Improving Daily Satellite-Based Precipitation Estimates Using Iterative Combination Methods in Bolivia

This study evaluated satellite-based precipitation in Bolivia using a combination framework with four equations: relative error, geometric, weighted linear, and sigmoidal. These were compared to enhance daily GSMaP_Gauge V8 estimates. Considering elevation variability, the interpolation method KED proved critical to obtain proper rainfall patterns. The conventional method OK showed weak performance indicators (R2 = 0.18, NSE = -4.19, PBias = 48.28%). In contrast, the KED approach restored spatial continuity and predictive capability nationwide (R2 = 0.71, NSE = 0.68, KGE = 0.79, PBias = 11.24%, RMSE = 1.3 mm/day). Therefore, it provided a more consistent bias correction. On the other hand, sensitivity heat maps demonstrate that iterative algorithms require strict bounding. Specifically, iterating beyond optimal thresholds (N > 6 for relative error; N > 5 for geometric; N > 2 for weighted linear and sigmoidal) triggers exponential residual amplification. The optimized framework successfully bridges the gap between rain-gauge accuracy and gridded estimates in ungauged zones. The relative error showed best performance in the Amazon region, with geometric on the Altiplano plateau, and weighted linear in La Plata basin. These products yield reliable daily precipitation estimates for hydrological modeling, water-resource management, and climate change assessments in Bolivia. Finally, this approach can also be applied in other countries, identifying proper combination methods per hydrologic units.