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| Titel |
Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps |
| VerfasserIn |
M. Herbst, M. C. Casper, J. Grundmann, O. Buchholz |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 9, no. 2 ; Nr. 9, no. 2 (2009-03-19), S.373-392 |
| Datensatznummer |
250006707
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| Publikation (Nr.) |
 copernicus.org/nhess-9-373-2009.pdf |
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| Zusammenfassung |
Distributed watershed models constitute a key component in flood forecasting
systems. It is widely recognized that models because of their structural
differences have varying capabilities of capturing different aspects of the
system behaviour equally well. Of course, this also applies to the
reproduction of peak discharges by a simulation model which is of particular
interest regarding the flood forecasting problem.
In our study we use a Self-Organizing Map (SOM) in combination with index
measures which are derived from the flow duration curve in order to examine
the conditions under which three different distributed watershed models are
capable of reproducing flood events present in the calibration data. These
indices are specifically conceptualized to extract data on the peak
discharge characteristics of model output time series which are obtained
from Monte-Carlo simulations with the distributed watershed models NASIM,
LARSIM and WaSIM-ETH. The SOM helps to analyze this data by producing a
discretized mapping of their distribution in the index space onto a two
dimensional plane such that their pattern and consequently the patterns of
model behaviour can be conveyed in a comprehensive manner. It is
demonstrated how the SOM provides useful information about details of model
behaviour and also helps identifying the model parameters that are relevant
for the reproduction of peak discharges and thus for flood prediction
problems. It is further shown how the SOM can be used to identify those
parameter sets from among the Monte-Carlo data that most closely approximate
the peak discharges of a measured time series. The results represent the
characteristics of the observed time series with partially superior accuracy
than the reference simulation obtained by implementing a simple calibration
strategy using the global optimization algorithm SCE-UA. The most prominent
advantage of using SOM in the context of model analysis is that it allows to
comparatively evaluating the data from two or more models. Our results
highlight the individuality of the model realizations in terms of the index
measures and shed a critical light on the use and implementation of simple
and yet too rigorous calibration strategies. |
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