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| Titel |
A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions |
| VerfasserIn |
P. Pokhrel, D. E. Robertson, Q. J. Wang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 2 ; Nr. 17, no. 2 (2013-02-22), S.795-804 |
| Datensatznummer |
250018803
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| Publikation (Nr.) |
 copernicus.org/hess-17-795-2013.pdf |
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| Zusammenfassung |
| 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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