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| Titel |
A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts |
| VerfasserIn |
G. Thirel, E. Martin, J.-F. Mahfouf, S. Massart, S. Ricci, F. Regimbeau, F. Habets |
| 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 ; 14, no. 8 ; Nr. 14, no. 8 (2010-08-24), S.1639-1653 |
| Datensatznummer |
250012405
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| Publikation (Nr.) |
 copernicus.org/hess-14-1639-2010.pdf |
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| Zusammenfassung |
| The use of ensemble streamflow forecasts is developing in the international
flood forecasting services. Ensemble streamflow forecast systems can provide
more accurate forecasts and useful information about the uncertainty of the
forecasts, thus improving the assessment of risks. Nevertheless, these
systems, like all hydrological forecasts, suffer from errors on
initialization or on meteorological data, which lead to hydrological
prediction errors. This article, which is the second part of a 2-part
article, concerns the impacts of initial states, improved by a streamflow
assimilation system, on an ensemble streamflow prediction system over France.
An assimilation system was implemented to improve the streamflow analysis of
the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes
the ensemble streamflow forecasts at Météo-France. This assimilation
system, using the Best Linear Unbiased Estimator (BLUE) and modifying the
initial soil moisture states, showed an improvement of the streamflow
analysis with low soil moisture increments. The final states of this suite
were used to initialize the ensemble streamflow forecasts of
Météo-France, which are based on the SIM model and use the European
Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction
System (EPS). Two different configurations of the assimilation system were
used in this study: the first with the classical SIM model and the second
using improved soil physics in ISBA. The effects of the assimilation system
on the ensemble streamflow forecasts were assessed for these two
configurations, and a comparison was made with the original (i.e. without
data assimilation and without the improved physics) ensemble streamflow
forecasts. It is shown that the assimilation system improved most of the
statistical scores usually computed for the validation of ensemble
predictions (RMSE, Brier Skill Score and its decomposition, Ranked
Probability Skill Score, False Alarm Rate, etc.), especially for the first
few days of the time range. The assimilation was slightly more efficient for
small basins than for large ones. |
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