SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River Watershed, U.S. 
Authors:Yuan, L. and K.J. Forshay 
Journal:PLOS ONE 
Volume (Issue):16(4) 
Article ID:e0248489 
URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Secondary Application Category:model comparison 
Watershed Description:4,200 km^2 Illinois River, which drains parts of northwest Arkansas and northeast Oklahoma, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWATSVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWATSVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.