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| Titel |
Evaluation of bias-correction methods for ensemble streamflow volume forecasts |
| VerfasserIn |
T. Hashino, A. A. Bradley, S. S. Schwartz |
| 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 ; 11, no. 2 ; Nr. 11, no. 2 (2007-02-27), S.939-950 |
| Datensatznummer |
250009225
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| Publikation (Nr.) |
 copernicus.org/hess-11-939-2007.pdf |
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| Zusammenfassung |
| Ensemble prediction systems are used operationally to make
probabilistic streamflow forecasts for seasonal time scales.
However, hydrological models used for ensemble streamflow prediction
often have simulation biases that degrade forecast quality and limit
the operational usefulness of the forecasts. This study evaluates
three bias-correction methods for ensemble streamflow volume
forecasts. All three adjust the ensemble traces using a
transformation derived with simulated and observed flows from a
historical simulation. The quality of probabilistic forecasts issued
when using the three bias-correction methods is evaluated using a
distributions-oriented verification approach. Comparisons are made
of retrospective forecasts of monthly flow volumes for a
north-central United States basin (Des Moines River, Iowa), issued
sequentially for each month over a 48-year record. The results show
that all three bias-correction methods significantly improve
forecast quality by eliminating unconditional biases and enhancing
the potential skill. Still, subtle differences in the attributes of
the bias-corrected forecasts have important implications for their
use in operational decision-making. Diagnostic verification
distinguishes these attributes in a context meaningful for
decision-making, providing criteria to choose among bias-correction
methods with comparable skill. |
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