Articles | Volume 22, issue 23
https://doi.org/10.5194/bg-22-7455-2025
© 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/bg-22-7455-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Dec 2025
Research article |
| 01 Dec 2025
| 01 Dec 2025
Unrecognised water limitation is a main source of uncertainty for models of terrestrial photosynthesis
Samantha Biegel
CORRESPONDING AUTHOR
Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
ETH AI Center, ETH Zürich, Zürich, Switzerland
Konrad Schindler
Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
ETH AI Center, ETH Zürich, Zürich, Switzerland
Benjamin D. Stocker
Institute of Geography, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Related authors
No articles found.
Hongruixuan Chen, Jian Song, Olivier Dietrich, Clifford Broni-Bediako, Weihao Xuan, Junjue Wang, Xinlei Shao, Yimin Wei, Junshi Xia, Cuiling Lan, Konrad Schindler, and Naoto Yokoya
Earth Syst. Sci. Data, 17, 6217–6253, https://doi.org/10.5194/essd-17-6217-2025, https://doi.org/10.5194/essd-17-6217-2025, 2025
Short summary
Short summary
Natural disasters often damage buildings and threaten lives, especially in areas with limited resources. To help improve emergency response, we created a global dataset called BRIGHT using both optical and radar images to detect building damage in any weather. We tested many artificial intelligence models and showed how well they work in real disaster scenes. This work can guide better tools for future disaster recovery and help save lives faster.
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
Short summary
Short summary
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.
Ghjulia Sialelli, Torben Peters, Jan D. Wegner, and Konrad Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 829–838, https://doi.org/10.5194/isprs-annals-X-G-2025-829-2025, https://doi.org/10.5194/isprs-annals-X-G-2025-829-2025, 2025
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
Short summary
Short summary
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.
Josefa Arán Paredes, Koen Hufkens, Mayeul Marcadella, Fabian Bernhard, and Benjamin D. Stocker
EGUsphere, https://doi.org/10.1101/2023.11.24.568574, https://doi.org/10.1101/2023.11.24.568574, 2025
Short summary
Short summary
Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here we present the {rsofun} R package, providing an implementation of a model for site-scale ecosystem photosynthesis including functions for Bayesian model-data integration. The package {rsofun} lowers the bar of entry to ecosystem modelling and model-data integration and serves as an open-access resource for model development and dissemination.
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
Short summary
Short summary
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.
Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 18, 3807–3823, https://doi.org/10.5194/tc-18-3807-2024, https://doi.org/10.5194/tc-18-3807-2024, 2024
Short summary
Short summary
For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
B. Xiang, T. Peters, T. Kontogianni, F. Vetterli, S. Puliti, R. Astrup, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 605–612, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, 2023
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
Short summary
Short summary
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
Short summary
Short summary
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.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
O. Kantarcioglu, K. Schindler, and S. Kocaman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 161–167, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, 2023
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
Short summary
Short summary
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.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
Short summary
Short summary
Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
C. Stucker, B. Ke, Y. Yue, S. Huang, I. Armeni, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 193–201, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, 2022
Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
Nico Lang, Andrea Irniger, Agnieszka Rozniak, Roni Hunziker, Jan Dirk Wegner, and Konrad Schindler
Hydrol. Earth Syst. Sci., 25, 2567–2597, https://doi.org/10.5194/hess-25-2567-2021, https://doi.org/10.5194/hess-25-2567-2021, 2021
Short summary
Short summary
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
Short summary
Short summary
In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Cited articles
Abramowitz, G., Ukkola, A., Hobeichi, S., Cranko Page, J., Lipson, M., De Kauwe, M. G., Green, S., Brenner, C., Frame, J., Nearing, G., Clark, M., Best, M., Anthoni, P., Arduini, G., Boussetta, S., Caldararu, S., Cho, K., Cuntz, M., Fairbairn, D., Ferguson, C. R., Kim, H., Kim, Y., Knauer, J., Lawrence, D., Luo, X., Malyshev, S., Nitta, T., Ogee, J., Oleson, K., Ottlé, C., Peylin, P., de Rosnay, P., Rumbold, H., Su, B., Vuichard, N., Walker, A. P., Wang-Faivre, X., Wang, Y., and Zeng, Y.: On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results, Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, 2024. a, b, c, d
Anderegg, W. R. L., Konings, A. G., Trugman, A. T., Yu, K., Bowling, D. R., Gabbitas, R., Karp, D. S., Pacala, S., Sperry, J. S., Sulman, B. N., and Zenes, N.: Hydraulic diversity of forests regulates ecosystem resilience during drought, Nature, 561, 538–541, https://doi.org/10.1038/s41586-018-0539-7, 2018. a
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer Normalization, arXiv [preprint], https://doi.org/10.48550/arXiv.1607.06450, 2016. a, b
Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.1803.01271, 2018. a, b
Baldocchi, D. D.: Must we incorporate soil moisture information when applying light use efficiency models with satellite remote sensing information?, New Phytologist, 218, 1293–1294, https://doi.org/10.1111/nph.15176, 2018. a
Baldocchi, D. D.: How eddy covariance flux measurements have contributed to our understanding of Global Change Biology, Global Change Biology, 26, 242–260, https://doi.org/10.1111/gcb.14807, 2020. a
Barber, J. and Andersson, B.: Too much of a good thing: light can be bad for photosynthesis, Trends in Biochemical Sciences, 17, 61–66, https://doi.org/10.1016/0968-0004(92)90503-2, 1992. a
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L., Wigneron, J. P., Weber, U., Reichstein, M., Fu, Z., Anthoni, P., Arneth, A., Haverd, V., Jain, A. K., Joetzjer, E., Knauer, J., Lienert, S., Loughran, T., McGuire, P. C., Tian, H., Viovy, N., and Zaehle, S.: Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity, Science Advances, 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724, 2020. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. a, b, c
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010. a
Bernacchi, C. J., PimenTEL, C., and Long, S. P.: In vivo temperature response functions of parameters required to model RuBP-limited photosynthesis, Plant, Cell & Environment, 26, 1419–1430, https://doi.org/10.1046/j.0016-8025.2003.01050.x, 2003. a
Berry, J. and Bjorkman, O.: Photosynthetic Response and Adaptation to Temperature in Higher Plants, Annual Review of Plant Biology, 31, 491–543, https://doi.org/10.1146/annurev.pp.31.060180.002423, 1980. a, b
Besnard, S., Carvalhais, N., Arain, M. A., Black, A., Brede, B., Buchmann, N., Chen, J., Clevers, J. G. P. W., Dutrieux, L. P., Gans, F., Herold, M., Jung, M., Kosugi, Y., Knohl, A., Law, B. E., Paul-Limoges, E., Lohila, A., Merbold, L., Roupsard, O., Valentini, R., Wolf, S., Zhang, X., and Reichstein, M.: Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests, PLOS ONE, 14, e0211510, https://doi.org/10.1371/journal.pone.0211510, 2019. a, b
Biegel, S.: SamanthaBiegel/gpp-ml, Zenodo [code], https://doi.org/10.5281/ZENODO.15236497, 2025. a
Booth, B. B. B., Jones, C. D., Collins, M., Totterdell, I. J., Cox, P. M., Sitch, S., Huntingford, C., Betts, R. A., Harris, G. R., and Lloyd, J.: High sensitivity of future global warming to land carbon cycle processes, Environmental Research Letters, 7, 024002, https://doi.org/10.1088/1748-9326/7/2/024002, 2012. a
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., and Grégoire, J.-M.: Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sensing of Environment, 77, 22–33, https://doi.org/10.1016/S0034-4257(01)00191-2, 2001. a
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martínez-Vilalta, J., Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Pittermann, J., Pratt, R. B., Sperry, J. S., Westoby, M., Wright, I. J., and Zanne, A. E.: Global convergence in the vulnerability of forests to drought, Nature, 491, 752–755, https://doi.org/10.1038/nature11688, 2012. a
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agricultural and Forest Meteorology, 301–302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021. a
Davis, T. W., Prentice, I. C., Stocker, B. D., Thomas, R. T., Whitley, R. J., Wang, H., Evans, B. J., Gallego-Sala, A. V., Sykes, M. T., and Cramer, W.: Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, 2017. a, b, c
Denissen, J. M. C., Teuling, A. J., Pitman, A. J., Koirala, S., Migliavacca, M., Li, W., Reichstein, M., Winkler, A. J., Zhan, C., and Orth, R.: Widespread shift from ecosystem energy to water limitation with climate change, Nature Climate Change, 12, 677–684, https://doi.org/10.1038/s41558-022-01403-8, 2022. a
Drought 2018 Team and ICOS Ecosystem Thematic Centre: Drought-2018 ecosystem eddy covariance flux product for 52 stations in FLUXNET-Archive format, ICOS Carbon Portal [data set], https://doi.org/10.18160/YVR0-4898, 2020. a
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013. a
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and Otero-Casal, C.: Hydrologic regulation of plant rooting depth, Proceedings of the National Academy of Sciences, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114, 2017. a
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980. a
Fu, Z., Ciais, P., Wigneron, J.-P., Gentine, P., Feldman, A. F., Makowski, D., Viovy, N., Kemanian, A. R., Goll, D. S., Stoy, P. C., Prentice, I. C., Yakir, D., Liu, L., Ma, H., Li, X., Huang, Y., Yu, K., Zhu, P., Li, X., Zhu, Z., Lian, J., and Smith, W. K.: Global critical soil moisture thresholds of plant water stress, Nature Communications, 15, 4826, https://doi.org/10.1038/s41467-024-49244-7, 2024. a
Gaber, M., Kang, Y., Schurgers, G., and Keenan, T.: Using automated machine learning for the upscaling of gross primary productivity, Biogeosciences, 21, 2447–2472, https://doi.org/10.5194/bg-21-2447-2024, 2024. a, b, c, d
Gamon, J. A., Huemmrich, K. F., Wong, C. Y. S., Ensminger, I., Garrity, S., Hollinger, D. Y., Noormets, A., and Peñuelas, J.: A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers, Proceedings of the National Academy of Sciences of the United States of America, 113, 13087–13092, https://doi.org/10.1073/pnas.1606162113, 2016. a
Giardina, F., Gentine, P., Konings, A. G., Seneviratne, S. I., and Stocker, B. D.: Diagnosing evapotranspiration responses to water deficit across biomes using deep learning, New Phytologist, 240, 968–983, https://doi.org/10.1111/nph.19197, 2023. a
Hahm, W. J., Dralle, D. N., Rempe, D. M., Bryk, A. B., Thompson, S. E., Dawson, T. E., and Dietrich, W. E.: Low Subsurface Water Storage Capacity Relative to Annual Rainfall Decouples Mediterranean Plant Productivity and Water Use From Rainfall Variability, Geophysical Research Letters, 46, 6544–6553, https://doi.org/10.1029/2019GL083294, 2019. a
Hendrycks, D. and Gimpel, K.: Gaussian Error Linear Units (GELUs), arXiv [preprint], https://doi.org/10.48550/arXiv.1606.08415, 2023. a, b
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R.: Improving neural networks by preventing co-adaptation of feature detectors, arXiv [preprint], https://doi.org/10.48550/arXiv.1207.0580, 2012. a
Hochreiter, S. and Schmidhuber, J.: Long Short-term Memory, Neural computation, 9, 1735–80, https://doi.org/10.1162/neco.1997.9.8.1735, 1997. a, b
Hufkens, K. and Stocker, B.: FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling, Zenodo [data set], https://doi.org/10.5281/zenodo.14808331, 2025. a, b
ICOS: ICOS Data Portal, https://www.icos-cp.eu/, last access: 27 January 2025. a
Isaac, P., Cleverly, J., McHugh, I., van Gorsel, E., Ewenz, C., and Beringer, J.: OzFlux data: network integration from collection to curation, Biogeosciences, 14, 2903–2928, https://doi.org/10.5194/bg-14-2903-2017, 2017. a
Joiner, J. and Yoshida, Y.: Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales, Agricultural and Forest Meteorology, 291, 108092, https://doi.org/10.1016/j.agrformet.2020.108092, 2020. a
Joshi, J., Stocker, B. D., Hofhansl, F., Zhou, S., Dieckmann, U., and Prentice, I. C.: Towards a unified theory of plant photosynthesis and hydraulics, Nature Plants, 8, 1304–1316, https://doi.org/10.1038/s41477-022-01244-5, 2022. a
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M., Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations, Journal of Geophysical Research: Biogeosciences, 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011. a
Kang, Y., Bassiouni, M., Gaber, M., Lu, X., and Keenan, T. F.: CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization, Earth Syst. Sci. Data, 17, 3009–3046, https://doi.org/10.5194/essd-17-3009-2025, 2025. a, b, c, d
Kannenberg, S. A., Anderegg, W. R. L., Barnes, M. L., Dannenberg, M. P., and Knapp, A. K.: Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems, Nature Geoscience, 17, 38–43, https://doi.org/10.1038/s41561-023-01351-8, 2024. a
Kattge, J. and Knorr, W.: Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species, Plant, Cell & Environment, 30, 1176–1190, https://doi.org/10.1111/j.1365-3040.2007.01690.x, 2007. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2017. a
Konings, A. G. and Gentine, P.: Global variations in ecosystem-scale isohydricity, Global Change Biology, 23, 891–905, https://doi.org/10.1111/gcb.13389, 2017. a, b
Kraft, B., Nelson, J. A., Walther, S., Gans, F., Weber, U., Duveiller, G., Reichstein, M., Zhang, W., Rußwurm, M., Tuia, D., Körner, M., Hamdi, Z., and Jung, M.: On the added value of sequential deep learning for the upscaling of evapotranspiration, Biogeosciences, 22, 3965–3987, https://doi.org/10.5194/bg-22-3965-2025, 2025. a, b, c
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Battaglia, M., Cano, F. J., Carter, K. R., Cavaleri, M. A., Cernusak, L. A., Chambers, J. Q., Crous, K. Y., De Kauwe, M. G., Dillaway, D. N., Dreyer, E., Ellsworth, D. S., Ghannoum, O., Han, Q., Hikosaka, K., Jensen, A. M., Kelly, J. W. G., Kruger, E. L., Mercado, L. M., Onoda, Y., Reich, P. B., Rogers, A., Slot, M., Smith, N. G., Tarvainen, L., Tissue, D. T., Togashi, H. F., Tribuzy, E. S., Uddling, J., Vårhammar, A., Wallin, G., Warren, J. M., and Way, D. A.: Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale, New Phytologist, 222, 768–784, https://doi.org/10.1111/nph.15668, 2019. a, b
LaThuile: La Thuile Synthesis Dataset, https://fluxnet.org/data/la-thuile-dataset/, last access: 27 January 2025. a
Li, X., Xiao, J., He, B., Altaf Arain, M., Beringer, J., Desai, A. R., Emmel, C., Hollinger, D. Y., Krasnova, A., Mammarella, I., Noe, S. M., Ortiz, P. S., Rey-Sanchez, A. C., Rocha, A. V., and Varlagin, A.: Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations, Global Change Biology, 24, 3990–4008, https://doi.org/10.1111/gcb.14297, 2018. a
Liu, G., Migliavacca, M., Reimers, C., Kraft, B., Reichstein, M., Richardson, A. D., Wingate, L., Delpierre, N., Yang, H., and Winkler, A. J.: DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology, Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, 2024a. a
Liu, J., Ryu, Y., Luo, X., Dechant, B., Stocker, B. D., Keenan, T. F., Gentine, P., Li, X., Li, B., Harrison, S. P., and Prentice, I. C.: Evidence for widespread thermal acclimation of canopy photosynthesis, Nature Plants, 10, 1919–1927, https://doi.org/10.1038/s41477-024-01846-1, 2024b. a
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E., and Meyer, H.: Assessing and improving the transferability of current global spatial prediction models, Global Ecology and Biogeography, 32, 356–368, https://doi.org/10.1111/geb.13635, 2023. a
Luo, X. and Keenan, T. F.: Global evidence for the acclimation of ecosystem photosynthesis to light, Nature Ecology & Evolution, 4, 1351–1357, https://doi.org/10.1038/s41559-020-1258-7, 2020. a
Luo, Y., Gessler, A., D'Odorico, P., Hufkens, K., and Stocker, B. D.: Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems, New Phytologist, 240, 984–1002, https://doi.org/10.1111/nph.19208, 2023. a, b, c
Maire, V., Martre, P., Kattge, J., Gastal, F., Esser, G., Fontaine, S., and Soussana, J.-F.: The Coordination of Leaf Photosynthesis Links C and N Fluxes in C3 Plant Species, PLOS ONE, 7, e38345, https://doi.org/10.1371/journal.pone.0038345, 2012. a
McCormick, E. L., Dralle, D. N., Hahm, W. J., Tune, A. K., Schmidt, L. M., Chadwick, K. D., and Rempe, D. M.: Widespread woody plant use of water stored in bedrock, Nature, 597, 225–229, https://doi.org/10.1038/s41586-021-03761-3, 2021. a
McDowell, N. G., Sapes, G., Pivovaroff, A., Adams, H. D., Allen, C. D., Anderegg, W. R., Arend, M., Breshears, D. D., Brodribb, T., Choat, B., Cochard, H., De Cáceres, M., De Kauwe, M. G., Grossiord, C., Hammond, W. M., Hartmann, H., Hoch, G., Kahmen, A., Klein, T., Mackay, D. S., Mantova, M., Martínez-Vilalta, J., Medlyn, B. E., Mencuccini, M., Nardini, A., Oliveira, R. S., Sala, A., Tissue, D. T., Torres-Ruiz, J. M., Trowbridge, A. M., Trugman, A. T., Wiley, E., and Xu, C.: Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit, Nature Reviews Earth and Environment, 3, 294–308, https://doi.org/10.1038/s43017-022-00272-1, 2022. a
Meyer, H. and Pebesma, E.: Machine learning-based global maps of ecological variables and the challenge of assessing them, Nature Communications, 13, 2208, https://doi.org/10.1038/s41467-022-29838-9, 2022. a
Monteith, J. L.: Solar Radiation and Productivity in Tropical Ecosystems, Journal of Applied Ecology, 9, 747–766, https://doi.org/10.2307/2401901, 1972. a
Montero, D., Mahecha, M. D., Martinuzzi, F., Aybar, C., Klosterhalfen, A., Knohl, A., Koebsch, F., Anaya, J., and Wieneke, S.: Recurrent Neural Networks for Modelling Gross Primary Production, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.12745, 2024. a, b, c
Myneni, R., Knyazikhin, Y., and Park, T.: MODIS/Terra+Aqua Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061, NASA Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD15A2H.061, 2021. a
Nakagawa, R., Chau, M., Calzaretta, J., Keenan, T., Vahabi, P., Todeschini, A., Bassiouni, M., and Kang, Y.: Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT), arXiv [preprint], https://doi.org/10.48550/arXiv.2306.13815, 2023. a, b
Nelson, J. A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L., Ibrom, A., Papale, D., Göckede, M., Duveiller, G., Knohl, A., Hörtnagl, L., Scott, R. L., Dušek, J., Zhang, W., Hamdi, Z. M., Reichstein, M., Aranda-Barranco, S., Ardö, J., Op de Beeck, M., Billesbach, D., Bowling, D., Bracho, R., Brümmer, C., Camps-Valls, G., Chen, S., Cleverly, J. R., Desai, A., Dong, G., El-Madany, T. S., Euskirchen, E. S., Feigenwinter, I., Galvagno, M., Gerosa, G. A., Gielen, B., Goded, I., Goslee, S., Gough, C. M., Heinesch, B., Ichii, K., Jackowicz-Korczynski, M. A., Klosterhalfen, A., Knox, S., Kobayashi, H., Kohonen, K.-M., Korkiakoski, M., Mammarella, I., Gharun, M., Marzuoli, R., Matamala, R., Metzger, S., Montagnani, L., Nicolini, G., O'Halloran, T., Ourcival, J.-M., Peichl, M., Pendall, E., Ruiz Reverter, B., Roland, M., Sabbatini, S., Sachs, T., Schmidt, M., Schwalm, C. R., Shekhar, A., Silberstein, R., Silveira, M. L., Spano, D., Tagesson, T., Tramontana, G., Trotta, C., Turco, F., Vesala, T., Vincke, C., Vitale, D., Vivoni, E. R., Wang, Y., Woodgate, W., Yepez, E. A., Zhang, J., Zona, D., and Jung, M.: X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X, Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, 2024. a, b, c, d
Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes, Nature Climate Change, 6, 1023–1027, https://doi.org/10.1038/nclimate3114, 2016. a
OzFlux: OzFlux Data Portal, https://www.ozflux.org.au/, last access: 27 January 2025. a
Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M., Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L., Montagnani, L., Moors, E., Olesen, J. E., Reichstein, M., Tramontana, G., van Gorsel, E., Wohlfahrt, G., and Ráduly, B.: Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks, Journal of Geophysical Research: Biogeosciences, 120, 1941–1957, https://doi.org/10.1002/2015JG002997, 2015. a
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., de Grandcourt, A., Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., di Tommasi, P., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., Gitelson, A., Goded, I., Goeckede, M., Goldstein, A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening, C., Grünwald, T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson, C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera, N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt, J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch, W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J.-M., Lion, M., Liska, A. J., Lohila, A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V., Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes, J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S., Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck, J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival, J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N., Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, Ü., Raz-Yaseef, N., Rebmann, C., Reed, D., de Dios, V. R., Restrepo-Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Cañete, E. P., Sanchez-Mejia, Z. M., Schmid, H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I., Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T., Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel, E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang, H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G., Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020. a, b
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, Journal of Advances in Modeling Earth Systems, 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016. a
Peng, J., Tang, J., Xie, S., Wang, Y., Liao, J., Chen, C., Sun, C., Mao, J., Zhou, Q., and Niu, S.: Evidence for the acclimation of ecosystem photosynthesis to soil moisture, Nature Communications, 15, 9795, https://doi.org/10.1038/s41467-024-54156-7, 2024. a
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.: Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology, Ecology Letters, 17, 82–91, https://doi.org/10.1111/ele.12211, 2014. a, b
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Monthly Weather Review, 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972. a, b
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005. a
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M. D., Seneviratne, S. I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D. C., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., and Wattenbach, M.: Climate extremes and the carbon cycle, Nature, 500, 287–295, https://doi.org/10.1038/nature12350, 2013. a
Ryu, Y., Berry, J. A., and Baldocchi, D. D.: What is global photosynthesis? History, uncertainties and opportunities, Remote Sensing of Environment, 223, 95–114, https://doi.org/10.1016/j.rse.2019.01.016, 2019. a
Smith, W. K., Dannenberg, M. P., Yan, D., Herrmann, S., Barnes, M. L., Barron-Gafford, G. A., Biederman, J. A., Ferrenberg, S., Fox, A. M., Hudson, A., Knowles, J. F., MacBean, N., Moore, D. J. P., Nagler, P. L., Reed, S. C., Rutherford, W. A., Scott, R. L., Wang, X., and Yang, J.: Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities, Remote Sensing of Environment, 233, 111401, https://doi.org/10.1016/j.rse.2019.111401, 2019. a
Stocker, B.: cwd v1.0: R package for cumulative water deficit calculation, Zenodo, Zenodo [code], https://doi.org/10.5281/ZENODO.5359053, 2021. a
Stocker, B., Hufkens, K., Marqués, L., Arán, P., Maspons, J., Bernhard, F., marcadella, and Peng, Y.: geco-bern/rsofun: v5.0, Zenodo [code], https://doi.org/10.5281/zenodo.14264892, 2024. a, b
Stocker, B. D., Wang, H., Smith, N. G., Harrison, S. P., Keenan, T. F., Sandoval, D., Davis, T., and Prentice, I. C.: P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production, Geosci. Model Dev., 13, 1545–1581, https://doi.org/10.5194/gmd-13-1545-2020, 2020. a, b, c
Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nature Geoscience, 16, 250–256, https://doi.org/10.1038/s41561-023-01125-2, 2023. a, b, c
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a, b, c, d
Tuzet, A., Perrier, A., and Leuning, R.: A coupled model of stomatal conductance, photosynthesis and transpiration, Plant, Cell & Environment, 26, 1097–1116, https://doi.org/10.1046/j.1365-3040.2003.01035.x, 2003. a
Ukkola, A. M., Abramowitz, G., and De Kauwe, M. G.: A flux tower dataset tailored for land model evaluation, Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, 2022. a
Walther, S., Besnard, S., Nelson, J. A., El-Madany, T. S., Migliavacca, M., Weber, U., Carvalhais, N., Ermida, S. L., Brümmer, C., Schrader, F., Prokushkin, A. S., Panov, A. V., and Jung, M.: Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set, Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, 2022. a
Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., and Peng, C.: Towards a universal model for carbon dioxide uptake by plants, Nature Plants, 3, 734–741, https://doi.org/10.1038/s41477-017-0006-8, 2017. a
Warm Winter 2020 Team and ICOS Ecosystem Thematic Centre: Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format—release 2022-1, ICOS Carbon Portal [data set], https://doi.org/10.18160/2G60-ZHAK, 2022. a
Whitley, R., Beringer, J., Hutley, L. B., Abramowitz, G., De Kauwe, M. G., Evans, B., Haverd, V., Li, L., Moore, C., Ryu, Y., Scheiter, S., Schymanski, S. J., Smith, B., Wang, Y.-P., Williams, M., and Yu, Q.: Challenges and opportunities in land surface modelling of savanna ecosystems, Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, 2017. a, b
Xiang, Y., Gubian, S., Suomela, B., and Hoeng, J.: Generalized Simulated Annealing for Global Optimization: The GenSA Package, The R Journal, 5, 13, https://doi.org/10.32614/RJ-2013-002, 2013. a
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M., and Guan, K.: Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests, New Phytologist, 212, 80–95, https://doi.org/10.1111/nph.14009, 2016. a, b
Yang, F., Ichii, K., White, M., Hashimoto, H., Michaelis, A., Votava, P., Zhu, A.-X., Huete, A., Running, S., and Nemani, R.: Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach, Remote Sensing of Environment, 110, 109–122, https://doi.org/10.1016/j.rse.2007.02.016, 2007. a
Yu, X., Orth, R., Reichstein, M., Bahn, M., Klosterhalfen, A., Knohl, A., Koebsch, F., Migliavacca, M., Mund, M., Nelson, J. A., Stocker, B. D., Walther, S., and Bastos, A.: Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest, Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, 2022. a
Zhang, Q., Wang, H., Dong, J., Zhong, G., and Sun, X.: Prediction of Sea Surface Temperature Using Long Short-Term Memory, IEEE Geoscience and Remote Sensing Letters, 14, 1745–1749, https://doi.org/10.1109/LGRS.2017.2733548, 2017. a
Zheng, Y., Shen, R., Wang, Y., Li, X., Liu, S., Liang, S., Chen, J. M., Ju, W., Zhang, L., and Yuan, W.: Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017, Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, 2020. a
Zscheischler, J., Mahecha, M. D., von Buttlar, J., Harmeling, S., Jung, M., Rammig, A., Randerson, J. T., Schölkopf, B., Seneviratne, S. I., Tomelleri, E., Zaehle, S., and Reichstein, M.: A few extreme events dominate global interannual variability in gross primary production, Environmental Research Letters, 9, 035001, https://doi.org/10.1088/1748-9326/9/3/035001, 2014. a
Short summary
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.
Our work addresses the predictability of carbon absorption by ecosystems across the globe,...
Altmetrics
Final-revised paper
Preprint