Assessing SEBAL Model Performance under Center Pivot Irrigation for Efficient and Accurate Irrigation Management: A Case Study on Sugar Beet Cultivation

Atef, Yousria; Abdel-Aziz, Ahmed Abou El-Hassan; Mehawed, Hazem Sayed; EL-Gindy, Abd El-Gany Mohamed

doi:10.21608/agro.2025.345755.1597

Assessing SEBAL Model Performance under Center Pivot Irrigation for Efficient and Accurate Irrigation Management: A Case Study on Sugar Beet Cultivation

Document Type : Original Article

Authors

¹ Agricultural Engineering Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt

² Agricultural Engineering Research Institute, Agricultural Research Center, Ministry of Agriculture

³ Faculty of Desert Agriculture, King Salman International University, El Tor, Egypt

10.21608/agro.2025.345755.1597

Abstract

Accurate evapotranspiration (ET) estimation is crucial for effective irrigation management, particularly in water-stressed agricultural regions. This study evaluates and compares three ET models, SEBAL, Penman-Monteith, and Priestley-Taylor, in estimating ET for sugar beet (Beta vulgaris L.) under center-pivot irrigation. The results of Penman-Monteith generated the highest cumulative ET (875 mm), reflecting its sensitivity to meteorological inputs but risking overestimation in data-limited contexts. SEBAL estimated a moderate ET of 759.6 mm, closely matching expected water demands due to its use of satellite-derived energy balance data. Priestley-Taylor yielded the lowest ET (633.2 mm), underestimating peak water needs, risking water stress and yield reduction. Model accuracy metrics confirmed SEBAL’s reliability, with a Mean Absolute Error (MAE) of 0.437 mm/day and Root Mean Square Error (RMSE) of 0.541 mm/day. In comparison, Priestley-Taylor showed higher errors (MAE: 0.721 mm/day, RMSE: 0.856 mm/day). The spatial analysis highlighted SEBAL’s ability to detect dynamic ET variations, with peak demands of 4.0–5.0 mm/day during the late midseason. Regression analysis further supported SEBAL’s predictive accuracy, achieving an R-squared value of 0.95 when correlating ET with DMP. The SEBAL model, integrated with remote sensing, proves valuable for advancing sustainable water management practices in agriculture.

Keywords