The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based streamflow and water-quality load estimates. Streamflow and load estimates considered for use in regional SPARROW model applications (2012 base year) are described in Saad and others, 2019 (https://dx.doi.org/10.3133/sir20195069). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2012 regional SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical⁄process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. Load estimates used in recent Midcontinent SPARROW model applications (2002 base year) are described in Saad and others, 2018 (https://doi.org/10.3133/sir20185051). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release, prepared in cooperation with the International Joint Commission, contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2002 Midcontinent SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

The United States Geological Survey’s (USGS) SPAtially Referenced Regressions On Watershed attributes (SPARROW) model was developed to aid in the interpretation of monitoring data and simulate water-quality conditions in streams across large spatial scales. SPARROW is a hybrid empirical⁄process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. Load estimates used in recent Midcontinent SPARROW model applications (2002 base year) are described in Saad and others, 2018 (https://doi.org/10.3133/sir20185051). Load estimation methods described in this report include the Beale Ratio Estimator and Fluxmaster models. This USGS data release, prepared in cooperation with the International Joint Commission, contains all of the input and output files necessary to reproduce the load estimates considered for inclusion in the 2002 Midcontinent SPARROW models. Data preparation for input to the load estimation models is also fully described in the above-mentioned report.

This dataset utilized available water-quality data from the Mississippi Department of Environmental Quality and streamflow from the U.S. Geological Survey to estimate total nitrogen and total phosphorus loads and changes in loads from water years 2008 through 2018. Nutrient loads and changes in loads were estimated at 22 state ambient water-quality network sites, and were estimated using LOADEST regression models, Beale-Ratio Estimator, or WRTDS (Weighted Regression on Time, Discharge, and Season). The method selected is based on the evaluation of the flux-bias statistic and use of multiple graphical tools through EGRET to identify and characterize issues with particular models for each given dataset and is included in this data release.

This dataset utilized available water-quality data from the Mississippi Department of Environmental Quality and streamflow from the U.S. Geological Survey to estimate total nitrogen and total phosphorus loads and changes in loads from water years 2008 through 2018. Nutrient loads and changes in loads were estimated at 22 state ambient water-quality network sites, and were estimated using LOADEST regression models, Beale-Ratio Estimator, or WRTDS (Weighted Regression on Time, Discharge, and Season). The method selected is based on the evaluation of the flux-bias statistic and use of multiple graphical tools through EGRET to identify and characterize issues with particular models for each given dataset and is included in this data release.

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.

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.

Data provided in this release support the findings in Choquette et al. (2019), utilizing methods for evaluating water-quality and daily-streamflow trends described also in Hirsch and DeCicco (2015 and 2018a) and Hirsch (2018). The trend results and model-input data focus on 10 locations in the Lake Erie watershed that have long-term (20 or more years) water-quality and streamflow monitoring records. The trend results include the years 1987 through 2016 or specified sub-periods during this time frame. The model-input data records spanned the time period 1974 through 2016 although record lengths varied by site, data type, and trend analysis. The water-quality records were provided by the National Center for Water Quality Research (NCWQR; Heidelberg University, Tiffin, Ohio) and the Indiana Department of Environmental Management (IDEM), and streamflow records were provided by the U.S. Geological Survey (USGS). The 10 water-quality trend sites were identified using abbreviated names of the nearby USGS streamgage that provided streamflow data for determining nutrient fluxes at these sites (see Site_map.pdf and Site-summary_table.csv). Trends in flow-normalized nutrient fluxes were determined using the method Weighted Regression on Time, Discharge, and Season (WRTDS) method (Hirsch and DeCicco, 2015, 2018a, and 2018b) and streamflow (discharge) trends were determined using the graphical-statistical method of Quantile-Kendall plots (Hirsch, 2018). The nutrient trend analyses focus on the parameters total phosphorus (TP, as P), soluble reactive phosphorus (SRP, as P), total nitrogen (TN, as N), nitrate plus nitrite (NO23, as N) filtered at NCWQR sites or unfiltered at IDEM sites, and total Kjeldahl nitrogen (TKN, as N). TN was calculated as TKN plus NO23. SRP was monitored at only 6 of the 10 trend sites. Additional information on field and laboratory methods appears in Choquette et al. (2019). The dataset is presented in two parts: 1. Nutrient and Streamflow Model-Input Data 2. Nutrient and Streamflow Trend Results References: Choquette, A.F., Hirsch, R.M., Murphy, J.C., Johnson, L.T., and Confesor, R.B. Jr., 2019, Tracking changes in nutrient delivery to western Lake Erie: approaches to compensate for variability and trends in streamflow: J. of Great Lakes Research, v. 45, no. 1, p. 21-39, https://doi.org/10.1016/j.jglr.2018.11.012. Hirsch, R.M., 2018, Daily streamflow trend analysis: U.S. Geological Survey Office of Water Information Blog, 38 p., at: https://owi.usgs.gov/blog/Quantile-Kendall/. Hirsch, R.M., and De Cicco, L.A., 2015 (revised). User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data, Version 2.0, U.S. Geological Survey Techniques Methods, 4-A10. U.S. Geological Survey, Reston, VA., 93 p. (at: http://dx.doi.org/10.3133/tm4A10). Hirsch, R.M., and De Cicco, L.A., 2018a, Guide to EGRET 3.0 Enhancements: at https://cran.r-project.org/web/packages/EGRET/vignettes/Enhancements.html. Hirsch, R.M., and De Cicco, L.A., 2018b, EGRET release 3.0, and EGRETci release 2.0, at: https://cran.r-project.org/ .

Data provided in this release support the findings in Choquette et al. (2019), utilizing methods for evaluating water-quality and daily-streamflow trends described also in Hirsch and DeCicco (2015 and 2018a) and Hirsch (2018). The trend results and model-input data focus on 10 locations in the Lake Erie watershed that have long-term (20 or more years) water-quality and streamflow monitoring records. The trend results include the years 1987 through 2016 or specified sub-periods during this time frame. The model-input data records spanned the time period 1974 through 2016 although record lengths varied by site, data type, and trend analysis. The water-quality records were provided by the National Center for Water Quality Research (NCWQR; Heidelberg University, Tiffin, Ohio) and the Indiana Department of Environmental Management (IDEM), and streamflow records were provided by the U.S. Geological Survey (USGS). The 10 water-quality trend sites were identified using abbreviated names of the nearby USGS streamgage that provided streamflow data for determining nutrient fluxes at these sites (see Site_map.pdf and Site-summary_table.csv). Trends in flow-normalized nutrient fluxes were determined using the method Weighted Regression on Time, Discharge, and Season (WRTDS) method (Hirsch and DeCicco, 2015, 2018a, and 2018b) and streamflow (discharge) trends were determined using the graphical-statistical method of Quantile-Kendall plots (Hirsch, 2018). The nutrient trend analyses focus on the parameters total phosphorus (TP, as P), soluble reactive phosphorus (SRP, as P), total nitrogen (TN, as N), nitrate plus nitrite (NO23, as N) filtered at NCWQR sites or unfiltered at IDEM sites, and total Kjeldahl nitrogen (TKN, as N). TN was calculated as TKN plus NO23. SRP was monitored at only 6 of the 10 trend sites. Additional information on field and laboratory methods appears in Choquette et al. (2019). The dataset is presented in two parts: 1. Nutrient and Streamflow Model-Input Data 2. Nutrient and Streamflow Trend Results References: Choquette, A.F., Hirsch, R.M., Murphy, J.C., Johnson, L.T., and Confesor, R.B. Jr., 2019, Tracking changes in nutrient delivery to western Lake Erie: approaches to compensate for variability and trends in streamflow: J. of Great Lakes Research, v. 45, no. 1, p. 21-39, https://doi.org/10.1016/j.jglr.2018.11.012. Hirsch, R.M., 2018, Daily streamflow trend analysis: U.S. Geological Survey Office of Water Information Blog, 38 p., at: https://owi.usgs.gov/blog/Quantile-Kendall/. Hirsch, R.M., and De Cicco, L.A., 2015 (revised). User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data, Version 2.0, U.S. Geological Survey Techniques Methods, 4-A10. U.S. Geological Survey, Reston, VA., 93 p. (at: http://dx.doi.org/10.3133/tm4A10). Hirsch, R.M., and De Cicco, L.A., 2018a, Guide to EGRET 3.0 Enhancements: at https://cran.r-project.org/web/packages/EGRET/vignettes/Enhancements.html. Hirsch, R.M., and De Cicco, L.A., 2018b, EGRET release 3.0, and EGRETci release 2.0, at: https://cran.r-project.org/ .

This dataset includes input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper White River Basin, Colorado, from 2000 to 2020. All input data, including discrete and continuous streamflow records and discrete concentrations of inorganic nitrogen, total nitrogen, and total phosphorus, were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS) database. Input data were used in multiple models including Maintenance of Variance Extension Type 2 (MOVE.2) and Weighted Regressions on Time, Discharge, and Season (WRTDS) to estimate continuous streamflow records, daily concentrations and loads, and streamflow-normalized annual mean concentrations and loads of selected water-quality constituents.