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| Titel |
A hydrologic post-processor for ensemble streamflow predictions |
| VerfasserIn |
L. Zhao, Q. Duan, J. Schaake, A. Ye, J. Xia |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Towards practical applications in ensemble hydro-meteorological forecasting ; Nr. 29 (2011-02-28), S.51-59 |
| Datensatznummer |
250016934
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| Publikation (Nr.) |
 copernicus.org/adgeo-29-51-2011.pdf |
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| Zusammenfassung |
This paper evaluates the performance of a statistical post-processor for
imperfect hydrologic model forecasts. Assuming that the meteorological
forecasts are well-calibrated, we employ a "General Linear Model (GLM)" to
post-process simulations produced by a hydrologic model. For a particular
forecast date, the observations and simulations from an "analysis window"
and hydrologic model forecasts for a "forecast window", the GLM
Post-Processor (GLMPP) is used to produce an ensemble of predictions of the
streamflow observations that will occur during the "forecast window". The
objectives of the GLMPP are to: (1) preserve any skill in the original
hydrologic ensemble forecast; (2) correct systematic model biases; (3)
retain the equal-likelihood assumption for the ensemble; (4) preserve
temporal scale dependency relationships in streamflow hydrographs and the
uncertainty in the predictions; and, (5) produce reliable ensemble
predictions.
Observed and simulated daily streamflow data from the Second Workshop on
Model Parameter Estimation Experiment (MOPEX) are used to test how well
these objectives are met when the GLMPP is applied to ensemble hydrologic
forecasts driven by well calibrated meteorological forecasts. A 39-year
hydrologic dataset from the French Broad basin is split into calibration and
verification periods. The results show that the GLMPP built using data from
the calibration period removes the mean bias when applied to hydrologic
model simulations from both the calibration and verification periods.
Probability distributions of the post-processed model simulations are shown
to be closer to the climatological probability distributions of observed
streamflow than the distributions of the unadjusted simulated flows. A
number of experiments with different GLMPP configurations were also
conducted to examine the effects of different configurations for forecast
and analysis window lengths on the robustness of the results. |
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