How do the properties of training scenarios influence the robustness of reservoir operating policies to climate uncertainty?. (July 2021)

• Record Type: Journal Article
• Authors: Cohen, Jonathan S.; Zeff, Harrison B.; Herman, Jonathan D.
• Is Part Of: Environmental modelling & software. Volume 141(2021)
• Publication Date: 2021-07
• Subjects: Policy search -- Reservoir operations -- Climate adaptation -- Robustness -- Scenario selection; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105047
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Reservoir control policies provide a flexible option to adapt to the uncertain hydrologic impacts of climate change. This challenge requires robust policies capable of navigating scenarios that are wetter, drier, or more variable than anticipated. While a number of prior studies have trained robust policies using large scenario ensembles, there remains a need to understand how the properties of training scenarios impact policy robustness. Specifically, this study investigates scenario properties including annual runoff, snowpack, and baseline regret—the difference between baseline policy and perfect foresight performance in an individual scenario. Results indicate that policies trained to scenario subsets with high baseline regret outperform those generated with other training sets in both wetter and drier futures, largely by adopting an intra-annual hedging strategy. The approach highlights the potential to improve the efficiency and robustness of policy training by considering both the hydrologic properties and baseline regret of the training ensemble. Highlights: Baseline regret quantifies maximum improvement in reservoir policies in individual climate scenarios. Scenarios are clustered based on baseline regret and hydrologic properties. We identify training scenario properties that yield robust policy generalization on test sets.