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| Titel |
The role of observation uncertainty in the calibration of hydrologic rainfall-runoff models |
| VerfasserIn |
T. Ghizzoni, F. Giannoni, G. Roth, R. Rudari |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Mediterranean storms and extreme events in an era of climate change ; Nr. 12 (2007-06-28), S.33-38 |
| Datensatznummer |
250010355
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| Publikation (Nr.) |
 copernicus.org/adgeo-12-33-2007.pdf |
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| Zusammenfassung |
Hydrologic rainfall-runoff models are usually calibrated with reference to a
limited number of recorded flood events, for which rainfall and runoff
measurements are available. In this framework, model's parameters
consistency depends on the number of both events and hydrograph points used
for calibration, and on measurements reliability. Recently, to make users
aware of application limits, major attention has been devoted to the
estimation of uncertainty in hydrologic modelling. Here a simple numerical
experiment is proposed, that allows the analysis of uncertainty in
hydrologic rainfall-runoff modelling associated to both quantity and quality
of available data.
A distributed rainfall-runoff model based on geomorphologic concepts has
been used. The experiment involves the analysis of an ensemble of model
runs, and its overall set up holds if the model is to be applied in
different catchments and climates, or even if a different hydrologic model
is used. With reference to a set of 100 synthetic rainfall events
characterized by a given rainfall volume, the effect of uncertainty in
parameters calibration is studied. An artificial truth – perfect
observation – is created by using the model in a known configuration. An
external source of uncertainty is introduced by assuming realistic, i.e.
uncertain, discharge observations to calibrate the model. The range of
parameters' values able to "reproduce" the observation is studied.
Finally, the model uncertainty is evaluated and discussed. The experiment
gives useful indications about the number of both events and data points
needed for a careful and stable calibration of a specific model, applied in a
given climate and catchment. Moreover, an insight on the expected and
maximum error in flood peak discharge simulations is given: errors ranging
up to 40% are to be expected if parameters are calibrated on insufficient
data sets. |
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