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| Titel |
State updating of a distributed hydrological model with Ensemble Kalman filtering: effects of updating frequency and observation network density on forecast accuracy |
| VerfasserIn |
O. Rakovec, A. H. Weerts, P. Hazenberg, P. J. J. F. Torfs, R. Uijlenhoet |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250061626
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| Zusammenfassung |
| This paper presents a study on optimal setup for discharge assimilation within a
spatially distributed hydrological model. The well-known ensemble Kalman filter
(EnKF) is employed to update the grid-based distributed states of the hourly HBV-96
model. Synthetic and real world experiments are carried out for the Upper Ourthe (1
600 km2), a quickly responding catchment in the Belgian Ardennes. We assess the
impact on the forecasted discharge of (1) various sets of the spatially distributed
discharge gauges and (2) the filtering frequency. The results show that the hydrological
forecast at the catchment outlet is improved by assimilating interior gauges. This
augmentation of the observation vector improves the forecast more than increasing
the updating frequency. In terms of the model states, the EnKF scheme is mainly
changing the pdf’s of the two routing model storages during situations when the
uncertainty in the discharge simulation is larger than the defined observation uncertainty. |
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