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

Qian Li, Liutong Chen, Zhengtao Yan and Yingjun Xu
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Qian Li: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Liutong Chen: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zhengtao Yan: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yingjun Xu: Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2022, vol. 14, issue 5, 1-12

Abstract: 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.

Keywords: risk assessment; bivariate return period; risk map; cold wave; snow disasters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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