Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2022
Research article |
| 16 Sep 2022
| 16 Sep 2022
Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis
Haiyang Shi
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Geping Luo
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Olaf Hellwich
CORRESPONDING AUTHOR
Department of Computer Vision & Remote Sensing, Technische Universität Berlin, 10587 Berlin, Germany
Mingjuan Xie
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Chen Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yu Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yuangang Wang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Xiuliang Yuan
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
Xiaofei Ma
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
Wenqiang Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Alishir Kurban
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Philippe De Maeyer
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Tim Van de Voorde
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Viewed
Total article views: 3,959 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Mar 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,894 | 996 | 69 | 3,959 | 294 | 71 | 96 |
- HTML: 2,894
- PDF: 996
- XML: 69
- Total: 3,959
- Supplement: 294
- BibTeX: 71
- EndNote: 96
Total article views: 2,677 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Sep 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,024 | 604 | 49 | 2,677 | 114 | 64 | 87 |
- HTML: 2,024
- PDF: 604
- XML: 49
- Total: 2,677
- Supplement: 114
- BibTeX: 64
- EndNote: 87
Total article views: 1,282 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Mar 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 870 | 392 | 20 | 1,282 | 180 | 7 | 9 |
- HTML: 870
- PDF: 392
- XML: 20
- Total: 1,282
- Supplement: 180
- BibTeX: 7
- EndNote: 9
Viewed (geographical distribution)
Total article views: 3,959 (including HTML, PDF, and XML)
Thereof 3,800 with geography defined
and 159 with unknown origin.
Total article views: 2,677 (including HTML, PDF, and XML)
Thereof 2,582 with geography defined
and 95 with unknown origin.
Total article views: 1,282 (including HTML, PDF, and XML)
Thereof 1,218 with geography defined
and 64 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
10 citations as recorded by crossref.
- Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge Y. Kim et al. 10.1371/journal.pone.0328798
- Revisiting and attributing the global controls over terrestrial ecosystem functions of climate and plant traits at FLUXNET sites via causal graphical models H. Shi et al. 10.5194/bg-20-2727-2023
- Integrating Load‐Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration S. Friedman et al. 10.1111/pce.70222
- Benchmarking transferable evapotranspiration against physical models: Uncertainty analysis of machine learning quasi-observation across Central Asia’s plant functional types and climate zones F. Ochege et al. 10.1016/j.ejrh.2025.102849
- Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis J. Zhao et al. 10.3390/agronomy15051237
- Comparing the use of all data or specific subsets for training machine learning models in hydrology: A case study of evapotranspiration prediction H. Shi et al. 10.1016/j.jhydrol.2023.130399
- Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks H. Shi & X. Cai 10.1016/j.envsoft.2025.106383
- Global dryland aridity changes indicated by atmospheric, hydrological, and vegetation observations at meteorological stations H. Shi et al. 10.5194/hess-27-4551-2023
- Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing M. Xie et al. 10.1038/s41597-023-02473-9
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
10 citations as recorded by crossref.
- Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge Y. Kim et al. 10.1371/journal.pone.0328798
- Revisiting and attributing the global controls over terrestrial ecosystem functions of climate and plant traits at FLUXNET sites via causal graphical models H. Shi et al. 10.5194/bg-20-2727-2023
- Integrating Load‐Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration S. Friedman et al. 10.1111/pce.70222
- Benchmarking transferable evapotranspiration against physical models: Uncertainty analysis of machine learning quasi-observation across Central Asia’s plant functional types and climate zones F. Ochege et al. 10.1016/j.ejrh.2025.102849
- Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis J. Zhao et al. 10.3390/agronomy15051237
- Comparing the use of all data or specific subsets for training machine learning models in hydrology: A case study of evapotranspiration prediction H. Shi et al. 10.1016/j.jhydrol.2023.130399
- Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks H. Shi & X. Cai 10.1016/j.envsoft.2025.106383
- Global dryland aridity changes indicated by atmospheric, hydrological, and vegetation observations at meteorological stations H. Shi et al. 10.5194/hess-27-4551-2023
- Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing M. Xie et al. 10.1038/s41597-023-02473-9
- Artificial intelligence and Eddy covariance: A review A. Lucarini et al. 10.1016/j.scitotenv.2024.175406
Latest update: 03 Nov 2025
Short summary
There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.
There have been many machine learning simulation studies based on eddy-covariance observations...
