Abstract
Rewetting peatlands is required to limit carbon dioxide (CO2) emissions, however, raising the groundwater level (GWL) will strongly increase the chance of methane (CH4) emissions which has a higher radiative forcing than CO2. Data sets of CH4 from different rewetting strategies and natural systems are scarce, and quantification and an understanding of the main drivers of CH4 emissions are needed to make effective peatland rewetting decisions. We present a large data set of CH4 fluxes (FCH4) measured across 16 sites with eddy covariance on Dutch peatlands. Sites were classified into six land uses, which also determined their vegetation and GWL range. We investigated the principal drivers of emissions and gapfilled the data using machine learning (ML) to derive annual totals. In addition, Shapley values were used to understand the importance of drivers to ML model predictions. The data showed the typical controls of FCH4 where temperature and the GWL were the dominant factors, however, some relationships were dependent on land use and the vegetation present. There was a clear average increase in FCH4 with increasing GWLs, with the highest emissions occurring at GWLs near the surface. Soil temperature was the single most important predictor for ML gapfilling but the Shapley values revealed the multi-driver dependency of FCH4. Mean annual FCH4 totals across all land uses ranged from 90 ± 11 to 632 ± 65 kg CH4 ha−1 year−1 and were on average highest for semi-natural land uses, followed by paludiculture, lake, wet grassland and pasture with water infiltration system. The mean annual flux was strongly correlated with the mean annual GWL (R2 = 0.80). The greenhouse gas balance of our sites still needs to be estimated to determine the net climate impact, however, our results indicate that considerable rates of CO2 uptake and long-term storage are required to fully offset the emissions of CH4 from land uses with high GWLs.
| Original language | English |
|---|---|
| Article number | e17590 |
| Number of pages | 22 |
| Journal | Global Change Biology |
| Volume | 30 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Dec 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
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SDG 15 Life on Land
Keywords
- CH
- eddy covariance
- flux driver
- greenhouse gas
- land use change
- machine learning
- rewet
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Dive into the research topics of 'Drivers and Annual Totals of Methane Emissions From Dutch Peatlands'. Together they form a unique fingerprint.Datasets
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Methane fluxes measured by eddy covariance on Dutch peatlands
Buzacott, A. J. V. (Creator), Kruijt, B. (Creator), Bataille, L. (Creator), van Giersbergen, Q. (Creator), Heuts, T. S. (Creator), Fritz, C. (Creator), Nouta, R. (Creator), Erkens, G. (Creator), Boonman, J. (Creator), van den Berg, M. (Creator), van Huissteden, J. (Creator) & van der Velde, Y. (Creator), VU University Amsterdam, 21 Nov 2024
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