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Hurricanes or typhoons are multi-hazard events that usually result in strong winds, storm surge, waves, and debris flow. A community-level multi-hazard hurricane risk analysis approach is proposed herein to account for the combined impacts of hazards driven by hurricanes including surge, wave, and wind. A tightly coupled ADCIRC and SWAN model is used to account for the surge and wave hazard. Community-level exposure analysis is conducted using a portfolio of building archetypes associated with each hazard. A building-level hurricane vulnerability model is developed using fragility functions to account for content, building envelope, and structural damage. These fragility functions calculate the exceedance probability of predefined damage states associated with each hazard. Then, a building damage state is calculated based on the maximum probability of being in each damage state corresponding to each hazard. The proposed hurricane risk model is then applied to Waveland, Mississippi, a community that was severely impacted by Hurricane Katrina in 2005. The main contribution of this research is modeling the community-level hurricane vulnerability in terms of damage to the building envelope and interior contents driven by surge, wave, and wind using fragility functions to provide a comprehensive model for resilience-informed decision-making.

Hurricane Ike made landfall on the U.S. Gulf of Mexico coast on September 13, 2008 over the Galveston Bay Entrance in Texas (Edge, 2013). The Bolivar Peninsula is a barrier island/peninsula on the east side of the Entrance that received peak overland storm surge and wave heights in the right front quadrant of the hurricane. Prior to the storm Bolivar had been developed with 6000+ buildings, primarily low-density, single-family houses elevated on piling foundations. Over 2000 buildings were destroyed during the storm.

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Past research has shown feedback between natural and human decision systems in coastal areas influence the efficiency of management actions. To capture these feedbacks, a coupled coastal town risk framework was developed (Karanci et. al., 2017) which uses storms and sea level rise as exogenous drivers and simulates the evolution of the morphological landscape, implementation of soft-engineered coastal protection measures and household’s occupation/abandonment decisions through the years. Employing scenario analysis, the framework can be used to illustrate and explore the ramifications of coastal management decisions and policies. Numerous scenarios with diverse conditions can be considered by varying natural (storm frequency, SLR) and socio-economic conditions (insurance rates, flooding risk perception, costs of prevention measures). The utilization of the process-based model XBeach (1-D) to determine the coastal response and inundation depths due to storms enables the framework to accurately estimate the morphological response (Roelvink et al., 2009). However, it also imposes steep computational time requirements when conducting scenario analysis which call for numerous XBeach simulations (~2100 simulation runs for a single scenario of 50-year time frame). Additionally, the implementation of XBeach requires broad knowledge of coastal processes and modeling skills which constrains the potential user community. To overcome this challenge, a Bayesian network (BN) was created to act as a surrogate for XBeach simulations in the framework. This study describes the surrogate storm impact estimation BN and demonstrates its integration to the framework through a scenario analysis study.