Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15589-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-15589-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches
Jingyu Yao
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
China
Zhongming Gao
Laboratory for Atmospheric Research, Department of Civil and
Environmental Engineering, Washington State University, Pullman, Washington,
USA
School of Atmospheric Sciences, Sun Yat-sen University, Southern
Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China
Heping Liu
Laboratory for Atmospheric Research, Department of Civil and
Environmental Engineering, Washington State University, Pullman, Washington,
USA
Guoyin Wang
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
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Short summary
Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes, though gap-filling CO2 fluxes is challenging at dryland sites due to small fluxes. Using data collected from a semiarid site, four machine learning methods are evaluated with different lengths of artificial gaps. The artificial neural network and random forest methods outperform the other methods. With these methods, uncertainties in the annual CO2 flux at this site are estimated to be within 16 g C m−2.
Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes,...
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