Resumen
Assessments of urban flood hazards are crucial for planning and early warning flood system design. Moreover, hazard risk assessment is useful for emergency planning and insurance. There are two common methods for conducting flood hazard risk assessments (FHRA): those based on physical models and those based on parameters. Although physical models are able to simulate flood propagation processes accurately, they also have obvious shortcomings. Parameter-based FHRAs are more comprehensive because they emphasize the analysis of hazard factors. However, this approach also has various flaws, including its qualitative, macro-scale and high subjective nature. In this study, the strengths of both methods were combined to develop a new micro-scale FHRA. Taking the FHRA of the flood storage and detention area of Dongting Lake as an example, this study used high-precision digital elevation model (DEM) data generated from an airborne light detection and ranging (LiDAR) point cloud to construct a two-dimensional (2-D) flood propagation model. Micro-scale FHRAs were then performed using eight selected FHR indicators based on catastrophe theory. By automatically calculating the FHR value of each assessment unit based on hierarchical recursion, the catastrophe theory and catastrophe progression method effectively avoided uncertainty in weight assignment, which is an issue commonly faced by parameter-based methods. The FHRA results obtained under 144 different sequences of assessment indicators also show that the proposed method has a low sensitivity to the ranking of FHR indicators, as well as a high fault tolerance for different assessment results arising from subjective rankings by humans.