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arXiv · 2605.18785

A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method

Abstract

Urban flood disaster is one of the most serious natural disasters. Numerous flood simulation models have been proposed and relatively matured. However, two major challenges persist: excessive simplification of the city system and high computational complexity. To break these limitations, this paper develops an Urban Flood Dynamical System Model (UFDSM) based on the concept of the Cellular Automata Urban Flood Model. This model allows flexible customization of cell types and selection of water motion or distribution rules based on actual urban environments to incorporate as much the urban system data as possible. The water motion and distribution rules can be simple, which could reduce the computational complexity, but not arbitrary. So, a sufficient condition is provided so that solutions of dynamical system align with macroscopic physical conditions governing water movement. Then, to preserve the evolutionary properties of the UFDSM, we propose a first-order conservation nonstandard finite difference algorithm. This numerical method ensures positive solutions and conservation of water while maintaining the same fixed-point characteristics as the dynamical system. And, this numerical method is validated by comparing it with an analytical solution.Furthermore, to verify the applicability of our model, we performed an urban flood simulation experiment and compared it to HEC-RAS. There is approximately a 2mm discrepancy in distance dp' and 0.02mm discrepancy in distance d2' , with the relative distance Rp about 7.5% and the relative distance R2 approximately 0.06%. Additionally, the proposed model is easily coupled with other hydrological processes and facilitates data assimilation, thereby offering promising practical applications.

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BibTeXRIS

Yongfu Tian, Shan Ding, Guofeng Su, Jianguo Chen. 2026-05-07. A Novel Urban Flood Dynamical System Model and a Corresponding Nonstandard Finite Difference Method. https://arxiv.org/abs/2605.18785

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