Confronting uncertainty: The future of hydropower in the himalayan region amidst climate ambiguity

Abstract

Uncertainty profoundly influences hydropower projections under climate change, involving both aleatory and epistemic factors and necessitating varied methodological approaches. This study integrates top-down (General Circulation Models (GCM)) and bottom-up machine learning-based projections, incorporating land use changes using the Soil and Water Assessment Tool. GCMs predict substantial precipitation increases, up to 52.39 % under SSP5–8.5 scenarios, with temperature changes ranging from 0.77 °C to 6.21 °C. In contrast, bottom-up approaches forecast declining basin-wide precipitation from September to December, followed by increases. Spring-fed and snow-fed rivers generally show higher flows in GCM scenarios but lower flows in bottom-up scenarios. Snow-fed rivers exhibit notably greater discharge and energy variability compared to spring-fed rivers, with secondary energy experiencing more significant fluctuations than primary energy. This study underscores the need for diverse methodologies in hydropower planning and highlights the importance of integrating modeling approaches and enhancing collaboration among hydrologists, climate scientists, and policymakers. Future research should focus on combining multiple methodologies for more comprehensive climate impact assessments.