Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections
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There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.
KeywordsRisks Uncertainty Adaptation Probabilistic projections Robust decision-making
The authors would like to acknowledge the Ethiopian Ministry of Water and Energy, the Tana Sub Basin Organization, and the International Water Management Institute for providing the data and models on which this analysis was based. Dr. Zaitchik’s contribution to this research was supported through NSF-ICER Grant 1624335. The source code and simulation model for the analyses described in this manuscript can be obtained from the corresponding author. We would also like to acknowledge two anonymous reviewers whose thorough review greatly enhanced the manuscript.
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