Randomized trial evaluates flood risk in urban areas, suggesting enhanced risk management strategies.
Dam-break floods pose severe threats to downstream urban areas due to their sudden onset, rapid propagation, and potentially catastrophic socioeconomic consequences. This study develops a multi-indicator fuzzy comprehensive evaluation framework for urban flood risk assessment under dam-break scenarios. The framework integrates hydrodynamic simulation outputs with geographic information system-based spatial analysis to characterize spatial variations in flood risk. It incorporates flood hazard factors (inundation depth, arrival time, and inundation duration) derived from a coupled one-dimensional/two-dimensional hydrodynamic model and socioeconomic vulnerability factors (population density and road network density) derived from spatial statistics. Indicator weights were determined using a combined Analytic Hierarchy Process and entropy-weight method, and risk levels were obtained through membership-function calculation and spatial overlay analysis. The framework was applied to the Dongpu and Dafangying reservoirs in Hefei, China, under a scenario of simultaneous dam failure during a probable maximum flood. Results show that, compared with hazard-only assessment, the comprehensive evaluation substantially reduced the extent of high-risk areas and altered their spatial distribution. Very high-risk zones were concentrated along the upstream main channel, where high-velocity floodwaters coincide with dense population and economic activity. Flood arrival time and population density were identified as the most influential indicators in the proposed risk assessment framework. The proposed framework provides a more comprehensive and spatially refined tool for urban dam-break flood risk management and emergency decision-making.
No takes yet. Share an insight, caveat, or question.
Lin et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: