Evaluating the convergence between eddy covariance and biometric methods for 
assessing carbon budgets of forests

Campioli, M.; Malhi, Y.; Vicca, S.; Luyssaert, S.; Papale, D.; Peñuelas, J.; Reichstein, Markus; Migliavacca, Mirco; Arain, M. A.; Janssens, I. A.

doi:10.1038/ncomms13717

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Evaluating the convergence between eddy covariance and biometric methods for assessing carbon budgets of forests

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Reichstein, Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Migliavacca, Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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http://dx.doi.org/10.1038/ncomms13717
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Citation

Campioli, M., Malhi, Y., Vicca, S., Luyssaert, S., Papale, D., Peñuelas, J., et al. (2016). Evaluating the convergence between eddy covariance and biometric methods for assessing carbon budgets of forests. Nature Communications, 7: 13717. doi:10.1038/ncomms13717.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-243D-B

Abstract

The eddy-covariance (EC) micro-meteorological technique and the ecology-based biometric methods (BM) are the primary methodologies to quantify CO2 exchange between terrestrial ecosystems and the atmosphere (net ecosystem production, NEP) and its two components, ecosystem respiration and gross primary production. Here we show that EC and BM provide different estimates of NEP, but comparable ecosystem respiration and gross primary production for forest ecosystems globally. Discrepancies between methods are not related to environmental or stand variables, but are consistently more pronounced for boreal forests where carbon fluxes are smaller. BM estimates are prone to underestimation of net primary production and overestimation of leaf respiration. EC biases are not apparent across sites, suggesting the effectiveness of standard post-processing procedures. Our results increase confidence in EC, show in which conditions EC and BM estimates can be integrated, and which methodological aspects can improve the convergence between EC and BM.