Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15589-2021
https://doi.org/10.5194/acp-21-15589-2021
Technical note
 | 
18 Oct 2021
Technical note |  | 18 Oct 2021

Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches

Jingyu Yao, Zhongming Gao, Jianping Huang, Heping Liu, and Guoyin Wang

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Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer, Dordrecht, the Netherlands, 438 pp., https://doi.org/10.1007/978-94-007-2351-1, 2012. 
Baldocchi, D. and Sturtevant, C.: Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration?, Agr. Forest Meteorol., 207, 117–126, https://doi.org/10.1016/j.agrformet.2015.03.010, 2015. 
Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future, Glob. Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003. 
Berg, A. and McColl, K. A.: No projected global drylands expansion under greenhouse warming, Nat. Clim. Chang., 11, 331–337, https://doi.org/10.1038/s41558-021-01007-8, 2021. 
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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.
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