Developing Surface Rainfall Data Based on Blending of Satellite-Based Products and Rain Gauge Observations in the Ngawi Region, East Java

Rainfall estimation can be performed using various methods, including direct satellite observations (RR-Satellite). However, these estimates show discrepancies when compared to actual observations in-situ rain gauges (RR-Obs). To address this challenge, one potential solution is integrating RR-Satellite with RR-Obs. The Kriging with External Drift (KED) interpolation method is a blending technique that incorporates RR-Satellite as external drift. This study utilized four satellite dataset, namely CHIRP, CMORPH, GSMAP_V8, and IMERG as auxiliary information to generate monthly rainfall estimates (RR-Blended) at 26 rain gauges in Ngawi, East Java, for the period 2001 - 2023. The performance of each satellite dataset was evaluated using Leave-One-Out Cross Validation (LOOCV). The results indicated that RR-Blended using CHIRP (bCHIRP) demonstrated the best accuracy at the climatological scale, with KGE > 0.3 and TSS > 0.65, outperforming other satellite dataset. At the monthly scale, bCHIRP, bCMORPH, and bIMERG showed better performance in different months throughout the year. In terms of spatial accuracy, bCMORPH achieved the highest performance. Our findings suggest that each satellite offers unique advantages based on the time and location of observation. Therefore, we recommend using a weighted combination of RR-Blended from four satellites as the most effective approach for obtaining the best rainfall estimates.