| 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 M. Buyukyildiz |
| Year: | 2019 |
| Journal: | Water |
| Volume (Issue): | 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 and/or data 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 |