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Uncertainties of extreme rainfall quantiles estimated by a stochastic rainfall model and by a generalized Pareto distribution

Abstract : The hourly rainfall stochastic model SHYPRE generates long hourly rainfall series and enables the estimation of distribution quantiles. Two different uncertainty analyses are proposed, based on frequential and Bayesian methods, to quantify the effect of sampling distribution and parameter uncertainties on the quantile estimations. The results are compared with those of a regional generalized Pareto distribution(GPD) based on extreme value analysis, with a regionally fixed value of the shape parameter. The GPD and SHYPRE are shown to have similar uncertainties. The application of regional approaches is shown to reduce sampling sensitivity in estimations, especially when few data are available. The study is based on a 122-year daily rainfall series in Marseille, France.
Keywords : ANALYSE BAYESIENNE
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Submitted on : Friday, May 15, 2020 - 3:35:34 PM
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A. Müller, P. Arnaud, M. Lang, J. Lavabre. Uncertainties of extreme rainfall quantiles estimated by a stochastic rainfall model and by a generalized Pareto distribution. Hydrological Sciences Journal, Taylor & Francis, 2009, 54 (3), pp.417-429. ⟨10.1623/hysj.54.3.417⟩. ⟨hal-02591891⟩

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