References

BGD

Biogeosciences Discussions

BGD

Biogeosciences Discuss.

1810-6285

Göttingen, Germany

10.5194/bgd-6-115-2009

Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions

Stolk

P. C.

¹ ² Jacobs

C. M. J.

² Moors

E. J.

² Hensen

³ Velthof

G. L.

² Kabat

Earth System Science and Climate Change Group, Wageningen University, Wageningen, The Netherlands

Alterra, Wageningen University and Research Centre Wageningen, The Netherlands

Department of Air Quality and Climate Change, Energy research Centre of the Netherlands (ECN), Petten, The Netherlands

06 01 2009

6 1 115 141

2009

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://bg.copernicus.org/preprints/6/115/2009/bgd-6-115-2009.html

The full text article is available as a PDF file from https://bg.copernicus.org/preprints/6/115/2009/bgd-6-115-2009.pdf

Chambers are widely used to measure surface fluxes of nitrous oxide (N2O). Usually linear regression is used to calculate the fluxes from the chamber data. Non-linearity in the chamber data can result in an underestimation of the flux. Non-linear regression models are available for these data, but are not commonly used. In this study we compared the fit of linear and non-linear regression models to determine significant non-linearity in the chamber data. We assessed the influence of this significant non-linearity on the annual fluxes. For a two year dataset from an automatic chamber we calculated the fluxes with linear and non-linear regression methods. Based on the fit of the methods 32% of the data was defined significant non-linear. Significant non-linearity was not recognized by the goodness of fit of the linear regression alone. Using non-linear regression for these data and linear regression for the rest, increases the annual flux with 21% to 53% compared to the flux determined from linear regression alone. We suggest that differences this large are due to leakage through the soil. Macropores or a coarse textured soil can add to fast leakage from the chamber. Yet, also for chambers without leakage non-linearity in the chamber data is unavoidable, due to feedback from the increasing concentration in the chamber. To prevent a possibly small, but systematic underestimation of the flux, we recommend comparing the fit of a linear regression model with a non-linear regression model. The non-linear regression model should be used if the fit is significantly better. Open questions are how macropores affect chamber measurements and how optimization of chamber design can prevent this.

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