The stationarity hypothesis is essential in hydrological frequency analysis
and statistical inference. This assumption is often not fulfilled for large observed
datasets, especially in the case of hydro-climatic variables. The Generalized Extreme Value
distribution with covariates allows to model data in the presence of non-stationarity
and/or dependence on covariates. Linear and non-linear dependence structures have
been proposed with the corresponding fitting approach. The objective of the present
study is to develop the GEV model with B-Spline in a Bayesian framework. A Markov
Chain Monte Carlo (MCMC) algorithm has been developed to estimate quantiles and
their posterior distributions. The methods are tested and illustrated using simulated
data and applied to meteorological data. Results indicate the better performance
of the proposed Bayesian method for rainfall quantile estimation according to BIAS
and RMSE criteria especially for high return period events.
Cite this paper
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