Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data
<p>Peatlands worldwide have been transformed from carbon sinks to carbon sources due to years of intensive agriculture requiring low water tables. In the Netherlands, carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>) emissions from drained pe...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/22/3867/2025/bg-22-3867-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849407078104301568 |
|---|---|
| author | L. M. van der Poel L. V. Bataille B. Kruijt W. Franssen W. Jans J. Biermann A. Rietman A. J. V. Buzacott Y. van der Velde R. Boelens R. W. A. Hutjes |
| author_facet | L. M. van der Poel L. V. Bataille B. Kruijt W. Franssen W. Jans J. Biermann A. Rietman A. J. V. Buzacott Y. van der Velde R. Boelens R. W. A. Hutjes |
| author_sort | L. M. van der Poel |
| collection | DOAJ |
| description | <p>Peatlands worldwide have been transformed from carbon sinks to carbon sources due to years of intensive agriculture requiring low water tables. In the Netherlands, carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>) emissions from drained peatlands mount up to 5.6 Mton annually and, according to the Dutch climate agreement, should be reduced by 1 Mton by 2030. It is generally accepted that mitigation measures should include raising the water level, and the exact influence of water table depth has been increasingly studied in recent years. Most studies do this by comparing annual eddy covariance (EC) site-specific CO<span class="inline-formula"><sub>2</sub></span> budgets to mean annual effective water table depths. However, here we apply a different approach: we integrate measurements from 16 EC towers with EC measurements from 141 flights by a low-flying research aircraft in an interpretable machine learning (ML) framework. We make use of the different strengths of tower and airborne data, temporal continuity, and spatial heterogeneity. We apply time frequency wavelet analysis and a footprint model to relate the measured fluxes to the underlying surface. Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula"><sub>2</sub></span> fluxes and use Shapley values and various simulations to interpret the model's outputs. We find that emissions increase by 4.6 t CO<span class="inline-formula"><sub>2</sub></span> ha<span class="inline-formula"><sup>−1</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> (90 % confidence interval: 4.0–5.4) for every 10 cm lowering of the water table, down to a water table depth of 0.8 m below the surface. For more drained conditions, emissions decrease again. Furthermore, we find that the sensitivity of CO<span class="inline-formula"><sub>2</sub></span> emissions to drainage is stronger in winter than in summer and that it varies between sites. This study shows the added value of using ML with different types of instantaneous data and holds potential for future applications.</p> |
| format | Article |
| id | doaj-art-4e7276ada6a748a9af3c7c5f43a0287a |
| institution | Kabale University |
| issn | 1726-4170 1726-4189 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj-art-4e7276ada6a748a9af3c7c5f43a0287a2025-08-20T03:36:11ZengCopernicus PublicationsBiogeosciences1726-41701726-41892025-08-01223867389810.5194/bg-22-3867-2025Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance dataL. M. van der Poel0L. V. Bataille1B. Kruijt2W. Franssen3W. Jans4J. Biermann5A. Rietman6A. J. V. Buzacott7Y. van der Velde8R. Boelens9R. W. A. Hutjes10Earth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the NetherlandsInstitute for Atmospheric and Earth System Research, University of Helsinki, 00014 Helsinki, FinlandFaculty of Science, Earth and Climate, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the NetherlandsHydroLogic, 3811 HN Amersfoort, the NetherlandsEarth Systems and Global Change Group, Wageningen University, 6708 PB Wageningen, the Netherlands<p>Peatlands worldwide have been transformed from carbon sinks to carbon sources due to years of intensive agriculture requiring low water tables. In the Netherlands, carbon dioxide (CO<span class="inline-formula"><sub>2</sub></span>) emissions from drained peatlands mount up to 5.6 Mton annually and, according to the Dutch climate agreement, should be reduced by 1 Mton by 2030. It is generally accepted that mitigation measures should include raising the water level, and the exact influence of water table depth has been increasingly studied in recent years. Most studies do this by comparing annual eddy covariance (EC) site-specific CO<span class="inline-formula"><sub>2</sub></span> budgets to mean annual effective water table depths. However, here we apply a different approach: we integrate measurements from 16 EC towers with EC measurements from 141 flights by a low-flying research aircraft in an interpretable machine learning (ML) framework. We make use of the different strengths of tower and airborne data, temporal continuity, and spatial heterogeneity. We apply time frequency wavelet analysis and a footprint model to relate the measured fluxes to the underlying surface. Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula"><sub>2</sub></span> fluxes and use Shapley values and various simulations to interpret the model's outputs. We find that emissions increase by 4.6 t CO<span class="inline-formula"><sub>2</sub></span> ha<span class="inline-formula"><sup>−1</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> (90 % confidence interval: 4.0–5.4) for every 10 cm lowering of the water table, down to a water table depth of 0.8 m below the surface. For more drained conditions, emissions decrease again. Furthermore, we find that the sensitivity of CO<span class="inline-formula"><sub>2</sub></span> emissions to drainage is stronger in winter than in summer and that it varies between sites. This study shows the added value of using ML with different types of instantaneous data and holds potential for future applications.</p>https://bg.copernicus.org/articles/22/3867/2025/bg-22-3867-2025.pdf |
| spellingShingle | L. M. van der Poel L. V. Bataille B. Kruijt W. Franssen W. Jans J. Biermann A. Rietman A. J. V. Buzacott Y. van der Velde R. Boelens R. W. A. Hutjes Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data Biogeosciences |
| title | Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data |
| title_full | Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data |
| title_fullStr | Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data |
| title_full_unstemmed | Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data |
| title_short | Groundwater–CO<sub>2</sub> emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data |
| title_sort | groundwater co sub 2 sub emissions relationship in dutch peatlands derived by machine learning using airborne and ground based eddy covariance data |
| url | https://bg.copernicus.org/articles/22/3867/2025/bg-22-3867-2025.pdf |
| work_keys_str_mv | AT lmvanderpoel groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT lvbataille groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT bkruijt groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT wfranssen groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT wjans groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT jbiermann groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT arietman groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT ajvbuzacott groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT yvandervelde groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT rboelens groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata AT rwahutjes groundwatercosub2subemissionsrelationshipindutchpeatlandsderivedbymachinelearningusingairborneandgroundbasededdycovariancedata |