Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

  • Ohn, Ilsang (Department of Statistics, Seoul National University) ;
  • Seo, Seung Beom (Institute of Engineering Research, Seoul National University) ;
  • Kim, Seonghyeon (Department of Statistics, Seoul National University) ;
  • Kim, Young-Oh (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Yongdai (Department of Statistics, Seoul National University)
  • Received : 2019.10.13
  • Accepted : 2019.12.03
  • Published : 2020.01.31
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Abstract

A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

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