APHRODITE Rainfall Data for Streamflow Estimation of the Ungauged Jalaur River Basin Using Rainfall-Run-off Model SWAT

Global reanalyzed data have been used in hydrological modeling and streamflow estimation of data-scarce river basins. This study quantified the streamflow of ungauged J alaur River Basin utilizing the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) rainfall dataset, with resolution of 0.25° having a time domain of 1981 to 2005, as input data to the hydrological model Soil and Water Assessment Tool (SWAT). Linear scaling bias correction improved the fitting of APHRODITE rainfall to the observed climate data, with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.57$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}=0.76$</tex> for daily and monthly time scales, respectively, despite lower mean values of APHRODITE indicating underestimation of rainfall. Statistical criteria for model performance evaluation included correlation coefficient (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), Nash-Sutcliffe Efficiency (NSE), percent bias (PBIAS), and Root Mean Square Error-observation standard deviation ratio (RSR) at the Calinog, Passi and Pototan stations. The APHRODITE-induced SWAT model demonstrated robust statistical agreement having R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>≥0.87 and NS≥0.75. Despite underestimation indicated by positive PBIAS values across all stations, the model exhibited higher predictive efficiency with RSR values less than or equal to 0.5. Peak stream flows were consistently underestimated at the Calinog and Passi stations, whereas better estimates were obtained at the Pototan station. The streamflow generated from the APHRODITE-induced SWAT hydrological model reveals a high degree of comparability to ground-based streamflow stations. This study has shown the suitability of global reanalyzed rainfall data as an alternative to observation data for hydrological modeling of data-scarce river basins.