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 files and data included in this archive allow readers to inspect and reproduce the model results reported in Neff et al. (2020). Please refer to the included ReadMe file for a further explanation of individual files and step-by-step instructions for running the models.

The dataset supported findings in the study: "Evaluation of SWAT reservoir, ponds, and wetlands tools in water and sediment simulation in the Rock River watershed". Results of this study demonstrate the impact of impoundments in SWAT modeling.The dataset includes sources of the SWAT input data. This dataset is associated with the following publication: Jalowska, A., and Y. Yuan. Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 55(1): 209-227, (2019).

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 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 U.S. Geological Survey (USGS) data release contains model scenario input and output files and nine hydrology and water-quality models developed using the Soil and Water Assessment Tool (SWAT). The models represent nine watersheds in eastern New York that drain to Lake Erie or the Niagara River and were created, calibrated, and validated for hydrology, sediment, and nutrients as baseline scenarios. Twenty-six additional scenarios were created to explore the effects of agricultural and urban best management practices, point source discharges, and green infrastructure on the water quality of tributaries to Lake Erie/Niagara River. Model documentation and scenario development are described in Merriman and others (2024).

This section of the data release supports an archive of the models used in the associated publication. The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek and West Plum Creek in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids – all of which are included in the associated data release. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the models included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events.

This section of the data release supports an archive of the models used in the associated publication. The U.S. Geological Survey and the University of Wisconsin – Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at Mainstem Plum Creek and West Plum Creek in northeastern Wisconsin. Monitoring data from 2010–2020 at Mainstem Plum and 2013–2020 at West Plum were used to detect changes in hydrologic and water-quality responses during runoff events. Runoff events were defined by hydrographers and used to compute event loads and event flow-weighted mean concentrations of total phosphorus and total suspended solids – all of which are included in the associated data release. Additionally, changes in these parameters were assessed between two time periods (“initial” and “post-CMP implementation”) using the models included in this model archive. Because event discharges, loads, and concentrations are influenced by factors such as weather and the conditions preceding events, random-forest and regression models were developed to control for these factors and to elucidate water-quality changes more directly associated with CMP implementation. Residuals from random-forest models were used to detect changes between the two time periods via Wilcoxon signed-rank tests, and multiple linear regression models were used to determine percent change in responses via time-period dummy variable coefficients. Results indicate statistically insignificant changes in most responses during runoff events.

This data release contains inputs for and outputs from hydrologic simulations of the southeastern U.S. using the Monthly Water Balance Model, the Precipitation Runoff Modeling System (PRMS), and statistically-based methods. These simulations were developed to provide estimates of water availability and statistics of streamflow for historical and potential future conditions for an area of approximately 1.16 million square miles. These model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (LaFontaine and others, 2019); they include four types of data: 1) model input parameters, 2) model output statistics, 3) GIS files of the model hydrologic response units and stream segments, and 4) statistically-based streamflow estimates for headwater watersheds. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., 2019, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U.S. Geological Survey Scientific Investigations Report, 2019-5039, 83 p., https://doi.org/10.3133/sir20195039.

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.