Assessment of Coastal Communities’ Vulnerability to Hurricane Surge under Climate Change via Probabilistic Map – A Case Study of the Southwest Coast of Florida

Abstract:

The US coastline, over the past few years, has been overwhelmed by major storms including Hurricane Katrina (2005), Ike (2008), Irene (2011), and Sandy (2012). Supported by a growing and extensive body of evidence, a majority of research agrees hurricane activities have been enhanced due to climate change. However, the precise prediction of hurricane induced inundation remains a challenge. This study proposed a probabilistic inundation map based on a Statistically Modeled Storm Database (SMSD) to assess the probabilistic coastal inundation risk of Southwest Florida for near-future (20 years) scenario considering climate change. This map was processed through a Joint Probability Method with Optimal-Sampling (JPM-OS), developed by Condon and Sheng in 2012, and accompanied by a high resolution storm surge modeling system CH3D-SSMS. The probabilistic inundation map shows a 25.5-31.2% increase in spatially averaged inundation height compared to an inundation map of present-day scenario.

To estimate climate change impacts on coastal communities, socioeconomic analyses were conducted using both the SMSD based probabilistic inundation map and the present-day inundation map. Combined with 2010 census data and 2012 parcel data from Florida Geographic Data Library, the differences of economic loss between the near-future and present day scenarios were used to generate an economic exposure map at census block group level to reflect coastal communities’ exposure to climate change. The results show that climate change induced inundation increase has significant economic impacts. Moreover, the impacts are not equally distributed among different social groups considering their social vulnerability to hazards. Social vulnerability index at census block group level were obtained from Hazards and Vulnerability Research Institute. The demographic and economic variables in the index represent a community’s adaptability to hazards. Local Moran's I was calculated to identify the clusters of highly exposed and vulnerable communities. The economic-exposure cluster map was overlapped with social-vulnerability cluster map to identify communities with low adaptive capability but high exposure. The result provides decision makers an intuitive tool to identify most susceptible communities for adaptation.