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| Titel |
Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates |
| VerfasserIn |
J. H. Spaaks, W. Bouten |
| 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 ; 17, no. 9 ; Nr. 17, no. 9 (2013-09-09), S.3455-3472 |
| Datensatznummer |
250085926
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| Publikation (Nr.) |
 copernicus.org/hess-17-3455-2013.pdf |
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| Zusammenfassung |
| In hydrological modeling, model structures are developed in an
iterative cycle as more and different types of measurements become
available and our understanding of the hillslope or watershed
improves. However, with increasing complexity of the model, it
becomes more and more difficult to detect which parts of the model
are deficient, or which processes should also be incorporated into
the model during the next development step. In this study, we first compare
two methods (the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) and
the Simultaneous parameter Optimization and Data Assimilation algorithm (SODA)) to calibrate a purposely deficient
3-D hillslope-scale model to error-free, artificially
generated measurements. We use a multi-objective approach based on
distributed pressure head at the soil–bedrock interface and
hillslope-scale discharge and water balance. For these idealized circumstances,
SODA's usefulness as
a diagnostic methodology is demonstrated by its ability to identify
the timing and location of processes that are missing in the
model. We show that SODA's state updates provide information
that could readily be incorporated into an improved model structure,
and that this type of information cannot be gained from parameter
estimation methods such as SCEM-UA. We then expand on the SODA result
by performing yet another calibration, in which we investigate whether
SODA's state updating patterns are still capable of providing insight
into model structure deficiencies when there are fewer measurements,
which are moreover subject to measurement noise. We conclude that SODA
can help guide the discussion between experimentalists and modelers by
providing accurate and detailed information on how to improve
spatially distributed hydrologic models. |
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