Developing countries have experienced significant challenges in meeting their needs for food, energy, and water security. This paper presents a country-level review of the current issues associated with Food-Energy-Water (FEW) security in the Middle East. In this study, sixteen countries in the Middle East are studied, namely Iraq, Iran, Syria, Lebanon, Israel, Palestine, Egypt, Turkey, and the Arabian Peninsula (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia (KSA), United Arab Emirates (UAE), and Yemen). Here, we conduct a comprehensive assessment to study and evaluate the emerging drivers of FEW systems in the region. The investigated drivers include water security, extreme events, economic growth, urbanization, population growth, poverty, and political stability. The results suggest that most of the studied countries are facing FEW resource insecurity or weak planning/management strategies. Our evaluation further revealed the current status of each country with respect to each factor, and suggested that climatic and socioeconomic factors have contributed to the subsequent stress on FEW resources, specifically on the water sector. In general, and with respect to the water-energy security, it was found that energy production in the Middle East is highly constrained by water deficiency, drought, and/or economic growth. The water-food security in the region is mainly affected by drought, water scarcity, population growth, urbanization, and/or political unrest.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

Tropical cyclones are among the most devastating natural disasters that pose risk to people and assets all around the globe. The Saffir-Simpson scale is commonly used to inform threatened communities about the severity of hazard, but lacks consideration of other potential drivers of a hazardous situation (e.g. terrestrial and coastal flooding). Here, we propose an alternative approach that accounts for multiple components and their likelihood of coincidence for appropriate characterization of hurricane hazard. We assess the marginal and joint probability of wind-speed and rainfall from landfalling Atlantic tropical cyclones in the United States between 1979 ∼ 2017 to characterize the hazard associated with these events. We then integrate the vulnerability of affected communities to have a better depiction of risk that is comparable to the actual cost of these hurricanes. Our results show that the multihazard indexing approach significantly better characterizes the hurricane hazard, and is more appropriate for risk-informed decision-making.

Flash floods are common natural hazards in the southeast United States (SEUS) as a consequence of frequent torrential rainfall caused by tropical storms, thunderstorms, and hurricanes. Understanding flash flood characteristics is essential for mitigating the associated risks and implementing proactive risk management strategies. In this study, flash flood characteristics including frequency, duration, and intensity are assessed in addition to their associated property damages. The National Oceanic and Atmospheric Administration (NOAA) Storm Events database as well as hourly precipitation data of the North American Land Data Assimilation System project phase‐2 (NLDAS‐2) are utilised, and more than 14,000 flash flood events during 1996–2017 are analysed. Flash flood hazard is investigated at county, state, and regional levels across the SEUS. Results indicate increasing pattern for the frequency and intensity of flash flooding over the SEUS. The frequency of flash flooding is found to be higher in spring and summer, whereas the duration and intensity of events are higher during winter and fall, respectively. The western parts of the SEUS are prone to more frequent and intense flash flooding compared to the eastern parts. Overall, our analyses suggest that flash flood hazard in Louisiana is higher than other states in the SEUS.