Classification of risk levels for snow damage estimation considering socioeconomic factors in South Korea

Abstract

Abstract In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

Keywords

• DPSIR
• Multiple regression
• Risk levels
• Snow damage estimation