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Divya Upadhyay
Sudhanshu Dixit
, and
Udit Bhatia


Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India’s energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable internal climate variability (ICV) for future projections of climate variables. Multiple initial condition ensembles (MICE) and multimodel ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants in India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensemble members using the Variable Infiltration Capacity hydrological model. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower potential for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing toward the far term (2075–2100). We also show that bias correction does not preserve ICV in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows a decrease toward the far term for February–May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.

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