Improvement in long-range streamflow forecasting accuracy using the Bayes’ theorem

Seung Beom Seo, Young Oh Kim, Shin Uk Kang, Gun Il Chun

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This study has developed a hydrologic forecasting system for correcting the systematic bias inherent in hydrologic simulations based on the Bayes’ theorem. The observed climatology was used as prior information, and results of a linear regression model that describes the relationship between ‘the observed streamflow’ and ‘the mean of the Ensemble Streamflow Prediction (ESP) forecasts’ was used to form a likelihood function. The Bayes’ theorem was then applied to produce posterior information for the streamflow forecast. Thirty-five watersheds, in which a dam is operated, were tested in this study, and the forecast accuracy was evaluated. It was found that the developed Bayesian ESP (B-ESP) model is capable of improving the forecast accuracy of the ESP. It was found that the forecasting accuracy was improved for all the different lengths of lead-times with the B-ESP model. Nonetheless, the B-ESP model obtained lower RPSS values than the ESP, while its deterministic forecasting accuracy was better than the ESP. This is due to the intrinsic attribute of the Bayesian inference.

Original languageEnglish
Pages (from-to)616-632
Number of pages17
JournalHydrology Research
Issue number2
StatePublished - 1 Apr 2019


  • Bayes’ theorem
  • Ensemble Streamflow Prediction
  • Hydrologic forecast
  • Likelihood
  • Posterior information


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