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고해상도 소기후모형을 이용한 국내 167개 시·군별 이상기상 발생빈도 자료

Extreme Weather Frequency Data over 167 Si-gun of S. Korea with High-resolution Topo-climatology Model

  • 조세라 (국립농업과학원 기후변화평가과) ;
  • 심교문 (국립농업과학원 기후변화평가과) ;
  • 박주현 (주식회사 에피넷) ;
  • 김용석 (국립농업과학원 기후변화평가과) ;
  • 허지나 (국립농업과학원 기후변화평가과)
  • Jo, Sera (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Shim, Kyo Moon (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Park, Joo Hyeon (R&D Center, EPINET Co., Ltd) ;
  • Kim, Yong Seok (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Hur, Jina (Climate Change Assessment Division, National Institute of Agricultural Sciences)
  • 투고 : 2020.09.07
  • 심사 : 2020.09.24
  • 발행 : 2020.09.30

초록

기상조건은 농업에 영향을 미치는 주요 환경요인이며, 특히 이상기상의 발생은 작물의 성장 및 작황에 큰 영향을 미친다. 그러므로 이상기상으로 인한 농업적 피해를 줄이기 위해 관측을 바탕으로 한 이상기상의 발생 빈도 분석 및 통계자료가 필요하다. 본 연구에서는 30m 및 270m 해상도의 고해상도 소기후 모형을 통해 상세화된 3종의 주요 기상변수(기온, 강수, 일사량)를 이용해, 남한의 167개 시·군의 1981년부터 2019년 동안 발생한 이상기상 발생에 대한 통계자료를 소개하였다. 소기후 모형을 통해 추정된 167개 시·군 이상기상 현상 발생 특징은 기상청의 종관 기상 관측자료와 비교해 보았을 때 전국적인 분포 및 변화 경향을 잘 반영하는 것으로 나타났다. 또한, 기상청 종관 기상 관측 시스템에서 관측하지 못하는 지역의 기상까지 반영한 고해상도의 자료를 활용하였으므로 해당 시·군의 이상기상을 더욱 현실적으로 나타내었다. 본 연구에서 소개하는 시·군별 이상기상 통계자료는 농업 부문의 기상재해 취약성 평가 및 피해 저감을 위한 정책 기초자료로 활용될 수 있을 것으로 생각된다.

The weather conditions, such as temperature, precipitation, and sunshine duration, play one of the key roles in Agriculture. In particular, extreme weather events have crucial impacts on growth and yields of crops. This study estimates statistics of extreme weather events in 167 Si-gun over South Korea derived from high-resolution(30 and 270m) topo-climatology model for key three meteorological variables(temperature, precipitation and sunshine duration). It is shown that the characteristic of each extreme weather frequency in the topo-climatology model is in good agreement with observation from Korean Meteorological Administration's Automatic Surface Observing System. Moreover, it is possible to analyze the statistics of extreme weather more realistically because this data can cover the weather at not-observed regions. Hence, this data is expected to be used as baseline data for assessing vulnerability to extreme weather and politic decisions for damage reduction in agricultural sector.

키워드

참고문헌

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