Integrated Multidimensional Water Security Framework for Classifying Major Global River Basins

Water security has emerged as a central challenge in the context of escalating climate change and rising socioeconomic inequalities. Its scope has expanded beyond the physical availability of freshwater incorporating multiple dimensions. River basins serve as fundamental natural units for understanding and managing these interconnected dimensions, offering a critical lens through which basin level vulnerabilities and inequalities can be assessed. However, sociohydrological perspectives that capture the interactions among these drivers remain underexplored, and traditional approaches that rely on static thresholds often fail to account for evolving climate induced and socioeconomic pressures. This study investigates the multidimensional nature of water security across major global river basins using an unsupervised machine learning framework. The framework classifies river basins into distinct spatial management units based on water security metrics. The resulting clusters reveal unique combinations of vulnerabilities, reflecting differences in exposure to climate hazards, ecological conditions, and socioeconomic inequalities. The findings highlight that global river basins experience disturbances and stressors at multiple levels, driven by both natural and human systems.  The analysis uncovers spatial patterns of similarity and differences, demonstrating how multiple dimensions jointly shape basin level water security. These insights provide a basis for more targeted, equitable, and resilience focused water management strategies. The outcomes support policymakers and stakeholders in designing basin specific interventions that strengthen water security under increasing climate and societal pressures.