Ensemble hydrological prediction-based real-time optimization of a multiobjective reservoir during flood season in a semiarid basin with global numerical weather predictions

Future streamflow uncertainties hinder reservoir real-time operation, but the ensemble prediction technique is effective for reducing the uncertainties. This study aims to combine ensemble hydrological predictions with real-time multiobjective reservoir optimization during flood season. The ensemble prediction-based reservoir optimization system (EPROS) takes advantage of 8 day lead time global numerical weather predictions (NWPs) by the Japan Meteorological Agency (JMA). Thirty-member ensemble streamflows are generated through running the water and energy budget-based distributed hydrological model fed with 30-member perturbed quantitative precipitation forecasts (QPFs) and deterministic NWPs. The QPF perturbation amplitudes are calculated from the QPF intensity and location errors during previous 8 day periods. The reservoir objective function is established to minimize the maximum reservoir water level (reservoir and upstream safety), the downstream flood peak (downstream safety), and the difference between simulated reservoir end water level and target level (water use). The system is evaluated on the Fengman reservoir basin (semiarid), which often suffers from extreme floods in summer and serious droughts in spring. The results show the ensemble QPFs generated by EPROS are comparable to those for JMA by using probability-based measures. The streamflow forecast error is significantly reduced by employing the ensemble prediction approach. The system has demonstrated high efficiency in optimizing reservoir objectives for both normal and critical flood events. Fifty-member ensembles generate a wider streamflow and reservoir release range than 10-member ensembles, but the ensemble mean end water levels and releases are comparable. The system is easy to operate and thereby feasible for practical operations in various reservoir basins.