Water reservoir operating rules are typically derived based on the assumption of streamflow stationarity, however, this assumption could be undermined by climate change. Adaptive reservoir operation is one of the most effective strategies to support water resources management under non-stationarity, yet until now, adaptive strategies considering non-stationarity across multiple time scales are rarely investigated. We propose an adaptive reservoir operation framework that incorporates streamflow non-stationarity across time scales simultaneously. Specifically, we first decompose the streamflow into four frequency categories to detect non-stationarity features through reservoir operation simulations. Next, we incorporate the non-stationarity information from each frequency category into adaptive reservoir operation by using Bayesian Model Averaging. We apply this framework to reservoir operation of the Grand Ethiopian Renaissance Dam on the Blue Nile River and evaluate its effectiveness with streamflow simulated from 21 general circulation models (GCMs) for two greenhouse gases emission scenarios. We find that streamflow non-stationarity from all GCMs varies by future period and frequency category. The proposed Bayesian adaptive reservoir operation framework can detect streamflow non-stationarity across all frequency categories and predominantly outperforms conventional adaptive strategies, especially in terms of firm power output. In general, firm output increases under the Bayesian framework as the power generation reliability increases. The proposed framework offers a robust approach to identify adaptive strategies for reservoir operation to address streamflow non-stationarity.
Yang, G; Zaitchik, B; Badr, H; Block, P
A Bayesian adaptive reservoir operation framework incorporating streamflow non-stationarity
Journal Of Hydrology