针对城市洪涝计算存在数据量大、复杂度高、交互性强的特点，本项目拟综合水力学、地理空间分析、数值计算和数学优化等相关知识，提出Quasi 2D-1D耦合水动力洪涝模拟系统的构建方法。首先，研究GIS地表空域结构的提取和表征方法，阐明特殊地形对局部流体水力学特性的影响机制，建立地表空域的精细化几何拓扑模型。其次，研究GIS拓扑模型与地表模拟元件的空间关联、自动配准和智能编译方法，提出地表水动力系统的构建方法；再次，研究地表和地下模型的水量交互作用规律和转化关系，提出双层系统的耦合机制和优化方法；最后，通过模型率定和验证评价，识别关键影响参数，阐明计算精度和运算效率的制约关系，为模型修正和优化提供依据。研究成果将改善地表淹没的概化处理和非节点处洪涝过程的动态描述，客观反映地下管流与地表径流的动态交互过程，实现高速率、精细化的城市洪涝计算，为城市排水防涝评估和预警预报分析提供科学支撑。

Urban inundation modeling has always been characterized as a challenging problem as the calculation process is data intensive, highly complex and often requires strong coupling and interaction between subsurface and surface systems. This project aims to develop a coupled Quasi 2D-1D hydrodynamic inundation modeling approach based on knowledge on hydraulic, geospatial analysis, numerical calculation and mathematical optimization. Firstly, GIS-based spatial feature extraction techniques are employed to explore the influence mechanism of special topography on local hydrodynamic characteristics and establish refined geometric topological model of surface spatial structure. Secondly, methods to automatically establish the spatial correlation, registration and intelligent compile of the GIS topological model and surface simulation components to propose the construction approach of surface hydrodynamic model. Next, the coupling optimization methods and interactive relations between the surface and underground models are investigated to develop the optimal coupling schemes of the two systems. Finally, the project aims to identify key influence parameters through model calibration and verification to show the relations between model accuracy and computation efficiency and thus provide basis for model modification. The goal is to achieve a high-speed and robust urban flood simulation model with fine descriptions of dynamic flooding process in non-node areas, and to appropriately reflect the dynamic interactions between pipe flows and surface runoffs. The system is expected to provide scientific support for decision making on flood risk management and early warning analysis.