Noah Hallisey

This paper discusses consequence prediction data useful to local emergency managers (EMs) when pre­paring for major coastal storms (hurricanes, tropical storms, nor’easters). Following an implementation research approach, findings are presented from a 2023 survey of 49 state and local EMs conducted in the New England region. Results suggest the categories of criti­cal infrastructure (CI) of greatest interest to local EMs. Results further indicate how respondents feel access to data can help them reduce unanticipated storm con­sequences. The progressive effects of climate change, together with growing societal dependence on complex CI, make it increasingly difficult for EMs to predict specific consequences of storms for coastal communi­ties. Advancements in high-resolution storm simula­tion and consequence modeling can help EMs make preparedness and response decisions, but these tools do not always deliver localized, actionable data, or they may deliver an overwhelming volume of informa­tion that is difficult to apply. To address the resulting data gap, researchers need to better understand end users’ data needs. The presented research leverages the Coastal Hazards Analysis, Modeling, and Prediction (CHAMP) project to explore the practical application of decision support tools to improve local storm pre­paredness and response. Results include best practices for simulation-based decision support tools based on stakeholder and end user input. This work contributes to a better understanding of EMs’ storm vulnerability assessments and informs continued development of decision support tools like CHAMP.

We discuss the novel application of homeland security functional exercises to evaluate emergency managers’ use of simulation-based decision support tools for response to major coastal storms (e.g., hurricanes, tropical storms, nor’easters), such as Hurricane Katrina. The U.S. Department of Homeland Security’s “Homeland Security Exercise and Evaluation Program” (HSEEP) is the universally accepted standard for emergency management exercises in the United States and provides a templated format familiar to emergency management practitioners. Using HSEEP in a scenario-based approach to end-user evaluation provides a plausible, realistic environment that encourages open and honest player feedback. We present the results of two HSEEP-based functional exercises run as part of implementation research with the Coastal Hazards Analysis, Modeling, and Prediction (CHAMP) system. CHAMP combines high-resolution storm models with a database of critical infrastructure vulnerabilities to predict storm consequences and aid decision-making. Findings suggest that the HSEEP exercise format provides a ready-made process for evaluating emergency management tools in a format comfortable and familiar to participants. User feedback and observation data collected during the CHAMP workshops and exercises are used to inform real-world activation protocols and to guide ongoing development of the CHAMP system.