Flood cascading on critical infrastructure with climate change: A spatial analysis of the extreme weather event in Xinxiang, China

Floods caused by extreme weather events and climate change have increased in occurrence and severity all over the world, resulting in devastation and disruption of activities. Researchers and policy practitioners have increasingly paid attention to the role of critical infrastructure (CI) in disaster risk reduction, flood resilience and climate change adaptation in terms of its backbone functions in maintaining societal services in hazard attacks. The analysed city in this study, Xinxiang (Henan province, China), was affected by an extreme flood event that occurred on 17-23 July 2021, which caused great socio-economic losses. However, few studies have focused on medium-sized cities and the flood cascading effects on CI during this event. Therefore, this study explores the damages caused by this flooding event with links to CI, such as health services, energy supply stations, shelters and transport facilities (HEST infrastructure). To achieve this, the study first combines RGB (red, green blue) composition and supervised classification for flood detection to monitor and map flood inundation areas. Second, it manages a multiscenario simulation and evaluates the cascading effects on HEST infrastructure. Diverse open-source data are employed, including Sentinel-1 synthetic aperture radar (SAR) data and Landsat-8 OIL data, point-of-interest (POI) and OpenStreetMap (OSM) data. The study reveals that this extreme flood event has profoundly affected croplands and villagers. Due to the revisiting period of Sentinel-1 SAR data, four scenarios are simulated to portray the retreated but 'omitted' floodwater: Scenario 0 is the flood inundation area on 27 July, and Scenarios 1, 2 and 3 are built based on this information with a buffer of 50, 100 and 150 m outwards, respectively. In the four scenarios, as the inundation areas expand, the affected HEST infrastructure becomes more clustered at the centre of the core study area, indicating that those located in the urban centre are more susceptible to flooding. Furthermore, the affected transport facilities assemble in the north and east of the core study area, implying that transport facilities located in the north and east of the core study area are more susceptible to flooding. The verification of the flood inundation maps and affected HEST infrastructure in the scenario simulation support the series method adopted in this study. The findings of this study can be used by flood managers, urban planners and other decision makers to better understand extreme historic weather events in China, improve flood resilience and decrease the negative impacts of such events on HEST infrastructure.