Ensemble Gap-Filling of Missing Daily Precipitation Data with CHIRPS, IMERG and Statistical Methods: A Case Study in Bolivia-Tarija

Precipitation data are essential for hydrological modeling, water resource management, and climate risk assessment. However, meteorological time series frequently contain missing records due to equipment failures, maintenance issues, and logistical limitations, particularly in remote regions with sparse monitoring networks. This study evaluates three gap-filling approaches, Linear Regression (LR), Multiple Linear Regression (MLR), and Inverse Distance Weighting squared (IDW2), for reconstructing missing daily precipitation records at the Saykan Perulas station in southern Bolivia. CHIRPS and IMERG satellite-derived precipitation products were incorporated as complementary data sources to support reconstruction under data-scarce conditions. Model performance was assessed by introducing continuous multi-year gaps representing approximately 27% of the observed record to simulate a realistic worst-case reconstruction scenario. Statistical evaluation included RMSE, MAE, NSE, R2, and intensity contingency matrices. The ensemble-based MLR approach, combining nearby station observations with CHIRPS data, achieved the best overall performance (RMSE = 43.0; NSE = 0.65), showing moderate improvements relative to the standalone approaches. CHIRPS generally outperformed IMERG, likely due to its finer spatial resolution (0.05° vs. 0.1°) and better representation of local precipitation variability. In contrast, IDW2 exhibited the weakest overall performance, particularly in reproducing temporal precipitation variability and intermediate rainfall intensities. The results suggest that integrating satellite-derived precipitation products with ground-based observations can improve the continuity and reconstruction of precipitation series in regions characterized by sparse observational coverage, complex topography, and high spatial rainfall variability.