  Machine learning-based hydrological prediction models, despite their high
accuracy, face limitations in extrapolation capabilities when applied globally
due to uneven data distribution. This study integrates Domain-Adversarial
Neural Networks (DANN) to improve the geographical adaptability of
evapotranspiration (ET) models. By employing DANN, we aim to mitigate
distributional discrepancies between different sites, significantly enhancing
the model's extrapolation capabilities. Our results show that DANN improves ET
prediction accuracy with an average increase in the Kling-Gupta Efficiency
(KGE) of 0.2 to 0.3 compared to the traditional Leave-One-Out (LOO) method.
DANN is particularly effective for isolated sites and transition zones between
biomes, reducing data distribution discrepancies and avoiding low-accuracy
predictions. By leveraging information from data-rich areas, DANN enhances the
reliability of global-scale ET products, especially in ungauged regions. This
study highlights the potential of domain adaptation techniques to improve the
extrapolation and generalization capabilities of machine learning models in
hydrological studies.
