Precipitation Forecasting and Monitoring in Degraded Land: A Study Case in Zaghouan

Abstract

The study aimed to forecast and monitor drought over degraded land based on monthly precipitation using the Seasonal Autoregressive Integrated Moving Average (SARIMA) approach. Several statistical parameters to select the most appropriate model were applied. The results indicate that the SARIMA (1,1,1) (0,1,1)12 is the most suitable for 1981 to 2019 CHIRPS time-series data. The combination of precipitation data and this approved model will subsequently be applied to compute, assess, and predict the severity of drought in the study area. The forecasting performance of the generated SARIMA model was evaluated according to the mean absolute percentage error (15%), which indicated that the proposed model showed high performance in forecasting drought. The forecasting trends showed adequate results, fitting well with the historical tendencies of drought events.