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Abstract

Article InfoStochastic weather generators used in meteorological and hydrological studies are mostly statistical models that produce random numbers resembling the observed data on which they have been fitted. They can generate weather sequences that statistically resemble the real observed data. In this study we developed a single-site daily rainfall generator for the simulations of rainfall occurrences and amounts in northwestern of Benin. A first-order two-state Markov chain was used to determine the occurrence of daily precipitation. The rainfall amounts on wet days were generated by using the oneparameter Exponential distribution and the two-parameter Gamma distribution. The Markov model was successful in simulating the rainfall occurrences and the Exponential distribution as the more reliability in preserving most of the important daily characteristics of the historical rainfall amounts. The test of the two distributions on data that are not taken into account in the elaboration of the model has shown that the exponential distribution reproduces well the statistics of the daily precipitation than the gamma distribution