Modeling of Climatic Variables using Stochastic Approaches in Sudan

Abstract

The climatic variables take a significant role in agricultural process and irrigation management because we need to know all changes related to the climate which absolutely will be affected agricultural yield. For this purpose, the ARIMA models were suggested in this study for modeling daily average temperature, relative humidity, and solar radiation variables related to five main meteorological stations (Wad Madani, Khartoum, Al Gadaref, Al Damazin, and Dongola) in Sudan. The daily variables were obtained from the period 2013-to 2020 years. Time series analysis methods are used for estimating and modeling the climatic variables using ARIMA (Autoregressive Integrated Moving Average Models) which are called Box Jenkins models. For modeling purposes, linear stochastic models were used to estimate future values of daily variables. The Augmented Dickey-Fuller test (ADF) was applied to the time series to check the stationarity at 1%, 5%, and 10% confidence levels. The time series of variables were stationarity and without trend. Diagnostic checks were used for checking models and selecting the best models from the autocorrelation (ACF) and partial autocorrelation (PACF) function graphs. The best models were selected according to the adjusted R2, Standard error (S.E), Akaike information criterion (AIC), and Bayesian information criterion (BIC) values. The best results were observed in ARIMA (1,0,1) and (1,0,2) which can be effective for predicting future values. The ARIMA models obtained satisfactory results for temperature, relative humidity, and solar radiation variables. So, this study might be extremely helpful for agricultural engineers to achieve all the processes related to agricultural practices.