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A comprehensive understanding of the intricate relationship between precipitation threshold and extreme river discharge, especially in mountainous regions, is challenging yet crucial for effective mountain risk management. This study, conducted in the Himalayan region of Nepal, employs an innovative approach using Intensity-Duration-Frequency (IDF) curves to identify potential precipitation extremes relevant to extreme river discharges. Detailed analysis of historical hydrometeorological events and their impacts in Nepal was carried out to select a specific event for thorough examination. Additionally, the research evaluates satellite-based precipitation data and utilizes frequency analysis techniques such as Gumbel and Log Pearson Type III distributions, customized to the region’s unique conditions. Furthermore, the study evaluates Probable Maximum Precipitation (PMP) and Depth-Area-Duration (DAD) curves at various sub-basin levels. The findings underscore the significant role of 3-day consecutive precipitation over 1-day events in generating extreme river discharge within the basin. Notably, the study reveals discrepancies between PMP values derived from gauge-based and satellite-based data, emphasizing the necessity for proper bias correction of the satellite data. Despite its limitations, the research highlights the potential of high-resolution satellite data for disaggregating gauge observed daily precipitation and underscores its applicability to other Himalayan basins, offering valuable insights into precipitation-related extreme events and their implications for water resource management and disaster risk reduction. [ABSTRACT FROM AUTHOR]

Investigating processes causing water resource depletion risks is key to understanding past, present, and future excessive irrigation water withdrawal. Quantitative spatiotemporal analysis of the factors driving excessive irrigation water withdrawal is currently inadequate. Although multiple drivers typically contribute to this risk, we studied, for the first time, whether and how Actual Irrigation Water Withdrawals (AIWW) could affect global future water resources projection. Here, we used AIWW datasets from five CMIP6 climate models based on two Shared Socioeconomic Scenarios (SSP370 and 585) and a global hydrological model (H08) to compare the historical (1981–2014) with the future periods: 2041–2070 and 2071–2100. Results show that the temporal AIWW average variations have shown a statistically significant increase during 1981–2014 (p<0.05 and R2 = 0.98) ranging from 101.82 to 136.24 mm yr−1 (mean = 120.5 mm yr−1). Under SSP370 and SSP585, the global spatial AIWW average during 2041–2070 and 2071–2100 are 142.55 and 145.55, and 144.91 and 149.74 mm yr−1, respectively. Under SSP370 and SSP585, the average AIWW changes for 2041–2070 and 2071–2100 are projected to be 96.71, 97.55, 103.52, and 106.98 %, respectively. Under SSP370 and 585, the average global AIWW anomaly changes for 2041–2070 and 2071–2100 are also projected to be −96.86, −106.29, −97.72, and −111.48 %, respectively. Higher AIWW increases are mainly concentrated in India, South China, parts of the United States, parts of Europe, and parts of South African and Latin American countries. Substantiated with population increase, and higher food demand, an increased AIWW will further aggravate globally. Thus, exploring future AIWW changes is a key step forward that requires greater attention in irrigation research interventions helpful to inform societies to reduce future risks of water resource depletion. Adaptation policies targeting the future use of water for irrigation are crucial to lessen water resource depletion risk, suggesting the need for large-scale policy interventions. • The AIWW showed a statistically significant increase globally with uncertainties varying across the time scales and SSPs. • Future AIWW change is projected to increase for each SSP as compared to the historical period. • Future AIWW anomaly changes are projected to decrease for each SSP as compared to historical period with higher variability. • High AIWW are mainly concentrated in India, South China, and United States, and countries in Europe and Latin America. • With increasing population, food demand, and living standards in future, higher AIWW are inevitable in developing countries. [ABSTRACT FROM AUTHOR]