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In the context of global climate change, floods have become one of the major natural disasters affecting the safety of human life, economic construction, and sustainable development. Despite significant improvements in flood risk and exposure modeling in some studies, there is still a lack of evidence on the spatiotemporal distribution patterns associated with flood risk across the globe. Meanwhile, numerous studies mostly explore flood risk distribution patterns based on specific spatial scales, ignoring to some extent the fact that flood risk has different distribution patterns on different scales. Here, on the basis of hazard–vulnerability components quantified using game theory (GT), we proposed a framework for analyzing the spatiotemporal distribution patterns of global flood risk and the influencing factors behind them on multiple scales. The results revealed that global flood risk increased during 2005–2020, with the percentages of high-risk areas being 4.3%, 4.48%, 4.6%, and 5.02%, respectively. There were 11 global risk hotspots, mainly located in areas with high population concentration, high economic density, abundant precipitation, and low elevation. On the national scale, high-risk countries were mainly concentrated in East Asia, South Asia, Central Europe, and Western Europe. In our experiment, developed countries accounted for the majority of the 20 highest risk countries in the world, with Singapore being the highest risk country and El Salvador having the highest positive risk growth rate (growing by 19.05% during 2015–2020). The findings of this study offer much-needed information and reference for academics researching flood risk under climate change. [ABSTRACT FROM AUTHOR]

Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads. [ABSTRACT FROM AUTHOR]

Flood is one of the most serious natural disasters in the world. Flood losses in the developing countries throughout the Belt and Road region are more than twice the global average. However, to date, the extent of the vulnerability of the Belt and Road region remains poorly understood. Therefore, this study sought to address this knowledge gap. In this study, we presented a vulnerability assessment model based on triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and support vector machine (SVM) model. Firstly, a geospatial database including 11 flood conditioning factors was built. Secondly, the exposure and disaster reduction capability were calculated based on TFN-AHP and SVM, respectively. Finally, the spatial distribution of vulnerability throughout the Belt and Road region was generated. According to the results, the exposure and disaster reduction capability in most areas are extremely low, accounting for 86.45% and 80.53%, respectively. Meanwhile, the vulnerability of 47,105,300 km2 areas is low or extremely low, accounting for 93% of the Belt and Road region. The high-vulnerable areas (accounting for 3.54%) are primarily concentrated in the southern and eastern parts of China, northern India, most areas of Bangladesh, the Indus Valley in Pakistan, the Nile River Basin in Egypt, and the central region of Indonesia. Obviously, these regions with high vulnerability are characterized by frequent economic activities and dense populations. As suggested of these results, this study provides scientific and technological evidence for the prevention and mitigation of flood disasters in the countries along the Belt and Road region. [ABSTRACT FROM AUTHOR]

Flood is a very destructive natural disaster in the world that is strongly influenced by land-use change. Therefore, a comprehensive flood risk modeling considering the change in land-use is essential for understanding, predicting, and mitigating flood risk. However, most existing single modeling ignored the derivative effect of land-use change, which may reduce the reality of results. To further address the issue, this study presented an integrated model chain by coupling the Markov-FLUS model, the multiple linear regression and the improved TOPSIS model. By applying it in Guangdong Province, the future land-use simulation, spatialization of hazard-bearing bodies, and determination of flood risk were realized. The results show that the coupled model chain allows for good prediction of flood risk under different scenarios, which could be expressed by flood risk composite index (FRSI). In the natural growth scenario, the flood risk will show markedly increasing trend from 2020 to 2030 (FRSI = 2.06), with the high and highest risk zones will expand significantly. Spatially, these increased high flood risk zones mainly distributed on the periphery of existing built-up lands. On the contrary, the flood risk in ecological protection scenario tends to stabilize (FRSI = 1.98), which may be a reference for alternative development paths. These dynamic information identified by this model chain provides a deeper insight into the spatiotemporal characteristics of future high flood risk areas, which can facilitate reasonable flood mitigation measures to be developed at the most critical locations in the region. In further applications, more efficient spatialization models and climate factor are suggested to be introduced. • Presenting a model chain for flood risk prediction under land-use changes. • Model chain considers the derivative effect of land-use changes. • Model chain allows for a more realistic flood risk maps base on multi-scenarios. [ABSTRACT FROM AUTHOR]