Detecting early warning signals of long-term water supply vulnerability using machine learning. (September 2020)

• Record Type: Journal Article
• Authors: Robinson, Bethany; Cohen, Jonathan S.; Herman, Jonathan D.
• Is Part Of: Environmental modelling & software. Volume 131(2020)
• Publication Date: 2020-09
• 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.2020.104781
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Adapting water resources systems to climate change requires identifying hydroclimatic signals that reliably indicate long-term transitions to vulnerable system states. While recent studies have classified the conditions under which vulnerability occurs (i.e., scenario discovery), there remains an opportunity to extend such methods into a dynamic planning context to design and assess early warning signals. This study contributes a machine learning approach to classifying the occurrence of long-term water supply vulnerability over lead times ranging from 0 to 20 years, using a case study of the northern California reservoir system. Results indicate that this approach predicts the occurrence of future vulnerabilities in validation significantly better than a random classifier, given a balanced set of training data. Accuracy decreases at longer lead times, and the most influential predictors include long-term monthly averages of reservoir storage. Dynamic early warning signals can be used to inform monitoring and detection of vulnerabilities under a changing climate. Highlights: Machine learning approaches to predict future occurrence of long-term water supply vulnerability. Significantly outperforms benchmark random classifier over lead times ranging from 0 to 20 years. The most influential predictors include long-term monthly averages of reservoir storage. Dynamic early warning signals can be used to inform monitoring and detection of environmental change.