Lookup NU author(s): Angelo Forestieri,
Dr Stephen Blenkinsop,
Professor Hayley Fowler
This is the authors' accepted manuscript of an article that has been published in its final definitive form by John Wiley & Sons Ltd., 2018.
For re-use rights please refer to the publisher's terms and conditions.
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 paper, 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 distribution (GEV), and the Two Component Extreme Value distribution (TCEV). 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 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.
Author(s): Forestieri A, Lo Conti F, Blenkinsop S, Cannarozzo M, Fowler HJ, Noto LV
Publication type: Article
Publication status: Published
Journal: International Journal of Climatology
Print publication date: 01/04/2018
Online publication date: 17/01/2018
Acceptance date: 07/12/2017
ISSN (print): 0899-8418
ISSN (electronic): 1097-0088
Publisher: John Wiley & Sons Ltd.
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