LEAPS-MPS: Computational Modeling to Characterize and Attribute Uncertainty in Future Coastal Risk

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The project aims to examine how coastal areas can mitigate damages from sea-level change and coastal flooding. Rising global sea levels and intensifying storms cause risks for people and property in coastal areas. Strategies to manage these risks include protective measures like building seawalls, elevating existing structures, and relocation away from the coast. However, uncertainty is inherent in the geophysical processes, the mathematical models, and the observational data used to calibrate those models. These modeling uncertainties lead to uncertainty in the optimal strategy to defend against coastal hazards. This research will assess how different geophysical and socioeconomic factors lead to uncertainty in the decisions and costs to effectively protect coastal areas, and uncertainty in the estimated damages from potentially under-protecting coastal assets. The project will provide training opportunities for students to develop software and conduct computer model experiments. These activities will support the representation and persistence of students from underrepresented minority groups by enhancing students’ sense of science identity through engaging in projects. Further, the research will be conducted at a Carnegie R2 university, where the resources made available through this project will have positive impacts. The research will investigate models for sea-level change and coastal impacts by exploiting mathematical structures for coastal adaptation decision-making and gridded global climate data. Machine learning and statistical tools will be integrated with existing geophysical and socioeconomic models, bridging coastal adaptation decisions to uncertainties in geophysical processes, in climate and socioeconomic models, and in observational data. This coupled modeling framework will serve as a laboratory to characterize uncertainty in future coastal adaptation costs and decisions. The uncertainty will be decomposed and attributed to geophysical drivers of risk and socioeconomic uncertainties using supervised machine learning and global sensitivity analysis methods. The research will assess the extent to which different uncertainties affect the optimal strategies for adapting coastal areas to the risks posed by sea-level change. As optimality is typically defined by minimizing expected loss, additional decision-making criteria will be implemented in the models employed, thereby enabling an exploration of the role of risk aversion and imperfect information. A standard modeling framework for integrated assessment will be used, which will facilitate future efforts to expand this work to examine the larger role of sea-level hazards in integrated assessments of climate risks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.