Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6445-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6445-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-spatial scale assessment and multi-dataset fusion of global terrestrial evapotranspiration datasets
Yi Wu
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yiying Wang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiachen Ji
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yuanfang Chai
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Short summary
Our study introduces BMA-ET, a novel multi-dataset fusion product. Spanning 1980 to 2020 with spatial resolution of 0.5° and 1°, BMA-ET uses Bayesian model averaging (BMA) to combine thirty evapotranspiration (ET) datasets. A key innovation is dynamic weighting scheme, which adjusts for vegetation types and non-common coverage years among ET datasets. BMA-ET provides a comprehensive resource for understanding global ET patterns and trends, addressing the limitation of prior ET fusion efforts.
Our study introduces BMA-ET, a novel multi-dataset fusion product. Spanning 1980 to 2020 with...
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