This paper presents linear stochastic models known as multiplicative seasonal
autoregressive integrated moving average model (SARIMA).The model is used to simulate monthly
rainfall in Nyala station, Sudan. For the analysis, monthly rainfall data for the years 1971–2010 were
used. The seasonality observed in Auto Correlation Function(ACF) and Partial Auto Correlation
Function(PACF) plots of monthly rainfall data was removed using first order seasonal differencing prior
to the development of the SARIMA model. Interestingly, the SARIMA (0,0,0)x(0,1,1)12 model developed
was found to be most suitable for simulating monthly rainfall over Nyala station. This model is
considered appropriate to forecast the monthly rainfall to assist decision makers to establish priorities for
water demand, storage, distribution and disaster management
