Planning for community resilience to disasters is a process that involves co‐ordinated action within and between relevant organizations and stakeholders, with the goal of reducing disaster risk. The effectiveness of this process is influenced by a range of factors, both positively and negatively, that need to be identified and understood so as to develop organizational capacity to build community resilience to disaster. This study investigates disaster planning and management in Oman, a country facing significant natural hazards, and with a relatively new system of institutional disaster management. Fuzzy cognitive mapping integrated with stakeholder analysis is used to identify relevant factors and their inter‐relationships, and hence provides an improved understanding of disaster governance. Developing an improved understanding of the complexity of this institutional behavior allows identification of opportunities to build greater resilience to disaster through improved planning and emergency response. We make recommendations for improved disaster management in Oman relating to governance (including improved plan dissemination and closer working with community organizations), risk assessment, public education, built environment development, and financing for disaster resilience.
Tropical cyclones [TCs] are a common natural hazard that have significantly impacted Oman. Over the period 1881–2019, 41 TC systems made landfall in Oman, each associated with extreme winds, storm surges and significant flash floods, often resulting in loss of life and substantial damage to infrastructure. TCs affect Omani coastal areas from Muscat in the north to Salalah in the south. However, developing a better understanding of the high-risk regions is needed, and is of particular interest in disaster risk reduction institutions in Oman. This study aims to find and map TC tracks and their spatio-temporal distribution to landfall in Oman to identify the high-risk areas. The analysis uses Kernel Density Estimation [KDE] and Linear Direction Mean [LDM] methods to better identify the spatio-temporal distribution of TC tracks and their landfall in Oman. The study reveals clear seasonal and monthly patterns. This knowledge will help to improve disaster planning for the high-risk areas.
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