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Quantifying uncertainties in estimating future hydropower production directlyor indirectly affects India's energy security, planning, and management. Thechaotic and nonlinear nature of atmospheric processes results in considerableInternal Climate Variability (ICV) for future projections of climate variables.Multiple Initial Condition Ensembles (MICE) and Multi-Model Ensembles (MME) areoften used to analyze the role of ICV and model uncertainty in precipitationand temperature. However, there are limited studies focusing on quantifying therole of internal variability on impact variables, including hydropowerproduction. In this study, we analyze the role of ICV and model uncertainty onthree prominent hydropower plants of India using MICE of EC-Earth3 and MME fromCMIP6. We estimate the streamflow projections for all ensembles using theVariable Infiltration Capacity hydrological model for four time periods,historical, near, mid and far-term. We estimate maximum hydropower productiongenerated using monthly release and hydraulic head available at the reservoir.We also analyzed the role of bias correction in hydropower production. Theresults show that ICV plays a significant role in estimating streamflow andhydropower estimation for monsoon and throughout the year, respectively. Modeluncertainty contributes more to total uncertainty than ICV in estimating thestreamflow and potential hydropower. However, ICV is increasing towards thefar-term. We also show that bias correction does not preserve the internalvariability in estimating the streamflow. Although there is an increase inuncertainty for estimated streamflow, mean hydropower shows the decreasetowards the far-term for February to May, more prominent for MICE than MME. Theresults suggest a need to incorporate uncertainty due to internal variabilityfor addressing power security in changing climate scenarios.

Translating the internal climate variability from climate variables to hydropower production