Using Existing Indicators to Bridge the Exposure Data Gap: A Novel Natural Hazard Assessment

• Published in: Sustainability 2024-12, Vol.16 (23), p.10778
• Main Authors: Williams, Adam K, Summers, James K, Harwell, Linda C
• Format: Article
• Language: English
• Subjects: Climate change; Climatic changes; Earthquakes; Emergency preparedness; Space debris
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su162310778

Extreme natural hazard events are increasing across the globe, compelling increased climate research on resiliency. Research concerning issues as integrative as climate change and natural hazard resiliency often requires complex methodologies to account for cumulative influences. Indicators can be used to parse complex data to assess the intersection of inputs and outcomes (i.e., cumulative impacts). The Climate Resilience Screening Index (CRSI) is a good example of an indicator framework as it integrates indicators and their associated metrics into five domains (e.g., natural environment, society, and risk), enabling the index to accommodate a variety of inputs in its assessment of resilience. Indicator research, however, is generally limited by the availability of pertinent data. Natural hazard data concerning exposure, loss, and risk are routinely collected by the Federal Emergency Management Agency (FEMA) to create and update the National Risk Index (NRI), a composite index. The NRI can be disaggregated to obtain individual underlying metrics about natural hazard exposure. Quantifying natural hazard exposure requires extensive computation, with each hazard type requiring multiple modifying considerations, such as meteorological adjustments made by subject matter experts. Commonly available natural hazard exposure data, like that from FEMA, combines the spatial extent of historical natural hazard events and the determined value of the affected area. Exposure-related data were retrieved from the National Risk Index and used to create a new composite value to represent only the spatial extent of natural hazard events. Utilizing this new methodology to represent natural hazard exposure alleviates the burden of complex computation. It allows exposure data to be more expeditiously integrated into research and indices relating to natural hazards.