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

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.

The Hazard Consequence Prediction System: A Participatory Action Research Approach to Enhance Emergency Management.

Emergency managers (EMs) need nuanced data that contextualize the local-scale risks and impacts posed by major storm events (e.g. hurricanes and nor'easters). Traditional tools available to EMs, such as weather forecasts or storm surge predictions, do not provide actionable data regarding specific local concerns, such as access by emergency vehicles and potential communication disruptions. However, new storm models now have sufficient resolution to make informed emergency management at the local scale. This paper presents a Participatory Action Research (PAR) approach to capture critical infrastructure managers concerns about hurricanes and nor'easters in Providence, Rhode Island (USA). Using these data collection approach, concerns can be integrated into numerical storm models and used in emergency management to flag potential consequences in real time during the advance of a storm. This paper presents the methodology and results from a pilot project conducted for emergency managers and highlights implications for practice and future academic research.