Ocean state rising: Storm simulation and vulnerability mapping to predict hurricane impacts for Rhode Island’s critical infrastructure

Samuel Adams, Austin Becker, Kyle McElroy, Noah Hallisey, Peter Stempel, Isaac Ginis, Deborah Crowley

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Predicting the consequences of a major coastal storm is increasingly difficult as the result of global climate change and growing societal dependence on critical infrastructure (CI). Past storms are no longer a reliable predictor of future weather events, and the traditional approach to vulnerability assessment presents accumulated loss in largely quantitative terms that lack the specificity local emergency managers need to develop effective plans and mitigation strategies. The Rhode Island Coastal Hazards Modeling and Prediction (RI-CHAMP) system is a geographic information system (GIS)-based modeling tool that combines high-resolution storm simulations with geolocated vulnerability data to predict specific consequences based on local concerns about impacts to CI. This case study discusses implementing RI-CHAMP for the State of Rhode Island to predict impacts of wind and inundation on its CI during a hurricane, tropical storm, or nor’easter. This paper addresses the collection and field verification of vulnerability data, along with RI-CHAMP’s process for integrating those data with storm models. The project deeply engaged end-users (emergency managers, facility managers, and other stakeholders) in developing RI-CHAMP’s ArcGIS Online dashboard to ensure it provides specific, actionable data. The results of real and synthetic storm models are presented along with discussion of how the data in these simulations are being used by state and local emergency managers, facility owners, and others.

Original languageEnglish (US)
Pages (from-to)47-61
Number of pages15
JournalJournal of Emergency Management
Volume22
Issue number7
DOIs
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Emergency Medicine
  • Safety, Risk, Reliability and Quality
  • Safety Research

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