A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

Pokhrel, P.; Robertson, D. E.; Wang, Q. J.

Hydrologic model predictions are often biased and subject to heteroscedastic errors originating from various sources including data, model structure and parameter calibration. Statistical post-processors are applied to reduce such errors and quantify uncertainty in the predictions. In this study, we investigate the use of a statistical post-processor based on the Bayesian joint probability (BJP) modelling approach to reduce errors and quantify uncertainty in streamflow predictions generated from a monthly water balance model. The BJP post-processor reduces errors through elimination of systematic bias and through transient errors updating. It uses a parametric transformation to normalize data and stabilize variance and allows for parameter uncertainty in the post-processor. We apply the BJP post-processor to 18 catchments located in eastern Australia and demonstrate its effectiveness in reducing prediction errors and quantifying prediction uncertainty.

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Pokhrel, P. / Robertson, D. E. / Wang, Q. J.: A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions. 2013. Copernicus Publications.

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