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

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.