During major ocean storms (hurricanes, tropical storms, nor’easters), coastal communities often experience negative outcomes local emergency managers (EMs) did not anticipate or sufficiently prepare for. Local EMs often have difficulty obtaining specific, local forecast data and consequence predictions they can act on to prevent losses and minimize avoidable consequences. Despite tremendous advances in forecasting science, uncertainty remains a constant storm decision making, requiring local EMs to make critical response choices under pressure of limited time and incomplete information. Rising sea levels, more frequent and intense storm events, growing coastal populations, and increasing societal dependence on complex infrastructure are introducing new variables that EMs must consider when trying to anticipate the consequences of a major ocean storm.
To build decision support tools that reduce EMs’ uncertainty and support data-driven decision making, researchers need to know more about the choices EMs make when preparing for a major ocean storm. This dissertation includes four related manuscripts that address the guiding question: How can local EMs implement simulation-based decision support tools to prevent losses during major ocean storms and reduce avoidable consequences for community health, safety, and economic security? Data is gathered from EM practitioners through interviews, workshops, exercises, and online surveys. This is implementation research using a mixed methods approach to study the practical application of a decision support tool developed at the University of Rhode Island, the Coastal Hazards Analysis Modeling and Prediction (CHAMP) system.
Manuscript 1 presents findings from a 2023 survey of emergency management practitioners to identify what consequence prediction data is useful to decision makers when preparing for major coastal storms. Manuscript 2 presents findings from a series of interviews with EMs, designed to better understand the decision process they follow during storm response. Manuscript 3 presents results of a workshop with CHAMP system end users, designed to evaluate CHAMP’s ArcGIS Online dashboard and explore how EMs anticipate using the tool. Manuscript 4 presents the results of Homeland Security exercises that evaluated the fully functional CHAMP system and explored how EMs apply it to real-world scenarios.
This work contributes to researchers’ understanding of the decisions EMs make in preparation for major ocean storms and provides insights others can use when implementing and evaluating decision support tools like CHAMP. My study represents a uncommon use of implementation research in the social sciences, an approach I’ve found invaluable when evaluating how a research-based intervention such as CHAMP functions in the real world. I present a novel solution of using Homeland Security exercises to evaluate research-derived tools and introduce a framework for “avoidable consequences” that others can use to measure the effectiveness of EM-related interventions.
Project Lead: Sam Adams, PhD Candidate
Project Type: Dissertation Research