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| Titel |
Data-driven catchment classification: application to the pub problem |
| VerfasserIn |
M. Prinzio, A. Castellarin, E. Toth |
| 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 ; 15, no. 6 ; Nr. 15, no. 6 (2011-06-23), S.1921-1935 |
| Datensatznummer |
250012860
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| Publikation (Nr.) |
 copernicus.org/hess-15-1921-2011.pdf |
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| Zusammenfassung |
| A promising approach to catchment classification makes use of unsupervised
neural networks (Self Organising Maps, SOM's), which organise input data
through non-linear techniques depending on the intrinsic similarity of the
data themselves. Our study considers ∼300 Italian catchments scattered
nationwide, for which several descriptors of the streamflow regime and
geomorphoclimatic characteristics are available. We compare a reference
classification, identified by using indices of the streamflow regime as
input to SOM, with four alternative classifications, which were identified
on the basis of catchment descriptors that can be derived for ungauged
basins. One alternative classification adopts the available catchment
descriptors as input to SOM, the remaining classifications are identified by
applying SOM to sets of derived variables obtained by applying Principal
Component Analysis (PCA) and Canonical Correlation Analysis (CCA) to the
available catchment descriptors. The comparison is performed relative to a
PUB problem, that is for predicting several streamflow indices in ungauged
basins. We perform an extensive cross-validation to quantify nationwide the
accuracy of predictions of mean annual runoff, mean annual flood, and flood
quantiles associated with given exceedance probabilities. Results of the
study indicate that performing PCA and, in particular, CCA on the available
set of catchment descriptors before applying SOM significantly improves the
effectiveness of SOM classifications by reducing the uncertainty of
hydrological predictions in ungauged sites. |
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