Regional frequency analysis of extreme rainfall in Sicily (Italy)

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24 Citations (Scopus)


Extreme rainfall events have large impacts on society and are likely to increase in intensity under climate change. For design and management decisions, particularly regarding hydraulic works, accurate estimates of precipitation magnitudes are needed at different durations. In this article, an objective approach of the regional frequency analysis (RFA) has been applied to precipitation data for the island of Sicily, Italy. Annual maximum series for rainfall with durations of 1, 3, 6, 12, and 24 h from about 130 rain gauges were used. The RFA has been implemented using principal component analysis (PCA) followed by a clustering analysis, through the k-means algorithm, to identify statistically homogeneous groups of stations for the derivation of regional growth curves. Three regional probability distributions were identified as appropriate from an initial wider selection of distributions and were compared – the three-parameter log-normal distribution (LN3), the generalized extreme value (GEV) distribution, and the two component extreme value (TCEV) distribution. The regional parameters of these distributions were estimated using L-moments and considering a hierarchical approach. Finally, assessment of the accuracy of the growth curves was achieved by means of the relative bias and relative root-mean-square error (RMSE) using a simulation analysis of regional L-moments. Results highlight that for the lower return periods, all distributions showed the same accuracy while for higher return periods the LN3 distribution provided the best result. The study provides an updated resource for the estimation of extreme precipitation quantiles for Sicily through the derivation of growth curves needed to obtain depth–duration–frequency (DDF) curves.
Original languageEnglish
Pages (from-to)e698-e716
Number of pages19
JournalInternational Journal of Climatology
Publication statusPublished - 2018

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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