Abstract
Abstract. Climate change is a global stressor that can undermine water management policies developed with the assumption of stationary climate. While the response-surface-based assessments provided a new paradigm for formulating actionable adaptive solutions, the uncertainty associated with the stress tests poses challenges. To address the risks of unsatisfactory performances in a climate domain, this study proposed the incorporation of the logistic regression into a decision-centric framework. The proposed approach replaces the “response surfaces” of the performance metrics typically used for the decision-scaling framework with the “logistic surfaces” that describes the risk of system failures against predefined performance thresholds. As a case study, water supply and environmental reliabilities were assessed within the eco-engineering decision-scaling framework for a complex river basin in South Korea. Results showed that human-demand-only operations in the river basin could result in the water deficiency at a location requiring environmental flows. To reduce the environmental risks, the stakeholders could accept increasing risks of unsatisfactory water supply performance at the sub-basins with small water demands. This study suggests that the logistic surfaces could provide a computational efficiency to measure system robustness to climatic changes from multiple perspectives together with the risk information for decision-making processes.
Highlights
Climate change is a global stressor that poses prodigious challenges to long-term management of water resources
This study shows that the response surface can be converted into a probabilistic domain by categorizing the performance samples from the stress tests against a threshold
Though higher precipitation variability (Pcv) could generate more direct runoff across the river basin, storage capacities of the agricultural reservoirs and dams seem to nullify the impacts of Pcv changes on the variation in water supply reliability
Summary
Climate change is a global stressor that poses prodigious challenges to long-term management of water resources. An established method for the impact assessment was to investigate outputs of relevant system models forced by projections of the general circulation models (GCMs) under hypothetical greenhouse gas (GHG) emission scenarios (e.g., Xu et al, 2015; Eum and Simonovic, 2010). This type of assessments takes the “predict--act” paradigm for which the first prerequisite is sufficiently reliable predictions. The GCM projections, are often biased by inappropriate model formulations and/or imperfectly understood physical processes (Stevens and Bony, 2013; Deser et al, 2012; Dufresne and Bony, 2008; Stainforth et al, 2005). They may contain unacceptable risk costs for policymakers (Brown et al, 2012), hindering utilization of GCM-led strategies (Weaver et al, 2013; Brown and Wilby, 2012)
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