Exploration of Copula Models Use in Risk Assessment for Freezing and Snow Events: A Case Study in Southern China

Due to cold waves, low and extremely low temperatures occur every winter. Sudden cooling can cause freezing and snow disasters, which seriously affect transportation, power, safety, and other activities, resulting in serious economic losses. Based on precipitation and average temperature data from 258 national meteorological stations over the past 70 years, this study established a historical freezing and snow event data set, extracting the accumulated precipitation intensity (API) and accumulated temperature intensity (ATI). We selected the optimal distribution function and joint distribution function for each station and calculated the univariate and bivariate joint return periods. The return period accuracy plays an important role in risk assessment results. By comparing the calculations with the real return period for historical extreme events, we found that the bivariate joint return period based on a copula model was more accurate than the univariate return period. This is important for the prediction and risk assessment of freezing and snow disasters. Additionally, a risk map based on the joint return period showed that Jiangsu and Anhui, as well as some individual stations in the central provinces, were high-risk areas; however, the risk level was lower in Chongqing and the southern provinces.