Dynamic flood risk modeling in urban metro systems considering station configuration

Abstract

Urban flooding poses a significant threat to the operational continuity and safety of metro systems. This study aimed to develop a spatiotemporally dynamic flood risk assessment framework for urban metro systems based on flood modeling. The framework was demonstrated through a case study of the extreme flooding triggered by a record-breaking rainstorm on September 7, 2023, in Hong Kong. A two-dimensional shallow water equations (2D-SWEs) based hydrodynamic model was employed to reproduce the extreme urban flooding, which agrees well with the observed inundation locations. The simulated grid-based inundation was then used to quantify spatiotemporal flood hazard posing to the metro system, with tailored criteria for aboveground, underground, and elevated metro stations. Exposure and vulnerability were assessed by analyzing the construction and operational characteristics of the metro system. By integrating flood hazard, exposure, and vulnerability maps, the spatiotemporal flood risk of Hong Kong's metro system during the historical extreme flood event was comprehensively assessed. In the case study, 46.4% of metro stations were exposed to high or very high flood hazards, while only 29.1% were classified as having high or greater overall flood risk. The temporal analysis further revealed that peak station risk occurred 1–12.5 h after peak rainfall, with an average lag of about 5 h. These findings demonstrate the effectiveness of the proposed framework in capturing the temporal and spatial variability of flood risk at the station scale, providing valuable insights for emergency preparedness and planning.