Document Type : Research Paper
Authors
1
MSc in Water Resources Management and Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
2
Assistant Professor, Department of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
3
Assistant Professor, Department of Surveying, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
4
Assistant Professor, Department of Environmental Engineering, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract
Runoff is a crucial hydrological variable that provides vital information for water resource management and planning. In this study, we used the Soil and Water Assessment Tool (SWAT) to simulate monthly runoff in the Neyshabur watershed, Khorasan Razavi province, for a ten-year period from 2000 to 2009. We considered all available rain gauges, synoptic, and evapotranspiration stations within and around the watershed. We calibrated the model parameters and coefficients using the SUFI2 algorithm in the SWAT-CUP software package. We found that the parameters related to the infiltration process, such as CH_K2, CN2, SOL_AWC, and REVAPMN, had the most significant impact on the runoff. We evaluated the model's performance during the calibration and validation periods using parameters such as P-factor, R-factor, Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2). The simulation results showed good agreement with the observed monthly runoff for both the calibration and validation periods. The NSE and R2 values were 0.84 and 0.87, respectively, at the Zarande Andarab station during the calibration period, and 0.74 and 0.78, respectively, during the validation period. The Hosseinabad Jangal station showed even better performance, with NSE and R2 values of 0.93 and 0.93, respectively, during the calibration period, and 0.90 and 0.90, respectively, during the validation period. Comparing our results with previous studies in the same watershed, we found that utilizing a more comprehensive monitoring network and increasing the statistical period of the study can significantly enhance the model's performance and reduce uncertainties in the calibration and validation stages.
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