Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 10
Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3283-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3283-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 10
Research article
 | Highlight paper
 | 
27 May 2026
Research article | Highlight paper |  | 27 May 2026

A novel classifier-guided ensemble framework for global terrestrial evapotranspiration estimates

Le Ni, Weiguang Wang, Jianyu Fu, and Mingzhu Cao

Viewed

Total article views: 3,378 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,038 1,151 189 3,378 711 1,258
  • HTML: 2,038
  • PDF: 1,151
  • XML: 189
  • Total: 3,378
  • BibTeX: 711
  • EndNote: 1,258
Views and downloads (calculated since 02 Dec 2025)
Cumulative views and downloads (calculated since 02 Dec 2025)

Viewed (geographical distribution)

Total article views: 3,378 (including HTML, PDF, and XML) Thereof 3,378 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 May 2026
Download
Editorial statement
Evapotranspiration estimates often contain substantial uncertainties, while direct observations remain sparse. This study offers a practical and innovative framework for selecting the most appropriate model for Global Terrestrial Evapotranspiration Estimates for specific locations. The authors present a Classifier-Guided Ensemble approach that integrates process-based algorithms, machine learning models, and hybrid methods to identify the most suitable model for a given setting and subsequently estimate evapotranspiration (ET).
Read more
Short summary
Existing global evapotranspiration algorithms rely on sparse local measurements and each comes with its own strengths and weaknesses. Here, we proposed an ensemble framework that employed a machine learning system to dynamically select the most appropriate algorithm to be used across spatial and temporal scales, thus fully utilizing the distinct strengths of each method. In multi-scale validations, our framework exhibited enhanced extrapolation performance, stability, and interpretability.
Read more
Share