Regionalization Of Droughts In Portugal
Price
Free (open access)
Transaction
Volume
146
Pages
11
Page Range
239 - 249
Published
2011
Size
966 kb
Paper DOI
10.2495/RM110211
Copyright
WIT Press
Author(s)
J. F. Santos, M. M. Portela & I. Pulido-Calvo
Abstract
Droughts are complex natural hazards that distress large worldwide areas every year with serious impacts on society, environment and economy. Despite their importance they are still among the least understood extreme weather events. This paper is focused on the identification of regional patterns of droughts in Mainland Portugal based on monthly precipitation data, from September 1910 to October 2004, in 144 rain gages distributed uniformly over the country. The drought events were described by means of the Standardized Precipitation Index (SPI) applied to different time scales. To assess the spatial and temporal patterns of droughts, a principal component analysis (PCA) and K-means clustering method (KMC) were applied to the SPI series. The study showed that, for the different times scales, both methods resulted in an equivalent areal zoning, with three regions with different behaviours: the north, the centre and the south of Portugal. These three regions are consistent with the precipitation spatial distribution in Portugal Mainland, which in general terms decrease from North to South, with the central mountainous region representing the transition between the wet north and the progressively dry south. As the mean annual precipitation decreases southwards the hydrological regime becomes more irregular and consequently more prone to droughts. Keywords: Standardized Precipitation Index (SPI), principal component analysis (PCA), clusters analysis. 1 Scope: data base The present paper addresses the identification of spatial patterns of droughts in Mainland Portugal by applying two procedures often utilized in regionalization studies of climatic variables, namely the principal components analysis (PCA)
Keywords
Standardized Precipitation Index (SPI), principal component analysis (PCA), clusters analysis