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| Titel |
Mapping model behaviour using Self-Organizing Maps |
| VerfasserIn |
M. Herbst, H. V. Gupta, M. C. Casper |
| 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 ; 13, no. 3 ; Nr. 13, no. 3 (2009-03-18), S.395-409 |
| Datensatznummer |
250011803
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| Publikation (Nr.) |
 copernicus.org/hess-13-395-2009.pdf |
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| Zusammenfassung |
| Hydrological model evaluation and identification essentially involves
extracting and processing information from model time series. However, the
type of information extracted by statistical measures has only very limited
meaning because it does not relate to the hydrological context of the data.
To overcome this inadequacy we exploit the diagnostic evaluation concept of
Signature Indices, in which model performance is measured using
theoretically relevant characteristics of system behaviour. In our study, a
Self-Organizing Map (SOM) is used to process the Signatures extracted from
Monte-Carlo simulations generated by the distributed conceptual watershed
model NASIM. The SOM creates a hydrologically interpretable mapping of
overall model behaviour, which immediately reveals deficits and trade-offs
in the ability of the model to represent the different functional behaviours
of the watershed. Further, it facilitates interpretation of the hydrological
functions of the model parameters and provides preliminary information
regarding their sensitivities. Most notably, we use this mapping to identify
the set of model realizations (among the Monte-Carlo data) that most closely
approximate the observed discharge time series in terms of the
hydrologically relevant characteristics, and to confine the parameter space
accordingly. Our results suggest that Signature Index based SOMs could
potentially serve as tools for decision makers inasmuch as model
realizations with specific Signature properties can be selected according to
the purpose of the model application. Moreover, given that the approach
helps to represent and analyze multi-dimensional distributions, it could be
used to form the basis of an optimization framework that uses SOMs to
characterize the model performance response surface. As such it provides a
powerful and useful way to conduct model identification and model
uncertainty analyses. |
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