STOCHASTIC GENERATION OF THE OCCURRENCE AND AMOUNT OF DAILY RAINFALL

Abstract

Rainfall is the main source of irrigation water in the northwest part of Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall. The first order Markov chain model was used to simulate the sequence of rainfall occurrence using the method of transitional probability matrices, while daily rainfall amount was generated using a gamma distribution. The model parameters were estimated from historical rainfall records. The shape and scale parameters were estimated by moment method and hence it became possible to find the parameter values at the study area and then to generate synthetic sequences according to the gamma distribution. The parameters necessary for the whole generation include the means, variance or standard deviation and conditional probabilities of wet and dry days. Results obtained showed that the model could be used to generate rainfall data satisfactorily.