Assessing the Performance of the Satellite-Based Precipitation Products (SPP) in the Data-Sparse Himalayan Terrain

Located on the south-facing slope of the Himalayas, Nepal receives intense, long-lasting precipitation during the Asian summer monsoon, making Nepal one of the most susceptible countries to flood and landslide hazards in the region. However, sparse gauging and irregular measurement constrain the vulnerability assessments of floods and landslides, which rely highly on the accuracy of precipitation. Therefore, this study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region by comparing different datasets and identifying the best alternative of gauge-based precipitation for hydro-meteorological applications. We compared eight SPPs using statistical metrics and then used the Multi-Criteria Decision-Making (MCDM) technique to rank them. Secondly, we assessed the hydrological utility of SPPs by simulating them through the GR4J hydrological model. We found a high POD (0.60-0.80) for all SPPs except CHIRPS and PERSIANN; however, a high CC (0.20-0.40) only for CHIRPS, IMERG_Final, and CMORPH. Based on MCDM, CMORPH and IMERG_Final rank first and second. While SPPs could not simulate daily discharge (NSE < 0.28), they performed better for monthly streamflow (NSE > 0.54). Overall, this study recommends CMORPH and IMERG_Final and improves the understanding of data quality to better manage hydrological disasters in the data-sparse Himalayas. This study framework can also be used in other Himalayan regions to systematically rank and identify the most suitable datasets for hydro-meteorological applications.

Automated summary

This study evaluates the performance of Satellite-based Precipitation Products (SPPs) in the Himalayas region and identifies the most suitable datasets for hydro-meteorological applications. CMORPH and IMERG_Final are recommended as the best SPPs for managing hydrological disasters in the data-sparse Himalayas. The study framework can be applied in other Himalayan regions to systematically rank and identify suitable datasets.