DROUGHT FORECAST USING ARIMA MODEL FOR THE STANDARDIZED PRECIPITATION INDEX (SPI) AND PRECIPITATION DATA

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.