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| Titel |
Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy |
| VerfasserIn |
X. Xie, S. Meng, S. Liang, Y. Yao |
| 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 ; 18, no. 10 ; Nr. 18, no. 10 (2014-10-07), S.3923-3936 |
| Datensatznummer |
250120488
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| Publikation (Nr.) |
 copernicus.org/hess-18-3923-2014.pdf |
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| Zusammenfassung |
| The challenge of streamflow predictions at ungauged locations is primarily
attributed to various uncertainties in hydrological modelling. Many studies
have been devoted to addressing this issue. The similarity regionalization
approach, a commonly used strategy, is usually limited by subjective
selection of similarity measures. This paper presents an application of a
partitioned update scheme based on the ensemble Kalman filter (EnKF) to
reduce the prediction uncertainties. This scheme performs real-time updating
for states and parameters of a distributed hydrological model by
assimilating gauged streamflow. The streamflow predictions are constrained
by the physical rainfall-runoff processes defined in the distributed
hydrological model and by the correlation information transferred from
gauged to ungauged basins. This scheme is successfully demonstrated in a
nested basin with real-world hydrological data where the subbasins have
immediate upstream and downstream neighbours. The results suggest that the
assimilated observed data from downstream neighbours have more important
roles in reducing the streamflow prediction errors at ungauged locations.
The real-time updated model parameters remain stable with reasonable spreads
after short-period assimilation, while their estimation trajectories have
slow variations, which may be attributable to climate and land surface
changes. Although this real-time updating scheme is intended for streamflow
predictions in nested basins, it can be a valuable tool in separate basins
to improve hydrological predictions by assimilating multi-source data sets,
including ground-based and remote-sensing observations. |
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