Valuing the Codesign of Streamflow Forecast and Reservoir Operation Models

Abstract

Seasonal streamflow forecasts are becoming widely used to improve water reservoir operations, especially in areas where climate teleconnections enable predictability on medium and long lead times. Most existing studies have focused on the assimilation of forecasts into operational decision models, an approach that typically banks on predeveloped forecasts to optimize water release decisions. However, this approach may overlook the potential synergies that stand in co-developing forecast and decision-making models. In other words, the opportunities that lie in coupling both forecast and operational decision models have not yet been explored. Here, we address this gap and contribute a novel approach building on the Evolutionary Multi-Objective Direct Policy Search algorithm to design forecast and decision-making models together. The proposed approach is benchmarked against operating policies not informed by any forecast, as well as by forecast-informed policies relying on predeveloped forecasts (data-driven and perfect). Numerical experiments are conducted on the Angat-Umiray water resources system, Philippines, which is operated primarily for ensuring municipal water supply to Metro Manila and irrigation supply to a large agricultural district. Our results show that the integrated design of forecast models and control policies provides a performance gain with respect to policies informed by predesigned forecasts. This result is particularly interesting because the skill of the integrated forecast models is lower than that of the predeveloped ones, thus suggesting that more accurate forecasts do not necessarily produce better water system operations. Overall, our analysis represents a step towards a deeper integration of streamflow forecast and reservoir operation models.