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| Titel |
Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe |
| VerfasserIn |
M. A. Sunyer, Y. Hundecha, D. Lawrence, H. Madsen, P. Willems, M. Martinkova, K. Vormoor, G. Bürger, M. Hanel, J. Kriaučiūnienė, A. Loukas, M. Osuch, I. Yucel |
| 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 ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-20), S.1827-1847 |
| Datensatznummer |
250120685
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| Publikation (Nr.) |
 copernicus.org/hess-19-1827-2015.pdf |
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| Zusammenfassung |
Information on extreme precipitation for future climate is needed to assess
the changes in the frequency and intensity of flooding. The primary source
of information in climate change impact studies is climate model
projections. However, due to the coarse resolution and biases of these
models, they cannot be directly used in hydrological models. Hence,
statistical downscaling is necessary to address climate change impacts at
the catchment scale.
This study compares eight statistical downscaling methods (SDMs) often used in
climate change impact studies. Four methods are based on change factors (CFs),
three are bias correction (BC) methods, and one is a perfect prognosis method. The
eight methods are used to downscale precipitation output from 15
regional climate models (RCMs) from the ENSEMBLES project for 11
catchments in Europe. The overall results point to an increase in extreme
precipitation in most catchments in both winter and summer. For individual
catchments, the downscaled time series tend to agree on the direction of the
change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season
analysed. Similarly, general conclusions cannot be drawn regarding the
differences between CFs and BC methods. The
performance of the BC methods during the control period also
depends on the catchment, but in most cases they represent an improvement
compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and
SDMs indicates that at least 30% and up to
approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the
expected changes in extreme precipitation and highlights the need for
considering an ensemble of both SDMs and climate
models. Recommendations are provided for the selection of the most suitable
SDMs to include in the analysis. |
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