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.
Highlights
Many countries in West Africa have suffered from catastrophic floods (Burkina Faso, Senegal, Togo, Benin, Cote d’Ivoire, and Niger)
Previous studies have applied the generalized extreme value (GEV) distribution, among other methods, to analyze extreme stream flow [12,41,42], and the results suggest that parameters of the GEV distribution can be a function of covariates, such as climate indexes and time
The GEV-1 model, whose location parameter is a linear function of covariates (SST or sea level pressure (SLP)) and whose other parameters are constant, is the best model for explaining change in the extreme annual maximal (AM) streamflow at the different stations
Summary
Many countries in West Africa have suffered from catastrophic floods (Burkina Faso, Senegal, Togo, Benin, Cote d’Ivoire, and Niger). These floods affected thousands of people through property damage and fatalities [1]. Floods have become increasingly frequent, and it is unknown whether they are caused by an increasing frequency in heavy rainfall, consequent change in discharge magnitude, or changes in land use. These devastating floods call for improvement in hydrological forecasts to reduce the vulnerability of local communities [2]. Similar conclusions were attained regarding the 2012 inundation in Niamey, the capital of the Niger Republic [6], and in Lusaka, the capital of Zambia [7], where the flood risk has strongly increased because of the fast growth of the city in a flood-prone area
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