Probabilistic Sea-Level Rise Hazard Analysis

Abstract eng:
The release of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) sent an alarming message of anthropogenic warming induced sea-level rise (SLR), as a result of thermal expansion as well as ice melt from glaciers and ice sheets. More alarmingly, recent satellite observations and tide gauge measurements (Church and White, 2006) showed even higher SLR than previously predicted by IPCC AR4. As SLR knowledge advanced, numerous researchers constructed and updated Sea-Level Rise Prediction Models (SLRPMs) that estimated different SLR over various time frames based on various emission scenarios (e.g., Vermeer and Rahmstorf, 2009). Significant uncertainties, both aleatory and epistemic, remain in SLR predictions. Engineering and policy decisions, on the other hand, need to be made in response to SLR regarding affected coastal infrastructure, populations, and ecosystems. This paper proposes a framework termed Probabilistic Sea-Level Rise Hazard Analysis (PSLRHA), to integrate the SLR knowledge of current climate change scientific communities for informed engineering and policy decisions. PSLRHA combines probabilities of all emission scenarios with predictions of the resulting SLR over time, in order to compute SLR hazard at any given site. PSLRHA also incorporates uncertainties in those SLR predictions, by considering multiple SLRPMs. While the PSLRHA framework sets the foundation for decision making, the advancement of this framework requires extensive scientific input and updates, including appropriate treatment of epistemic uncertainties, as SLR knowledge advances. The output of the PSLRHA framework could be a Global Sea-Level Rise Hazard Map (GSLRHM), similar to that of the United States National Seismic Hazard Map. This map can be used for Performance-Based Sea-Level Rise Engineering (PBSLRE) over geographically distributed locations in the long run. Ultimately, informed decisions are enabled through GSLRHM and PBSLRE by the probabilistic framework that incorporates SLR hazard based on multiple scenarios, from projections by multiple modelers, for the time frame and design/policy of interest.

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