,The Soil and Water Hub is jointly developed by USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, part of The Texas A&M University System. Modeling dataset resources are available for download for use with software tools Agricultural Policy/Environmental eXtender Model (APEX), Soil and Water Assessment Tool (SWAT), ArcSWAT, and related Conservation practices.,,

,[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526332 ],This dataset contains soil water content data developed from neutron probe readings taken in access tubes in two of the four large, precision weighing lysimeters and in the fields surrounding each lysimeter that were planted to winter wheat at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) beginning in 1989. Data in each spreadsheet are for one winter wheat growing season, either 1989-1990, 1991-1992, or 1992-1993. Other readings taken in those years for other crops are reported elsewhere. Data for the 1989-1990 season and the 1992-1993 season are from the northwest (NW) and southwest (SW) weighing lysimeters and surrounding fields. Data for the 1991-1992 season are from the northeast (NE) and southeast (SE) weighing lysimeters and surrounding fields. Readings were taken periodically with a field-calibrated neutron probe at depths from 10 cm to 230 cm (maximum of 190 cm depth in the lysimeters) in 20-cm depth increments. Periods between readings were typically one to two weeks, sometimes longer according to experimental design and need for data. Field calibrations in the Pullman soil series were done every few years. Calibrations typically produced a regression equation with RMSE <= 0.01 m3 m-3 (e.g., Evett and Steiner, 1995). Data were used to guide irrigation scheduling to achieve full or deficit irrigation as required by the experimental design. Data may be used to calculate the soil profile water content in mm of water from the surface to the maximum depth of reading. Profile water content differences between reading times in the same access tube are considered the change in soil water storage during the period in question and may be used to compute evapotranspiration (ET) using the soil water balance equation: ET = (change in storage + P + I + F + R, where P is precipitation during the period, I is irrigation during the period, F is soil water flux (drainage) out of the bottom of the soil profile during the period, and R is the sum of runon and runoff during the period. Typically, R is taken as zero because the fields were furrow diked to prevent runon and runoff during most of each growing season.,,

,[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526332 ],This dataset contains soil water content data developed from neutron probe readings taken in access tubes in each of the four large, precision weighing lysimeters and in the fields surrounding each lysimeter at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) beginning in 1989. Readings were taken periodically with a field-calibrated neutron probe at depths from 10 cm to 230 cm (maximum of 190 cm depth in the lysimeters) in 20-cm depth increments. Periods between readings were typically one to two weeks, sometimes longer according to experimental design and need for data. Field calibrations in the Pullman soil series were done every few years. Calibrations typically produced a regression equation with RMSE <= 0.01 m3 m-3 (e.g., Evett and Steiner, 1995). Data were used to guide irrigation scheduling to achieve full or deficit irrigation as required by the experimental design. Data may be used to calculate the soil profile water content in mm of water from the surface to the maximum depth of reading. Profile water content differences between reading times in the same access tube are considered the change in soil water storage during the period in question and may be used to compute evapotranspiration (ET) using the soil water balance equation: ET = (change in storage + P + I + F + R, where P is precipitation during the period, I is irrigation during the period, F is soil water flux (drainage) out of the bottom of the soil profile during the period, and R is the sum of runon and runoff during the period. Typically, R is taken as zero because the fields were furrow diked to prevent runon and runoff during most of each growing season.,,

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" (https:// doi:10.5066/pending), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW models.

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" (https:// doi:10.5066/pending), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW models.

,The Soil and Water Assessment Tool (SWAT) is a public domain model jointly developed by USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, part of The Texas A&M University System. SWAT is a small watershed to river basin-scale model to simulate the quality and quantity of surface and ground water and predict the environmental impact of land use, land management practices, and climate change. SWAT is widely used in assessing soil erosion prevention and control, non-point source pollution control and regional management in watersheds.,,

This dataset is part of the National Water Census Water Budget Estimation and Evaluation Project's ongoing development of best estimates of daily historical water budgets for about 100,000 hydrologic units across the United States. In this release, estimates of soil moisture and recharge are added to the already released estimates of streamflow and precipitation. All these estimates are made available per twelve-digit hydrologic unit code watershed as contained in the NHDPlusV2 dataset. As this project progresses, it is expected that a complete closed water budget generated from the same water budget model will succeed this data release. For background on generation of these wate rbudget variables, see: Hay, L.E., 2019, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), by HRU Calibrated Version: U.S. Geological Survey data release, https://doi.org/10.5066/P9NM8K8W The water budget variables were converted to a hydrologic unit basis using area-weighted spatial interpolation. All code used for conversions are included as an attachment to this data release. Summary of Files Included: 1) hu12_ids.csv - twelve digit hydrologic unit code identifiers used in all files. 2) timesteps.csv - timesteps used in all files. 3) nhm_recharge.nc - NetCDF version of recharge data 4) nhm_recharge_grid.csv - One timestep per row version of recharge data 5) nhm_recharge_timeseries.csv - One HUC12 per row version of recharge data 6) nhm_soil_moist.nc - NetCDF version of soil moisture data 7) nhm_soil_moisture_grid.csv - One timestep per row version of soil moisture data 8) nhm_soil_moisture_timeseries.csv - One HUC12 per row version of soil moisture data 9) scripts.zip - A reproducible R workflow to create outputs implemented with the drake package.

This dataset is part of the National Water Census Water Budget Estimation and Evaluation Project's ongoing development of best estimates of daily historical water budgets for about 100,000 hydrologic units across the United States. In this release, estimates of soil moisture and recharge are added to the already released estimates of streamflow and precipitation. All these estimates are made available per twelve-digit hydrologic unit code watershed as contained in the NHDPlusV2 dataset. As this project progresses, it is expected that a complete closed water budget generated from the same water budget model will succeed this data release. For background on generation of these wate rbudget variables, see: Hay, L.E., 2019, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), by HRU Calibrated Version: U.S. Geological Survey data release, https://doi.org/10.5066/P9NM8K8W The water budget variables were converted to a hydrologic unit basis using area-weighted spatial interpolation. All code used for conversions are included as an attachment to this data release. Summary of Files Included: 1) hu12_ids.csv - twelve digit hydrologic unit code identifiers used in all files. 2) timesteps.csv - timesteps used in all files. 3) nhm_recharge.nc - NetCDF version of recharge data 4) nhm_recharge_grid.csv - One timestep per row version of recharge data 5) nhm_recharge_timeseries.csv - One HUC12 per row version of recharge data 6) nhm_soil_moist.nc - NetCDF version of soil moisture data 7) nhm_soil_moisture_grid.csv - One timestep per row version of soil moisture data 8) nhm_soil_moisture_timeseries.csv - One HUC12 per row version of soil moisture data 9) scripts.zip - A reproducible R workflow to create outputs implemented with the drake package.

As part of the Coastal Carolinas Focus Area Study of the U.S. Geological Survey National Water Census Program, the Soil and Water Assessment Tool (SWAT) was used to develop models for the Pee Dee River Basin, North Carolina and South Carolina, to simulate future streamflow and irrigation demand based on land use, climate, and water demand projections. SWAT is a basin-scale, process-based watershed model with the capability of simulating water-management scenarios. Model basins were divided into approximately two-square mile subbasins and subsequently divided into smaller, discrete hydrologic response units based on land use, slope, and soil type. The calibration period for the historic model was 2000 to 2014. The best available data on water-use from this time period were used, including public water supply, industrial water use, irrigation needs and golf courses. Six future scenario models simulated streamflow during the period 2055 to 2065 based on incorporation of two alternative land use projections, an ensemble of three global climate models, and water demand forecasts. This USGS data release contains all the input and output files for the simulations described in the associated model documentation report (https://doi.org/10.3133/sir20235036).

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.