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| Titel |
How does bias correction of regional climate model precipitation affect modelled runoff? |
| VerfasserIn |
J. Teng, N. J. Potter, F. H. S. Chiew, L. Zhang, B. Wang, J. Vaze, J. P. Evans |
| 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. 2 ; Nr. 19, no. 2 (2015-02-04), S.711-728 |
| Datensatznummer |
250120618
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| Publikation (Nr.) |
 copernicus.org/hess-19-711-2015.pdf |
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| Zusammenfassung |
| Many studies bias correct daily precipitation from climate models to match
the observed precipitation statistics, and the bias corrected data are then
used for various modelling applications. This paper presents a review of
recent methods used to bias correct precipitation from regional climate
models (RCMs). The paper then assesses four bias correction methods applied
to the weather research and forecasting (WRF) model simulated precipitation,
and the follow-on impact on modelled runoff for eight catchments in southeast
Australia. Overall, the best results are produced by either quantile mapping
or a newly proposed two-state gamma distribution mapping method. However, the
differences between the methods are small in the modelling experiments
here (and as reported in the literature), mainly due to the substantial
corrections required and inconsistent errors over time (non-stationarity).
The errors in bias corrected precipitation are typically amplified
in modelled runoff. The tested methods cannot overcome limitations of the RCM in
simulating precipitation sequence, which affects runoff generation. Results
further show that whereas bias correction does not seem to alter change
signals in precipitation means, it can introduce additional uncertainty to
change signals in high precipitation amounts and, consequently, in runoff.
Future climate change impact studies need to take this into account when
deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs
will continue to improve and will become increasingly useful for hydrological
applications as the bias in RCM simulations reduces. |
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