Temporal dynamics of coupling 1D and 2D hydrodynamic models for urban community rainstorm flooding simulation

In the context of global climate change and rapid urbanization, the risk of urban flood disasters caused by heavy rainfall is continuously increasing. To effectively address this challenge, this study developed a stormwater flooding simulation model at the urban community scale by coupling a one-dimensional pipe network hydrodynamic model (SWMM) with a two-dimensional surface water hydrodynamic model (LISFLOOD-FP), with particular emphasis on the impact of temporal dynamics on simulation outcomes.

The coupling process integrates time step synchronization, data transmission, and updating model configuration to ensure accurate and dynamic simulations. Initially, an appropriate time step for the SWMM model was selected to ensure its output data provided a suitable temporal resolution for LISFLOOD-FP, optimizing data exchange frequency and detail. At the end of each time step, the overflow node coordinates and volumes from SWMM were converted into the required .bci and .bdy file formats and promptly transmitted to LISFLOOD-FP as input conditions for the next time step, ensuring real-time interaction between the models. Meanwhile, the LISFLOOD-FP configuration files (.par) were updated in real-time based on the latest SWMM output data, incorporating the most recent overflow information as boundary conditions. This continuous feedback loop allowed LISFLOOD-FP to dynamically adjust its simulations, enhancing the precision of inundation predictions.

Validation using actual precipitation data from July 11, 2023, and design storms with various return periods (1a, 5a, 10a, 20a, 50a, and 100a) demonstrated high accuracy in simulating stormwater network loads, inundation extents, and depths. High-risk areas were primarily located at the boundaries of academic zones, the southern side of residential areas, and their intersections. The study concludes that the real-time coupled simulation method, grounded in temporal sequence, not only enhances the precision of inundation predictions but also fully accounts for the complexity of urban stormwater systems, providing robust support for urban planning and disaster mitigation strategies.