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Area of Science:

  • Environmental Science
  • Machine Learning
  • Hydrology

Background:

  • Terrestrial evapotranspiration (ET) is vital for water, energy, and carbon cycles.
  • Pure machine learning (ML) models struggle with ET estimation, particularly under extreme conditions.
  • Physics-informed ML, specifically hybrid models, show promise for improved ET accuracy.

Purpose of the Study:

  • To investigate the mechanisms behind the improved performance of hybrid ET models.
  • To compare six novel hybrid ET models with a pure ML model.
  • To identify optimal parameterizations for hybrid ET modeling.

Main Methods:

  • Developed six hybrid models integrating different physical ET formulations with the random forest algorithm.
  • Trained and compared models using daily ET observations, meteorological data, and satellite remote sensing.
  • Analyzed the correlation between model error (RMSE) and sensitivity to machine-learned parameters.

Main Results:

  • A strong correlation (r=0.93) was found between ET estimate sensitivity and model error (RMSE).
  • Reduced sensitivity to machine-learned parameters minimizes error propagation and enhances model performance.
  • The most accurate hybrid model used a novel, stable empirical parameter, outperforming pure ML and other hybrid models.

Conclusions:

  • Conventional parameterizations may need reevaluation for optimal integration of physical models with ML.
  • Domain knowledge is crucial for developing effective hybrid models.
  • This study provides insights for advancing hybrid modeling beyond ET estimation.