Dynamic equivalent drainage method for urban flood modeling: A rainfall-adaptive fusion approach

Abstract The scarcity of detailed drainage network data severely constrains urban flood modeling and risk assessment. To address this challenge, this study proposes a novel Dynamic Fusion Method (DFM) for equivalent drainage modeling in data-scarce areas. The DFM dynamically integrates three existing approaches—the Rainfall Reduction Method (RRM), Road-based Equivalent Drainage Method (REDM), and Stormwater Inlet Equivalent Drainage Method (SIEDM)—using a rainfall-adaptive nonlinear weighting function. A high-resolution 1D/2D coupled model (SWMM/HEC-RAS), validated against historical inundation records, was established as a benchmark (HRPN) to evaluate the DFM against individual methods under various design storm scenarios in a typical urbanized catchment in Nanjing, China. The comparative results reveal a critical trade-off between volumetric error and spatial reliability. While the RRM produced the lowest total area error, it suffered from significant under-prediction, failing to identify critical flood-prone zones. In contrast, the DFM demonstrated superior spatial consistency, achieving the highest Intersection over Union (IoU) with the benchmark (average IoU of 0.268), outperforming RRM and SIEDM by 16.0% and 10.7%, respectively. Mechanistically, the DFM’s adaptive weighting system effectively acts as a proxy for the drainage system's nonlinear state transition, shifting dominance from global capacity reduction during light rain to localized, surface-based drainage representation during extreme peaks. Although the DFM tends toward a conservative over-prediction of inundated areas, it avoids the dangerous underestimation risks associated with traditional static methods. These findings suggest that the DFM provides a robust and safer alternative framework for high-precision flood risk banding and management in regions lacking detailed infrastructure data.