Study region: We investigate samples from two Italian regions, i.e. Lazio and Sicily, located in central and south Italy, respectively, and characterized by two diverse climates. Study focus: In engineering practice, the study of maxima daily rainfall values is commonly dealt with light-tailed probability distribution functions, such as the Gumbel. The choice of a distribution rather than another may cause estimation errors of rainfall values associated to specific return periods. Recently, several studies demonstrate that heavy-tailed distributions are preferable for extreme events modelling. Here, we opt for six theoretical probability distribution functions and evaluate their performance in fitting extreme precipitation samples. We select the samples with two common methods, i.e. the Peak-Over-Threshold and the Annual Maxima. We assess the best fitting distribution to the empirical samples of extreme values through the Ratio Mean Square Error Method and the Kolmogorov-Smirnov test. New hydrological insights for the region: The assessment of the best fitting distribution to daily rainfall of the two different areas investigated here leads to interesting remarks. Despite the diversity of their climate, results suggest that heavy-tailed distributions describe more accurately empirical data rather than light-tailed ones. Therefore, extreme events may have been largely underestimated in the past in both areas. The proposed investigation can prompt the choice of the best fitting probability distribution to evaluate the design hydrological quantities supporting common engineering practice.

Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences / Moccia, Benedetta; Mineo, Claudio; Ridolfi, Elena; Russo, Fabio; Napolitano, Francesco. - In: JOURNAL OF HYDROLOGY. REGIONAL STUDIES. - ISSN 2214-5818. - 33:(2021). [10.1016/j.ejrh.2020.100771]

Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences

Moccia, Benedetta
;
Mineo, Claudio;Ridolfi, Elena;Russo, Fabio;Napolitano, Francesco
2021

Abstract

Study region: We investigate samples from two Italian regions, i.e. Lazio and Sicily, located in central and south Italy, respectively, and characterized by two diverse climates. Study focus: In engineering practice, the study of maxima daily rainfall values is commonly dealt with light-tailed probability distribution functions, such as the Gumbel. The choice of a distribution rather than another may cause estimation errors of rainfall values associated to specific return periods. Recently, several studies demonstrate that heavy-tailed distributions are preferable for extreme events modelling. Here, we opt for six theoretical probability distribution functions and evaluate their performance in fitting extreme precipitation samples. We select the samples with two common methods, i.e. the Peak-Over-Threshold and the Annual Maxima. We assess the best fitting distribution to the empirical samples of extreme values through the Ratio Mean Square Error Method and the Kolmogorov-Smirnov test. New hydrological insights for the region: The assessment of the best fitting distribution to daily rainfall of the two different areas investigated here leads to interesting remarks. Despite the diversity of their climate, results suggest that heavy-tailed distributions describe more accurately empirical data rather than light-tailed ones. Therefore, extreme events may have been largely underestimated in the past in both areas. The proposed investigation can prompt the choice of the best fitting probability distribution to evaluate the design hydrological quantities supporting common engineering practice.
2021
daily precipitation; extreme rainfall; fitting method; Kolmogorov-Smirnov test; tail behaviour
01 Pubblicazione su rivista::01a Articolo in rivista
Probability distributions of daily rainfall extremes in Lazio and Sicily, Italy, and design rainfall inferences / Moccia, Benedetta; Mineo, Claudio; Ridolfi, Elena; Russo, Fabio; Napolitano, Francesco. - In: JOURNAL OF HYDROLOGY. REGIONAL STUDIES. - ISSN 2214-5818. - 33:(2021). [10.1016/j.ejrh.2020.100771]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1477397
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