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• W2912247852 abstract "Abstract 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." @default.
• W2912247852 created "2019-02-21" @default.
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• W2912247852 date "2019-01-23" @default.
• W2912247852 modified "2023-10-14" @default.
• W2912247852 title "Improvement in long-range streamflow forecasting accuracy using the Bayes' theorem" @default.
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• W2912247852 doi "https://doi.org/10.2166/nh.2019.098" @default.
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