Simulating Streamflow Through The SWAT Model in The Keduang Sub-Watershed, Wonogiri Regency, Indonesia

• Main Authors: Andrianto Ansari, Tasuku Kato, Atiqotun Fitriah
• Format: Article
• Language: English
• Published: Universitas Gadjah Mada 2019-09-01
• Series: Agritech
• Subjects: discharges; keduang sub-watershed; sufi-2; swat-cup; swat model
• Online Access: https://jurnal.ugm.ac.id/agritech/article/view/42884

Water resource modelling has been used to analyze the sustainability of the watershed affected by human activity and natural disasters. The objective of this research was to evaluate the SWAT model and its applicability in the Keduang Sub-Watershed for streamflow prediction, which is part of Bengawan Solo Watershed. A SWAT integrated with Geographic Information Systems (ArcGIS, version 10.4.1) was used to simulate Keduang Sub-Watershed streamflow for the period from 2008 to 2017. Model calibration and validation were performed for monthly and daily periods using Sequential Uncertainty Fitting 2 (SUFI-2) within SWAT-CUP using daily observed streamflow data at the catchment outlet. The results during calibration and validation periods showed that the value of the Nash-Sutcliffe Efficiency (NSE), the coefficient of determination (R2), Percent Bias (PBIAS) and Root Mean Square (RSR) had different values for daily and monthly simulation. The calibration and validation outputs for daily and monthly simulation showed a good model performance for discharges. In the daily simulation, the value of NSE, R2, PBIAS and RSR were 0.57; 0.58; -3.4 and 0.67 for calibration periods, whereas in the validation period the values of NSE, R2, PBIAS and RSR were 0.50; 0.51; -10.7 and 0.65, respectively. The monthly simulation had better results than the daily simulation where the value of NSE, R2, PBIAS, RSR were 0.79; 0.81; -6.2 and 0.54 for calibration periods, as well as 0.73; 0.69;-1.9 and 0.71 for validation periods, respectively. Those results indicated that the SWAT model was acceptable for Keduang watershed simulation based on the model performance which was higher than the minimum standard acceptance.