Rainfall Drought Simulating Using Stochastic SARIMA Models for Gadaref Region, Sudan (original) (raw)
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A SEASONAL ARIMA MODEL FOR FORECASTING MONTHLY RAINFALL IN GEZIRA SCHEME, SUDAN
Monthly rainfall in the Gezira irrigation scheme of Sudan is being modelled by Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. The realization analyzed is from 1971 to 2000. A visual inspection of the time plot gives the expected impression of a generally horizontal trend and 12-month seasonal periodicity. The Augmented Dickey-Fuller (ADF) Test adjudges the series as stationary. However its correlogram gives a contrary impression of non-stationarity. A seasonal (i.e. 12-point) differencing yields a stationary series on ADF test and correlogram grounds. On the basis of its correlogram three models are proposed and fitted: 1) A SARIMA(0, 0, 0)x(0, 1, 1) 12 model; 2) A SARIMA(0, 0, 1)x(0, 1, 1) 12 model; 3) A SARIMA(0, 0, 1)x(2, 1, 1) 2 model. On minimum AIC grounds, the SARIMA(0, 0, 0)x(0, 1, 1) 12 model is adjudged the most adequate. This model adequacy claim is further corroborated by a residual analysis. This may be used as basis for rainfall forecasting for planning purposes in the region.
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Drought forecasting is considering an important tool to help the decision makers. Standardized Precipitation Index (SPI) is a tool, which was primarily developed to identify meteorological drought and wet events by using only series of monthly rainfall. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecasting both the SPI series and precipitation data. As the land station data at the selected stations in Ethiopia is not available/ cost expensive after the year 2010, therefore the satellite image data, which is available and less costly, is used after correction using the available land data. The land station data for the stations which have more than 30 years land data recorded are used to correct the deviation in the satellite image data. After comparing the land station data and the satellite image data three stations are selected. The selected stations have overlap between both satellite and land stations data for more than 16 years. SPSS program was used to analyses and forecasting the data. As a result, it is concluded that forecasting using SPSS program is recommending for both drought and precipitation using ARIMA model.
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