Chloride data used to assess trends over time, using both USGS data for trends in loads and USEPA NRSA data used to assess trends in concentrations. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. To describe the long-term trends in the surface water quality component of water availability, data from the USGS and other Federal, State, and local agencies were accessed primarily through the US EPA's Water Quality Portal (https://www.waterqualitydata.us/) in 2023 and used in trend analyses. This USGS data release contains all the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models, using data preparation methods described in Oelsner and others, 2017 for individual monitoring locations. Models were calibrated for each combination of site and parameter using the screened input data. Models were run on Tallgrass, the USGS supercomputer, in separate run for each parameter. Once calibrated, the WRTDS models were initially evaluated using a logistic regression equation that estimated a probability of acceptance for each model (e.g., "a good fit") based on a set of diagnostic metrics derived from the observed, estimated, and residual values from each model and data set (Murphy and Chanat, 2023). Each WRTDS model was assigned to one of three categories: “auto-accept,” “auto-reject,” or “manual evaluation". Models assigned to the latter category were visually evaluated for appropriate model fit using residual and diagnostic plots. Models assigned to the first two categories were automatically included or rejected from the final results, respectively. Seven water-quality parameters were assessed, including nutrients (nitrate, filtered orthophosphate, total nitrogen, and total phosphorus), salinity indicators (chloride and specific conductance), and sediment (suspended sediment concentration). Trends are reported for three trend periods: 1980-2020, 2000-2020, and the longest period of record at each site.

The U.S. Geological Survey (USGS) developed a systematic, quantitative approach to prioritize candidate basins that can support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC4) with some of the smaller HUC4s being combined (hereafter referred to as modified HUC4 basins). Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Thirty-three geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were initially considered to assist in ranking candidate basins for study. The two highest ranking candidate basins in each of the 18 regions were identified as semi-finalists for selection as “Integrated Water Science Basins.” The initial 33 geospatial variables are included within this data release. Also included in this data release are the polygon layers of the modified HUC4 basins and the hydrologic regions used for the analyses and a separate data dictionary to define the variables.

The U.S. Geological Survey (USGS) developed a systematic, quantitative approach to prioritize candidate basins that can support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC4) with some of the smaller HUC4s being combined (hereafter referred to as modified HUC4 basins). Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Thirty-three geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were initially considered to assist in ranking candidate basins for study. The two highest ranking candidate basins in each of the 18 regions were identified as semi-finalists for selection as “Integrated Water Science Basins.” The initial 33 geospatial variables are included within this data release. Also included in this data release are the polygon layers of the modified HUC4 basins and the hydrologic regions used for the analyses and a separate data dictionary to define the variables.

The U.S. Geological Survey (USGS) developed a systematic, quantitative approach to prioritize candidate basins that can support the assessment and forecasting objectives of the major USGS water science programs. Candidate basins were the level-4 hydrologic units (HUC4) with some of the smaller HUC4s being combined (hereafter referred to as modified HUC4 basins). Candidate basins for the contiguous United States (CONUS) were grouped into 18 hydrologic regions. Thirty-three geospatial variables representing land use, climate change, water use, water-balance components, streamflow alteration, fire risk, and ecosystem sensitivity were initially considered to assist in ranking candidate basins for study. The two highest ranking candidate basins in each of the 18 regions were identified as semi-finalists for selection as “Integrated Water Science Basins.” The initial 33 geospatial variables are included within this data release. Also included in this data release are the polygon layers of the modified HUC4 basins and the hydrologic regions used for the analyses and a separate data dictionary to define the variables.

Nonstationary streamflow due to environmental and human-induced causes can affect water quality over time, yet these effects are poorly accounted for in water-quality trend models. This data release provides instream water-quality trends and estimates of two components of change, for sites across the Nation previously presented in Oelsner et al. (2017). We used previously calibrated Weighted Regressions on Time, Discharge, and Season (WRTDS) models published in De Cicco et al. (2017) to estimate instream water-quality trends and associated uncertainties with the generalized flow normalization procedure available in EGRET version 3.0 (Hirsch et al., 2018a) and EGRETci version 2.0 (Hirsch et al., 2018b). The procedure allows for nonstationarity in the flow regime, whereas previous versions of EGRET assumed streamflow stationarity. Water-quality trends of annual mean concentrations and loads (also referred to as fluxes) are provided as an annual series and the change between the start and end year for four trend periods (1972-2012, 1982-2012, 1992-2012, and 2002-2012). Information about the sites, including the collecting agency and associated streamflow gage, and information about site selection and the data screening process can be found in Oelsner et al. (2017). This data release includes results for 19 water-quality parameters including nutrients (ammonia, nitrate, filtered and unfiltered orthophosphate, total nitrogen, total phosphorus), major ions (calcium, chloride, magnesium, potassium, sodium, sulfate), salinity indicators (specific conductance, total dissolved solids), carbon (alkalinity, dissolved organic carbon, total organic carbon), and sediment (total suspended solids, suspended-sediment concentration) at over 1,200 sites. Note, the number of parameters with data varies by site with most sites having data for 1-4 parameters. Each water-quality trend was parsed into two components of change: (1) the streamflow trend component (QTC) and (2) the watershed management trend component (MTC). The QTC is an indicator of the amount of change in the water-quality trend attributed to changes in the streamflow regime, and the MTC is an indicator of the amount of change in the water-quality trend that may be attributed to human actions and changes in point and non-point sources in a watershed. Note, the MTC is referred to as the concentration-discharge trend component (CQTC) in the EGRET version 3.0 software. For our work, we chose to refer to this trend component as the MTC because it provides a more conceptual description (Murphy and Sprague, 2019). The trend results presented here expand upon the results in De Cicco et al. (2017) and Oelsner et al. (2017), which were analyzed using flow-normalization under the stationary streamflow assumption. The results presented in this data release are intended to complement these previously published results and support investigations into natural and human effects on water-quality trends across the United States. Data preparation information and WRTDS model specifications are described in Oelsner et al. (2017) and Murphy and Sprague (2019). This work was completed as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. De Cicco, L.A., Sprague, L.A., Murphy, J.C., Riskin, M.L., Falcone, J.A., Stets, E.G., Oelsner, G.P., and Johnson, H.M., 2017, Water-quality and streamflow datasets used in the Weighted Regressions on Time, Discharge, and Season (WRTDS) models to determine trends in the Nation’s rivers and streams, 1972-2012 (ver. 1.1 July 7, 2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7KW5D4H. Hirsch, R., De Cicco, L., Watkins, D., Carr, L., and Murphy, J., 2018a, EGRET: Exploration and Graphics for RivEr Trends, version 3.0, https://CRAN.R-project.org/package=EGRET. Hirsch, R., De Cicco, L., and Murphy, J., 2018b, EGRETci: Exploration and Graphics for RivEr Trends (EGRET) Confidence Intervals, version 2.0.

Nonstationary streamflow due to environmental and human-induced causes can affect water quality over time, yet these effects are poorly accounted for in water-quality trend models. This data release provides instream water-quality trends and estimates of two components of change, for sites across the Nation previously presented in Oelsner et al. (2017). We used previously calibrated Weighted Regressions on Time, Discharge, and Season (WRTDS) models published in De Cicco et al. (2017) to estimate instream water-quality trends and associated uncertainties with the generalized flow normalization procedure available in EGRET version 3.0 (Hirsch et al., 2018a) and EGRETci version 2.0 (Hirsch et al., 2018b). The procedure allows for nonstationarity in the flow regime, whereas previous versions of EGRET assumed streamflow stationarity. Water-quality trends of annual mean concentrations and loads (also referred to as fluxes) are provided as an annual series and the change between the start and end year for four trend periods (1972-2012, 1982-2012, 1992-2012, and 2002-2012). Information about the sites, including the collecting agency and associated streamflow gage, and information about site selection and the data screening process can be found in Oelsner et al. (2017). This data release includes results for 19 water-quality parameters including nutrients (ammonia, nitrate, filtered and unfiltered orthophosphate, total nitrogen, total phosphorus), major ions (calcium, chloride, magnesium, potassium, sodium, sulfate), salinity indicators (specific conductance, total dissolved solids), carbon (alkalinity, dissolved organic carbon, total organic carbon), and sediment (total suspended solids, suspended-sediment concentration) at over 1,200 sites. Note, the number of parameters with data varies by site with most sites having data for 1-4 parameters. Each water-quality trend was parsed into two components of change: (1) the streamflow trend component (QTC) and (2) the watershed management trend component (MTC). The QTC is an indicator of the amount of change in the water-quality trend attributed to changes in the streamflow regime, and the MTC is an indicator of the amount of change in the water-quality trend that may be attributed to human actions and changes in point and non-point sources in a watershed. Note, the MTC is referred to as the concentration-discharge trend component (CQTC) in the EGRET version 3.0 software. For our work, we chose to refer to this trend component as the MTC because it provides a more conceptual description (Murphy and Sprague, 2019). The trend results presented here expand upon the results in De Cicco et al. (2017) and Oelsner et al. (2017), which were analyzed using flow-normalization under the stationary streamflow assumption. The results presented in this data release are intended to complement these previously published results and support investigations into natural and human effects on water-quality trends across the United States. Data preparation information and WRTDS model specifications are described in Oelsner et al. (2017) and Murphy and Sprague (2019). This work was completed as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. De Cicco, L.A., Sprague, L.A., Murphy, J.C., Riskin, M.L., Falcone, J.A., Stets, E.G., Oelsner, G.P., and Johnson, H.M., 2017, Water-quality and streamflow datasets used in the Weighted Regressions on Time, Discharge, and Season (WRTDS) models to determine trends in the Nation’s rivers and streams, 1972-2012 (ver. 1.1 July 7, 2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7KW5D4H. Hirsch, R., De Cicco, L., Watkins, D., Carr, L., and Murphy, J., 2018a, EGRET: Exploration and Graphics for RivEr Trends, version 3.0, https://CRAN.R-project.org/package=EGRET. Hirsch, R., De Cicco, L., and Murphy, J., 2018b, EGRETci: Exploration and Graphics for RivEr Trends (EGRET) Confidence Intervals, version 2.0.

This dataset is the basis for the U.S. Geological Survey Scientific Investigations Report "An evaluation of methods for computing annual water quality loads", which utilized available data from Heidelberg University and the U.S. Geological Survey to evaluate the accuracy of various methods for computing annual water-quality loads. This dataset contains two files used in the report: "QW_FLOW.csv", which contains the water-quality sample and streamflow data used to develop water-quality load estimates, and "QW_LOAD.csv", which contains observed and estimated annual loads.

This dataset is the basis for the U.S. Geological Survey Scientific Investigations Report "An evaluation of methods for computing annual water quality loads", which utilized available data from Heidelberg University and the U.S. Geological Survey to evaluate the accuracy of various methods for computing annual water-quality loads. This dataset contains two files used in the report: "QW_FLOW.csv", which contains the water-quality sample and streamflow data used to develop water-quality load estimates, and "QW_LOAD.csv", which contains observed and estimated annual loads.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project. One of the major goals of the NAWQA project was to determine how river water quality has changed over time. To support that goal, long-term consistent and comparable monitoring has been conducted by the USGS on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water quality. In 2017, data from these multiple sources were combined to support one of the most comprehensive assessments to date of water-quality trends in the United States (Oelsner and others, 2017; De Cicco and others, 2017). This data release updates these water quality trends, which ended in 2012, with 5 more years of data and now end in 2017. This USGS data release contains all the input and output files necessary to reproduce the results from the Weighted Regressions on Time, Discharge, and Season (WRTDS) models, using data preparation methods described in Oelsner and others, 2017. Models were calibrated for each combination of site and parameter using the screened input data. Models were run on Yeti, the USGS supercomputer, in 3 separate runs, using the scripts in the "Script.zip" folder. See readMe.txt for details on how the files in this data release are related and the modeling process. "SiteTable.csv" gives information on sites used in this analysis. Once calibrated, the WRTDS models were initially evaluated using a logistic regression equation that estimated a probability of acceptance for each model (e.g., "a good fit") based on a set of diagnostic metrics derived from the observed, estimated, and residual values from each model and data set. Each WRTDS model was assigned to one of three categories: “auto-accept,” “auto-reject,” or “manual evaluation". Models assigned to the latter category were visually evaluated for appropriate model fit using residual and diagnostic plots. Models assigned to the first two categories were automatically included or rejected from the final results, respectively. Twenty-two water-quality parameters were assessed, including nutrients (ammonia, nitrate, filtered orthophosphate, total nitrogen, total phosphorus, and unfiltered orthophosphate), major ions (calcium, bromide, fluoride, chloride, magnesium, potassium, sodium, and sulfate), salinity indicators (total dissolved solids and specific conductance), sediment (total suspended solids and suspended sediment concentration), carbon (dissolved organic carbon, total organic carbon, and particulate organic carbon), and alkalinity. Trends are reported for six periods: 1972-2017, 1982-2017, 1987-2017, 1992-2017, 2002-2017, and 2007-2017.