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| Titel |
Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows |
| VerfasserIn |
U. Haberlandt, I. Radtke |
| 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. 1 ; Nr. 18, no. 1 (2014-01-30), S.353-365 |
| Datensatznummer |
250120263
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| Publikation (Nr.) |
 copernicus.org/hess-18-353-2014.pdf |
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| Zusammenfassung |
| Derived flood frequency analysis allows the estimation of design floods with
hydrological modeling for poorly observed basins considering change and
taking into account flood protection measures. There are several possible
choices regarding precipitation input, discharge output and consequently
the calibration of the model. The objective of this study is to
compare different calibration strategies for a hydrological model
considering various types of rainfall input and runoff output data sets and
to propose the most suitable approach. Event based and continuous, observed
hourly rainfall data as well as disaggregated daily rainfall and
stochastically generated hourly rainfall data are used as input for the
model. As output, short hourly and longer daily continuous flow time series
as well as probability distributions of annual maximum peak flow series are
employed. The performance of the strategies is evaluated using the obtained
different model parameter sets for continuous simulation of discharge in an
independent validation period and by comparing the model derived flood
frequency distributions with the observed one. The investigations are
carried out for three mesoscale catchments in northern Germany with the
hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of
precipitation input data should be used for calibration and application of
the hydrological model, (II) a model calibrated using a small sample of
extreme values works quite well for the simulation of continuous time series
with moderate length but not vice versa, and (III) the best performance with
small uncertainty is obtained when stochastic precipitation data and the
observed probability distribution of peak flows are used for model
calibration. This outcome suggests to calibrate a hydrological model
directly on probability distributions of observed peak flows using
stochastic rainfall as input if its purpose is the application for derived
flood frequency analysis. |
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