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Generation of Sub-Hourly Rainfall Events through a Point Stochastic Rainfall Model

Department of Engineering, University of Messina, c/da Di Dio, 98166 Vill. S Agata, Messina, Italy
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Geosciences 2019, 9(5), 226; https://doi.org/10.3390/geosciences9050226
Received: 8 February 2019 / Revised: 7 May 2019 / Accepted: 8 May 2019 / Published: 16 May 2019
(This article belongs to the Special Issue Hydrology of Urban Catchments)
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Abstract

The aim of this paper is to present a stochastic model to generate sub-hourly rainfall events at a given point. Historical events used as the input have been extracted by the sub-hourly rainfall series available for a defined rain gauge station based on a fixed inter-event time and selected if their average intensity was larger than a critical fixed one. The sub-hourly events generated by applying the proposed methodology are completely stochastic and their main characteristics, i.e., shape, duration and average intensity, have been derived as a function of the statistics of the historical events analyzed. In order to characterize the shape, dimensionless hyetographs have been derived. They have been statistically modelled by using the Beta cumulative distribution. Average intensity and duration of the historical events were first modelled separately by fitting several probability distributions and selecting the best one using the more common statistical criteria. Then, their correlation was modelled using the Frank’s copula. In order to test the methodology, two sites in Sicily, Italy, where 10 min’ recorded rainfall data were available, were analyzed. Finally, comparison between the statistics of the simulated events and those of the measured data demonstrates the good performance of the model. View Full-Text
Keywords: sub-hourly rainfall events; stochastic rainfall generator; frank’s copula; mass curves sub-hourly rainfall events; stochastic rainfall generator; frank’s copula; mass curves
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Brigandì, G.; Aronica, G.T. Generation of Sub-Hourly Rainfall Events through a Point Stochastic Rainfall Model. Geosciences 2019, 9, 226.

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