Integrating Hydrological Ensemble Prediction System and Optimization for Limiting Downstream Flood Risk in Dam Operations

Advanced Hydrological Ensemble Prediction Systems (HEPSs) offer significant potential to enhance real-time water management by providing probabilistic ensemble water forecasts that can help dam operators better anticipate and mitigate risks. However, fully utilizing HEPS forecasts in real-time decision-making presents major challenges. To address this for dam operations, we developed an integrated HEPS-optimization program to determine the required dam releases to meet a downstream target flow, considering short-term ensemble tributary inflow forecasts. We specially designed the program to have the ability to explicitly limit downstream flood risk through chance constraints. This ability is highly desirable for more effective risk-based operations but is lacking in the large majority of existing methods. A complicating factor however was that the ensemble nature of the tributary inflow forecasts necessitated formulating the chance constraints as discontinuous mixed-integer equations, which makes the problem nondeterministic polynomial-time hard. Thus, to solve the program, we adopted an innovative approach combining a novel ranking mechanism with nonlinear programming. We favoured this approach over conventional branch-and-bound methods and stochastic dynamic programming as it is considerably faster. We evaluated the viability of our methods using a case study of Hume Dam and Lake Mulwala in the Murray-Darling Basin, Australia. The results demonstrate their efficacy.