differed, the maximum 

score was used for the assessment. For example, if the local catchment score was 80 and the 

network score was 50, the higher score of 80 was used for the assessment. 

Sites were flagged for further evaluation if they received a score of ≥75%. 

The same procedure was followed when assessing the likelihood of impact from road 

crossings.

As a final step, we consulted with local biologists who were familiar with the sites to get 

input on the degree of impact at flagged sites. 

D.2.3.2. Atmospheric Deposition 

Two aspects of atmospheric deposition were assessed: 

• Concentrations of NO3

• Concentrations of SO4

In addition, TNC geology class (Olivero and Anderson, 2008) was considered as a 

potential mediating factor. First the data listed in Table C-3 were gathered. Using GIS software 

(ArcGIS 10.0), the NO3 and SO4 deposition grid data (1-km resolution) were linked to the sites. 

Next, the NO3 and SO4 values were averaged. Then, the following formula was used to convert 
D-13 

these values to a scoring scale ranging from 0 (no nitrogen and sulfate deposition) to 100 

(highest average concentration of NO3 and SO4; note: the minimum and maximum values used in 

this formula are based on the range of values found across the entire study area): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

Sites were flagged for further evaluation if they received a score of ≥75%. 

Geology can potentially mediate some of the effects of atmospheric deposition. To assess 

this potential, GIS software (ArcGIS 10.0) was used to link the TNC geology class (Olivero and 

Anderson, 2008) to the sites (note: at this time the TNC geology class data are only available for 

Northeast and Mid-Atlantic regions). 

Sites were scored as follows:

• Sites located in areas designated as “low buffered, acidic” received a score of 100. 

• Sites located in areas designated as “moderately buffered, neutral” or “assume 

moderately buffered (Size 3+ rivers)” received a score of 50. 

• Sites located in areas designated as “highly buffered, calcareous” received a score of 0. 

• Sites located in areas that lacked data or were designated as “unknown buffering/missing 

geology” were not assessed.

Sites were flagged if they received a score of 100%.

As a final step, we consulted with local biologists who were familiar with the sites to get

input on the degree of impact at flagged sites, and to see if they had access to more detailed data, 

such as pH and ANC measurements, to help us better assess the potential degree of impact. 

D.2.3.3. Coal Mining

Two aspects of coal mining were assessed: 

• Potential for mining

• Permit activity

First the data listed in Table D-3 were gathered. 

To assess the potential for coal mining, the following were considered: 
D-14 

• Whether the site is located in an area that has been designated as a mountaintop removal 

(MTR) area and/or a coal field (USGS, 2001). 

o If the site is located in a coal field, is it designated as “potentially minable” or is it 

tagged for “other uses”? 

• What the total coal production is for the state where the site is located [source: Table 6 in 

the 2011 Annual Coal Report (U.S. EIA, 2012)]. 

The following steps were performed when assessing a site for mining potential: 

1. First a coal field score was assigned, as follows: 

• Using GIS software (ArcGIS 10.0), the coal field and MTR GIS layers were linked to 

the sites.

• If the site is located in a catchment that has been designated as a “potentially 

minable” coal field (USGS, 2001) and/or a mountaintop removal (MTR) area, it was 

assigned a score of 1. 

• If the site is located in a catchment that has been designated as a coal field with “other 

uses” (USGS, 2001), it was assigned a score of 0.5. 

• If the site is located in a catchment that is not part of a coal field or MTR area, it 

received a score of 0. 

2. Then a coal production score was assigned, as follows: 

• Total coal production values for each state were taken from Table 6 in the 2011 

Annual Coal Report (U.S. EIA, 2012). 

• Those values were converted to a scale of 0 to 100 using this formula (note: the 

minimum and maximum values used in this formula are based on the range of values 

found in the states in our study area): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

• Sites were assigned scores based on what state they were located in. For example, 

West Virginia had the highest total coal production of all of the states in the study 

area, so any sites located in West Virginia received a coal production score of 100. 
D-15 

3. To get the final score for mining potential, the coal field score was multiplied by the 

coal production score. Scores ranged from 0 (no mining potential) to 100 (highest 

potential for mining). 

Sites were flagged for further evaluation if they received a score of ≥75%. 

Permit data were not available for all the states, and where those data were available, data 

type and quality varied, as did the attribute data. Therefore, permit activity was assessed on a 

state-by-state basis. If sites were located in states where permit data were available, the following 

steps were performed to assess the intensity of permit activity: 

1. Gather the permit data listed in Table D-3. 

2. Using GIS software (ArcGIS 10.0), create a 1-km buffer around the candidate RMN sites 

(this included both the upstream and downstream areas). 

3. Using GIS software (ArcGIS 10.0), perform a procedure to determine how many mining 

permits had been issued within the 1-km buffer.

4. The following formula was used to convert those values to a scale of 0 to 100 (note: since 

the type of data available for each state varied, the minimum and maximum values used 

in this formula were based on the range of data found in each state): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

Sites were flagged for further evaluation if they received a score of >0. 

As a final step, we consulted with local biologists who were familiar with the sites to get

input on the degree of impact at flagged sites, and to see if they had access to more detailed data

to help us better assess the potential degree of impact. Just because mining permits have been 

issued in an area does not mean that mining activities are actually taking place. And even if 

mining activities are taking place, impacts can vary greatly depending on site-specific factors 

such as the size and type of mine. 

D.2.3.4. Shale Gas Drilling

Two aspects of shall gas drilling were assessed: 

• Potential for drilling

• Permit activity
D-16 

First the data listed in Table D-3 were gathered. 

To assess the potential for shale gas drilling, the following screening procedure was 

performed: 

• Using GIS software (ArcGIS 10.0), the shale play GIS layer (see Table D-3) was linked 

to the sites.

• If the site is located in a shale play region, it was assigned a score of 100 and flagged it 

for further evaluation. 

Permit data were only available for the states of West Virginia and Pennsylvania. The 

following steps were performed to assess the intensity of permit activity at sites in those sites:

1. Gather the permit data listed in Table D-3. 

2. Using GIS software (ArcGIS 10.0), create a 1-km buffer around the candidate RMN sites 

(this included both the upstream and downstream areas). 

3. Using GIS software (ArcGIS 10.0), perform a procedure to determine how many 

unconventional permits had been issued within the 1-km buffer.

4. The following formula was used to convert those values to a scale of 0 to 100 (note: since 

the type of data available for each state varied, the minimum and maximum values used 

in this formula were based on the range of data found in each state): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

Sites were flagged for further evaluation if they received a score of >0%. 

As a final step, we consulted with local biologists who were familiar with the sites to get

input on the degree of impact at flagged sites, and to see if they had access to more detailed data

to help us better assess the potential degree of impact. Just because drilling permits have been 

issued in an area does not mean that drilling activities are actually taking place. And even if 

drilling activities are taking place, impacts can vary greatly depending on site-specific factors.

D.2.3.5. Potential for Future Urban Development

EPA’s ICLUS tools and data sets (Version 1.3 and 1.3.1; U.S. EPA, 2011) were used to 

assess the potential that a site will experience future urban development. The ICLUS Tools were 

used to project the percentage change in imperviousness in each NHDPlusV1 local catchment by 
D-17 

2050 based on high (A2) and low (B1) emissions scenarios (note: the ICLUS data have a 

resolution of 1-km). 

GIS software (ArcGIS 10.0) was used to link sites with NHDPlusV1 local catchments. 

Sites were flagged for further evaluation if the following conditions occurred: 

• The percentage impervious value in the NHDPlusV1 local catchment where the site is 

located is currently ≤10% (based on values derived from the 2001 NLCD version 1 data

set), and

• The future projection is for a positive value ≥0.5% (this is based on an average of the 

high [A2] and low [B1] emissions scenarios). 

As a final step, we checked with local biologists who were familiar with the sites to find 

out their thoughts on the potential for future development at flagged sites, and to see if they had 

access to more detailed data to help us better assess the potential degree of impact. 

D.2.3.6. Water Withdrawals 

Three aspects of water use were assessed: 

• Irrigation, total withdrawals, fresh; 

• Total withdrawals, fresh only; and

• Total withdrawals, total. 

First the data listed in Table D-3 were gathered. These data are based on 2005 water use 

and are only available at the county-level (USGS, 2010). Then GIS software (ArcGIS 10.0) were 

used to associate the county-level data with NHDPlusV1 local catchments. Next sites were 

linked with NHDPlusV1 local catchments. For each parameter, the following formula was used 

to convert the values to a scoring scale ranging from 0 (no withdrawals) to 100 (highest 

withdrawals; note: the minimum and maximum values used in this formula are based on the 

range of values found across the entire study area): 

100 × (Value − Minimum) ÷ (Maximum − Minimum)

Sites were flagged for further evaluation if they received a score of ≥50% for any of the 

three parameters. 
D-18 

As a final step, we checked with local biologists who were familiar with the sites to find 

out their thoughts on the potential for future development at flagged sites, and to see if they had 

access to more detailed data to help us better assess the potential degree of impact. 
D-19 

D.3. REFERENCES

Alabama Surface Mining Commission. (2013) Alabama coal mine geospatial data – permit boundaries (1983-

present). Jasper, AL: State of Alabama http://surface-mining.alabama.gov/. 

Carlisle, DM; Hawkins, CP; Meador, MR; Potapova, M; Falcone, J. (2008) Biological assessments of Appalachian 

streams based on predictive models for fish, macroinvertebrate, and diatom assemblages. J N Am Benthol 

Soc 27(1): 6–37.

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 (NFHAP) 2010 HCI scores and human disturbance data for 

conterminous United States linked to NHDPLUSV1. Denver, CO: National Fish Habitat Action Plan 

(NFHAP). Available online at https://www.sciencebase.gov/catalog/item/514afb90e4b0040b38150dbc 

Esselman, PC; Infante, DM; Wang, L; Taylor, WW; Daniel, WM; Tingley, R; Fenner, J; Cooper, C; Wieferich, D; 

Thornbrugh, D; Ross, R, (2011a) A landscape assessment of fish habitat conditions in United States rivers 

and their watersheds. Denver, Co: National Fish Habitat Action Plan (NFHAP). Available online at 

http://fishhabitat.org/. 

Esselman, PC; Infante, DM; Wang, L; Wu, D; Cooper, AR; Taylor, WW. (2011b) An index of cumulative 

disturbance to river fish habitats of the conterminous United States from landscape anthropogenic 

activities. Ecol Restor 29:133–151.

Frac Tracker. (2013) Frac Tracker – data [web page]. http://www.fractracker.org/downloads/

Homer, C; Dewitz, J; Fry, J; Coan, M; Hossain, N; Larson, C; Herold, N; McKerrow, A; VanDriel, JN; Wickham, J. 

(2007) Completion of the 2001 National Land Cover Database for the Conterminous United States. PE&RS 

73(4):337−341. Available online at 

http://www.asprs.org/a/publications/pers/2007journal/april/highlight.pdf

King, RS; Baker, ME. (2010) Considerations for identifying and interpreting ecological community thresholds. J N 

Am Benthol Assoc 29(3):998-1008 

Nowak, DJ; Greenfield, EJ. (2010) Evaluating the National Land Cover Database tree canopy and impervious cover 

estimates across the conterminous United States: a comparison with photo-interpreted estimates. Environ 

Manage 46:378–390.

Olivero, AP; Anderson, MG. (2008) Northeast aquatic habitat classification. Denver, CO: The Nature Conservancy. 

Available online at http://rcngrants.org/content/northeastern-aquatic-habitat-classification-project

PASDA (Pennsylvania Spatial Data Access). (2013) Data download –mine and refuse permits. Available online at 

www.pasda.psu.edu. 

U.S. Bureau of the Census. (2000) Census 2000 TIGER/Line data. Available online at 

http://www.icpsr.umich.edu/icpsrweb/ICPSR/themes/tiger/2000/

U.S. EIA (Energy Information Administration). (2013) Maps: exploration, resources, reserves, and production - 

United States shale gas maps - Lower 48 states shale plays. Available online at 

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.eia.gov/coal/annual/

U.S. EPA/USGS (Environmental Protection Agency/U.S. Geological Survey). (2005). National hydrography dataset 

plus, NHDPlus Version 1.0. (NHDPlusV1). Available online at http://www.fws.gov/r5gomp/gom/nhdgom/metadata.pdf 
D-20 

U.S. EPA (Environmental Protection Agency). (2011) ICLUS tools and datasets (Version 1.3 & 1.3.1). [EPA/600/R09/143F]. Washington, DC: U.S. Environmental Protection Agency. Available on line at 

http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=205305 

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]. 

Available online at 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 at http://nationalatlas.gov/atlasftp.html#coalfdp

USGS (U.S. Geological Survey). (2005) Active mines and mineral processing plants in the United States in 2003. 

Reston, VA: USGS. Available online at 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. Available online at 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 at : 

http://nationalatlas.gov/atlasftp.html#dams00x

VDEQ (Virginia Department of Environmental Quality. (2013) Surface mine permit boundaries. Richmond, VA: 

Division of Mined Land Reclamation. Available online at 

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). Available online at 

http://tagis.dep.wv.gov/home/Downloads

WVGES (West Virginia Geological Ecomic Survey). (2014). West Virginia coal bed mapping [online mapper]. 

Available online at http://www.wvgs.wvnet.edu/www/coal/cbmp/coalims.html
E-1 

APPENDIX E. __________________________ 

SECONDARY REGIONAL 

MONITORING NETWORK (RMN) 

SITES IN THE NORTHEAST AND

MID-ATLANTIC REGIONS
E-2

Table E-1. 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. For this 

exercise, natural land cover included open water, forest, wetlands, barren, and grassland/herbaceous. Data were 

accumulated for the entire upstream catchment. Land cover screenings were estimates based on data associated 

with the National Hydrography Dataset Plus Version 1 (NHDPlusV1) catchments in which the sites are located 

(http://www.horizon-systems.com/NHDPlus). Land use data were based on the 2001 National Land Cover 

Database (NLCD; Homer et al., 2007) (http://www.mrlc.gov/nlcd01_data.php).

Longitude Latitude State Entity Station ID Water body name

Percentage

natural 

(%) Notes

−72.7464 43.7708 VT VT DEC 130000000324 White River 92.4 VT DEC sentinel site

−72.9458 43.8556 VT VT DEC 135411000013 Smith Brook 99 VT DEC sentinel site

−71.6356 44.7550 VT VT DEC 280000000003 Nulhegan River 90 VT DEC sentinel site

−72.7819 44.5036 VT VT DEC 493238200015 Ranch Brook 96 VT DEC sentinel site; USGS 

gage (04288230)

−73.2292 44.2483 VT VT DEC 530000000037 Lewis Creek 64 VT DEC sentinel site

−72.9384 42.0356 CT CT DEEP 1156 Hubbard Brook 96 CT DEEP sentinel site; 

colocated with USGS gage 

(01187300)

−72.3289 41.4100 CT CT DEEP 1236 Beaver Brook 89 CT DEEP sentinel site

−72.3343 41.4603 CT CT DEEP 1239 Burnhams Brook 85 CT DEEP sentinel site

−72.82146 41.93717 CT CT DEEP 359 West Branch Salmon 86 CT DEEP sentinel site

−73.2155 41.5575 CT CT DEEP 1468 Weekepeemee River 72 CT DEEP sentinel site; 

colocated with USGS gage 

(01203805)

−72.5365 41.6615 CT CT DEEP 2295 Mott Hill Brook 92 CT DEEP sentinel site
E-3

Table E-1. continued...

Longitude Latitude State Entity Station ID Water body name

Percentage

Natural 

(%) Notes

−72.4226 41.4283 CT CT DEEP 2297 Hemlock Valley 

Brook

81 CT DEEP sentinel site

−73.1214 41.9328 CT CT DEEP 2299 Rugg Brook 91 CT DEEP sentinel site

−72.4338 41.5623 CT CT DEEP 2304 Day Pond Brook -- CT DEEP sentinel site

−73.3200 41.9459 CT CT DEEP 2309 Flat Brook 90 CT DEEP sentinel site

−73.1679 41.8646 CT CT DEEP 2312 Jakes Brook 91 CT DEEP sentinel site

−72.1509 41.7812 CT CT DEEP 2331 Stonehouse Brook 89 CT DEEP sentinel site

−73.3678 41.2931 CT CT DEEP 2346 Little River 81 CT DEEP sentinel site

−73.1745 41.5783 CT CT DEEP 2676 Nonewaug River 53 CT DEEP sentinel site; USGS 

gage (01203600)

−72.9630 41.7807 CT CT DEEP 2711 Bunnell Brook 71 CT DEEP sentinel site; USGS 

gage (01188000)

−72.4640 41.8272 CT CT DEEP 345 Tankerhoosen River 66 CT DEEP sentinel site

−69.5933 44.2232 ME ME DEP MEDEP_56817 Sheepscot 

River―Station 74

87 ME DEP long-term 

monitoring site; USGS gage 

(01038000)―water and air 

temperature, discharge

−69.5313 44.3679 ME ME DEP MEDEP_57011 West Branch 

Sheepscot 

River―Station 268

85 ME DEP long-term biological 

monitoring site
E-4

Table E-1. continued...

Longitude Latitude State Entity Station ID Water body name

Percentage

Natural 

(%) Notes

−68.2346 44.3934 ME ME DEP MEDEP_57065 Duck Brook―Station 

322

83 ME DEP long-term biological 

monitoring site
E-5

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 NPS―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. Land use data, 

which were accumulated for the entire upstream catchment, were estimates based on data associated with the National 

Hydrography Dataset Plus Version 1 (NHDPlusV1) catchments in which the sites are located (http://www.horizonsystems.com/NHDPlus). Land use data were based on the 2001 National Land Cover Database (NLCD; Homer et al., 

2007) (http://www.mrlc.gov/nlcd01_data.php).

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage

Forest (%)

Percentage

Urban (%)

Percentage

Hay (%)

Percentage

Crop (%)

−75.323216 41.73465 PA DRBC MB_Dyberry Middle Branch 

Dyberry Creek

79.9 0.0 14.0 0.6

−74.88980 40.77471 NJ EPA R2 1 Unnamed 

tributary to 

Musconetcong 

River

long-term 

monitoring 

site―Jim 

Kurtenbach 

(U.S. EPA R2)

67.9 0.0 4.2 23.9

−74.50486 40.95164 NJ EPA R2 17 Hibernia Brook long-term 

monitoring 

site―Jim 

Kurtenbach 

(U.S. EPA R2)

82.1 2.1 0.0 0.8

−74.84479 40.75211 NJ EPA R2 2 Teetertown 

Brook 

long-term 

monitoring 

site―Jim 

Kurtenbach 

(U.S. EPA R2)

53.9 1.3 14.6 17.7
E-6

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−77.54528 39.65833 MD MD DNR ANTI-101-S Unnamed 

tributary to 

Edgemont 

Reservoir

MD DNR 

sentinel 

site―Highlands

90.6 0.4 4.4 0.5

−76.09499 39.08754 MD MD DNR CORS-102-S Unnamed 

tributary to 

Emory Creek

MD DNR 

sentinel 

site―Coastal 

Plain

71.6 0.2 4.6 7.2

−78.45571 39.68672 MD MD DNR FIMI-207-S Fifteen Mile 

Creek

MD DNR 

sentinel 

site―Highlands

88.3 0.1 5.6 0.9

−76.04611 39.61055 MD MD DNR FURN-101-S Unnamed 

tributary to 

Principio Creek

MD DNR 

sentinel 

site―Highlands

80.5 0.5 9.3 8.1

−76.69843 39.43951 MD MD DNR JONE-109-S Unnamed 

tributary to 

Dipping Pond 

Run

MD DNR 

sentinel 

site―Highlands

45.3 2.5 32.4 6.7

−76.71875 39.42925 MD MD DNR JONE-315-S North Branch 

of Jones Falls

MD DNR 

sentinel 

site―Highlands

52.8 1.1 23.8 16.3

−76.86417 39.44055 MD MD DNR LIBE-102-S Timber Run MD DNR 

sentinel 

site―Highlands

70.1 0.3 12.7 15.5

−76.69829 39.48052 MD MD DNR LOCH-120-S Baisman Run MD DNR 

sentinel 

site―Highlands

71.2 0.3 24.4 2.4
E-7

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−76.21896 39.19352 MD MD DNR LOCR-102-S Swan Creek MD DNR 

sentinel 

site―Coastal 

Plain

20.8 0.0 0.0 17.7

−77.09766 38.58225 MD MD DNR MATT-033-S Mattawoman 

Creek

MD DNR 

sentinel 

site―Coastal 

Plain

48.3 12.8 1.8 7.5

−77.08594 38.48386 MD MD DNR NANJ-331-S Mill Run MD DNR 

sentinel 

site―Coastal 

Plain

72.5 0.2 1.8 9.9

−75.49247 38.2495 MD MD DNR NASS-108-S Millville Creek MD DNR 

sentinel 

site―Coastal 

Plain

8.9 0.8 0.6 8.0

−75.46182 38.26359 MD MD DNR NASS-302-S Nassawango 

Creek

MD DNR 

sentinel 

site―Coastal 

Plain

18.8 0.4 4.4 17.0

−76.76012 38.56392 MD MD DNR PAXL-294-S Swanson Creek MD DNR 

sentinel 

site―Coastal 

Plain

67.3 0.2 2.4 15.3

−77.02912 38.51108 MD MD DNR PTOB-002-S Hoghole Run MD DNR 

sentinel 

site―Coastal 

Plain

74.2 1.0 0.7 12.1
E-8

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−76.97198 39.16949 MD MD DNR RKGR-119-S Unnamed 

tributary to 

Patuxent River

MD DNR 

sentinel 

site―Highlands

31.8 0.5 44.0 14.2

−79.21349 39.54119 MD MD DNR SAVA-276-S Double Lick 

Run

MD DNR 

sentinel 

site―Highlands

92.1 0.0 6.6 0.0

−76.73717 38.36662 MD MD DNR STCL-051-S Unnamed 

tributary to St. 

Clements Creek

MD DNR 

sentinel 

site―Coastal 

Plain

69.8 0.0 2.2 15.8

−77.48935 39.58739 MD MD DNR UMON-119-

S 

Buzzard 

Branch

MD DNR 

sentinel 

site―Highlands

97.5 0.0 0.2 0.0

−75.96062 38.72408 MD MD DNR UPCK-113-S Unnamed 

tributary to 

Skeleton Creek

MD DNR 

sentinel 

site―Coastal 

Plain

23.9 0.3 10.7 29.4

−75.78362 39.28768 MD MD DNR UPCR-208-S Cypress Branch MD DNR 

sentinel 

site―Coastal 

Plain

40.3 0.2 4.0 26.7

−75.59259 38.41408 MD MD DNR WIRH-220-S Leonard Pond 

Run

MD DNR 

sentinel 

site―Coastal 

Plain

26.7 8.0 6.3 30.3
E-9

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−76.90348 38.49936 MD MD DNR ZEKI-012-S Unnamed 

tributary to 

Zekiah Swamp 

Run

MD DNR 

sentinel 

site―Coastal 

Plain

89.8 0.0 0.6 5.3

−78.45247 40.41597 PA NPS―ERMN Blair Gap 

Run―Foot 

Ten

of 

92.9 1.4 1.8 0.2

−78.51846 40.43269 PA NPS―ERMN Blair Gap 

Run―

Muleshoe

96.1 0.4 0.5 0.1

−79.92348 39.78393 PA NPS―ERMN Dublin Run -- -- -- --

−79.58149 39.81449 PA NPS―ERMN Great Meadows 

Run

75.2 2.4 9.4 3.5

−79.93024 39.78248 PA NPS―ERMN Ice Pond Run -- -- -- --

−80.97161 37.58466 PA NPS―ERMN Little Bluestone 

River

75.7 1.5 15.3 0.9

−78.48373 40.41876 PA NPS―ERMN Milllstone Run 97.0 0.3 0.8 0.0

−81.02055 37.53483 PA NPS―ERMN Mountain 

Creek

75.9 1.6 14.9 0.6

−79.59970 39.81014 PA NPS―ERMN Unnamed 

tributary 

(Scotts Run)

95.9 0.0 0.0 0.0

−75.14398 40.97139 PA NPS―ERMN DEWA.3001 Caledonia 

Creek 13

NPS―ERMN 

high priority

78.0 1.0 8.0 1.5

−74.98444 41.11381 PA NPS―ERMN DEWA.3002 Van Campen 

Creek 12

74.2 2.4 0.1 0.1
E-10

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−74.90309 41.19744 PA NPS―ERMN DEWA.3003 Deckers Creek 

03

82.0 0.0 0.3 2.4

−74.87464 41.22245 PA NPS―ERMN DEWA.3004 Dingmans 

Creek 05

71.3 2.0 0.1 0.0

−75.12652 40.974 NJ NPS―ERMN DEWA.3005 Dunnfield 

Creek 03

96.8 0.0 0.3 0.0

−74.96252 41.13729 PA NPS―ERMN DEWA.3006 Toms Creek 20 80.2 0.6 0.0 0.0

−74.90598 41.1756 PA NPS―ERMN DEWA.3007 Spackmans 

Creek 08

91.1 0.5 0.7 0.4

−74.91831 41.23772 PA NPS―ERMN DEWA.3008 Dingmans 

Creek 57

69.8 2.0 0.1 0.0

−74.92372 41.09674 NJ NPS―ERMN DEWA.3010 Vancampens 

Brook 95

93.8 0.0 0.0 0.0

−74.87711 41.24882 PA NPS―ERMN DEWA.3011 Adams Creek 

14

86.9 0.9 0.1 0.1

−74.89043 41.2578 PA NPS―ERMN DEWA.3012 Adams Creek 

33

82.8 1.2 0.1 0.1

−75.00533 41.09383 PA NPS―ERMN DEWA.3013 Little Bushkill 

Creek 01

76.9 0.5 0.0 0.0

−74.96505 41.07109 NJ NPS―ERMN DEWA.3014 Vancampens 

Brook 43

95.3 0.0 0.0 0.0

−74.90343 41.23052 PA NPS―ERMN DEWA.3015 Dingmans 

Creek 39

69.8 2.0 0.1 0.0

−74.95916 41.12946 PA NPS―ERMN DEWA.3018 Toms Creek 07 80.2 0.6 0.0 0.0

−74.92673 41.16889 PA NPS―ERMN DEWA.3020 Mill Creek 25 83.5 0.3 0.4 0.4
E-11

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−74.96279 41.1415 PA NPS―ERMN DEWA.3022 Toms Creek 25 80.2 0.6 0.0 0.0

−74.88573 41.23542 PA NPS―ERMN DEWA.3023 Unnamed 

tributary 

Dingmans 

Creek 07

71.3 2.0 0.1 0.0

−74.98445 41.0647 NJ NPS―ERMN DEWA.3025 Vancampens 

Brook 22

NPS―ERMN 

high priority

95.3 0.0 0.0 0.0

−74.94059 41.08567 NJ NPS―ERMN DEWA.3026 Unnamed 

tributary 

Vancampens 

Brook 05

97.8 0.0 0.0 0.0

−74.86975 41.24147 PA NPS―ERMN DEWA.3027 Adams Creek 

03

NPS―ERMN 

high priority

86.9 0.9 0.1 0.1

−74.79550 41.29461 NJ NPS―ERMN DEWA.3028 White Brook 

15

53.2 1.6 13.6 7.6

−75.01434 41.08235 PA NPS―ERMN DEWA.3029 Sand Hill 

Creek 08

63.5 6.1 0.8 0.0

−75.00528 41.03179 NJ NPS―ERMN DEWA.3030 Yards Creek 07 86.0 0.0 0.0 0.0

−74.89481 41.23067 PA NPS―ERMN DEWA.3031 Dingmans 

Creek 30

69.8 2.0 0.1 0.0

−74.84545 41.2952 PA NPS―ERMN DEWA.3032 Raymondskill 

Creek 13

73.7 1.0 0.9 0.1

−75.10517 40.98337 NJ NPS―ERMN DEWA.3033 Dunnfield 

Creek 26

96.8 0.0 0.3 0.0

−74.95645 41.12711 PA NPS―ERMN DEWA.3034 Toms Creek 03 81.2 0.5 0.0 0.2
E-12

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−74.89987 41.19356 PA NPS―ERMN DEWA.3035 Hornbecks 

Creek 15

82.0 0.8 0.7 1.0

−74.92431 41.15917 PA NPS―ERMN DEWA.3036 Mill Creek 12 83.5 0.3 0.4 0.4

−74.94123 41.09062 NJ NPS―ERMN DEWA.3038 Vancampens 

Brook 76

93.8 0.0 0.0 0.0

−74.88168 41.25185 PA NPS―ERMN DEWA.3039 Adams Creek 

21

85.0 1.0 0.1 0.1

−81.09167 37.9441 WV NPS―ERMN NERI.3001 Meadow Fork 1 74.1 6.1 6.1 0.1

−81.01278 37.91956 WV NPS―ERMN NERI.3005 Buffalo Creek 

16

98.5 0.0 0.0 0.2

−81.02305 37.88808 WV NPS―ERMN NERI.3009 Slater Creek 20 97.7 0.0 0.0 0.0

−80.91077 37.81927 WV NPS―ERMN NERI.3011 Meadow Creek 

17

84.9 2.5 4.6 1.1

−81.04551 37.87994 WV NPS―ERMN NERI.3013 Dowdy Creek 

16

99.1 0.0 0.0 0.0

−81.04918 37.82895 WV NPS―ERMN NERI.3016 River Branch 4 95.9 0.0 3.8 0.3

−81.02102 38.03256 WV NPS―ERMN NERI.3018 Keeney Creek 

10

89.7 2.3 1.5 0.8

−81.02453 37.94417 WV NPS―ERMN NERI.3021 Fire Creek 17 99.2 0.0 0.0 0.2

−80.90375 37.714 WV NPS―ERMN NERI.3024 Big Branch 10 95.3 0.2 2.9 0.0

−81.03647 37.87402 WV NPS―ERMN NERI.3025 Dowdy Creek 

30

99.1 0.0 0.0 0.0

−80.97903 37.85864 WV NPS―ERMN NERI.3026 Little Laurel 

Creek 6

91.3 0.0 3.8 1.5
E-13

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−81.08293 38.04904 WV NPS―ERMN NERI.3029 Wolf Creek 30 56.6 6.5 25.5 0.1

−81.04749 37.82782 WV NPS―ERMN NERI.3032 River Branch 6 WV DEP 

reference site

95.9 0.0 3.8 0.3

−81.02506 37.98267 WV NPS―ERMN NERI.3034 Unnamed 

tributary 21 

New River 1

69.3 2.2 20.8 0.7

−81.06012 38.06032 WV NPS―ERMN NERI.3035 Fern Creek 11 88.1 2.9 3.1 0.4

−80.94296 37.74477 WV NPS―ERMN NERI.3036 Unnamed 

tributary Fall 

Branch 2

97.8 0.0 1.4 0.1

−81.00490 37.85802 WV NPS―ERMN NERI.3037 Laurel Creek 

47

93.0 0.6 1.5 0.6

−81.08737 37.96331 WV NPS―ERMN NERI.3038 Arbuckle Creek 

2

47.7 24.9 12.2 0.3

−81.01654 37.78795 WV NPS―ERMN NERI.3040 Polls Branch 14 70.0 1.2 20.5 0.1

−81.01984 37.8583 WV NPS―ERMN NERI.3041 Unnamed 

tributary Laurel 

Creek 3

93.0 0.6 1.5 0.6

−80.95156 37.87324 WV NPS―ERMN NERI.3042 Bucklick 

Branch 3

98.8 0.0 0.2 0.0

−80.89788 37.83271 WV NPS―ERMN NERI.3043 Meadow Creek 

39

84.3 2.6 4.8 1.1

−81.03925 37.8512 WV NPS―ERMN NERI.3044 Laurel Creek 8 93.0 0.6 1.5 0.6

−80.92717 37.80196 WV NPS―ERMN NERI.3047 Sewell Branch 

2

71.0 0.4 22.0 0.1
E-14

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−81.05710 37.82369 WV NPS―ERMN NERI.3048 Slate 

Fork―

Creek 12

Mill 

81.9 2.9 9.9 0.1

−81.01080 37.91417 WV NPS―ERMN NERI.3049 Unnamed 

tributary 

Buffalo Creek 

6

98.5 0.0 0.0 0.2

−81.01287 37.96168 WV NPS―ERMN NERI.3050 Ephraim Creek 

8 

NPS―ERMN 

high priority; 

WV DEP 

reference site

99.3 0.0 0.0 0.0

−80.93170 37.74969 WV NPS―ERMN NERI.3052 Fall Branch 7 NPS―ERMN 

high priority; 

WV DEP 

reference site

95.3 0.5 2.7 0.2

−81.02849 37.89156 WV NPS―ERMN NERI.3053 Slater Creek 13 97.7 0.0 0.0 0.0

−81.09031 37.96421 WV NPS―ERMN NERI.3054 Arbuckle Creek 

5

47.7 24.9 12.2 0.3

−80.95197 37.86122 WV NPS―ERMN NERI.3058 Richlick 

Branch 17

97.1 0.0 0.3 0.5

−80.88025 37.83799 WV NPS―ERMN NERI.3059 Meadow Creek 

58

81.4 3.5 5.8 1.2

−81.10399 37.84261 WV NPS―ERMN NERI.3064 Batoff Creek 7 75.6 6.6 5.9 0.0

−81.09510 37.94727 WV NPS―ERMN NERI.3065 Meadow Fork 6 74.0 5.1 6.5 0.1
E-15

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−81.02195 37.91346 WV NPS―ERMN NERI.3069 Buffalo Creek 

4 

NPS―ERMN 

high priority; 

WV DEP 

reference site

98.5 0.0 0.0 0.2

−80.90266 37.71391 WV NPS―ERMN NERI.3072 Big Branch 9 95.3 0.2 2.9 0.0

−81.05947 37.88203 WV NPS―ERMN NERI.3077 Dowdy Creek 2 99.1 0.0 0.0 0.0

−81.05316 37.83172 WV NPS―ERMN NERI.3080 Slate 

Fork―Mill 

Creek 1

86.3 2.0 7.9 0.1

−81.01693 38.03013 WV NPS―ERMN NERI.3082 Keeney Creek 

15

89.7 2.3 1.5 0.8

−80.98218 37.86476 WV NPS―ERMN NERI.3085 Laurel Creek 

61

91.9 0.8 1.7 0.7

−81.08257 38.04763 WV NPS―ERMN NERI.3093 Wolf Creek 32 56.6 6.5 25.5 0.1

−81.05947 38.06101 WV NPS―ERMN NERI.3099 Fern Creek 12 88.1 2.9 3.1 0.4

−80.93452 37.74875 WV NPS―ERMN NERI.3100 Fall Branch 10 95.3 0.5 2.7 0.2

−76.91134 41.32519 PA PA DEP WQN_408 Loyalsock 

Creek

long-term data, 

EV (protected)

81.6 0.2 6.3 4.4

−76.15029 42.06312 NY SRBC Apal Apalachin 

Creek

precip gage 69.5 0.6 23.1 1.5

−77.60667 41.24694 PA SRBC BAKR0.1 Baker Run pressure 

transducer 

(real-time) and 

precip gage

97.0 0.0 0.0 0.0

−76.72019 42.04209 NY SRBC Baldwin Baldwin Creek precip gage 73.0 0.1 15.4 5.0
E-16

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−77.23044 41.47393 PA SRBC BLOC Blockhouse 

Creek

precip gage 74.1 0.8 13.6 5.4

−78.59258 40.26388 PA SRBC BOBS Bobs Creek pressure 

transducer 

(real-time) and 

precip gage

88.5 0.2 4.7 2.1

−76.02756 41.42725 PA SRBC BOWN Bowman Creek 89.1 0.2 1.8 5.0

−77.73670 42.31903 NY SRBC CANA Canacadea 

Creek

precip gage 69.5 1.1 19.8 1.3

−76.47508 42.20472 NY SRBC Catatonk Catatonk Creek pressure 

transducer 

(stand-alone)

71.4 0.5 11.2 4.5

−74.79921 42.70639 NY SRBC Cherry Cherry Valley 

Creek

66.1 0.3 15.1 5.1

−78.64757 40.63052 PA SRBC CHEST Chest Creek 58.1 0.8 21.5 13.2

−76.00931 42.01582 NY SRBC CHOC Choconut 

Creek

pressure 

transducer 

(stand-alone)

72.7 0.2 19.9 2.3

−77.29313 41.85752 PA SRBC CROK Crooked Creek 45.9 0.1 27.1 21.6

−78.27008 41.52649 PA SRBC Driftwood Driftwood 

Branch 

Sinnemahoning 

Creek

pressure 

transducer 

(real-time)

92.0 0.0 1.0 0.1
E-17

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−77.91244 41.57467 PA SRBC East Fork East Fork First 

Fork 

Sinnemahoning 

Creek

89.1 0.0 0.2 0.0

−76.34434 41.32261 PA SRBC EBFC East Branch 

Fishing Creek

92.4 0.0 0.1 0.0

−76.07111 41.78832 PA SRBC EBWC East Branch 

Wyalusing 

Creek

50.0 0.4 32.3 10.4

−77.58154 41.73642 PA SRBC ELKR Elk Run 81.8 0.0 10.4 0.4

−76.91233 41.99164 PA SRBC HAMM Hammond 

Creek

46.6 0.2 33.6 14.0

−78.25348 41.36235 PA SRBC Hicks Hicks Run 91.6 0.0 1.6 0.0

−78.17458 41.45256 PA SRBC Hunts Hunts Run precip gage 90.7 0.0 0.0 0.0

−76.24282 41.23366 PA SRBC Kitchen Kitchen Creek 85.9 0.2 3.5 0.5

−75.47324 41.68331 PA SRBC LACK Lackawanna 

River

68.2 0.4 11.3 9.0

−77.18943 41.32739 PA SRBC LARR Larrys Creek 75.7 0.1 19.1 1.0

−78.40722 40.97 PA SRBC LCLF0.1 Little Clearfield 

Creek

68.5 0.2 19.1 2.4

−76.06980 41.58154 PA SRBC LMEHOOP Little 

Mehoopany 

Creek

pressure 

transducer 

(real-time)

66.8 0.1 8.1 16.7

−76.64148 41.19353 PA SRBC LMUN Little Muncy 

Creek

56.1 0.2 23.1 13.9
E-18

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−77.55928 41.76142 PA SRBC Long Long Run 82.0 0.0 10.4 1.9

−77.36278 41.31 PA SRBC LPIN0.2 Little Pine 

Creek

82.7 0.4 8.4 3.5

−76.33104 41.4588 PA SRBC LYSK5.0 Loyalsock 

Creek

pressure 

transducer 

(real-time) and 

precip gage

85.5 0.1 0.6 0.7

−77.60997 41.06022 PA SRBC MARS Marsh Creek 83.7 0.7 7.9 3.4

−77.41333 41.76306 PA SRBC Marsh Tioga Marsh Creek 72.0 1.3 15.5 5.0

−75.98474 41.61164 PA SRBC MESH Meshoppen 

Creek

pressure 

transducer 

(stand-alone)

47.1 0.2 18.5 27.3

−76.05357 42.20426 NY SRBC Nanticoke Nanticoke 

Creek

62.2 0.4 24.9 7.8

−77.76387 41.79146 PA SRBC Ninemile Ninemile Run precip gage 84.4 0.4 3.8 2.8

−78.46158 41.04564 PA SRBC PA_Moose Moose Creek 90.3 -- -- --

−77.45056 41.64694 PA SRBC Pine 

Blackwell

Pine Creek 80.5 0.4 7.7 2.0

−76.92300 41.49143 PA SRBC Ples Pleasant Stream 87.6 0.0 1.4 1.0

−78.22029 41.51169 PA SRBC Portage Portage Creek 91.9 0.3 2.5 0.1

−75.50220 42.77596 NY SRBC Sangerfield Sangerfield 

River

35.8 0.4 17.0 14.0

−75.77788 41.55783 PA SRBC SBTK South Branch 

Tunkhannock 

Creek

pressure 

transducer 

(real-time)

53.4 3.4 5.9 24.1
E-19

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−76.92222 42.10278 NY SRBC SING 0.9 Sing Sing 

Creek

61.5 3.1 9.9 10.9

−75.84137 41.92994 PA SRBC SNAK Snake Creek pressure 

transducer 

(stand-alone)

67.1 0.2 23.0 2.8

−75.52351 41.95946 PA SRBC STAR Starrucca Creek 73.1 0.2 15.9 1.1

−76.27436 41.62644 PA SRBC Sugar Run Sugar Run 64.4 0.1 9.9 18.6

−76.76835 41.78974 PA SRBC SUGR Sugar Creek 44.9 1.0 34.3 13.2

−76.91416 41.70931 PA SRBC TIOG Tioga River 83.0 0.0 1.7 2.9

−76.60723 41.78132 PA SRBC TOMJ Tomjack Creek 42.7 0.3 35.9 15.6

−76.76011 41.65262 PA SRBC TOWA Towanda Creek 54.9 0.8 25.5 13.1

−78.36118 41.07359 PA SRBC TROT Trout Run pressure 

transducer 

(real-time) and 

precip gage

90.4 0.0 0.4 0.0

−76.10589 42.59277 NY SRBC Trout Brook Trout Brook precip gage 62.7 0.5 24.8 5.3

−77.37918 42.0752 NY SRBC Tuscarora Tuscarora 

Creek

pressure 

transducer 

(real-time)

42.9 0.3 35.1 13.6

−77.76123 41.79011 PA SRBC Upper Pine Pine Creek pressure 

transducer 

(real-time)

75.1 0.0 11.4 4.0

−76.28083 41.96661 PA SRBC WAPP Wappasening 

Creek

63.4 0.0 30.2 1.8
E-20

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−78.80331 40.69289 PA SRBC WB SUS West Branch 

Susquehanna 

River

pressure 

transducer 

(stand-alone)

68.7 3.5 15.8 3.0

−78.27484 41.49444 PA SRBC West West Creek 83.8 0.5 6.5 0.4

−77.66985 41.72483 PA SRBC WPIN West Branch 

Pine Creek

87.0 0.0 0.5 0.0

−77.68520 41.40016 PA SRBC Young Young 

Woman's Creek

96.8 0.0 0.1 0.0

−74.50528 39.885 NJ USGS USGS 

01466500 

McDonalds 

Branch

USGS gage in 

Byrne State 

Forest (Pine 

Barrens)

-- -- -- --

−82.12353 38.48514 WV WV DEP 11897 Unnamed 

tributary/Left 

Fork river mile 

1.69/Mill Creek

-- -- -- --

−79.69583 38.73825 WV WV DEP 12455 Laurel 

Fork/Dry Fork

89.5 0.0 0.1 0.1

−80.86781 38.88133 WV WV DEP 12689 Long Lick Run -- -- -- --

−81.09958 39.22211 WV WV DEP 12690 Unnamed 

tributary/North 

Fork river mile 

22.26/Hughes 

River

-- -- -- --
E-21

Table E-2. continued...

Longitude Latitude State Entity Station ID

Water body 

name Notes

Percentage 

Forest (%)

Percentage 

Urban (%)

Percentage 

Hay (%)

Percentage 

Crop (%)

−80.32127 38.25981 WV WV DEP 2046 North 

Fork/Cranberry 

River

long-term 

monitoring site 

impacted by acid 

rain

98.9 0.0 0.0 0.0

−81.14683 37.50275 WV WV DEP 2359 Mash Fork long-term 

monitoring site 

impacted by acid 

rain

93.1 0.0 5.2 0.0

−82.28014 38.06845 WV WV DEP 4513 Little Laurel 

Creek

-- -- -- --

−79.39594 38.97394 WV WV DEP 8255 Red Creek long-term 

monitoring site 

impacted by acid 

rain

97.7 0.0 0.1 0.0

−79.61147 39.04225 WV WV DEP 8357 Otter Creek long-term 

monitoring site 

impacted by acid 

rain

99.5 0.0 0.0 0.0

−81.93119 38.38489 WV WV DEP 8482 Sams Fork -- -- -- --

−80.37117 38.33544 WV WV DEP 9315 Middle 

Fork/Williams 

River

long-term 

monitoring site 

impacted by acid 

rain

99.5 0.0 0.0 0.0
E-22

E.1. REFERENCES

Homer, C; Dewitz, J; Fry, J; Coan, M; Hossain, N; Larson, C; Herold, N; McKerrow, A; VanDriel, JN; Wickham, J. 

(2007) Completion of the 2001 National Land Cover Database for the Conterminous United States. PE&RS 

73(4):337−341. Available online at 

http://www.asprs.org/a/publications/pers/2007journal/april/highlight.pdf
F-1 

APPENDIX F. __________________________ 

MACROINVERTEBRATE 

COLLECTION METHODS
F-2

Table F-1. Macroinvertebrate methods for medium-high gradient freshwater wadeable streams with abundant 

riffle habitat and rocky substrate. These are the methods that were agreed upon by the Northeast, Mid-Atlantic, 

and Southeast regional working groups during the pilot phase of the RMNs (before the QAPP was developed). 

At this time (fall 2015), the regional working groups are working on the region-specific QAPP addendums. It is 

possible that some updates will be made during this process.

Regional 

network Effort

Reach 

length Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

Northeast 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. 

150 m D-frame net 

(46 cm wide ×

30 cm high) 

with 500-μm 

mesh

Riffles Approximately 

1 m2

September−

midOctober 

300 Lowest 

practical 

(species 

whenever 

possible) 

MidAtlanticData 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.

100 m Varies by 

entity (either 

square frame 

kick nets or 

D-frame nets, 

with mesh 

size ranging 

from 

450−600 μm) 

Abundant 

riffles

Minimum of 

1 m2

Spring 

(March−

April) and 

summer 

(July− 

August) 

300 Lowest 

practical 

(species 

whenever 

possible) 
F-3

Table F-1. continued...

Regional 

network Effort

Reach 

length Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

Southeast 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

100 m Kick-net with 

500-μm mesh

Riffles Approximately 

2 m2

April 2013. 

Subsequent 

samples will 

be collected 

annually 

within 

2 weeks of 

the original 

collection

300 ± 10% Lowest 

practical 

(species 

whenever 

possible) 

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)

100 m Dip-net with 

500-μm mesh

Multihabitat NA 

(qualitative)

NA 

(qualitative)
F-4

Table F-2. Macroinvertebrate methods used by Northeastern states for routine sampling events in medium-high 

gradient freshwater wadeable streams with riffle habitat and rocky substrate

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

CT 

DEEP

Streams with 

riffle habitat

12 kick samples 

are taken 

throughout riffle 

habitats within 

the sampling 

reach

Rectangular 

net (46 cm × 

46 cm × 

25 cm) with 

500-μm mesh

Riffles Approximately 

2 m2

October 1− 

November 

30 

200 Lowest practical 

(species whenever 

possible) 

VT 

DEC

Moderate to 

high gradient 

streams with 

riffle habitat

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.

D-frame net 

(46 cm

wide × 30 cm

high) with 

500-μm mesh

Riffles Approximately 

1 m2

September−

midOctober 

300 Lowest practical 

(species whenever 

possible) 

ME 

DEP

Streams with 

riffle and run 

habitat 

3 cylindrical 

rock-filled wire 

baskets are 

placed in 

locations with 

similar habitat 

characteristics for 

28 ± 4 days.

Contents are 

washed into a 

sieve bucket 

with 600-μm 

mesh

Riffle/run is 

the 

preferred 

habitat. 

Approximately 

0.3 m2 per 

basket

July 1−

September 

30 

Entire samples 

are processed 

and identified, 

with 

exceptions

Lowest practical 

(species whenever 

possible) 
F-5

Table F-2. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

NH DES Streams with 

riffle and run 

habitat 

3 cylindrical rockfilled 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.

Contents are 

washed into a 

sieve bucket 

with 600-μm 

mesh

Riffle/run 

is the 

preferred 

habitat. 

Approximately 

0.3 m2 per 

basket

late July−

September

100 Genus, except 

Chironomidae 

(family-level)

RI DEM Routine 

monitoring in 

streams with 

riffle habitat

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.

D-frame net 

(30-cm width) 

with 500-μm 

mesh

Riffle Within reach 

(100 linear 

meters)

August−

September

100 Mostly 

genus-level. 

Chironomidae are 

identified to the 

subfamily or 

tribe-level

NY DEC Routine 

monitoring in 

streams with 

riffle habitat

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

Rectangular 

net (23 cm × 

46 cm) with 

800−900-μm 

mesh

Riffle 2.5 m2 July−

September

100 Lowest practical 

(mostly genus- or 

species-level, some 

family-level [e.g., 

Gastropoda and 

Pelecypoda]) 
F-6

Table F-2. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

MA DEP Routine 

monitoring in 

streams with 

riffle habitat

10 kick-samples 

are taken in riffle 

habitats within the 

sampling reach 

and composited

Kick-net, 

46-cm wide 

opening, 

500-μm mesh

Riffle/run 

is the 

preferred 

habitat 

Approximately 

2 m2

July 1−

September 

30 

100 Lowest practical 

level
F-7

Table F-3. Macroinvertebrate methods used by Mid-Atlantic states and RBCs for routine sampling events in

medium-high gradient freshwater wadeable streams with riffle habitat and rocky substrate 

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

NJ DEP Riffle/run 10−20 kicks are 

taken from riffle/run 

areas and 

composited

D-frame net 

(30 cm) with 

800 × 900-μm 

mesh

Riffle/run 10−20 net 

dimensions 

April−

November 

100 ± 10% Genus

DE 

DNREC

Piedmont 2 kicks composited Kick-net (1-m2

area) with 

600 μm mesh

Riffle 2 m2 October−

November

200 ± 20% Genus or 

lowest 

practical

PA DEP Smaller 

freestone 

riffle-run 

streams 

(<25−50 mi2)

6 kicks are taken 

from riffle areas and 

composited 

D-frame net 

(30 cm wide × 

20 cm high) 

with 500-μm 

mesh

Riffle 6 m2 Year-round 200 ± 20% Genus, except 

Chironomidae, 

snails, clams, 

mussels 

(family); 

Nematoda, 

Nemertea, 

Bryozoa 

(phylum); 

Turbellaria, 

Hirudenia, 

Oligochaeta 

(class); water 

mites 

(artificial)

Limestone 

spring 

streams

2 kicks are taken 

from riffle-run areas 

(1 fast, 1 slow) and 

composited 

D-frame net 

(30 cm wide 

20 cm high) 

with 500-μm 

mesh

Riffle-run (1 fast, 1 

slow)

2 m2 January−

May

300 ± 20%
F-8

Table F-3. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

MD Maryland Approximately D-frame net Multihabitat (in order About 2 m2 March− 100 ± 20% Genus (or lowest 

DNR Biological 

Stream 

Survey 

(MBSS)

20 kicks/jabs/sweeps

/rubs from multiple 

habitats (sampled in 

proportion to 

availability in reach) 

are composited 

(about 30 cm

wide) with 

450-μm mesh

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

April practical); 

crayfish and 

mussels identified 

to species 

(sometime 

subspecies?) in 

the field along 

with fish, reptiles, 

amphibians, and 

some invasive 

plants
F-9

Table F-3. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

WV

DEP

Wadeable 

streams 

(WVSCI)

4 kicks composited Rectangular 

kick net (50 cm 

wide × 30 cm 

high × 50 cm 

deep) with 

600-μm net 

mesh (595-μm 

sieve); D-frame 

net (30 cm 

wide) can be 

used for 

smaller streams

riffle-run 1 m2 April 15−

October 15 

200 ± 20% Family (all 

insects)

Wadeable 

streams 

(GLIMPSS)

―Mountain 

and Plateau

1 m2 Winter 

(December−

midFebruary), 

spring 

(March−

May)

―Plateau 

only, 

summer 

(June−midOctober)

200 ± 20% Genus (all insects 

minus 

Collembola) 

VA 

DEQ

Noncoastal 

Plain (VSCI)

6 kicks from riffle 

habitat (unless 

absent, then 

multihabitat) are 

composited 

D-frame net 

(50 cm wide × 

30 cm high × 

50 cm deep) 

with 500 μm 

net mesh 

Riffle, unless absent, 

then multihabitat 

2 m2 Spring 

(March−

May) and 

fall 

(September−

November)

110 ± 10% Family (working 

toward developing 

a genus-level 

index) 
F-10

Table F-3. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

SRBC Aquatic 

Resource 

Surveys

6 kicks composited 

or 5 minutes for a 

distance of 5 m (PA 

or NY)

D-frame 

net/aquatic net 

(30 cm × 20 to 

23 cm × 46 cm

[PA or NY]); 

500-μm; 

800 μm × 

900 μm 

(depending on 

PA or NY)

Riffle-run 6 m2 or 

distance of 

5 m (PA or 

NY)

Typically 

late April 

into May, 

late June into 

July, and 

October 

PADEP or 

NYSDEC 

protocol 

Genus, except 

Chironomidae, 

snails, clams 

mussels (family); 

Nematoda, 

Nemertea, 

Bryozoa 

(phylum); 

Turbellaria, 

Hirudenia, 

Oligochaeta 

(class); water 

mites (artificial)

Subbasin 

Survey, Year 

1/Interstate 

Streams

2 kicks composited Kick-net (1 m2) 

with 600-μm 

mesh

2 m2 Year 1―

historically 

spring−fall, 

now 

spring−May 

30. 

Interstate―

May (Group 

3) or August

(Group 1 and

2); varies

depending

on site

classification

200 ± 20%

Remote 

Water 

Quality 

Monitoring 

Network

6 kicks composited D-Frame Net 

(46 cm × 

20 cm) with 

500-μm mesh

6 m2 October 200 ± 20%
F-11

Table F-3. continued...

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

NPS Eastern 

Rivers and 

Mountains 

Network

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.

Slack sampler, 

500-μm nets 

and sieves

Riffle Each 

discrete 

sample = 

0.25 m2

area; total 

area 

sampled = 

1.25 m2

April−early 

June

300 Genus, except 

Chironomidae, 

snails, clams 

mussels (family); 

Nematoda, 

Nemertea, 

Bryozoa 

(phylum); 

Turbellaria, 

Hirudenia, 

Oligochaeta 

(class); water 

mites (artificial)
F-12

Table F-4. Macroinvertebrate methods used by Southeast states for routine sampling events in medium-high 

gradient freshwater wadeable streams with riffle habitat and rocky substrate

Entity

Stream 

type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

AL

DEM

WMB-I 

protocols 

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

Kick net, 2 

A-frame nets, 2 

#30 sieve buckets, 

2 #30 sieves, 

plastic elutriation 

treys, 100% 

denatured ethanol, 

and plastic sample 

containers 

Riffle, rock-log, 

Rootbank, CPOM, 

sand, and 

macrophytes 

(macrophytes not 

always available 

and excluded from 

index) 

Approximately 

4 m2

Late April−

early July

100 

organisms 

per habitat 

Genus or lowest 

possible level

GA

DNR

High 

(riffle/run) 

gradient

20 jabs from 

multiple habitats 

are composited 

D-frame net 

(30-cm width) 

with 500-μm net 

mesh

Multihabitat― 

riffles, woody 

debris/snags, 

undercut 

banks/rootwads, 

leafpacks, soft 

sediment/sandy 

substrate, and 

submerged 

macrophytes (when 

present)

20 jabs, each 

for a linear 

distance of 

1 m 

MidSeptember−

February 

200 ± 20% Lowest practical 

level (generally 

genus or 

species)
F-13

Table F-4. continued...

Entity Stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

KY DEP Wadeable, 

moderate/hig

h gradient 

streams

Combination of 

quantitative 

(composite of 4 

riffle kicks) and 

qualitative 

(multihabitat) 

samples

Quantitative

―kick net 

(600-μm 

mesh); 

qualitative―d

ip net, mesh 

bucket, 

forceps 

600-μm mesh

Quantitative 

samples are taken 

from riffles; 

qualitative are 

taken from 

multiple habitats 

(undercut 

banks/roots, wood, 

vegetation, leaf 

packs, soft and 

rocky substrates)

1 m2

(quantitative)

Summer 

(June−Septe

mber)

300 Lowest practical 

level (generally 

genus or 

species)

Headwater, 

moderate/

high gradient 

streams

Spring 

index period 

(February−

May)

NC Standard Composite of 2 Multiple gear Multihabitat NA Year-round Organisms All of the 

DENR qualitative kicks, 3 sweeps, types (riffles, bank areas, (qualitative are field field-picked 

method for 1 leaf pack (kick net with macrophyte beds, only) picked organisms are 

wadeable sample, 2 fine 600-μm woody debris, leaf roughly in identified in the 

flowing mesh rock and/or mesh; packs, sand, etc.) proportion laboratory to the 

streams and log wash triangular to their lowest practical 

rivers samples, 1 sand sweep net; abundance. level (generally 

sample and visual fine-mesh Abundance genus or 

collections from samplers data are species)

habitats and [300-μm recorded as 

substrate types mesh]; sieve rare (1−2 

missed or bucket) specimens), 

under-sampled common 

by the other (3−9 

collection specimens) 

techniques or abundant 

(≥10 

specimens)
F-14

Table F-4. continued...

Entity Stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

SC Ambient Same as NC DENR Feb 1 to Same as NC DENR

DHEC monitoring March 15: 

Middle 

Atlantic 

Coastal 

Plain 

Ecoregion 

(EPA Level 

III 63); June 

15 to Sept 1: 

Statewide, 

minus EPA 

Level III 

Ecoregion 

63

TN DEC Streams with 

riffles 

Single habitat, 

semiquantitative; 

composite of 

2 riffle kicks

Kick net 

(1-m2, 

500-μm 

mesh)

Riffle 2 m2 Year-round 200 ± 20% Genus level
F-15

Table F-5. Macroinvertebrate methods used in national surveys conducted by EPA and USGS

Entity

Project or 

stream type Effort Gear Habitat

Sampling 

area

Index 

period

Target # 

organisms

Taxonomic 

resolution

EPA 

National 

Aquatic 

Resource 

Surveys

WSA and 

NRSA

A 0.1-m2 area was 

sampled for 

30 seconds at a 

randomly selected 

location at each of 

the 11 transects. The 

samples were 

composited into one 

sample per site.

Modified 

D-frame 

net (30 cm 

wide) with 

500-μm 

mesh

Multihabitat 

Composite 

Approximately

1 m2

June−

September

500 Genus level

USGS NAWQA 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.

Slack 

sampler, 

500-μm 

nets and 

sieves

Riffle Each discrete 

sample = 

0.25-m2 area; 

total area 

sampled = 

1.25 m2

Late 

June−midOctober 

300 Lowest 

practical 

level
G-1 

APPENDIX G. __________________________ 

LEVEL OF TAXONOMIC 

RESOLUTION
G-2 

When possible, all taxa should be taken to the lowest practical taxonomic level (ideally 

species level). If this is not possible, efforts should be made to identify the taxa listed in Table 

G-1 to the level of resolution described in the table. Ephemeroptera, Plecoptera, Trichoptera, and

Chironomidae that are not listed in Table G-1 should be identified to at least the genus level, 

where possible. 

The taxa in Table G-1 were selected based on differences in thermal tolerances that were 

evident in analyses (U.S. EPA, 2012; unpublished Northeast pilot study) and from best

professional judgment. The list in Table G-1 should be regarded as a starting point and should be 

updated as better data become available in the future. Table G-2 contains a list of taxa that were 

considered for inclusion in Table G-1 but for various reasons, were not selected. 
G-3

Table G-1. At RMN sites, we recommend that the taxa listed below be taken 

to the specified level of resolution, where practical. The Chironomidae 

require a slide mount and a compound microscope to identify to the 

species-level.

Order Family Genus

Level of 

resolution Notes

Coleoptera Elmidae Promoresia adults to 

species

Potential variability in thermal 

preferences of P. tardella (cold) 

and P. elegans (warm).

Diptera Chironomidae Eukiefferiella species Potential variability in thermal 

preferences of E. brevicalar, E. 

brehmi, and E. tirolensis (cold); 

and E. claripennis and E. 

devonica (warm).

Diptera Chironomidae Polypedilum species P. aviceps is generally regarded

as a cold water taxon.

Diptera Chironomidae Tvetenia species 

group 

T. vitracies is warm water

oriented in the Northeast.

Diptera Simuliidae genus 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.

Ephemeroptera Baetidae Baetis species Potential variability in thermal 

preferences (e.g., B. 

tricaudatus―cold; B. 

intercalaris and B. 

flavistriga―warm).

Ephemeroptera Ephemerellidae Ephemerella species (as 

maturity 

allows)

Potential variability in thermal 

preferences (e.g., E.

subvaria―colder); need mature 

individuals (early instars are 

difficult to speciate).

Plecoptera Perlidae Acroneuria species Potential variability in thermal 

preferences of A. abnormis

(warmer) and A. carolinensis 

(cooler).
G-4

Table G-1. continued...

Order Family Genus

Level of 

resolution Notes

Plecoptera Perlidae Paragnetina species Potential variability in thermal 

preferences of P. immarginata

(cold) and P. media and P.

kansanensis.

Plecoptera Pteronarcyidae Pteronarcys species P. dorsata may be warmer water

oriented.

Trichoptera Brachycentridae Brachycentrus species Potential variability in thermal 

preferences in the Northeast. 

Trichoptera Hydropsychidae Ceratopsyche species Potential variability in thermal 

preferences.

Trichoptera Rhyacophilidae Rhyacophila species Most species are cold water, but 

some variability has been 

documented in the Northeast 

(U.S. EPA, 2012, unpublished 

data).

Trichoptera Uenoidae Neophylax species Some variability was noted in a 

pilot study in North Carolina 

(U.S. EPA, 2012).
G-5

Table G-2. Taxa that were considered for inclusion in Table G-1 

Order Family Genus

Level of 

resolution Notes

Coleoptera Elmidae Oulimnius species 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.

Diptera Chironomidae Micropsectra species General agreement that there is 

variability in thermal preferences, 

but the taxonomy for this genus 

needs to be further developed.

Diptera Ceratopogonidae species General agreement that there is 

variability in thermal preferences, 

but the taxonomy for this family 

needs to be further developed. 

Ephemeroptera Ephemerellidae Drunella species 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.

Ephemeroptera Ephemerellidae Eurylophella species Some variability was noted in a 

pilot study in North Carolina 

(U.S. EPA, 2012); could be 

seasonal phenology vs. thermal 

preference.

Ephemeroptera Heptageniidae Epeorus species Some variability was noted in a 

pilot study in Utah (U.S. EPA, 

2012); can be difficult to speciate.

Ephemeroptera Heptageniidae Stenacron species 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. 
G-6

Table G-2. continued...

Order Family Genus

Level of 

resolution Notes

Trichoptera Goeridae Goera species 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.

Trichoptera Hydropsychidae Hydropsyche species 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).

Trichoptera Leptoceridae Oecetis species 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.

Trichoptera Philopotamidae Chimarra species 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.

Oligochaeta family 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.
G-7

Table G-2. continued...

Order Family Genus

Level of 

resolution Notes

Amphipoda Gammaridae Gammarus species 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).

Amphipoda Hyalellidae Hyallela species 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.

Isopoda Asellidae Caecidotea species C. brevicauda has been noted as a

potential cold-water indicator in

the Midwest (Gerritsen and Stamp,

2012).

Neoophora Planariidae Dugesia species 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.

Neoophora Dugesiidae Cura species C. formanii is regarded as a

cold-water taxon in Vermont. Can

be difficult to speciate in speciose

regions.
G-8

G.1. 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. Available online at http://www.uwsp.edu/cnrap/biomonitoring/Documents/pdf/USEPA-BCG-Report-Final-2012.pdf 

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. Available online at 

http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=239585 
H-1 

APPENDIX H. __________________________ 

SUMMARIZING 

MACROINVERTEBRATE 

DATA
H-2

H.1. LIST OF CANDIDATE BIOLOGICAL INDICATORS

Table H-1. Recommendations on candidate biological indicators to 

summarize from the macroinvertebrate data collected at regional monitoring 

network (RMN) sites; many of these indicators are commonly used by 

biomonitoring programs for site assessments. 

Type of 

indicator Biological indicator Expected response Source

Taxonomicbased metricTotal number of taxa 

(richness)

Predicted to decrease 

in response to 

increasing 

anthropogenic stress 

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

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, and 

Hemiptera (OCH) taxa

Expected to be more 

prevalent during 

summer, low flow 

(more pool-like) 

periods 

Bonada et al., 2007a

Percentage OCH 

individuals
H-3

Table H-1. continued...

Type of 

indicator Biological indicator Expected response Source

Traits-based 

metric related 

to temperature

(for list of 

thermal 

indicator taxa, 

see 

Appendix I)

Number of cold water taxa Predicted to decrease 

in response to 

warming 

temperatures

Lake, 2003; Hamilton 

et al., 2010; Stamp et al., 

2010; U.S. EPA, 2012 Percentage cold water 

individuals 

Number of warm water 

taxa

Predicted to increase 

in response to 

warming 

temperatures Percentage warm 

individuals

water 

Traits-based 

metric related 

to hydrology 

Collector filterer Predicted to decrease 

during low flow 

conditions

Wills et al., 2006; Bogan 

and Lytle, 2007; Walters 

and Post, 2011

Collector gatherer Predicted to increase 

during slow velocity 

conditions

Heino, 2009

Scraper/herbivore Predicted to increase 

during conditions of 

stable flow and 

habitat availability; 

decrease during 

drought conditions

Richards et al., 1997;

McKay and King, 2006; 

Wills et al., 2006; 

Fenoglio et al., 2007; 

Griswold et al., 2008; 

Diaz et al., 2008

Shredder Expected to respond 

to changing thermal 

and hydrologic 

conditions

Richards et al., 1997; 

Buzby and Perry, 2000; 

McKay and King, 2006; 

Foucreau et al., 2013

Predator Predicted to increase 

during low flow 

conditions

Bogan and Lytle, 2007; 

Miller et al., 2007; 

Walters and Post, 2011

Swimmer Predicted to comprise 

higher proportion of 

assemblage during 

drier, harsher, 

climatic conditions

Béche et al., 2006; 

Bonada et al., 2007b; 

Diaz et al., 2008 

Rheophily―depositional Favor low flow/slow 

velocity conditions

Richards et al., 1997; 

Lake, 2003; Wills et al., 

2006; Poff et al., 2010; 

Brooks et al., 2011 Rheophily―erosional Favor high flow/fast 

velocity conditions
H-4

Table H-1. continued...

Type of 

indicator Biological indicator Expected response Source

Biological 

condition 

Bioassessment score (e.g., 

multimetric index, 

predictive, biological 

condition gradient)

Expected to worsen 

in response to 

increasing 

anthropogenic stress

Barbour et al., 1995; 

DeShon, 1995; Hawkins 

et al., 2000; Davies and 

Jackson, 2006

Individual taxa Presence―absence Hypotheses have 

been developed for 

some individual taxa 

(e.g., the cold and 

warm water taxa 

listed in Appendix I)

Becker et al., 2010

Relative abundance

Spatial distribution

Variability Persistence (variability in 

presence/absence; see 

Section H.2 of this 

appendix) 

Expect lower 

persistence in 

disturbed or 

climatically harsh 

environments

Holling, 1973; Bradley 

and Ormerod, 2001; 

Milner et al., 2006; 

Durance and Ormerod, 

2007

Stability (variability in 

relative abundance; see 

Section H.2 of this 

appendix)

Expect lower stability 

in disturbed or 

climatically harsh 

environments

Scarsbrook, 2002; Milner 

et al., 2006 

H.2. FORMULAS FOR CALCULATING PERSISTENCE AND STABILITY

Persistence between samples can be calculated using Jaccard’s similarity coefficient (J):

J(AB) = j

a + b − j (H-1) 

Here j 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]):
H-5

BC(AB) = 1 − ∑ |nnAAAA −nnBBAA| i

NA + NB

(H-2)

Here nAi and nBi are the number of individuals of taxa i 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 H-2: 

J(AB) = 3

3 + 5 − 3 = 35 = 0.60 

BC(AB) = 1 − |10 - 19| + |0 - 35| + |5 - 5| + |8 - 13| + |0 - 1|

23 + 73

= 1 − 9 + 35 + 0 + 5 + 1

23 + 73 = 1 − 5096 = 0.48 

(H-3) 

(H-4)

(H-5) 

Table H-2. Sample data for calculating persistence and stability 

Samples Taxa V Taxa W Taxa X Taxa Y Taxa Z Sum

Sample year (or site) A 10 0 5 8 0 23

Sample year (or site) B 19 35 5 13 1 73

High persistence and stability are thought to occur where environmental conditions are similar or 

relatively constant, or where change occurs incrementally. For additional background and an 

example of these techniques applied to long-running surveys in Alaskan streams, see Milner 

et al. (2006). At these sites, mean persistence and stability between study years ranged from 0.49 

to 0.70 and from 0.29 to 0.44, respectively, which suggests that even among the most persistent 

sites, substantial year-to-year shifts in relative abundances can occur. 

H.1. REFERENCE:

Barbour, MT; Stribling, JB; Karr, J. (1995) Multimetric approach for establishing biocriteria and measuring 

biological condition. In Davis, W.S. & T.P. Simon (eds). Biological Assessment and Criteria; Tools for Water 

Resource Planning and Decision Making. Lewis Publishers. Boca Raton, FL: 63-77.
H-6

Barbour, MT; Gerritsen, J; Griffith, GE; Frydenborg, R; McCarron, E; White, JS; Bastian, ML. (1996) A framework 

for biological criteria for Florida streams using benthic macroinvertebrates. J N Am Benthol Soc 15(2):185−211.

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.

Bêche, LA; McElracy, EP; Resh, VH. (2006) Long-term seasonal variation in the biological traits of benthicmacroinvertebrates in two Mediterranean-climate streams in California U.S.A. Freshw Biol 51:56−75.

Becker, AJ; Stranko, SA; Klauda, RJ; Prochaska, AP; Schuster, JD; Kashiwagi, MR; Graves, PH. (2010) Maryland 

biological stream survey’s sentinel site network: A multi-purpose monitoring program. Annapolis, MD: Maryland 

Department of Natural Resources.

Bonada, N; Rieradevall, M; Prat, N. (2007a). Macroinvertebrate community structure and biological traits related to 

flow permanence in a Mediterranean river network. Hydrobiologia 589:91−106.

Bonada, N; Dolédec, S; Statzner, B. (2007b). Taxonomic and biological trait differences of stream 

macroinvertebrate communities between Mediterranean and temperate regions: implications for future climatic 

scenarios. Global Change Biol 13:1658−1671.

Bogan, MT; Lytle, DA. (2007) Seasonal flow variation allows ‘time-sharing’ by disparate aquatic insect 

communities in montane desert streams. Freshw Biol 52(2):290−304. doi:10.1111/j.1365-2427.2006.01691.x

Bradley, DC; Ormerod, SJ. (2001) Community persistence among upland stream invertebrates tracks the North 

Atlantic Oscillation. J Animal Ecol 70(6):987−996.

Brooks, A; Chessman, B; Haeusler, T. (2011) Macroinvertebrate traits distinguish unregulated rivers subject to 

water abstraction. J N Am Benthol Soc 30(2):419−435.

Buzby, KM; Perry, SA. (2000). Modeling the potential effects of climate change on leaf pack processing in central 

Appalachian streams. Can J Fish Aquatic Sci 57(9):1773−1783.

Davies, SP; Jackson, SK. (2006) The Biological Condition Gradient: A descriptive model for interpreting change in 

aquatic ecosystems. Ecol Appl 16(4):1251−1266.

DeShon, J. (1995) Development and application of the Invertebrate Community Index (ICI). [p. 217−243]. In Davis, 

WS; Simon, TP (eds). Biological assessment and criteria: tools for water resource planning and decision making. 

Boca Raton:CRC Press.

Diaz, AM; Suarez Alonso, ML; Vidal-Abarca Gutierrez, MR. (2008) Biological traits of stream macroinvertebrates 

from a semi-arid catchment: patterns along complex environmental gradients. Freshw Biol 53:1−21.

Durance, I; Ormerod, SJ. (2007) Climate change effects on upland stream macroinvertebrates over a 25-year period. 

Glob Change Biol 13:942−957.

Fenoglio, S; Bo, T; Cucco, M; Malacarne, G. (2007) Response of benthic invertebrate assemblages to varying 

drought conditions in the Po river (NW Italy). Ital J Zool 74(2):191−201.

Fore, LS; Karr, JR; Wisseman, RW. (1996) Assessing invertebrate responses to human activities: Evaluating 

alternative approaches. J N Am Benthol Soc 15(2):212−231.

Foucreau, N; Piscart, C; Puijalon, S; Hervant, F. (2013) Effect of climate-related change in vegetation on leaf litter 

consumption and energy storage by Gammarus pulex from Continental or Mediterranean populations. PLoS ONE 

8(10): e77242.
H-7

Griswold, MW; Berzinis, RW; Crisman, TL; Golladay, SW. (2008) Impacts of climatic stability on the structural 

and functional aspects of macroinvertebrate communities after severe drought. Freshw Biol 53(12):2465-2483. 

doi:10.1111/j.1365-2427.2008.02067.x.

Hamilton, AT; Stamp, J; Bierwagen, BG. (2010) Vulnerability of biological metrics and multimetric indices to 

effects of climate change. J N Am Benthol Soc 29(4):1379−1396.

Hawkins, CP; Norris, RH; Hogue, JN; Feminella, JW. (2000) Development and evaluation of predictive models for 

measuring the biological integrity of streams. Ecol Appl 10:1456−1477.

Heino, J. (2009) Species co-occurrence, nestedness and guild− environment relationships in stream 

macroinvertebrates. Freshw Biol 54(9):1947−1959.

Holling, CS. (1973) Resilience and stability of ecological systems. Ann Rev Ecol Systems 4:1−23.

Lake, PS. (2003) Ecological effects of perturbation by drought in flowing waters. Freshw Biol 48:1161−1172.

McKay, SF; King, AJ. (2006) Potential ecological effects of water extraction in small, unregulated streams. River 

Res Appl 22:1023−1037.

Miller, S; Wooster, D; Li, J. (2007) Resistance and resilience of macroinvertebrates to irrigation water withdrawals. 

Freshw Biol 52:2494−2510.

Milner, AM; Conn, SC; Brown, LE. (2006) Persistence and stability of macroinvertebrate communities in streams of 

Denali National Park, Alaska: implications for biological monitoring. Freshw Biol 51:373−387.

Poff, NL; Allan, JD; Bain, MB; Karr, JR; Prestegaard, KL; Richter, BD; Sparks, RE; Stromberg, JC. (1997) The 

natural flow regime: a paradigm for river conservation and restoration. BioScience 47(11):769−784.

Richards, C; Haro, RJ; Johnson, LB; Host, GE. (1997) Catchment and reach-scale properties as indicators of 

macroinvertebrate species traits. Freshw Biol 37:219−230.

Scarsbrook MR. (2002) Persistence and stability of lotic invertebrate communities in New Zealand. Freshw Biol 

47:417−431.

Smith, EP; Voshell, Jr, JR. (1997) Studies of benthic macroinvertebrates and fish in streams within EPA Region 3 

for development of biological indicators of ecological condition. Blacksburg, VA: Virginia Polytechnic Institute and 

State University.

Stamp, J; Hamilton, A; Zheng, L; Bierwagen, B. (2010) Use of thermal preference metrics to examine state 

biomonitoring data for climate change effects. J N Am Benthol Soc 29(4):1410−1423.

U.S. EPA (Environmental Protection Agency). (2012) Implications of climate change for state 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.

Walters, AW; Post, D. (2011) How low can you go? Impacts of a low-flow disturbance on aquatic insect 

communities. Ecol Appl 21:163−174.

Wills, TC; Baker, EA; Nuhfer, AJ; Zorn, TG. (2006) Response of the benthic macroinvertebrate community in a 

northern Michigan stream to reduced summer streamflows. River Res Appl 22:819−836.

H.2. REFERENCE:
H-8

Bray, JR; Curtis, JT. (1957) An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 

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. Freshw Biol 51:373−387.
I-1

APPENDIX I. __________________________

MACROINVERTEBRATE 

THERMAL INDICATOR 

TAXA
I-2

This appendix contains lists of macroinvertebrate taxa that are believed to have strong thermal 

preferences based on analyses conducted by EPA (U.S. EPA, 2012; unpublished Northeast pilot 

study) and state biomonitoring programs (Maryland Department of Natural Resources [MD 

DNR], Pennsylvania Department of Environmental Protection [PA DEP], Tennessee Department 

of Environment and Conservation [TN DEC], Vermont Department of Environmental 

Conservation [VT DEC]). Best professional judgment from regional taxonomists was also 

considered.

Table I-1 contains a list of cold- and warm-water preference taxa (benthic macroinvertebrates) 

for the eastern United States, based on Generalized Additive Models (GAM)―full temperature 

range model rank results from the Northeast pilot study (U.S. EPA, 2012) and the best 

professional judgment of regional experts.

Table I-2 contains lists of taxa that have been identified as thermal indicators by VT DEC (Steve 

Fiske, Aaron Moore, and Jim Kellogg, unpublished).

Table I-3 contains the list of taxa that have been identified as cold water taxa by MD DNR 

(Becker et al., 2010) and also contains information that was provided by PA DEP (Amy 

Williams and Dustin Shull, unpublished data).

Table I-4 contains a list of indicator taxa identified based on thermal tolerance analyses (per 

Yuan, 2006) conducted on data from North Carolina (U.S. EPA, 2012), and also contains 

information that was provided by Debbie Arnwine from TN DEC.

All of these lists are intended to be starting points, which can be revised as more data become 

available.
I-3

Table I-1. List of cold- and warm-water preference taxa (benthic 

macroinvertebrates) for the eastern United States, based on GAM―full 

temperature range model rank results from the Northeast pilot study 

(EPA, 2012) and the best professional judgment of regional experts

Order Family Genus Final identification Indicator

Coleoptera Elmidae Oulimnius Oulimnius Cold

Coleoptera Elmidae Promoresia Promoresia tardella Cold

Diptera Chironomidae Brillia Brillia Cold

Diptera Chironomidae Eukiefferiella Eukiefferiella 

brevicalcar

Cold

Diptera Chironomidae Eukiefferiella Eukiefferiella 

claripennis

Cold

Diptera Chironomidae Heleniella Heleniella Cold

Diptera Chironomidae Parachaetocladius Parachaetocladius Cold

Diptera Chironomidae Polypedilum Polypedilum tritum Cold

Diptera Dixidae Dixa Dixa Cold

Diptera Psychodidae Pericoma Pericoma Cold

Diptera Simuliidae Prosimulium Prosimulium Cold

Ephemeroptera Ameletidae Ameletus Ameletus Cold

Ephemeroptera Baetidae Baetis Baetis tricaudatus Cold

Ephemeroptera Baetidae Diphetor Diphetor Cold

Ephemeroptera Ephemerellidae Drunella Drunella Cold

Ephemeroptera Ephemerellidae Ephemerella Ephemerella Cold

Ephemeroptera Ephemerellidae Eurylophella Eurylophella Cold

Ephemeroptera Heptageniidae Cinygmula Cinygmula Cold

Ephemeroptera Heptageniidae Epeorus Epeorus Cold

Ephemeroptera Heptageniidae Rhithrogena Rhithrogena Cold

Ephemeroptera Leptophlebiidae Habrophlebia Habrophlebia Cold

Odonata Gomphidae Lanthus Lanthus Cold

Plecoptera Capniidae Capniidae Cold

Plecoptera Chloroperlidae Chloroperlidae Cold

Plecoptera Nemouridae Nemouridae Cold

Plecoptera Peltoperlidae Peltoperla Peltoperla Cold
I-4

Table I-1. continued...

Order Family Genus Final identification Indicator

Plecoptera Peltoperlidae Tallaperla Tallaperla Cold

Plecoptera Perlodidae Isoperla Isoperla Cold

Plecoptera Perlodidae Malirekus Malirekus Cold

Plecoptera Pteronarcyidae Pteronarcys Pteronarcys Cold

Plecoptera Taeniopterygidae Taenionema Taenionema Cold

Plecoptera Taeniopterygidae Taeniopteryx Taeniopteryx Cold

Trichoptera Apataniidae Apatania Apatania Cold

Trichoptera Brachycentridae Brachycentrus Brachycentrus 

americanus

Cold

Trichoptera Glossosomatidae Agapetus Agapetus Cold

Trichoptera Glossosomatidae Glossosoma Glossosoma Cold

Trichoptera Hydropsychidae Arctopsyche Arctopsyche Cold

Trichoptera Hydropsychidae Ceratopsyche Ceratopsyche alhedra Cold

Trichoptera Hydropsychidae Ceratopsyche Ceratopsyche macleodi Cold

Trichoptera Hydropsychidae Ceratopsyche Ceratopsyche ventura Cold

Trichoptera Hydropsychidae Diplectrona Diplectrona Cold

Trichoptera Hydropsychidae Parapsyche Parapsyche Cold

Trichoptera Hydroptilidae Palaeagapetus Palaeagapetus Cold

Trichoptera Odontoceridae Psilotreta Psilotreta Cold

Trichoptera Philopotamidae Dolophilodes Dolophilodes Cold

Trichoptera Philopotamidae Wormaldia Wormaldia Cold

Trichoptera Rhyacophilidae Rhyacophila Rhyacophila Cold

- - - Turbellariaa Warm

Basommatophora Physidae Physella Physella Warm

Coleoptera Elmidae Stenelmis Stenelmis Warm

Coleoptera Hydrophilidae Berosus Berosus Warm

Diptera Chironomidae Ablabesmyia Ablabesmyia Warm

Diptera Chironomidae Cardiocladius Cardiocladius Warm
I-5

Table I-1. continued...

Order Family Genus Final identification Indicator

Diptera Chironomidae Dicrotendipes Dicrotendipes Warm

Diptera Chironomidae Glyptotendipes Glyptotendipes Warm

Diptera Chironomidae Nilotanypus Nilotanypus Warm

Diptera Chironomidae Nilothauma Nilothauma Warm

Diptera Chironomidae Pentaneura Pentaneura Warm

Diptera Chironomidae Polypedilum Polypedilum convictum Warm

Diptera Chironomidae Polypedilum Polypedilum flavum Warm

Diptera Chironomidae Stenochironomus Stenochironomus Warm

Diptera Chironomidae Tanytarsus Tanytarsus Warm

Diptera Chironomidae Tvetenia Tvetenia vitracies Warm

Ephemeroptera Baetidae Baetis Baetis intercalaris Warm

Ephemeroptera Caenidae Caenis Caenis Warm

Ephemeroptera Heptageniidae Stenacron Stenacron Warm

Ephemeroptera Leptohyphidae Tricorythodes Tricorythodes Warm

Neotaenioglossa Hydrobiidae Hydrobiidae Warm

Odonata Coenagrionidae Argia Argia Warm

Odonata Coenagrionidae Ischnura Ischnura Warm

Odonata Corduliidae Helocordulia Helocordulia Warm

Odonata Corduliidae Macromia Macromia Warm

Trichoptera Hydropsychidae Macrostemum Macrostemum Warm

Trichoptera Hydroptilidae Hydroptila Hydroptila Warm

Trichoptera Leptoceridae Oecetis Oecetis Warm

Trichoptera Philopotamidae Chimarra Chimarra obscura Warm

Trichoptera Polycentropodidae Neureclipsis Neureclipsis Warm

Veneroida Pisidiidae Sphaerium Sphaerium Warm

a

Class Turbellaria 
I-6

Table I-2. Thermal indicator taxa that have been identified by VT DEC

(Steve Fiske, Aaron Moore and Jim Kellogg, unpublished data)

Order Genus Species Indicator

Diptera Polypedilum aviceps Cold

Diptera Neostempellina reissi Cold

Diptera Tvetenia bavarica grp Cold

Ephemeroptera Rhithrogena sp Cold

Ephemeroptera Ameletus sp Cold

Trichoptera Arctopsyche sp Cold

Trichoptera Arctopsyche ladogensis Cold

Trichoptera Rhyacophila carolina Cold

Trichoptera Rhyacophila torva Cold

Trichoptera Rhyacophila nigrita Cold

Trichoptera Rhyacophila invaria Cold

Trichoptera Rhyacophila acutiloba Cold

Plecoptera Peltoperla sp Cold

Plecoptera Tallaperla sp Cold

Plecoptera Taenionema sp Cold

Decapoda Cambarus bartoni Cold

Trichoptera Palaeagapetus sp Cold

Diptera Eukiefferella brevicalar, brehmi, and tirolensis Cold

Coleoptera Oulimnius latiusculus Cold

Coleoptera Promoresia tardella Cold

Amphipoda Gammarus pseudolimnaeus Cold/cool

Amphipoda Hyallela azteca Cold/cool

Neoophora Cura formanii Cold

Diptera Eukiefferella claripennis Warm

Diptera Polypedilum flavum Warm

Diptera Tvetenia discoloripes, vitracies Warm
I-7

Table I-2. continued...

Order Genus Species Indicator

Trichoptera Leucotrichia sp Warm

Trichoptera Rhyacophila mainensis Warm

Trichoptera Rhyacophila manistee Warm

Trichoptera Rhyacophila minora Warm

Plecoptera Neoperla sp Warm

Plecoptera Taeniopteryx sp Warm

Coleoptera Promoresia elegans Warm

Neoophora Dugesia tigrina Warm
I-8

Table I-3. 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 Order Genus MD PA

Occurrence in 

PA DEP data set

Cold Diptera Bittacomorpha Yes

Cold Diptera Dixa Yes

Cold Diptera Heleniella Yes

Cold Diptera Prodiamesa Yes

Cold Ephemeroptera Ameletus Yes Common

Cold Ephemeroptera Cinygmula Yes Yes Common

Cold Ephemeroptera Diphetor Yes Yes Common

Cold Ephemeroptera Drunella Yes Common

Cold (MD)/cool (PA) Ephemeroptera Epeorus Yes Yes Common

Cold Ephemeroptera Ephemera Yes

Cold Ephemeroptera Ephemerella Yes Common

Cold Ephemeroptera Eurylophella Yes Common

Cold (MD)/cool (PA) Ephemeroptera Habrophlebia Yes Yes Rare

Cold Ephemeroptera Paraleptophlebia Yes

Cold Plecoptera Alloperla Yes Yes Common

Cold Plecoptera Amphinemura Yes Common

Cold Plecoptera Diploperla Yes Rare

Cold Plecoptera Haploperla Yes Rare

Cold Plecoptera Isoperla Yes Common

Cold Plecoptera Leuctra Yes

Cold Plecoptera Malirekus Yes Rare

Cold Plecoptera Peltoperla Yes Rare

Cold Plecoptera Pteronarcys Yes Rare

Cold Plecoptera Remenus Yes Rare

Cold Plecoptera Sweltsa Yes Yes Common
I-9

Table I-3. continued...

Type Order Genus MD PA

Occurrence in 

PA DEP data set

Cold Plecoptera Tallaperla Yes Yes Common

Cold Plecoptera Yugus Yes Rare

Cold Trichoptera Diplectrona Yes

Cold Trichoptera Wormaldia Yes Yes Common
I-10

Table I-4. 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 Order Genus

NC

(U.S. EPA, 

2012) TN Notes―TN

Cold (NC)/ 

cool (TN)

Coleoptera Promoresia Yes Yes

Cold (NC)/ 

cool (TN)

Diptera Antocha Yes Yes

Cold (NC)/ 

cool (TN)

Diptera Atherix Yes Yes

Cold Diptera Cardiocladius Yes

Cold Diptera Diamesa Yes

Cold Diptera Dicranota Yes

Cold Diptera Eukiefferiella Yes

Cold Diptera Heleniella Yes

Cold (NC)/ 

cool (TN)

Diptera Pagastia Yes Yes

Cold Diptera Potthastia Yes

Cold Diptera Rheopelopia Yes

Cold Ephemeroptera Acentrella Yes

Cold Ephemeroptera Cinygmula Yes

Cold (NC)/ 

cool (TN)

Ephemeroptera Drunella Yes Yes

Cold (NC)/ 

cool (TN)

Ephemeroptera Epeorus Yes Yes

Cold Ephemeroptera Nixe Yes

Cold (NC)/ 

cool (TN)

Ephemeroptera Rhithrogena Yes Yes

Cold (NC)/ 

cool (TN)

Odonata Lanthus Yes Yes

Cold Plecoptera Amphinemura Yes
I-11

Table I-4. continued...

Type Order Genus

NC

(U.S. 

EPA, 

2012) TN Notes―TN

Cold Plecoptera Clioperla Yes

Cold Plecoptera Cultus Yes

Cold Plecoptera Diploperla Yes Yes Uncommon in 

TN data set

Cold Plecoptera Isoperla Yes

Cold Plecoptera Malirekus Yes Yes Uncommon in 

TN data set

Cold Plecoptera Peltoperla Yes Uncommon

TN data set

in 

Cold Plecoptera Pteronarcys Yes

Cold Plecoptera Tallaperla Yes Yes

Cold Plecoptera Zapada Yes

Cold (NC)/ 

cool (TN)

Trichoptera Agapetus Yes Yes

Cold Trichoptera Apatania Yes Yes Uncommon in 

TN data set

Cold Trichoptera Arctopsyche Yes Yes Uncommon in 

TN data set

Cold Trichoptera Dolophilodes Yes Yes Mostly cool 

or cold

Cold Trichoptera Glossosoma Yes Yes Mostly cool 

or cold

Cold Trichoptera Parapsyche Yes Yes Uncommon in 

TN data set

Cold/cool Ephemeroptera Ameletus Yes

Cold/cool Trichoptera Lepidostoma Yes

Cool Ephemeroptera Habrophlebia Yes Uncommon in 

TN data set

Cool Plecoptera Alloperla Yes
I-12

Table I-4. continued...

Type Order Genus

NC

(U.S. 

EPA, 

2012) TN Notes―TN

Cool Plecoptera Sweltsa Yes Warm and 

cold but 

mostly cool

Cool Plecoptera Taenionema Yes uncommon in 

TN data set

Cool Trichoptera Diplectrona Yes Warm and 

cold―more 

common in 

cool or cold

Cool Trichoptera Wormaldia Yes

Warm Arhynchobdellida Erpobdella Yes

Warm Arhynchobdellida Mooreobdella Yes

Warm Basommatophora Physella Yes

Warm Coleoptera Berosus Yes

Warm Coleoptera Lioporeus Yes

Warm Decapoda Palaemonetes Yes

Warm Diptera Nilothauma Yes

Warm Diptera Parachironomus Yes

Warm Diptera Pentaneura Yes

Warm Diptera Procladius Yes

Warm Diptera Stenochironomus Yes

Warm Ephemeroptera Diphetor Yes

Warm Ephemeroptera Tricorythodes Yes

Warm Hemiptera Belostoma Yes

Warm Isopoda Caecidotea Yes

Warm Odonata Epicordulia Yes

Warm Odonata Helocordulia Yes

Warm Odonata Hetaerina Yes

Warm Odonata Ischnura Yes
I-13

Table I-4. continued...

Type Order Genus

NC

(U.S. 

EPA, 

2012) TN Notes―TN

Warm Odonata Macromia Yes

Warm Odonata Neurocordulia Yes

Warm Odonata Tetragoneuria Yes

Warm Rhynchobdellida Helobdella Yes

Warm Rhynchobdellida Placobdella Yes

Warm Trichoptera Chimarra Yes

Warm Trichoptera Macrostemum Yes

Warm Trichoptera Neureclipsis Yes

Warm Trichoptera Phylocentropus Yes

Warm Unionoida Elliptio Yes
I-14

I.1. REFERENCES

Becker, AJ; Stranko, SA; Klauda, RJ; Prochaska, AP; Schuster, JD; Kashiwagi, MR; Graves, PH. (2010) Maryland 

biological stream survey’s sentinel site network: A multi-purpose monitoring program. Annapolis, MD: Maryland 

Department of Natural Resources. 

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. 

Yuan, L. (2006) Estimation and application of macroinvertebrate tolerance values. National Center for 

Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, 

Washington, DC; EPA/600/P-04/116F. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=154869.
J-1 

APPENDIX J. __________________________ 

THERMAL SUMMARY 

STATISTICS
J-2

Table J-1. Recommended thermal statistics to calculate for each year of 

continuous temperature data at regional monitoring network (RMN) sites. 

Timeframe Thermal statistic Calculation

Daily Daily mean Mean temperature for each day

Daily maximum Maximum temperature for each day

Daily minimum Minimum temperature for each day

Daily difference 

(maximum−minimum)

Difference between the maximum and 

minimum temperatures for each day

Daily variance Standard deviation for each day

Monthly Monthly mean Mean of the daily means for each month

Monthly maximum Maximum value for each month

Monthly minimum Minimum value for each month

Monthly difference 

(maximum−minimum)

Difference between the maximum and 

minimum temperatures for each month

Monthly variance Standard deviation for each month

Seasonala Seasonal mean Mean of the daily means for each season

Seasonal maximum Maximum value for each season

Seasonal minimum Minimum value for each season

Seasonal difference 

(maximum−minimum)

Difference between the maximum and 

minimum temperatures for each season

Seasonal variance Standard deviation for each season
J-3

Table J.1. continued...

Timeframe Thermal statistic Calculation

Annual Annual mean Mean of the daily means for the year 

(January 1−December 31)

Annual maximum Maximum value for the year 

(January 1−December 31)

Annual minimum Minimum value for the year 

(January 1−December 31)

Mean annual difference Mean of the daily difference 

(January 1−December 31)

Maximum annual difference Maximum of the daily difference 

(January 1−December 31)

Minimum annual difference Minimum of the daily difference 

(January 1−December 31)

Variance of the annual mean 

difference

Standard deviation of the daily difference 

(January 1−December 31)

Percentage exceedance ([Number of measurements that exceed a 

thresholdb] ÷ [total number of measurements 

in a year]) × 100

a

Seasons are defined as follows. Winter: December, January, February; Spring: March, April, May; Summer: June, 

July, August; Fall: September, October, November. b

Thresholds may vary by entity and location.
K-1 

APPENDIX K. __________________________ 

HYDROLOGIC SUMMARY 

STATISTICS AND TOOLS

FOR CALCULATING 

ESTIMATED 

STREAMFLOW 

STATISTICS
K-2

K.1. LIST OF RECOMMENDED HYDROLOGIC SUMMARY STATISTICS FOR REGIONAL MONITORING

NETWORK (RMN) SITES

Table K-1. Recommended 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. For more 

details on these studies, see Sections K.2−K.4.

Timeframe Metric Calculation

Daily Daily mean Mean stage or flow for each day

Daily median Median stage or flow for each day

Daily maximum Maximum stage or flow for each day

Daily minimum Minimum stage or flow for each day

Daily difference 

(maximum−minimum)

Difference between the maximum and minimum stage or flows for each day

Coefficient of variation Standard deviation for stage or flow for each day/mean daily stage or flow
K-3

Table K-1. continued...

Timeframe Metric Calculation

Monthly Monthly mean Mean stage or flow for each month

Monthly maximuma Maximum stage or flow for each month

Monthly minimumb Minimum stage or flow for each month

Monthly difference 

(maximum−minimum)

Difference between the maximum and minimum stage or flow values for each month

High flow magnitude 

(90th percentile)

90th percentile of monthly stage or flow values; this represents high flows 

similar to the Q10 measurement used in DePhilip and Moberg (2013)

and is 

Median magnitude (50th percentile) 50th percentile of monthly stage or flow values; this represents the monthly median

Low 

(25th

flow magnitude 

percentile)

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 Q75 measurement used in DePhilip and Moberg (2013)

Low 

(10th

flow magnitude 

percentile)

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 Q90 measurement used in DePhilip and Moberg (2013)

Extreme low flow magnitude 

(1st percentile)

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 high flow and floods Percentage of stage or flow 

90th percentile

measurements in each month that exceed the monthly 

Percentage low flows Percentage of stage or flow measurements in each 

monthly 25th and 1st percentiles (similar to the Q75

DePhilip and Moberg [2013])

month that are between the 

and Q99 measurements used in 

Percentage typical Percentage of stage or flow measurements in each month that are between the 

monthly 25th and 90th percentiles (similar to the Q75 and Q10 measurements used in 

DePhilip and Moberg [2013])
K-4

Table K-1. continued...

Timeframe Metric Calculation

Seasonal Percentage high flows and floods in 

spring and fall

Percentage of stage or flow measurements in each month that exceed the monthly 

90th percentile in spring (March−May) and fall (September−November)

Annual 

(January 1−

December 31) 

Annual mean Mean of the daily mean stage or flow

Annual maximum Maximum stage or flow

Julian date of annual maximum Julian date of annual maximum stage or flow

Annual minimum Minimum stage or flow

Julian date of annual minimum Julian date of annual minimum stage or flow

Mean annual difference Mean of the daily difference

Maximum annual difference Maximum of the daily difference

Minimum annual difference Minimum of the daily difference

Variance of the annual mean 

difference

Standard deviation of the daily difference

Number of zero flow days Number of days having stage or flow measurements of 0

a

In Olden and Poff (2003), mean maximum August flow and mean maximum October flow captured important aspects of high flow conditions.

b

In Olden and Poff (2003), mean minimum April flow captured important aspects of low flow conditions.
K-5

K.2. HYDROLOGIC METRICS DERIVED FROM DEPHILIP AND MOBERG (2013)

FOR THE UPPER OHIO RIVER BASIN

The Nature Conservancy and several partners (states, river basin commissions, 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 K-2 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 K-1 can be generated for data from RMN sites.

Table K-2. 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, Q10 equals the 10% exceedance probability 

(Q10), 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 Flow statistic

High flows

Annual/interannual (≥bankfull)

Large flood Magnitude and frequency of 20‐year flood

Small flood Magnitude and frequency of 5‐year flood

Bankfull Magnitude and frequency of 1- to 2-year high flow event

High flow pulses (<bankfull)

Frequency of high flow pulses Number of events > monthly Q10 in spring and fall

High pulse magnitude Monthly Q10

Seasonal flows

Monthly magnitude Monthly median

Typical monthly range Area under monthly flow duration curve between Q75 and 

Q10 (or some part of this range)

Low flows

Monthly low flow range Area under monthly flow duration curve between Q75 and 

Q99

Monthly low flow magnitude Monthly Q75

Monthly Q90
K-6

Figure K-1. 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). 
7 

K.3. HYDROLOGIC METRICS DERIVED FROM OLDEN AND POFF (2003)

Olden and Poff (2003) did a comprehensive review of 171 hydrologic metrics, including Indicators of Hydrologic Alteration. 

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 K-3 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.

Table K-3. 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 Metric Description

Abbreviated

metric

Magnitude―

average flow 

conditions 

Skewness in daily flows Mean daily flows divided by median daily flows Ma5

Mean annual runoff Mean annual flow divided by catchment area Ma41

Variability in daily flows 1 Coefficient of variation in daily flows Ma3

Spreads in daily flows Ranges in daily flows (25th/75th percentiles) divided by median 

daily flows

Ma11

Magnitude―

low flow 

conditions 

Baseflow index 1 7-day minimum flow divided by mean annual daily flows

averaged across all years

Ml17

Mean minimum April flow Mean minimum monthly flow in April Ml4

Variability across annual 

minimum flows

Coefficient of variation in annual minimum flows averaged across 

all years

Ml21

Variability in baseflow index 1 Coefficient of variation in baseflow index (Ml17) Ml18
8 

Table K-3. continued...

Abbreviated 

Category Metric Description metric

Magnitude― High flow discharge Mean of the 10th percentile from the flow duration curve divided Mh16

high flow by median daily flow across all years

conditions Mean maximum August flow Mean maximum monthly flow in August Mh8

Mean maximum October flow Mean maximum monthly flow in October Mh10

Median of annual maximum Median of the highest annual daily flow divided by the median Mh14

flows annual daily flow averaged across all years

Frequency of Frequency of low flow spells Total number of low flow spells (threshold equal to 5% of mean Fl3

flow daily flow) divided by record length in years

events―low 

flow 

conditions 

Variability in low flow pulse 

count

Coefficient of variation in Fl1 Fl2

Low flow pulse count Number of annual occurrences during which the magnitude of Fl1

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.
9 

Table K-3. continued...

Category Metric Description

Abbreviated 

metric

Frequency of 

flow 

events―high 

flow 

conditions 

High flood pulse count 2 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 three times the median daily flow and the value is an 

average instead of a tabulated count.

Fh3

Flood frequency Mean number of high flow events per year using an upper 

threshold of three times median flow over all years

Fh6

Flood frequency Mean number of high flow events per 

threshold of seven times median flow 

year using an upper 

over all years

Fh7

Variability in high flood pulse 

count

Coefficient of variation in high pulse count (defined as 75th

percentile)

Fh2

Duration Number of zero flow days Mean annual number of days having zero daily flow Dl18

Variability in low flow pulse 

duration

Coefficient of variation in low flow pulse duration Dl17

Low flow pulse duration Mean duration of Fl1 Dl16

Means of 30-day minimum 

daily discharge

Mean annual 30-day minimum divided by median flow Dl13

Means of 30-day maximum

daily discharge

Mean annual 30-day maximum divided by median flow Dh13

Variability in high flow pulse 

duration

Coefficient of variation in Fh1 Dh16

High flow duration Upper threshold is defined as the 75th percentile of median flows Dh20

High flow pulse duration Mean duration of Fh1 Dh15
10 

Table K-3. continued...

Category Metric Description

Abbreviated 

metric

Timing of 

flow events 

Constancy See Colwell (1974) Ta1

Seasonal predictability of 

nonflooding

Maximum proportion of the year (number of days/365) during 

which no floods have ever occurred over the period of record

Th3

Variability in Julian date of 

annual minimum

Coefficient of variation in Tl1 Tl2

Rate of 

change

Variability in reversals Coefficient of variation in Ra8 Ra9

Reversals Number of negative and positive changes in water conditions from 

1 day to the next

Ra8

Change of flow Median of difference between natural logarithm of flows between 

2 consecutive days with increasing/decreasing flow

Ra6

No day rises Ratio of days where flow is higher than the previous day Ra5
11 

K.4. HYDROLOGIC METRICS USED BY HAWKINS ET AL. (2013)

Hawkins et al. (2013) used an iterative process to identify 16 streamflow variables that, in 

their judgment, could characterize those general aspects of streamflow regimes relevant to 

stream ecosystem structure and function. These variables are listed in Table K-4. 

Table K-4. These 16 streamflow variables were selected by Hawkins et al. 

(2013) to quantify aspects of hydrologic regimes believed to be important to 

stream biota

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 (HFE) 

Day of year of 50% of flow (T50) 

Day of year of peak flow (Tp) 

Predictability (P) 

Constancy (C) 
12 

K.5. TOOLS FOR ESTIMATING STREAMFLOW AT UNGAGED SITES

Table K-5. 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 StreamStats1 Varies by state http://water.usgs.gov/osw/streamstats/ 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.

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://tagis.dep.wv.gov/streamflow/ This free, publicly available tool utilizes a 

point-and-click interface. Seven Q10, annual 

and monthly flow estimates are generated 

when you click on a location.

1

http://water.usgs.gov/osw/streamstats/
13

USGS = U.S. Geological Survey; BaSE = Baseline Streamflow Estimator; MA SYE = Massachusetts Sustainable Yield Estimator; DEP = Department of 

Environmental Protection
14

K.6. REFERENCES

Archfield, SA; Vogel, RM; Steeves, PA; Brandt, SL: Weiskel, PK; Garabedian, SP. (2010) The Massachusetts

sustainable‐yield estimator: A decision‐support tool to assess water availability at ungaged stream locations 

in Massachusetts. [USGS Scientific Investigations Report 2009−5227]. Northborough, MA: MassachusettsRhode Island Water Science Center, U.S. Geological Survey. 41 pp. Available online: 

http://pubs.usgs.gov/sir/2009/5227/. 

Buchanan, C; Moltz, HLN; Haywood, HC; Palmer, JB; Griggs, AN. (2013) A test of The Ecological Limits of 

Hydrologic Alteration (ELOHA) method for determining environmental flows in the Potomac River basin, 

U.S.A. Freshw Biol 58(12):2632−2647.

Colwell RK. (1974) Predictability, constancy, and contingency of periodic phenomena. Ecol. 55:1148–1153.

Cummins, J; Buchanan, C; Haywood, HC; Moltz, H; Griggs, A; Jones, C; Kraus, R; Hitt, NP; R. Bumgardner, R. 

(2010). Potomac large river ecologically sustainable water management report. [ICPRB Report 10-3]. 

Interstate Commission on the Potomac River Basin for The Nature Conservancy. Available online at 

www.potomacriver.org/pubs. 

DePhilip, M; Moberg, T. (2010) Ecosystem flow recommendations for the Susquehanna River Basin. Harrisburg, 

PA: The Nature Conservancy. Available online at 

http://www.srbc.net/policies/docs/TNCFinalSusquehannaRiverEcosystemFlowsStudyReport_Nov10_2012

0327_fs135148v1.PDF

DePhilip, M; Moberg, T. (2013). Ecosystem flow recommendations for the Upper Ohio River basin in western 

Pennsylvania. Harrisburg, PA: The Nature Conservancy.

Hawkins, CP; Tarboton, DG; Jin, J. (2013) Consequences of global climate change for stream biodiversity and 

implications for the application and interpretation of biological indicators of aquatic ecosystem condition. 

Final Report. [EPA Agreement Number: RD834186]. Logan Utah: Utah State University. 

http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/9063 

Olden, JD; Poff, NL. (2003) Redundancy and the choice of hydrologic indices for characterizing streamflow 

regimes. River Res Appl 19:101−121.

Stuckey, MH; Koerkle, EH; Ulrich, J. (2012) Estimation of baseline daily mean streamflows for ungagged location 

on Pennsylvania streams, water years 1960‐2008. [USGS Scientific Investigations Report 2012‐5142]. New 

Cumberland, PA: U.S. Geological Survey. Available online at http://pubs.usgs.gov/sir/2012/5142/. 

Shank, M. (2011) West Virginia DEP 7Q10 report tool [web page]. Accessed 1 August 2014. Available online at 

http://tagis.dep.wv.gov/7q10/. 
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