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

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 Rsquared 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.