Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6773-2023
© 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/gmd-16-6773-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Nov 2023
Model description paper |
| 22 Nov 2023
| 22 Nov 2023
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Tanya J. R. Lippmann
CORRESPONDING AUTHOR
Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
Ype van der Velde
Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
Monique M. P. D. Heijmans
Department of Environmental Sciences, Wageningen University and Research, Wageningen, the Netherlands
Han Dolman
Royal Netherlands Institute for Sea Research, Texel, the Netherlands
Netherlands Earth System Science Center, Utrecht University, Utrecht, the Netherlands
Dimmie M. D. Hendriks
Department of Soil and Groundwater Systems, Deltares Research Institute, Utrecht, the Netherlands
Ko van Huissteden
Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands
VOF Kytalyk Carbon Cycle Research, Epse, the Netherlands
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Tanya Juliette Rebecca Lippmann, Monique Heijmans, Han Dolman, Ype van der Velde, Dimmie Hendriks, and Ko van Huissteden
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-143, https://doi.org/10.5194/gmd-2022-143, 2022
Preprint withdrawn
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To assess the impact of vegetation on GHG fluxes in peatlands, we developed a new model, Peatland-VU-NUCOM (PVN). These results showed that plant communities impact GHG emissions, indicating that plant community re-establishment is a critical component of peatland restoration. This is the first time that a peatland emissions model investigated the role of re-introducing peat forming vegetation on GHG emissions.
Tanya J. R. Lippmann, Michiel H. in 't Zandt, Nathalie N. L. Van der Putten, Freek S. Busschers, Marc P. Hijma, Pieter van der Velden, Tim de Groot, Zicarlo van Aalderen, Ove H. Meisel, Caroline P. Slomp, Helge Niemann, Mike S. M. Jetten, Han A. J. Dolman, and Cornelia U. Welte
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This paper is a step towards understanding the basal peat ecosystem beneath the North Sea. Plant remains followed parallel sequences. Methane concentrations were low with local exceptions, with the source likely being trapped pockets of millennia-old methane. Microbial community structure indicated the absence of a biofilter and was diverse across sites. Large carbon stores in the presence of methanogens and in the absence of methanotrophs have the potential to be metabolized into methane.
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We combine two types of carbon dioxide (CO2) data from Dutch peatlands in a machine learning model: from fixed measurement towers and from a light research aircraft. We find that emissions increase with deeper water table depths (WTD) by 4.6 tonnes CO2 per hectare per year, per 10 cm deeper WTD on average. The effect is stronger in winter than in summer and varies between locations. This variability should be taken into account when developing mitigation measures.
Ralf C. H. Aben, Daniël van de Craats, Jim Boonman, Stijn H. Peeters, Bart Vriend, Coline C. F. Boonman, Ype van der Velde, Gilles Erkens, and Merit van den Berg
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Drained peatlands cause high CO2 emissions. We assessed the effectiveness of subsurface water infiltration systems (WISs) in reducing CO2 emissions related to increases in water table depth (WTD) on 12 sites for up to 4 years. Results show WISs markedly reduced emissions by 2.1 t CO2-C ha-1 yr-1. The relationship between the amount of carbon above the WTD and CO2 emission was stronger than the relationship between WTD and emission. Long-term monitoring is crucial for accurate emission estimates.
Merit van den Berg, Thomas M. Gremmen, Renske J. E. Vroom, Jacobus van Huissteden, Jim Boonman, Corine J. A. van Huissteden, Ype van der Velde, Alfons J. P. Smolders, and Bas P. van de Riet
Biogeosciences, 21, 2669–2690, https://doi.org/10.5194/bg-21-2669-2024, https://doi.org/10.5194/bg-21-2669-2024, 2024
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An Arctic catchment with permafrost responds in a linear fashion: water in=water out. As permafrost thaws, 9 of 10 nested catchments become more non-linear over time. We find upstream catchments have stronger streamflow seasonality and exhibit the most nonlinear storage-discharge relationships. Downstream catchments have the greatest increases in non-linearity over time. These long-term shifts in the storage-discharge relationship are not typically seen in current hydrological models.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Cindy Quik, Ype van der Velde, Jasper H. J. Candel, Luc Steinbuch, Roy van Beek, and Jakob Wallinga
Biogeosciences, 20, 695–718, https://doi.org/10.5194/bg-20-695-2023, https://doi.org/10.5194/bg-20-695-2023, 2023
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In NW Europe only parts of former peatlands remain. When these peatlands formed is not well known but relevant for questions on landscape, climate and archaeology. We investigated the age of Fochteloërveen, using radiocarbon dating and modelling. Results show that peat initiated at several sites 11 000–7000 years ago and expanded rapidly 5000 years ago. Our approach may ultimately be applied to model peat ages outside current remnants and provide a view of these lost landscapes.
Jim Boonman, Mariet M. Hefting, Corine J. A. van Huissteden, Merit van den Berg, Jacobus (Ko) van Huissteden, Gilles Erkens, Roel Melman, and Ype van der Velde
Biogeosciences, 19, 5707–5727, https://doi.org/10.5194/bg-19-5707-2022, https://doi.org/10.5194/bg-19-5707-2022, 2022
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Draining peat causes high CO2 emissions, and rewetting could potentially help solve this problem. In the dry year 2020 we measured that subsurface irrigation reduced CO2 emissions by 28 % and 83 % on two research sites. We modelled a peat parcel and found that the reduction depends on seepage and weather conditions and increases when using pressurized irrigation or maintaining high ditchwater levels. We found that soil temperature and moisture are suitable as indicators of peat CO2 emissions.
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To assess the impact of vegetation on GHG fluxes in peatlands, we developed a new model, Peatland-VU-NUCOM (PVN). These results showed that plant communities impact GHG emissions, indicating that plant community re-establishment is a critical component of peatland restoration. This is the first time that a peatland emissions model investigated the role of re-introducing peat forming vegetation on GHG emissions.
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Biogeosciences, 19, 1515–1525, https://doi.org/10.5194/bg-19-1515-2022, https://doi.org/10.5194/bg-19-1515-2022, 2022
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An important uncertainty in the modelling of methane emissions from natural wetlands is the wetland area. It is important to get the spatiotemporal covariance between the variables that drive methane emissions right for accurate quantification. Using high-resolution wetland and soil carbon maps, in combination with a simplified methane emission model that is coarsened in six steps from 0.005° to 1°, we find a strong relation between wetland emissions and the model resolution.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
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The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
Tanya J. R. Lippmann, Michiel H. in 't Zandt, Nathalie N. L. Van der Putten, Freek S. Busschers, Marc P. Hijma, Pieter van der Velden, Tim de Groot, Zicarlo van Aalderen, Ove H. Meisel, Caroline P. Slomp, Helge Niemann, Mike S. M. Jetten, Han A. J. Dolman, and Cornelia U. Welte
Biogeosciences, 18, 5491–5511, https://doi.org/10.5194/bg-18-5491-2021, https://doi.org/10.5194/bg-18-5491-2021, 2021
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This paper is a step towards understanding the basal peat ecosystem beneath the North Sea. Plant remains followed parallel sequences. Methane concentrations were low with local exceptions, with the source likely being trapped pockets of millennia-old methane. Microbial community structure indicated the absence of a biofilter and was diverse across sites. Large carbon stores in the presence of methanogens and in the absence of methanotrophs have the potential to be metabolized into methane.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Thomas Janssen, Ype van der Velde, Florian Hofhansl, Sebastiaan Luyssaert, Kim Naudts, Bart Driessen, Katrin Fleischer, and Han Dolman
Biogeosciences, 18, 4445–4472, https://doi.org/10.5194/bg-18-4445-2021, https://doi.org/10.5194/bg-18-4445-2021, 2021
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Satellite images show that the Amazon forest has greened up during past droughts. Measurements of tree stem growth and leaf litterfall upscaled using machine-learning algorithms show that leaf flushing at the onset of a drought results in canopy rejuvenation and green-up during drought while simultaneously trees excessively shed older leaves and tree stem growth declines. Canopy green-up during drought therefore does not necessarily point to enhanced tree growth and improved forest health.
Vince P. Kaandorp, Hans Peter Broers, Ype van der Velde, Joachim Rozemeijer, and Perry G. B. de Louw
Hydrol. Earth Syst. Sci., 25, 3691–3711, https://doi.org/10.5194/hess-25-3691-2021, https://doi.org/10.5194/hess-25-3691-2021, 2021
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We reconstructed historical and present-day tritium, chloride, and nitrate concentrations in stream water of a catchment using
land-use-based input curves and calculated travel times of groundwater. Parameters such as the unsaturated zone thickness, mean travel time, and input patterns determine time lags between inputs and in-stream concentrations. The timescale of the breakthrough of pollutants in streams is dependent on the location of pollution in a catchment.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
Ove H. Meisel, Joshua F. Dean, Jorien E. Vonk, Lukas Wacker, Gert-Jan Reichart, and Han Dolman
Biogeosciences, 18, 2241–2258, https://doi.org/10.5194/bg-18-2241-2021, https://doi.org/10.5194/bg-18-2241-2021, 2021
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Arctic permafrost lakes form thaw bulbs of unfrozen soil (taliks) beneath them where carbon degradation and greenhouse gas production are increased. We analyzed the stable carbon isotopes of Alaskan talik sediments and their porewater dissolved organic carbon and found that the top layers of these taliks are likely more actively degraded than the deeper layers. This in turn implies that these top layers are likely also more potent greenhouse gas producers than the underlying deeper layers.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
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NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Liang Yu, Joachim C. Rozemeijer, Hans Peter Broers, Boris M. van Breukelen, Jack J. Middelburg, Maarten Ouboter, and Ype van der Velde
Hydrol. Earth Syst. Sci., 25, 69–87, https://doi.org/10.5194/hess-25-69-2021, https://doi.org/10.5194/hess-25-69-2021, 2021
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The assessment of the collected water quality information is for the managers to find a way to improve the water environment to satisfy human uses and environmental needs. We found groundwater containing high concentrations of nutrient mixes with rain water in the ditches. The stable solutes are diluted during rain. The change in nutrients over time is determined by and uptaken by organisms and chemical processes. The water is more enriched with nutrients and looked
dirtierduring winter.
Maitane Iturrate-Garcia, Monique M. P. D. Heijmans, J. Hans C. Cornelissen, Fritz H. Schweingruber, Pascal A. Niklaus, and Gabriela Schaepman-Strub
Biogeosciences, 17, 4981–4998, https://doi.org/10.5194/bg-17-4981-2020, https://doi.org/10.5194/bg-17-4981-2020, 2020
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Changes on plant traits associated with climate warming might alter vegetation–climate interactions. We investigated experimentally the effects of enhanced permafrost thaw and soil nutrients on a wide set of tundra shrub traits. We found a coordinated trait response to some treatments, which suggests a shift in shrub resource, growth and defence strategies. This shift might feed back into permafrost thaw – through mechanisms associated with water demand – and into carbon and energy fluxes.
Srijana Lama, Sander Houweling, K. Folkert Boersma, Henk Eskes, Ilse Aben, Hugo A. C. Denier van der Gon, Maarten C. Krol, Han Dolman, Tobias Borsdorff, and Alba Lorente
Atmos. Chem. Phys., 20, 10295–10310, https://doi.org/10.5194/acp-20-10295-2020, https://doi.org/10.5194/acp-20-10295-2020, 2020
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Rapid urbanization has increased the consumption of fossil fuel, contributing the degradation of urban air quality. Burning efficiency is a major factor determining the impact of fuel burning on the environment. We quantify the burning efficiency of fossil fuel use over six megacities using satellite remote sensing data. City governance can use these results to understand air pollution scenarios and to formulate effective air pollution control strategies.
Thomas Janssen, Katrin Fleischer, Sebastiaan Luyssaert, Kim Naudts, and Han Dolman
Biogeosciences, 17, 2621–2645, https://doi.org/10.5194/bg-17-2621-2020, https://doi.org/10.5194/bg-17-2621-2020, 2020
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The frequency and severity of droughts are expected to increase in the tropics, impacting the functioning of tropical forests. Here, we synthesized observed responses to drought in Neotropical forests. We find that, during drought, trees generally close their leaf stomata, resulting in reductions in photosynthesis, growth and transpiration. However, on the ecosystem scale, these responses are not visible. This indicates that resistance to drought increases from the leaf to ecosystem scale.
Ana Maria Roxana Petrescu, Glen P. Peters, Greet Janssens-Maenhout, Philippe Ciais, Francesco N. Tubiello, Giacomo Grassi, Gert-Jan Nabuurs, Adrian Leip, Gema Carmona-Garcia, Wilfried Winiwarter, Lena Höglund-Isaksson, Dirk Günther, Efisio Solazzo, Anja Kiesow, Ana Bastos, Julia Pongratz, Julia E. M. S. Nabel, Giulia Conchedda, Roberto Pilli, Robbie M. Andrew, Mart-Jan Schelhaas, and Albertus J. Dolman
Earth Syst. Sci. Data, 12, 961–1001, https://doi.org/10.5194/essd-12-961-2020, https://doi.org/10.5194/essd-12-961-2020, 2020
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up GHG anthropogenic emissions from agriculture, forestry and other land use (AFOLU) in the EU28. The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models, aiming at reconciling GHG budgets with official country-level UNFCCC inventories. We provide comprehensive emission assessments in support to policy, facilitating real-time verification procedures.
Olli Peltola, Timo Vesala, Yao Gao, Olle Räty, Pavel Alekseychik, Mika Aurela, Bogdan Chojnicki, Ankur R. Desai, Albertus J. Dolman, Eugenie S. Euskirchen, Thomas Friborg, Mathias Göckede, Manuel Helbig, Elyn Humphreys, Robert B. Jackson, Georg Jocher, Fortunat Joos, Janina Klatt, Sara H. Knox, Natalia Kowalska, Lars Kutzbach, Sebastian Lienert, Annalea Lohila, Ivan Mammarella, Daniel F. Nadeau, Mats B. Nilsson, Walter C. Oechel, Matthias Peichl, Thomas Pypker, William Quinton, Janne Rinne, Torsten Sachs, Mateusz Samson, Hans Peter Schmid, Oliver Sonnentag, Christian Wille, Donatella Zona, and Tuula Aalto
Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, https://doi.org/10.5194/essd-11-1263-2019, 2019
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Here we develop a monthly gridded dataset of northern (> 45 N) wetland methane (CH4) emissions. The data product is derived using a random forest machine-learning technique and eddy covariance CH4 fluxes from 25 wetland sites. Annual CH4 emissions from these wetlands calculated from the derived data product are comparable to prior studies focusing on these areas. This product is an independent estimate of northern wetland CH4 emissions and hence could be used, e.g. for process model evaluation.
Martijn Westhoff, Axel Kleidon, Stan Schymanski, Benjamin Dewals, Femke Nijsse, Maik Renner, Henk Dijkstra, Hisashi Ozawa, Hubert Savenije, Han Dolman, Antoon Meesters, and Erwin Zehe
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2019-6, https://doi.org/10.5194/esd-2019-6, 2019
Publication in ESD not foreseen
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Even models relying on physical laws have parameters that need to be measured or estimated. Thermodynamic optimality principles potentially offer a way to reduce the number of estimated parameters by stating that a system evolves to an optimum state. These principles have been applied successfully within the Earth system, but it is often unclear what to optimize and how. In this review paper we identify commonalities between different successful applications as well as some doubtful applications.
Joshua F. Dean, Jurgen R. van Hal, A. Johannes Dolman, Rien Aerts, and James T. Weedon
Biogeosciences, 15, 7141–7154, https://doi.org/10.5194/bg-15-7141-2018, https://doi.org/10.5194/bg-15-7141-2018, 2018
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Lakes, rivers, ponds and streams are significant contributors of the greenhouse gas carbon dioxide to the atmosphere. This is partly due to the decomposition of plant and soil organic matter transported through these aquatic systems by microbial communities. In determining how vulnerable this organic material is to decomposition during aquatic transport, we show that standardized treatments in experiments can affect the way microbial communities behave and potentially the experimental outcome.
Joachim Rozemeijer, Janneke Klein, Dimmie Hendriks, Wiebe Borren, Maarten Ouboter, and Winnie Rip
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-636, https://doi.org/10.5194/hess-2017-636, 2018
Revised manuscript not accepted
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In lowland deltas surface water levels are often tightly controlled by inlet of diverted river water during dry periods and discharge via large-scale pumping stations during wet periods. The objective of this study was to assess the effects of changing the water level management from a fixed level to a flexible regime for 10 study catchments in The Netherlands. Water quality risks appeared and our methods could prevent such effects in the growing number of regulated catchments worldwide.
Fernando Jaramillo, Neil Cory, Berit Arheimer, Hjalmar Laudon, Ype van der Velde, Thomas B. Hasper, Claudia Teutschbein, and Johan Uddling
Hydrol. Earth Syst. Sci., 22, 567–580, https://doi.org/10.5194/hess-22-567-2018, https://doi.org/10.5194/hess-22-567-2018, 2018
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Which is the dominant effect on evapotranspiration in northern forests, an increase by recent forests expansion or a decrease by the water use response due to increasing CO2 concentrations? We determined the dominant effect during the period 1961–2012 in 65 Swedish basins. We used the Budyko framework to study the hydroclimatic movements in Budyko space. Our findings suggest that forest expansion is the dominant driver of long-term and large-scale evapotranspiration changes.
Stefanie R. Lutz, Ype van der Velde, Omniea F. Elsayed, Gwenaël Imfeld, Marie Lefrancq, Sylvain Payraudeau, and Boris M. van Breukelen
Hydrol. Earth Syst. Sci., 21, 5243–5261, https://doi.org/10.5194/hess-21-5243-2017, https://doi.org/10.5194/hess-21-5243-2017, 2017
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This study presents concentration and carbon isotope data of two herbicides from a small agricultural catchment. Herbicide concentrations at the catchment outlet were highest after intense rainfall events. The isotope data indicated herbicide degradation within 2 months after application. The system was modelled with a conceptual mathematical model using the transport formulation by travel-time distributions, which allowed testing of various assumptions of pesticide transport and degradation.
Henk-Jan van der Kolk, Monique M. P. D. Heijmans, Jacobus van Huissteden, Jeroen W. M. Pullens, and Frank Berendse
Biogeosciences, 13, 6229–6245, https://doi.org/10.5194/bg-13-6229-2016, https://doi.org/10.5194/bg-13-6229-2016, 2016
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Changes in tundra vegetation structure may amplify Arctic climate warming. Our simulations with a new tundra vegetation model suggest that precipitation increases favour grass abundance, whereas warming favours shrub dominance. However, abrupt permafrost thaw initiating wetland formation leads to grass dominance. Our simulations show that a wetter tundra, due to increased precipitation or abrupt permafrost thaw, could result in local shrub decline instead of the widely expected shrub expansion.
Inge Juszak, Werner Eugster, Monique M. P. D. Heijmans, and Gabriela Schaepman-Strub
Biogeosciences, 13, 4049–4064, https://doi.org/10.5194/bg-13-4049-2016, https://doi.org/10.5194/bg-13-4049-2016, 2016
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Changes in Arctic vegetation composition and structure feed back to climate and permafrost. Using field observations at a Siberian tundra site, we find that dwarf shrubs absorb more solar radiation than wet sedges and thus amplify surface warming, especially during snow melt. On the other hand, permafrost thaw was enhanced below sedges as a consequence of high soil moisture. Standing dead sedge leaves affected the radiation budget strongly and deserve more scientific attention.
Patrick W. Bogaart, Ype van der Velde, Steve W. Lyon, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 20, 1413–1432, https://doi.org/10.5194/hess-20-1413-2016, https://doi.org/10.5194/hess-20-1413-2016, 2016
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We analyse how stream discharge declines after rain storms. This "recession" behaviour contains information about the capacity of the catchment to hold or release water. Looking at many rivers in Sweden, we were able to link distinct recession regimes to land use and catchment characteristics. Trends in recession behaviour are found to correspond to intensifying agriculture and extensive reforestation. We conclude that both humans and nature reorganizes the soil in order to enhance efficiency.
D. G. Miralles, C. Jiménez, M. Jung, D. Michel, A. Ershadi, M. F. McCabe, M. Hirschi, B. Martens, A. J. Dolman, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, https://doi.org/10.5194/hess-20-823-2016, 2016
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The WACMOS-ET project aims to advance the development of land evaporation estimates on global and regional scales. Evaluation of current evaporation data sets on the global scale showed that they manifest large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into several components. Different models perform better under different conditions, highlighting the potential for considering biome- or climate-specific model ensembles.
J. C. Rozemeijer, A. Visser, W. Borren, M. Winegram, Y. van der Velde, J. Klein, and H. P. Broers
Hydrol. Earth Syst. Sci., 20, 347–358, https://doi.org/10.5194/hess-20-347-2016, https://doi.org/10.5194/hess-20-347-2016, 2016
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Controlled drainage has been recognized as an effective option to optimize soil moisture conditions for agriculture and to reduce unnecessary losses of fresh water and nutrients. For a grassland field in the Netherlands, we measured the changes in the field water and solute balance after introducing controlled drainage. We concluded that controlled drainage reduced the drain discharge and increased the groundwater storage in the field, but did not have clear positive effects for water quality.
G. Martins, C. von Randow, G. Sampaio, and A. J. Dolman
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-671-2015, https://doi.org/10.5194/hessd-12-671-2015, 2015
Revised manuscript not accepted
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Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. We study how the general circulation models of the CMIP5 simulate the inter-relations between regional precipitation, moisture convergence and SST in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia.
B. J. Dermody, R. P. H. van Beek, E. Meeks, K. Klein Goldewijk, W. Scheidel, Y. van der Velde, M. F. P. Bierkens, M. J. Wassen, and S. C. Dekker
Hydrol. Earth Syst. Sci., 18, 5025–5040, https://doi.org/10.5194/hess-18-5025-2014, https://doi.org/10.5194/hess-18-5025-2014, 2014
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Our virtual water network of the Roman World shows that virtual water trade and irrigation provided the Romans with resilience to interannual climate variability. Virtual water trade enabled the Romans to meet food demands from regions with a surplus. Irrigation provided stable water supplies for agriculture, particularly in large river catchments. However, virtual water trade also stimulated urbanization and population growth, which eroded Roman resilience to climate variability over time.
B. van der Grift, J. C. Rozemeijer, J. Griffioen, and Y. van der Velde
Hydrol. Earth Syst. Sci., 18, 4687–4702, https://doi.org/10.5194/hess-18-4687-2014, https://doi.org/10.5194/hess-18-4687-2014, 2014
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Exfiltration of anoxic groundwater containing Fe(II) to surface water is an important mechanism controlling P speciation in the lowland catchments. Due to changes in pH and temperature, the Fe(II) oxidation rates were much lower in winter than in summer. This study also shows a fast transformation of dissolved P to structural P during the initial stage of the Fe oxidation process resulting in low dissolved P concentrations in the surface water throughout the year.
G. R. van der Werf and A. J. Dolman
Earth Syst. Dynam., 5, 375–382, https://doi.org/10.5194/esd-5-375-2014, https://doi.org/10.5194/esd-5-375-2014, 2014
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Climate sensitivity can be quantified using measured changes in temperature and forcings. This approach requires disentangling natural and anthropogenic influences on global climate. We focused on the role of the Atlantic Multidecadal Oscillation (AMO) in this and show how different AMO characterizations influence the anthropogenic temperature trends (we found they were in between previously published values) and transient climate sensitivity, which we found to be 1.6 (1.0-3.3)°C.
A. Budishchev, Y. Mi, J. van Huissteden, L. Belelli-Marchesini, G. Schaepman-Strub, F. J. W. Parmentier, G. Fratini, A. Gallagher, T. C. Maximov, and A. J. Dolman
Biogeosciences, 11, 4651–4664, https://doi.org/10.5194/bg-11-4651-2014, https://doi.org/10.5194/bg-11-4651-2014, 2014
A. P. Schrier-Uijl, P. S. Kroon, D. M. D. Hendriks, A. Hensen, J. Van Huissteden, F. Berendse, and E. M. Veenendaal
Biogeosciences, 11, 4559–4576, https://doi.org/10.5194/bg-11-4559-2014, https://doi.org/10.5194/bg-11-4559-2014, 2014
Y. Mi, J. van Huissteden, F. J. W. Parmentier, A. Gallagher, A. Budishchev, C. T. Berridge, and A. J. Dolman
Biogeosciences, 11, 3985–3999, https://doi.org/10.5194/bg-11-3985-2014, https://doi.org/10.5194/bg-11-3985-2014, 2014
T. Chen, G. R. van der Werf, N. Gobron, E. J. Moors, and A. J. Dolman
Biogeosciences, 11, 3871–3880, https://doi.org/10.5194/bg-11-3871-2014, https://doi.org/10.5194/bg-11-3871-2014, 2014
Y. Mi, J. van Huissteden, and A. J. Dolman
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-3603-2014, https://doi.org/10.5194/tcd-8-3603-2014, 2014
Revised manuscript not accepted
P. Ciais, A. J. Dolman, A. Bombelli, R. Duren, A. Peregon, P. J. Rayner, C. Miller, N. Gobron, G. Kinderman, G. Marland, N. Gruber, F. Chevallier, R. J. Andres, G. Balsamo, L. Bopp, F.-M. Bréon, G. Broquet, R. Dargaville, T. J. Battin, A. Borges, H. Bovensmann, M. Buchwitz, J. Butler, J. G. Canadell, R. B. Cook, R. DeFries, R. Engelen, K. R. Gurney, C. Heinze, M. Heimann, A. Held, M. Henry, B. Law, S. Luyssaert, J. Miller, T. Moriyama, C. Moulin, R. B. Myneni, C. Nussli, M. Obersteiner, D. Ojima, Y. Pan, J.-D. Paris, S. L. Piao, B. Poulter, S. Plummer, S. Quegan, P. Raymond, M. Reichstein, L. Rivier, C. Sabine, D. Schimel, O. Tarasova, R. Valentini, R. Wang, G. van der Werf, D. Wickland, M. Williams, and C. Zehner
Biogeosciences, 11, 3547–3602, https://doi.org/10.5194/bg-11-3547-2014, https://doi.org/10.5194/bg-11-3547-2014, 2014
M. Van Damme, L. Clarisse, C. L. Heald, D. Hurtmans, Y. Ngadi, C. Clerbaux, A. J. Dolman, J. W. Erisman, and P. F. Coheur
Atmos. Chem. Phys., 14, 2905–2922, https://doi.org/10.5194/acp-14-2905-2014, https://doi.org/10.5194/acp-14-2905-2014, 2014
C. T. Berridge, L. H. Hadju, and A. J. Dolman
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-1977-2014, https://doi.org/10.5194/bgd-11-1977-2014, 2014
Revised manuscript not accepted
T. Chen, G. R. Werf, R. A. M. Jeu, G. Wang, and A. J. Dolman
Hydrol. Earth Syst. Sci., 17, 3885–3894, https://doi.org/10.5194/hess-17-3885-2013, https://doi.org/10.5194/hess-17-3885-2013, 2013
B. Ringeval, P. O. Hopcroft, P. J. Valdes, P. Ciais, G. Ramstein, A. J. Dolman, and M. Kageyama
Clim. Past, 9, 149–171, https://doi.org/10.5194/cp-9-149-2013, https://doi.org/10.5194/cp-9-149-2013, 2013
Related subject area
Biogeosciences
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
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Inferring the tree regeneration niche from inventory data using a dynamic forest model
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
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Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Cited articles
Alhoz, K., Kenesei, K., Papageorgiou, M., Keurentjes, E. E. M., and de Jong, M.: Improved AHN3 Gridded DTM/DSM, Delft University of Technology, https://repository.tudelft.nl/islandora/object/uuid:153b2a82-05ba-412b-8a0a-119cb5689afd (last access: 15 November 2023) 2020. a
Aulakh, M. S., Wassmann, R., Bueno, C., and Rennenberg, H.: Impact of root exudates of different cultivars and plant development stages of rice (Oryza sativa L.) on methane production in a paddy soil, Plant Soil, 230, 77–86, https://doi.org/10.1023/A:1004817212321, 2001. a
Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: Past, present and future, Glob. Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003. a
Baldocchi, D. D. and Harley, P. C.: Scaling carbon dioxide and water vapour exchange from leaf to canopy in a deciduous forest. II. Model testing and application, Plant Cell Environ., 18, 1157–1173, https://doi.org/10.1111/j.1365-3040.1995.tb00626.x, 1995. a
Ballantyne, D. M., Hribljan, J. A., Pypker, T. G., and Chimner, R. A.: Long-term water table manipulations alter peatland gaseous carbon fluxes in Northern Michigan, Wetl. Ecol. Manag., 22, 35–47, https://doi.org/10.1007/s11273-013-9320-8, 2014. a
Bansal, S., Johnson, O. F., Meier, J., and Zhu, X.: Vegetation Affects Timing and Location of Wetland Methane Emissions, J. Geophys. Res.-Biogeo., 125, e2020JG005777, https://doi.org/10.1029/2020JG005777, 2020. a, b
Bao, T., Jia, G., and Xu, X.: Weakening greenhouse gas sink of pristine wetlands under warming, Nat. Clim. Change, 13, 462–469, https://doi.org/10.1038/s41558-023-01637-0, 2023. a
Basile-Doelsch, I., Balesdent, J., and Pellerin, S.: Reviews and syntheses: The mechanisms underlying carbon storage in soil, Biogeosciences, 17, 5223–5242, https://doi.org/10.5194/bg-17-5223-2020, 2020. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Boonman, J., Hefting, M. M., van Huissteden, C. J. A., van den Berg, M., van Huissteden, J. (., Erkens, G., Melman, R., and van der Velde, Y.: Cutting peatland CO2 emissions with water management practices, Biogeosciences, 19, 5707–5727, https://doi.org/10.5194/bg-19-5707-2022, 2022. a
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M., Parmentier, F. J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S., Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W., Meier, W. N., Wouters, B., Mernild, S., Mård, J., Pawlak, J., and Olsen, M. S.: Key indicators of Arctic climate change: 1971-2017, Environ. Res. Lett., 14, 045010, https://doi.org/10.1088/1748-9326/aafc1b, 2019. a
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane emissions from wetlands: Biogeochemical, microbial, and modeling perspectives from local to global scales, Glob. Change Biol., 19, 1325–1346, https://doi.org/10.1111/gcb.12131, 2013. a
Bubier, J.: The Relationship of Vegetation to Methane Emission and Hydrochemical Gradients in Northern Peatlands, J. Ecol., 83, 403–420, 2016. a
Chaney, N. W., Sheffield, J., Villarini, G., and Wood, E. F.: Development of a high-resolution gridded daily meteorological dataset over sub-Saharan Africa: Spatial analysis of trends in climate extremes, J. Climate, 27, 5815–5835, https://doi.org/10.1175/JCLI-D-13-00423.1, 2014. a
Christensen, T. R., Ekberg, A., Ström, L., Mastepanov, M., Panikov, N., Öquist, M., Svensson, B. H., Nykänen, H., Martikainen, P. J., and Oskarsson, H.: Factors controlling large scale variations in methane emissions from wetlands, Geophys. Res. Lett., 30, 67, https://doi.org/10.1029/2002GL016848, 2003. a
Churkina, G., Running, S. W., and Schloss, A. L.: Comparing global models of terrestrial net primary productivity (NPP): The importance of water availability, Glob. Change Biol., 5, 46–55, https://doi.org/10.1046/j.1365-2486.1999.00006.x, 1999. a
Clemmensen, K. E., Bahr, A., Ovaskainen, O., Dahlberg, A., Ekblad, A., Wallander, H., Stenlid, J., Finlay, R. D., Wardle, D. A., and Lindahl, B. D.: Roots and Associated Fungi Drive Long-Term Carbon Sequestration in Boreal Forest, Science, 339, 1615–1618, https://doi.org/10.1126/science.1231923, 2013. a
Crow, S. E. and Wieder, R. K.: Sources of CO2 emission from a northern peatland: Root respiration, exudation, and decomposition, Ecology, 86, 1825–1834, https://doi.org/10.1890/04-1575, 2005. a
Davidson, E. and Janssens, I. A.: Temperature sensitivity of soil carbon decomposition and feedbacks to climate change, Nature, 440, 165–173, https://doi.org/10.1038/nature04514, 2006. a
De Boeck, H. J., Dreesen, F. E., Janssens, I. A., and Nijs, I.: Whole-system responses of experimental plant communities to climate extremes imposed in different seasons, New Phytol., 189, 806–817, https://doi.org/10.1111/j.1469-8137.2010.03515.x, 2011. a
De La Motte, L. G., Beauclaire, Q., Heinesch, B., Cuntz, M., Foltýnová, L., Šigut, L., Kowalska, N., Manca, G., Ballarin, I. G., Vincke, C., Roland, M., Ibrom, A., Lousteau, D., Siebicke, L., Neiryink, J., and Longdoz, B.: Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought: Edaphic drought in forest ecosystems, Philos. T. Roy. Soc. B, 375, 20190527, https://doi.org/10.1098/rstb.2019.0527, 2020. a
De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H., Van Walsum, P. E., Delsman, J. R., Hunink, J. C., Massop, H. T. L., and Kroon, T.: An operational, multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument, Environ. Model. Softw., 59, 98–108, https://doi.org/10.1016/j.envsoft.2014.05.009, 2014. a
Dick, J. J., Tetzlaff, D., Birkel, C., and Soulsby, C.: Modelling landscape controls on dissolved organic carbon sources and fluxes to streams, Biogeochemistry, 122, 361–374, https://doi.org/10.1007/s10533-014-0046-3, 2015. a
Dorrepaal, E., Aerts, R., Cornelissen, J. H., Van Logtestijn, R. S., and Callaghan, T. V.: Sphagnum modifies climate-change impacts on subarctic vascular bog plants, Funct. Ecol., 20, 31–41, https://doi.org/10.1111/j.1365-2435.2006.01076.x, 2006. a, b
Dorrepaal, E., Cornelissen, J. H., and Aerts, R.: Changing leaf litter feedbacks on plant production across contrasting sub-arctic peatland species and growth forms, Oecologia, 151, 251–261, https://doi.org/10.1007/s00442-006-0580-3, 2007. a, b, c
Dorrepaal, E., Toet, S., Van Logtestijn, R. S., Swart, E., Van De Weg, M. J., Callaghan, T. V., and Aerts, R.: Carbon respiration from subsurface peat accelerated by climate warming in the subarctic, Nature, 460, 616–619, https://doi.org/10.1038/nature08216, 2009. a, b, c
Erkens, G., Van Der Meulen, M. J., and Middelkoop, H.: Double trouble: Subsidence and CO2 respiration due to 1,000 years of Dutch coastal peatlands cultivation, Hydrogeol. J., 24, 551–568, https://doi.org/10.1007/s10040-016-1380-4, 2016. a
Evans, C. D., Peacock, M., Baird, A. J., Artz, R. R., 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. a, b
Frolking, S. and Roulet, N. T.: Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions, Glob. Change Biol., 13, 1079–1088, https://doi.org/10.1111/j.1365-2486.2007.01339.x, 2007. a
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.: Terrestrial vegetation and water balance – Hydrological evaluation of a dynamic global vegetation model, J. Hydrol., 286, 249–270, https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004. a
Geurts, J. and Fritz, C.: Paludiculture pilots and experiments with focus on cattail and reed in the Netherlands. Technical, Tech. rep., Radboud University Nijmegen, Nijmegen, https://doi.org/10.13140/RG.2.2.12916.24966, 2018. a
Gorham, E.: Shoot height, weight and standing crop in relation to density of monospecific plant stands, Nature, 279, 148–150, https://doi.org/10.1038/279148a0, 1979. a
Graf, M. and Rochefort, L.: Examining the peat-accumulating potential of fen vegetation in the context of fen restoration of harvested peatlands, Ecoscience, 16, 158–166, https://doi.org/10.2980/16-2-3128, 2009. a, b, c
Granberg, G., Grip, H., Ottosson Löfvenius, M., Sundh, I., Svensson, B. H., and Nilsson, M.: A simple model for simulation of water content, soil frost, and soil temperatures in boreal mixed mires, Water Resour. Res., 35, 3771–3782, https://doi.org/10.1029/1999WR900216, 1999. a
Gunnarsson, U., Granberg, G., and Nilsson, M.: Growth, production and interspecific competition in Sphagnum: Effects of temperature, nitrogen and sulphur treatments on a boreal mire, New Phytol., 163, 349–359, https://doi.org/10.1111/j.1469-8137.2004.01108.x, 2004. a
Günther, A., Barthelmes, A., Huth, V., Joosten, H., Jurasinski, G., Koebsch, F., and Couwenberg, J.: Prompt rewetting of drained peatlands reduces climate warming despite methane emissions, Nat. Commun., 11, 1644, https://doi.org/10.1038/s41467-020-15499-z, 2020. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Harpenslager, S. F., van den Elzen, E., Kox, M. A., Smolders, A. J., Ettwig, K. F., and Lamers, L. P.: Rewetting former agricultural peatlands: Topsoil removal as a prerequisite to avoid strong nutrient and greenhouse gas emissions, Ecol. Eng., 84, 159–168, https://doi.org/10.1016/j.ecoleng.2015.08.002, 2015. a, b, c
Haxeltine, A. and Prentice, I. C.: BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochem. Cy., 10, 693–709, https://doi.org/10.1029/96GB02344, 1996. a, b, c
Heijmans, M. M., Mauquoy, D., Van Geel, B., and Berendse, F.: Long-term effects of climate change on vegetation and carbon dynamics in peat bogs, J. Veg. Sci., 19, 307–320, https://doi.org/10.3170/2008-8-18368, 2008. a, b, c
Heijmans, M. M., Mauquoy, D., Van Geel, B., and Berendse, F.: Long-term effects of climate change on vegetation and carbon dynamics in peat bogs, J. Veg. Sci., 19, 307–320, https://doi.org/10.3170/2008-8-18368, 2008. a
Hendriks, D. M. D., van Huissteden, J., Dolman, A. J., and van der Molen, M. K.: The full greenhouse gas balance of an abandoned peat meadow, Biogeosciences, 4, 411–424, https://doi.org/10.5194/bg-4-411-2007, 2007. a, b, c
Hobbie, S. E., Schimel, J. P., Trumbore, S. E., and Randerson, J. R.: Controls over carbon storage and turnover in high-latitude soils, Glob. Change Biol., 6, 196–210, https://doi.org/10.1046/j.1365-2486.2000.06021.x, 2000. a
Högberg, P., Nordgren, A., Buchmann, N., Taylor, A. F., Ekblad, A., Högberg, M. N., Nyberg, G., Ottosson-Löfvenius, M., and Read, D. J.: Large-scale forest girdling shows that current photosynthesis drives soil respiration, Nature, 411, 789–792, https://doi.org/10.1038/35081058, 2001. a
Huang, S., Titus, S. J., and Wiens, D. P.: Comparison of nonlinear height-diameter functions for major Alberta tree species, Can. J. Forest Res., 22, 1297–1304, https://doi.org/10.1139/x92-172, 1992. a
Iversen, C. M., Sloan, V. L., Sullivan, P. F., Euskirchen, E. S., Mcguire, A. D., Norby, R. J., Walker, A. P., Warren, J. M., and Wullschleger, S. D.: The unseen iceberg: Plant roots in arctic tundra, New Phytol., 205, 34–58, https://doi.org/10.1111/nph.13003, 2015. a, b
Jaatinen, K., Laiho, R., Vuorenmaa, A., Del Castillo, U., Minkkinen, K., Pennanen, T., Penttilä, T., and Fritze, H.: Responses of aerobic microbial communities and soil respiration to water-level drawdown in a northern boreal fen, Environ. Microbiol., 10, 339–353, https://doi.org/10.1111/j.1462-2920.2007.01455.x, 2008. a
Jackowicz-Korczyński, M., Christensen, T. R., Bäckstrand, K., Crill, P., Friborg, T., Mastepanov, M., and Ström, L.: Annual cycle of methane emission from a subarctic peatland, J. Geophys. Res.-Biogeo., 115, G02009, https://doi.org/10.1029/2008JG000913, 2010. a, b
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E., and Schulze, E. D.: A global analysis of root distributions for terrestrial biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996. a
Järveoja, J., Laht, J., Maddison, M., Soosaar, K., Ostonen, I., and Mander, Ü.: Mitigation of greenhouse gas emissions from an abandoned Baltic peat extraction area by growing reed canary grass: Life-cycle assessment, Reg. Environ. Change, 13, 781–795, https://doi.org/10.1007/s10113-012-0355-9, 2013. a
Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H., Violle, C., Harrison, S. P., Van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C. C., Blonder, B., Bond, W. J., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J. Q., Chapin, F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Duarte, L., Durka, W., Elser, J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernández-Méndez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R. V., Green, W. A., Gutierrez, A. G., Hickler, T., Higgins, S. I., Hodgson, J. G., Jalili, A., Jansen, S., Joly, C. A., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J., Llusià, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B. E., Messier, J., Moles, A. T., Müller, S. C., Nadrowski, K., Naeem, S., Niinemets, Ü., Nöllert, S., Nüske, A., Ogaya, R., Oleksyn, J., Onipchenko, V. G., Onoda, Y., Ordoñez, J., Overbeck, G., Ozinga, W. A., Patiño, S., Paula, S., Pausas, J. G., Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Prinzing, A., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado-Negret, B., Sardans, J., Shiodera, S., Shipley, B., Siefert, A., Sosinski, E., Soussana, J. F., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., White, S., Wright, S. J., Yguel, B., Zaehle, S., Zanne, A. E., and Wirth, C.: TRY – a global database of plant traits, Glob. Change Biol., 17, 2905–2935, https://doi.org/10.1111/j.1365-2486.2011.02451.x, 2011. a, b
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D., Aakala, T., Abedi, M., Acosta, A. T., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara, J. M., Alcázar C, C., Aleixo, I., Ali, H., Amiaud, B., Ammer, C., Amoroso, M. M., Anand, M., Anderson, C., Anten, N., Antos, J., Apgaua, D. M. G., Ashman, T. L., Asmara, D. H., Asner, G. P., Aspinwall, M., Atkin, O., Aubin, I., Baastrup-Spohr, L., Bahalkeh, K., Bahn, M., Baker, T., Baker, W. J., Bakker, J. P., Baldocchi, D., Baltzer, J., Banerjee, A., Baranger, A., Barlow, J., Barneche, D. R., Baruch, Z., Bastianelli, D., Battles, J., Bauerle, W., Bauters, M., Bazzato, E., Beckmann, M., Beeckman, H., Beierkuhnlein, C., Bekker, R., Belfry, G., Belluau, M., Beloiu, M., Benavides, R., Benomar, L., Berdugo-Lattke, M. L., Berenguer, E., Bergamin, R., Bergmann, J., Bergmann Carlucci, M., Berner, L., Bernhardt-Römermann, M., Bigler, C., Bjorkman, A. D., Blackman, C., Blanco, C., Blonder, B., Blumenthal, D., Bocanegra-González, K. T., Boeckx, P., Bohlman, S., Böhning-Gaese, K., Boisvert-Marsh, L., Bond, W., Bond-Lamberty, B., Boom, A., Boonman, C. C., Bordin, K., Boughton, E. H., Boukili, V., Bowman, D. M., Bravo, S., Brendel, M. R., Broadley, M. R., Brown, K. A., Bruelheide, H., Brumnich, F., Bruun, H. H., Bruy, D., Buchanan, S. W., Bucher, S. F., Buchmann, N., Buitenwerf, R., Bunker, D. E., Bürger, J., Burrascano, S., Burslem, D. F., Butterfield, B. J., Byun, C., Marques, M., Scalon, M. C., Caccianiga, M., Cadotte, M., Cailleret, M., Camac, J., Camarero, J. J., Campany, C., Campetella, G., Campos, J. A., Cano-Arboleda, L., Canullo, R., Carbognani, M., Carvalho, F., Casanoves, F., Castagneyrol, B., Catford, J. A., Cavender-Bares, J., Cerabolini, B. E., Cervellini, M., Chacón-Madrigal, E., Chapin, K., Chapin, F. S., Chelli, S., Chen, S. C., Chen, A., Cherubini, P., Chianucci, F., Choat, B., Chung, K. S., Chytrý, M., Ciccarelli, D., Coll, L., Collins, C. G., Conti, L., Coomes, D., Cornelissen, J. H., Cornwell, W. K., Corona, P., Coyea, M., Craine, J., Craven, D., Cromsigt, J. P., Csecserits, A., Cufar, K., Cuntz, M., da Silva, A. C., Dahlin, K. M., Dainese, M., Dalke, I., Dalle Fratte, M., Dang-Le, A. T., Danihelka, J., Dannoura, M., Dawson, S., de Beer, A. J., De Frutos, A., De Long, J. R., Dechant, B., Delagrange, S., Delpierre, N., Derroire, G., Dias, A. S., Diaz-Toribio, M. H., Dimitrakopoulos, P. G., Dobrowolski, M., Doktor, D., Dřevojan, P., Dong, N., Dransfield, J., Dressler, S., Duarte, L., Ducouret, E., Dullinger, S., Durka, W., Duursma, R., Dymova, O., E-Vojtkó, A., Eckstein, R. L., Ejtehadi, H., Elser, J., Emilio, T., Engemann, K., Erfanian, M. B., Erfmeier, A., Esquivel-Muelbert, A., Esser, G., Estiarte, M., Domingues, T. F., Fagan, W. F., Fagúndez, J., Falster, D. S., Fan, Y., Fang, J., Farris, E., Fazlioglu, F., Feng, Y., Fernandez-Mendez, F., Ferrara, C., Ferreira, J., Fidelis, A., Finegan, B., Firn, J., Flowers, T. J., Flynn, D. F., Fontana, V., Forey, E., Forgiarini, C., François, L., Frangipani, M., Frank, D., Frenette-Dussault, C., Freschet, G. T., Fry, E. L., Fyllas, N. M., Mazzochini, G. G., Gachet, S., Gallagher, R., Ganade, G., Ganga, F., García-Palacios, P., Gargaglione, V., Garnier, E., Garrido, J. L., de Gasper, A. L., Gea-Izquierdo, G., Gibson, D., Gillison, A. N., Giroldo, A., Glasenhardt, M. C., Gleason, S., Gliesch, M., Goldberg, E., Göldel, B., Gonzalez-Akre, E., Gonzalez-Andujar, J. L., González-Melo, A., González-Robles, A., Graae, B. J., Granda, E., Graves, S., Green, W. A., Gregor, T., Gross, N., Guerin, G. R., Günther, A., Gutiérrez, A. G., Haddock, L., Haines, A., Hall, J., Hambuckers, A., Han, W., Harrison, S. P., Hattingh, W., Hawes, J. E., He, T., He, P., Heberling, J. M., Helm, A., Hempel, S., Hentschel, J., Hérault, B., Hereş, A. M., Herz, K., Heuertz, M., Hickler, T., Hietz, P., Higuchi, P., Hipp, A. L., Hirons, A., Hock, M., Hogan, J. A., Holl, K., Honnay, O., Hornstein, D., Hou, E., Hough-Snee, N., Hovstad, K. A., Ichie, T., Igić, B., Illa, E., Isaac, M., Ishihara, M., Ivanov, L., Ivanova, L., Iversen, C. M., Izquierdo, J., Jackson, R. B., Jackson, B., Jactel, H., Jagodzinski, A. M., Jandt, U., Jansen, S., Jenkins, T., Jentsch, A., Jespersen, J. R. P., Jiang, G. F., Johansen, J. L., Johnson, D., Jokela, E. J., Joly, C. A., Jordan, G. J., Joseph, G. S., Junaedi, D., Junker, R. R., Justes, E., Kabzems, R., Kane, J., Kaplan, Z., Kattenborn, T., Kavelenova, L., Kearsley, E., Kempel, A., Kenzo, T., Kerkhoff, A., Khalil, M. I., Kinlock, N. L., Kissling, W. D., Kitajima, K., Kitzberger, T., Kjøller, R., Klein, T., Kleyer, M., Klimešová, J., Klipel, J., Kloeppel, B., Klotz, S., Knops, J. M., Kohyama, T., Koike, F., Kollmann, J., Komac, B., Komatsu, K., König, C., Kraft, N. J., Kramer, K., Kreft, H., Kühn, I., Kumarathunge, D., Kuppler, J., Kurokawa, H., Kurosawa, Y., Kuyah, S., Laclau, J. P., Lafleur, B., Lallai, E., Lamb, E., Lamprecht, A., Larkin, D. J., Laughlin, D., Le Bagousse-Pinguet, Y., le Maire, G., le Roux, P. C., le Roux, E., Lee, T., Lens, F., Lewis, S. L., Lhotsky, B., Li, Y., Li, X., Lichstein, J. W., Liebergesell, M., Lim, J. Y., Lin, Y. S., Linares, J. C., Liu, C., Liu, D., Liu, U., Livingstone, S., Llusià, J., Lohbeck, M., López-García, Á., Lopez-Gonzalez, G., Lososová, Z., Louault, F., Lukács, B. A., Lukeš, P., Luo, Y., Lussu, M., Ma, S., Maciel Rabelo Pereira, C., Mack, M., Maire, V., Mäkelä, A., Mäkinen, H., Malhado, A. C. M., Mallik, A., Manning, P., Manzoni, S., Marchetti, Z., Marchino, L., Marcilio-Silva, V., Marcon, E., Marignani, M., Markesteijn, L., Martin, A., Martínez-Garza, C., Martínez-Vilalta, J., Mašková, T., Mason, K., Mason, N., Massad, T. J., Masse, J., Mayrose, I., McCarthy, J., McCormack, M. L., McCulloh, K., McFadden, I. R., McGill, B. J., McPartland, M. Y., Medeiros, J. S., Medlyn, B., Meerts, P., Mehrabi, Z., Meir, P., Melo, F. P., Mencuccini, M., Meredieu, C., Messier, J., Mészáros, I., Metsaranta, J., Michaletz, S. T., Michelaki, C., Migalina, S., Milla, R., Miller, J. E., Minden, V., Ming, R., Mokany, K., Moles, A. T., Molnár, A., Molofsky, J., Molz, M., Montgomery, R. A., Monty, A., Moravcová, L., Moreno-Martínez, A., Moretti, M., Mori, A. S., Mori, S., Morris, D., Morrison, J., Mucina, L., Mueller, S., Muir, C. D., Müller, S. C., Munoz, F., Myers-Smith, I. H., Myster, R. W., Nagano, M., Naidu, S., Narayanan, A., Natesan, B., Negoita, L., Nelson, A. S., Neuschulz, E. L., Ni, J., Niedrist, G., Nieto, J., Niinemets, Ü., Nolan, R., Nottebrock, H., Nouvellon, Y., Novakovskiy, A., Nystuen, K. O., O'Grady, A., O'Hara, K., O'Reilly-Nugent, A., Oakley, S., Oberhuber, W., Ohtsuka, T., Oliveira, R., Öllerer, K., Olson, M. E., Onipchenko, V., Onoda, Y., Onstein, R. E., Ordonez, J. C., Osada, N., Ostonen, I., Ottaviani, G., Otto, S., Overbeck, G. E., Ozinga, W. A., Pahl, A. T., Paine, C. E., Pakeman, R. J., Papageorgiou, A. C., Parfionova, E., Pärtel, M., Patacca, M., Paula, S., Paule, J., Pauli, H., Pausas, J. G., Peco, B., Penuelas, J., Perea, A., Peri, P. L., Petisco-Souza, A. C., Petraglia, A., Petritan, A. M., Phillips, O. L., Pierce, S., Pillar, V. D., Pisek, J., Pomogaybin, A., Poorter, H., Portsmuth, A., Poschlod, P., Potvin, C., Pounds, D., Powell, A. S., Power, S. A., Prinzing, A., Puglielli, G., Pyšek, P., Raevel, V., Rammig, A., Ransijn, J., Ray, C. A., Reich, P. B., Reichstein, M., Reid, D. E., Réjou-Méchain, M., de Dios, V. R., Ribeiro, S., Richardson, S., Riibak, K., Rillig, M. C., Riviera, F., Robert, E. M., Roberts, S., Robroek, B., Roddy, A., Rodrigues, A. V., Rogers, A., Rollinson, E., Rolo, V., Römermann, C., Ronzhina, D., Roscher, C., Rosell, J. A., Rosenfield, M. F., Rossi, C., Roy, D. B., Royer-Tardif, S., Rüger, N., Ruiz-Peinado, R., Rumpf, S. B., Rusch, G. M., Ryo, M., Sack, L., Saldaña, A., Salgado-Negret, B., Salguero-Gomez, R., Santa-Regina, I., Santacruz-García, A. C., Santos, J., Sardans, J., Schamp, B., Scherer-Lorenzen, M., Schleuning, M., Schmid, B., Schmidt, M., Schmitt, S., Schneider, J. V., Schowanek, S. D., Schrader, J., Schrodt, F., Schuldt, B., Schurr, F., Selaya Garvizu, G., Semchenko, M., Seymour, C., Sfair, J. C., Sharpe, J. M., Sheppard, C. S., Sheremetiev, S., Shiodera, S., Shipley, B., Shovon, T. A., Siebenkäs, A., Sierra, C., Silva, V., Silva, M., Sitzia, T., Sjöman, H., Slot, M., Smith, N. G., Sodhi, D., Soltis, P., Soltis, D., Somers, B., Sonnier, G., Sørensen, M. V., Sosinski, E. E., Soudzilovskaia, N. A., Souza, A. F., Spasojevic, M., Sperandii, M. G., Stan, A. B., Stegen, J., Steinbauer, K., Stephan, J. G., Sterck, F., Stojanovic, D. B., Strydom, T., Suarez, M. L., Svenning, J. C., Svitková, I., Svitok, M., Svoboda, M., Swaine, E., Swenson, N., Tabarelli, M., Takagi, K., Tappeiner, U., Tarifa, R., Tauugourdeau, S., Tavsanoglu, C., te Beest, M., Tedersoo, L., Thiffault, N., Thom, D., Thomas, E., Thompson, K., Thornton, P. E., Thuiller, W., Tichý, L., Tissue, D., Tjoelker, M. G., Tng, D. Y. P., Tobias, J., Török, P., Tarin, T., Torres-Ruiz, J. M., Tóthmérész, B., Treurnicht, M., Trivellone, V., Trolliet, F., Trotsiuk, V., Tsakalos, J. L., Tsiripidis, I., Tysklind, N., Umehara, T., Usoltsev, V., Vadeboncoeur, M., Vaezi, J., Valladares, F., Vamosi, J., van Bodegom, P. M., van Breugel, M., Van Cleemput, E., van de Weg, M., van der Merwe, S., van der Plas, F., van der Sande, M. T., van Kleunen, M., Van Meerbeek, K., Vanderwel, M., Vanselow, K. A., Vårhammar, A., Varone, L., Vasquez Valderrama, M. Y., Vassilev, K., Vellend, M., Veneklaas, E. J., Verbeeck, H., Verheyen, K., Vibrans, A., Vieira, I., Villacís, J., Violle, C., Vivek, P., Wagner, K., Waldram, M., Waldron, A., Walker, A. P., Waller, M., Walther, G., Wang, H., Wang, F., Wang, W., Watkins, H., Watkins, J., Weber, U., Weedon, J. T., Wei, L., Weigelt, P., Weiher, E., Wells, A. W., Wellstein, C., Wenk, E., Westoby, M., Westwood, A., White, P. J., Whitten, M., Williams, M., Winkler, D. E., Winter, K., Womack, C., Wright, I. J., Wright, S. J., Wright, J., Pinho, B. X., Ximenes, F., Yamada, T., Yamaji, K., Yanai, R., Yankov, N., Yguel, B., Zanini, K. J., Zanne, A. E., Zelený, D., Zhao, Y. P., Zheng, J., Zheng, J., Ziemińska, K., Zirbel, C. R., Zizka, G., Zo-Bi, I. C., Zotz, G., and Wirth, C.: TRY plant trait database – enhanced coverage and open access, Glob. Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020. a, b
Keenan, T., Sabate, S., and Gracia, C.: The importance of mesophyll conductance in regulating forest ecosystem productivity during drought periods, Glob. Change Biol., 16, 1019–1034, https://doi.org/10.1111/j.1365-2486.2009.02017.x, 2010. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a
Knox, S. H., Sturtevant, C., Matthes, J. H., Koteen, L., Verfaillie, J., and Baldocchi, D.: Agricultural peatland restoration: Effects of land-use change on greenhouse gas (CO2 and CH4) fluxes in the Sacramento-San Joaquin Delta, Glob. Change Biol., 21, 750–765, https://doi.org/10.1111/gcb.12745, 2015. a, b, c
Koebsch, F., Sonnentag, O., Järveoja, J., Peltoniemi, M., Alekseychik, P., Aurela, M., Arslan, A. N., Dinsmore, K., Gianelle, D., Helfter, C., Jackowicz-Korczynski, M., Korrensalo, A., Leith, F., Linkosalmi, M., Lohila, A., Lund, M., Maddison, M., Mammarella, I., Mander, Ü., Minkkinen, K., Pickard, A., Pullens, J. W., Tuittila, E. S., Nilsson, M. B., and Peichl, M.: Refining the role of phenology in regulating gross ecosystem productivity across European peatlands, Glob. Change Biol., 26, 876–887, https://doi.org/10.1111/gcb.14905, 2020. a
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, 1–33, https://doi.org/10.1029/2003GB002199, 2005. a, b
Lafleur, P. M., Roulet, N. T., Bubier, J. L., Frolking, S., and Moore, T. R.: Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog, Global Biogeochem. Cy., 17, 1–14, https://doi.org/10.1029/2002gb001983, 2003. a
Laine, A. M., Korrensalo, A., and Tuittila, E. S.: Plant functional traits play the second fiddle to plant functional types in explaining peatland CO2 and CH4 gas exchange, Sci. Total Environ., 834, 155352, https://doi.org/10.1016/j.scitotenv.2022.155352, 2022. a
Li, T., Raivonen, M., Alekseychik, P., Aurela, M., Lohila, A., Zheng, X., Zhang, Q., Wang, G., Mammarella, I., Rinne, J., Yu, L., Xie, B., Vesala, T., and Zhang, W.: Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland, Sci. Total Environ., 572, 1111–1122, https://doi.org/10.1016/j.scitotenv.2016.08.020, 2016. a
Lippmann, T. J. R., in 't Zandt, M. H., Van der Putten, N. N. L., Busschers, F. S., Hijma, M. P., van der Velden, P., de Groot, T., van Aalderen, Z., Meisel, O. H., Slomp, C. P., Niemann, H., Jetten, M. S. M., Dolman, H. A. J., and Welte, C. U.: Microbial activity, methane production, and carbon storage in Early Holocene North Sea peats, Biogeosciences, 18, 5491–5511, https://doi.org/10.5194/bg-18-5491-2021, 2021. a
Lippmann, T. J. R. and van Huissteden, J.: Peatland-VU-NUCOM (PVN 1.0): A peatland GHG emissions model using dynamic plant functional types, Zenodo, https://doi.org/10.5281/zenodo.7701698, 2023. a
Littleton, E. W., Harper, A. B., Vaughan, N. E., Oliver, R. J., Duran-Rojas, M. C., and Lenton, T. M.: JULES-BE: representation of bioenergy crops and harvesting in the Joint UK Land Environment Simulator vn5.1, Geosci. Model Dev., 13, 1123–1136, https://doi.org/10.5194/gmd-13-1123-2020, 2020. a
Loisel, J., Gallego-Sala, A. V., Amesbury, M. J., Magnan, G., Anshari, G., Beilman, D. W., Benavides, J. C., Blewett, J., Camill, P., Charman, D. J., Chawchai, S., Hedgpeth, A., Kleinen, T., Korhola, A., Large, D., Mansilla, C. A., Müller, J., van Bellen, S., West, J. B., Yu, Z., Bubier, J. L., Garneau, M., Moore, T., Sannel, A. B., Page, S., Väliranta, M., Bechtold, M., Brovkin, V., Cole, L. E., Chanton, J. P., Christensen, T. R., Davies, M. A., De Vleeschouwer, F., Finkelstein, S. A., Frolking, S., Gałka, M., Gandois, L., Girkin, N., Harris, L. I., Heinemeyer, A., Hoyt, A. M., Jones, M. C., Joos, F., Juutinen, S., Kaiser, K., Lacourse, T., Lamentowicz, M., Larmola, T., Leifeld, J., Lohila, A., Milner, A. M., Minkkinen, K., Moss, P., Naafs, B. D., Nichols, J., O'Donnell, J., Payne, R., Philben, M., Piilo, S., Quillet, A., Ratnayake, A. S., Roland, T. P., Sjögersten, S., Sonnentag, O., Swindles, G. T., Swinnen, W., Talbot, J., Treat, C., Valach, A. C., and Wu, J.: Expert assessment of future vulnerability of the global peatland carbon sink, Nat. Clim. Change, 11, 70–77, https://doi.org/10.1038/s41558-020-00944-0, 2021. a
Malmer, N., Albinsson, C., Svensson, B. M., and Wallén, B.: Interferences between Sphagnum and vascular plants: Effects on plant community structure and peat formation, Oikos, 100, 469–482, https://doi.org/10.1034/j.1600-0706.2003.12170.x, 2003. a
Malmer, N., Johansson, T., Olsrud, M., and Christensen, T. R.: Vegetation, climatic changes and net carbon sequestration in a North-Scandinavian subarctic mire over 30 years, Glob. Change Biol., 11, 1895–1909, https://doi.org/10.1111/j.1365-2486.2005.01042.x, 2005. a
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. (Eds.): Climate Change 2021 – The Physical Science Basis, in: Climate Change 2021 – The Physical Science Basis Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 34, F0003–F0003, https://doi.org/10.1017/9781009157896, 2021. a
Mazzola, V., Perks, M. P., Smith, J., Yeluripati, J., and Xenakis, G.: Assessing soil carbon dioxide and methane fluxes from a Scots pine raised bog-edge-woodland, J. Environ. Manage., 302, 114061, https://doi.org/10.1016/j.jenvman.2021.114061, 2022. a
Melillo, J. M., Steudler, P. A., Aber, J. D., Newkirk, K., Lux, H., Bowles, F. P., Catricala, C., Magill, A., Ahrens, T., and Morrisseau, S.: Soil warming and carbon-cycle feedbacks to the climate system, Science, 298, 2173–2176, https://doi.org/10.1126/science.1074153, 2002. a
Melton, J. R., Wania, R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Beerling, D. J., Chen, G., Eliseev, A. V., Denisov, S. N., Hopcroft, P. O., Lettenmaier, D. P., Riley, W. J., Singarayer, J. S., Subin, Z. M., Tian, H., Zürcher, S., Brovkin, V., van Bodegom, P. M., Kleinen, T., Yu, Z. C., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, 2013. a
Metzger, C., Jansson, P.-E., Lohila, A., Aurela, M., Eickenscheidt, T., Belelli-Marchesini, L., Dinsmore, K. J., Drewer, J., van Huissteden, J., and Drösler, M.: CO2 fluxes and ecosystem dynamics at five European treeless peatlands – merging data and process oriented modeling, Biogeosciences, 12, 125–146, https://doi.org/10.5194/bg-12-125-2015, 2015. a
Mi, Y., van Huissteden, J., Parmentier, F. J. W., Gallagher, A., Budishchev, A., Berridge, C. T., and Dolman, A. J.: Improving a plot-scale methane emission model and its performance at a northeastern Siberian tundra site, Biogeosciences, 11, 3985–3999, https://doi.org/10.5194/bg-11-3985-2014, 2014. a, b, c, d, e
Moore, T. R., Bubier, J. L., and Bledzki, L.: Litter decomposition in temperate peatland ecosystems: The effect of substrate and site, Ecosystems, 10, 949–963, https://doi.org/10.1007/s10021-007-9064-5, 2007. a
Morin, T. H., Bohrer, G., Frasson, R. P., Naor-Azreli, L., Mesi, S., Stefanik, K. C., and Schäfer, K. V.: Environmental drivers of methane fluxes from an urban temperate wetland park, J. Geophys. Res.-Biogeo., 119, 2188–2208, https://doi.org/10.1002/2014JG002750, 2014a. a
Morin, T. H., Bohrer, G., Naor-Azrieli, L., Mesi, S., Kenny, W. T., Mitsch, W. J., and Schäfer, K. V. R.: The seasonal and diurnal dynamics of methane flux at a created urban wetland, Ecol. Eng., 72, 74–83, https://doi.org/10.1016/j.ecoleng.2014.02.002, 2014b. a
Morin, T. H., Bohrer, G., Stefanik, K. C., Rey-Sanchez, A. C., Matheny, A. M., and Mitsch, W. J.: Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park, Agr. Forest Meteorol., 237-238, 160–170, https://doi.org/10.1016/j.agrformet.2017.01.022, 2017. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Ostle, N. J., Smith, P., Fisher, R., Ian Woodward, F., Fisher, J. B., Smith, J. U., Galbraith, D., Levy, P., Meir, P., McNamara, N. P., and Bardgett, R. D.: Integrating plant-soil interactions into global carbon cycle models, J. Ecol., 97, 851–863, https://doi.org/10.1111/j.1365-2745.2009.01547.x, 2009. a
Pangala, S. R., Moore, S., Hornibrook, E. R., and Gauci, V.: Trees are major conduits for methane egress from tropical forested wetlands, New Phytol., 197, 524–531, https://doi.org/10.1111/nph.12031, 2013. a
Peltoniemi, K., Fritze, H., and Laiho, R.: Response of fungal and actinobacterial communities to water-level drawdown in boreal peatland sites, Soil Biol. Biochem., 41, 1902–1914, https://doi.org/10.1016/j.soilbio.2009.06.018, 2009. a
Puma, M. J., Koster, R. D., and Cook, B. I.: Phenological versus meteorological controls on land-atmosphere water and carbon fluxes, J. Geophys. Res.-Biogeo., 118, 14–29, https://doi.org/10.1029/2012JG002088, 2013. a
Saarnio, S., Wittenmayer, L., and Merbach, W.: Rhizospheric exudation of Eriophorum vaginatum L. – Potential link to methanogenesis, Plant Soil, 267, 343–355, https://doi.org/10.1007/s11104-005-0140-3, 2004. a
Saleska, S. R., Shaw, M. R., Fischer, M. L., Dunne, J. A., Still, C. J., Holman, M. L., and Harte, J.: Plant community composition mediates both large transient decline and predicted long-term recovery of soil carbon under climate warming, Global Biogeochem. Cy., 16, 3-1–3-18, https://doi.org/10.1029/2001gb001573, 2002. a
Schipper, L. A. and Reddy, K. R.: Determination of Methane Oxidation in the Rhizosphere of Sagittaria lancifolia Using Methyl Fluoride, Soil Sci. Soc. Am. J., 60, 611–616, https://doi.org/10.2136/sssaj1996.03615995006000020039x, 1996. a, b
Schrier-Uijl, A. P., Kroon, P. S., Hendriks, D. M. D., Hensen, A., Van Huissteden, J., Berendse, F., and Veenendaal, E. M.: Agricultural peatlands: towards a greenhouse gas sink – a synthesis of a Dutch landscape study, Biogeosciences, 11, 4559–4576, https://doi.org/10.5194/bg-11-4559-2014, 2014. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003. a
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, https://doi.org/10.1046/j.1466-822x.2001.t01-1-00256.x, 2001. a, b
Smithwick, E. A., Lucash, M. S., McCormack, M. L., and Sivandran, G.: Improving the representation of roots in terrestrial models, Ecol. Model., 291, 193–204, https://doi.org/10.1016/j.ecolmodel.2014.07.023, 2014. a, b
Speckman, H. N., Frank, J. M., Bradford, J. B., Miles, B. L., Massman, W. J., Parton, W. J., and Ryan, M. G.: Forest ecosystem respiration estimated from eddy covariance and chamber measurements under high turbulence and substantial tree mortality from bark beetles, Glob. Change Biol., 21, 708–721, https://doi.org/10.1111/gcb.12731, 2015. a
Strack, M., Waller, M. F., and Waddington, J. M.: Sedge succession and peatland methane dynamics: A potential feedback to climate change, Ecosystems, 9, 278–287, https://doi.org/10.1007/s10021-005-0070-1, 2006. a
Ström, L., Mastepanov, M., and Christensen, T. R.: Species-specific Effects of Vascular Plants on Carbon Turnover and Methane Emissions from Wetlands, Biogeochemistry, 75, 65–82, https://doi.org/10.1007/s10533-004-6124-1, 2005. a, b
Ström, L., Falk, J. M., Skov, K., Jackowicz-Korczynski, M., Mastepanov, M., Christensen, T. R., Lund, M., and Schmidt, N. M.: Controls of spatial and temporal variability in CH4 flux in a high arctic fen over three years, Biogeochemistry, 125, 21–35, https://doi.org/10.1007/s10533-015-0109-0, 2015. a
Tiemeyer, B., Albiac Borraz, E., Augustin, J., Bechtold, M., Beetz, S., Beyer, C., Drosler, M., Ebli, M., Eickenscheidt, T., Fiedler, S., Forster, C., Freibauer, A., Giebels, M., Glatzel, S., Heinichen, J., Hoffmann, M., Hoper, 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. a
Toet, S., Cornelissen, J. H., Aerts, R., Van Logtestijn, R. S., De Beus, M., and Stoevelaar, R.: Moss responses to elevated CO2 and variation in hydrology in a temperate lowland peatland, Plant Ecol., 182, 27–40, https://doi.org/10.1007/s11258-005-9029-8, 2006. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a
Van den Hoof, C., Hanert, E., and Vidale, P. L.: Simulating dynamic crop growth with an adapted land surface model – JULES-SUCROS: Model development and validation, Agr. Forest Meteorol., 151, 137–153, https://doi.org/10.1016/j.agrformet.2010.09.011, 2011. a
van Geel, B., Bos, J. M., and Pals, J. P.: Archaeological and palaeoecological aspects of a medieval house terp in a reclaimed raiseg bog area in North Holland, Ber. Rijksd. Oudheidkd. Bodemonderz, 33, 419–444, 1983. a
van Huissteden, J., Petrescu, A. M. R., Hendriks, D. M. D., and Rebel, K. T.: Sensitivity analysis of a wetland methane emission model based on temperate and arctic wetland sites, Biogeosciences, 6, 3035–3051, https://doi.org/10.5194/bg-6-3035-2009, 2009. a, b
Waddington, J. M. and Roulet, N. T.: Carbon balance of a Boreal patterned peatland, Glob. Change Biol., 6, 87–97, https://doi.org/10.1046/j.1365-2486.2000.00283.x, 2000. a
Waldo, N. B., Hunt, B. K., Fadely, E. C., Moran, J. J., and Neumann, R. B.: Plant root exudates increase methane emissions through direct and indirect pathways, Biogeochemistry, 145, 213–234, https://doi.org/10.1007/s10533-019-00600-6, 2019. a
Walter, B. P. and Heimann, M.: A process-based, climate-sensitive model to derive methane emissions from natural wetlands: Application to five wetland sites, sensitivity to model parameters, and climate, Global Biogeochem. Cy., 14, 745–765, https://doi.org/10.1029/1999GB001204, 2000. a, b, c, d, e, f
Wania, R., Ross, I., and Prentice, I. C.: Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1, Geosci. Model Dev., 3, 565–584, https://doi.org/10.5194/gmd-3-565-2010, 2010. a, b
Wania, R., Melton, J. R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Chen, G., Eliseev, A. V., Hopcroft, P. O., Riley, W. J., Subin, Z. M., Tian, H., van Bodegom, P. M., Kleinen, T., Yu, Z. C., Singarayer, J. S., Zürcher, S., Lettenmaier, D. P., Beerling, D. J., Denisov, S. N., Prigent, C., Papa, F., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP), Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, 2013. a
WGI: Climate Change 2021 The Physical Science Basis WGI, vol. 34, John Wiley and Sons Ltd., ISBN 9781009157896, 2021. a
Whipps, J. M.: Carbon economy, The rhizosphere, 59–97, 1990. a
Wu, Y. and Blodau, C.: PEATBOG: a biogeochemical model for analyzing coupled carbon and nitrogen dynamics in northern peatlands, Geosci. Model Dev., 6, 1173–1207, https://doi.org/10.5194/gmd-6-1173-2013, 2013. a, b
WWu, Y., Verseghy, D. L., and Melton, J. R.: Integrating peatlands into the coupled Canadian Land Surface Scheme (CLASS) v3.6 and the Canadian Terrestrial Ecosystem Model (CTEM) v2.0, Geosci. Model Dev., 9, 2639–2663, https://doi.org/10.5194/gmd-9-2639-2016, 2016. a
Wullschleger, S. D., Epstein, H. E., Box, E. O., Euskirchen, E. S., Goswami, S., Iversen, C. M., Kattge, J., Norby, R. J., Van Bodegom, P. M., and Xu, X.: Plant functional types in Earth system models: Past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems, Ann. Bot., 114, 1–16, https://doi.org/10.1093/aob/mcu077, 2014. a
Yrjälä, K., Tuomivirta, T., Juottonen, H., Putkinen, A., Lappi, K., Tuittila, E. S., Penttilä, T., Minkkinen, K., Laine, J., Peltoniemi, K., and Fritze, H.: CH4 production and oxidation processes in a boreal fen ecosystem after long-term water table drawdown, Glob. Change Biol., 17, 1311–1320, https://doi.org/10.1111/j.1365-2486.2010.02290.x, 2011. a
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W., and Hunt, S. J.: Global peatland dynamics since the Last Glacial Maximum, Geophys. Res. Lett., 37, 1–5, https://doi.org/10.1029/2010GL043584, 2010. a
Zeng, X.: Global Vegetation Root Distribution for Land Modeling, J. Hydrometeorol., 2, 525–530, https://doi.org/10.1175/1525-7541(2001)002<0525:gvrdfl>2.0.co;2, 2001. a
Zhao, J., Lange, H., and Meissner, H.: Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods, Agr. Forest Meteorol., 285–286, 107912, https://doi.org/10.1016/j.agrformet.2020.107912, 2020. a
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked...
