Avoidable consequences: Using advanced storm simulation to help emergency managers improve outcomes

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Abstract
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.

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