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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 4
Nat. Hazards Earth Syst. Sci., 8, 763–773, 2008
https://doi.org/10.5194/nhess-8-763-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Mediterranean Storms (Plinius 2007)

Nat. Hazards Earth Syst. Sci., 8, 763–773, 2008
https://doi.org/10.5194/nhess-8-763-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Jul 2008

30 Jul 2008

Using stochastic space-time models to map extreme precipitation in southern Portugal

A. C. Costa1, R. Durão2, M. J. Pereira2, and A. Soares2 A. C. Costa et al.
  • 1ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal
  • 2CERENA, Instituto Superior Técnico, Lisbon, Portugal

Abstract. 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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