SWAT Condition Study
SWAT is a watershed-scale model that applies empirical and physically based approaches to determine runoff responses to differences in land management. The model is primarily intended for application to agricultural watersheds and can simulate a wide variety of agricultural management practices and crop rotations under varying climate scenarios (Arnold et al. 1998 ; Arnold and Fohrer 2005 ; Gassman et al. 2010 ). The key inputs are soils, land cover, and topography (slope), which were intersected to generate Hydrologic Response Units (HRUs): the basic computational unit of the model. Land cover data are based on the 2008 Cropland Data Layer (National Agricultural Statistics Service, U.S. Department of Agriculture 2012 ; Han et al. 2014 ), which kenyancupid profil arama includes data for major and minor crop types as well as nonagricultural land classes. Pixel resolution for the 2008 Cropland Data Layer is 30 m. We reclassified some data classes (<0.25% of the original Cropland Data Layer) into available similar classes to simplify the spatial data and reduce the number of model HRUs. For example, sweet corn (0.17%) was reclassified as corn. Topography data are based on a digital elevation model (pixel size = 30 m) from the National Elevation Dataset (U.S. Geological Survey 2016 ), whereas soils data are from the county-level Soil Survey Geographic (SSURGO) database downloaded from the Web Soil Survey (Soil Survey Staff et al. 2019 ). SSURGO data for the study watershed had a minimum map unit size of 684 m 2 (excluding edge polygons). The model extent was the 12-digit watershed for headwater streams Rock and Pratt Creek, and the entire drainage area for Wolf Creek (10-digit HUC 0708020508) and Miller Creek (0708020509) (Figure 1). The final number of model HRUs ranged from 1,684 (Rock Creek) to 7,528 (Wolf Creek).
SWAT requires every day environment enters from precipitation, temperature, relative moisture, wind-speed, and you may solar power rays. Climate enter in study were produced by second age group environment radar (NEXRAD) data having precipitation and you can Climate Forecast System Reanalysis (CFSR) (Fuka ainsi que al. 2014 ) having kept weather data. New spatial weather data (NEXRAD and you can CSFR) is actually portrayed in the design as the a plastic material circle setup towards the good grid (NEXRAD: 4km spacing; CSFR: roughly 29 kilometer spacing). SWAT automatically chooses determine points that are near the centroid from model subbasins. Getting NEXRAD research, how many man-made gauges found from inside the watershed line varied off six (Rock Creek) so you can 49 (Wolf Creek). To have CSFR analysis, how many gauges ranged of two to three. SWAT operates during the a daily timestep and you will secret design outputs is streamflow plus deposit and you may mineral export. Because of it study, we made use of only the everyday streamflow outputs to target the brand new hydrologic aftereffects of BMPs into the flooding wreck.
Discharge–Frequency Investigation (Module step one)
We began with a Baseline scenario to simulate current conventional agricultural practices, in which corn and soybean crops are grown in a two-year rotation typical for the Upper Midwest. Tillage, fertilizer application, and planting/harvest dates are based on farmer surveys (Minnesota Department of Agriculture 2007 ) and feedback from local stakeholders and commodity groups. We calibrated and validated SWAT against measured flow data from the Wolf Creek watershed. To do so, we ran the model for 12 years from 2002, , December 31. The first two years of model results were treated as a warm-up period and we discarded the results, leaving 10 years of model results to compare against observed flow data. We calibrated SWAT for the five-year period from 2009, , December 31. We assessed agreement between observed and modeled flow using mean daily flow and Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS) (cf. Moriasi et al. 2015 ; Ahmadisharaf et al. 2019 ). For NSE, a value of 1 indicates perfect agreement between observed and predicted values, whereas values >0.5 are generally considered satisfactory for monthly flow. For PBIAS, values <15% are generally considered satisfactory (Moriasi et al. 2015 ; Ahmadisharaf et al. 2019 ), with 0 being the ideal PBIAS.