Bayesian uncertainty decomposition for hydrological projections

Ilsang Ohn, Seonghyeon Kim, Seung Beom Seo, Young Oh Kim, Yongdai Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


There is a considerable uncertainty in a hydrological projection, which arisen from the multiple stages composing the hydrological projection. Uncertainty decomposition analysis evaluates contribution of each stage to the total uncertainty in the hydrological projection. Some uncertainty decomposition methods have been proposed, but they still have some limitations: (1) they do not consider nonstationarity in data and (2) they only use summary statistics of the projected data instead of the full time-series and lack a principled way to choose the summary statistic. We propose a novel Bayesian uncertainty decomposition method which can alleviate such problems. In addition, the proposed method provides probabilistic statements about the uncertainties. We apply the proposed method to the streamflow projection data for Yongdam Dam basin located at Geum River in South Korea.

Original languageEnglish
Pages (from-to)953-975
Number of pages23
JournalJournal of the Korean Statistical Society
Issue number3
StatePublished - 1 Sep 2020


  • Bayesian statistics
  • Heavy-tailed distribution
  • Hydrological projection
  • Uncertainty decomposition


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