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.

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 Yampa River Basin, Colorado, from 1999 to 2018. All input data, including discrete and continuous streamflow records and discrete concentrations of suspended sediment, Kjeldahl 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), LOAD ESTimator (LOADEST), 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.

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 Yampa River Basin, Colorado, from 1999 to 2018. All input data, including discrete and continuous streamflow records and discrete concentrations of suspended sediment, Kjeldahl 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), LOAD ESTimator (LOADEST), 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.

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 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.

This data release contains the input and output used to assess the potential effect of climate on streamflow and salinity (measured as total dissolved solids [TDS]) yields across the Upper Colorado River Basin from water years 1986 to 2021. This analysis included estimation of the spatiotemporal variability in mean annual climatic variables (air temperature, snow water equivalent, precipitation and antecedent precipitation), mean annual streamflow yields, and mean annual TDS yields at 34 sites within the basin. Generalized Additive Models (GAMs) were used to look at non-linear trends in streamflow and TDS yields in the Upper Colorado River Basin. GAMs were also used to create attribution models that explain temporal variability in streamflow and TDS using climate variables (precipitation, snow, and air temperature). A detailed description of the analysis is provided in the associated journal article.

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.

The primary data set consists of continuous water-quality data (temperature, specific conductance, pH, dissolved oxygen, turbidity, nitrate plus nitrite, and streamflow) from in-situ equipment, and discrete water-quality samples (total nitrogen, total phosphorus, suspended sediment concentration, and suspended sediment sieve diameter) collected during site visits at the USGS streamgage Iroquois River near Foresman, Indiana, April 7, 2015 to July 19, 2018. These continuous and discrete measurements were used to develop regression models which may be used to compute concentrations and loads of total nitrogen and total phosphorus. The secondary data set consists of daily streamflow, daily nitrate, daily turbidity and daily specific conductance values collected continuously by in-situ monitors at Iroquois River near Foresman, Indiana March 20, 2015 to July 19, 2018 which serve as input explanatory variables for the developed regression models to compute total nitrogen and total phosphorus at Iroquois River near Foresman. The tertiary data set for March 20, 2015 to July 19, 2018 is the output data set that was developed by application of the regression models and includes the computed daily mean total nitrogen and total phosphorus concentrations (concentration, upper 95-percent prediction interval, and lower 95-percent prediction interval) and daily mean total nitrogen and total phosphorus loads (load, upper 95-percent prediction interval, and lower 95-percent prediction interval).

The primary data set consists of continuous water-quality data (temperature, specific conductance, pH, dissolved oxygen, turbidity, nitrate plus nitrite, and streamflow) from in-situ equipment, and discrete water-quality samples (total nitrogen, total phosphorus, suspended sediment concentration, and suspended sediment sieve diameter) collected during site visits at the USGS streamgage Iroquois River near Foresman, Indiana, April 7, 2015 to July 19, 2018. These continuous and discrete measurements were used to develop regression models which may be used to compute concentrations and loads of total nitrogen and total phosphorus. The secondary data set consists of daily streamflow, daily nitrate, daily turbidity and daily specific conductance values collected continuously by in-situ monitors at Iroquois River near Foresman, Indiana March 20, 2015 to July 19, 2018 which serve as input explanatory variables for the developed regression models to compute total nitrogen and total phosphorus at Iroquois River near Foresman. The tertiary data set for March 20, 2015 to July 19, 2018 is the output data set that was developed by application of the regression models and includes the computed daily mean total nitrogen and total phosphorus concentrations (concentration, upper 95-percent prediction interval, and lower 95-percent prediction interval) and daily mean total nitrogen and total phosphorus loads (load, upper 95-percent prediction interval, and lower 95-percent prediction interval).