Engineering

Sensible heat

Ecology

Biology

Metallurgy

Materials science

Energy budget

Energy balance

Vegetation (pathology)

Civil engineering

Surface (topology)

Latent heat

Thermodynamics

Physics

Mathematics

Water cycle

Eddy covariance

Geometry

Tower

Medicine

Meteorology

Atmosphere (unit)

Atmospheric sciences

Flux (metallurgy)

Evapotranspiration

Ecosystem

Pathology

Geophysical Research Letters

Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land‐atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance ( r s ), and we find that vegetation height and soil moisture are the main regulators of ET and r s .

Physics‐Constrained Machine Learning of Evapotranspiration