SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Effect of soil data resolution on identification of critical source areas of sediment 
Authors:Singh, H.V., L. Kalin and P. Srivastava 
Journal:Journal of Hydrologic Engineering 
Volume (Issue):16(3) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:soil data resolution effects 
Secondary Application Category:critical source area assessment 
Watershed Description:398 km^2 Fish River in southwest Alabama, USA 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Identification of critical source areas (CSA) of pollution in a watershed is important for effective implementation of best management practices (BMPs). Process-based, watershed models are often used for this purpose. One of the main inputs to these models is the spatially-explicit soils data. The objective of this study was to evaluate if the use of two commonly used soil datasets, the State Soil Geographic (STATSGO) and the Soil Survey Geographic (SSURGO) data, can lead to differences in location of CSAs of sediment. A watershed model, Soil and Water Assessment Tool (SWAT) in combination with the Tukey-Kramer test was used for locating CSAs in the Fish River watershed, which is located in coastal Alabama, U.S.A. The model was calibrated and validated using flow data from a USGS gauging station located within the watershed. The locations of the CSAs of sediment were analyzed at subwatershed and Hydrologic Response Unit (HRU) levels. Results show that the locations of the CSAs were different for the two soil datasets. The locations of the CSAs varied at both subwatershed and HRU levels. The use of STATSGO soil data resulted in higher soil erodibility factor and surface runoff. As a result, higher sediment yield was obtained from the use of the STATSGO data as compared to the sediment yield obtained from the use of the SSURGO data. Therefore, for accurate identification of CSAs of sediment (and potentially other pollutants) and for effective implementation of economically-feasible BMPs, it is important to use the most detailed spatial dataset available. 
Keywords:Critical Source Area, Best Management Practice, SWAT, Sediment, Modeling, STATSGO, SSURGO