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Korean Meteorological Society (한국기상학회)
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Spring Forest-Fire Variability over Korea Associated with Large-Scale Climate Factors

대규모 기후인자와 관련된 우리나라 봄철 산불위험도 변동

  • Jeong, Ji-Yoon (Department of Oceanography, Chonnam National University) ;
  • Woo, Sung-Ho (Department of Oceanography, Chonnam National University) ;
  • Son, Rack-Hun (School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology) ;
  • Yoon, Jin-Ho (School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology) ;
  • Jeong, Jee-Hoon (Department of Oceanography, Chonnam National University) ;
  • Lee, Suk-Jun (National Institute of Forest Science) ;
  • Lee, Byung-Doo (National Institute of Forest Science)
  • 정지윤 (전남대학교 지구환경과학부 해양환경전공) ;
  • 우성호 (전남대학교 지구환경과학부 해양환경전공) ;
  • 손락훈 (광주과학기술원 지구환경공학과) ;
  • 윤진호 (광주과학기술원 지구환경공학과) ;
  • 정지훈 (전남대학교 지구환경과학부 해양환경전공) ;
  • 이석준 (국립산림과학원 산림보전부 산림방재연구과) ;
  • 이병두 (국립산림과학원 산림보전부 산림방재연구과)
  • Received : 2018.11.21
  • Accepted : 2018.12.17
  • Published : 2018.12.31
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Abstract

This study investigated the variability of spring (March-May) forest fire risk in Korea for the period 1991~2017 and analyzed its relationship with large-scale climate factors. The Forest Weather Index (FWI) representing the meteorological risk for forest fire occurrences calculated based on observational data and its relationship with large-scale climate factors were analyzed. We performed the empirical orthogonal function (EOF) analysis on the spring FWI. The leading EOF mode of FWI accounting for about 70% of total variability was found to be highly correlated with total number of forest fire occurrences in Korea. The high FWI, forest fire occurrence risk, in Korea, is associated with warmer atmosphere temperature in midwest Eurasia-China-Korea peninsula, cyclonic circulation anomaly in northeastern China-Korea peninsula-northwest pacific, westerly wind anomaly in central China-Korea peninsula, and low humidity in Korea. These are further related with warmer sea surface temperature and enhanced outgoing longwave radiation over Western Pacific, which represents a typical condition for a La $Ni\tilde{n}a$ episode. This suggests that large-scale climate factors over East Asia and ENSO could have a significant influence on the occurrence of spring forest fires in Korea.

Keywords

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Fig. 1. (a) Climatology of Monthly forest fire occurrences (shading), Burned area (blue line) and FWI (purple line) during 1991~2017 in South Korea. (b) Comparison between forest fire occurrences (shading) and FWI (purple line), DWI (pink line) and KBDI (orange line) over spring (March~May) during 1991~2017 in South Korea.

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Fig. 2. Spatial Correlation between FWI and occurrence of forest fire over Spring (March~May) in South Korea during 1991~2017.

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Fig. 3. (a) Spatial Patterns and (b) Principal component (PC) time series of EOF analysis for averaged spring (March~May) FWI in South Korea during 1981~2016.

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Fig. 5. Spatial correlation between FWI 1st PCT and Relative humidity (a), Sea level pressure (b), 850 hPa zonal wind (c) and 2 m air Temperature (d) over spring (March~May) during 1980/1981~2015/16. Significant at 95% (90%) correlations are indicated with black (gray) dotted pattern. All data were detrended before the analysis.

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Fig. 6. Spatial correlation between FWI 1st PCT and Sea Surface Temperature (a, c, e) and Outgoing Longwave Radiation (b, d, f) over previous autumn (September~November), winter (December~February) and spring (March~May) during 1980/1981~2015/16. Significant at 95% (90%) correlations are indicated with black (gray) dotted pattern. All data were detrended before the analysis.

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Fig. 7. Linear regression of 850 hPa zonal wind (a), 500 hPa Geopotential height (b) and Relative humidity (c) with respect to averaged western pacific Outgoing Longwave Radiation over spring (March~May) during 1981~2015. Regressions statistically significant at the 0.05 (0.1) level are indicated by black (gray) dotted pattern. All time-series and data were detrended before the analysis.

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Fig. 8. Spatial correlation between FWI 1st PCT and 850 hPa zonal wind (a) and Sea Surface Temperature (b) over winter (December~February) during 1980/1981~2015/16. (c) Time series of multiple linear regression (MLR) model for predicting FWI 1st PCT (red line), FWI 1st PCT (blue line) and occurrence of forest fire (black line) during 1981~2016 over spring (March~May) in South Korea. Significant at 95% (90%) correlations are indicated with black (gray) dotted pattern. All data were detrended before the analysis.

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Fig. 4. Time series of occurrence of forest fire (black line) during 1991~2016, FWI 1st PCT (red dashed line) and averaged FWI anomaly (blue dashed line) during 1981~2016 over spring (March~May) in South Korea.

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