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

Abstract. Gap-filling eddy covariance CO2 fluxes is
challenging at dryland sites due to small CO2 fluxes. Here, four
machine 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 a
semiarid sagebrush ecosystem with different lengths of artificial gaps. The
ANN and RF algorithms outperform the KNN and SVM in filling gaps ranging
from 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 gaps
of 2 months. In addition, our results suggest that there is no need to
fill the daytime and nighttime net ecosystem exchange (NEE) gaps separately when using the ANN and
RF. With the ANN and RF, the gap-filling-induced uncertainties in the annual
NEE at this site are estimated to be within 16 g C m−2, whereas the
uncertainties by the KNN and SVM can be as large as 27 g C m−2. To
better fill extremely long gaps of a few months, we test a two-layer
gap-filling framework based on the RF. With this framework, the model
performance is improved significantly, especially for the nighttime data.
Therefore, this approach provides an alternative in filling extremely long
gaps to characterize annual carbon budgets and interannual variability in
dryland ecosystems.
