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| Titel |
Comparative assessment of predictions in ungauged basins – Part 1: Runoff-hydrograph studies |
| VerfasserIn |
J. Parajka, A. Viglione, M. Rogger, J. L. Salinas, M. Sivapalan  , G. Blöschl |
| 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. 5 ; Nr. 17, no. 5 (2013-05-07), S.1783-1795 |
| Datensatznummer |
250018871
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| Publikation (Nr.) |
 copernicus.org/hess-17-1783-2013.pdf |
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| Zusammenfassung |
| The objective of this assessment is to compare studies predicting runoff
hydrographs in ungauged catchments. The aim is to learn from the differences
and similarities between catchments in different locations, and to interpret
the differences in performance in terms of the underlying climate and
landscape controls. The assessment is performed at two levels. The Level 1
assessment is a meta-analysis of 34 studies reported in the literature
involving 3874 catchments. The Level 2 assessment consists of a more focused
and detailed analysis of individual basins from selected studies from Level
1 in terms of how the leave-one-out cross-validation performance depends on
climate and catchment characteristics as well as on the chosen
regionalisation method. The results indicate that runoff-hydrograph
predictions in ungauged catchments tend to be more accurate in humid than in
arid catchments and more accurate in large than in small catchments. The
dependence of performance on elevation differs by regions and depends on how
aridity varies with elevation and air temperature. The effect of the parameter
regionalisation method on model performance differs between studies.
However, there is a tendency towards a somewhat lower performance of
regressions than other methods in those studies that apply different methods
in the same region. In humid catchments spatial proximity and similarity
methods perform best while in arid catchments similarity and parameter
regression methods perform slightly better. For studies with a large number
of catchments (dense stream gauge network) there is a tendency for spatial
proximity and geostatistics to perform better than regression or
regionalisation based on simple averaging of model parameters from gauged
catchments. There was no clear relationship between predictive performance
and the number of regionalised model parameters. The implications of the
findings are discussed in the context of model building. |
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