WIT Press


Regionalization Of Droughts In Portugal

Price

Free (open access)

Paper DOI

10.2495/RM110211

Volume

146

Pages

11

Page Range

239 - 249

Published

2011

Size

966 kb

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