한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제46권7호
  • /
  • Pages.755-765
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  • 2013
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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관측자료 동화기법과 댐운영을 고려한 실시간 댐 수문량 예측모형 개발

Development of Realtime Dam's Hydrologic Variables Prediction Model using Observed Data Assimilation and Reservoir Operation Techniques

  • 이병주 (국립기상연구소 응용기상연구과 수문자원연구팀) ;
  • 정일원 (APEC 기후센터 기후변화연구팀) ;
  • 정현숙 (국립기상연구소 응용기상연구과) ;
  • 배덕효 (세종대학교 물자원연구소.토목환경공학과)
  • Lee, Byong Ju (Applied Meteorology Research Division, National Institute of meteorological Research) ;
  • Jung, Il-Won (Climate Change Research Team, Climate Research Department) ;
  • Jung, Hyun-Sook (Applied Meteorology Research Division, National Institute of meteorological Research) ;
  • Bae, Deg Hyo (Department of Civil and Environmental Engineering, Sejong University)
  • 투고 : 2013.01.30
  • 심사 : 2013.04.30
  • 발행 : 2013.07.31
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초록

본 연구의 목적은 앙상블 칼만필터링 기법과 연속형 강우-유출모형을 연계한 SURF 모형과Auto ROM을 결합한 실시간 댐 수문량 예측모형(DHVPM)을 개발하고 그 적용성을 평가하는데 있다. 대상유역은 충주댐 상류유역을 선정하였으며 2006~2009년 동안 연최대 유입량이 발생한 4개 사례를 선정하였다. 관측유량 자료동화 적용에 따른 선행시간 1시간 유입량에 대한 첨두유량 상대오차, 평균제곱근오차, 모형효율성계수를 산정한 결과, 2007년 첨두유량 상대오차 결과를 제외한 모든 사례에서 자료동화기법을 적용한 결과가 우수한 것으로 나타났다. 현시점으로 가정한 가상시점에서 예측 선행시간 10시간에 대해 유입량을 예측한 결과에서, 유역평균 강우량의 오차가 큰 경우에 대해 자료동화기법을 적용함으로써 예측 유입량의 오차가 줄어드는 것을 확인하였다. 이상의 결과로부터 실시간 예측유입량의 정확도를 향상시키기 위해서는 관측유입량의 실시간 활용이 가능한 환경에서 자료동화기법을 연계한 유입량 예측모형을 이용하는 것이 바람직할 것으로 판단된다.

This study developed a real-time dam's hydrologic variables prediction model (DHVPM) and evaluated its performance for simulating historical dam inflow and outflow in the Chungju dam basin. The DHVPM consists of the Sejong University River Forecast (SURF) model for hydrologic modeling and an autoreservoir operation method (Auto ROM) for dam operation. SURF model is continuous rainfall-runoff model with data assimilation using an ensemble Kalman filter technique. The four extreme events including the maximum inflow of each year for 2006~2009 were selected to examine the performance of DHVPM. The statistical criteria, the relative error in peak flow, root mean square error, and model efficiency, demonstrated that DHVPM with data assimilation can simulate more close to observed inflow than those with no data assimilation at both 1-hour lead time, except the relative error in peak flow in 2007. Especially, DHVPM with data assimilation until 10-hour lead time reduced the biases of inflow forecast attributed to observed precipitation error. In conclusion, DHVPM with data assimilation can be useful to improve the accuracy of inflow forecast in the basin where real-time observed inflow are available.

키워드

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