Urban flood prediction under heavy precipitation

Increasing city resilience to floods under climate change has become one of the major challenges for decision makers, urban planners, and engineering practitioners around the world. Accurate prediction of urban floods under heavy precipitation is critically important to address such a challenge as it can help understand the vulnerability of a city to future climate change and simulate the effectiveness of various sustainable engineering techniques in reducing urban flooding risks in real urban settings. Here, we propose a new model for urban flood prediction under heavy precipitation. The model divides an irregular urban area into many grid cells with no limitation on the spatial resolution as long as the DEM data of the same resolution are available. It is capable of reflecting the frequent inflow or outflow interactions among grid cells and capturing the rapid generation of surface runoff in urban areas during heavy rainfall. The model also accounts for typical characteristics of urban areas, such as large-scale impermeable surfaces and urban drainage systems, in order to simulate urban floods more realistically. In addition, the model uses both surface elevation and instantaneous surface water depth of all grid cells to dynamically determine the directions of horizontal inflow and outflow during each time step of model simulation. This enables the model to capture the reverse-flow phenomenon which is commonly seen in flat urban areas during heavy storms. By applying the proposed model for reproducing the 2016 flood in Lafayette Parish, Louisiana, we demonstrate its effectiveness in predicting real-world flood events.