Bayesian identification and validation of a daily rainfall model under monsoon conditions
1999
CHAOUCHE, ALI | PARENT, ERIC
A model for daily rainfall occurrence and daily rainfall amount is fitted to the data of Dedougou, in the Sudano-Sahelian zone. The fitting is performed in a Bayesian framework using the Hastings procedure which allows for a full representation of parameter uncertainty. The method is based on a comparison of any observed characteristic of the rainy season to its predictive law. If and only if the observation falls into the central part of the predictive distribution the model is judged to describe the characteristic under scrutiny. Using this method, several records were studied. It is shown that the model preserves the commonly used hydrological records like daily and annual precipitation, number of rainy days per year, rainy sequence length. The model depicts the climatic change which occurred in the late sixties. The Markovian hypothesis, on which the model is based, is validated. The independence hypothesis between rainfall amounts in two successive days does not hold. The extreme daily rainfall values are consistent with the largest values generated by the model.
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