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| Titel |
Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria |
| VerfasserIn |
A. Viglione, J. Parajka, M. Rogger, J. L. Salinas, G. Laaha, 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. 6 ; Nr. 17, no. 6 (2013-06-21), S.2263-2279 |
| Datensatznummer |
250018904
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| Publikation (Nr.) |
 copernicus.org/hess-17-2263-2013.pdf |
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| Zusammenfassung |
| This is the third of a three-part paper series through which we assess the
performance of runoff predictions in ungauged basins in a comparative way.
Whereas the two previous papers by Parajka et al. (2013) and Salinas
et al. (2013) assess the regionalisation performance of
hydrographs and hydrological extremes on the basis of a comprehensive
literature review of thousands of case studies around the world, in this
paper we jointly assess prediction performance of a range of runoff
signatures for a consistent and rich dataset. Daily runoff time series are
predicted for 213 catchments in Austria by a regionalised rainfall–runoff
model and by Top-kriging, a geostatistical estimation method that accounts
for the river network hierarchy. From the runoff time-series, six runoff
signatures are extracted: annual runoff, seasonal runoff, flow duration
curves, low flows, high flows and runoff hydrographs. The predictive
performance is assessed in terms of the bias, error spread and proportion of
unexplained spatial variance of statistical measures of these signatures in
cross-validation (blind testing) mode. Results of the comparative assessment
show that, in Austria, the predictive performance increases with catchment
area for both methods and for most signatures, it tends to increase with
elevation for the regionalised rainfall–runoff model, while the dependence
on climate characteristics is weaker. Annual and seasonal runoff can be
predicted more accurately than all other signatures. The spatial variability
of high flows in ungauged basins is the most difficult to estimate followed
by the low flows. It also turns out that in this data-rich study in Austria,
the geostatistical approach (Top-kriging) generally outperforms the
regionalised rainfall–runoff model. |
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