Divisive and hierarchical clustering techniques to analyse variability of rainfall distribution patterns in a Mediterranean region

Abstract One of the main characteristics of the Mediterranean climate is the irregular distribution of rainfalls and its high variability from year to year, which makes it difficult to know if Mediterranean rainfalls are changing. In this work, a combination of divisive (K-means algorithm) and hierarchical (average and Ward's methods) cluster techniques were used to analyse rainfall distribution patterns over the year and their changes over time. The study was performed using daily data (1889–1999) from the Alt Penedes region (NE Spain), and annual, spring and autumn rainfall were analysed. The variables included in the cluster analysis were spring and autumn rainfalls, which represent on average 70% of the annual rainfall. The K-means and Ward's methods led to similar classifications of the observations, whereas the average method had a lesser discrimination power. However, the combination of all of them was useful in the identification of the rainfall distribution patterns over the years and their changes with time. Patterns including dry springs and wet autumns have increased during the last decade, although any consistent trend in annual rainfall was not found.

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