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Standardisation of eddy-covariance flux measurements of methane and nitrous oxide

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Nemitz Eiko, Mammarella Ivan, Ibrom Andreas, Aurela Mika, Burba George G., Dengel Sigrid, Gielen Bert, Grelle Achim, Heinesch Bernard, Herbst Mathias, Hörtnagl Lukas, Klemedtsson Leif, Lindroth Anders, Lohila Annalea, McDermitt Dayle K., Meier Philip, Merbold Lutz, Nelson David, Nicolini Giacomo, Nilsson Mats B., Peltola Olli, Rinne Janne, Zahniser Mark,
Project Robust models for assessing the effectiveness of technologies and managements to reduce N2O emissions from grazed pastures (Models4Pastures)
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Original article (peer-reviewed)

Journal International Agrophysics
Volume (Issue) 32(4)
Page(s) 517 - 549
Title of proceedings International Agrophysics
DOI 10.1515/intag-2017-0042

Open Access

URL http://doi.org/10.1515/intag-2017-0042
Type of Open Access Publisher (Gold Open Access)

Abstract

Abstract Commercially available fast-response analysers for methane (CH 4 ) and nitrous oxide (N 2 O) have recently become more sensitive, more robust and easier to operate. This has made their application for long-term flux measurements with the eddy-covariance method more feasible. Unlike for carbon dioxide (CO 2 ) and water vapour (H 2 O), there have so far been no guidelines on how to optimise and standardise the measurements. This paper reviews the state-of-the-art of the various steps of the measurements and discusses aspects such as instrument selection, setup and maintenance, data processing as well as the additional measurements needed to aid interpretation and gap-filling. It presents the methodological protocol for eddy covariance measurements of CH 4 and N 2 O fluxes as agreed for the ecosystem station network of the pan-European Research Infrastructure Integrated Carbon Observation System and provides a first international standard that is suggested to be adopted more widely. Fluxes can be episodic and the processes controlling the fluxes are complex, preventing simple mechanistic gap-filling strategies. Fluxes are often near or below the detection limit, requiring additional care during data processing. The protocol sets out the best practice for these conditions to avoid biasing the results and long-term budgets. It summarises the current approach to gap-filling.
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