基于洪涝情景模拟的城市公共服务灾害应急响应空间可达性评价--以医疗急救为例

In the context of climate change and urbanization, increase of flood disasters has been a great challenge for Chinese cities and one of the hotspots in natural hazards research. This paper aims to develop a commonly used paradigm for urban flood emergency assessment at city scale. The city center (within outer ring) of Shanghai, China was selected as the study area because it exhibits enhanced consequences of flooding. A simplified hydrodynamic model (FloodMap) that tightly couples a 1D river flow model with a 2D floodplain flow model in urban environment, was used to predict 100-year and 1000-year flood inundation in the current state, the 2030s and the 2050s with relative sea level rise taken into account. Moreover, GIS-based network analysis (service area and closest facility) was employed to evaluate the transportation conditions and emergency responses accessibility of critical public service sectors (Medical Treatment) under normal conditions and multiple flood scenarios. The results show that the performance of the emergency medical services was largely dependent on flood magnitude (extent and depth), traffic conditions (travel speed) and emergency station positioning. In normal conditions, when no flood restrictions are in place, emergency medical services would be able to reach most parts of the study area within 15 minutes even under significant traffic congestion. As inundation would mainly occur within 2-3 km of Huangpu river banks, flood has a limited influence on emergency medical treatment for the entire region of central Shanghai. Even during 1000-year flood scenario in the 2050s, over half (51%) of the area is predicted to be accessible within 15 minutes. Floodwater may directly compromise a number of hospitals, leading to travel delays and obvious disruption of emergency services in riparian areas. This study suggests that the framework developed for coupling flood modeling with urban emergency response assessment, is proved to be effective and practical, and will provide a support to the decision making of urban flood emergency management.