,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.,,

,[NOTE - 2022-09-07: this dataset is superseded by an updated version https://doi.org/10.15482/USDA.ADC/1526332 ],This dataset contains soil water content data developed from neutron probe readings taken in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.,,

,SPAW is a daily hydrologic budget model for agricultural fields and ponds (wetlands, lagoons, ponds and reservoirs). Included are irrigation scheduling and soil nitrogen. Data input and results are graphical screens.,The SPAW (Soil-Plant-Air-Water) computer model simulates the daily hydrologic water budgets of agricultural landscapes by two connected routines, one for farm fields and a second for impoundments such as wetland ponds, lagoons or reservoirs. Climate, soil and vegetation data files for field and pond projects are selected from those prepared and stored with a system of interactive screens. Various combinations of the data files readily represent multiple landscape and ponding variations.,

,[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.,,

This model archive makes available the calibrated Soil-Water-Balance (SWB) model used to simulate potential recharge for the Mississippi Alluvial Aquifer for 1915 to 2018. The model was calibrated using monthly values of evapotranspiration and annual values of runoff and recharge for 19 drainage basins selected from within or nearby the Mississippi Alluvial Aquifer system. The calibrated SWB model and its use are described in the associated U.S. Geological Survey Open-File Report 2021-1008. The model was used to create output at two different scales: 1,609-meter and 1,000-meter grid cells. Also included are files used to generate a high-resolution (100-meter) subset of output for an area near Shellmound, Mississippi. The directory structure of the model archive contains all of the files needed to document and run the model for a short example time period. This archive *does not* include all daily weather data drivers needed to replicate the model output; those files consume tens of gigabytes of storage space and are available elsewhere on the Internet (sources and online links to these data are provided in the source information section of the metadata). The directories in the archive are presented each as a separate .zip file and include a "bin" directory, a "georef" directory, a "model directory, an "output" directory, and a "source" directory. There is a README file describing all the files and directories in the archive and information on how to run the model. Each primary folder also contains a README file describing the contents.

,Spatial data on soils, land use, and topography, combined with knowledge of conservation effectiveness can be used to identify alternatives to reduce nutrient discharge from small watersheds. This database was developed to be used in conjunction with the Agricultural Conservation Planning Framework Toolkit.,Data comprise soil survey information and land use. Soil characterization data were extracted from the Natural Resources Conservation Service (NRCS) Web Soil Survey (Soil Survey Staff, 2013). Land use coverages were developed to represent agricultural fields and the types and rotations of agricultural crops and other land cover types. Land use boundaries were produced by editing a publicly available USDA field boundaries dataset (pre-2008), with all ownership and county-level attributes removed. To ensure these field polygons were consistent with recent land use, the 2009 Cropland Data Layer (USDA-NASS, 2013) was examined for all fields larger than 16 ha. For those fields with multiple cover types, 2009 National Agricultural Imagery Program (NAIP) aerial photography was used as a basis to manually edit field boundaries. A field was considered to have multiple cover types and was edited if the dominant cover occupied <75% of the field, as indicated by the 2009 Cropland Data Layer. Updated field boundaries were then overlaid with data from USDA-National Agricultural Statistics Service (2013) Cropland Data Layer for 2000 – 2014, and each field was classified to represent crop rotations and land cover using the most recent six-year (2009-2014) sequence of land cover. Six-year land-cover strings (e.g., corn-corn-soybean-corn-soybean-corn) generated for each field were classified to represent major crop rotations, which were dominantly comprised of corn (Zea mays L.) and soybean (Glycine max (L.) Merr) annual row crops.,,The database does not include high-resolution digital elevation models (DEMs) derived from LiDAR (light detection and ranging) survey data, although these are needed by the Agricultural Conservation Planning Framework Toolkit and must be obtained independently.,Database is scheduled to become available on October 1, 2015.,

This model archive provides input and output for Soil-Water-Balance (SWB) models developed for the Central Sands Lake study in central Wisconsin; this archive supplements the technical appendix in a report to the Wisconsin State Legislature written by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed DNR to determine whether existing and potential groundwater withdrawals are causing or are likely to cause significant reduction of mean seasonal water levels at Pleasant Lake, Long Lake, and Plainfield Lake (s. 281.34(7m)(2)(b), Wis. Stats.) in Waushara County, Wisconsin. The Soil-Water-Balance code (Westenbroek and others, 2018) partitions precipitation into rainfall and snowmelt, simulates the change in soil moisture within the root zone of crops and other vegetation, and estimates potential crop irrigation water requirements based on the needs of the vegetation. The amount of water escaping the root zone of plants (net infiltration or potential recharge) and the estimated crop water demand were fed into a related groundwater flow model in order to evaluate how landscape-level changes in crop type and irrigation requirements end up affecting groundwater and lake levels over time. The associated groundwater flow model is contained in a separate ScienceBase archive (https://doi.org/10.5066/P9BVFSGJ). The period of 2012-2018 was used for parameter estimation (synonymously referred to as "history matching") for the groundwater models. This time period was chosen because it includes the most complete water use records to simulate groundwater withdrawals. The SWB2 model run for this period (called 'regional' under the directory that contains simulation scenarios, run at a resolution of 100m) was used to supply only net infiltration (potential recharge) values to the groundwater flow model. History matching was performed using groundwater elevations, lake stages, and streamflow observations over the 2012-2018 time period and processed observations derived from those raw data. A set of lower-resolution (200m) scenario runs were made to support the WDNR in their charge to evaluate the impact of water withdrawals on lake elevations. Three scenarios were created, driven by daily weather data as estimated by PRISM data (PRISM Climate Group, 2020) spanning the period 1981 to 2018. These scenarios, although based on real daily weather data, rely on three synthetic sets of input data and therefore should not be viewed as representing any specific time period. The scenarios represent: 1) 'current irrigation', in which land-use patterns and irrigation mask inputs are statistically generated based on the current frequency of crop rotations; 2) 'no irrigation, pre-development land-use', in which agricultural lands are converted to some non-irrigated agriculture or other non-agricultural land-use; 3) 'full development', where all lands with potential use for agricultural purposes (appropriate drainage and slope, for example) are converted to land-use and irrigation masks in a manner similar to scenario 1 development. The assumptions behind the scenario generation are detailed in Fienen and others, 2021.

,The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The High Plains Climate Network (HPCN) dataset is one of various datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains HPCN data from 15 stations in the SGP97 domain. This dataset covers the complete SGP97 time period (18 June 1997 through 18 July 1997) and for the SGP97 domain. The SGP97 domain is approximately 97W to 99W longitude and 34.5N to 37N latitude. The HPCN dataset contains different parameters depending upon the reporting station. Each station provides Station Name, State, and Identification Number preceding that station's data within the dataset. Each parameter column has a self explanatory title indicating the data available for that station and parameter units.,

This USGS data release contains 2013 streamflow, baseflow, and precipitation data from three hydrologically-diverse streams in the United States used to develop a conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities. The framework combined generalized concepts on the movement of water, the environmental behavior of chemicals and eroded soil, and the designed functions of various agricultural activities. The framework addresses the impacts on water quality of a broad range of agricultural chemicals and sediment across a variety of hydrologic settings. • Chesterville Branch near Crumpton, Maryland, (USGS site ID - 01493112) had substantial baseflow throughout the year with increased streamflow within a day of rainfall. • Indian Creek at State Line RD, Leawood, Kansas (USGS site ID - 06893390) was a fastflow-dominated urban steam that was not well connected to shallow groundwater. • The watershed of Leary-Weber Ditch at Mohawk, Indiana (USGS site ID - 03361638) has an extensive subsurface drainage network within its watershed. These data support the following publication: Capel, P.D., Wolock, D.M., Coupe, R.H., and Roth, J.L., 2017, A conceptual framework for effectively anticipating water-quality changes resulting from changes in agricultural activities: U.S. Geological Survey Scientific Investigations Report 2017-5095, 35 p., https://doi.org/10.3133/sir20175095.