SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Calibration of SWAT and two data-driven models for a data-scarce mountainous headwater in semi-arid Konya closed basin 
Authors:Koycegiz, C. and Buyukyildiz, M. 
Year:2019 
Journal:Water 
Volume:11(1) 
Pages: 
Article ID:147 
DOI:10.3390/w11010147 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:model comparison 
Secondary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Watershed Description:153.87 km^2 Konya River, a tributary of the Çarşamba river, located in southwest Turkey. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çar¸samba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coeffcient (R2) = 0.787 and Nash–Sutcliffe effciency coeffcient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models. 
Language:English 
Keywords:SWAT; SUFI-2; RBNN; SVM; hydrological modelling