Who Will Bear the Burden of Increased Coastal Flooding as Sea Level Rises in the San Francisco Bay Area? An analysis of the Factors Contributing to Community Vulnerability.

Abstract:

Climate change and the sea-level rise it entails present significant challenges to low-lying, coastal communities, particularly in urban settings. In the San Francisco Bay Area studied here, many of these communities have witnessed rapid change associated with urbanization and often inequitable access to political influence and economic development over the last century. The combination of an uncertain and increasing flood hazard with increased vulnerability implies significant and evolving risk. To prepare for the challenges ahead, community leaders are evaluating different scenarios to get a comprehensive assessment of the different sources of vulnerability.

The starting point for most evaluations of risk are direct damages defined as the monetary loss due to flood damage to building structures and contents. Here, we use the Stanford University risk framework (SURF) for the San Francisco Bay area that uses Census data to link hazard, exposure, and vulnerability with household income to quantify direct damages as a consequence of coastal flooding for different income groups over the next three decades. The main contribution of this study is to assess the degree to which these direct damages are distributed equitably among different population groups or not.

Social inequalities among households can increase a household’s susceptibility to risks and amplify the impact of the hazards. Although SURF identifies the distribution of direct flood damage for different income groups, it has not been used to assess vulnerability in a broader sense including social and racial attributes, which contribute to making marginalized members of a community more vulnerable. Since these factors are important indicators in determining how households prepare, respond, and recover from hazards, decision-makers need to identify these areas of increased vulnerability to implement policies that allow a community to keep its integrity.

Instead of using a principal component analysis (PCA), which is commonly used in indices to identify socially vulnerable communities, we use a k-means clustering approach to look at individual and interacting attributes of social vulnerability and to be able to assess changes in vulnerability over time. K-means clustering enables a more interpretable and hence more actionable perspective on how demographic drivers such as race, income, and immigration status are coupled with each other and related to the physical hazards in communities in the San Francisco Bay Area.