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Transboundary watersheds supply water, food, and energy to vast populations and play a critical role in sustaining natural ecosystems. Precipitation variability and drought directly influence water resources and security, particularly impacting regions dominated by rainfed agriculture. In the Mekong River Basin, over 70% of rice paddies are rainfed, and are often inhabited by low-income populations. Water security is also threatened within irrigated regions by droughts that reduce surface water availability and often lead to unmanaged declines in groundwater systems. Understanding how precipitation variability affects surface and subsurface water fluxes is key to identifying drought-prone areas. To address this, Land Surface Models (LSMs) have been used effectively to simulate hydrologic cycles and dominant processes at regional to global scales, especially in data scarce areas and at relatively low cost. In this study, we integrate remotely sensed data into the Landscape Hydrology Model (LHM), an integrated surface and subsurface hydrologic model, to estimate water balance components at a 3-hourly and 2 km spatial resolution across the Mekong River Basin.

This study aims to quantitatively assess the relationship between delayed monsoonal onset and soil moisture drought, and to identify regions prone to moderate to severe drought conditions. Our results show that droughts tend to be more severe in years when the monsoon is delayed, with the strongest correlations observed within the same year. Furthermore, regions receiving high annual precipitation (greater than 1000 mm/year) are more severely affected by drought under delayed monsoon scenarios. These findings offer insights into the basin’s water cycle, highlight the influence of precipitation anomalies on water distribution, and support the development of more effective adaptive strategies to mitigate drought impacts.