Abstract:
A statistical model to predict the probability and magnitude of floods in
non-stationary conditions is presented. The model uses a time-dependent and/or
covariate-dependent generalized extreme value (GEV) distribution to fit the annual maximal
(AM) discharge, and it is applied to five gauging stations in the Ouémé River Basin in Benin
Republic, West Africa. Different combinations of the model parameters, which vary with
respect to time and/or climate covariates, were explored with the stationary model based on
three criteria of goodness of fit. The non-stationary model more adequately explains a
substantial amount of variation in the data. The GEV-1 model, which incorporates a linear
trend in its location parameter, surpasses the other models. Non-stationary return levels for
different return periods have been proposed for the study area. This case study tested the
hypothesis of stationarity in estimating flood events in the basin and it demonstrated the
strong need to account for changes over time when performing flood frequency analyses.
