Uncertainty assessment of ensemble streamflow prediction method

앙상블 유량예측기법의 불확실성 평가

  • Kim, Seon-Ho (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kang, Shin-Uk (National Drought Information Analysis Center, Korea Water Resources Cooperation) ;
  • Bae, Deg-Hyo (Department of Civil and Environmental Engineering, Sejong University)
  • 김선호 (세종대학교 공과대학 건설환경공학과) ;
  • 강신욱 (한국수자원공사 국가가뭄정보분석센터) ;
  • 배덕효 (세종대학교 공과대학 건설환경공학과)
  • Received : 2018.02.10
  • Accepted : 2018.03.21
  • Published : 2018.06.30


The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP method and error factors of ensemble-based streamflow prediction method.


Supported by : 기상청, 국토교통과학기술진흥원


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