What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis

Abstract

AbstractMultireservoir systems are designed to serve multiple conflicting demands over varying time scales that may be out of phase with the system's hydroclimatic inputs. Adaptive, nonlinear reservoir control policies are often best suited to serve these needs. However, nonlinear operating policies are hard to interpret, so water managers tend to favor simple, static rules that may not effectively manage conflicts between the system's multisectoral demands. In this study, we introduce an analytical framework for opening the black box of optimized nonlinear operating policies, decomposing their time‐varying information sensitivities to show how their adaptive and coordinated release prescriptions better manage hydrologic variability. Interestingly, these information sensitivities vary significantly across policies depending on how they negotiate tradeoffs between conflicting objectives. We illustrate this analysis in the Red River basin of Vietnam, where four major reservoirs serve to protect the capital of Hanoi from flooding while also providing the surrounding region with electric power and meeting multisectoral water demands for the agricultural and urban economies. Utilizing Evolutionary Multi‐Objective Direct Policy Search, we are able to design policies that, using the same information as sequential if/then/else‐based operating guidelines developed by the government, outperform these traditional rules with respect to every objective. Policy diagnostics using time‐varying sensitivity analysis illustrate how the Evolutionary Multi‐Objective Direct Policy Search operations better adapt and coordinate information use to reduce food‐energy‐water conflicts in the basin. These findings accentuate the benefits of transitioning to dynamic operating policies in order to manage evolving hydroclimatic variability and socioeconomic demands in multipurpose reservoir networks.