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| Titel |
A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts |
| VerfasserIn |
M. Li, Q. J. Wang, J. C. Bennett, D. E. Robertson |
| 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 ; 19, no. 1 ; Nr. 19, no. 1 (2015-01-06), S.1-15 |
| Datensatznummer |
250120578
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| Publikation (Nr.) |
 copernicus.org/hess-19-1-2015.pdf |
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| Zusammenfassung |
| For streamflow forecasting, rainfall–runoff models are often augmented with
updating procedures that correct forecasts based on the latest available
streamflow observations of streamflow. A popular approach for updating
forecasts is autoregressive (AR) models, which exploit the "memory" in
hydrological model simulation errors. AR models may be applied to raw errors
directly or to normalised errors. In this study, we demonstrate that AR
models applied in either way can sometimes cause over-correction of
forecasts. In using an AR model applied to raw errors, the over-correction
usually occurs when streamflow is rapidly receding. In applying an AR model
to normalised errors, the over-correction usually occurs when streamflow is
rapidly rising. In addition, when parameters of a hydrological model and an
AR model are estimated jointly, the AR model applied to normalised errors
sometimes degrades the stand-alone performance of the base hydrological
model. This is not desirable for forecasting applications, as forecasts
should rely as much as possible on the base hydrological model, with updating
only used to correct minor errors. To overcome the adverse effects of the
conventional AR models, a restricted AR model applied to normalised errors is
introduced. We show that the new model reduces over-correction and improves
the performance of the base hydrological model considerably. |
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