Spatial and temporal variability of droughts in Portugal

Abstract

An analysis 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 is presented. The drought events were characterized by means of the Standardized Precipitation Index (SPI) applied to different time scales (1, 6, and 12 consecutive months and 6 months from April to September and 12 months from October to September). To assess spatial and temporal patterns of droughts, a principal component analysis (PCA) and K‐means clustering (KMC) were applied to the SPI series. In this way, three different and spatially well‐defined regions with different temporal evolution of droughts were identified (north, central, and south regions of Portugal). A spectral analysis of the SPI patterns obtained with principal component analysis and clusters analysis, using the fast Fourier transform algorithm (FFT), showed that there is a manifest 3.6‐year cycle in the SPI pattern in the south of Portugal and evident 2.4‐year and 13.4‐year cycles in the north of Portugal. The observation of the drought periods supports the occurrence of more frequent cycles of dry events in the south (droughts from moderate to extreme approximately every 3.6 years) than in the north (droughts from severe to extreme approximately every 13.4 years). These results suggest a much stronger immediate influence of the NAO in the south than in the north of Portugal, although these relations remain a challenging task.

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Spatial and temporal characteristics of droughts in the Northeast China Transect

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“…The use of clustering techniques in spatial and temporal patterns in climatic and meteorological research increased in recent decades. For instance, Santos et al (2010) and Dinpashoh et al (2004) used an algorithm that combined clusters analysis and PCA for dividing the research region into climate subregions. Since time series of precipitation are dynamic, certain non-stationary or chaotic properties may be embedded in the data, requiring appropriate methods to extract the features of interest (Hsu and Li 2010).…”

Comprehensive Characterization of Droughts in Slovakia

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