This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Peatlands store large amounts of carbon. However, when they are drained, they can become substantial sources of the greenhouse gas carbon dioxide (CO₂).
An international research team led by the University of Münster has now used the largest international dataset on CO₂ budgets of northern peatlands to show how water level and temperature effects are linked. The lower the water table, the more strongly CO₂ emissions respond to rising temperatures. A higher water table can therefore dampen this effect.
The results have been published in the journal Nature Communications. Peatlands develop in areas with permanently or temporarily waterlogged soils. Under oxygen-poor conditions, dead plant material decomposes only slowly, allowing peat to accumulate over long periods and store large amounts of carbon.
When a peatland is drained, more oxygen enters the peat. Microorganisms then break down the organic material more rapidly, releasing CO₂, among other gases. "Water level has long been recognized as an important factor controlling the CO₂ balance of peatlands.
Our analyses show, however, that its importance cannot be considered independently of temperature," explains Nicolas Behrens, first author of the study and a researcher at the Institute of Landscape Ecology at the University of Münster. For the study, the team analyzed measurements from 276 so-called site-years covering a total of 114 peatlands in temperate and boreal regions. These were located, among other places, in Germany, Estonia, France, the United Kingdom and North America.
A site-year refers to measurements from a particular site over the course of one year. The researchers examined both natural fens and bogs as well as croplands, grasslands and former peat extraction sites. Because the relationships are nonlinear and other factors such as vegetation and solar radiation also play a role, the research team used explainable machine-learning methods.
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