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| Titel |
Towards automatic calibration of 2-D flood propagation models |
| VerfasserIn |
P. Fabio, G. T. Aronica, H. Apel |
| 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 ; 14, no. 6 ; Nr. 14, no. 6 (2010-06-07), S.911-924 |
| Datensatznummer |
250012332
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| Publikation (Nr.) |
 copernicus.org/hess-14-911-2010.pdf |
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| Zusammenfassung |
| Hydraulic models for flood propagation description are an essential tool in
many fields and are used, for example, for flood hazard and risk
assessments, evaluation of flood control measures, etc. Nowadays there are
many models of different complexity regarding the mathematical foundation
and spatial dimensions available, and most of them are comparatively easy to
operate due to sophisticated tools for model setup and control. However, the
calibration of these models is still underdeveloped in contrast to other
models like e.g. hydrological models or models used in ecosystem analysis.
This has two primary reasons: first, lack of relevant data against which the
models can be calibrated, because flood events are very rarely monitored due
to the disturbances inflicted by them and the lack of appropriate measuring
equipment in place. Second, 2-D models are computationally very
demanding and therefore the use of available sophisticated automatic
calibration procedures is restricted in many cases. This study takes a well
documented flood event in August 2002 at the Mulde River in Germany as an
example and investigates the most appropriate calibration strategy for a
simplified 2-D hyperbolic finite element model. The model independent
optimiser PEST, that enables automatic calibrations without changing model
code, is used and the model is calibrated against over 380 surveyed maximum
water levels. The application of the parallel version of the optimiser
showed that (a) it is possible to use automatic calibration in combination of
2-D hydraulic model, and (b) equifinality of model parameterisation can also
be caused by a too large number of degrees of freedom in the calibration
data in contrast to a too simple model setup. In order to improve model
calibration and reduce equifinality, a method was developed to identify
calibration data, resp. model setup with likely errors that obstruct model
calibration. |
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