Abstract
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.
Highlights
Flooding can represent a severe natural threat, with large flood events causing substantial casualties and property losses every year [1]
Many methods for FHR assessment (FHRA) have been proposed. These can be divided into two main categories: (i) traditional flood hazard risk assessments (FHRA) methods based on physical models (PMs) and (ii) parameter-based flood vulnerability assessment methods [4]
To address the aforementioned problems, this study fully considered the advantages and disadvantages of both the PM- and parameter-based FHRA methods before proposing a micro-scale FHRA method based on catastrophe theory and with the integration of airborne light detection and ranging (LiDAR) point cloud data and a two-dimensional (2-D) hydraulic model
Summary
Flooding can represent a severe natural threat, with large flood events causing substantial casualties and property losses every year [1]. The main difference between the two categories is that the former employs PMs to perform FHRAs for specific flood events [5,6,7,8] and can be combined with flood loss assessment models to determine the economic losses in affected areas [9]. The latter uses collectable data to construct assessment indicators on a spatiotemporal scale, and explores useful information to conduct qualitative assessments of regions’ flood vulnerability. Examples of parameter-based flood vulnerability assessment include the flood vulnerability index (FVI) [10,11], the set pair analysis–variable fuzzy sets (SPA–VFS) model [12], spatial multi-criteria analysis (SMCA) [13]), the block maxima model (BMM) [14] and the decision tree (DT) approach [15]
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