Strong hurricane winds often cause severe infrastructure damage and pose social and economic consequences in coastal communities. In the context of community resilience planning, estimating such impacts can facilitate developing more risk-informed mitigation plans in the community of interest. This study presents a new framework for synthetically simulating scenario-hurricane winds using a parametric wind field model for predicting community-level building damage, direct economic loss, and social consequences. The proposed synthetic scenario approach uses historical hurricane data and adjusts its original trajectory to create synthetic change scenarios and estimates peak gust wind speed at the location of each building. In this research, a stochastic damage simulation algorithm is applied to assess the buildings’ physical damage. The algorithm assigns a damage level to each building using the corresponding damage-based fragility functions, predicted maximum gust speed at the building’s location, and a randomly generated number. The monetary loss to the building inventory due to its physical damage is determined using FEMA’s direct loss ratios and buildings’ replacement costs considering uncertainty. To assess the social impacts of the physical damage exposure, three likely post-disaster social disruptions are measured, including household dislocation, employment disruption, and school closures. The framework is demonstrated by its application to the hurricane-prone community of Onslow County, North Carolina. The novel contribution of the developed framework, aside from the introduced approach for spatial predicting hurricane-induced wind hazards, is its ability to illuminate some aspects of the social consequences of substantial physical damages to the building inventory in a coastal community due to the hurricane-induced winds. These advancements enable community planners and decision-makers to make more risk-informed decisions for improving coastal community resilience.
We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision‐making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high‐risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane‐induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation‐optimization model to optimize decision‐making by a government entity whose objective is to minimize region‐wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take‐up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high‐risk properties effectively reduces total (uninsured and insured) losses.
more » « less- Award ID(s):
- 1830511
- NSF-PAR ID:
- 10445416
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Risk Management and Insurance Review
- Volume:
- 25
- Issue:
- 2
- ISSN:
- 1098-1616
- Format(s):
- Medium: X Size: p. 173-199
- Size(s):
- ["p. 173-199"]
- Sponsoring Org:
- National Science Foundation
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