A new tool for correcting the spatial and temporal pattern of global precipitation products across mountainous terrain: precipitation and hydrological analysis

This study primarily aims to integrate global precipitation data into hydrological models at the catchment scale, a common practice in hydrological research. Specifically, the study investigates how biased spatial and temporal patterns in precipitation data affect model performance and uncertainty. The European Meteorological Observations (EMO) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) global datasets are utilized as inputs for the GEOframe-NewAGE hydrological model to simulate the hydrological processes of the mountainous Aosta Valley catchment in northwestern Italy. The uncertainty of the hydrological model forced with global precipitation data is assessed using a proposed method called Empirical Conditional Probability (EcoProb). The results show that, although traditional performance metrics suggest similar outcomes for the model forced with EMO and CHIRPS, the proposed uncertainty analysis reveals higher uncertainty when CHIRPS is used as the precipitation input. To leverage all useful information in the global precipitation data, the spatial correlation of CHIRPS is combined with a subset of rain gauges using the EcoProb method to modify the EMO precipitation data. This approach enabled the integration of the advantages of EMO and CHIRPS, which offer higher temporal and spatial correlation with ground observation, respectively, into a unified precipitation product. The combined dataset, referred to as the EcoProbSet product in this study, outperformed both the CHIRPS and EMO products, reducing the uncertainty introduced into hydrological models compared to the original global datasets.