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.

River sand and gravel are mined worldwide at volumes that now rival their natural replenishment, yet quantitative knowledge of this mining activity and its consequences lags far behind many other global environmental pressures. We synthesize 411 peer‐reviewed studies published since 1974 within a new Driver‐to‐Management Pathway for Sustainable Sand and Gravel Mining framework that links socio‐economic demand, spatial‐temporal extent of extraction, hydrogeomorphic change, socio‐ecological disruption, and management response. Evidence shows a pronounced scale mismatch: regional‐to‐global drivers fuel largely unreported local extraction, while impacts propagate well beyond mining sites. In most documented cases, annual removal exceeds bed‐material supply several‐fold, producing channel incision, bank collapse, declining groundwater tables, deteriorating water quality, and inland migration of saline water in coastal areas. These physical changes cascade into habitat loss, infrastructure damage, and livelihood insecurity, especially across rapidly developing regions of Asia and Africa. Despite the proliferation of impact studies (69% of the literature), research is dominated by hydrogeomorphic perspectives, with ecological and socio‐economic impacts far more often treated as secondary or co‐occurring components rather than primary foci; meanwhile, only 27% of studies quantify extraction extent and 24% analyze demand drivers, hampering the design of effective interventions. Together, these findings highlight the need for basin‐scale monitoring, cross‐boundary governance, and demand‐side interventions that explicitly link extraction limits to sediment budgets.

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 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.