Social vulnerability to tropical cyclones: A case study in Muscat Governorate, Oman

Social vulnerability (SV) assessment reveals the hidden weaknesses in the human system that make populations susceptible to loss following exposure to external stress. In this study, SV to natural hazards, such as tropical cyclones, are studied and assessed at the local level for coastal cities in Oman. Vulnerability is determined using the underlying social characteristics specific to people in Oman that put them at risk from cyclones. Oman is a developing country exposed to frequent tropical cyclones that create devastating impacts on its coastal cities, yet disaster risk reduction is undeveloped, with limited understanding of the spatial and temporal distribution of risk and vulnerability, and limited investment in resources and skills in this field. In particular, Oman lacks a natural hazard risk assessment system, hence the response to cyclone events is still reactive and not scientifically based. Some unpublished biophysical vulnerability studies exist that focus mainly on the coastal vulnerability to tsunami in Oman, but there have been no prior studies of SV to natural hazards. In this research, an SV model is adopted and applied at the local level (smallest administration boundary) for four coastal cities in the Muscat capital region. Drawing on a conceptual framework of social vulnerability, based on the work of Susan Cutter, the study identified appropriate SV variables reported by the 2010 census. From a preliminary list of 38 potential variables, 24 variables in 9 social dimensions were selected following exclusion of variables due to multicollinearity and singularity. These variables were then used in a principal component analysis (PCA) to further reduce the number of factors to a few meaningful components/factors/indicators. This process produced three indicators, each consisting of a cluster of variables that make up a construct representative of a vulnerable social group. The subsequent aggregation of these variables created a social vulnerability index (SVI) used in GIS to map the spatial distribution of SV to cyclones across Muscat region. This analysis was then repeated for the 1993 and 2003 censuses, which along with the 2010 analysis, allowed an exploration of the temporal variation of SV over two decades. The results show that for Muscat’s coastal cities, in addition to their exposure to physical hazards, there are clusters of municipal blocks with high SV to cyclones, and others with very low social vulnerability. The level of SV also increases over time. In 1993 there were only three municipal blocks with high SV to cyclones, but by 2010 there were 20 high SV municipal blocks, and a decline in low vulnerability areas. This increase in SV is attributed mainly to an increase in population (particularly rural to urban migration for employment), and an increase in the number of non-Omanis arriving for work, especially those in low wage categories. The study thus demonstrates the need to consider the dynamic nature of SV in natural hazard risk assessment and management. The results can be useful in practice, with the spatial SV maps supporting decision makers in planning and resource allocation before and during an emergency event. The Muscat case study can also be replicated elsewhere in Oman, based on the common nationally available small area data