Abstract:
West African populations are increasingly exposed to heavy rainfall events which cause devastating
floods. For the design of rainwater drainage facilities (to protect populations), practitioners systematically use the
Gumbel distribution regardless of rainfall statistical behaviour. The objective of this study is twofold. The first is
to update existing knowledge on heavy rainfall frequency analysis inWest Africa to check whether the systematic
preference for Gumbel’s distribution is not misleading, and subsequently to quantify biases induced by the use of
the Gumbel distribution on stations fitting other distributions. Annual maximum daily rainfall of 12 stations located
in the Benin sections of the Niger and Volta Rivers’ basins covering a period of 96 years (1921–2016) were
used. Five statistical distributions (Gumbel, GEV, Lognormal, Pearson type III, and Log-Pearson type III) were
used for the frequency analysis and the most appropriate distribution was selected based on the Akaike (AIC)
and Bayesian (BIC) criteria. The study shows that the Gumbel’s distribution best represents the data of 2=3 of
the stations studied, while the remaining 1=3 of the stations fit better GEV, Lognormal, and Pearson type III
distributions. The systematic application of Gumbel’s distribution for the frequency analysis of extreme rainfall
is therefore misleading. For stations whose data best fit the other distributions, annual daily rainfall maxima were
estimated both using these distributions and the Gumbel’s distribution for different return periods. Depending
on the return period, results demonstrate that the use of the Gumbel distribution instead of these distributions
leads to an overestimation (of up to C6:1 %) and an underestimation (of up to 45:9 %) of the annual daily
rainfall maxima and therefore to an uncertain design of flood protection facilities. For better validity, the findings
presented here should be tested on larger datasets.