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Analysis of the Relationship between the Number of Forest Fires and Non-Rainfall Days during the 30-year in South Korea

  • Songhee, Han (Department of Forestry and Environmental Systems, Kangwon National University) ;
  • Heemun, Chae (Department of Forest Science, Kangwon National University)
  • Received : 2022.10.11
  • Accepted : 2022.10.19
  • Published : 2022.12.31

Abstract

This study examined the relationship between the number of forest fires and days with no rainfall based on the national forest fire statistics data of the Korea Forest Service and meteorological data from the Open MET Data Portal of the Korea Meteorological Administration (KMA; data.kma.go.kr) for the last 30 years (1991-2021). As for the trend in precipitation amount and non-rainfall days, the rainfall and the days with rainfall decreased in 2010 compared to those in 1990s. In terms of the number of forest fires that occurred in February-May accounted for 75% of the total number of forest fires, followed by 29% in April and 25% in March. In 2000s, the total number of forest fires was 5,226, indicating the highest forest fire activity. To analyze the relationship between regional distribution of non-rainfall periods (days) and number of forest fires, the non-rainfall period was categorized into five groups (0 days, 1-10 days, 11-20 days, 21-30 days, and 31 days or longer). During the spring fire danger season, the number of forest fires was the largest when the non-rainfall period was 11-20 days; during the autumn fire precaution period, the number of forest fires was the largest when the non-rainfall period was 1-10 days, 11-20 days, and 21-30 days, showing differences in the duration of forest fire occurrence by region. The 30-year trend indicated that large forest fires occurred only between February and May, and in terms of the relationship with the non-rainfall period groups, large fires occurred when the non-rainfall period was 1-10 days. This signifies that in spring season, the dry period continued throughout the country, indicating that even a short duration of consecutive non-rainfall days poses a high risk of large forest fires.

Keywords

Acknowledgement

Development of Urban Forest Fire Response Techniques Caused by Strong Wind based on Big data. This research was supported by a grant (20010162) of Regional Customized Disaster-Safety R&D Program funded by Ministry of Interior and Safety (MOIS, Korea).

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