English abstract
Exposure to natural hazards in the Sultanate of Oman is common in recent years due
to the effects of the Sultanate's location, topography, climatic conditions, and
urbanization activities. The negative effects of natural hazards are often exacerbated
by their interactions with other hazards. Therefore, early planning and assessment of
multiple hazards are of great importance to prevent, mitigate, and control these
hazards. This study aims to estimate the risk from multiple hazards in the Governorate
of Muscat considering all possible interactions between hazards using the MmhRisk HI quantitative model. The model is made up of five modules: hazard's identification,
analysis, interaction analysis, exposure analysis, and vulnerability analysis. In hazard
identification, Shannon's entropy model was used to identify the role of 15 factors in
causing the hazards of coastal floods, flash-floods, and slope failures in each wilayat.
According to the findings, the land-use factor plays a major role in causing coastal
flooding in the Wilayats of Muscat and Qurayyat, while in the Wilayat of Seeb, the
elevation factor plays a major role. Whereas the distance-to-road is the most
contributor factor in the Wilayats of Bawshar and Muttrah, Considering the hazards of
flash-floods and slope failures, the land-use factor plays an important role in causing
these two hazards in the governorate. Accordingly, the spatial distribution of these
three hazards in each wilayat was determined. In the hazard analysis, HEC-SSP v2.2
software was used to analyze the frequency and magnitude of hazards using rainfall
records for the period between 1998 and 2016. The spatial interpolation method was
then used to estimate the distribution of rainfall over the entire governorate. Based on
the hazard's identification and analysis results, the probability of interaction between
hazards was analyzed based on the relationship between them. In the exposure
analysis, official census data was used to analyze the social's exposure to potential
hazards using five elements: population density, percentage of the population aged ≤14
and ≥ 65 years, percentage of females, percentage of non-Omani, and percentage of
people receiving government assistance. In the vulnerability analysis, the Bayesian
Network (BN) model was used to estimate the percentage of the population likely to
be affected by multiple hazards of different degrees. The analysis showed that the
Wilayats of Bawshar and Seeb recorded the highest vulnerability at 9.1% and 8.6%,
respectively, due to the higher proportion of non-Omanis, females, and the population
aged ≤14 and ≥ 65 years residing in these wilayats compared to other wilayats in the
governorate. Based on the results of the analysis for all modules, the percentage of
population at risk from multiple hazards in each wilayat was estimated. According to
the analysis, the Wilayats of Seeb and Bawshar showed the highest level of risk from
multiple hazards with a probability of 3.2% and 2.7%, respectively. This is mainly due
to their high level of vulnerability and the large number of residents living in these
wilayats. The study, therefore. reveals the importance of the results and scientific
information obtained for use by local authorities to implement appropriate measures
in order to mitigate and improve preparedness for multiple hazards risks.
