Evaluation of satellite rainfall estimates for surface runoff modelling in the Maha Oya Basin, Sri Lanka

Abstract

Temporal and spatial variations in rainfall are inherent in tropical monsoonal catchments with varying topographical characteristics, causing corresponding variations in river runoff. Global satellite rainfall estimates offer advantages in mitigating scale discrepancies and overcoming the critical requirement of reliable rainfall data sources in regions with limited ground-based observations for hydrological studies. This study comprehensively analyses the applicability of high-resolution satellite rainfall estimates (SREs) for surface runoff modeling. The efficacy of satellite estimates necessitates rigorous evaluation due to intrinsic biases, requiring bias correction. Hydrological simulations conducted using bias-corrected SREs reveal significant differences in model performance, emphasizing the importance of accurate rainfall inputs for reliable streamflow predictions. Overall, this research addresses the research gap in utilizing satellite rainfall estimates for hydrological modeling, particularly in regions with diverse terrain conditions and tropical monsoonal climates. CHIRPS, AgERA5, and PERSIANN rainfall estimates were selected considering their applicability in recent research studies. CHIRPS and AgERA5 rainfall estimates follow the monsoonal climate signal within the Maha Oya basin, fitting to ground-based rainfall observations. Further, CHIRPS estimates generate the best runoff simulations compared to other SREs, resulting in coefficient of determination of 0.53 and 0.57 for model calibration and validation.