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| Titel |
Robust estimation of hydrological model parameters |
| VerfasserIn |
A. Bárdossy, S. K. Singh |
| 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 ; 12, no. 6 ; Nr. 12, no. 6 (2008-11-28), S.1273-1283 |
| Datensatznummer |
250010956
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| Publikation (Nr.) |
 copernicus.org/hess-12-1273-2008.pdf |
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| Zusammenfassung |
| The estimation of hydrological model parameters is a challenging task. With
increasing capacity of computational power several complex optimization
algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models
depend upon the input data. The quality of input data cannot be assured as
there may be measurement errors for both input and state variables. In this
study a methodology has been developed to find a set of robust parameter
vectors for a hydrological model. To see the effect of observational error on
parameters, stochastically generated synthetic measurement errors were
applied to observed discharge and temperature data. With this modified data,
the model was calibrated and the effect of measurement errors on parameters
was analysed. It was found that the measurement errors have a significant
effect on the best performing parameter vector. The erroneous data led to
very different optimal parameter vectors. To overcome this problem and to
find a set of robust parameter vectors, a geometrical approach based on
Tukey's half space depth was used. The depth of the set of N randomly
generated parameters was calculated with respect to the set with the best
model performance (Nash-Sutclife efficiency was used for this study) for each
parameter vector. Based on the depth of parameter vectors, one can find a set
of robust parameter vectors. The results show that the parameters chosen
according to the above criteria have low sensitivity and perform well when
transfered to a different time period. The method is demonstrated on the
upper Neckar catchment in Germany. The conceptual HBV model was used for this
study. |
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