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| Titel |
Influence of rainfall observation network on model calibration and application |
| VerfasserIn |
A. Bárdossy, T. Das |
| 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. 1 ; Nr. 12, no. 1 (2008-01-25), S.77-89 |
| Datensatznummer |
250010460
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| Publikation (Nr.) |
 copernicus.org/hess-12-77-2008.pdf |
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| Zusammenfassung |
| The objective in this study is to investigate the influence of the spatial
resolution of the rainfall input on the model calibration and application.
The analysis is carried out by varying the distribution of the raingauge
network. A meso-scale catchment located in southwest Germany has been
selected for this study. First, the semi-distributed HBV model is calibrated
with the precipitation interpolated from the available observed rainfall of
the different raingauge networks. An automatic calibration method based on
the combinatorial optimization algorithm simulated annealing is applied. The
performance of the hydrological model is analyzed as a function of the
raingauge density. Secondly, the calibrated model is validated using
interpolated precipitation from the same raingauge density used for the
calibration as well as interpolated precipitation based on networks of
reduced and increased raingauge density. Lastly, the effect of missing
rainfall data is investigated by using a multiple linear regression approach
for filling in the missing measurements. The model, calibrated with the
complete set of observed data, is then run in the validation period using
the above described precipitation field. The simulated hydrographs obtained
in the above described three sets of experiments are analyzed through the
comparisons of the computed Nash-Sutcliffe coefficient and several
goodness-of-fit indexes. The results show that the model using different
raingauge networks might need re-calibration of the model parameters,
specifically model calibrated on relatively sparse precipitation information
might perform well on dense precipitation information while model calibrated
on dense precipitation information fails on sparse precipitation
information. Also, the model calibrated with the complete set of observed
precipitation and run with incomplete observed data associated with the data
estimated using multiple linear regressions, at the locations treated as
missing measurements, performs well. |
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