Modeling and Forecasting Rainfall in Ethiopia

Abstract

Ethiopian economy is extremely dependent on agricultural sector, which contributes 45% to the Gross Domestic Product (GDP), 85% foreign earnings and provides livelihood to 80% of the population. Ethiopian agriculture is highly dependent on natural rainfall, with irrigation agriculture accounting for less than 1% of the country’s total cultivated land. Therefore, modeling and forecasting the rainfall dynamics of the country has a great importance. This paper aims at examining the rainfall dynamics and fit appropriate model for forecasting Ethiopian rainfall. In this research, we apply Box-Jenkins approach, Seasonal Autoregressive Integrated Moving Average (SARIMA) model in order to forecast monthly rainfall of Ethiopia for the period of twelve months ahead. Monthly rainfall data from 1901 to 2015 were used from world bank group (climate change portal). Appropriate SARIMA model has been identified based on an Akaike information criteria (AIC) and Bayesian information criteria (BIC) for forecasting the amount of monthly average rainfall. Farmers, in general agricultural sectors, policy makers, tourists, and investors engaged in the construction industry are some of the sectors benefited from this result.