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| Titel |
Comparative assessment of predictions in ungauged basins – Part 2: Flood and low flow studies |
| VerfasserIn |
J. L. Salinas, G. Laaha, M. Rogger, J. Parajka, A. Viglione, 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. 7 ; Nr. 17, no. 7 (2013-07-09), S.2637-2652 |
| Datensatznummer |
250018928
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| Publikation (Nr.) |
 copernicus.org/hess-17-2637-2013.pdf |
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| Zusammenfassung |
| The objective of this paper is to assess the performance of methods that
predict low flows and flood runoff in ungauged catchments. The aim is to
learn from the similarities and differences between catchments in different
places, and to interpret the differences in performance in terms of the
underlying climate-landscape controls. The assessment is performed at two
levels. The Level 1 assessment is a meta-analysis of 14 low flow prediction
studies reported in the literature involving 3112 catchments, and 20 flood
prediction studies involving 3023 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 regionalisation method. The results
indicate that both flood and low flow predictions in ungauged catchments
tend to be less accurate in arid than in humid climates and more accurate in
large than in small catchments. There is also a tendency towards a somewhat
lower performance of regressions than other methods in those studies that
apply different methods in the same region, while geostatistical methods
tend to perform better than other methods. Of the various flood
regionalisation approaches, index methods show significantly lower
performance in arid catchments than regression methods or geostatistical
methods. For low flow regionalisation, regional regressions are generally
better than global regressions. |
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