Snow Disaster Hazard Assessment on the Tibetan Plateau Based on Copula Function

Abstract

In the context of global climate change, the Tibetan Plateau is particularly susceptible to meteorological disasters, including snow disasters. This study utilized daily temperature and precipitation data from 44 meteorological stations on the Tibetan Plateau spanning from 1960 to 2018 to construct a snow event dataset. Optimal marginal distribution and the copula function were chosen to calculate the joint return period and joint probability, which effectively assess the hazard of snow disasters in the region. Additionally, the study analyzed the comprehensive risk of snow disasters under various return periods by integrating social and economic data. The results indicate the following: (1) Based on the five different Archimedean copula functions, the joint return period of an error rate of each station was calculated to be less than 36%, which is significantly lower than the recurrence interval of univariate analysis; (2) High-hazard areas are predominantly concentrated in the northwest region of the Tanggula Mountains and the eastern foothills of the Bayankara Mountains. As the return period increases, the spatial distribution of snow disaster hazard probability shifts gradually from “double-core” to continuous distribution; and (3) the northwestern Karakorum Mountains and Bayankara Mountains are two distinct high-risk areas for snow disasters. The range of high-risk areas in the region expands with an increase in the return period.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

“…k = 1 ln(n) (10) where x ij represents the ith sample value under the jth indicator and p ij represents the proportion of the ith sample value under the jth indicator (i = 1, 2, 3, . .…”

“…Xu et al leveraged daily temperature and precipitation data from meteorological stations on the QTP to compile a snow disaster event dataset. By utilizing optimal marginal distribution and conjugate functions, they calculated joint regression periods and joint probabilities, effectively assessing the hazards of snow disasters in the region [10]. Lu et al considered the snow depth, snowfall temperature, snow accumulation duration, and total snowfall to establish grading criteria for snow accumulation in the Nagqu Region.…”