SWAT Literature Database for Peer-Reviewed Journal Articles

Title:The use of soil taxonomy as a soil type identifier for the Shasta Lake watershed using SWAT 
Authors:Ficklin, D. L., Y. Luo, M. Zhang and S. E. Gatzke 
Journal:Transactions of the ASABE 
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URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:soil data resolution effects 
Secondary Application Category:hydrologic assessment 
Watershed Description:18,839 km^2 Shasta Lake, located primarily in northeastern California with a small portion in southeast Oregon, U.S. 
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Abstract:We tested a methodology for aggregating soil properties across multiple soil survey areas according to soil taxonomic information available within the Soil Survey Geographic (SSURGO) dataset. Most hydrologic modeling studies using SSURGO assume that soil property data are adequately grouped into a “soil type” that is represented by a map unit key within SSURGO. The map unit key in SSURGO, however, is not intended for this purpose because the map unit key is not guaranteed to be the same between adjacent surveys. As a result, similar soil types are assigned a different map unit key across soil survey boundaries resulting in an artificial increase of soil types. The Soil and Water Assessment Tool (SWAT) was used to simulate hydrology using data from the low-resolution State Soil Geographic Database (STATSGO) dataset, the SSURGO dataset using map unit key as a soil type identifier (MUKEY), and the SSURGO dataset using soil taxonomy as a soil type identifier (TAXSUB; TAXa by SUBbasin) in the Shasta Lake watershed in northern California and southern Oregon. Results indicate that, of the soil properties tested, only TAXSUB maximum soil depth was significantly different from the STATSGO maximum soil depth, while both TAXSUB maximum soil depth and saturated hydraulic conductivity were significantly different from the MUKEY dataset. Based on SWAT streamflow output, the TAXSUB soil dataset generated streamflow results closer to observed streamflow data as compared to the STATSGO or MUKEY inputs, which generated streamflow values much higher than observed data. The TAXSUB soil dataset had a greater maximum soil depth, resulting in more soil water infiltration. During large precipitation events, the soil column for TAXSUB may still be able to accommodate more water, while the STATSGO and MUKEY soil columns are completely saturated with water, leading to surface runoff. The soil taxonomy grouping method within SWAT produced more accurate streamflow results than using MUKEY and STATSGO but should still be further tested in other environmental settings. 
Keywords:Hydrology, Soils, SSURGO, STATSGO, Streamflow, SWAT, Watershed modeling