12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Methane emissions from soils
View graph of relations

« Back

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

Research output: Contribution to journalJournal article

Published

  • P. M. Levy
  • A. Burden
  • M.D.A. Cooper
  • K.J. Dinsmore
  • J. Drewer
  • C. Evans
  • D. Fowler
  • J. Gaiawyn
  • A. Gray
  • S.K. Jones
  • T. Jones
  • N.P. McNamara
  • R. Mills
  • Nick Ostle
  • L.J. Sheppard
  • U. Skiba
  • A. Sowerby
  • Sue Ward
  • P. Zielinski
Journal publication date05/2012
JournalGlobal Change Biology
Journal number5
Volume18
Number of pages13
Pages1657-1669
Original languageEnglish

Abstract

Nearly 5000 chamber measurements of CH4 flux were collated from 21 sites across the United Kingdom, 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 ca. 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 nonlinear 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 United Kingdom, an emission factor of +0.4 g CH4 m−2 yr−1 per cm increase in water table height was derived from the data.