한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제40권3호
  • /
  • Pages.191-204
  • /
  • 2007
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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수자원에 대한 기후변화 영향평가를 위한 고해상도 시나리오 생산(I): 유역별 기후시나리오 구축

Generation of High Resolution Scenarios for Climate Change Impacts on Water Resources (I): Climate Scenarios on Each Sub-basins

  • 배덕효 (세종대학교 물자원연구소.토목환경공학과) ;
  • 정일원 (세종대학교 토목환경공학과) ;
  • 권원태 (기상연구소 기후연구실)
  • Bae, Deg-Hyo (Dept. of Civil and Environmental Engrg., Sejong Univ) ;
  • Jung, Il-Won (Dept. of Civil and Environmental Engrg., Sejong Univ) ;
  • Kwon, Won-Tae (Climate Research Lab., Meteorological Research Institute)
  • 발행 : 2007.03.31
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초록

본 연구에서는 기후변화가 국내 수자원에 미치는 영향을 평가하기 위해 고해상도($27km\;{\times}\;27km$)의 SRES A2 시나리오와 LARS-WG를 이용하여 국내 139개 소유역별 기후시나리오를 생산하였다. 본 연구에서 사용된 고해상도 시나리오는 약 350km 수평해상도의 ECHO-G 자료를 NCAR/PSU MM5를 이용하여 27km 수평해상도로 상세화한 것이다. A2 시나리오는 우리나라의 공간적 강수특성을 비교적 잘 모사하였으나, 한강과 금강유역의 강수량이 적게 모의되는 문제점을 보였다. 이러한 기후모형의 한계를 극복하고 유역스케일의 신뢰성 높은 기후시나리오를 생산하기 위해 일기상발생기인 LARS-WG를 선정하고 국내 기후모의에 대한 적요성을 평가하였다. LARS-WG를 이용한 기후모의 결과 월평균최대.최소기온과 월평균강수량은 관측치에 평균에서는 ${\pm}20%$, 표준편차에서는 ${\pm}50%$ 이내로 기후변화에 따른 수자원 영향평가의 목적으로 적용성이 높다고 판단되었다. 또한 LARS-WG를 이용하여 유역별 시나리오를 생산하고 관측치와 비교한 결과 기후모형에서 모의하지 못하는 지역적인 기후특성을 잘 반영하는 것으로 분석되었다.

To evaluate the climate change impacts on water resources, this study generates and analyzes the climate change scenarios for 139 sub-basins in Korea using high resolution ($27km\;{\times}\; 27km$) SHES A2 scenario and LARS-WG. The $27km\;{\times}\; 27km$ high resolution NCAR/PSU MM5 scenario is downscaled from 350km horizontal resolution ECHO-G data. The A2 scenario relatively well reproduced Korean spatial precipitation characteristics, but it underestimated the precipitation over the Han River and the Gum River basins. The LARS-WG was selected and evaluated to overcome the limitation of climate model and to create a highly reliable climate scenario. The results show that the monthly mean minimum and maximum temperature and monthly mean precipitation are within ${\pm}20%$ from the observed mean, and ${\pm}50%$ from the standard deviation that represents the generated data are highly reliable. Moreover, the comparison results between observed data and generated data from LARS-WG show that the latter can reflect the regional climate characteristic very well that can not be simulated from the former.

키워드

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