Freshwater Explorer

A tool for exploring the quality of freshwater resources

EPA's Freshwater Explorer is an interactive web-based mapping tool that provides information about background and observed salt and mineral content for freshwater streams, lakes, and wells in all 50 U.S. states, Puerto Rico, and the U.S. Virgin Islands. It can be used by anyone, including citizens and non-governmental organizations, to better understand national and local water quality issues and provides water quality information to help federal, state, territory, tribal, and local partners make decisions about freshwater resources.

Compatibility and Availability

The Freshwater Explorer is map-centric and mobile-friendly, and works on all different screen sizes ranging from desktop computers and tablets to mobile phones. The application can be download to a third-party PC with full access to modify with an ArcGIS account.

Preferred citation:  Cormier S., Wharton C., Olson J., U.S. EPA Freshwater Explorer. V: 0.1. United States Environmental Protection Agency.  July 2021. https://arcg.is/KHb9S

Capabilities

EPA’s Freshwater Explorer allows users to quickly access and visualize predicted background salt and mineral levels for approximately 2.65 million stream segments of a few kilometers each. Users can conduct geographical searches, access information about how maps and water quality values are estimated in Freshwater Explorer, and access datasets and published papers. The tool includes summary statistics for observed salinity and mineral levels for approximately 289,000 stream locations, 10,000 lakes and 50,000 wells. New water quality parameters, such as phosphorus, will be added to Freshwater Explorer later in 2021. Users can use basic views or perform complex searches. Users can modify what is shown on a customizable map and pull in data from other EPA sources such as ECHO (Enforcement Compliance History Online) and ATTAINS (Assessment Total Maximum Daily Load [TMDL] Tracking and Implementation System) web services.

Exchange Portal

EPA’s Freshwater Explorer uses data stored in EPA’s Water Quality Exchange (WQX) through the Water Quality Portal (WQP) but makes those data more accessible and intuitive to use. The Freshwater Explorer curates and summarizes the Portal’s hundreds of millions of water quality data records from more than 500 federal, state, tribal and other partners. Using color-coded displays of specific information about water quality, the tool was developed to help users better visualize these datasets. EPA curated data from the WQX, removing some and flagging very low or high values submitted by contributors. However, data still include undetected errors of magnitude and location and therefore, the tool is most appropriate for performing screening level analysis. Users should assess the suitability for their specific applications.

• EPA’s Water Quality Portal
• Water Quality Exchange

Data and Metadata

EPA’s Freshwater Explorer provides unique data and also pulls data from multiple databases at EPA and other federal agencies through web services. Below is a summary of data included in Freshwater Explorer.

• Predicted background conductivity: Predicted values of natural background conductivity from empirical data from EPA’s Water Quality Portal (WQP).
• Model Validation: Shows the predictive performance of the natural Predicted Background Stream Conductivity model at reference sites that were used for model development.
• Observed Conductivity - WQP: Observational data from EPA’s Water Quality Portal (WQP), the nation's largest source for water quality monitoring data cooperatively sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA) and the National Water Quality Monitoring Council (NWQMC).
• Observed Conductivity - NWIS: Observational data from the U.S. Geological Survey (USGS) National Water Information System (NWIS).
• ECHO Data/System:  Location and basic information from Enforcement and Compliance History Online (ECHO).
• ATTAINS Data/System: Highlights impaired streams and basic information about water quality assessments and total maximum daily loads (TMDLs), through data submitted by states under Clean Water Act sections 303(d) and 305(b).

Applications

EPA’s Freshwater Explorer is most appropriate for assessments to see how measurements in watersheds differ from predicted background conductivity (a measure of salinity) and relationships among other water quality parameters, such as nutrients and land cover. It allows users to perform their own geographical searches and visualize calculated background and measured data for water quality parameters. This combination of information can be useful for states to work with communities and regulated entities to find the right balance of protection and use of freshwater resources. Background is estimated with a national model that may need to be calibrated for local conditions.

U.S. EPA Freshwater Explorer Quick Guide

1. EPA’s Freshwater Explorer works best when launched with Google Chrome.  From the opening view, scroll past the opening title until a colorful map of the United States appears. Wait for it to load.

   

2. Colors—Maps in Freshwater Explorer show predicted fresher water in blue, and more mineralized water in yellow.
    
3. Tabs allow you to explore pre-loaded searches or do your own deep dive. The first four tabs at the top are fixed views. It’s a good way to try the Explorer.  Information about these views are in the pull-down menus on the left of each view.  The fifth tab, “Deep Dive,” allows you to customize your view and pull in data from other publicly available web servers.

   

4. Data and metadata—Links are in the information at the top right “Data Access,” in pop boxes, or scroll down for the link list and disclaimer as shown here.

   

5. Search:
   • Expand or contract map by using the + and - symbols or other navigation tools.
   • Type the name or zip code in the space near the magnifying glass icon.
   • As you zoom in with your mouse or other navigation tool, more detail and layer options become available and the stream network becomes more complex.
   • Point and click on a colored line or shape. A data box will appear with information about the location.
   • The specific conductivity (SC) (µS/cm) color coding is listed in the pull-down Legend in the upper right. Background was not estimated for streams delineated in gray.
     Inset map on lower left shows relative location of large map.

     

6. Predicted Background Conductivity—This is the first view as you open Freshwater Explorer.  At the continental scale, colors represent predicted background conductivity in river drainages as they would occur if water quality was minimally disturbed by people. As you zoom in on an area, the background switches to satellite imagery showing vegetation and development. A network of streams appears as a spectrum from low (violet) to high (red) calculated natural background conductivity. Calculated dissolved mineral content is the average conductivity modeled monthly for years 2000-2015. Click on a stream segment. It will become emphasized and a pop-up data box will appear with predicted background conductivity.

   

7. National Measured Conductivity-WQP—The second tab shows sample stations from WQX on a simple background with the stream network. Shapes are water body types listed in the legend on the right. Click on a station or a stream. A data box will appear. At the top of the box, numbers list the number of stations at this point. Use the triangle to see information from WQP or the stream segment background.

   

8. National Measured Conductivity -NWIS—The third tab shows sample stations from the National Water Information System (NWIS) on a simple background with the stream network. Shapes are water body types listed in the legend on the right. Click on a station or a stream segment. A data box will appear. At the top of the box, numbers list the number of stations at this point. Use the triangle to see information from WQP or the stream segment background.

   

9. Model Validation—Map shows how well the Predicted Background Conductivity (PBC) model performed by comparing conductivity measured at reference sites (dots) that were used to develop the empirical PBC model and the prediction from the model.

   

10. Deep Dive—Widgets in the upper right allow you to select background base maps, data layers such as measured data or predicted background, water body type, and pull in data from other public web services. The legends for each data layer are below the data layer you select.

    

Disclaimer: Some streams, especially headwaters, are not always captured in the NHDPlusV2 stream network. Observed data are from secondary sources. Use caution as some data have incorrect units (i.e. measured as mS/cm but entered as µS/cm, or vice versa, an error of 1000-fold). Data evaluation is a continuous process. Gray shapes indicate that the data has been flagged due to uncertain data quality.

Background Information

What is Fresh Water?

Fresh water is characterized by the concentration of dissolved mineral ions in the water, and is essential for drinking water, agriculture, industry, and aquatic life. Increases in minerals and ion levels sometimes indicate a source of pollution in freshwater systems. Higher levels of minerals in the water can cause harmful algal blooms and affect aquatic wildlife. These conditions can increase costs for making water suitable for drinking by people and livestock, for use in agricultural and industrial processes, and for water reuse.

Ions carry a positive or negative charge which facilitates the flow of electricity in water. The more ions, the easier electricity flows between electrodes, giving scientists a way to measure the total concentration of ions. Individual kinds of dissolved ions are usually observed with ion selective probes. Total dissolved mineral and nutrient ions are usually reported as conductivity in micro Siemens per centimeter (µS/cm) or mg/L. Within the U.S. EPA Freshwater Explorer, conductivity always refers to specific conductivity (SC) in µS/cm calibrated at 25°C.

Predicted Background Conductivity

Predicted dissolved mineral content is the average conductivity predicted monthly for years 2000-2015 as they would occur if water quality was minimally influenced by people. The model is based on geophysical and other data at thousands of sites in the United States that were judged to be relatively unaffected by people (Olson and Cormier 2019). The predictor variables were generated for each stream line within the National Hydrography Dataset Plus version 2 (NHDPlusV2) with algorithms and code from the StreamCat Dataset (ESRI 2012, Hill et al 2016).

For more detail see the predictive background conductivity model.

The Natural Background Stream Conductivity Model predicts background specific conductivity (SC) for stream segments in the contiguous United States to enable comparison with measured in-stream conductivity (Olson and Cormier 2019). This random forest model was developed using geology, soil, vegetation, climate and other empirically measured predictors. It was developed for streams with natural background SC < 2000 µS/cm. Above this level, inland water is considered brackish and the estimates from the Natural Background Model may be less reliable.

Data for some parameters that affect background SC were not readily available and were not included in the model. These include freshwater and marine interfaces, natural mineral springs, salt deposits which may affect groundwater and streams, and other natural sources of salts. In such areas the model is likely to underestimate SC. Local knowledge is necessary when assessing differences between predicted and measured background SC. The model is less precise with intermittent flows and very arid climates.

Related Resources

• Olson, J.R. and Cormier, S.M. 2019. Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity. Environmental Science & Technology.  DOI: 10.1021/acs.est.8b06777. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153567/
• Hill, R.A., Weber, M.H., Leibowitz, S.G., Olsen, A.R. and Thornbrugh, D.J., 2016. The Stream‐Catchment (StreamCat) Dataset: A database of watershed metrics for the conterminous United States. JAWRA Journal of the American Water Resources Association, 52(1), pp.120-128.

Technical Support

For questions, comments, or suggestions, contact us at [email protected].

---

List of Abbreviations

% Pleco	Percentage of plecopteran taxa
%Chiro	Percentage of chronimid taxa
%Cllct	Percentage of collectors
%Dip	Percentage of dipteran taxa
%Ephem	Percentage of ephemeropteran taxa
%EPT	Percentage of ephemeropteran, plecopteran, and trichopteran taxa
%Filtr	Percentage of filterers
%Oligo	Percentage of oligochaete taxa
%Pred	Percentage of predators
%Scrap	Percentage of scrapers
%Shred	Percentage of shredders
%Toler	Percentage of tolerant taxa
%Trich	Percentage of trichopteran taxa
B-C	background-to-criterion
CCC	criterion continuous concentration
CDF	cumulative distribution function
CI	confidence interval
CMEC	criterion maximum exposure concentration
DO	dissolved oxygen
EMAP	Environmental Monitoring and Assessment Program
EPA	U.S. Environmental Protection Agency
EPA	Environmental Protection Agency
GAM	generalized additive model
GIS	Geographic Information System
HCx	hazardous concentration of the “x” centile of a taxonomic sensitivity distribution
LOWESS	Locally Weighted Scatterplot Smoothing
MAHA	Mid‑Atlantic Highland Assessment
MAIA	Mid‑Atlantic Integrated Assessment
NAPAP	National Acid Precipitation Assessment Program
NRSA	National Rivers and Streams Assessment
NWSA	National Wadeable Streams Assessment
ORD	Office of Research and Development
ORD	U.S. EPA Office of Research and Development
OW	U.S. EPA Office of Water
OWOW	U.S. EPA Office of Water Oceans and Wetlands
PL	prediction limit
QA/QC	quality assurance/quality control
RBP	rapid bioassessment protocol
Region 6	U.S. EPA Region 6
R-EMAP	Regional Environmental Monitoring and Assessment Program
S	Siemens
SAB	Science Advisory Board
SC	specific conductivity
TDS	total dissolved solids
TMDL	total maximum daily load
TSS	total suspended solids
USGS	U.S. Geological Survey
WABbase	Watershed Assessment Branch database
WDE	Washington Department of Ecology
WoE	Weight of Evidence
WQP	Water Quality Portal
WQX	Water Quality Exchange
WSA	Wadeable Streams Assessment
WVDEP	West Virginia Department of Environmental Protection
XCD	extirpation concentration distribution
XCx	extirpation concentration affecting “x” percentage of individuals of a taxon

Glossary

Term	Definition
Anion	A negatively charge ion, e.g., Cl-
Assemblage, stream	A taxonomic or sampled subset of a community as may be collected from a stream.
Assessment endpoint	An explicit expression of the actual environmental value that is to be protected, operationally defined by an ecological entity and its attribute or characteristics.  An assessment endpoint may be identified at any level of organization (e.g., organism, population, community).
Background	The range of naturally occurring substances in waters that have not been substantially influenced by human activity.
Background specific conductivity	The specific conductivity (SC) in streams in a region that occurs naturally and not as the result of human activity.  Background may also be characterized as a population of minimally affected sites or low SC sites using a weight of evidence.
Background-to-Criterion (B-C) Model	A log-log linear regression model of the background conductivity values associated with the estimated 5% extirpation of stream organisms from 24 ecoregions in the United States (Cormier et al 2018).
Benchmark	A dose or concentration of a pollutant that, if exceeded, is expected to produce an adverse effect (called the benchmark response) in one or more assessment endpoints, signifying a decline in water quality or human health.
Bootstrapping	A statistical technique of repeated random sampling from a data set that is often used in environmental studies to estimate confidence and prediction limits of a parameter.
Box plot	A depiction of the 25th, 50th, and 75th quantiles of a distribution as a rectangle with a central line.  The two standard deviation range is depicted as “whiskers” extending from the box.  Data beyond two standard deviations are indicated by individual circles or dots beyond the whiskers.
Catchment area	The spatial extent of the surrounding landscape that drains into a particular river, stream, or other waterbody.
Cation	A positively charged ion, e.g., Na+
Community	The full complement of interacting organisms within a defined area of an ecosystem.
Conductance, specific	Conductance is the inverse of resistance for a particular sample expressed as Seimens (S) usually at 25°C.  In the literature, it is sometimes used synonymously with specific conductivity, but to avoid confusion, the term conductance is not used in this document.
Conductivity, specific (or specific electrical conductivity)	A measure of ionic concentration based on the electrical property of water and dissolved ions.  As ionic concentration increases, conductivity increases.  Standardized measurements in this document refer to specific conductivity, μS/cm (also seen as: μmho/cm) at 25°C.
Confounder	An extraneous variable that correlates with both the dependent and independent variable.  The presence of confounders can interfere with the ability to characterize a causal relationship.
Criterion continuous concentration (CCC)	An estimate of the highest concentration of a material in surface water to which an aquatic community can be exposed indefinitely without resulting in an unacceptable effect.
Criterion maximum exposure concentration (CMEC)	An estimate of the maximum concentration of a material in surface water to which an aquatic community can be exposed for a short time without resulting in an unacceptable effect.  In this document, the CMEC is estimated at the 90th centile of specific conductivity observations that contribute to the annual CCC.
Cumulative frequency distribution (CFD)	The probabilities that a random variable with a given probability distribution will be found at a value less than or equal to x.  Weighted CFDs are used to estimate extirpation concentrations of individual genera or species and unweighted CFDs to estimate a SC level that is expected to extirpate 5% of aquatic invertebrate genera. Similar to cumulative distribution function (CDF)
Ephemeral stream	A stream that flows briefly only in direct response to local precipitation, and whose channel is above the local ground water table at all times.
Extirpation	The depletion of a population of a species or genus to the point that it is no longer a viable resource or is unlikely to fulfill its function in the ecosystem.
Extirpation concentration	The level above which a genus is effectively absent from its normal habitat.  The threshold for extirpation is operationally defined by the level below which 95% of the observations of the genus occur.
Extrapolation	The process of extending the applicability of a model beyond the measured range of the original data set from which the model was derived.
Flowing waters	Inland waters with a unidirectional flow including permanent, intermittent and ephemeral streams.
Fresh water	Naturally occurring water characterized by low concentrations of dissolved salts and other total dissolved solids, not brackish or marine water.
Freshwater	Adjective indicating low salt content, typically less than 1,000 ppm or 1500uS/cm.
Generalized additive model	A nonparametric, likelihood based local regression model that replaces the linear function of a generalized linear model with a locally smoothed additive function.
Hazardous concentration	A concentration threshold that is hazardous for a proportion of taxa.  In this document, it is the concentration that is hazardous to 5% of genera calculated as the 5th centile of a taxonomic extirpation concentration distribution.
Hydroline	A feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. The National Hydrography Dataset (NHD) data was originally developed at 1:100,000-scale and exists at that scale for the whole country.
Hydrology	Distribution and connectivity of water such as streams and lakes
Intermittent stream	A stream that flows continuously for only part of the time.  During low flow there may be dry reaches alternating with wetted, nonflowing reaches.  The stream bed may lie below the local ground water table for at least part of the year.
Interpolate	Process of estimating an unknown value that lies between known values.
Ion	an atom or molecule with a net electric charge due to the loss or gain of one or more electrons, e.g., Na+ or Cl-
Ionic composition	The specific ions dissolved in water.  In this document, the ionic composition is used to distinguish water dominated by chloride salts from those dominated by bicarbonate and sulfate salts.
Ionic mixture	An undefined or defined blend of dissolved ions.  In this document, the example case studies refer to the most common mixture of ions contaminating U.S. streams, specifically those dominated by calcium, magnesium, sulfate, and bicarbonate ions.
Ionic regulation	The passive and active physiological processes that maintain the ionic composition, pH, and water content of tissues that is necessary for life.
Least disturbed condition	The best available physical, chemical, and biological habitat conditions given today’s state of the landscape or the least disturbed by human activities (Stoddard et al., 2006).  Contrast with “minimally affected condition.”
Major ions	The most common contributors to ionic concentration in surface waters, consisting of the following cations: Ca2+, Mg2+, Na+, K+; and anions: HCO3−, CO32−, SO42−, Cl−.
Measure of effect	A measurable ecological characteristic that is related to the valued characteristic chosen as the assessment endpoint and is a measure of biological effects (e.g., survival, reproduction, growth).  In this document it is the presence/absence of macroinvertebrate genera along a specific conductivity gradient.
Measure of exposure	An observed or estimated characteristic that is used to characterize the level of exposure to the stressor.  In this document, the measure of ionic exposure is specific conductivity.
Minimally affected condition	The physical, chemical, and biological habitat found in the absence of significant human disturbance (Stoddard et al., 2006).  Contrast with “least disturbed condition.”
Osmoregulation	The physiological control of water content of an organism's tissues to maintain fluid and electrolyte balance within a cell or organism relative to the surrounding environment.
Perennial stream	A stream with continuous surface or shallow interstitial flow year round, and whose stream bed intersects the local ground water table throughout the year.  Also referred to as a permanent stream.
Predicted 5% Extirpation Concentration	The concentration that is hazardous to 5% of genera calculated as the 5th centile of a taxonomic extirpation concentration distribution.
Predictive background conductivity	Mineral content is the average conductivity predicted monthly for years 2000 – 2015 as they would occur if water quality was minimally influenced by people.
Predictive background conductivity model	A random forest regression model that predicts background conductivity based on geophysical and other data at thousands of sites that were judged to be relatively unaffected by people (Olson and Cormier 2019).
Produced water	Waters that are produced by oil and gas development, mine dewatering, and related activities (e.g., coal bed methane mining, hydraulic fracturing).
Reference site	Sampling locations that have been identified as minimally affected or least disturbed based on land use, habitat, and water quality characteristics other than specific conductivity.
Salinity	The amount of salts dissolved in water.  Traditionally expressed as parts per thousand (‰) or grams of salt per kilogram of water. Salinity (‰) is often reserved for describing marine waters where sodium and chloride are the dominant ions.  In this report, salinity is equivalent to total dissolved salts and may be composed of any ionic mixture.
Sample	A single water or biological collection, multiple samples can be taken at different times.
Sample site	Location where a water or biological collection was taken.
Sensitivity analysis	A process that involves changing input values of a model in various ways to see the effect on the output value.  The main goal of sensitivity analysis is to gain insight into which assumptions are most critical for model building.
Station	Location where a water or biological collection was taken.
Topography	Mapped forms and features of land surfaces.
Total dissolved solids (TDS)	A measure of the combined content of all inorganic and organic substances dissolved in water, conventionally expressed as mg/L and operationally defined as those solids that pass through a filter, typically 0.45 μm.
Total Nitrogen	Sum of nitrate+nitrite and total Kjeldahl nitrogen (TKN) in mg/L.
Total Phosphorus	Data reported as phosphorus were converted to standardized forms and where appropriate summed as Total Phosphorus, Dissolved Phosphorus, Total Orthophosphate, Dissolved Orthophosphate.
Univoltine	An organism having one brood or generation per year.
Validation	Confirmation of the quality of a model and its results, typically by applying an independent data set.
Valley fill	A headwater valley filled with mining overburden.  This practice usually occurs in steep terrain where there are limited disposal alternatives.
Verification	Demonstrating the accuracy of measurements or calculations.
Weight of Evidence	(1) A process of making inferences from multiple pieces of evidence, adapted from the legal metaphor of the scales of justice. (2) The relative degree of support for a conclusion provided by evidence. The result of “weighing the body of evidence.”
Wells	May refer to freshwater wells or oil and gas wells.
Widget	A tool or subroutine used in the U.S. EPA Freshwater Explorer.

Relevant Citations

Hill, R.A., Weber, M.H., Leibowitz, M.H., Olsen, A.R., Thornbrugh, D.J., 2016. The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States. JAWRA Journal of the American Water Resources Association, 52(1), 120-128. doi: https://doi.org/10.1111/1752-1688.12372

McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., Rea, A., 2012. NHDPlus Version 2: User Guide. National Operational Hydrologic Remote Sensing Center, Washington, DC. Available at: https://nctc.fws.gov/courses/references/tutorials/geospatial/CSP7306/Readings/NHDPlusV2_User_Guide.pdf

NASA, 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Available at: https://modis.gsfc.nasa.gov/data/

Olson, J.R., Hawkins, C.P., 2012. Predicting natural base‐flow stream water chemistry in the western United States. Water Resources Research, 48(2). doi: https://doi.org/10.1029/2011WR011088

Olson, J.R. and Cormier, S.M., 2019.  Modeling spatial and temporal variation in natural background specific conductivity. Environmental Science and Technology. doi: https://dx.doi.org/10.1021/acs.est.8b06777

Olson, J.R. and Cormier, S.M., 2019. Modeling spatial and temporal variation in natural background specific conductivity: Data sets and R-code. doi: https://doi.org/010.23719/1500945

Omernik, J. M.; Griffith, G. E., 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental. Management. 54 (6), 1249−1266. doi: https://doi.org/10.1007/s00267-014-0364-1

U.S. Environmental Protection Agency (U.S. EPA), 2016. STORET.  Available at: https://www.epa.gov/storet/

U.S. Geological Survey (USGS), 2016. National Water Information System. Available at: https://waterdata.usgs.gov/nwis

Stoddard, J. L., Larsen, D. P., Hawkins, C. P., Johnson, R. K., Norris, R. H., 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol. Appl. 2006, 16 (4), 1267−1276. doi: https://doi.org/10.1890/1051-0761(2006)016[1267:SEFTEC]2.0.CO;2

Cormier, S.M., Suter, G.W., Fernandez, M.B. and Zheng, L., 2020. Adequacy of sample size for estimating a value from field observational data. Ecotoxicology and Environmental Safety, 203, p.110992

Olson, J.R. and Cormier, S.M. 2019. Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity. Environmental Science & Technology.  DOI: 10.1021/acs.est.8b06777. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153567/

Cormier, S.M., Zheng, L. and Flaherty, C.M., 2018. Field-based method for evaluating the annual maximum specific conductivity tolerated by freshwater invertebrates. Science of the Total Environment, 633, pp.1637-1646. doi:  10.1016/j.scitotenv.2018.01.136.

Cormier, S.M., 2017. Data for: Field-based methods for evaluating the annual maximum specific conductivity tolerated by freshwater invertebrates. 10.23719/1371704.

Cormier, S.M., Zheng, L. and Flaherty, C.M., 2018. A field-based model of the relationship between extirpation of salt-intolerant benthic invertebrates and background conductivity. Science of The Total Environment, 633, pp.1629-1636.doi: 10.1016/j.scitotenv.2018.02.044.

Cormier, S.M., 2017. Dataset for: A Field-based Model of the Relationship Between Extirpation of Salt-intolerant Benthic Invertebrates and Background Conductivity. https://doi.org/10.23719/1371707. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106560/

Cormier, S.M., Zheng, L., Suter, G.W. and Flaherty, C.M., 2018. Assessing background levels of specific conductivity using weight of evidence. Science of The Total Environment, 628, pp.1637-1649. doi: 10.1016/j.scitotenv.2018.02.017.

Cormier, S.M., Zheng, L., Hayslip, G. and Flaherty, C.M., 2018. A field-based characterization of conductivity in areas of minimal alteration: A case example in the Cascades of northwestern United States. Science of The Total Environment, 633, pp.1657-1666. https://doi.org/10.1016/j.scitotenv.2018.02.018.

Cormier, S.M., Zheng, L., Hill, R.A., Novak, R.M. and Flaherty, C.M., 2018. A flow-chart for developing water quality criteria from two field-based methods. Science of The Total Environment, 633, pp.1647-1656. doi: 10.1016/j.scitotenv.2018.01.137.

ESRI, 2014. ArcGIS Desktop: Release 10.2.2. Environmental Systems Research Institute, Redlands, California.

Griffith, M.B., Zheng, L. and Cormier, S.M., 2018. Using extirpation to evaluate ionic tolerance of freshwater fish. Environmental toxicology and chemistry, 37(3), pp.871-883.

Hill, R.A., Weber, M.H., Leibowitz, S.G., Olsen, A.R. and Thornbrugh, D.J., 2016. The Stream‐Catchment (StreamCat) Dataset: A database of watershed metrics for the conterminous United States. JAWRA Journal of the American Water Resources Association, 52(1), pp.120-128.

Cormier, S.M., Zheng, L., Leppo, E.W. and Hamilton, A., 2018. Step‐by‐step calculation and spreadsheet tools for predicting stressor levels that extirpate genera and species. Integrated Environmental Assessment and Management, 14(2), pp.174-180.

Coffey, D.B., Cormier, S.M., Harwood, J. 2014. Using field-based species sensitivity distributions to infer multiple causes. Human and Ecological Risk Assessment: An International Journal. 20(2): 402-432.

Cormier, S.M., Suter II, G.W. 2013. Editorial. Sources of data for water quality criteria. Env. Tox. Chem. 32(2): 254.

Cormier S.M., Suter II, GW. 2013. A method for deriving water-quality benchmarks using field data. Env. Tox. Chem.2(2):  255–262.

Cormier, S.M., Suter II, G.W., Zheng L. 2013. Derivation of a benchmark for freshwater ionic strength Env. Tox. Chem.2(2): 263–271.

Cormier, S.M., Suter II, G.W II. A method for assessing causation of field exposure–response relationships. Env. Tox. Chem.32(2): 272–276.

Cormier, S.M., Suter II, GW, Zheng L., Pond G. J. 2013. Assessing causation of the extirpation of stream macroinvertebrates by a mixture of ions. Env. Tox. Chem.32(2): 277–287.

Suter II G.W., Cormier, S.M. 2013. A method for assessing the potential for confounding applied to ionic strength in central Appalachian streams. Env. Tox. Chem.32(2): 288–295.

Cormier, S.M., Wilkes S.P., Zheng L. 2013. Relationship of land use and elevated ionic strength in Appalachian watersheds. Env. Tox. Chem. 32(2): 296–303.

Datasets and R-code

Cormier, S.M., 2017. Data for Assessing Background Levels of Specific Conductivity Using Weight of Evidence 508 Compliant. (dataset). https://doi.org/10.23719/1402418.

Cormier, S.M., 2017. Distribution Link for Data for: A Field-based Characterization of Conductivity in Areas of Minimal Alteration: A Case Example in the Cascades of Northwestern United States. https://doi.org/10.23719/1396168.

Cormier, S.M., 2017. Data for: Estimation of field-based benchmarks from background specific conductivity. https://doi.org/10.23719/1371706

Griffith, M.B., Zheng, L. and Cormier, S.M., 2018. Data for: Using extirpation to evaluate ionic tolerance of freshwater fish. Environmental toxicology and chemistry https://doi.org/10.23719/

Cormier, S. M., 2017. Data Set for: Step-by-Step Calculation and Spreadsheet Tools for Predicting Stressor Levels that Extirpate Genera and Species. Doi: 10.23719/1371705 

Cormier, S.M., 2017. https://github.com/smcormier/Biological-Extirpation-Analysis-Tools-BEAT/releases/tag/v.1.0.2.