Articles | Volume 20, issue 12
https://doi.org/10.5194/bg-20-2387-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2387-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jun 2023
Research article |
| 22 Jun 2023
| 22 Jun 2023
Water-table-driven greenhouse gas emission estimates guide peatland restoration at national scale
Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark
Lars Elsgaard
Aarhus University, Department of Agroecology, Tjele, Denmark
Mogens H. Greve
Aarhus University, Department of Agroecology, Tjele, Denmark
Steen Gyldenkærne
Aarhus University, Department of Environmental Science, Roskilde, Denmark
Cecilie Hermansen
Aarhus University, Department of Agroecology, Tjele, Denmark
Gregor Levin
Aarhus University, Department of Environmental Science, Roskilde, Denmark
Shubiao Wu
Aarhus University, Department of Agroecology, Tjele, Denmark
Simon Stisen
Geological Survey of Denmark and Greenland, Department of Hydrology, Copenhagen, Denmark
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Cited articles
Adhikari, K., et al.: High‐resolution 3‐D mapping of soil texture in Denmark, Soil Sci. Soc. Am. J., 77, 860–876, 2013.
Andersen, R., Farrell, C., Graf, M., Muller, F., Calvar, E., Frankard, P., Caporn, S., and Anderson, P.:
An overview of the progress and challenges of peatland restoration in Western Europe, Restor. Ecol., 25, 271–282, https://doi.org/10.1111/rec.12415, 2017.
Audet, J., Elsgaard, L., Kjaergaard, C., Larsen, S. E., and Hoffmann, C. C.:
Greenhouse gas emissions from a Danish riparian wetland before and after restoration, Ecol. Eng., 57, 170–182, https://doi.org/10.1016/j.ecoleng.2013.04.021, 2013.
Bauer-Marschallinger, B., Cao, S., Navacchi, C., Freeman, V., Reuß, F., Geudtner, D., et al.: The normalised Sentinel-1 Global Backscatter Model, mapping Earth’s land surface with C-band microwaves, Sci. Data, 8, 277, https://doi.org/10.1038/s41597-021-01059-7, 2021.
Bechtold, M., Tiemeyer, B., Laggner, A., Leppelt, T., Frahm, E., and Belting, S.:
Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling, Hydrol. Earth Syst. Sci., 18, 3319–3339, https://doi.org/10.5194/hess-18-3319-2014, 2014.
Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., Bourgault, M. A., Brümmer, C., Burdun, I., Desai, A. R., Devito, K., Grünwald, T., Grygoruk, M., Humphreys, E. R., Klatt, J., Kurbatova, J., Lohila, A., Munir, T. M., Nilsson, M. B., Price, J. S., Röhl, M., Schneider, A., and Tiemeyer, B.:
PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model, J. Adv. Model. Earth Sy., 11, 2130–2162, https://doi.org/10.1029/2018MS001574, 2019.
Breuning-Madsen, H. and Jensen, N. H.: Pedological regional variations in well-drained soils, Denmark, Geogr. Tidsskr. J. Geogr., 92, 61–69, https://doi.org/10.1080/00167223.1992.10649316, 1992.
Digital elevation model: Map service by the Danish Agency for Data Supply and Infrastructure (SDFI), https://dataforsyningen.dk/ (last access: 1 June 2022), 2018.
Dorogush, A. V., Ershov, V., and Gulin, A.:
CatBoost: Gradient boosting with categorical features support, arXiv, https://arxiv.org/abs/1810.11363 (last access: 1 June 2022), 2018.
Elsgaard, L., Görres, C. M., Hoffmann, C. C., Blicher-Mathiesen, G., Schelde, K., and Petersen, S. O.:
Net ecosystem exchange of CO2 and carbon balance for eight temperate organic soils under agricultural management, Agr. Ecosyst. Environ., 162, 52–67, https://doi.org/10.1016/j.agee.2012.09.001, 2012.
Evans, C. D., Peacock, M., Baird, A. J., Artz, R. R. E., Burden, A., Callaghan, N., Chapman, P. J., Cooper, H. M., Coyle, M., Craig, E., Cumming, A., Dixon, S., Gauci, V., Grayson, R. P., Helfter, C., Heppell, C. M., Holden, J., Jones, D. L., Kaduk, J., Levy, P., Matthews, R., McNamara, N. P., Misselbrook, T., Oakley, S., Page, S. E., Rayment, M., Ridley, L. M., Stanley, K. M., Williamson, J. L., Worrall, F., and Morrison, R.:
Overriding water table control on managed peatland greenhouse gas emissions, Nature, 593, 548–552, https://doi.org/10.1038/s41586-021-03523-1, 2021.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021.
Gong, J., Wang, K., Kellomäki, S., Zhang, C., Martikainen, P. J., and Shurpali, N.:
Modeling water table changes in boreal peatlands of Finland under changing climate conditions, Ecol. Model., 244, 65–78, https://doi.org/10.1016/j.ecolmodel.2012.06.031, 2012.
Greve, M. H., Christensen, O. F., Greve, M. B., and Kheir, R. B.:
Change in peat coverage in Danish cultivated soils during the past 35 years, Soil Sci., 179, 250–257, https://doi.org/10.1097/SS.0000000000000066, 2014.
Greve, M. H., Greve, M. B., and Pedersen, B. F.:
Kortlægning af jordens kulstofindhold i Danmark. Redegørelse for metode og usikkerheder, Århus University, https://pure.au.dk/portal/files/172171967/Jordens_kulstofindhold_metode_og_usikkerhed_Oktober_2019.pdf (last access: 15 November 2022), 2019 (in Danish).
Hambäck, P. A., Dawson, L., Geranmayeh, P., Jarsjö, J., Kačergytė, I., Peacock, M., Collentine, D., Destouni, G., Futter, M., Hugelius, G., Hedman, S., Jonsson, S., Klatt, B. K., Lindström, A., Nilsson, J. E., Pärt, T., Schneider, L. D., Strand, J. A., Urrutia-Cordero, P., Åhlén, D., Åhlén, I., and Blicharska, M.:
Tradeoffs and synergies in wetland multifunctionality: A scaling issue, Sci. Total Environ., 862, 160746, https://doi.org/10.1016/j.scitotenv.2022.160746, 2023.
Hancock, J. T. and Khoshgoftaar, T. M.:
CatBoost for big data: an interdisciplinary review, J. Big Data, 7, 1–45, https://doi.org/10.1186/s40537-020-00369-8, 2020.
Hansen, M. and Pjetursson, B.:
Free, online Danish shallow geological data, Geol. Surv. Den. Greenl., 23, 53–56, 2011.
Henriksen, H. J., et al.: Dokumentationsrapport vedr. modelleverancer til Hydrologisk Informations og Prognosesystem, Udarbejdet som en del af Den Fællesoffentlige Digitaliseringsstrategi 2016–2020, Initiativet Fælles Data om Terræn, Klima og Vand, GEUS, https://doi.org/10.22008/gpub/38113, 2021.
Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W., and Zhou, H.:
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions, J. Hydrol., 574, 1029–1041, https://doi.org/10.1016/j.jhydrol.2019.04.085, 2019.
Huang, Y., Ciais, P., Luo, Y., Zhu, D., Wang, Y., Qiu, C., Goll, D. S., Guenet, B., Makowski, D., De Graaf, I., Leifeld, J., Kwon, M. J., Hu, J., and Qu, L.:
Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown, Nat. Clim. Change, 11, 618–622, https://doi.org/10.1038/s41558-021-01059-w, 2021.
IPCC:
Guidelines for National Greenhouse Gas Inventories: Wetlands. Methodological Guidance on Lands with Wet and Drained Soils, and Constructed Wetlands for Wastewater Treatment, Intergovernmental Panel on Climate Change, 2014.
Jakobsen, P. R. and Hermansen, B.: Danmarks digitale jordartskort 1:25.000, Version 3.0, Danmarks og Grønlands Geologiske Undersøgelse Rapport, Bind 2007, Nr. 84, GEUS, https://doi.org/10.22008/gpub/27122, 2008.
Kandel, T. P., Lærke, P. E., and Elsgaard, L.:
Annual emissions of CO2, CH4 and N2O from a temperate peat bog: Comparison of an undrained and four drained sites under permanent grass and arable crop rotations with cereals and potato, Agr. Forest Meteorol., 256, 470–481, https://doi.org/10.1016/j.agrformet.2018.03.021, 2018.
Karki, S., Elsgaard, L., Audet, J., and Lærke, P. E.:
Mitigation of greenhouse gas emissions from reed canary grass in paludiculture: effect of groundwater level, Plant Soil, 383, 217–230, https://doi.org/10.1007/s11104-014-2164-z, 2014.
Klimarådet:
Carbon rich peat soils, The Danish Council on Climate Change Secretariat, https://klimaraadet.dk/en/analyser/kulstofrige-lavbundsjorder (last access: 1 January 2023), 2020.
Koch, J.: Water table depth for Danish lowland soils, GEUS Dataverse [data set], https://doi.org/10.22008/FK2/0AFGQT, 2022.
Koch, J., Berger, H., Henriksen, H. J., and Sonnenborg, T. O.:
Modelling of the shallow water table at high spatial resolution using random forests, Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, 2019.
Koch, J., Gotfredsen, J., Schneider, R., Troldborg, L., Stisen, S., and Henriksen, H. J.:
High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model, Front. Water, , https://doi.org/10.3389/frwa.2021.701726, 2021.
Lendzioch, T., Langhammer, J., Vlček, L., and Minařík, R.:
Mapping the groundwater level and soil moisture of a montane peat bog using uav monitoring and machine learning, Remote Sens.-Basel, 13, 907, https://doi.org/10.3390/rs13050907, 2021.
Levin, G.:
BASEMAP03 – Technical documentation of the method for elaboration of a land-use and landcover map for Denmark, Århus University [data set], https://dce2.au.dk/pub/TR159.pdf (last access: 1 August 2022), 2019.
Levin, G. and Gyldenkærne, S.:
Estimating Land Use/Land Cover and Changes in Denmark, no. Tech. Rep. (227), DCE–Danish Cent. Environ. Energy, 2022.
Lundberg, S. M. and Lee, S. I.:
A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems, vol. 2017-December, edited by: Guyon, I., Von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R.,
ISBN: 9781510860964, 2017.
Minkkinen, K., Ojanen, P., Koskinen, M., and Penttilä, T.:
Nitrous oxide emissions of undrained, forestry-drained, and rewetted boreal peatlands, Forest Ecol. Manag., 478, 118494, https://doi.org/10.1016/j.foreco.2020.118494, 2020.
Nielsen, O.-K., Plejdrup, M. S., Winther, M., Nielsen, M., Gyldenkærne, S., Mikkelsen, M. H., Albrektsen, R., Thomsen, M., Hjelgaard, K., Fauser, P., Bruun, H. G., Johannsen, V. K., Nord-Larsen, T., Vesterdal, L., Stupak, I., Scott-Bentsen, N., Rasmussen, E., Petersen, S. B., Baunbæk, L., and Hansen, M. G.:
Denmark's National Inventory Report 2022. Emission Inventories 1990–2020 – Submitted under the United Nations Framework Convention on Climate Change and the Kyoto Protocol, Aarhus University, DCE – Danish Centre for Environment and Energy, , 969 pp., Scientific Report No. 494, http://dce2.au.dk/pub/SR494.pdf (last access: 15 January 2023), 2022.
Petersen, A. B., Wittig, C., and Leone, S. R.:
Electronic-to-vibrational pumped CO2 laser operating at 4.3, 10.6, and 14.1 µm, J. Appl. Phys., 47, 1051–1054, https://doi.org/10.1063/1.322744, 1976.
Petersen, S. O., Hoffmann, C. C., Schäfer, C.-M., Blicher-Mathiesen, G., Elsgaard, L., Kristensen, K., Larsen, S. E., Torp, S. B., and Greve, M. H.:
Annual emissions of CH4 and N2O, and ecosystem respiration, from eight organic soils in Western Denmark managed by agriculture, Biogeosciences, 9, 403–422, https://doi.org/10.5194/bg-9-403-2012, 2012.
Potapov, P., Hansen, M. C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., et al.: Landsat analysis ready data for global land cover and land cover change mapping, Remote Sens., 12, 426, https://doi.org/10.3390/rs12030426, 2020.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A.:
Catboost: Unbiased boosting with categorical features, in: Advances in Neural Information Processing Systems, edited by: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R.,
ISBN: 9781510884472, 2018.
Refsgaard, J. C., Højberg, A. L., He, X., Hansen, A. L., Rasmussen, S. H., and Stisen, S.:
Where are the limits of model predictive capabilities?, Hydrol. Process., 30, 4956–4965, https://doi.org/10.1002/hyp.11029, 2016.
Sechu, G. L., Nilsson, B., Iversen, B. V., Greve, M. B., Børgesen, C. D., and Greve, M. H.:
A stepwise gis approach for the delineation of river valley bottom within drainage basins using a cost distance accumulation analysis, Water (Switzerland), 13, 827, https://doi.org/10.3390/w13060827, 2021.
Smith, P., Bustamante, M., Uk, P. S., and Brazil, M. B.:
Agriculture, forestry and other land use (AFOLU), in: Climate change 2014: mitigation of climate change, Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 811–922, Cambridge University Press, 2014.
Stisen, S., Ondracek, M., Troldborg, L., Schneider, R. J. M., and Til, M. J. V.: National Vandressource Model. Modelopstilling og kalibrering af DK-model 2019, anmarks og Grønlands Geologiske Undersøgelse Rapport; Bind 2019, Nr. 31, GEUS, https://doi.org/10.22008/gpub/32631, 2020.
Tiemeyer, B., Albiac Borraz, E., Augustin, J., Bechtold, M., Beetz, S., Beyer, C., Drösler, M., Ebli, M., Eickenscheidt, T., Fiedler, S., Förster, C., Freibauer, A., Giebels, M., Glatzel, S., Heinichen, J., Hoffmann, M., Höper, H., Jurasinski, G., Leiber-Sauheitl, K., Peichl-Brak, M., Roßkopf, N., Sommer, M., and Zeitz, J.:
High emissions of greenhouse gases from grasslands on peat and other organic soils, Glob. Change Biol., 22, 4134–4149, https://doi.org/10.1111/gcb.13303, 2016.
Tiemeyer, B., Freibauer, A., Borraz, E. A., Augustin, J., Bechtold, M., Beetz, S., Beyer, C., Ebli, M., Eickenscheidt, T., Fiedler, S., Förster, C., Gensior, A., Giebels, M., Glatzel, S., Heinichen, J., Hoffmann, M., Höper, H., Jurasinski, G., Laggner, A., Leiber-Sauheitl, K., Peichl-Brak, M., and Drösler, M.:
A new methodology for organic soils in national greenhouse gas inventories: Data synthesis, derivation and application, Ecol. Indic., 109, 105838, https://doi.org/10.1016/j.ecolind.2019.105838, 2020.
Wilson, D., Dixon, S. D., Artz, R. R. E., Smith, T. E. L., Evans, C. D., Owen, H. J. F., Archer, E., and Renou-Wilson, F.:
Derivation of greenhouse gas emission factors for peatlands managed for extraction in the Republic of Ireland and the United Kingdom, Biogeosciences, 12, 5291–5308, https://doi.org/10.5194/bg-12-5291-2015, 2015.
Wilson, D., Blain, D., Couwenberg, J., Evans, C. D., Murdiyarso, D., Page, S. E., Renou-Wilson, F., Rieley, J. O., Strack, M., and Tuittila, E. S.:
Greenhouse gas emission factors associated with rewetting of organic soils, http://hdl.handle.net/10012/11532 (last access: 1 December 2022), 2016.
Short summary
Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The emissions are triggered by lowering the water table, which is a necessary step in order to make peatlands arable. Many countries aim at reducing their emissions by restoring peatlands, which can be achieved by stopping agricultural activities and thereby raising the water table. We estimate a total emission of 2.6 Mt CO2-eq for organic-rich peatlands in Denmark and a potential reduction of 77 %.
Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The...
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