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Adaptation Triggers and Indicator Interpretability for Dynamic Reoptimization of Reservoir Control Policies Under Climate Change.

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Název: Adaptation Triggers and Indicator Interpretability for Dynamic Reoptimization of Reservoir Control Policies Under Climate Change.
Autoři: Sunkara, Sai Veena, Herman, Jonathan D., Giuliani, Matteo
Zdroj: Water Resources Research; Jan2026, Vol. 62 Issue 1, p1-22, 22p
Témata: CLIMATE change, WATER demand management, RESOURCE allocation, OROVILLE Dam (Calif.), FLOOD risk, KEY performance indicators (Management), SENSITIVITY analysis, MATHEMATICAL optimization, ADAPTIVE natural resource management
Geografický termín: CALIFORNIA
Abstrakt: Recent studies have shown the potential for reservoir control policies to adapt to uncertain future climate and demand by reoptimizing on a fixed time interval. However, this strategy is independent of the system evolution and might implement late or unnecessary adaptation. This study develops a framework to identify dynamic decisions on two levels: an "outer loop" adaptation policy that establishes indicator thresholds for reoptimization based on recently observed data, and an "inner loop" control policy that undergoes reoptimization according to these thresholds. We demonstrate this method for a case study of Oroville Reservoir, California, using an ensemble of climate model projections split into training and testing sets. The control policy uses inputs of storage, day of year, and a 5‐day inflow forecast, while the adaptation policy indicators include long‐term statistics of climate and demand as well as the recent system performance. Both policy levels are optimized simultaneously using heuristic policy search and analyzed with policy interpretation methods, including Shapley Additive Explanations (SHAP) and global sensitivity analysis. Results show that the adaptation solutions provide equal or better performance compared to the historical benchmark and are robust to out‐of‐sample scenarios. Additionally, the decision to reoptimize is primarily driven by demand, flood cost and mean annual flow indicators on different timescales. The proposed methodology identifies how control policy reoptimization can be initiated using observed thresholds of climate, demand, and system performance to improve adaptation under future uncertainty. Plain Language Summary: As climate and water demand conditions change in the future, traditional reservoir operations that rely on fixed schedules may fail to respond in a timely way to changing conditions. In this study, we develop a new approach that allows reservoir operations to adapt dynamically based on how the system is performing over time. Our framework has two levels: the first identifies key indicators such as climate trends, water demand, and reservoir performance that signal when operating policies need to be updated. The second level reoptimizes the reservoir's control policy based on these indicator thresholds. We apply this framework to California's Oroville Reservoir. We find that indicators related to water demand, flood risk, and mean annual flow are especially important for deciding when to update operations. We also use sensitivity analysis to understand which indicators are important and how they influence decisions. Finally, we compare our adaptive strategies to a baseline policy and a benchmark of perfect information, showing that the adaptive method improves performance. This work demonstrates how dynamic policy updates can better prepare water systems for an uncertain future. Key Points: Two‐level dynamic adaptation framework; outer loop identifies indicator thresholds, and inner loop reoptimizes reservoir control policyAdaptive policies achieve equal or better performance in all objectives compared to the historical benchmark and are robust to uncertaintyAdaptation decisions are influenced by recently observed indicators such as demand, flood cost and mean annual flow [ABSTRACT FROM AUTHOR]
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Databáze: Biomedical Index
Popis
Abstrakt:Recent studies have shown the potential for reservoir control policies to adapt to uncertain future climate and demand by reoptimizing on a fixed time interval. However, this strategy is independent of the system evolution and might implement late or unnecessary adaptation. This study develops a framework to identify dynamic decisions on two levels: an "outer loop" adaptation policy that establishes indicator thresholds for reoptimization based on recently observed data, and an "inner loop" control policy that undergoes reoptimization according to these thresholds. We demonstrate this method for a case study of Oroville Reservoir, California, using an ensemble of climate model projections split into training and testing sets. The control policy uses inputs of storage, day of year, and a 5‐day inflow forecast, while the adaptation policy indicators include long‐term statistics of climate and demand as well as the recent system performance. Both policy levels are optimized simultaneously using heuristic policy search and analyzed with policy interpretation methods, including Shapley Additive Explanations (SHAP) and global sensitivity analysis. Results show that the adaptation solutions provide equal or better performance compared to the historical benchmark and are robust to out‐of‐sample scenarios. Additionally, the decision to reoptimize is primarily driven by demand, flood cost and mean annual flow indicators on different timescales. The proposed methodology identifies how control policy reoptimization can be initiated using observed thresholds of climate, demand, and system performance to improve adaptation under future uncertainty. Plain Language Summary: As climate and water demand conditions change in the future, traditional reservoir operations that rely on fixed schedules may fail to respond in a timely way to changing conditions. In this study, we develop a new approach that allows reservoir operations to adapt dynamically based on how the system is performing over time. Our framework has two levels: the first identifies key indicators such as climate trends, water demand, and reservoir performance that signal when operating policies need to be updated. The second level reoptimizes the reservoir's control policy based on these indicator thresholds. We apply this framework to California's Oroville Reservoir. We find that indicators related to water demand, flood risk, and mean annual flow are especially important for deciding when to update operations. We also use sensitivity analysis to understand which indicators are important and how they influence decisions. Finally, we compare our adaptive strategies to a baseline policy and a benchmark of perfect information, showing that the adaptive method improves performance. This work demonstrates how dynamic policy updates can better prepare water systems for an uncertain future. Key Points: Two‐level dynamic adaptation framework; outer loop identifies indicator thresholds, and inner loop reoptimizes reservoir control policyAdaptive policies achieve equal or better performance in all objectives compared to the historical benchmark and are robust to uncertaintyAdaptation decisions are influenced by recently observed indicators such as demand, flood cost and mean annual flow [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2025WR040531