Assessing Pluvial Flooding Risk in Urban Areas with High Spatial Heterogeneity Using a Fused Physically-Based and Data-Driven Framework

Abstract

Urban flood risk poses an escalating threat to urban safety and sustainable development amid climate change and rapid urbanization. Although various flood risk assessment methods exist, most studies rely on single analytical approaches, neglecting the advantages of methodological integration and the multifaceted nature of risk characterization. Furthermore, prevailing assessment frameworks apply uniform hydrodynamic models across entire urban areas, inadequately capturing the diverse inundation processes arising from spatial heterogeneity of urban surfaces. This research developed an integrated multi-method framework for cities exhibiting significant spatial heterogeneity in environmental, infrastructural, and socioeconomic characteristics, enabling efficient high-precision flood simulation and risk assessment by coupling the indicator system method (ISM) with the cloud model (CM). The framework comprises: (1) spatially-differentiated hydrodynamic modeling for pipeline-dense and pipeline-sparse areas; (2) entropy-analytic hierarchy process weighted grid-based flood risk assessment across multiple rainfall scenarios; and (3) cloud model-driven risk evaluation at sub-drainage functional zones to address uncertainty in assessment. The results indicate that the integrated multi-model approach effectively captures flood formation mechanisms and identify an expansion of high-risk areas. Grid-based assessment delineates fine-grained risk distribution, while the cloud model assessment reveals the stability and uncertainty of risk levels. This research advances flood risk assessment methodology by bridging sophisticated hydrodynamic modeling integration with multi-scale risk evaluation, providing a robust framework for urban flood management.

Keywords

Cloud model / Hydrological and hydrodynamic / Indicator system / Multi-scale assessment / Spatial heterogeneity / Urban pluvial flooding

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