Climate change and water resource management: Exploring hybrid modeling approaches

Azerbaijan State Oil and Industry University, Baku, Azerbaijan

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Abstract

This study examines the complex effects of climate change on water resources, highlighting the critical need for comprehensive analysis and adaptive management approaches. Projections suggest a 10–30% decrease in freshwater availability across arid tropical regions by 2080, with substantial economic impacts anticipated in South Asia and Africa. While global assessments exist, systematic evaluations of Asian river basins remain limited. Recent investigations in India reveal marked climatic influences, including temperature increases and precipitation anomalies. State-of-the-art modeling frameworks, including the Coupled Model Intercomparison Project (CMIP), enhance climate projections and inform adaptation strategies. Alterations in precipitation patterns, especially within the Indian monsoon system, present serious challenges to agricultural, industrial, and municipal water supplies. Elevated temperatures exacerbate evaporation rates and intensify hydrological extremes, including droughts and flood events. Hydrological models such as WetSpa, SWAT, and MODFLOW simulate climate-induced changes in groundwater recharge and flow regimes, offering critical insights for sustainable resource management. Artificial intelligence (AI) and machine learning (ML) techniques significantly improve groundwater prediction and management capabilities. Advanced methods like Artificial Neural Networks (ANNs) enable precise forecasting, while hybrid models integrating AI/ML with conventional approaches enhance predictive accuracy by addressing nonlinear relationships and optimizing computational efficiency. This research emphasizes the necessity of combining cutting-edge modeling, AI/ML innovations, and traditional methodologies to develop resilient water management systems in response to climate change.