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| Titel |
Using stochastic space-time models to map extreme precipitation in southern Portugal |
| VerfasserIn |
A. C. Costa, R. Durão, M. J. Pereira, A. Soares |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 8, no. 4 ; Nr. 8, no. 4 (2008-07-30), S.763-773 |
| Datensatznummer |
250005648
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| Publikation (Nr.) |
 copernicus.org/nhess-8-763-2008.pdf |
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| Zusammenfassung |
| The topographic characteristics and spatial climatic diversity are
significant in the South of continental Portugal where the rainfall regime
is typically Mediterranean. Direct sequential cosimulation is proposed for
mapping an extreme precipitation index in southern Portugal using elevation
as auxiliary information. The analysed index (R5D) can be considered a flood
indicator because it provides a measure of medium-term precipitation total.
The methodology accounts for local data variability and incorporates
space-time models that allow capturing long-term trends of extreme
precipitation, and local changes in the relationship between elevation and
extreme precipitation through time. Annual gridded datasets of the flood
indicator are produced from 1940 to 1999 on 800 m×800 m grids by
using the space-time relationship between elevation and the index.
Uncertainty evaluations of the proposed scenarios are also produced for each
year. The results indicate that the relationship between elevation and
extreme precipitation varies locally and has decreased through time over the
study region. In wetter years the flood indicator exhibits the highest
values in mountainous regions of the South, while in drier years the spatial
pattern of extreme precipitation has much less variability over the study
region. The uncertainty of extreme precipitation estimates also varies in
time and space, and in earlier decades is strongly dependent on the density
of the monitoring stations network. The produced maps will be useful in
regional and local studies related to climate change, desertification, land
and water resources management, hydrological modelling, and flood mitigation
planning. |
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