Risk assessment and zoning of thunderstorm-gale disasters in Jiangsu Province based on grey correlation degree and other methods

Thunderstorm-gale disasters are frequent in the Jiangsu Province. It is an important technical means to improve the local meteorological disaster prevention and mitigation capabilities by carrying out risk assessment of thunderstorm-gale disasters. In this paper, multiple types of data were used to focus on the risk research of the thunderstorm-gale disasters in Jiangsu Province, by adopting the Grey correlation degree, path analysis, and AHP methods. The various data contain daily meteorological data from national stations and regional stations in Jiangsu Province from 2008 to 2022, disaster information from cities and counties, as well as geographic information data such as river network density and digital elevation, and seven indicators were selected for analysis, which were including maximum wind speed, daily maximum precipitation, frequency of hail occurrence, population density, per capital GDP, farmland area, and safe housing area ratio. A thunderstorm-gale disaster risk assessment model is established by using grey relational analysis and path analysis methods, combined with the sensitivity of disaster prone environments and the exposure analysis of disaster bearing bodies such as population, economy, crops, and safe housing ratio, to obtain the risk zoning results for thunderstorm-gale disasters in Jiangsu. The results are as follows. (1) The higher-level dangerous area of thunderstorm-gales disasters in Jiangsu Province was mainly concentrated in most areas of Xuzhou, central Suqian, and some parts of Huai'an and Taizhou. (2) The high risk areas of thunderstorm-gale disasters in Jiangsu were distributed in the western part of Huaibei, the central region between the Huai River and the Yangtz River, and the zoning results were consistent with the actual disaster situation highly. The risk zoning results could reflect the potential risks of thunderstorm-gale disasters in Jiangsu Province.