Enhancing urban pluvial flood modeling through graph reconstruction of incomplete sewer networks

Abstract

This work presents an efficient graph-reconstruction-based approach for generating physical sewer models from incomplete information, addressing the challenge of representing the sewer drainage effect in urban pluvial flood simulations. The approach utilizes graph-based topological analysis and hydraulic design constraints to derive gravitational flow directions and nodal invert elevations in decentralized sewer networks with multiple outfalls. By incorporating linearized programming formulation to solve reconstruction problems, this approach can achieve high computational efficiency, making it suitable for application to city-scale sewer networks with thousands of nodes and links. Tested in Yinchuan, China, the approach integrates with a 1D/2D coupled hydrologic–hydrodynamic model and accurately reproduces maximum inundation depths (R2 = 0.95) when the complete network layout and regulated facilities are available. Simplifications, such as the adoption of road-based layouts and the omission of regulated facilities, can degrade simulation performance for extreme rainfall events compared to calibrated equifinal methods. However, design rainfall analysis demonstrates that the physical reconstruction approach can reliably outperform equifinal methods, achieving reduced variation and higher accuracy in simulating inundation areas. However, proper configuration of regulated facilities and network connectivity remains crucial, particularly for simulating local inundation during extreme rainfall. Thus, it is recommended that the proposed algorithm be integrated with targeted field investigations to further improve urban pluvial flood simulation performance in data-scarce regions.