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. Gap-filling eddy covariance CO2 fluxes ischallenging at dryland sites due to small CO2 fluxes. Here, fourmachine learning (ML) algorithms including artificial neural network (ANN), k-nearest neighbors (KNNs), random forest (RF), and support vector machine(SVM) are employed and evaluated for gap-filling CO2 fluxes over asemiarid sagebrush ecosystem with different lengths of artificial gaps. TheANN and RF algorithms outperform the KNN and SVM in filling gaps rangingfrom hours to days, with the RF being more time efficient than the ANN.Performances of the ANN and RF are largely degraded for extremely long gapsof 2 months. In addition, our results suggest that there is no need tofill the daytime and nighttime net ecosystem exchange (NEE) gaps separately when using the ANN andRF. With the ANN and RF, the gap-filling-induced uncertainties in the annualNEE at this site are estimated to be within 16 g C m−2, whereas theuncertainties by the KNN and SVM can be as large as 27 g C m−2. Tobetter fill extremely long gaps of a few months, we test a two-layergap-filling framework based on the RF. With this framework, the modelperformance is improved significantly, especially for the nighttime data.Therefore, this approach provides an alternative in filling extremely longgaps to characterize annual carbon budgets and interannual variability indryland ecosystems.

atmospheric chemistry and physics

Technical note: Uncertainties in eddy covariance CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches