Articles | Volume 19, issue 13
https://doi.org/10.5194/gmd-19-6335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-6335-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Jul 2026
Development and technical paper |
| 15 Jul 2026
| 15 Jul 2026
mLDNDCv1.0: a machine learning-based surrogate of LandscapeDNDC for optimising cropping systems in Denmark
Meshach Ojo Aderele
Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, Denmark
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Edwin Haas
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
Licheng Liu
Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA
João Serra
Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, Denmark
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Forest Research Centre CEF, Associate Laboratory TERRA, Instituto Superior de Agronomía, Universidade de Lisboa, 1349-017, Lisbon, Portugal
David Kraus
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
Klaus Butterbach-Bahl
Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, Denmark
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, Denmark
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
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Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2026-4010, https://doi.org/10.5194/egusphere-2026-4010, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Countries produce a yearly greenhouse gas inventory that is used to monitor and design climate policies. Agricultural N2O emissions are especially uncertain, due to measurement difficulties. Using a process-based model combined with high resolution management data we show that, in comparison with current methods, N2O emissions can be mapped with high spatial and temporal resolution.
Henri Kajasilta, Stephanie Gerin, Milla Niiranen, Miika Läpikivi, Maarit Liimatainen, David Kraus, Henriikka Vekuri, Mika Korkiakoski, Liisa Kulmala, Jari Liski, and Julius Vira
Biogeosciences, 23, 3567–3590, https://doi.org/10.5194/bg-23-3567-2026, https://doi.org/10.5194/bg-23-3567-2026, 2026
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We modelled different water table scenarios in drained agricultural peatlands to investigate the impact of water management on greenhouse gas emissions. Our results show that raising the water table reduces emissions, even in fields with thinner peat layers and conservative water management practices. Carbon dioxide emissions were more affected than nitrous oxide emissions. This study sheds light on the role of peatlands in mitigating emissions. Simulations were run using a process-based model.
Xiao Bai, Tom Cripps, João Serra, Klaus Butterbach-Bahl, and Zhisheng Yao
Biogeosciences, 23, 3207–3223, https://doi.org/10.5194/bg-23-3207-2026, https://doi.org/10.5194/bg-23-3207-2026, 2026
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This study examines the spatiotemporal variability of soil CH4, N2O and CO2 fluxes based on measurements across 56 spatial sites in an urban park. Our results show that soils in urban greenspaces function as sources of N2O and weak sinks of CH4. We developed random forest models to predict the probability of hot and cold spots of gas fluxes. Our study offers valuable insights into scaling gas fluxes in urban greenspaces, enabling a better assessment of how urbanization affects landscape fluxes.
Ahmed Hasan Shahriyer, David Kraus, Tiina Markkanen, Mika Korkiakoski, Helena Rautakoski, Suvi Orttenvuori, Yao Gao, Henri Kajasilta, Rüdiger Grote, Annalea Lohila, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-5197, https://doi.org/10.5194/egusphere-2025-5197, 2026
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We successfully represented hydrology and carbon cycle associated with different forestry managements (Rotational and continuous cover forestry) for a drained peatland ecosystem using the processed based model LDNDC. This provides a robust framework for investigating future management scenarios and develop forest management strategies that supports climate neutrality in peatland ecosystems.
Odysseas Sifounakis, Edwin Haas, Klaus Butterbach-Bahl, Eleni Katragkou, Maria Chara Karypidou, and Maria P. Papadopoulou
EGUsphere, https://doi.org/10.5194/egusphere-2025-5311, https://doi.org/10.5194/egusphere-2025-5311, 2025
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Using bio-geochemical models and future climate scenarios, we mapped all nitrogen and carbon fluxes from croplands. Climate change will strain food production – especially under climate change – yet soil carbon losses stay moderate where farming sustains. Nitrous oxide (a major greenhouse gas) tends to fall, while ammonia losses rise. Reporting the full balance improves transparency and guides smarter fertilizer use and soil management.
Roxanne Daelman, Marijn Bauters, Matti Barthel, Emmanuel Bulonza, Lodewijk Lefevre, José Mbifo, Johan Six, Klaus Butterbach-Bahl, Benjamin Wolf, Ralf Kiese, and Pascal Boeckx
Biogeosciences, 22, 1529–1542, https://doi.org/10.5194/bg-22-1529-2025, https://doi.org/10.5194/bg-22-1529-2025, 2025
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The increase in atmospheric concentrations of several greenhouse gases (GHGs) since 1750 is attributed to human activity. However, natural ecosystems, such as tropical forests, also contribute to GHG budgets. The Congo Basin hosts the second largest tropical forest and is understudied. In this study, measurements of soil GHG exchange were carried out during 16 months in a tropical forest in the Congo Basin. Overall, the soil acted as a major source of CO2 and N2O and a minor sink of CH4.
Carolin Boos, Sophie Reinermann, Raul Wood, Ralf Ludwig, Anne Schucknecht, David Kraus, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2024-2864, https://doi.org/10.5194/egusphere-2024-2864, 2024
Preprint archived
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We applied a biogeochemical model on grasslands in the pre-Alpine Ammer region in Germany and analyzed the influence of soil and climate on annual yields. In drought affected years, total yields were decreased by 4 %. Overall, yields decrease with rising elevation, but less so in drier and hotter years, whereas soil organic carbon has a positive impact on yields, especially in drier years. Our findings imply, that adapted management in the region allows to mitigate yield losses from drought.
Daniel Nadal-Sala, Rüdiger Grote, David Kraus, Uri Hochberg, Tamir Klein, Yael Wagner, Fedor Tatarinov, Dan Yakir, and Nadine K. Ruehr
Biogeosciences, 21, 2973–2994, https://doi.org/10.5194/bg-21-2973-2024, https://doi.org/10.5194/bg-21-2973-2024, 2024
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A hydraulic model approach is presented that can be added to any physiologically based ecosystem model. Simulated plant water potential triggers stomatal closure, photosynthesis decline, root–soil resistance increases, and sapwood and foliage senescence. The model has been evaluated at an extremely dry site stocked with Aleppo pine and was able to represent gas exchange, soil water content, and plant water potential. The model also responded realistically regarding leaf senescence.
Patrick Olschewski, Mame Diarra Bousso Dieng, Hassane Moutahir, Brian Böker, Edwin Haas, Harald Kunstmann, and Patrick Laux
Nat. Hazards Earth Syst. Sci., 24, 1099–1134, https://doi.org/10.5194/nhess-24-1099-2024, https://doi.org/10.5194/nhess-24-1099-2024, 2024
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We applied a multivariate and dependency-preserving bias correction method to climate model output for the Greater Mediterranean Region and investigated potential changes in false-spring events (FSEs) and heat–drought compound events (HDCEs). Results project an increase in the frequency of FSEs in middle and late spring as well as increases in frequency, intensity, and duration for HDCEs. This will potentially aggravate the risk of crop loss and failure and negatively impact food security.
Odysseas Sifounakis, Edwin Haas, Klaus Butterbach-Bahl, and Maria P. Papadopoulou
Biogeosciences, 21, 1563–1581, https://doi.org/10.5194/bg-21-1563-2024, https://doi.org/10.5194/bg-21-1563-2024, 2024
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We performed a full assessment of the carbon and nitrogen cycles of a cropland ecosystem. An uncertainty analysis and quantification of all carbon and nitrogen fluxes were deployed. The inventory simulations include greenhouse gas emissions of N2O, NH3 volatilization and NO3 leaching from arable land cultivation in Greece. The inventory also reports changes in soil organic carbon and nitrogen stocks in arable soils.
Elizabeth Gachibu Wangari, Ricky Mwangada Mwanake, Tobias Houska, David Kraus, Gretchen Maria Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl
Biogeosciences, 20, 5029–5067, https://doi.org/10.5194/bg-20-5029-2023, https://doi.org/10.5194/bg-20-5029-2023, 2023
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Agricultural landscapes act as sinks or sources of the greenhouse gases (GHGs) CO2, CH4, or N2O. Various physicochemical and biological processes control the fluxes of these GHGs between ecosystems and the atmosphere. Therefore, fluxes depend on environmental conditions such as soil moisture, soil temperature, or soil parameters, which result in large spatial and temporal variations of GHG fluxes. Here, we describe an example of how this variation may be studied and analyzed.
Ricky Mwangada Mwanake, Gretchen Maria Gettel, Elizabeth Gachibu Wangari, Clarissa Glaser, Tobias Houska, Lutz Breuer, Klaus Butterbach-Bahl, and Ralf Kiese
Biogeosciences, 20, 3395–3422, https://doi.org/10.5194/bg-20-3395-2023, https://doi.org/10.5194/bg-20-3395-2023, 2023
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Despite occupying <1 %; of the globe, streams are significant sources of greenhouse gas (GHG) emissions. In this study, we determined anthropogenic effects on GHG emissions from streams. We found that anthropogenic-influenced streams had up to 20 times more annual GHG emissions than natural ones and were also responsible for seasonal peaks. Anthropogenic influences also altered declining GHG flux trends with stream size, with potential impacts on stream-size-based spatial upscaling techniques.
Joseph Okello, Marijn Bauters, Hans Verbeeck, Samuel Bodé, John Kasenene, Astrid Françoys, Till Engelhardt, Klaus Butterbach-Bahl, Ralf Kiese, and Pascal Boeckx
Biogeosciences, 20, 719–735, https://doi.org/10.5194/bg-20-719-2023, https://doi.org/10.5194/bg-20-719-2023, 2023
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The increase in global and regional temperatures has the potential to drive accelerated soil organic carbon losses in tropical forests. We simulated climate warming by translocating intact soil cores from higher to lower elevations. The results revealed increasing temperature sensitivity and decreasing losses of soil organic carbon with increasing elevation. Our results suggest that climate warming may trigger enhanced losses of soil organic carbon from tropical montane forests.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
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By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
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Short summary
This study develops a fast, data‑driven tool to virtually test millions of ways to manage winter wheat fields in Denmark, without running slow process-based crop models each time. It finds fertilizer, residue, manure, catch crop and irrigation strategies that cut nitrogen pollution and greenhouse gases while increasing yields and soil carbon, all without using more fertilizer overall.
This study develops a fast, data‑driven tool to virtually test millions of ways to manage...
