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

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.

SUBMITTER: Campioli M

PROVIDER: S-EPMC5171944 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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

Campioli M M Malhi Y Y Vicca S S Luyssaert S S Papale D D Peñuelas J J Reichstein M M Migliavacca M M Arain M A MA Janssens I A IA

Nature communications 20161214

The eddy-covariance (EC) micro-meteorological technique and the ecology-based biometric methods (BM) are the primary methodologies to quantify CO<sub>2</sub> 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 ...[more]

PMID: 27966534

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Project description:Wetlands represent the dominant natural source of methane (CH4) to the atmosphere. Thus, substantial effort has been spent examining the CH4 budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH4 fluxes using a 3.5 year time series of simultaneous open- and closed path CH4 flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH4 flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH4 fluxes were typically 6%-41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH4 fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g - CH4 m-2 yr-1). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH4 emissions for both EC systems were in excellent agreement (within 0.6 g - CH4 m-2 yr-1) and averaged 18 g - CH4 m-2 yr-1. Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7-17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Dataset Information

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

Publications

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

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