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| Titel |
Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? |
| VerfasserIn |
S. A. Archfield, A. Pugliese, A. Castellarin, J. O. Skøien, J. E. Kiang |
| 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. 4 ; Nr. 17, no. 4 (2013-04-23), S.1575-1588 |
| Datensatznummer |
250018858
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| Publikation (Nr.) |
 copernicus.org/hess-17-1575-2013.pdf |
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| Zusammenfassung |
| In the United States, estimation of flood frequency quantiles at ungauged
locations has been largely based on regional regression techniques that
relate measurable catchment descriptors to flood quantiles. More recently,
spatial interpolation techniques of point data have been shown to be
effective for predicting streamflow statistics (i.e., flood flows and low-flow
indices) in ungauged catchments. Literature reports successful applications
of two techniques, canonical kriging, CK (or physiographical-space-based
interpolation, PSBI), and topological kriging, TK (or top-kriging). CK
performs the spatial interpolation of the streamflow statistic of interest in
the two-dimensional space of catchment descriptors. TK predicts the
streamflow statistic along river networks taking both the catchment area and
nested nature of catchments into account. It is of interest to understand how
these spatial interpolation methods compare with generalized least squares
(GLS) regression, one of the most common approaches to estimate flood
quantiles at ungauged locations. By means of a leave-one-out cross-validation
procedure, the performance of CK and TK was compared to GLS regression
equations developed for the prediction of 10, 50, 100 and 500 yr floods for
61 streamgauges in the southeast United States. TK substantially outperforms
GLS and CK for the study area, particularly for large catchments. The
performance of TK over GLS highlights an important distinction between the
treatments of spatial correlation when using regression-based or spatial
interpolation methods to estimate flood quantiles at ungauged locations. The
analysis also shows that coupling TK with CK slightly improves the
performance of TK; however, the improvement is marginal when compared to the
improvement in performance over GLS. |
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