Precipitation modeling is vital for water resource management in basins with limited gauged data. In this study, the 5 km Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product was downscaled to 1 km at the annual and mean monthly scales for the Catamayo–Chira catchment, a key water source for Ecuador and Peru. Single-variable and multivariable machine learning (ML) methods were applied to data from 10 gauged stations from 2001 to 2023. Predictors included longitude (Long), latitude (Lat), altitude, Normalized Difference Vegetation Index, and Land Surface Temperature. Performance metrics were utilized to assess the methods. The results demonstrated a notable improvement after downscaling compared to the original CHIRPS estimates. The most effective single-variable methods were simple linear regression (LR) for Long and Lat, and non-linear ML methods such as support vector machine with linear kernel (SVM-lin) and with radial basis function kernel (SVM-rbf), and artificial neural networks (ANN), employing all predictors. Surprisingly, single-variable linear methods yield better results than multivariable non-linear ones. These models provided acceptable fits to annual and mean monthly observations, and their performance tended to be better during the drier months. Downscaled annual precipitation distributions successfully captured differences between “El Niño” and non-“El Niño” years. The current study could be replicated in basins with limited gauging data, thereby enhancing water resource management.
Gaona et al. (Tue,) studied this question.