Journal of Geography, Environment and Earth Science International

Accurate prediction of potential evapotranspiration (PET) is crucial for effective irrigation management and sustainable water resource planning. Evapotranspiration (ET) is one of the most significant components of the hydrologic cycle, as a large proportion of the precipitation falling on land surfaces returns to the atmosphere through this process. The ET process is primarily driven by meteorological variables such as air temperature, relative humidity, solar radiation and wind speed. The study aims to assess the accuracy of the Box-Jenkins SARIMA Model for Forecasting Potential Evapotranspiration in the Bidar district. This study focuses on modelling and forecasting PET for various locations in the Bidar district, Karnataka, India. PET was estimated using the Thornthwaite method based on daily maximum and minimum temperature data. For forecasting, Seasonal Autoregressive Integrated Moving Average (SARIMA) models were developed utilising the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. Model selection was based on the lowest values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The selected SARIMA models were viz., ARIMA (2,0,2)(2,1,0)₁₂ for Bidar, ARIMA(1,0,0)(2,1,0)₁₂ for Aurad, ARIMA(2,0,2)(2,1,0)₁₂ for Bhalki and Humnabad and ARIMA(1,0,0)(2,1,0)₁₂ for Basavakalyan. Among these, the models for Aurad and Bidar demonstrated superior predictive performance. All five models showed satisfactory accuracy for short-term forecasting, particularly for a lead time of one month. The study underscores the applicability of SARIMA models in enhancing decision-making for irrigation scheduling and command area management, thereby contributing to improved agricultural water use efficiency.