Matthew J. Cashman Taylor E. Woods Kelly O. Maloney Daren M. Carlisle Lindsey J. Boyle 20260302 tabular digital data Sciencebase US Geological Survey https://doi.org/10.5066/P1FBQTVT This data release provides a variety of water quality benchmarks, compiled from multiple publications and attributed to the NHDPlus v2.1 hydrography, for the conterminous United States (CONUS) for use in integrated water availability studies for evaluating altered water conditions and potential ecological impacts. To avoid ambiguity in terms, for the purpose of this data release, we use the following definitions. Benchmark is used as a generic term to refer to any value used to evaluate or compare condition against. A variety of benchmark sub-categories exist, which are not mutually exclusive. Criteria are a type of benchmark that are used or suggested for regulatory purposes, including enforceable and nonenforceable standards. Thresholds are a class of benchmark that indicate a significant, pre-defined quantifiable change in condition. Background reference values are a class of benchmark that indicate conditions representative of no to minimal amounts of human activity or disturbance. Presumptive standards are a class of benchmark that lack a clear and quantifiable degree of impact and where negative effects are merely presumed to occur. Specifically, this data release primarily focuses on ecologically relevant water quality benchmarks for nutrients (nitrogen, phosphorus), salinity as specific conductance, and temperature. Additional benchmarks that exist in the source datasets for other factors (e.g., chlorophyll-a, turbidity, and fish and macroinvertebrate multimetric indices) are also provided for completeness and value-added utility, even if they were not the primary focus of this compilation. This dataset includes a compilation of ecologically relevant benchmarks from the following sources: 1. Environmental Protection Agency National Rivers and Streams Assessment. Benchmarks used in the 2018-2019 assessment cycle are provided for total nitrogen, total phosphorus, specific conductance, and fish and macroinvertebrate multimetric indices. Specifically, the background reference value cutoff of Good/Fair and the Fair/Poor benchmark used as a presumptive standard to evaluate potential ecosystem impacts are included for each water quality parameter and biological multimetric index. These benchmarks are spatially variable based on the specific National Rivers and Streams Assessment Ecoregion Level III aggregation (9 units). 2. Environmental Protection Agency Recommended Ecoregional Nutrient Criteria for Rivers and Streams, developed in the National Nutrient Strategy. Although not directly used for regulatory purposes, these criteria are intended as a recommended starting place to support development of jurisdiction-specific regulatory criteria. Criteria are provided for total nitrogen, total phosphorus, turbidity, and chlorophyll-a. These data are variable based on a specific National Nutrient Ecoregion Level III aggregation (14 units). 3. Specific Conductance benchmark of a natural background reference value from Olson and Cormier (2019). The natural background benchmark was derived from a machine-learning model from data at least-impacted sites across the conterminous United States. These data are spatially variable at each NHDPlus v2.1 COMID. 4. Newly calculated fish community temperature thresholds, combining previously published fish species distribution models and laboratory-derived maximum critical thermal limits (CTmax) for each species. Available for individual species or via summary metrics of CTmax thresholds for the fish community in each local stream reach. These data are spatially variable for each NHDPlus v2.1 COMID. As the various benchmarks originate from different methods, were derived for comparison against different condition levels, represent different time-scales of condition, and/or may have different quantified levels of impact, a thorough reading of the metadata is highly encouraged, especially Attribute Definitions, Logical Consistency, and Purpose. This may have profound implications for use in water availability and ecological condition studies. This data release contains four primary tabular datasets. The two primary national-scale datasets are provided in Parquet format (.parquet), an open-source, efficient, cloud-optimized tabular format for big which can be easily read in R, Python, and other open-source languages. Alternative .csv formats of these two conus datasets, with otherwise identical content, are provided for accessibility purposes and are compressed in a .zip format for long-term storage. In addition, two other files are provided exclusively in .csv format, including a crosswalk of taxonomic names used to reconcile across source datasets, and a data dictionary for the primary files. The files are: 1. water_quality_benchmarks.parquet - The main ecological benchmark dataset for all CONUS NHDPlus v2.1 network flowlines. 2. Temperature_CTmax_taxa_NHD_CONUS.parquet - The taxa-level temperature thresholds for all fish taxa predicted to be present in all CONUS NHDPlus v2.1 network flowlines. In tall format, with one row per unique taxa per unique flowline identifier. Used to produce the community level summaries contained in water_quality_benchmarks.parquet. 3. conus_benchmarks_csv_archive.zip - A compressed archive containing identical copies of the main CONUS ecological benchmark datasets listed above (files #1 and #2), but provided in .csv format for accessibility purposes. Files are contained in .zip compressed archives to minimize size for long-term storage purposes. 4. unique_tsn_crosswalk.csv - A dataset that lists the unique taxonomic identifiers and taxonomic serial numbers (TSN) used in processing the temperature threshold data and align taxa names used in source thermal benchmark data and in the fish species distribution models. 5. variable_lookup.csv - A data dictionary containing entity and attribute information about the variable names, descriptions, units, and data sources contained in the main water quality benchmark files provided at the CONUS scale (files #1 and #2). File also contains sciencebase identifiers to facilitate optional programmatic access to these data through cloud-based parquet retrieval tools. This data release was created to facilitate the analysis and comparison of water quality observation and model output data at national and large regional scales. Specifically, this is a compilation of nationally-consistent and spatially allocated benchmarks for evaluating potential departure from natural background near-reference conditions and potential levels of ecological impact. As none of these benchmarks are explicit, enforceable regulatory criteria, these should not be used to make regulatory evaluations. In addition, these benchmarks were created for use in large national and regional evaluations. More relevant, locally-derived benchmarks and thresholds are likely available elsewhere for addressing local scale questions of water availability and ecological condition. While many of the data sources that we have compiled were previously available in various formats, this compilation was created to overcome the following main challenges: 1) many of these data were published in tabular form based on different spatial regional aggregations in report documents, requiring manual translation; 2) allocation of stream reaches to various spatial regional aggregations requires additional GIS spatial processing and attribution, which can be unwieldy at a national scale for many users; 3) one dataset is available only through a web-hosted interactive map viewer which is not easy to programmatically access or download for use in subsequent scientific model applications. These data are primarily recommended for use in direct comparisons with observational data or modeled water quality conditions. Specific use will vary based on data source, temporal scale of analysis, and the specific type of benchmark or a threshold's quantified level of impact, which can vary across the dataset. Example of their use is as follows: 1) background reference value conditions can be used to create ratios of alteration from background conditions; 2) benchmark(s) used for potential ecological impact can be used to flag areas above or below the benchmark(s), or used as a ratio to indicate the degree of alteration beyond the potential impact benchmark(s); 3) acute limits, such as the fish community thermal thresholds, can be used as an index of thermal stress, sometimes called the thermal buffer, that is calculated as the magnitude of difference between current water conditions and thermal thresholds. 20260227 publication date Complete None planned -125.1563 -66.6211 49.6107 24.3671 ISO 19115 Topic Category biota inlandWaters USGS Thesaurus salinity surface water quality water chemistry water temperature nutrient content (water) biota aquatic biology freshwater ecosystems USGS Metadata Identifier USGS:6733c24dd34ee3976592da4e Common Geographic Areas United States of America None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Matthew J Cashman USGS - WATER Physical Scientist mailing and physical

MD/DE/DC WSC UMBC Campus, UMBC Research Park

Catonsville MD 21228 USA 443-498-5511 mcashman@usgs.gov This data release is a compilation, spatial allocation, and/or dataset join and summarization of previous datasets compiled by other groups. Please see processing notes and source reference credits. Windows 11 Enterprise, R version 4.4.1, and the R packages tidyverse (2.0.0), arrow (19.0.1), duckdb (1.2.1), dbplyr (2.5.0), duckplyr (1.0.1), DBI (1.2.3) Hao Yu Arthur Cooper Dana M. Infante Jared A. Ross 20220520 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9yx3ex6 Lise Comte Julian D. Olden 20170911 publication https://www.nature.com/ Nature Climate Change Nature Climate Change https://doi.org/10.1038/nclimate3382 https://www.nature.com/articles/nclimate3382 Jonathan Chang Daniel L. Rabosky Stephen A. Smith Michael E. Alfaro 20190328 publication https://doi.org/10.1111/2041-210X.13182 John R. Olson Charles P Hawkins 20130604 publication Freshwater Science BioOne https://doi.org/10.1899/12-113.1 https://bioone.org/journals/freshwater-science/volume-32/issue-3/12-113.1/Developing-site-specific-nutrient-criteria-from-empirical-models/10.1899/12-113.1.short James M. Omernik Glenn E. Griffith 20140916 publication Environmental Management Springer https://doi.org/10.1007/s00267-014-0364-1 ITIS US Geological Survey Smithsonian Institution 20250130 application/service www.itis.gov https://doi.org/10.5066/F7KH0KBK Alan T. Herlihy Steven G. Paulsen John Van Sickle John L. Stoddard Charles P. Hawkins Lester L. Yuan 200812 publication Journal of the North American Benthological Society vol. 27, issue 4 n/a University of Chicago Press ppg. 860-877 https://doi.org/10.1899/08-081.1 Alan T. Herlihy Jean C. Sifneos 200812 publication Journal of the North American Benthological Society vol. 27, issue 4 n/a University of Chicago Press ppg. 932-948 https://doi.org/10.1899/08-041.1 John W. Clune J. Kent Crawford Elizabeth W. Boyer 20201217 publication Water vol. 12, issue 12 n/a MDPI AG ppg. 3550 https://doi.org/10.3390/w12123550 Raymond B. Cowles Charles M. Bogert 19440913 publication Bulletin of the American Museum of Natural History Volume 83, Article 5 New York American Museum of Natural History https://digitallibrary.amnh.org/bitstreams/f73745e6-81a7-4c50-821a-e4c38453118a/download John R. Olson Susan M. Cormier 20190312 publication Environmental Science & Technology vol. 53, issue 8 n/a American Chemical Society (ACS) ppg. 4316-4325 https://doi.org/10.1021/acs.est.8b06777 U.S. Environmental Protection Agency 2024 publication EPA 841-R-22-004 Washington, DC U.S. Environmental Protection Agency, Office of Water and Office of Research and Development. https://riverstreamassessment.epa.gov/webreport These benchmarks were compiled for use in large national and regional evaluations. More accurate, locally derived thresholds are likely to be available elsewhere for addressing local scale questions of water availability and ecological condition. Validity of each benchmark is based on the accuracy of the original source dataset, its assumptions, and methods used to derive the values. Taxa-specific critical thermal limits (CTmax) derived from lab-studies have uncertainties reported in these datasets. Benchmarks derived from statistical modeling studies have uncertainty evaluated and reported in the original study publications. Other benchmarks based on distributions of sample observations have their own assumptions about landscape representativeness, degree of human impact, and ecological relevance. Please see source publications for more description on limitations and evaluated accuracies, but general concerns are reproduced below. A variety of spatial units are used to derive the benchmarks compiled in this dataset. EPA ecoregions are frequently used as the spatial units for developing benchmarks, such as those aggregated version of the EPA Ecoregions Level III that are used by the EPA National Rivers and Streams Assessment (EPA NRSA) and the EPA National Nutrient Strategy (EPA NNS). EPA NRSA created benchmarks based on an aggregation of Ecoregion Level III units into 9 aggregated regions. EPA NNS created benchmarks based on a different aggregation of Ecoregion Level III units into 14 aggregated regions. Due to high amount of natural variation within regions, a single value per region is typically insufficient, and more-spatially variable and site-specific benchmarks are recommended (Olson and Hawkins 2013). Along these lines, both the temperature benchmarks and background salinity reference value by Olson and Cormier (2019) compiled in this dataset are spatially variable at the NHDPlus v2.1 comid stream reach scale, although additional attribute fields make it possible to aggregate or summarize these benchmark values to the aggregated ecoregions used by the EPA NRSA and EPA NNS. Presumptive standard benchmarks derived from percentiles of found observational data, such as recommended criteria by the EPA National Nutrient Strategy, have known limitations (e.g. Clune and others 2020, Herlihy and Sifneos 2008). Notably, found observational data often are biased towards problematic areas not representative of the region. Random, probabilistic sampling of a region, such as by the EPA National Rivers and Streams Assessment, may therefore provide a better representation of conditions across a region (Herlihy and Sifneos 2008). In addition, using near-reference sites, such as done by the EPA National Rivers and Streams Assessment, has been argued to be a superior method of defining region-based benchmarks that capture reference value conditions better than observed found data (Herlihy and Sifneos 2008). However, even using near-reference sites confronts the issue that quality reference sites may not equally exist in all regions (Herlihy and others 2008). When available, modeled estimates of natural background condition are recommended for use due to the ability to better capture in-region variability and better represent potential background conditions (Herlihy and Sifneos 2008; Olson and Hawkins 2013). In addition, benchmarks suggesting ecological impact, especially when based on a distribution of observations, are presumptive standards and do not directly quantify ecological impact in their benchmark creation. Within EPA NRSA, exceeding the Poor presumptive standard was statistically evaluated post-hoc in NRSA reports, and was found to be associated with varying level of ecological impacts among regions when quantified. The model created by Olson and Cormier (2019) to predict the background conductivity reference value benchmark was designed for streams with natural background specific conductance (SC) < 2000 microSiemens per centimeter. Above this level, the model estimates may be less reliable. In addition, freshwater and marine interfaces, natural mineral springs, and salt deposits which may affect groundwater and streams are not included in the model, and thus may result in underestimated values in areas where these conditions are prevalent (Olson and Cormier 2019). As a result, values in arid areas may be less reliable, and the use of local knowledge is suggested for assessing the differences between predicted and measured background SC as provided in the original publication (Olson and Cormier 2019) and explorable in interactive format through the Freshwater Explorer (Cormier and others 2021). More details on model validation and accuracy can be found in the source publication at Olson and Cormier (2019). Lastly, all reported benchmarks, with the exception of the temperature thresholds, are chronic benchmarks, analogous to longer, time-averaged conditions. Specifically, NNS benchmarks are created through averaging found data collected across seasons. NRSA benchmarks were created using samples collected typically at baseflow and during spring and/or summer months corresponding with biological collections. In contrast, the temperature thresholds are indicated by acute, short-term lab studies that result in severe effects to individual fishes. The benchmarks contained in this data release are not functionally equivalent as to their implication for water availability or ecological condition, even for the same water quality constituent. Values may indicate different conditions as indicated by their benchmark class, from background reference value conditions, thresholds indicating explicit defined ecological impacts, or a presumptive standard of a presumed impact. To note, NRSA and NNS benchmarks were created using distributions of observed data, rather than based on a defined ecological level of impact; the Poor presumptive standard for water quality constituents used in the NRSA is associated with different magnitudes of increased probability of poor ecological condition in different ecoregions (USEPA 2024). Some indicate a general community stress level, and others indicate conditions leading to die-off of taxa quickly due to acute thresholds. Please see information about the individual entity and attributes (and source citations) for full information about the methods used to derive each benchmark. Water quality benchmarks are often developed and presented for discrete areas, such as the aggregated Level III Ecoregions used by the EPA NRSA and EPA NNS. There are 181 Level III Ecoregions for North America (EPA Ecoregions Level III), which represent similar ecosystem types and are used as a framework for research, assessment, management, and monitoring of ecosystems. More detail on the Ecoregion framework can be found in Omernik and Griffith 2014. Level III Ecoregion boundaries provided by the EPA (EPA Ecoregions Level III) were downloaded and manually and visually compared to maps of the aggregated ecoregions used in the EPA NNS and presented in Rohm and others (2007) and to maps of the aggregated ecoregions used in the EPA NRSA and presented in Herlihy (2008). Notably, in Herlihy (2008) there were missing Ecoregions Level III in the aggregation table (Table 3), and so the figure of mapped aggregated units for CONUS (Figure 3) was used to fill in the corresponding aggregated ecoregion for the missing Ecoregion Level III regions. In Rohm and others (2007), mapped boundaries for Level III Ecoregions did not match the current GIS data for Ecoregions Level III currently available by EPA (EPA Ecoregions Level III). Specifically, this causes differences in the mapped extent of Nutrient Aggregated Region IV in Figure 1 of Rohm and others (2007), specifically the locations of Ecoregion Level III numeric classes 26 to 30 in Texas. The current Ecoregion Level III boundaries provided by EPA (EPA Ecoregions Level III) are used in this project to crosswalk to NNS aggregated regions, rather than the maps of aggregated units in the original publications. Data are based off the extent of the conterminous United States in the NHDPlus v2.1 and represent condition for flowlines and streams and rivers. While Level III Ecoregions exist for all of North America, only Ecoregion Level III for the extent of the conterminous United States were used, and no data are provided for rivers and streams falling outside the conterminous United States. Similarly, as this data release focuses on flowlines for rivers and streams, benchmark data may be omitted for flowlines associated with coastal open water lacking catchment polygons. For example, WQ benchmarks are missing for open water areas of the Delaware River estuary or for many flowlines near or along the border with Canada. In addition, the EPA National Nutrient Strategy does not provide any recommended ecoregional benchmarks for the Southern Florida Coastal Plain region, NNS aggregated region XIII, and thus those benchmark values are NA in that region. Stream catchments can intersect with multiple ecoregion boundaries based on exact horizontal positioning of the source Ecoregion Level III spatial dataset provided by EPA (EPA Ecoregions Level III). A single Ecoregion Level III attribution for each catchment is based off the dominant areal-based overlap for each Ecoregion Level III that intersects with each catchment. Ambiguity in exact Ecoregional boundaries may result in slight mis-attribution of catchments around the boundaries of multiple Ecoregions. N/A McKay, L. Bondelid, T. Dewald, T Johnston, J. Moore, R. Rea, A. 2012 Hydrologic Network Washington, DC United States EPA https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data 100000 Digital and/or Hardcopy 2012 publication date NHDPlus v2.1 Served as spatial framework for benchmark criteria association U.S. Environmental Protection Agency 202409 publication Washington, D.C. 20460 Office of Water: Office of Wetlands, Oceans and Watersheds; and Office of Research and Development https://www.epa.gov/system/files/documents/2024-12/nrsa-2018-19-tsd-final-11252024.pdf Digital and/or Hardcopy 2018 2019 publication date EPA NRSA Source for ecoregion benchmark values for fish and benthic macroinvertebrate assemblage condition and stream nutrient concentrations U.S. Environmental Protection Agency 2001 publication https://www.epa.gov/system/files/documents/2021-07/ecoregion-table-rivers-streams.pdf Digital and/or Hardcopy 2000 2001 publication date EPA NNS summary table Source for National Nutrient Strategy recommended ecoregional criteria for TP, TN, Chlorophyll a, and FTU/NTU Susan M. Cormier Christopher L. Wharton John O. Olson 20210727 vector digital data Washington DC U.S. Environmental Protection Agency https://epa.maps.arcgis.com/home/item.html?id=85c2000098e446cb979af577fd95e821 https://arcg.is/KHb9S 100000 Digital and/or Hardcopy 20210727 publication date EPA Freshwater Explorer Source of predicted background reference SC values for NHDPlusv2.1 COMIDs in the conterminous United States Lise Comte J. D. Olden 2017 publication Nature Climate Change Springer Nature https://doi.org/10.1038/nclimate3382 Digital and/or Hardcopy 2017 publication date Comte and Olden (2017) Source of thermal data for fish Johnathan Chang Daniel L. Rabosky Stephen A. Smith Michael E. Alfaro 2019 publication https://doi.org/10.1111/2041-210X.13182 Digital and/or Hardcopy 2019 publication date Chang and others (2019) Name of the phylogenetic tree from the Fish Tree of Life, which is considered in the imputation procedure Emmanual A. Frimpong Paul L. Angermeier 2009 publication https://doi.org/10.1577/1548-8446-34.10.487 Digital and/or Hardcopy 2009 publication date Frimpong and Angermeir (2009) Source of monthly probability weight of spawning occurring for fish taxa Daniel J Wieferich Madison B Most Hao Yu Arthur Cooper Dana M. Infante Jared A. Ross 2024 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p1uv25fw Digital and/or Hardcopy 2024 publication date Yu and others (2022) Source for fish species distribution models for 419 species across CONUS Michael E Wieczorek Shannon E. Jackson Gregory E Schwarz Andrew J Sekellick Leah E Staub 20181002 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7765d7v Digital and/or Hardcopy 202506 publication date Wieczorek and others (2018) Contributes the percentage overlap between EPA Ecoregion Level III and each catchment COMID in CONUS Alan T. Herlihy Steven G. Paulsen John Van Sickle John L. Stoddard Charles P. Hawkins Lester L. Yuan 200812 publication Journal of the North American Benthological Society The University of Chicago Press Journals https://doi.org/10.1899/08-081.1 https://www.journals.uchicago.edu/doi/abs/10.1899/08-081.1 Digital and/or Hardcopy 200812 publication date Herlihy and others (2008) Crosswalk from Ecoregion Level III to aggregated ecoegions used by EPA National Rivers and Streams Assessment (NRSA) Christina M. Rohm James M. Omernik Alan J. Woods John L. Stoddard 20070708 publication Journal of the American Water Resources Association Wiley https://doi.org/10.1111/j.1752-1688.2002.tb01547.x https://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2002.tb01547.x Digital and/or Hardcopy 20070708 publication date Rohm and others (2007) Crosswalk from Ecoregion Level III to Aggregated Ecoregions used by EPA National Nutrient Strategy (NNS) S. Chamberlain 2021 application/service R Application to the Integrated Taxonomic Information System available at: https://www.itis.gov/ https://doi.org/10.32614/CRAN.package.ritis https://www.itis.gov/ Digital and/or Hardcopy 20250703 observed ritis Taxonomic Names Validation U.S. Environmental Protection Agency 20251105 publication https://www.epa.gov/nutrientpollution/ecoregional-nutrient-criteria-rivers-and-streams Digital and/or Hardcopy 2025 publication date EPA NNS website Figure 1 of the Ecoregion Level III boundaries and the NNS aggregations were used to visually compare with the aggregation table by Rohm and others (2007). John R. Olson Susan M. Cormier 20190312 publication Environmental Science &amp; Technology vol. 53, issue 8 n/a American Chemical Society (ACS) ppg. 4316-4325 https://doi.org/10.1021/acs.est.8b06777 Digital and/or Hardcopy 2000 2015 ground condition Olson and Cormier (2019) Original study that produced the natural background specific conductance values that are ultimately retrieved from EPA Freshwater Explorer U.S. Environmental Protection Agency 20100501 vector digital data https://www.epa.gov/eco-research/ecoregions-north-america https://dmap-prod-oms-edc.s3.us-east-1.amazonaws.com/ORD/Ecoregions/cec_na/NA_CEC_Eco_Level3.zip Digital and/or Hardcopy 20100501 publication date EPA Ecoregions Level III Spatial data for the boundaries of the EPA Ecoregions Level III used in Wieczorek and others (2018) and for visual validation of regional aggregations within this study. The U.S. EPA's National Rivers and Streams Assessment (EPA NRSA) is a collaborative survey through the U.S. Environmental Protection Agency. Data are collected from flowing rivers and streams across the United States. The EPA uses these data to develop benchmark values for ecoregions denoting "poor", "fair", or "good" condition for metrics of stream health such as benthic macroinvertebrate assemblage condition and stream nutrient concentrations. For NRSA, the benchmark value associated with each condition category is typically based on a deviation from a least-disturbed condition, inferred from a set of reference sites. Please see more details in individual attributes or the NRSA technical support document (EPA NRSA) for more details. For nutrients, the value at and below the 75th percentile of the reference-site distribution for each ecoregion was used to define the good-fair boundary, and is analogous to a background reference value. The 95th percentile and above of the reference-site distribution was used to define the most disturbed fair-poor boundary and is used as a presumptive standard for ecological impact, which is later used by NRSA to quantify increased odds ratios of fish and macroinvertebrate conditions also being in poor condition. The percent overlap of each NHDPlus V2.1 catchment with each EPA Ecoregion Level III polygon was obtained from Wieczorek and others (2018). Catchments were preprocessed to find the single dominant Ecoregion Level III, which were then crosswalked to the EPA NRSA aggregated ecoregions using Table 3 from Herlihy and others (2008). Some ecoregions were missing from the aggregation list in Table 3, but were indicated on the aggregated region map for CONUS provided in Figure 3, which matched aggregated maps provided by EPA NRSA reports . These missing ecoregions were then filled in manually based on spatial positioning and aggregated region indicated in Figure 3 from Herlihy (2008). Lastly, the benchmark cutoffs for good and poor condition for benthic macroinvertebrate, fish, total nitrogen, total phosphorus, and salinity from the EPA NRSA 2018/2019 technical support document (EPA NRSA) were joined to the EPA NRSA aggregated ecoregions associated with each NHDPlus v2.1 catchment. The dataset at this step contained each NHDPlus V2.1 catchment with associated EPA NRSA aggregated ecoregion and the NRSA benchmark for that aggregated ecoregion. NHDPlus v2.1 EPA NRSA Herlihy and others (2008) Wieczorek and others (2018) EPA Ecoregions Level III 20240419 As part of the U.S. EPA's National Nutrient Strategy (EPA NNS), the U.S. EPA uses found historical and recent nutrient observed data and reference site data to develop total phosphorus, total nitrogen, Chlorophyll a and turbidity benchmark criteria for aggregated Ecoregion level III across the United States. These benchmarks were obtained by ecoregion from the EPA NNS summary table. Additional ecoregion specific documents are available which document how each benchmark was derived. The percent overlap of each NHDPlus v2.1 catchment with each Ecoregion Level III was obtained from Wieczorek (2018). Catchments were preprocessed to find the single dominant Ecoregion Level III, which were then crosswalked to the EPA National Nutrient Strategy aggregated ecoregions using Rohm and others (2007). Crosswalk was visually compared with Figure 1 provided on the EPA NNS website that visually shows Ecoregion Level III boundaries and the associated NNS aggregation numbers. Lastly, the benchmark cutoffs used by the National Nutrient Strategy Ecoregional recommended nutrient criteria were then joined to the EPA NNS aggregated ecoregions associated with each NHDPlus v2.1 catchment. The dataset produced by this step contained each NHDPlus v2.1 catchment with associated NNS aggregated ecoregion and the NNS benchmark criteria for that aggregated ecoregion. These data were then joined to the output of Step 1 by catchment COMID. NHDPlus v2.1 EPA NNS summary table Rohm and others (2007) Wieczorek and others (2018) EPA NNS website 20240419 The EPA Freshwater Explorer is an interactive web-based mapping tool developed by the U.S. EPA for displaying water quality data (Cormier and others, 2021). This web tool contains visualizations of predicted background salinity for all NHDPlus v2.1 catchments in the continental U.S., as originally developed using machine learning methods by Olson and Cormier (2019) . Values from background reference value modeling efforts were downloaded from the EPA Freshwater Explorer landing page for Predicted Background Conductivity Data (Cormier and others, 2021). Predictions were downloaded as a shapefile and then exported from ArcGIS Pro as a data table. Predictions were added to the water_quality_benchmark dataset by joining by NHDPlusv2.1 COMID. More information about the development of the predicted background specific conductance can be found in Olson and Cormier (2019). EPA Freshwater Explorer 20240419 John R Olson California State University Monterey Bay mailing and physical

100 Campus Center

Seaside California 93955 United States 831-582-3873 joolson@csumb.edu Taxonomic thermal thresholds were produced by combining several distinct data sources. Specifically, we accessed data on fish community diversity across CONUS by accessing species distribution models at the NHDPlus v2.1 for 419 species across CONUS from Yu and others (2022). We accessed taxa-specific CTmax thermal tolerance data from Comte and Olden (2017), and ancillary fish trait data on spawning months from Fimpong and Angermeir (2008). All taxonomic data were harmonized through the Information Taxonomic Information System (ITIS) using the ritis package in R. Thermal tolerances were imputed for taxa missing tolerance data using phylogenetic imputation using the fishtree package in R (Chang and others 2019). This created a tall table of all fish taxa predicted to be present for each COMID in CONUS, along with their CTmax values (contained in the dataset CTmax_taxa_NHD_CONUS.parquet in this data release). A summary dataset was then created to represent community-level metrics calculated from the individual-taxon data compiled in the previous step, including the minimum,was then created off the taxa-specific raw data in each COMID via calculating the minima, mean, and quantiles (5%, 10%, 25% and 50%/median) for the CTmax of all fish taxa present in a COMID, as well as the number of taxa predicted in that COMID. The summarized community-level metrics were appended to the compiled benchmark dataset in water_quality_benchmarks.parquet. Comte and Olden (2017) Chang and others (2019) ritis Yu and others (2022) Frimpong and Angermeir (2009) 20250130 Matthew J Cashman USGS - WATER Supervisory Hydrologist mailing and physical

MD/DE/DC WSC UMBC Campus, UMBC Research Park

Catonsville MD 21228 443-498-5511 mcashman@usgs.gov water_quality_benchmarks.csv Comma Separated Value (CSV) file containing data on ecologically relevant benchmarks across CONUS. Each row is one COMID and each column contains a benchmark, threshold, or related ancillary information. COMIDs are not included where no benchmarks are available. Producer Defined comid NHDPlus V2.1 COMID, a unique identifier for each catchment in the NHDPlus V2 hydrographic network NHDPlus v2.1 -504212 948100740 ECO3_id EPA Ecoregion level III numeric identification code (1-85) EPA Ecoregion Level III NA NA values for this field typically indicate that the flowlines and/or catchments do not intersect the Ecoregion Level III boundaries used in this study. For example, this often would occur in areas of open water, such as flowlines associated with the open water areas of the Delaware River, or where the COMID was outside of the conterminuous US, which included many areas near or along the border with Canada. Producer defined EPA Ecoregion level III Number EPA Ecoregion Level III ECO3_name EPA Ecoregion level III text name EPA Ecoregion Level III NA No Data This study The text name for each of the Ecoregion Level III used by EPA in EPA Ecoregion Level III ECO3_NRSA_agg_name Text name of the EPA National Rivers and Streams Assessment aggregated version of Ecoregion level III EPA NRSA NA No Data This study The full text name of the aggregated Ecoregion Level III regions that are used by the EPA National Rivers and Streams Assessment and first defined in Herlihy and others (2008) ECO3_NRSA_agg_abrv Abbreviated form of the EPA National Rivers and Streams Assessment aggregated version of Ecoregion level III EPA NRSA NA No Data This study An abbreviated version of full text name of the aggregated Ecoregion Level III regions that are used by the EPA National Rivers and Streams Assessment and first defined in Herlihy and others (2008) NRSA_macro_MMI_good Benchmark boundary between Good (above) and Fair condition (below) for the macroinvertebrate multi-metric index defined by the EPA National Rivers and Streams Assessment. Benchmark was calculated via a process of benchmark regression adjustment modeling, as detailed in Herlihy and others (2008), which uses a PCA disturbance gradient to reduce the effects of disturbance on benchmark values within the reference site population. The adjusted benchmarks are intended to be analogous to the 5th (Poor) and 25th (Good) percentile of reference sites in each region based on the slope of the MMI-disturbance relationship in each region. Samples used in this determination were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 35.5 57 Unitless NRSA_macro_MMI_poor Benchmark boundary between Poor (below) and Fair condition (above) for the macroinvertebrate multi-metric index defined by the EPA National Rivers and Streams Assessment. Benchmark was calculated via a process of benchmark regression adjustment modeling, as detailed in Herlihy and others (2008), which uses a PCA disturbance gradient to reduce the effects of disturbance on benchmark values within the reference site population. The adjusted benchmarks are intended to be analogous to the 5th (Poor) and 25th (Good) percentile of reference sites in each region based on the slope of the MMI-disturbance relationship in each region. Samples used in this determination were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 21.3 42.8 Unitless NRSA_fish_MMI_good Benchmark boundary between Good (above) and Fair condition (below) for the fish multi-metric index defined by the EPA National Rivers and Streams Assessment. Benchmark was calculated via a process of benchmark regression adjustment modeling, as detailed in Herlihy and others (2008), which uses a PCA disturbance gradient to reduce the effects of disturbance on benchmark values within the reference site population. The adjusted benchmarks are intended to be analogous to the 5th (Poor) and 25th (Good) percentile of reference sites in each region based on the slope of the MMI-disturbance relationship in each region. Samples used in this determination were collected during spring and summer low-flow conditions. EPA NRSA NA No Data Producer defined 39.8 76.8 Unitless NRSA_fish_MMI_poor Benchmark boundary between Fair (above) and Poor condition (below) for the fish multi-metric index defined by the EPA National Rivers and Streams Assessment. Benchmark was calculated via a process of benchmark regression adjustment modeling, as detailed in Herlihy and others (2008), which uses a PCA disturbance gradient to reduce the effects of disturbance on benchmark values within the reference site population. The adjusted benchmarks are intended to be analogous to the 5th (Poor) and 25th (Good) percentile of reference sites in each region based on the slope of the MMI-disturbance relationship in each region. Samples used in this determination were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 29.3 65.4 Unitless NRSA_TN_ugL_good Benchmark boundary between Good (below) and Fair condition (above) for total nitrogen as defined by the EPA National Rivers and Streams Assessment. Benchmark calculated by taking the 75th percentile of samples collected in least-disturbed, reference sites within the ecoregion. Samples were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 139 700 Microgram per liter NRSA_TN_ugL_poor Benchmark boundary between Fair (below) and Poor condition (above) for total nitrogen as defined by the EPA National Rivers and Streams Assessment. Benchmark calculated by taking the 95th percentile of samples collected in least-disturbed, reference sites within the ecoregion. Samples were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 249 1274 Microgram per liter NRSA_TP_ugL_good Benchmark boundary between Good (below) and Fair condition (above) for total phosphorus as defined by the EPA National Rivers and Streams Assessment. Benchmark calculated by taking the 75th percentile of samples collected in least-disturbed, reference sites within the ecoregion. Samples were collected during spring and summer low-flow conditions. EPA NRSA NA No Data This study 14.8 88.6 Microgram per liter NRSA_TP_ugL_poor National Rivers and Streams assessment aggregated ecoregion-specific benchmark for stream total phosphorus concentration above which stream total phosphorus concentration is considered in poor condition. Benchmark boundary between Fair (below) and Poor condition (above) for total phosphorus as defined by the EPA National Rivers and Streams Assessment. Benchmark calculated by taking the 95th percentile of samples collected in least-disturbed, reference sites within the ecoregion. Samples were collected during spring and summer low-flow conditions. EPA NRSA NA No Data Producer defined 24.4 143 Microgram per liter NRSA_salinity_SC_uScm_good Benchmark boundary between Good (below) and Fair condition (above) for specific conductance as salinity as defined by the EPA National Rivers and Streams Assessment. Benchmark was not defined by field collected samples, as per nitrogen and phosphorus benchmarks, but instead given an ecoregional value without justification. EPA NRSA NA No Data This study 500 1000 microSiemens per centimeter NRSA_salinity_SC_uScm_poor Benchmark boundary between Fair (below) and Poor condition (above) for specific conductance as salinity as defined by the EPA National Rivers and Streams Assessment. Benchmark was not defined by field collected samples at reference sites, as per nitrogen and phosphorus benchmarks, but instead given an ecoregional value without justification. EPA NRSA NA No Data This study 1000 2000 microSiemens per centimeter ECO3_NNS_agg_name Text name of the EPA National Nutrient Strategy aggregated version of Ecoregion level III EPA NNS NA No Data This study The full text name of the aggregated Ecoregion Level III regions that are used by the National Nutrient Strategy and first defined in Rohm and others (2007) ECO3_NNS_agg_id Roman numeral identity code of the EPA National Nutrient Strategy aggregated version of Ecoregion level III, as used in source publications. EPA NNS NA No Data This study The roman numeral identity code values for the aggregated Ecoregion Level III regions that are used by the National Nutrient Strategy and first defined in Rohm and others (2007) ECO3_NNS_agg_abrv Abbreviated form of the EPA National Nutrient Strategy aggregated version of Ecoregion level III EPA NNS NA No Data This study The abbreviated form of the full next names of the aggregated Ecoregion Level III regions that are used by the National Nutrient Strategy and first defined in Rohm and others (2007) NNS_TN_mgL EPA recommended regional criteria for total nitrogen concentration for rivers and streams as presented in the National Nutrient Strategy. Criterion was developed from the median value across the 25th percentile of the distribution of found data for data categorized by season. Please see original source data for more details about calculations within each specific ecoregion. EPA NNS NA No Data This study 0.12 2.18 Milligrams per liter NNS_TP_ugL EPA recommended regional criteria for total phosphorus concentration for rivers and streams as presented in the National Nutrient Strategy. Criterion was developed from the median value across the 25th percentile of the distribution of found data for data categorized by season. Please see original source data for more details about calculations within each specific ecoregion. EPA NNS NA No Data This study 10 128 Micrograms per liter NNS_TP_notes Specific notes column from source dataset that indicated suspect and/or outlier values for investigation. EPA NNS NA No Data This study Specific notes that indicated suspect and/or outlier values for investigation. NNS_turb EPA recommended regional criteria for turbidity for rivers and streams as presented in the National Nutrient Strategy. Criterion was developed from the median value across the 25th percentile of the distribution of found data for data categorized by season. Please see original source data for more details about calculations within each specific ecoregion. EPA NNS NA No Data This study 1.3 17.5 Variable, see NNS_turb_units column NNS_turb_units Turbidity units used to develop the turbidity benchmark threshold within the ecoregion, based on the availability of data at the time of creation. EPA NNS NA No Data This study FTU Units = Formazin Turbidity Unit This study NTU Units = Nephelometric Turbidity Units This study NNS_chla_ugL EPA recommended regional criteria for chlorophyll-a for rivers and streams as presented in the National Nutrient Strategy. Criterion was developed from the median value across the 25th percentile of the distribution of found data for data categorized by season. Please see original source data for more details about calculations within each specific ecoregion. EPA NNS NA No Data This study 0.4 3.75 Micrograms per liter NNS_chla_method Specific method used to determine chlorophyll-a concentration thresholds for the region. Can be determined from flourometric or spectrophotometric methods, which are not distinctly interchangeable EPA NNS NA No Data This study Fluorometric Method measures the flourescence of chlorophyll when excited by a specific wavelength of light using a flourometer This study Spectrophotometric Method measures the absorbance of specific wavelengths of light by chlorophyll using a spectrophotometer. This study OlsonCormier_background_SC Reference value of predicted natural background specific conductance by each unique stream reach. Predicted background specific conductance is the average specific conductance from a model using reference sites modeled monthly between 2000 and 2015. Olson and others 2021 NA No Data Producer defined 10.2933701657459 1703.74806629834 microSiemens per centimeter community_CTmax_mean Threshold corresponding to the average critical thermal maximum (CTmax) of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_min Threshold corresponding to the minimal critical thermal maximum (CTmax) of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_q5 Threshold corresponding to the critical thermal maximum (CTmax) of the 5th percentile of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_q10 Threshold corresponding to the critical thermal maximum (CTmax) of the 10th percentile of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_q25 Threshold corresponding to the critical thermal maximum (CTmax) of the 25th percentile of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_q50 Threshold corresponding to the critical thermal maximum (CTmax) of the median (50th percentile) of all fish taxa predicted to be present in the stream reach, according to the modeled species distributions by Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the fish present do not have CTmax values. This study 25.83 40.29 Degrees Celsius community_CTmax_num_taxa Total number of fish taxa predicted for this stream reach, out of the total number of taxa modeled in the species distribution models of Yu and others, 2022 This study NA No Data. This is due to none of the species from Yu and others (2022) predicted to be present in this COMID and/or the COMID was outside of the modeling domain by Yu and others (2022). This study 1 79 Count Temperature_CTmax_taxa_NHD_CONUS.parquet A parquet file containing data on species-specific fish thermal thresholds across CONUS. Each row is the unique combination of fish taxa predicted to be present by each COMID in CONUS. Columns are information about the fish taxa, thermal threshold, information on how that was derived, and related spawning probability trait information. COMIDs are not included where no fish taxa predicted by the species distribution models of Yu et al (2022) Producer Defined comid NHDPlus V2.1 COMID, a unique identifier for each catchment in the NHDPlus V2 hydrographic network This study 101 948100736 accepted_tsn The unique Integrated Taxonomic Information System's (ITIS's) Taxonomic Serial Number (TSN) for each taxon. This attribute matches the accepted_tsn attribute present in the unique_tsn_crosswalk.csv dataset. This study ITIS Taxonomic Serial Number Integrated Taxonomic Information System (ITIS) accepted_name Scientific name returned by ITIS This study Scientific names of the various taxa in this study, as returned and updated by ITIS CTmax Critical thermal limit maxima, the upper thermal tolerance, for the fish species from lab-controlled experiments. Historically, CTmax has been defined as 'the thermal point at which locomotory activity becomes disorganized and the animal loses its ability to escape from conditions that will promptly lead to its death' (Cowles & Bogert, 1944). This study NA No CTmax data were in the source dataset or able to be imputed for this species This study 25.83 40.29 degrees Celsius CTmax_SD Standard deviation of the CTmax as derived from lab-experiments. Not available when threshold was phylogenetically interpolated This study NA Not available when threshold was phylogenetically interpolated or when standard deviation was not available for the lab-experiment as reported in the source dataset. Producer defined 0.14 4.22 degrees Celsius imputed True/False whether thermal tolerance threshold was imputed for this fish taxa for this study This study NA No CTmax data were in the source dataset or able to be imputed for this species Producer defined 0 1 phy_tree_name Name of the phylogenetic tree from the Fish Tree of Life, which is considered in the imputation procedure This study Polyodon_spathula Phylogenetic tree associated with Polyodon spathula was used in imputation This study Lepisosteus_osseus Phylogenetic tree associated with Lepisosteus osseus was used in imputation This study Lepisosteus_oculatus Phylogenetic tree associated with Lepisosteus oculatus was used in imputation This study Amia_calva Phylogenetic tree associated with Amia calva was used in imputation This study Dorosoma_cepedianum Phylogenetic tree associated with Dorosoma cepedianum was used in imputation This study Esox_americanus_americanus Phylogenetic tree associated with Esox americanus americanus was used in imputation This study Notemigonus_crysoleucas Phylogenetic tree associated with Notemigonus crysoleucas was used in imputation This study Notropis_buchanani Phylogenetic tree associated with Notropis buchanani was used in imputation This study Notropis_texanus Phylogenetic tree associated with Notropis texanus was used in imputation This study Notropis_volucellus Phylogenetic tree associated with Notropis volucellus was used in imputation This study jan Probability weight of spawning occurring for this taxon in January This study 0.0 1.0 feb Probability weight of spawning occurring for this taxon in Febuary This study 0.0 1.0 mar Probability weight of spawning occurring for this taxon in March This study 0.0 1.0 apr Probability weight of spawning occurring for this taxon in April This study 0.0 1.0 may Probability weight of spawning occurring for this taxon in May This study 0.0 1.0 jun Probability weight of spawning occurring for this taxon in June This study 0.0 1.0 jul Probability weight of spawning occurring for this taxon in July This study 0.0 1.0 aug Probability weight of spawning occurring for this taxon in August This study 0.0 1.0 sep Probability weight of spawning occurring for this taxon in September This study 0.0 1.0 oct Probability weight of spawning occurring for this taxon in October This study 0.0 1.0 nov Probability weight of spawning occurring for this taxon in November This study 0.0 1.0 dec Probability weight of spawning occurring for this taxon in December This study 0.0 1.0 unique_tsn_crosswalk.csv Comma Separated Value (CSV) file containing data indicating the crosswalk of names used by Yu and others (2022) and those used for this study after updates from The Integrated Taxonomic Information System (ITIS). Producer Defined agap_itis_tsn The unique Integrated Taxonomic Information System's (ITIS's) Taxonomic Serial Number (TSN) for each taxon, as originally returned, in the AGAP modeling done by Yu and others (2022) Producer Defined 159705 913996 agap_scientific_name Scientific name returned by ITIS and used in the AGAP modeling done by Yu and others (2022) Producer Defined Scientific names of various taxa in this study, as originally reported by Yu and others (2022) agap_common_name Common names used in the AGAP modeling project as done by Yu and others (2022) Producer Defined Common names of various taxa in this study, as originally reported by Yu and others (2022) agap_accepted_tsn The unique Integrated Taxonomic Information System's (ITIS's) Taxonomic Serial Number (TSN) for each taxon, as accepted and used in the AGAP modeling done by Yu and others (2022) Producer Defined 159705 1199065 accepted_tsn The unique Integrated Taxonomic Information System's (ITIS's) Taxonomic Serial Number (TSN) for each taxon, used in this study. This attribute matches the accepted_tsn attribute present in the Temperature_CTmax_taxa_NHD_CONUS.parquet dataset. Producer Defined 159705 1199065 accepted_name Scientific name returned by ITIS and used in this study Producer Defined Scientific names of the various taxa in this study, as returned and updated by ITIS variable_lookup.csv Data dictionary of variables contained in other files of this data release. Also used to facilitate (optional) remote data querying capabilities of the parquet files when hosted in the cloud. Producer Defined variable Variable names of columns contained in other files of this dataset. Producer Defined Variable names description Variable description for columns contained in other files of this dataset. Should match the attribute description for the respective entities in this metadata record Producer Defined Custom variable descriptions units Reporting units for the benchmarks contained in this dataset Producer Defined << empty cell >> Not applicable for this variable Producer defined mg/L milligrams per liter Producer defined μg/L microgram per liter Producer defined Multiple units, please see turb_units column Multiple units of this variable exist in the source data. Please see the turb_units column for specific unit used in this region Producer defined μS/cm microSiemens per centimeter Producer defined degrees celsius degrees celsius Producer defined Count count of the total number of species reported to be present Producer defined Unitless Does not have an explicit unit Producer defined benchmark_class Sub-category of benchmark corresponding to each benchmark. Producer Defined << empty cell >> This variable is not an explicit benchmark, and thus has no class Producer defined criterion A criterion is a type of benchmark that is used or suggested for regulatory purposes, including enforceable and nonenforceable standards. Producer defined background reference value Background reference values are a class of benchmark that indicate conditions representative of no to minimal amounts of human activity or disturbance Producer defined presumptive standard Presumptive standards are a class of benchmark that lack a clear and quantifiable degree of impact and where negative effects are merely presumed to occur. Producer defined threshold A threshold indicate a class of benchmark that indicate a significant, pre-defined quantifiable change in condition. Producer defined category Type of water quality constituent the benchmark relates to, or category of related non-constituent metadata. Producer Defined Region This variable contains information on the regional aggregation used in the source dataset Producer defined Nitrogen This variable is related to a nitrogen benchmark Producer defined Phosphorus This variable is related to a phosphorus benchmark Producer defined Sediment This variable is related to a sediment benchmark Producer defined Chlorophyll This variable is related to a chlorophyll benchmark Producer defined Biotic This variable is related to a non-chlorophyll biological benchmark Producer defined Salinity This variable is related to a salinity benchmark Producer defined Temperature This variable is related to a temperature benchmark Producer defined Traits This variable is related to life history or functional traits of an aquatic species Producer defined source The original source that the benchmark data were compiled from Frimpong and Angermeier 2009. Producer Defined EPA National Nutrient Strategy Ecoregional Nutrient Criteria for rivers and streams; https://www.epa.gov/system/files/documents/2021-07/ecoregion-table-rivers-streams.pdf The EPA National Nutrient Strategy recommended regional criteria Producer defined EPA 2013-2014 National Rivers and Streams Assessment; https://www.epa.gov/system/files/documents/2024-10/nrsa-2013-14-final-tsd-version-1.1-september-2024_.pdf EPA National Rivers and Streams Assessment 2013-2014 cycle, taken from the Technical document Producer defined Olson and Cormier 2019: https://pubs.acs.org/doi/10.1021/acs.est.8b06777; Data accessed from the EPA Freshwater Explorer: https://arcg.is/KHb9S Manuscript published by Olson and Cormier 2019 but data visualized and accessed through the EPA Freshwater Explorer online data visualization platform Producer defined This study, derived from Yu and others, 2022; https://doi.org/10.5066/P1UV25FW This benchmark was created for the purposes of this data release by the project team. Even though the benchmark uses other source datasets (see Processing Steps) these values did not originate from another source. Producer defined ITIS; https://www.itis.gov Value taken from the Integrated Taxonomic Information System available at https://www.itis.gov Producer defined Comte and Olden 2017; https://doi.org/10.1038/nclimate3382 Value taken from Comte and Olden's 2017 in Nature Climate Change, "Climactic vulnerability of the world's freshwater and marine fishes" Producer defined Chang and others, 2019; https://doi.org/10.1111/2041-210X.13182 Phylogenetic tree used was obtained from the R package fishtree which accesses the Fish Tree of Life website Producer defined Frimpong and Angermeier, 2011; https://doi.org/10.1577/1548-8446-34.10.487 Values obtained from the Fish Traits database by Frimpong and Angermeier 2009. Producer defined dataset_file Which data file contains this variable and can be used to access the variable programatically in a cloud-hosted environment Producer Defined water_quality_benchmarks.parquet The primary dataset containing the water quality benchmarks for CONUS Producer defined Temperature_CTmax_taxa_NHD_CONUS.parquet The dataset containing all the individual taxa-by-taxa records of fishes and their critical thermal maxima by COMID across CONUS Producer defined sb_item_name The name of the Sciencebase Item containing this variable Producer Defined Ecological Water Quality Benchmarks for the Conterminous United States, Attributed to the NHDPlus version 2.1 The title of this data release Producer defined sb_item_url The url to get to the Sciencebase item that contains this variable Producer Defined Website url sb_id The unique Sciencebase identifier that is used for the item containing this variable Producer Defined Sciencebase ID Sciencebase An additional data dictionary table, called variable_lookup.csv is also provided which provides information on entity and attributes in a tabular format, as well as additional metadata to to facilitate (optional) programmatic access and data querying capabilities of the parquet files when hosted in the cloud and using the USGS ScienceBase application programming interface (API). Matthew J. Cashman, Taylor E. Woods, Kelly O. Maloney, Daren M. Carlisle, and Lindsey J. Boyle, 20250901, Ecological Water Quality Benchmarks for the Conterminous United States, Attributed to the NHDPlus version 2.1: U.S. Geological Survey, https://doi.org/10.5066/P1FBQTVT. GS ScienceBase U.S. Geological Survey mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P1FBQTVT None 20260302 Matthew J Cashman USGS - WATER Physical Scientist mailing and physical

MD/DE/DC WSC UMBC Campus, UMBC Research Park

Catonsville MD 21228 443-498-5511 mcashman@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998