The data are: 1) compilation of field observed nutrient and hydrometeorological data for the Upper Fish River Watershed (UFRW); 2) wetland and GIS data downloaded from national repositories for UFRW; 3) wetland nutrient data generated by the models for the UFRW; 4) output data (nutrient loads and removal rates) produced by the SWAT-WetQual (watershed-wetland) model framework for the UFRW; 5) global wetland nutrient function data obtained from literature; and 6) model data used in developing statistical regression relationships for nutrient removal rates and efficiencies. Nutrients: Nitrate and Orthophosphate.

The dataset includes information to make Figure 4 within the manuscript. This dataset is associated with the following publication: Golden, H., A. Rajib, C. Lane, J. Christensen, Q. Wu, and S. Mengistu. Non-floodplain Wetlands Affect Watershed Nutrient Dynamics: A Critical Review. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(13): 7203-7214, (2019).

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 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.

The data are comprised of input and output data from Machine Learning models that were developed to predict watershed health (WH) values in HUC-10 sub-watersheds within three major Midwest river basins. The input data included timeseries of hydro-meteorological and reconstructed WQ parameters (sediment, nitrogen, and phosphorus) as well as GIS shape files of watershed attributes (soil, landcover/land use, geomorphology, drainage classes, fertilizer sale data, etc. ). The output data is ensemble-model estimated annual WH values in HUC-10 sub-watersheds within the three river basins. The ensemble-model predicted WH values are derived from WH values obtained from three trained and validated machine learning models. This dataset is associated with the following publication: Mallya, G., M.M. Hantush, and R.S. Govindaraju. A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins. WATER. MDPI, Basel, SWITZERLAND, 15(3): 586, (2023).

The data are comprised of input and output data from Machine Learning models that were developed to predict watershed health (WH) values in HUC-10 sub-watersheds within three major Midwest river basins. The input data included timeseries of hydro-meteorological and reconstructed WQ parameters (sediment, nitrogen, and phosphorus) as well as GIS shape files of watershed attributes (soil, landcover/land use, geomorphology, drainage classes, fertilizer sale data, etc. ). The output data is ensemble-model estimated annual WH values in HUC-10 sub-watersheds within the three river basins. The ensemble-model predicted WH values are derived from WH values obtained from three trained and validated machine learning models. This dataset is associated with the following publication: Mallya, G., M.M. Hantush, and R.S. Govindaraju. A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins. WATER. MDPI, Basel, SWITZERLAND, 15(3): 586, (2023).

This data release contains the watershed ancillary data that were used as input for a set of dynamic Spatially Referenced Regression On Watershed attributes (SPARROW) models for watersheds draining to Puget Sound and the Strait of Juan de Fuca, Washington for the years 2005 - 2020. The SPARROW models were used to estimate mean seasonal total nitrogen and total phosphorous conditions and the delivery of those nutrients to Puget Sound and the Strait of Juan de Fuca. The data sets in each child item, which consists of a collection of CSV files, represents landscape conditions in the incremental catchments that made up the hydrologic network used in the SPARROW modeling. The conditions for catchments that were partially or fully outside the domain of the original landscape data were estimated by extrapolating the conditions from nearby catchments.

This data release contains the watershed ancillary data that were used as input for a set of dynamic Spatially Referenced Regression On Watershed attributes (SPARROW) models for watersheds draining to Puget Sound and the Strait of Juan de Fuca, Washington for the years 2005 - 2020. The SPARROW models were used to estimate mean seasonal total nitrogen and total phosphorous conditions and the delivery of those nutrients to Puget Sound and the Strait of Juan de Fuca. The data sets in each child item, which consists of a collection of CSV files, represents landscape conditions in the incremental catchments that made up the hydrologic network used in the SPARROW modeling. The conditions for catchments that were partially or fully outside the domain of the original landscape data were estimated by extrapolating the conditions from nearby catchments.