<p>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 storm scenarios in a typical urbanized catchment in Nanjing, China. The results reveal a critical tradeoff between volumetric error and spatial reliability. While the RRM produced the lowest 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 explicitly models the nonlinear hierarchical response of the urban drainage system, with intensity-adaptive weights shifting from global pipe conveyance dominance during light rain to localized surface drainage during extreme peaks. Although the DFM tends toward a conservative over-prediction of inundated areas, it avoids the dangerous underestimation risks associated with static methods. These findings establish the DFM as a robust, safety-biased framework for flood risk identification in data-scarce regions, and offer a transferable dynamic fusion paradigm for equivalent drainage modeling without detailed pipe data.</p>