Drivers and Annual Totals of Methane Emissions From Dutch Peatlands
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.
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Subjects: | CH, eddy covariance, flux driver, greenhouse gas, land use change, machine learning, rewet, |
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dig-wur-nl-wurpubs-6389102024-12-20 Buzacott, Alexander J.V. Kruijt, Bart Bataille, Laurent van Giersbergen, Quint Heuts, Tom S. Fritz, Christian Nouta, Reinder Erkens, Gilles Boonman, Jim van den Berg, Merit van Huissteden, Jacobus van der Velde, Ype Article/Letter to editor Global Change Biology 30 (2024) 12 ISSN: 1354-1013 Drivers and Annual Totals of Methane Emissions From Dutch Peatlands 2024 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. en application/pdf https://research.wur.nl/en/publications/drivers-and-annual-totals-of-methane-emissions-from-dutch-peatlan 10.1111/gcb.17590 https://edepot.wur.nl/683506 CH eddy covariance flux driver greenhouse gas land use change machine learning rewet https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
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CH eddy covariance flux driver greenhouse gas land use change machine learning rewet CH eddy covariance flux driver greenhouse gas land use change machine learning rewet Buzacott, Alexander J.V. Kruijt, Bart Bataille, Laurent van Giersbergen, Quint Heuts, Tom S. Fritz, Christian Nouta, Reinder Erkens, Gilles Boonman, Jim van den Berg, Merit van Huissteden, Jacobus van der Velde, Ype Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
description |
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. |
format |
Article/Letter to editor |
topic_facet |
CH eddy covariance flux driver greenhouse gas land use change machine learning rewet |
author |
Buzacott, Alexander J.V. Kruijt, Bart Bataille, Laurent van Giersbergen, Quint Heuts, Tom S. Fritz, Christian Nouta, Reinder Erkens, Gilles Boonman, Jim van den Berg, Merit van Huissteden, Jacobus van der Velde, Ype |
author_facet |
Buzacott, Alexander J.V. Kruijt, Bart Bataille, Laurent van Giersbergen, Quint Heuts, Tom S. Fritz, Christian Nouta, Reinder Erkens, Gilles Boonman, Jim van den Berg, Merit van Huissteden, Jacobus van der Velde, Ype |
author_sort |
Buzacott, Alexander J.V. |
title |
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
title_short |
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
title_full |
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
title_fullStr |
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
title_full_unstemmed |
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands |
title_sort |
drivers and annual totals of methane emissions from dutch peatlands |
url |
https://research.wur.nl/en/publications/drivers-and-annual-totals-of-methane-emissions-from-dutch-peatlan |
work_keys_str_mv |
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