Exploiting multiple hydrologic forecasts to inform real-time reservoir operation for drought mitigation

To timely prompt anticipatory operations and relieve prolonged droughts effectively, multiple hydrological forecasts that provide thorough information about future streamflow need to be employed. However, it is challenging to integrate multiple forecasts in reservoir optimization and obtain a proper trading-off between current and future water supply benefits. This study proposes a novel Model Predictive Control (MPC) that can not only utilize the streamflow forecast but also utilize other two categorical forecasts, including the regime state forecast to capture the long-term persistence of the streamflow process and the annual streamflow volume state forecast. In the novel MPC, a discount factor depending on the annual streamflow volume state forecast weighs up the current deficit cost decided by the streamflow forecast and the future cost function determined by the regime state forecast. Taking the Biliuhe Reservoir in China as a case study, the value of each forecast in drought mitigation is evaluated. Results show that with an appropriate discount factor, using seasonal streamflow forecast can relieve intense deficits for extreme droughts and avoid unnecessary intense limitations for slight droughts. Utilizing the annual streamflow volume state forecast contributes to guaranteeing a reliable water supply. And employing the regime state forecast relieves the intensity of severe water deficit. By incorporating these hydrological forecasts, a 31.86% performance gain can be obtained with only a 5.03% reliability decrease compared to the baseline Stochastic Dynamic Programming informed by no forecast information. Nevertheless, forecast value is beset by forecast uncertainty. For streamflow forecast, forecast value decreases with increasing forecast uncertainty. But for the other two categorical forecasts, their forecast value depends on not only the forecast accuracy but also the hydrologic conditions and forecast bias.