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<p>Advances in soil moisture monitoring techniques and sensor networks have made data assimilation a powerful tool for estimating evapotranspiration (ET). The commonly used simple water balance (SWB) model provides reliable ET estimates within the data assimilation framework. However, this approach often neglects the influence of ET on vertical fluxes. While this assumption is reasonable during the drying period in low-drainage soils, it may not hold in soils with high hydraulic conductivity. This study introduces a comprehensive water balance (CWB) model that explicitly accounts for ET-driven percolation. The model captures ET effects on vertical flux by comparing soil water depletion with and without ET, thereby highlighting the role of root water uptake (RWU) in percolation. The CWB model, combined with the ensemble Kalman filter, predicts daily ET by using soil moisture sensor data across different soil types. Within this framework, RWU rather than soil moisture serves as the observable for updating. The model’s performance was evaluated against that of a SWB model under varying drainage conditions and with a reduced number of soil moisture sensors. The CWB model performed better than the conventional model in ET prediction, particularly in coarse-textured soils, reducing error by 45% and achieving higher accuracy (NSE = 0.918 vs. 0.727). ET prediction using eight sensors showed high accuracy (RV ≈ 1, NSE = 0.9, RMSE = 0.3 mmd<sup>− 1</sup>), while ET prediction using five sensors had slightly lower accuracy (RV &gt; 0.75, NSE &gt; 0.84, RMSE = 0.4 mmd<sup>− 1</sup>) but still provided reliable estimates under normal ET variations. Model testing with sensor errors showed that fine-textured soils exhibit lower sensitivity to uncertainty, enabling more reliable ET estimates. This finding highlights the necessity of incorporating vertical flux effects to avoid underestimation. Further improvement in prediction accuracy may be achieved by refining the bottom flux uncertainty within the framework. Even though this synthetic study shows the potential of RWU assimilation considering ET-affected percolation, future studies that focus on the use of real-field observations are necessary.</p>

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A comprehensive water balance approach for improved assimilation of evapotranspiration estimates derived from soil moisture

  • 期刊论文

  • 发表日期：

    2025年11月20日

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  • Diksha Chaudhary,
  • Ickkshaanshu Sonkar

摘要

Advances in soil moisture monitoring techniques and sensor networks have made data assimilation a powerful tool for estimating evapotranspiration (ET). The commonly used simple water balance (SWB) model provides reliable ET estimates within the data assimilation framework. However, this approach often neglects the influence of ET on vertical fluxes. While this assumption is reasonable during the drying period in low-drainage soils, it may not hold in soils with high hydraulic conductivity. This study introduces a comprehensive water balance (CWB) model that explicitly accounts for ET-driven percolation. The model captures ET effects on vertical flux by comparing soil water depletion with and without ET, thereby highlighting the role of root water uptake (RWU) in percolation. The CWB model, combined with the ensemble Kalman filter, predicts daily ET by using soil moisture sensor data across different soil types. Within this framework, RWU rather than soil moisture serves as the observable for updating. The model’s performance was evaluated against that of a SWB model under varying drainage conditions and with a reduced number of soil moisture sensors. The CWB model performed better than the conventional model in ET prediction, particularly in coarse-textured soils, reducing error by 45% and achieving higher accuracy (NSE = 0.918 vs. 0.727). ET prediction using eight sensors showed high accuracy (RV ≈ 1, NSE = 0.9, RMSE = 0.3 mmd− 1), while ET prediction using five sensors had slightly lower accuracy (RV > 0.75, NSE > 0.84, RMSE = 0.4 mmd− 1) but still provided reliable estimates under normal ET variations. Model testing with sensor errors showed that fine-textured soils exhibit lower sensitivity to uncertainty, enabling more reliable ET estimates. This finding highlights the necessity of incorporating vertical flux effects to avoid underestimation. Further improvement in prediction accuracy may be achieved by refining the bottom flux uncertainty within the framework. Even though this synthetic study shows the potential of RWU assimilation considering ET-affected percolation, future studies that focus on the use of real-field observations are necessary.