Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4931-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Jun 2026
Model description paper |
| 12 Jun 2026
| 12 Jun 2026
TREED (v1.0): a trait- and optimality-based eco-evolutionary vegetation model for the deep past and the present
Julian Rogger
CORRESPONDING AUTHOR
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Department of Earth and Planetary Sciences, ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
Khushboo Gurung
School of Earth and Environment, University of Leeds, Leeds, United Kingdom
Emanuel B. Kopp
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
William J. Matthaeus
School of Natural Sciences, Trinity College, Dublin, Ireland
Benjamin J. W. Mills
School of Earth and Environment, University of Leeds, Leeds, United Kingdom
Benjamin D. Stocker
Institute of Geography, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Taras V. Gerya
Department of Earth and Planetary Sciences, ETH Zurich, Zurich, Switzerland
Loïc Pellissier
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
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Biogeosciences, 23, 3829–3854, https://doi.org/10.5194/bg-23-3829-2026, https://doi.org/10.5194/bg-23-3829-2026, 2026
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Soil carbon sequestration supports climate mitigation and may enhance water availability. Using a global land model, we show that increased soil organic carbon improves water retention in the root zone and reduces runoff, particularly in dry, sandy regions. Although hydrological changes are modest, they are systematic and suggest co-benefits for vegetation productivity and ecosystem resilience in water-limited areas.
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere, https://doi.org/10.5194/egusphere-2025-6547, https://doi.org/10.5194/egusphere-2025-6547, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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This study introduces a new way to track Earth’s surface and other boundaries in computer models of the planet’s interior. It replaces noisy, tracer-based methods with a technique that cleanly follows surfaces while conserving volume. The approach produces smoother, more accurate results in both 2D and 3D, reduces dependence on large numbers of tracers, and supports future links between deep Earth processes, oceans, and surface environments.
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere, https://doi.org/10.5194/egusphere-2025-6546, https://doi.org/10.5194/egusphere-2025-6546, 2026
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This study presents a new way to model how Earth’s surface changes over time as the deep interior moves. The method tracks the surface directly, allowing clearer and more detailed results worldwide while using less computing power. It improves accuracy compared to existing approaches and makes it easier to connect deep Earth processes with oceans, climate, landscapes, and life through time.
Timothy Stephen Gray, Paul James Tackley, and Taras Gerya
EGUsphere, https://doi.org/10.5194/egusphere-2025-6354, https://doi.org/10.5194/egusphere-2025-6354, 2025
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We developed a new way to model how planetary surfaces rise and sink as the deep interior slowly flows. Existing approaches are either costly or unstable. Our method represents the surface smoothly within a fixed grid, which avoids artificial air layers and numerical problems. Tests show it matches established results while running faster and working in more realistic settings, such as loaded surfaces and global models. This makes simulations of surface evolution more reliable and accessible.
Josefa Arán Paredes, Fabian Bernhard, Koen Hufkens, Mayeul Marcadella, and Benjamin D. Stocker
Geosci. Model Dev., 18, 9855–9878, https://doi.org/10.5194/gmd-18-9855-2025, https://doi.org/10.5194/gmd-18-9855-2025, 2025
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Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here, we demonstrate and evaluate the rsofun R package, which provides a computationally efficient implementation of the P-model for site-scale simulations of ecosystem photosynthesis. Bayesian model fitting to observed fluxes and traits and evaluation on an independent test data set indicated robust calibration and unbiased prediction capabilities.
Samantha Biegel, Konrad Schindler, and Benjamin D. Stocker
Biogeosciences, 22, 7455–7481, https://doi.org/10.5194/bg-22-7455-2025, https://doi.org/10.5194/bg-22-7455-2025, 2025
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Our work addresses the predictability of carbon absorption by ecosystems across the globe, particularly in dry regions. We compare 3 different models, including a deep learning model that can learn from past environmental conditions, and show that this helps improve predictions. Still, challenges remain in dry areas due to varying vulnerabilities to drought. As drought conditions intensify globally, it's crucial to understand the varying impacts on ecosystem function.
Christoph von Matt, Benjamin Stocker, and Olivia Martius
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-383, https://doi.org/10.5194/essd-2025-383, 2025
Revised manuscript accepted for ESSD
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Low flow conditions (hydrological droughts) in Switzerland pose challenges to agriculture and energy production. Improved understanding of droughts supports warning applications and infrastructure planning. The HYD-responses data set provides data to study the the evolution of drought conditions. The data set combines weather data, snow cover data, soil moisture data, and numerous drought indicators. The data set supports process studies, statistical analyses, and the training of AI models.
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
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Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
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.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
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Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Piersilvio De Bartolomeis, Alexandru Meterez, Zixin Shu, and Benjamin David Stocker
EGUsphere, https://doi.org/10.5194/egusphere-2023-1826, https://doi.org/10.5194/egusphere-2023-1826, 2023
Preprint withdrawn
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Our research highlights the effectiveness of a recurrent neural network, LSTM, in predicting plant carbon absorption using weather and satellite data. LSTM outperforms other models, even for new locations, suggesting its broad application. Yet, challenges remain in predicting diverse ecosystems globally due to varying plant and climate factors. Our work enhances understanding of Earth's complex ecosystems using advanced models.
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
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Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
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Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Ionuț Iosifescu Enescu, David Hanimann, Dominik Haas-Artho, Marius Rüetschi, Dirk Nikolaus Karger, Gian-Kasper Plattner, Martin Hägeli, Rebecca Kurup Buchholz, Lucia de Espona, Niklaus E. Zimmermann, and Loïc Pellissier
Abstr. Int. Cartogr. Assoc., 3, 119, https://doi.org/10.5194/ica-abs-3-119-2021, https://doi.org/10.5194/ica-abs-3-119-2021, 2021
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Abstr. Int. Cartogr. Assoc., 3, 120, https://doi.org/10.5194/ica-abs-3-120-2021, https://doi.org/10.5194/ica-abs-3-120-2021, 2021
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Short summary
Vegetation plays a fundamental role in regulating Earth’s climate on time scales ranging from seconds to millions of years. Here, we develop and test a new vegetation model that uses evolutionary principles to predict vegetation structure, functioning, and diversity under environmental conditions fundamentally different from the present. Using the model in combination with fossil data from Earth's past may help to better understand the response of vegetation systems to environmental change.
Vegetation plays a fundamental role in regulating Earth’s climate on time scales ranging from...
