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Methane emissions from soils: synthesis and analysis of a large UK data set

Levy, Peter E. ORCID: https://orcid.org/0000-0002-8505-1901; Burden, Annette ORCID: https://orcid.org/0000-0002-7694-1638; Cooper, Mark D.A.; Dinsmore, Kerry J.; Drewer, Julia ORCID: https://orcid.org/0000-0002-6263-6341; Evans, Chris ORCID: https://orcid.org/0000-0002-7052-354X; Fowler, David; Gaiawyn, Jenny; Gray, Alan ORCID: https://orcid.org/0000-0002-6785-0590; Jones, Stephanie K.; Jones, Timothy; McNamara, Niall P. ORCID: https://orcid.org/0000-0002-5143-5819; Mills, Robert; Ostle, Nick; Sheppard, Lucy J.; Skiba, Ute ORCID: https://orcid.org/0000-0001-8659-6092; Sowerby, Alwyn; Ward, Susan E.; Zieliński, Piotr. 2012 Methane emissions from soils: synthesis and analysis of a large UK data set. Global Change Biology, 18 (5). 1657-1669. 10.1111/j.1365-2486.2011.02616.x

Abstract
Nearly 5000 chamber measurements of CH4 flux were collated from 21 sites across the UK, covering a range of soil and vegetation types, to derive a parsimonious model that explains as much of the variability as possible, with the least input requirements. Mean fluxes ranged from -0.3 to 27.4 nmol CH4 m−2 s−1, with small emissions or low rates of net uptake in mineral soils (site means of -0.3 to 0.7 nmol m−2 s−1) and much larger emissions from organic soils (site means of -0.3 to 27.4 nmol m−2 s−1). Less than half of the observed variability in instantaneous fluxes could be explained by independent variables measured. The reasons for this include measurement error, stochastic processes and, probably most importantly, poor correspondence between the independent variables measured and the actual variables influencing the processes underlying methane production, transport and oxidation. When temporal variation was accounted for, and the fluxes averaged at larger spatial scales, simple models explained up to ~75% of the variance in CH4 fluxes. Soil carbon, peat depth, soil moisture and pH together provided the best sub-set of explanatory variables. However, where plant species composition data were available, this provided the highest explanatory power. Linear and non-linear models generally fitted the data equally well, with the exception that soil moisture required a power transformation. To estimate the impact of changes in peatland water table on CH4 emissions in the UK, an emission factor of +0.4 g CH4 m−2 y−1 per cm increase in water table height was derived from the data.
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