Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed

Abstract A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources data through combining algorithms. The main objective this study assess and importance fused multistage approach bias correction. Accordingly, two versions resampled spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) were merged with ground measurements using a conditional merging procedure. Results applied performance measures (i.e. seven) on corrected CHIRP SREs show that Percent Detection (POD) Volume Error (PVE) have improved. Depending combination coupled stations for validation, up 70 50% PVE improvement was achieved at some wet dry periods, respectively. Moreover, conditionally outperformed by resampling station dataset (CHIRPS) over sparsely populated western part watershed. However, devised method limited in considering dry-day events during correction, which turn has affected correction CHIRPS product. Finally, research should concentrate such methods fusing understand benefits various approaches produce more precise records.