Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9855-2025
© Author(s) 2025. 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-18-9855-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Dec 2025
Development and technical paper |
| 10 Dec 2025
| 10 Dec 2025
rsofun v5.1: a model-data integration framework for simulating ecosystem processes
Josefa Arán Paredes
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
Fabian Bernhard
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
Koen Hufkens
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
Mayeul Marcadella
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
Benjamin D. Stocker
CORRESPONDING AUTHOR
Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland
Oeschger Center for Climate Change Research (OCCR), University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
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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.
Adam M. Young, Thomas Milliman, Koen Hufkens, Keith L. Ballou, Christopher Coffey, Kai Begay, Michael Fell, Mostafa Javadian, Alison K. Post, Christina Schädel, Zakary Vladich, Oscar Zimmerman, Dawn M. Browning, Christopher R. Florian, Minkyu Moon, Michael D. SanClements, Bijan Seyednasrollah, Mark A. Friedl, and Andrew D. Richardson
Earth Syst. Sci. Data, 17, 6531–6556, https://doi.org/10.5194/essd-17-6531-2025, https://doi.org/10.5194/essd-17-6531-2025, 2025
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Here, we describe the PhenoCam V3.0 public data release, which characterizes vegetation phenology in ecosystems across the US and globally using repeat digital photography. This V3.0 release includes new data records (a camera-derived normalized difference vegetation index and simplified data sets) and provides >4800 site years of phenological time series and transition dates, a 170 % increase relative to the previous release (V2.0). Over 450 of the time series are 5 years or longer in length.
Marco M. Lehmann, Josie Geris, Ilja van Meerveld, Daniele Penna, Youri Rothfuss, Matteo Verdone, Pertti Ala-Aho, Matyas Arvai, Alise Babre, Philippe Balandier, Fabian Bernhard, Lukrecija Butorac, Simon D. Carrière, Natalie C. Ceperley, Zuosinan Chen, Alicia Correa, Haoyu Diao, David Dubbert, Maren Dubbert, Fabio Ercoli, Marius G. Floriancic, Alligin Ghazoul, Teresa E. Gimeno, Damien Gounelle, Frank Hagedorn, Christophe Hissler, Frédéric Huneau, Alberto Iraheta, Tamara Jakovljević, Nerantzis Kazakis, Zoltan Kern, Laura Kinzinger, Karl Knaebel, Johannes Kobler, Jiri Kocum, Charlotte Koeber, Gerbrand Koren, Angelika Kübert, Dawid Kupka, Samuel Le Gall, Aleksi Lehtonen, Thomas Leydier, Philippe Malagoli, Francesca Sofia Manca di Villahermosa, Chiara Marchina, Núria Martínez-Carreras, Nicolas Martin-StPaul, Hannu Marttila, Aline Meyer Oliveira, Gael Monvoisin, Natalie Orlowski, Kadi Palmik-Das, Aurel Persoiu, Andrei Popa, Egor Prikaziuk, Cécile Quantin, Katja T. Rinne-Garmston, Clara Rohde, Martin Sanda, Matthias Saurer, Daniel Schulz, Michael P. Stockinger, Christine Stumpp, Jean-Stéphane Vénisse, Lukas Vlcek, Stylianos Voudouris, Björn Weeser, Mark E. Wilkinson, Giulia Zuecco, and Katrin Meusburger
Earth Syst. Sci. Data, 17, 6129–6147, https://doi.org/10.5194/essd-17-6129-2025, https://doi.org/10.5194/essd-17-6129-2025, 2025
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This study describes a unique large-scale isotope dataset to study water dynamics in European forests. Researchers collected data from 40 beech and spruce forest sites in spring and summer 2023, using a standardized method to ensure consistency. The results show that water sources for trees change between seasons and vary by tree species. This large dataset offers valuable information for understanding plant water use, improving ecohydrological models, and mapping water cycles across Europe.
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
Preprint under review 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.
Inne Vanderkelen, Marie-Estelle Demoury, Sean Swenson, David M. Lawrence, Benjamin D. Stocker, Myke Koopmans, and Édouard L. Davin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2637, https://doi.org/10.5194/egusphere-2025-2637, 2025
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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.
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-3779, https://doi.org/10.5194/egusphere-2024-3779, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Archives worldwide host vast records of observed weather data crucial for understanding climate variability. However, most of these records are still in paper form, limiting their use. To address this, we developed MeteoSaver, an open-source tool, to transcribe these records to machine-readable format. Applied to ten handwritten temperature sheets, it achieved a median accuracy of 74%. This tool offers a promising solution to preserve records from archives and unlock historical weather insights.
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.
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.
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.
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Short summary
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.
Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on...
