Study of Differences in Runoff and SWAT Model Parameters Using Observed Versus Satellite Rainfall Data

Ketvara Sittichok; Jutithep Vongphet; Praewwadee Hongsawong; Therasak Saiaon

Authors

Ketvara Sittichok Irrigation Engineering Department, Faculty of Engineering, Kasetsart University, Kamphaengsaen Campus, Nakhon Pathom, Thailand
Jutithep Vongphet Irrigation Engineering Department, Faculty of Engineering, Kasetsart University, Kamphaengsaen Campus, Nakhon Pathom, Thailand
Praewwadee Hongsawong Irrigation Engineering Department, Faculty of Engineering, Kasetsart University, Kamphaengsaen Campus, Nakhon Pathom, Thailand
Therasak Saiaon Irrigation Engineering Department, Faculty of Engineering, Kasetsart University, Kamphaengsaen Campus, Nakhon Pathom, Thailand

Keywords:

SWAT, Satellite Rainfall, Kaengkrachan Reservoir, Runoff, Reservoir Inflow

Abstract

The objective of this study was to investigate the differences in runoff and SWAT model parameters that were obtained using observed rainfall data (SWAT-Station) and satellite rainfall data as prepared by JAXA Global Rainfall Watch System (SWAT-GSMaP_NRT). A bias correction method was initially employed for satellite rainfall data, which were then introduced to the SWAT model. Both SWAT-Station and SWAT-GSMaP_NRT models were calibrated and validated; R2 NSE and PBIAS were used to estimate the model performance. Low to medium performance of SWAT-Station model were noted in both calibration/validation stages, with R2 NSE and PBIAS of 0.26/0.26, 0.25/0.14 and 26.75%/-26.50%, respectively. On the other hand, better performance was noted when satellite rainfall data were introduced to the model instead of the observed data (R2 of 0.45/0.46, NSE of 0.41/0.48 and PBIAS of 25.16%/-21.19%). Calibration/validation results of SWAT-GSMaP_NRT model showed the highest performance, with R2 of 0.68/0.51, NSE of 0.68/0.45 and PBIAS of 11.93%/-13.94%. Top five sensitive parameters of both models were then investigated; most sensitive ones were noted to belong to the soil moisture parameters. The maximum value of the sensitive parameters of SWAT-GSMaP_NRT model were slightly higher than those of SWAT-Station model. Finally, annual inflows predicted by both models were examined. Difference in the annual inflows predicted by both models was 12.37%. SWAT-GSMaP_NRT estimated slightly higher inflows when compared to SWAT-Station in rainy season; lower inflows were noticeably estimated starting from the end of wet season until the end of dry season, however. Average monthly inflows in rainy/dry season were predicted to be 119/48 and 129/36 mcm by SWAT-Station and SWAT-GSMaP_NRT models, respectively.

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