Total crop water consumption increased by 10% from 1987 to 2019 in Central Asia’s Amu Darya Basin, mainly driven by climate change as less water-intensive crops were adopted, according to analyses of ...

Abstract. Evapotranspiration comprises transpiration, soil evaporation, and interception. The partitioning of evapotranspiration is challenging due to the lack of direct measurements and uncertainty o...

DRI's Charles Morton, Associate Research Scientist of Geography, who had a key role in the expansion of OpenET said, "Delivering wall-to-wall evapotranspiration data across the contiguous United States at the field scale is a major milestone for OpenET,...

What is an extreme ET event? To illustrate what an extreme ET event is, we first consider the seven days of every year when a region in Central Western Europe experienced the highest cumulative ET (ETx7d) in ERA5 Land (Fig...

**Abstract:** This research introduces a novel closed-loop system for optimizing evapotranspiration in algae biomass production facilities leveraging a hybrid deep learning model coupled with Bayesian optimization. The system dynamically models and controls environmental factors to maximize water usage efficiency and algae growth rate, yielding a potentially 30% increase in biomass production compared to traditional methods. This approach addresses the significant challenge of minimizing freshwater input and energy expenditure in algae cultivation, a critical factor for large-scale sustainable biofuel and bioproduct production.

Surface energy balance (SEB) models are widely employed for remote-sensing-based evapotranspiration estimation. A critical parameter in most SEB models is the surface temperature of wet or dry boundaries where sensible heat (H) or latent heat (LE) equals 0, which is difficult to measure or estimate. The wide application of SEB models is seriously limited due to this challenge. Therefore, this study introduces ‘critical canopy temperature ( )’, defined as the canopy temperature at which LE equals 0, corresponding to the dry boundary in SEB models. We develop a physics-constrained machine learning (ML) model (hybrid model) that conserves the SEB equation to predict using meteorological measurements from 103 eddy-covariance (EC) stations combined with remote-sensing data. The predicted is integrated into the Surface Energy Balance Algorithm for Land (SEBAL) model to replace the dry boundary, thereby to improve the estimation of LE estimation. Results demonstrate that the hybrid model effectively captures canopy temperature anomalies during stomatal closure and achieve better generalization than pure ML approaches in LE estimation, particularly under extreme conditions. Compared with conventional dry-boundary selection scheme without SEB constraints, incorporating significantly improve SEBAL performance, reducing the root mean square error for LE from 119.33 to 81.71 W m−2 against EC observations (at 31.52% reduction). At regional scales, the hybrid model enables pixel-level estimation of , addressing the long-standing challenge of dry-boundary underrepresentation. Overall, the hybrid model provides a robust and accurate framework for predicting theoretical dry-boundary temperatures while conserving the SEB, supporting improved monitoring of vegetation physiological status and enhancing the accuracy of SEB models.

Understanding how water moves through the Earth system is fundamental to predicting climate impacts and ensuring sustainable water management.

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