한국습지학회지 (Journal of Wetlands Research)

한국습지학회 (Korean Wetlands Society)
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HadGEM3-RA 기후모델 일강우자료를 이용한 빈도해석 성능 평가

Assessment of Frequency Analysis using Daily Rainfall Data of HadGEM3-RA Climate Model

  • 김성훈 (연세대학교 건설환경공학과) ;
  • 김한빈 (연세대학교 건설환경공학과) ;
  • 정영훈 (연세대학교 건설환경공학과) ;
  • 허준행 (연세대학교 건설환경공학과)
  • Kim, Sunghun (chool of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Hanbeen (chool of Civil and Environmental Engineering, Yonsei University) ;
  • Jung, Younghun (chool of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Jun-Haeng (chool of Civil and Environmental Engineering, Yonsei University)
  • 투고 : 2019.10.07
  • 심사 : 2019.11.28
  • 발행 : 2019.12.30
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초록

본 연구에서는 기후변화 시나리오 자료를 이용하여 지점빈도해석(At-site Frequency Analysis, AFA)과 지역빈도해석(Regional Frequency Analysis, RFA) 등을 수행하였고, Monte Carlo simulation을 통한 RRMSE(relative root mean squared error) 값을 비교·분석함으로써 각 빈도해석 방법에 따른 성능을 평가하고자 하였다. 확률강우량 산정을 위하여 기상청에서 국가표준시나리오로 제공하는 RCM(Regional Climate Model) 자료 중 하나인 HadGEM3-RA(12.5km) 기후모델 자료로부터 우리나라 615개 지점에 대한 일 강우 자료를 추출하였고, 자료의 편의보정(bias correction)과 공간상세화(spatial disaggregation)를 위하여 분위사상법(quantile mapping)과 역거리제곱법(inverse distance squared method)을 적용하였다. 분석 결과 지역빈도해석 방법이 지점빈도해석보다 정확하게 확률강우량을 산정하는 것으로 나타났으며, 이는 기후변화 시나리오 기반의 확률강우량 산정시 지역빈도해석의 결과가 보다 합리적인 전망 결과를 도출할 것으로 판단된다.

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

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