An indicator-based approach to assess social vulnerability of coastal areas to sea-level rise and flooding: A case study of Bandar Abbas city, Iran

Abstract

The sea-level rise (SLR) resulting from climate change and flooding will threaten residents living in low-lying coastal zones in the coming decades. In this regard, evaluating social vulnerability as a significant component of flood risk reduction is necessary. Indicators, in conjunction with multi-criteria decision-making (MCDM) methods, have been recently employed to quantify social vulnerability. In the present study, an indicator-based approach was developed to assess social vulnerability to SLR and flooding in Bandar Abbas city coastal district, southern Iran. To build a social vulnerability index (SoVI) indicators were firstly categorized in, exposure, sensitivity, and adaptive capacity components. Indicators were then weighted using MCDM methods (i.e., analytical hierarchy process (AHP) and fuzzy AHP models). Subsequently, an additive weighting model was employed to produce social vulnerability maps at the block level, and under different combined flooding scenarios in 2050 and 2100. Results showed that using the fuzzy AHP does not necessarily change the ranks of indicators and components compared to the AHP model. However, the spatial extents of social vulnerability were entirely different for both models due to differences in indicators weight. It confirms that using the AHP model as a simplified method can be acceptable when determining indicators and components weight is required. Although, in the case of identifying the detailed spatial extents of social vulnerability using the fuzzy AHP might be appropriate. For all scenarios, using the AHP and fuzzy AHP model, most districts were classified as medium and low vulnerable, respectively. Moreover, the impact of SLR resulting from climate change was significantly evident in the eastern and western parts, where more districts were recognized as highly and very highly vulnerable in the worst-case scenario (S4-2100). The results of this study provide valuable comparative information that can be employed by decision-makers in coastal planning and risk reduction at local scales.