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| Titel |
Catchment classification based on characterisation of streamflow and precipitation time series |
| VerfasserIn |
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 ; 17, no. 3 ; Nr. 17, no. 3 (2013-03-15), S.1149-1159 |
| Datensatznummer |
250018829
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| Publikation (Nr.) |
 copernicus.org/hess-17-1149-2013.pdf |
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| Zusammenfassung |
The formulation of objective procedures for the delineation of homogeneous
groups of catchments is a fundamental issue in both operational and research
hydrology. For assessing catchment similarity, a variety of hydrological
information may be considered; in this paper, gauged sites are characterised
by a set of streamflow signatures that include a representation, albeit
simplified, of the properties of fine time-scale flow series and in
particular of the dynamic components of the data, in order to keep into
account the sequential order and the stochastic nature of the streamflow
process.
The streamflow signatures are provided in input to a clustering algorithm
based on unsupervised SOM neural networks, obtaining groups of catchments
with a clear hydrological distinctiveness, as highlighted by the
identification of the main patterns of the input variables in the different
classes and the interpretation of their interrelations. In addition, even if
no geographical, morphological nor climatological information is provided in
input to the SOM network, the clusters exhibit an overall consistency as far
as location, altitude and precipitation regime are concerned.
In order to assign ungauged sites to such groups, the catchments are
represented through a parsimonious set of morphometric and pluviometric
variables, including also indexes that attempt to synthesise the variability
and correlation properties of the precipitation time series, thus providing
information on the type of weather forcing that is specific to each basin.
Following a principal components analysis, needed for synthesizing and better
understanding the morpho-pluviometric catchment properties, a discriminant
analysis finally assigns the ungauged catchments, through a leave-one-out
cross validation, to one of the above identified hydrologic response classes.
The approach delivers a quite satisfactory identification of the membership
of ungauged catchments to the streamflow-based classes, since the comparison
of the two cluster sets shows a misclassification rate
of around 20%.
Overall results indicate that the inclusion of information on the properties
of the fine time-scale streamflow and rainfall time series may be a promising
way for better representing the hydrologic and climatic character of the
study catchments. |
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