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| Titel |
Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model |
| VerfasserIn |
M. Coustau, S. Ricci, V. Borrell-Estupina, C. Bouvier, O. Thual |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 3 ; Nr. 13, no. 3 (2013-03-06), S.583-596 |
| Datensatznummer |
250018381
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| Publikation (Nr.) |
 copernicus.org/nhess-13-583-2013.pdf |
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| Zusammenfassung |
| Mediterranean catchments in southern France are threatened by potentially
devastating fast floods which are difficult to anticipate. In order to
improve the skill of rainfall-runoff models in predicting such flash floods,
hydrologists use data assimilation techniques to provide real-time updates
of the model using observational data. This approach seeks to reduce the
uncertainties present in different components of the hydrological model
(forcing, parameters or state variables) in order to minimize the error in
simulated discharges. This article presents a data assimilation procedure,
the best linear unbiased estimator (BLUE), used with the goal of improving
the peak discharge predictions generated by an event-based hydrological
model Soil Conservation Service lag and route (SCS-LR). For a given
prediction date, selected model inputs are corrected by assimilating
discharge data observed at the basin outlet. This study is conducted on the
Lez Mediterranean basin in southern France. The key objectives of this article
are (i) to select the parameter(s) which allow for the most efficient and
reliable correction of the simulated discharges, (ii) to demonstrate the
impact of the correction of the initial condition upon simulated discharges,
and (iii) to identify and understand conditions in which this technique fails
to improve the forecast skill. The correction of the initial moisture
deficit of the soil reservoir proves to be the most efficient control
parameter for adjusting the peak discharge. Using data assimilation, this
correction leads to an average of 12% improvement in the flood peak
magnitude forecast in 75% of cases. The investigation of the other 25%
of cases points out a number of precautions for the appropriate use of this
data assimilation procedure. |
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