A Multi-dimensional and Integrated Socio-Environmental Vulnerability Assessment in Coastal Systems

Abstract:

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of spatially homogenous geo-physical and hydroclimate data. The representation of coupled socio-environmental factors, in particular adaptive capacity valuation, has been often ignored in vulnerability assessment. These call for development of a novel and inclusive vulnerability assessment framework, which comprises various hydroclimatic, physical, socio-economic, and ecological factors. For example, the hydroclimate vulnerability class should provide information on the coastal hazard events, including hurricane track density, surge height, rainfall intensity, and sea level rise rate, and the physical vulnerability class convey information about land cover and land use, elevation, and distance from the coastline. In addition, the socio-economic and ecological classes can inform the decision-making process by representing information about the social vulnerability, historical and archeological value, damage cost, and species richness. Here, the Multi Criteria Decision Making (MCDM) method and Probabilistic Principle Component Analysis (PPCA) have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. The datasets are analyzed at 30m spatial resolution, where the fuzzy logic-based normalization is used to scale the comprising factors and entropy-based weighting technique is coupled with sensitivity analysis to represent the spatial variation of the classes importance across counties. A range of percentile ranks has been used to form distinct levels and maps of combined vulnerability index (CVI) for each county. The sensitivity analysis of CVIs shows that the Charleston County is more sensitive to socio-economic factors, whereas the physical factors contribute to higher degree of vulnerability in Horry County. Finally, the PPCA method has been used for dimensionality reduction and exploratory analysis of data and to explore the MCDM objective weighting biases. Results suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while MCDM approach accounts for decision makers opinion while providing information for decision-making.