Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4783-2022
© Author(s) 2022. 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-15-4783-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 Jun 2022
Development and technical paper |
| 21 Jun 2022
| 21 Jun 2022
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
Félicien Meunier
CORRESPONDING AUTHOR
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Sruthi M. Krishna Moorthy
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Marc Peaucelle
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
now at: INRAE, Université de Bordeaux, UMR 1391 ISPA, 33140
Villenave-d'Ornon, France
Kim Calders
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Louise Terryn
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Wim Verbruggen
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark
Chang Liu
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
Ninni Saarinen
Department of Forest Sciences, University of Helsinki, Helsinki, Finland
School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
Niall Origo
NPL – Climate and Earth Observation (CEO) group, National Physical
Laboratory, Teddington, UK
Joanne Nightingale
NPL – Climate and Earth Observation (CEO) group, National Physical
Laboratory, Teddington, UK
Mathias Disney
UCL Department of Geography, Gower Street, London, WC1E 6BT, UK
NERC National Centre for Earth Observation (NCEO), UCL Geography,
Gower Street, London, WC1E 6BT, UK
Yadvinder Malhi
Environmental Change Institute, School of Geography and the
Environment, University of Oxford, Oxford, UK
Hans Verbeeck
CAVElab – Computational and Applied Vegetation Ecology, Department of
Environment, Ghent University, Ghent, Belgium
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Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Flossie Brown, Gerd A. Folberth, Stephen Sitch, Susanne Bauer, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Makoto Deushi, Inês Dos Santos Vieira, Corinne Galy-Lacaux, James Haywood, James Keeble, Lina M. Mercado, Fiona M. O'Connor, Naga Oshima, Kostas Tsigaridis, and Hans Verbeeck
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Marina Corrêa Scalon, Imma Oliveras Menor, Renata Freitag, Karine S. Peixoto, Sami W. Rifai, Beatriz Schwantes Marimon, Ben Hur Marimon Junior, and Yadvinder Malhi
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Jan Vanderborght, Valentin Couvreur, Felicien Meunier, Andrea Schnepf, Harry Vereecken, Martin Bouda, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 25, 4835–4860, https://doi.org/10.5194/hess-25-4835-2021, https://doi.org/10.5194/hess-25-4835-2021, 2021
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Paula Alejandra Lamprea Pineda, Marijn Bauters, Hans Verbeeck, Selene Baez, Matti Barthel, Samuel Bodé, and Pascal Boeckx
Biogeosciences, 18, 413–421, https://doi.org/10.5194/bg-18-413-2021, https://doi.org/10.5194/bg-18-413-2021, 2021
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Tropical forest soils are an important source and sink of greenhouse gases (GHGs) with tropical montane forests having been poorly studied. In this pilot study, we explored soil fluxes of CO2, CH4, and N2O in an Ecuadorian neotropical montane forest, where a net consumption of N2O at higher altitudes was observed. Our results highlight the importance of short-term variations in N2O and provide arguments and insights for future, more detailed studies on GHG fluxes from montane forest soils.
Wim Verbruggen, Guy Schurgers, Stéphanie Horion, Jonas Ardö, Paulo N. Bernardino, Bernard Cappelaere, Jérôme Demarty, Rasmus Fensholt, Laurent Kergoat, Thomas Sibret, Torbern Tagesson, and Hans Verbeeck
Biogeosciences, 18, 77–93, https://doi.org/10.5194/bg-18-77-2021, https://doi.org/10.5194/bg-18-77-2021, 2021
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A large part of Earth's land surface is covered by dryland ecosystems, which are subject to climate extremes that are projected to increase under future climate scenarios. By using a mathematical vegetation model, we studied the impact of single years of extreme rainfall on the vegetation in the Sahel. We found a contrasting response of grasses and trees to these extremes, strongly dependent on the way precipitation is spread over the rainy season, as well as a long-term impact on CO2 uptake.
Hannes P. T. De Deurwaerder, Marco D. Visser, Matteo Detto, Pascal Boeckx, Félicien Meunier, Kathrin Kuehnhammer, Ruth-Kristina Magh, John D. Marshall, Lixin Wang, Liangju Zhao, and Hans Verbeeck
Biogeosciences, 17, 4853–4870, https://doi.org/10.5194/bg-17-4853-2020, https://doi.org/10.5194/bg-17-4853-2020, 2020
Short summary
Short summary
The depths at which plants take up water is challenging to observe directly. To do so, scientists have relied on measuring the isotopic composition of xylem water as this provides information on the water’s source. Our work shows that this isotopic composition changes throughout the day, which complicates the interpretation of the water’s source and has been currently overlooked. We build a model to help understand the origin of these composition changes and their consequences for science.
Valentin Couvreur, Youri Rothfuss, Félicien Meunier, Thierry Bariac, Philippe Biron, Jean-Louis Durand, Patricia Richard, and Mathieu Javaux
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Short summary
Short summary
Isotopic labeling of soil water is a broadly used tool for tracing the origin of water extracted by plants and computing root water uptake (RWU) profiles with multisource mixing models. In this study, we show how a method such as this may misconstrue time series of xylem water isotopic composition as the temporal dynamics of RWU by simulating data collected during a tall fescue rhizotron experiment with an isotope-enabled physical soil–root model accounting for variability in root traits.
Sophie Flack-Prain, Patrick Meir, Yadvinder Malhi, Thomas Luke Smallman, and Mathew Williams
Biogeosciences, 16, 4463–4484, https://doi.org/10.5194/bg-16-4463-2019, https://doi.org/10.5194/bg-16-4463-2019, 2019
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Across the Amazon rainforest, trees take in carbon through photosynthesis. However, photosynthesis across the basin is threatened by predicted shifts in rainfall patterns. To unpick how changes in rainfall affect photosynthesis, we use a model which combines climate data with our knowledge of photosynthesis and other plant processes. We find that stomatal constraints are less important, and instead shifts in leaf surface area and leaf properties drive changes in photosynthesis with rainfall.
James Brennan, Jose L. Gómez-Dans, Mathias Disney, and Philip Lewis
Biogeosciences, 16, 3147–3164, https://doi.org/10.5194/bg-16-3147-2019, https://doi.org/10.5194/bg-16-3147-2019, 2019
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We estimate the uncertainties associated with three global satellite-derived burned area estimates. The method provides unique uncertainties for the three estimates at the global scale for 2001–2013. We find uncertainties of 4 %–5.5 % in global burned area and uncertainties of 8 %–10 % in the frequently burning regions of Africa and Australia.
Anja Rammig, Jens Heinke, Florian Hofhansl, Hans Verbeeck, Timothy R. Baker, Bradley Christoffersen, Philippe Ciais, Hannes De Deurwaerder, Katrin Fleischer, David Galbraith, Matthieu Guimberteau, Andreas Huth, Michelle Johnson, Bart Krujit, Fanny Langerwisch, Patrick Meir, Phillip Papastefanou, Gilvan Sampaio, Kirsten Thonicke, Celso von Randow, Christian Zang, and Edna Rödig
Geosci. Model Dev., 11, 5203–5215, https://doi.org/10.5194/gmd-11-5203-2018, https://doi.org/10.5194/gmd-11-5203-2018, 2018
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We propose a generic approach for a pixel-to-point comparison applicable for evaluation of models and remote-sensing products. We provide statistical measures accounting for the uncertainty in ecosystem variables. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest.
Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
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This paper presents and analyzes a global database of soil infiltration data, the SWIG database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists or they were digitized from published articles. We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models.
Sathyanarayan Rao, Félicien Meunier, Solomon Ehosioke, Nolwenn Lesparre, Andreas Kemna, Frédéric Nguyen, Sarah Garré, and Mathieu Javaux
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-280, https://doi.org/10.5194/bg-2018-280, 2018
Revised manuscript not accepted
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This paper illustrates the impact of electrical property of maize root segments on the Electrical Resistivity Tomography (ERT) inversion results with the help of numerical model. The model includes explicit root representation in the finite element mesh with root growth, transpiration and root water uptake. We show that, ignoring root segments could lead to wrong estimation of water content using ERT method.
Tommaso Jucker, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, Craig Brelsford, David F. R. P. Burslem, Nicolas J. Deere, Robert M. Ewers, Jakub Kvasnica, Simon L. Lewis, Yadvinder Malhi, Sol Milne, Reuben Nilus, Marion Pfeifer, Oliver L. Phillips, Lan Qie, Nathan Renneboog, Glen Reynolds, Terhi Riutta, Matthew J. Struebig, Martin Svátek, Edgar C. Turner, and David A. Coomes
Biogeosciences, 15, 3811–3830, https://doi.org/10.5194/bg-15-3811-2018, https://doi.org/10.5194/bg-15-3811-2018, 2018
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Efforts to protect tropical forests hinge on recognizing the ecosystem services they provide, including their ability to store carbon. Airborne laser scanning (ALS) captures information on the 3-D structure of forests, allowing carbon stocks to be mapped. By combining ALS with data from 173 field plots on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks from the air. Our model underpins ongoing efforts to restore Borneo's unique tropical forests.
Félicien Meunier, Valentin Couvreur, Xavier Draye, Mohsen Zarebanadkouki, Jan Vanderborght, and Mathieu Javaux
Hydrol. Earth Syst. Sci., 21, 6519–6540, https://doi.org/10.5194/hess-21-6519-2017, https://doi.org/10.5194/hess-21-6519-2017, 2017
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To maintain its yield, a plant needs to transpire water that it acquires from the soil. A deep understanding of the mechanisms that lead to water uptake location and intensity is required to correctly simulate the water transfer in the soil to the atmosphere. This work presents novel and general solutions of the water flow equation in roots with varying hydraulic properties that deeply affect the uptake pattern and the transpiration rate and can be used in ecohydrological models.
Marijn Bauters, Hans Verbeeck, Miro Demol, Stijn Bruneel, Cys Taveirne, Dries Van der Heyden, Landry Cizungu, and Pascal Boeckx
Biogeosciences, 14, 5313–5321, https://doi.org/10.5194/bg-14-5313-2017, https://doi.org/10.5194/bg-14-5313-2017, 2017
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We assessed community-weighted functional canopy traits and indicative δ15N shifts along two new altitudinal transects in the tropical forest biome of both South America and Africa. We found that the functional forest composition and δ15N response along both transects was parallel, with a species shift towards more nitrogen-conservative species at higher elevations.
N. Saarinen, M. Vastaranta, R. Näsi, T. Rosnell, T. Hakala, E. Honkavaara, M. A. Wulder, V. Luoma, A. M. G. Tommaselli, N. N. Imai, E. A. W. Ribeiro, R. B. Guimarães, M. Holopainen, and J. Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W3, 171–175, https://doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017, 2017
Matthieu Guimberteau, Philippe Ciais, Agnès Ducharne, Juan Pablo Boisier, Ana Paula Dutra Aguiar, Hester Biemans, Hannes De Deurwaerder, David Galbraith, Bart Kruijt, Fanny Langerwisch, German Poveda, Anja Rammig, Daniel Andres Rodriguez, Graciela Tejada, Kirsten Thonicke, Celso Von Randow, Rita C. S. Von Randow, Ke Zhang, and Hans Verbeeck
Hydrol. Earth Syst. Sci., 21, 1455–1475, https://doi.org/10.5194/hess-21-1455-2017, https://doi.org/10.5194/hess-21-1455-2017, 2017
Fabien H. Wagner, Bruno Hérault, Damien Bonal, Clément Stahl, Liana O. Anderson, Timothy R. Baker, Gabriel Sebastian Becker, Hans Beeckman, Danilo Boanerges Souza, Paulo Cesar Botosso, David M. J. S. Bowman, Achim Bräuning, Benjamin Brede, Foster Irving Brown, Jesus Julio Camarero, Plínio Barbosa Camargo, Fernanda C. G. Cardoso, Fabrício Alvim Carvalho, Wendeson Castro, Rubens Koloski Chagas, Jérome Chave, Emmanuel N. Chidumayo, Deborah A. Clark, Flavia Regina Capellotto Costa, Camille Couralet, Paulo Henrique da Silva Mauricio, Helmut Dalitz, Vinicius Resende de Castro, Jaçanan Eloisa de Freitas Milani, Edilson Consuelo de Oliveira, Luciano de Souza Arruda, Jean-Louis Devineau, David M. Drew, Oliver Dünisch, Giselda Durigan, Elisha Elifuraha, Marcio Fedele, Ligia Ferreira Fedele, Afonso Figueiredo Filho, César Augusto Guimarães Finger, Augusto César Franco, João Lima Freitas Júnior, Franklin Galvão, Aster Gebrekirstos, Robert Gliniars, Paulo Maurício Lima de Alencastro Graça, Anthony D. Griffiths, James Grogan, Kaiyu Guan, Jürgen Homeier, Maria Raquel Kanieski, Lip Khoon Kho, Jennifer Koenig, Sintia Valerio Kohler, Julia Krepkowski, José Pires Lemos-Filho, Diana Lieberman, Milton Eugene Lieberman, Claudio Sergio Lisi, Tomaz Longhi Santos, José Luis López Ayala, Eduardo Eijji Maeda, Yadvinder Malhi, Vivian R. B. Maria, Marcia C. M. Marques, Renato Marques, Hector Maza Chamba, Lawrence Mbwambo, Karina Liana Lisboa Melgaço, Hooz Angela Mendivelso, Brett P. Murphy, Joseph J. O'Brien, Steven F. Oberbauer, Naoki Okada, Raphaël Pélissier, Lynda D. Prior, Fidel Alejandro Roig, Michael Ross, Davi Rodrigo Rossatto, Vivien Rossi, Lucy Rowland, Ervan Rutishauser, Hellen Santana, Mark Schulze, Diogo Selhorst, Williamar Rodrigues Silva, Marcos Silveira, Susanne Spannl, Michael D. Swaine, José Julio Toledo, Marcos Miranda Toledo, Marisol Toledo, Takeshi Toma, Mario Tomazello Filho, Juan Ignacio Valdez Hernández, Jan Verbesselt, Simone Aparecida Vieira, Grégoire Vincent, Carolina Volkmer de Castilho, Franziska Volland, Martin Worbes, Magda Lea Bolzan Zanon, and Luiz E. O. C. Aragão
Biogeosciences, 13, 2537–2562, https://doi.org/10.5194/bg-13-2537-2016, https://doi.org/10.5194/bg-13-2537-2016, 2016
K. E. Clark, A. J. West, R. G. Hilton, G. P. Asner, C. A. Quesada, M. R. Silman, S. S. Saatchi, W. Farfan-Rios, R. E. Martin, A. B. Horwath, K. Halladay, M. New, and Y. Malhi
Earth Surf. Dynam., 4, 47–70, https://doi.org/10.5194/esurf-4-47-2016, https://doi.org/10.5194/esurf-4-47-2016, 2016
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The key findings of this paper are that landslides in the eastern Andes of Peru in the Kosñipata Valley rapidly turn over the landscape in ~1320 years, with a rate of 0.076% yr-1. Additionally, landslides were concentrated at lower elevations, due to an intense storm in 2010 accounting for ~1/4 of the total landslide area over the 25-year remote sensing study. Valley-wide carbon stocks were determined, and we estimate that 26 tC km-2 yr-1 of soil and biomass are stripped by landslides.
E. Joetzjer, C. Delire, H. Douville, P. Ciais, B. Decharme, D. Carrer, H. Verbeeck, M. De Weirdt, and D. Bonal
Geosci. Model Dev., 8, 1709–1727, https://doi.org/10.5194/gmd-8-1709-2015, https://doi.org/10.5194/gmd-8-1709-2015, 2015
L. Rowland, A. Harper, B. O. Christoffersen, D. R. Galbraith, H. M. A. Imbuzeiro, T. L. Powell, C. Doughty, N. M. Levine, Y. Malhi, S. R. Saleska, P. R. Moorcroft, P. Meir, and M. Williams
Geosci. Model Dev., 8, 1097–1110, https://doi.org/10.5194/gmd-8-1097-2015, https://doi.org/10.5194/gmd-8-1097-2015, 2015
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This study evaluates the capability of five vegetation models to simulate the response of forest productivity to changes in temperature and drought, using data collected from an Amazonian forest. This study concludes that model consistencies in the responses of net canopy carbon production to temperature and precipitation change were the result of inconsistently modelled leaf-scale process responses and substantial variation in modelled leaf area responses.
K. E. Clark, M. A. Torres, A. J. West, R. G. Hilton, M. New, A. B. Horwath, J. B. Fisher, J. M. Rapp, A. Robles Caceres, and Y. Malhi
Hydrol. Earth Syst. Sci., 18, 5377–5397, https://doi.org/10.5194/hess-18-5377-2014, https://doi.org/10.5194/hess-18-5377-2014, 2014
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This paper presents measurements of the balance of water inputs and outputs over 1 year for a river basin in the Andes of Peru. Our results show that the annual water budget is balanced within a few percent uncertainty; that is to say, the amount of water entering the basin was the same as the amount leaving, providing important information for understanding the water cycle. We also show that seasonal storage of water is important in sustaining the flow of water during the dry season.
M. Réjou-Méchain, H. C. Muller-Landau, M. Detto, S. C. Thomas, T. Le Toan, S. S. Saatchi, J. S. Barreto-Silva, N. A. Bourg, S. Bunyavejchewin, N. Butt, W. Y. Brockelman, M. Cao, D. Cárdenas, J.-M. Chiang, G. B. Chuyong, K. Clay, R. Condit, H. S. Dattaraja, S. J. Davies, A. Duque, S. Esufali, C. Ewango, R. H. S. Fernando, C. D. Fletcher, I. A. U. N. Gunatilleke, Z. Hao, K. E. Harms, T. B. Hart, B. Hérault, R. W. Howe, S. P. Hubbell, D. J. Johnson, D. Kenfack, A. J. Larson, L. Lin, Y. Lin, J. A. Lutz, J.-R. Makana, Y. Malhi, T. R. Marthews, R. W. McEwan, S. M. McMahon, W. J. McShea, R. Muscarella, A. Nathalang, N. S. M. Noor, C. J. Nytch, A. A. Oliveira, R. P. Phillips, N. Pongpattananurak, R. Punchi-Manage, R. Salim, J. Schurman, R. Sukumar, H. S. Suresh, U. Suwanvecho, D. W. Thomas, J. Thompson, M. Uríarte, R. Valencia, A. Vicentini, A. T. Wolf, S. Yap, Z. Yuan, C. E. Zartman, J. K. Zimmerman, and J. Chave
Biogeosciences, 11, 6827–6840, https://doi.org/10.5194/bg-11-6827-2014, https://doi.org/10.5194/bg-11-6827-2014, 2014
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Forest carbon mapping may greatly reduce uncertainties in the global carbon budget. Accuracy of such maps depends however on the quality of field measurements. Using 30 large forest plots, we found large local spatial variability in biomass. When field calibration plots are smaller than the remote sensing pixels, this high local spatial variability results in an underestimation of the variance in biomass.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
N. M. Fyllas, E. Gloor, L. M. Mercado, S. Sitch, C. A. Quesada, T. F. Domingues, D. R. Galbraith, A. Torre-Lezama, E. Vilanova, H. Ramírez-Angulo, N. Higuchi, D. A. Neill, M. Silveira, L. Ferreira, G. A. Aymard C., Y. Malhi, O. L. Phillips, and J. Lloyd
Geosci. Model Dev., 7, 1251–1269, https://doi.org/10.5194/gmd-7-1251-2014, https://doi.org/10.5194/gmd-7-1251-2014, 2014
M. Guimberteau, A. Ducharne, P. Ciais, J. P. Boisier, S. Peng, M. De Weirdt, and H. Verbeeck
Geosci. Model Dev., 7, 1115–1136, https://doi.org/10.5194/gmd-7-1115-2014, https://doi.org/10.5194/gmd-7-1115-2014, 2014
T. R. Marthews, C. A. Quesada, D. R. Galbraith, Y. Malhi, C. E. Mullins, M. G. Hodnett, and I. Dharssi
Geosci. Model Dev., 7, 711–723, https://doi.org/10.5194/gmd-7-711-2014, https://doi.org/10.5194/gmd-7-711-2014, 2014
G. P. Asner, C. B. Anderson, R. E. Martin, D. E. Knapp, R. Tupayachi, F. Sinca, and Y. Malhi
Biogeosciences, 11, 843–856, https://doi.org/10.5194/bg-11-843-2014, https://doi.org/10.5194/bg-11-843-2014, 2014
R. Valentini, A. Arneth, A. Bombelli, S. Castaldi, R. Cazzolla Gatti, F. Chevallier, P. Ciais, E. Grieco, J. Hartmann, M. Henry, R. A. Houghton, M. Jung, W. L. Kutsch, Y. Malhi, E. Mayorga, L. Merbold, G. Murray-Tortarolo, D. Papale, P. Peylin, B. Poulter, P. A. Raymond, M. Santini, S. Sitch, G. Vaglio Laurin, G. R. van der Werf, C. A. Williams, and R. J. Scholes
Biogeosciences, 11, 381–407, https://doi.org/10.5194/bg-11-381-2014, https://doi.org/10.5194/bg-11-381-2014, 2014
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
A. D. A. Castanho, M. T. Coe, M. H. Costa, Y. Malhi, D. Galbraith, and C. A. Quesada
Biogeosciences, 10, 2255–2272, https://doi.org/10.5194/bg-10-2255-2013, https://doi.org/10.5194/bg-10-2255-2013, 2013
Related subject area
Biogeosciences
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
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 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
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
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)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
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.
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.
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.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
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Parameterisation 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, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
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.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Cited articles
Åkerblom, M., Raumonen, P., Casella, E., Disney, M. I., Danson, F. M.,
Gaulton, R., Schofield, L. A., and Kaasalainen, M.: Non-intersecting leaf
insertion algorithm for tree structure models, Interface Focus, 8, 20170045,
https://doi.org/10.1098/rsfs.2017.0045, 2018.
Albani, M., Medvigy, D., Hurtt, G. C., and Moorcroft, P. R.: The
contributions of land-use change, CO2 fertilization, and climate variability
to the Eastern US carbon sink, Glob. Change Biol., 12, 2370–2390,
https://doi.org/10.1111/j.1365-2486.2006.01254.x, 2006.
Antonarakis, A., Saatchi, S., Chazdon, R., and Moorcroft, P.: Using Lidar
and Radar measurements to constrain predictions of forest ecosystem
structure and function., Ecol. Appl. Publ. Ecol. Soc. Am., 21, 1120–1137,
https://doi.org/10.1890/10-0274.1, 2011.
Antonarakis, A. S., Munger, J. W., and Moorcroft, P. R.: Imaging
spectroscopy- and lidar-derived estimates of canopy composition and
structure to improve predictions of forest carbon fluxes and ecosystem
dynamics, Geophys. Res. Lett., 41, 2535–2542,
https://doi.org/10.1002/2013GL058373, 2014.
Asner, G. P., Martin, R. E., Anderson, C. B., Kryston, K., Vaughn, N.,
Knapp, D. E., Bentley, L. P., Shenkin, A., Salinas, N., Sinca, F.,
Tupayachi, R., Huaypar, K. Q., Pillco, M. M., Álvarez, F. D. C.,
Díaz, S., Enquist, B. J., and Malhi, Y.: Scale dependence of canopy
trait distributions along a tropical forest elevation gradient, New Phytol.,
214, 973–988, https://doi.org/10.1111/nph.14068, 2017.
Babst, F., Friend, A. D., Karamihalaki, M., Wei, J., von Arx, G., Papale,
D., and Peters, R. L.: Modeling Ambitions Outpace Observations of Forest
Carbon Allocation, Trends Plant Sci., 26, 210–219,
https://doi.org/10.1016/j.tplants.2020.10.002, 2020.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein,
A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel,
W., U, K. T. P., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S.,
Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the
Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water
Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434,
https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Bonan, G.: Climate Change and Terrestrial Ecosystem Modeling, 1st edn.,
Cambridge University Press, https://doi.org/10.1017/9781107339217, 2019.
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the
Climate Benefits of Forests, Science, 320, 1444–1449, 2008.
Bürkner, P.-C.: brms: An R Package for Bayesian Multilevel Models Using
stan, J. Stat. Softw., 80, 1–27, https://doi.org/10.18637/jss.v080.i01, 2017.
Burt, A., Disney, M., and Calders, K.: Extracting individual trees from
lidar point clouds using treeseg, Methods Ecol. Evol., 10, 438–445,
https://doi.org/10.1111/2041-210X.13121, 2019.
Butt, N., Campbell, G., Malhi, Y., Morecroft, M., Fenn, K., and Thomas, M.:
Initial Results from Establishment of a Long-term Broadleaf Monitoring Plot
at Wytham Woods, University of Oxford Report, Oxford, UK, 2009.
Calders, K., Armston, J., Newnham, G., Herold, M., and Goodwin, N.:
Implications of sensor configuration and topography on vertical plant
profiles derived from terrestrial LiDAR, Agric. Forest Meteorol., 194,
104–117, https://doi.org/10.1016/j.agrformet.2014.03.022, 2014.
Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M.,
Culvenor, D., Avitabile, V., Disney, M., Armston, J., and Kaasalainen, M.:
Nondestructive estimates of above-ground biomass using terrestrial laser
scanning, Methods Ecol. Evol., 6, 198–208,
https://doi.org/10.1111/2041-210X.12301, 2015.
Calders, K., Disney, M. I., Armston, J., Burt, A., Brede, B., Origo, N.,
Muir, J., and Nightingale, J.: Evaluation of the Range Accuracy and the
Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments
for Data Interoperability, IEEE T. Geosci. Remote, 55, 2716–2724,
https://doi.org/10.1109/TGRS.2017.2652721, 2017.
Calders, K., Origo, N., Burt, A., Disney, M., Nightingale, J., Raumonen, P.,
Åkerblom, M., Malhi, Y., and Lewis, P.: Realistic Forest Stand
Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling,
Remote Sens., 10, 933, https://doi.org/10.3390/rs10060933, 2018a.
Calders, K., Origo, N., Disney, M., Nightingale, J., Woodgate, W., Armston,
J., and Lewis, P.: Variability and bias in active and passive ground-based
measurements of effective plant, wood and leaf area index, Agric. Forest
Meteorol., 252, 231–240, https://doi.org/10.1016/j.agrformet.2018.01.029,
2018b.
Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley,
L. P., Chave, J., Danson, F. M., Demol, M., Disney, M., Gaulton, R., Krishna
Moorthy, S. M., Levick, S. R., Saarinen, N., Schaaf, C., Stovall, A.,
Terryn, L., Wilkes, P., and Verbeeck, H.: Terrestrial laser scanning in
forest ecology: Expanding the horizon, Remote Sens. Environ., 251, 112102,
https://doi.org/10.1016/j.rse.2020.112102, 2020.
Cano, I. M., Shevliakova, E., Malyshev, S., Wright, S. J., Detto, M.,
Pacala, S. W., and Muller-Landau, H. C.: Allometric constraints and
competition enable the simulation of size structure and carbon fluxes in a
dynamic vegetation model of tropical forests (LM3PPA-TV), Glob. Change
Biol., 26, 4478–4494, https://doi.org/10.1111/gcb.15188, 2020.
Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan,
M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R.
C., Henry, M., Martínez-Yrízar, A., Mugasha, W. A., Muller-Landau, H. C.,
Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M.,
Ortiz-Malavassi, E., Pélissier, R., Ploton, P., Ryan, C. M., Saldarriaga,
J. G., and Vieilledent, G.: Improved allometric models to estimate the
aboveground biomass of tropical trees, Glob. Change Biol., 20, 3177–3190,
https://doi.org/10.1111/gcb.12629, 2014.
Collalti, A., Marconi, S., Ibrom, A., Trotta, C., Anav, A., D'Andrea, E., Matteucci, G., Montagnani, L., Gielen, B., Mammarella, I., Grünwald, T., Knohl, A., Berninger, F., Zhao, Y., Valentini, R., and Santini, M.: Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites, Geosci. Model Dev., 9, 479–504, https://doi.org/10.5194/gmd-9-479-2016, 2016.
Collalti, A., Thornton, P. E., Cescatti, A., Rita, A., Borghetti, M.,
Nolè, A., Trotta, C., Ciais, P., and Matteucci, G.: The sensitivity of
the forest carbon budget shifts across processes along with stand
development and climate change, Ecol. Appl., 29, e01837,
https://doi.org/10.1002/eap.1837, 2019.
Dietze, M. C. and Moorcroft, P. R.: Tree mortality in the eastern and
central United States: patterns and drivers, Glob. Change Biol., 17,
3312–3326, https://doi.org/10.1111/j.1365-2486.2011.02477.x, 2011.
Dietze, M. C., Wolosin, M. S., and Clark, J. S.: Capturing diversity and
interspecific variability in allometries: A hierarchical approach, For.
Ecol. Manag., 256, 1939–1948, https://doi.org/10.1016/j.foreco.2008.07.034,
2008.
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly,
R., Kooper, R., LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A
quantitative assessment of a terrestrial biosphere model's data needs across
North American biomes, J. Geophys. Res.-Biogeo., 119, 286–300,
https://doi.org/10.1002/2013JG002392, 2014.
Dokoohaki, H., Kivi, M. S., Martinez-Feria, R., Miguez, F. E., and
Hoogenboom, G.: A comprehensive uncertainty quantification of large-scale
process-based crop modeling frameworks, Environ. Res. Lett., 16, 084010,
https://doi.org/10.1088/1748-9326/ac0f26, 2021.
Ehbrecht, M., Schall, P., Ammer, C., and Seidel, D.: Quantifying stand
structural complexity and its relationship with forest management, tree
species diversity and microclimate, Agric. For. Meteorol., 242, 1–9,
https://doi.org/10.1016/j.agrformet.2017.04.012, 2017.
Esprey, L. J., Sands, P. J., and Smith, C. W.: Understanding 3-PG using a
sensitivity analysis, For. Ecol. Manag., 193, 235–250,
https://doi.org/10.1016/j.foreco.2004.01.032, 2004.
Fan, G., Nan, L., Dong, Y., Su, X., and Chen, F.: AdQSM: A New Method for
Estimating Above-Ground Biomass from TLS Point Clouds, Remote Sens., 12,
3089, https://doi.org/10.3390/rs12183089, 2020.
Fenn, K., Malhi, Y., Morecroft, M., Lloyd, C., and Thomas, M.: The Carbon
Cycle of a Maritime Ancient Temperate Broadleaved Woodland at Seasonal and
Annual Scales, Ecosystems, 18, 1–15,
https://doi.org/10.1007/s10021-014-9793-1, 2015.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fischer, F. J., Maréchaux, I., and Chave, J.: Improving plant allometry
by fusing forest models and remote sensing, New Phytol., 223, 1159–1165,
https://doi.org/10.1111/nph.15810, 2019.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Ian Woodward, F.: Assessing uncertainties in
a second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681,
https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.: Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O.,
Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G.,
Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Glob. Change Biol., 24, 35–54,
https://doi.org/10.1111/gcb.13910, 2018.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Bloh, W. von, Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C.,
Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H. D.,
Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K.-G., Schnur,
R., Strassmann, K., Weaver, A. J., Yoshikawa, C., and Zeng, N.:
Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model
Intercomparison, J. Climate, 19, 3337–3353,
https://doi.org/10.1175/JCLI3800.1, 2006.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections
due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Hackenberg, J., Spiecker, H., Calders, K., Disney, M., and Raumonen, P.:
SimpleTree – An Efficient Open Source Tool to Build Tree Models from TLS
Clouds, Forests, 6, 4245–4294, https://doi.org/10.3390/f6114245, 2015.
Hall, J., Kirby, K., and Whitbread, A.: National Vegetation Classification:
Field Guide to Woodland, JNCC,
ISBN 1 86107 554 5, 2001.
Hopkinson, C., Chasmer, L., Young-Pow, C., and Treitz, P.: Assessing forest
metrics with a ground-based scanning lidar, Can. J. For. Res., 34, 573–583,
https://doi.org/10.1139/x03-225, 2011.
Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R., Campbell, E.,
Dolan, K., Dubayah, R., Fisk, J. P., Flanagan, S., Huang, C., Huang, W.,
Johnson, K., Lamb, R., Ma, L., Marks, R., O'Leary, D., O'Neil-Dunne, J.,
Swatantran, A., and Tang, H.: Beyond MRV: high-resolution forest carbon
modeling for climate mitigation planning over Maryland, USA, Environ. Res.
Lett., 14, 045013, https://doi.org/10.1088/1748-9326/ab0bbe, 2019.
Hurtt, G. C., Fisk, J., Thomas, R. Q., Dubayah, R., Moorcroft, P. R., and
Shugart, H. H.: Linking models and data on vegetation structure, J. Geophys.
Res.-Biogeo., 115, G00E10, https://doi.org/10.1029/2009JG000937, 2010.
Jones, H. G. and Vaughan, R. A.: Remote Sensing of Vegetation: Principles,
Techniques, and Applications, OUP Oxford, 381 pp., 2010.
Jupp, D. L. B., Culvenor, D. S., Lovell, J. L., Newnham, G. J., Strahler, A.
H., and Woodcock, C. E.: Estimating forest LAI profiles and structural
parameters using a ground-based laser called “Echidna®”, Tree
Physiol., 29, 171–181, https://doi.org/10.1093/treephys/tpn022, 2009.
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY
plant trait database – enhanced coverage and open access, Glob. Change
Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Keeling, H. C. and Phillips, O. L.: The global relationship between forest
productivity and biomass, Glob. Ecol. Biogeogr., 16, 618–631,
https://doi.org/10.1111/j.1466-8238.2007.00314.x, 2007.
Keenan, T. F. and Niinemets, Ü.: Global leaf trait estimates biased due
to plasticity in the shade, Nat. Plants, 3, 1–6,
https://doi.org/10.1038/nplants.2016.201, 2016.
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M.
C.: Facilitating feedbacks between field measurements and ecosystem models,
Ecol. Monogr., 83, 133–154, 2013.
Lin, J. C., Pejam, M. R., Chan, E., Wofsy, S. C., Gottlieb, E. W., Margolis,
H. A., and McCaughey, J. H.: Attributing uncertainties in simulated
biospheric carbon fluxes to different error sources, Glob. Biogeochem.
Cycles, 25, 1–17, https://doi.org/10.1029/2010GB003884, 2011.
Lloyd, J., Patiño, S., Paiva, R. Q., Nardoto, G. B., Quesada, C. A., Santos, A. J. B., Baker, T. R., Brand, W. A., Hilke, I., Gielmann, H., Raessler, M., Luizão, F. J., Martinelli, L. A., and Mercado, L. M.: Optimisation of photosynthetic carbon gain and within-canopy gradients of associated foliar traits for Amazon forest trees, Biogeosciences, 7, 1833–1859, https://doi.org/10.5194/bg-7-1833-2010, 2010.
Longo, M., Knox, R. G., Medvigy, D. M., Levine, N. M., Dietze, M. C., Kim, Y., Swann, A. L. S., Zhang, K., Rollinson, C. R., Bras, R. L., Wofsy, S. C., and Moorcroft, P. R.: The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description, Geosci. Model Dev., 12, 4309–4346, https://doi.org/10.5194/gmd-12-4309-2019, 2019a.
Longo, M., Knox, R., Medvigy, D. M., Levine, N. M., Dietze, M., Swann, A. L. S., Zhang, K., Rollinson, C., di Porcia e Brugnera, M., Scott, D., Serbin, S. P., Kooper, R., Pourmokhtarian, A., Shiklomanov, A., Viskari, T., and Moorcroft, P.: Ecosystem Demography Model, version 2.2 (ED-2.2) (rev-86), Zenodo [code], https://doi.org/10.5281/zenodo.3365659, 2019b.
Longo, M., Saatchi, S., Keller, M., Bowman, K., Ferraz, A., Moorcroft, P.
R., Morton, D. C., Bonal, D., Brando, P., Burban, B., Derroire, G.,
dos-Santos, M. N., Meyer, V., Saleska, S., Trumbore, S., and Vincent, G.:
Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon
Tropical Forests, J. Geophys. Res.-Biogeo., 125, e2020JG005677,
https://doi.org/10.1029/2020JG005677, 2020.
Lovell, J. L., Jupp, D. L. B., Culvenor, D. S., and Coops, N. C.: Using
airborne and ground-based ranging lidar to measure canopy structure in
Australian forests, Can. J. Remote Sens., 29, 607–622,
https://doi.org/10.5589/m03-026, 2003.
Lovenduski, N. S. and Bonan, G. B.: Reducing uncertainty in projections of
terrestrial carbon uptake, Environ. Res. Lett., 12, 044020,
https://doi.org/10.1088/1748-9326/aa66b8, 2017.
Luo, Y., Weng, E., Wu, X., Gao, C., Zhou, X., and Zhang, L.: Parameter
identifiability, constraint, and equifinality in data assimilation with
ecosystem models, Ecol. Appl., 19, 571–574,
https://doi.org/10.1890/08-0561.1, 2009.
Maas, H.-G., Bienert, A., Scheller, S., and Keane, E.: Automatic forest
inventory parameter determination from terrestrial laser scanner data, Int.
J. Remote Sens., 29, 1579–1593, https://doi.org/10.1080/01431160701736406,
2008.
Massoud, E. C., Xu, C., Fisher, R. A., Knox, R. G., Walker, A. P., Serbin, S. P., Christoffersen, B. O., Holm, J. A., Kueppers, L. M., Ricciuto, D. M., Wei, L., Johnson, D. J., Chambers, J. Q., Koven, C. D., McDowell, N. G., and Vrugt, J. A.: Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES), Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, 2019.
McGuire, A. D., Sitch, S., Clein, J. S., Dargaville, R., Esser, G., Foley,
J., Heimann, M., Joos, F., Kaplan, J., Kicklighter, D. W., Meier, R. A.,
Melillo, J. M., Moore, B., Prentice, I. C., Ramankutty, N., Reichenau, T.,
Schloss, A., Tian, H., Williams, L. J., and Wittenberg, U.: Carbon balance
of the terrestrial biosphere in the Twentieth Century: Analyses of CO2,
climate and land use effects with four process-based ecosystem models, Glob.
Biogeochem. Cycles, 15, 183–206, https://doi.org/10.1029/2000GB001298,
2001.
Medvigy, D. and Moorcroft, P. R.: Predicting ecosystem dynamics at regional
scales: an evaluation of a terrestrial biosphere model for the forests of
northeastern North America, Philos. Trans. R. Soc. Lond. B Biol. Sci., 367,
222–235, https://doi.org/10.1098/rstb.2011.0253, 2012.
Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft,
P. R.: Mechanistic scaling of ecosystem function and dynamics in space and
time: Ecosystem Demography model version 2, J. Geophys. Res., 114, 1–21,
https://doi.org/10.1029/2008JG000812, 2009.
Meunier, F.: Data associated to the paper “Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)”, Zenodo [data set], https://doi.org/10.5281/zenodo.6363617, 2022.
Meunier, F., Verbeeck, H., Cowdery, B., Schnitzer, S. A., Smith-Martin, C.
M., Powers, J., Xu, X., Slot, M., De Deurwaerder, H. P. T., Detto, M.,
Bonal, D., Longo, M., Santiago, L. S., and Dietze, M.: Unraveling the
relative role of light and water competition between lianas and trees in
tropical forests, J. Ecol., 109, 519–540,
https://doi.org/10.1111/1365-2745.13540, 2020.
Meunier, F., Visser, M. D., Shiklomanov, A., Dietze, M. C., Guzmán Q.,
J. A., Sanchez-Azofeifa, G. A., De Deurwaerder, H. P. T., Krishna Moorthy,
S. M., Schnitzer, S. A., Marvin, D. C., Longo, M., Liu, C., Broadbent, E.
N., Almeyda Zambrano, A. M., Muller-Landau, H. C., Detto, M., and Verbeeck,
H.: Liana optical traits increase tropical forest albedo and reduce
ecosystem productivity, Glob. Change Biol., 28, 227–244,
https://doi.org/10.1111/gcb.15928, 2022.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling
vegetation dynamics: the ecosystem demography model (ED), Ecol. Monogr., 71,
557–586, 2001.
Oleson, K., Lawrence, M., Bonan, B., Drewniak, B., Huang, M., Koven, D.,
Levis, S., Li, F., Riley, J., Subin, M., Swenson, S., Thornton, E.,
Bozbiyik, A., Fisher, R., Heald, L., Kluzek, E., Lamarque, J.-F., Lawrence,
J., Leung, R., Lipscomb, W., Muszala, P., Ricciuto, M., Sacks, J., Sun, Y.,
Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the
Community Land Model (CLM), NCAR,
ISBN NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M, 2013.
Peaucelle, M., Bacour, C., Ciais, P., Vuichard, N., Kuppel, S.,
Peñuelas, J., Marchesini, L. B., Blanken, P. D., Buchmann, N., Chen, J.,
Delpierre, N., Desai, A. R., Dufrene, E., Gianelle, D., Gimeno-Colera, C.,
Gruening, C., Helfter, C., Hörtnagl, L., Ibrom, A., Joffre, R., Kato,
T., Kolb, T. E., Law, B., Lindroth, A., Mammarella, I., Merbold, L.,
Minerbi, S., Montagnani, L., Šigut, L., Sutton, M., Varlagin, A.,
Vesala, T., Wohlfahrt, G., Wolf, S., Yakir, D., and Viovy, N.: Covariations
between plant functional traits emerge from constraining parameterization of
a terrestrial biosphere model, Glob. Ecol. Biogeogr., 28, 1351–1365,
https://doi.org/10.1111/geb.12937, 2019.
Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S., Koffi, E., Kane, A., Maignan, F., Chevallier, F., Ciais, P., and Prunet, P.: A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle, Geosci. Model Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, 2016.
Poulter, B., Hattermann, F., Hawkins, E., Zaehle, S., Sitch, S.,
Restrepo-Coupe, N., Heyder, U., and Cramer, W.: Robust dynamics of Amazon
dieback to climate change with perturbed ecosystem model parameters, Glob.
Change Biol., 16, 2476–2495,
https://doi.org/10.1111/j.1365-2486.2009.02157.x, 2010.
Raczka, B., Dietze, M. C., Serbin, S. P., and Davis, K. J.: What Limits
Predictive Certainty of Long-Term Carbon Uptake?, J. Geophys. Res.-Biogeo., 123, 3570–3588, https://doi.org/10.1029/2018JG004504, 2018.
Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen,
H., Vastaranta, M., Holopainen, M., Disney, M., and Lewis, P.: Fast
Automatic Precision Tree Models from Terrestrial Laser Scanner Data, Remote
Sens., 5, 491–520, https://doi.org/10.3390/rs5020491, 2013.
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 12 June 2022), 2019.
Rezende, L. F. C., Arenque-Musa, B. C., Moura, M. S. B., Aidar, S. T., Von
Randow, C., Menezes, R. S. C., Ometto, J. P. B. H., Rezende, L. F. C.,
Arenque-Musa, B. C., Moura, M. S. B., Aidar, S. T., Von Randow, C., Menezes,
R. S. C., and Ometto, J. P. B. H.: Calibration of the maximum carboxylation
velocity (Vcmax) using data mining techniques and ecophysiological data from
the Brazilian semiarid region, for use in Dynamic Global Vegetation Models,
Braz. J. Biol., 76, 341–351, https://doi.org/10.1590/1519-6984.14414, 2016.
Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J. P., Dail, D.
B., Davidson, E. A., Scott, N. A., Evans, R. S., Hughes, H., Lee, J. T.,
Rodrigues, C., and Savage, K.: Estimating parameters of a forest ecosystem C
model with measurements of stocks and fluxes as joint constraints,
Oecologia, 164, 25–40, 2010.
Roberts, J., Hopkins, R., and Morecroft, M.: Towards a predictive
description of forest canopies from litter properties, Funct. Ecol., 13,
265–272, https://doi.org/10.1046/j.1365-2435.1999.00312.x, 1999.
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., Caemmerer, S. von,
Dietze, M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets,
Ü., Prentice, I. C., Serbin, S. P., Sitch, S., Way, D. A., and Zaehle,
S.: A roadmap for improving the representation of photosynthesis in Earth
system models, New Phytol., 213, 22–42, https://doi.org/10.1111/nph.14283,
2017.
Saarinen, N., Calders, K., Kankare, V., Yrttimaa, T., Junttila, S., Luoma,
V., Huuskonen, S., Hynynen, J., and Verbeeck, H.: Understanding 3D
structural complexity of individual Scots pine trees with different
management history, Ecol. Evol., 11, 2561–2572, https://doi.org/10.1002/ece3.7216,
2021.
Savill, P., Perrins, C., Kirby, K., and Fisher, N.: Wytham Woods: Oxford's
Ecological Laboratory, 1st edn., Oxford University Press, 288 pp., ISBN-10 0199605181, ISBN-13 978-0199605187, 2010.
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, Int.
J. Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283,
1985.
Shiklomanov, A. N., Bond-Lamberty, B., Atkins, J. W., and Gough, C. M.:
Structure and parameter uncertainty in centennial projections of forest
community structure and carbon cycling, Glob. Change Biol., 26, 6080–6096,
https://doi.org/10.1111/gcb.15164, 2020.
Stiers, M., Willim, K., Seidel, D., Ehbrecht, M., Kabal, M., Ammer, C., and
Annighöfer, P.: A quantitative comparison of the structural complexity
of managed, lately unmanaged and primary European beech (Fagus sylvatica L.)
forests, For. Ecol. Manag., 430, 357–365,
https://doi.org/10.1016/j.foreco.2018.08.039, 2018.
Takoudjou, S. M., Ploton, P., Sonké, B., Hackenberg, J., Griffon, S.,
Coligny, F. de, Kamdem, N. G., Libalah, M., Mofack, G. I., Moguédec, G.
L., Pélissier, R., and Barbier, N.: Using terrestrial laser scanning
data to estimate large tropical trees biomass and calibrate allometric
models: A comparison with traditional destructive approach, Methods Ecol.
Evol., 9, 905–916, https://doi.org/10.1111/2041-210X.12933, 2018.
Tanago, J. G. de, Lau, A., Bartholomeus, H., Herold, M., Avitabile, V.,
Raumonen, P., Martius, C., Goodman, R. C., Disney, M., Manuri, S., Burt, A.,
and Calders, K.: Estimation of above-ground biomass of large tropical trees
with terrestrial LiDAR, Methods Ecol. Evol., 9, 223–234,
https://doi.org/10.1111/2041-210X.12904, 2018.
Thomas, M. V., Malhi, Y., Fenn, K. M., Fisher, J. B., Morecroft, M. D., Lloyd, C. R., Taylor, M. E., and McNeil, D. D.: Carbon dioxide fluxes over an ancient broadleaved deciduous woodland in southern England, Biogeosciences, 8, 1595–1613, https://doi.org/10.5194/bg-8-1595-2011, 2011.
Thomas, R. Q., Hurtt, G. C., Dubayah, R., and Schilz, M. H.: Using lidar
data and a height-structured ecosystem model to estimate forest carbon
stocks and fluxes over mountainous terrain, Can. J. Remote Sens., 34,
S351–S363, https://doi.org/10.5589/m08-036, 2008.
van Breugel, M., Ransijn, J., Craven, D., Bongers, F., and Hall, J. S.:
Estimating carbon stock in secondary forests: Decisions and uncertainties
associated with allometric biomass models, For. Ecol. Manag., 262,
1648–1657, https://doi.org/10.1016/j.foreco.2011.07.018, 2011.
Vicari, M. B., Pisek, J., and Disney, M.: New estimates of leaf angle
distribution from terrestrial LiDAR: Comparison with measured and modelled
estimates from nine broadleaf tree species, Agric. Forest Meteorol., 264,
322–333, https://doi.org/10.1016/j.agrformet.2018.10.021, 2019.
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the Community
Land Model, NCAR, https://doi.org/10.5065/PZ8F-F017, 2018.
Viskari, T., Shiklomanov, A., Dietze, M. C., and Serbin, S. P.: The
influence of canopy radiation parameter uncertainty on model projections of
terrestrial carbon and energy cycling, PLOS ONE, 14, e0216512,
https://doi.org/10.1371/journal.pone.0216512, 2019.
Wang, Y., Hyyppä, J., Liang, X., Kaartinen, H., Yu, X., Lindberg, E.,
Holmgren, J., Qin, Y., Mallet, C., Ferraz, A., Torabzadeh, H., Morsdorf, F.,
Zhu, L., Liu, J., and Alho, P.: International Benchmarking of the Individual
Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture
and Forest Ecology Using Airborne Laser Scanning, IEEE T. Geosci. Remote, 54, 5011–5027, https://doi.org/10.1109/TGRS.2016.2543225, 2016.
Wang, Y. P.: A comparison of three different canopy radiation models
commonly used in plant modelling, Funct. Plant Biol. FPB, 30, 143–152,
https://doi.org/10.1071/FP02117, 2003.
Wei, H., Xia, Y., Mitchell, K. E., and Ek, M. B.: Improvement of the Noah
land surface model for warm season processes: evaluation of water and energy
flux simulation, Hydrol. Process., 27, 297–303,
https://doi.org/10.1002/hyp.9214, 2013.
Williams, I. N. and Torn, M. S.: Vegetation controls on surface heat flux
partitioning, and land-atmosphere coupling, Geophys. Res. Lett., 42,
9416–9424, https://doi.org/10.1002/2015GL066305, 2015.
Wramneby, A., Smith, B., Zaehle, S., and Sykes, M. T.: Parameter
uncertainties in the modelling of vegetation dynamics – Effects on tree
community structure and ecosystem functioning in European forest biomes,
Ecol. Model., 216, 277–290,
https://doi.org/10.1016/j.ecolmodel.2008.04.013, 2008.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z.,
Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer,
M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont,
B. B., Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M.-L., Niinemets,
Ü., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov,
V. I., Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and
Villar, R.: The worldwide leaf economics spectrum, Nature, 428, 821–827,
https://doi.org/10.1038/nature02403, 2004.
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrial biosphere dynamics, Glob.
Biogeochem. Cycles, 19, 1–16, https://doi.org/10.1029/2004GB002395, 2005.
Zhao, F., Strahler, A. H., Schaaf, C. L., Yao, T., Yang, X., Wang, Z.,
Schull, M. A., Román, M. O., Woodcock, C. E., Olofsson, P., Ni-Meister,
W., Jupp, D. L. B., Lovell, J. L., Culvenor, D. S., and Newnham, G. J.:
Measuring gap fraction, element clumping index and LAI in Sierra Forest
stands using a full-waveform ground-based lidar, Remote Sens. Environ., 125,
73–79, https://doi.org/10.1016/j.rse.2012.07.007, 2012.
Zhao, W. and Qualls, R. J.: A multiple-layer canopy scattering model to
simulate shortwave radiation distribution within a homogeneous plant canopy,
Water Resour. Res., 41, 1–16, https://doi.org/10.1029/2005WR004016, 2005.
Short summary
We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
We integrated state-of-the-art observations of the structure of the vegetation in a temperate...
