Public perception of the risk of disasters in a developing economy: the case of Saudi Arabia

Saudi Arabia has in recent years experienced frequent disasters, including flooding, epidemics, and dust storms, while many parts of the country are subject to regular earthquake and volcanic activity. The paper examines public perception of the risk of disasters in this interesting socio-cultural and regional environment not already covered by existing literature. A wide national survey conducted between March and May 2012 resulted in 1,164 responses across the 13 regions of Saudi Arabia. The study showed that the majority of the participants have faith that God is in control of the world and that disasters may be a punishment from him. However, this does not hinder their desire to be prepared to cope with disasters. It also highlighted that direct experience with such disasters does not directly influence perception. The research findings lead to the emergence of a number of recommendations regarding raising awareness of hazards and the risk of disasters, including education, training, encouraging voluntary work, and improving public access to vital information resources. A requirement for research into resilience also emerges, to prepare communities to cope with disasters; this is the focus of the authors’ future research.     

Using adaptively coupled models and high-performance computing for enabling the computability of dust storm forecasting

Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model (Eta-8bin) performs a quick forecasting with low resolution (22 km) to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model (NMM-dust) performs high-resolution (3 km) forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust.     

Disaster community resilience assessment method: a consensus-based Delphi and AHP approach

Natural Hazards - Several studies have highlighted the importance of community resilience in disaster management. The paper focuses on Saudi Arabia and proposes a ‘community resilience to...