Policy tree optimization for threshold-based water resources management over multiple timescales. (January 2018)

• Record Type: Journal Article
• Authors: Herman, Jonathan D.; Giuliani, Matteo
• Is Part Of: Environmental modelling & software. Volume 99(2018)
• Page Start: 39
• Page End: 51
• Publication Date: 2018-01
• Subjects: 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.2017.09.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Water resources systems face irreducible uncertainty in supply and demand, requiring policies to respond to changing conditions on multiple timescales. For both short-term operation and long-term adaptation, thresholds or "decision triggers", where a policy links observed indicators to actions, have featured prominently in recent studies. There remains a need for a general method to conceptualize threshold-based policies in an easily interpretable structure, and a corresponding search algorithm to design them. Here we propose a conceptual and computational framework where policies are formulated as binary trees, using a simulation-optimization approach. Folsom Reservoir, California serves as an illustrative case study, where policies define the thresholds triggering flood control and conservation actions. Candidate operating rules are generated across an ensemble of climate scenarios, incorporating indicator variables describing longer-term climate shifts to investigate opportunities for adaptation. Policy tree optimization and corresponding open-source software provide a generalizable, interpretable approach to policy design under uncertainty.