  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 Multi-Model 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 of India using MICE of EC-Earth3 and MME from
CMIP6. We estimate the streamflow projections for all ensembles using the
Variable Infiltration Capacity hydrological model for four time periods,
historical, near, mid and far-term. 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 estimation 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 towards the
far-term. We also show that bias correction does not preserve the internal
variability in estimating the streamflow. Although there is an increase in
uncertainty for estimated streamflow, mean hydropower shows the decrease
towards the far-term for February to 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.
