Simulation and Risk Assessment of Flood Disaster at the Entrance to a Rail Transit Station under Extreme Weather Conditions—A Case Study of Wanqingsha Station of Guangzhou Line 18

Abstract

With the rapid development of urbanization and underground transportation, as well as the frequent occurrence of extreme weather conditions such as extreme rainfall, flooding disasters for rail transit are becoming severe, and need to be urgently clarified in terms of the mechanism causing them. In this study, a comprehensive model for water damage at the entrance to a rail transit station is proposed, emphasizing the entire process of extreme weather–surface ponding–underground intrusion. The model is validated by the inundation process of Line 5 of the Zhengzhou Metro during the “7.20” event and further applied to Wanqingsha Station of Guangzhou Metro Line 18 in China to determine the surrounding water depth, distribution, total water inflow volume, and water damage time under different rainfall intensities, rain patterns and protection scenarios. It was found that when rainfall reaches the level of a 1-in-2000-years event, the surface water begins to invade the internal rail transit system through the rail transit entrances. When facing extreme rainfall akin to the “7.20” event in Zhengzhou, the rail transit system in Wanqingsha Station meets a heightened risk of water damage, resulting in significantly deeper water levels compared to 1-in-5000-year rainfall event in Guangzhou and exceeds the height of the subway entrances. Analysis of the water intrusion process reveals that, as rainfall intensity escalates, the total inflow water volume into the rail transit system increases while escape time diminishes. Moreover, under identical rainfall intensity, pre-type rainfall yields the highest total water inflow, whereas mid-type rainfall exhibits the shortest escape time. Enhancing the protection conditions can markedly attenuate surface water intrusion into the subterranean rail transit system, thereby enhancing the evacuation time for individuals within the system.