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| Titel |
Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region |
| VerfasserIn |
N. Guyennon, E. Romano, I. Portoghese, F. Salerno, S. Calmanti, A. B. Petrangeli, G. Tartari, D. Copetti |
| 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. 2 ; Nr. 17, no. 2 (2013-02-19), S.705-720 |
| Datensatznummer |
250017719
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| Publikation (Nr.) |
 copernicus.org/hess-17-705-2013.pdf |
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| Zusammenfassung |
| Various downscaling techniques have been developed to bridge the scale gap
between global climate models (GCMs) and finer scales required to assess
hydrological impacts of climate change. Such techniques may be grouped into
two downscaling approaches: the deterministic dynamical downscaling (DD) and
the statistical downscaling (SD). Although SD has been traditionally seen as
an alternative to DD, recent works on statistical downscaling have aimed to
combine the benefits of these two approaches. The overall objective of this
study is to assess whether a DD processing performed before the SD permits
to obtain more suitable climate scenarios for basin scale hydrological
applications starting from GCM simulations. The case study presented here
focuses on the Apulia region (South East of Italy, surface area about 20 000 km2),
characterised by a typical Mediterranean climate; the monthly
cumulated precipitation and monthly mean of daily minimum and maximum
temperature distribution were examined for the period 1953–2000. The
fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology
was adopted as GCM. The DD was carried out with the Protheus system (ENEA),
while the SD was performed through a monthly quantile-quantile correction.
The SD resulted efficient in reducing the mean bias in the spatial
distribution at both annual and seasonal scales, but it was not able to
correct the miss-modelled non-stationary components of the GCM dynamics. The
DD provided a partial correction by enhancing the spatial heterogeneity of
trends and the long-term time evolution predicted by the GCM. The best
results were obtained through the combination of both DD and SD approaches. |
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