Eddy covariance for quantifying trace gas fluxes from soils

Abstract

Abstract. Soils are highly complex physical and biological systems, and hence measuring soil gas exchange fluxes with high accuracy and adequate spatial representativity remains a challenge. A technique which has become increasingly popular is the eddy covariance (EC) method. This method takes advantage of the fact that surface fluxes are mixed into the near-surface atmosphere via turbulence. As a consequence, measurements with an EC system can be done at some distance above the surface, providing accurate and spatially integrated flux density estimates. In this paper we provide a basic overview targeting scientists who are not familiar with the EC method. This review gives examples of successful deployments from a wide variety of ecosystems. The primary focus is on the three major greenhouse gases: carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O). Several limitations to the application of EC systems exist, requiring a careful experimental design, which we discuss in detail. Thereby we group these experiments into two main classes: (1) manipulative experiments, and (2) survey-type experiments. Recommendations and examples of successful studies using various approaches are given, including the combination of EC flux measurements with online measurements of stable isotopes. We conclude that EC should not be considered a substitute to traditional (e.g., chamber based) flux measurements but instead an addition to them. The greatest strength of EC measurements in soil science are (1) their uninterrupted continuous measurement of gas concentrations and fluxes that can also capture short-term bursts of fluxes that easily could be missed by other methods and (2) the spatial integration covering the ecosystem scale (several square meters to hectares), thereby integrating over small-scale heterogeneity in the soil.

Mitigating N<sub>2</sub>O emissions from soil: from patching leaks to transformative action

“…Tremendous progress has been made during the last decennia with respect to the scientific understanding of N 2 O emissions from soils: various pathways and mechanisms have been elucidated (Butterbach-Bahl et al, 2013); molecular and isotopic tools to assess mechanisms have been advanced (Baggs, 2008(Baggs, , 2011Decock and Six, 2013); we have a general idea of temporal and spatial patterns of N 2 O emissions (Groffman et al, 2009); micrometeorological methods are available to monitor spatially integrated N 2 O emissions at high temporal resolution (Eugster and Merbold, 2015); various data sources have been synthesized in qualitative and quantitative reviews (Bouwman, 1996;Decock, 2014); and biogeochemical models have been developed and improved to predict N 2 O emissions under various scenarios (Chen et al, 2008). These efforts have paved the way to identify the major causes of soil-derived N 2 O and to isolate the strategies that have the greatest potential for reducing global N 2 O emissions (e.g.…”

Emissions of carbon dioxide and methane from fields fertilized with digestate from an agricultural biogas plant

“…The main advantage of the EC method compared to other methods is its spatial scale of integration, ranging from several square meters, to a hectare and more, depending on measurement height. Furthermore, EC measures fluxes directly, in contrast to methods where the flux is deduced from the change of concentration over time inside an enclosure (Eugster and Merbold, 2015).…”

The influence of tillage on N<sub>2</sub>O fluxes from an intensively managed grazed grassland in Scotland

“…Eddy covariance is able to integrate over a large area of the field (several hundred square metres) (Eugster and Merbold, 2015), but these measurements are still subject to an element of spatial variability which is difficult to fully account for given the spatially het-erogeneous nature of N 2 O fluxes. Any study which plans to report cumulative flux estimates should consider how to minimise the uncertainties which arise when interpolating and/or extrapolating measurements to larger temporal and spatial scales (e.g.…”