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SC-PDSI를 이용한 북한지역 가뭄분석 및 가뭄심도-지속기간-생기빈도 곡선의 유도

Drought Analysis using SC-PDSI and Derivation of Drought Severity-Duration-Frequency Curves in North Korea

  • 강신욱 (한국수자원공사 K-water연구원 수자원연구소) ;
  • 문장원 (한국건설기술연구원 수자원환경연구본부 수자원연구실)
  • Kang, Shin Uk (Korea Water Resources Corporation (K-water)) ;
  • Moon, Jang Won (Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2014.08.06
  • 심사 : 2014.08.22
  • 발행 : 2014.09.30

초록

본 연구에서는 가뭄지수를 이용하여 북한지역의 가뭄분석을 수행하였다. 분석 대상지점으로는 평양 등 27개 지점을 선정하였으며, 월 강수량 및 평균기온 자료를 수집하여 분석에 이용하였다. 가뭄분석을 위한 가뭄지수로는 SC-PDSI를 이용하였으며, 27개 지점별로 1984~2013년(30년)의 월 단위 가뭄지수를 산정하였다. 가뭄지수를 이용하여 과거 가뭄에 대해 검토한 결과, 대부분의 지점에서 2000년대 초반과 중반에 심각한 가뭄이 발생하였음을 확인할 수 있었다. 북한의 과거 가뭄사상에 대한 정량적 평가를 위해 6개 지점(평양, 함흥, 청진, 원산, 해주, 신의주)을 대상으로 지속기간별 최대 가뭄심도 자료계열을 구성하였으며, 빈도분석을 통해 지점별 가뭄심도-지속기간-재현기간(Severity-Duration-Frequency, SDF) 곡선을 유도하였다. 6개 지점의 과거 주요 가뭄사상을 추출한 후 SDF 곡선을 이용하여 각 사상별 재현기간을 추정한 결과, 2000년대 초반과 중반에 발생했던 가뭄은 최대 20~50년의 재현기간을 갖는 가뭄사상인 것으로 나타났다.

In this study, drought of North Korea are analyzed using drought index. 27 weather stations are selected and monthly precipitation and average temperature data are collected for drought analysis. SC-PDSI is used for drought analysis and calculated using collected weather data during 1984~2013 (30 years) in 27 weather stations. From the analysis result of historical drought event using drought index, it is confirmed that severe droughts occurred in the early and mid 2000's at most stations. Secondly, drought frequency analysis was carried out for the derivation of drought severity-duration-frequency (SDF) curves to enable quantitative evaluations of past historical droughts having been occurred in 6 stations (Pyeongyang, Hamheung, Cheongjin, Wonsan, Haeju, Sinuiju). This study can suggest return periods for historical major drought events by using derived SDF curves for each station. In the result, drought events in the early and mid 2000's had return periods of 20~50 years.

키워드

참고문헌

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  3. Investigation of drought-vulnerable regions in North Korea using remote sensing and cloud computing climate data vol.190, pp.3, 2018, https://doi.org/10.1007/s10661-018-6466-0
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