Drivers and Annual Totals of Methane Emissions From Dutch Peatlands

Alexander J.V. Buzacott*, Bart Kruijt, Laurent Bataille, Quint van Giersbergen, Tom S. Heuts, Christian Fritz, Reinder Nouta, Gilles Erkens, Jim Boonman, Merit van den Berg, Jacobus van Huissteden, Ype van der Velde

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Article numbere17590
Pages (from-to)1-22
Number of pages22
JournalGlobal Change Biology
Volume30
Issue number12
Early online date6 Dec 2024
DOIs
Publication statusPublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.

Funding

This work was supported by Ministerie van Landbouw, Natuur en Voedselkwaliteit, Stichting Toegepast Onderzoek Waterbeheer, Natuurmonumenten and Provincie Frysl\u00E2n. Funding: We kindly thank two anonymous reviewers for their insightful comments and suggestions that helped improve this manuscript. This study was conducted as part of the Netherlands Research Programme on Greenhouse Gas Dynamics in Peatlands and Organic Soils [in Dutch: Nationaal onderzoeksprogramma broeikasgassen veenweiden (NOBV)] that was funded by the Dutch government [Ministry of Agriculture, Nature, and Food Quality (LNV)]. The Frisian measurement sites (Hommerts, Lytse Deelen, De Burd) are part of research financed by the province of Frysl\u00E2n. Onlanden and Camphuys were co\u2010financed and managed by Natuurmonumenten and their local staff and volunteers. We would like to thank the support of field workers and technical support staff: Jan Biermann, Wietse Franssen, Hanne Berghuis, Wilma Jans, Corine van Huissteden, Ron Lootens and Arie Bikker.

FundersFunder number
Natuur en Voedselkwaliteit
Stichting Toegepast Onderzoek Waterbeheer
Dutch Government
Nationaal onderzoeksprogramma broeikasgassen veenweiden
Ministerie van Landbouw
Ministry of Agriculture of the People's Republic of China
Netherlands Research Programme on Greenhouse Gas Dynamics in Peatlands and Organic Soils

    Keywords

    • CH
    • eddy covariance
    • flux driver
    • greenhouse gas
    • land use change
    • machine learning
    • rewet

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