Development of a Multi-objective Optimal Operation Model of a Dam using Meteorological Ensemble Forecasts for Flood Control

Reservoir operation in flood conditions on hourly time scales poses significant difficulties due to the inherent uncertainty of inflow forecasts. Hence, incorporating ensemble flood forecasts into real-time reservoir operation optimization has rarely been investigated to effectively address uncertainties in inflow forecasts for flood control, let alone its added value compared to no-forecast operating scheme in multi-objective reservoir operating system. This research first investigates the potential of sequential Long Short-Term Memory networks to forecast one-day-ahead ensemble inflow floods based on numerical weather prediction data. Next, a new framework has been developed to incorporate ensemble inflow forecasts into multi-objective reservoir operation optimization using grid search-based genetic algorithm. Finally, we implemented our proposed ensemble-based operating method on the Dez dam basin in Iran and compared it with the benchmark no-forecast operating approach. The results revealed that the Long Short-Term Memory model reasonably performed well to propagate uncertainty in short-term ensemble inflow forecasts. In addition, the ensemble-based operating method could reduce release peaks and increase reservoir storage more than that of a deterministic (no-forecast) operating approach. Our findings demonstrated that the applicability of a skillful ensemble-based scheme in reservoir flood control operations could be effective in decreasing flood damage and water shortage.