Articles | Volume 20, issue 13
https://doi.org/10.5194/bg-20-2671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2671-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2023
Research article |
| 06 Jul 2023
| 06 Jul 2023
A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations
Doaa Aboelyazeed
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Chonggang Xu
Earth and Environmental Sciences Division, Los Alamos National
Laboratory, Los Alamos, NM 87544, USA
Forrest M. Hoffman
Computational Sciences & Engineering Division and the Climate
Change Science Institute, Oak Ridge National Laboratory, Oak Ridge,
TN 37830, USA
Department of Civil and Environmental Engineering, University of
Tennessee, Knoxville, TN 37996, USA
Jiangtao Liu
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Alex W. Jones
SciML, Open Source Software Organization, Cambridge, MA, USA
Chris Rackauckas
Computer Science and Artificial Intelligence Laboratory (CSAIL),
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Kathryn Lawson
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
Civil and Environmental Engineering, The Pennsylvania State
University, University Park, PA 16802, USA
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Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
EGUsphere, https://doi.org/10.5194/egusphere-2025-1708, https://doi.org/10.5194/egusphere-2025-1708, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
Jiangtao Liu, Chaopeng Shen, Fearghal O'Donncha, Yalan Song, Wei Zhi, Hylke E. Beck, Tadd Bindas, Nicholas Kraabel, and Kathryn Lawson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1706, https://doi.org/10.5194/egusphere-2025-1706, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.
Mohammad Sina Jahangir, John Quilty, Chaopeng Shen, Andrea Scott, Scott Steinschneider, and Jan Adamowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-846, https://doi.org/10.5194/egusphere-2025-846, 2025
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This study presents a novel hybrid approach to streamflow prediction, significantly improving the efficiency and accuracy of fine-tuning deep learning models for hydrological prediction. Tested across numerous catchments in the U.S. and Europe, this method accelerates the fine-tuning process and improves prediction accuracy in locations beyond the training data. This innovative approach sets the stage for future hydrological models leveraging transfer learning.
Peijun Li, Yalan Song, Ming Pan, Kathryn Lawson, and Chaopeng Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-483, https://doi.org/10.5194/egusphere-2025-483, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194, https://doi.org/10.5194/egusphere-2024-4194, 2025
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Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
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Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
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Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
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.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
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We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
The Cryosphere, 17, 3987–4006, https://doi.org/10.5194/tc-17-3987-2023, https://doi.org/10.5194/tc-17-3987-2023, 2023
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This study combines field observations, non-parametric statistical analyses, and thermodynamic modeling to characterize the environmental causes of the spatial variability in soil pore water solute concentrations across two Arctic catchments with varying extents of permafrost. Vegetation type, soil moisture and redox conditions, weathering and hydrologic transport, and mineral solubility were all found to be the primary drivers of the existing spatial variability of some soil pore water solutes.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
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We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
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We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Polly C. Buotte, Charles D. Koven, Chonggang Xu, Jacquelyn K. Shuman, Michael L. Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, and Lara M. Kueppers
Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, https://doi.org/10.5194/bg-18-4473-2021, 2021
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We present an approach for ensuring the definitions of plant types in dynamic vegetation models are connected to the underlying ecological processes controlling community composition. Our approach can be applied regionally or globally. Robust resolution of community composition will allow us to use these models to address important questions related to future climate and management effects on plant community composition, structure, carbon storage, and feedbacks within the Earth system.
Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu
Biogeosciences, 18, 4005–4020, https://doi.org/10.5194/bg-18-4005-2021, https://doi.org/10.5194/bg-18-4005-2021, 2021
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We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.
Charles D. Koven, Ryan G. Knox, Rosie A. Fisher, Jeffrey Q. Chambers, Bradley O. Christoffersen, Stuart J. Davies, Matteo Detto, Michael C. Dietze, Boris Faybishenko, Jennifer Holm, Maoyi Huang, Marlies Kovenock, Lara M. Kueppers, Gregory Lemieux, Elias Massoud, Nathan G. McDowell, Helene C. Muller-Landau, Jessica F. Needham, Richard J. Norby, Thomas Powell, Alistair Rogers, Shawn P. Serbin, Jacquelyn K. Shuman, Abigail L. S. Swann, Charuleka Varadharajan, Anthony P. Walker, S. Joseph Wright, and Chonggang Xu
Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, https://doi.org/10.5194/bg-17-3017-2020, 2020
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Tropical forests play a crucial role in governing climate feedbacks, and are incredibly diverse ecosystems, yet most Earth system models do not take into account the diversity of plant traits in these forests and how this diversity may govern feedbacks. We present an approach to represent diverse competing plant types within Earth system models, test this approach at a tropical forest site, and explore how the representation of disturbance and competition governs traits of the forest community.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
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We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
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We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai
Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, https://doi.org/10.5194/hess-22-5639-2018, 2018
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Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
Bradley O. Christoffersen, Manuel Gloor, Sophie Fauset, Nikolaos M. Fyllas, David R. Galbraith, Timothy R. Baker, Bart Kruijt, Lucy Rowland, Rosie A. Fisher, Oliver J. Binks, Sanna Sevanto, Chonggang Xu, Steven Jansen, Brendan Choat, Maurizio Mencuccini, Nate G. McDowell, and Patrick Meir
Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, https://doi.org/10.5194/gmd-9-4227-2016, 2016
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We developed a plant hydraulics model for tropical forests based on established plant physiological theory, and parameterized it by conducting a pantropical hydraulic trait survey. We show that a substantial amount of trait diversity can be represented in the model by a reduced set of trait dimensions. The fully parameterized model is able capture tree-level variation in water status and improves simulations of total ecosystem transpiration, showing how to incorporate hydraulic traits in models.
A. A. Ali, C. Xu, A. Rogers, R. A. Fisher, S. D. Wullschleger, E. C. Massoud, J. A. Vrugt, J. D. Muss, N. G. McDowell, J. B. Fisher, P. B. Reich, and C. J. Wilson
Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, https://doi.org/10.5194/gmd-9-587-2016, 2016
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We have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA V1.0) to predict the photosynthetic capacities at the global scale based on the optimization of key leaf-level metabolic processes. LUNA model predicts that future climatic changes would mostly affect plant photosynthetic capabilities in high-latitude regions and that Earth system models using fixed photosynthetic capabilities are likely to substantially overestimate future global photosynthesis.
W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, https://doi.org/10.5194/hess-18-2463-2014, 2014
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Biogeosciences, 22, 2087–2096, https://doi.org/10.5194/bg-22-2087-2025, https://doi.org/10.5194/bg-22-2087-2025, 2025
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Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti
Biogeosciences, 22, 1509–1528, https://doi.org/10.5194/bg-22-1509-2025, https://doi.org/10.5194/bg-22-1509-2025, 2025
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The Amazon is facing unprecedented disturbance. Determining trends in Amazonia's carbon balance and its sensitivity to disturbance requires reliable vegetation models that adequately capture how its ecosystems exchange carbon with the atmosphere. Using ground- and satellite-based ecosystem products, we present an improved model of land–atmosphere vegetation carbon exchange across the Amazon. Our model agrees with independent aircraft observations from different locations.
Andrew S. Kowalski
Biogeosciences, 22, 785–789, https://doi.org/10.5194/bg-22-785-2025, https://doi.org/10.5194/bg-22-785-2025, 2025
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The laws of physics show that leaf oxygen is not photosynthetically enriched but extremely dilute due to the overwhelming effects of humidification. This challenges the prevailing diffusion-only paradigm regarding leaf gas exchanges because non-diffusive transport is required. Such transport also explains why fluxes of carbon dioxide and water vapour become decoupled at very high temperatures, as has been observed but not explained by plant physiologists.
Florian Wieland, Nadine Bothen, Ralph Schwidetzky, Teresa M. Seifried, Paul Bieber, Ulrich Pöschl, Konrad Meister, Mischa Bonn, Janine Fröhlich-Nowoisky, and Hinrich Grothe
Biogeosciences, 22, 103–115, https://doi.org/10.5194/bg-22-103-2025, https://doi.org/10.5194/bg-22-103-2025, 2025
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Betula pendula is a widespread birch tree species containing ice nucleation agents that can trigger the freezing of cloud droplets and thereby alter the evolution of clouds. Our study identifies three distinct ice-nucleating macromolecule (INM) aggregates of varying size that can nucleate ice at temperatures up to –5.4°C. Our findings suggest that these vegetation-derived particles may influence atmospheric processes, weather, and climate more strongly than previously thought.
Nicholas Cowan, Toby Roberts, Mark Hanlon, Aurelia Bezanger, Galina Toteva, Alex Tweedie, Karen Yeung, Ajinkya Deshpande, Peter Levy, Ute Skiba, Eiko Nemitz, and Julia Drewer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3654, https://doi.org/10.5194/egusphere-2024-3654, 2024
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We measured soil hydrogen (H2) fluxes from two field sites, a managed grassland and a planted deciduous woodland, with flux measurements of H2 covering full seasonal cycles. We estimate annual H2 uptake of -3.1 ± 0.1 and -12.0 ± 0.4 kg H2 ha-1 yr-1 for the grassland and woodland sites, respectively. Soil moisture was found to be the primary driver of H2 uptake, with the clay content of the soils providing a physical barrier which limited H2 uptake.
Matthew G. Davis, Kevin Yan, and Jennifer G. Murphy
Biogeosciences, 21, 5381–5392, https://doi.org/10.5194/bg-21-5381-2024, https://doi.org/10.5194/bg-21-5381-2024, 2024
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Ammonia applied as fertilizer can volatilize into the atmosphere. This can threaten vulnerable ecosystems and human health. We investigated the partitioning of ammonia between an immobile adsorbed phase and mobile aqueous phase using several adsorption models. Using the Temkin model we determined that previous approaches to this issue may overestimate the quantity available for exchange by a factor of 5–20, suggesting that ammonia emissions from soil may be overestimated.
Annika Einbock and Franz Conen
Biogeosciences, 21, 5219–5231, https://doi.org/10.5194/bg-21-5219-2024, https://doi.org/10.5194/bg-21-5219-2024, 2024
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A small fraction of particles found at great heights in the atmosphere can freeze cloud droplets at temperatures of ≥ −10 °C and thus influence cloud properties. We provide a novel type of evidence that plant canopies are a major source of such biological ice-nucleating particles in the air above the Alps, potentially affecting mixed-phase cloud development.
Stephen E. Schwartz
Biogeosciences, 21, 5045–5057, https://doi.org/10.5194/bg-21-5045-2024, https://doi.org/10.5194/bg-21-5045-2024, 2024
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Anticorrelation in uptake of atmospheric CO2 following pulse emission or abrupt cessation of emissions is examined in two key model intercomparison studies. In both studies net transfer coefficients from the atmosphere to the world ocean and the terrestrial biosphere are anticorrelated across models, reducing inter-model diversity in decrease of atmospheric CO2 following the perturbation, increasing uncertainties of global warming potentials and consequences of prospective emission reductions.
Laura Dénise Nadolski, Tarek Sebastian El Madany, Jacob Allen Nelson, Arnaud Carrara, Gerardo Moreno, Richard K. F. Nair, Yunpeng Luo, Anke Hildebrandt, Victor Rolo, Markus Reichstein, and Sung-Ching Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-3190, https://doi.org/10.5194/egusphere-2024-3190, 2024
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Semi-arid ecosystems are crucial for Earth's carbon balance and are sensitive to changes in nitrogen (N) and phosphorus (P) levels. Their carbon dynamics are complex and not fully understood. We studied how long-term nutrient changes affect carbon exchange. In summer, N+P changed plant composition and productivity. In transitional seasons, carbon exchange was less weather-dependent with N. Adding N and N+P are increasing carbon exchange variability, driven by grass greenness.
Qian Li, Gil Lerner, Einat Bar, Efraim Lewinsohn, and Eran Tas
Biogeosciences, 21, 4133–4147, https://doi.org/10.5194/bg-21-4133-2024, https://doi.org/10.5194/bg-21-4133-2024, 2024
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Our research indicates that instantaneous changes in meteorological parameters better reflect drought-induced changes in the emission rates of biogenic volatile organic compounds (BVOCs) from natural vegetation than their absolute values. However, following a small amount of irrigation, this trend became more moderate or reversed, accompanied by a dramatic increase in BVOC emission rates. These findings advance our understanding of BVOC emissions under climate change.
Pia Gottschalk, Aram Kalhori, Zhan Li, Christian Wille, and Torsten Sachs
Biogeosciences, 21, 3593–3616, https://doi.org/10.5194/bg-21-3593-2024, https://doi.org/10.5194/bg-21-3593-2024, 2024
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To improve the accuracy of spatial carbon exchange estimates, we evaluated simple linear models for net ecosystem exchange (NEE) and gross primary productivity (GPP) and how they can be used to upscale the CO2 exchange of agricultural fields. The models are solely driven by Sentinel-2-derived vegetation indices (VIs). Evaluations show that different VIs have variable power to estimate NEE and GPP of crops in different years. The overall performance is as good as results from complex crop models.
Liliana Scapucci, Ankit Shekhar, Sergio Aranda-Barranco, Anastasiia Bolshakova, Lukas Hörtnagl, Mana Gharun, and Nina Buchmann
Biogeosciences, 21, 3571–3592, https://doi.org/10.5194/bg-21-3571-2024, https://doi.org/10.5194/bg-21-3571-2024, 2024
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Forests face increased exposure to “compound soil and atmospheric drought” (CSAD) events due to global warming. We examined the impacts and drivers of CO2 fluxes during CSAD events at multiple layers of a deciduous forest over 18 years. Results showed reduced net ecosystem productivity and forest-floor respiration during CSAD events, mainly driven by soil and atmospheric drought. This unpredictability in forest CO2 fluxes jeopardises reforestation projects aimed at mitigating CO2 emissions.
Piaopiao Ke, Anna Lintunen, Pasi Kolari, Annalea Lohila, Santeri Tuovinen, Janne Lampilahti, Roseline Thakur, Maija Peltola, Otso Peräkylä, Tuomo Nieminen, Ekaterina Ezhova, Mari Pihlatie, Asta Laasonen, Markku Koskinen, Helena Rautakoski, Laura Heimsch, Tom Kokkonen, Aki Vähä, Ivan Mammarella, Steffen Noe, Jaana Bäck, Veli-Matti Kerminen, and Markku Kulmala
EGUsphere, https://doi.org/10.5194/egusphere-2024-1967, https://doi.org/10.5194/egusphere-2024-1967, 2024
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Our research explores diverse ecosystems’ role in climate cooling via the concept of CarbonSink+ Potential. We measured CO2 uptake and loaal aerosol production in forests, farms, peatlands, urban gardens, and coastal areas across Finland and Estonia. The long-term data reveal that while forests are vital regarding CarbonSink+ Potential, farms and urban gardens also play significant roles. These insights can help optimize management policy of natural resource to mitigate global warming.
Tamara Emmerichs, Yen-Sen Lu, and Domenico Taraborrelli
Biogeosciences, 21, 3251–3269, https://doi.org/10.5194/bg-21-3251-2024, https://doi.org/10.5194/bg-21-3251-2024, 2024
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We assess the representation of the plant response to surface water in a global atmospheric chemistry model. This sensitivity is crucial for the return of precipitation back into the atmosphere and thus significantly impacts the representation of weather as well as air quality. The newly implemented response function reduces this process and has a better comparison with satellite observations. This yields a higher intensity of unusual warm periods and higher production of air pollutants.
Nina L. H. Kinney, Charles A. Hepburn, Matthew I. Gibson, Daniel Ballesteros, and Thomas F. Whale
Biogeosciences, 21, 3201–3214, https://doi.org/10.5194/bg-21-3201-2024, https://doi.org/10.5194/bg-21-3201-2024, 2024
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Molecules released from plant pollen induce the formation of ice from supercooled water at temperatures warm enough to suggest an underlying function for this activity. In this study we show that ice nucleators are ubiquitous in pollen. We suggest the molecules responsible fulfil some unrelated biological function and nucleate ice incidentally. The ubiquity of ice-nucleating molecules in pollen and particularly active examples reveal a greater potential for pollen to impact weather and climate.
Max Gaber, Yanghui Kang, Guy Schurgers, and Trevor Keenan
Biogeosciences, 21, 2447–2472, https://doi.org/10.5194/bg-21-2447-2024, https://doi.org/10.5194/bg-21-2447-2024, 2024
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Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
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Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Luana Krebs, Susanne Burri, Iris Feigenwinter, Mana Gharun, Philip Meier, and Nina Buchmann
Biogeosciences, 21, 2005–2028, https://doi.org/10.5194/bg-21-2005-2024, https://doi.org/10.5194/bg-21-2005-2024, 2024
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This study explores year-round forest-floor greenhouse gas (GHG) fluxes in a Swiss spruce forest. Soil temperature and snow depth affected forest-floor respiration, while CH4 uptake was linked to snow cover. Negligible N2O fluxes were observed. In 2022, a warm year, CO2 emissions notably increased. The study suggests rising forest-floor GHG emissions due to climate change, impacting carbon sink behavior. Thus, for future forest management, continuous year-round GHG flux measurements are crucial.
Simone Rodrigues, Glauber Cirino, Demerval Moreira, Andrea Pozzer, Rafael Palácios, Sung-Ching Lee, Breno Imbiriba, José Nogueira, Maria Isabel Vitorino, and George Vourlitis
Biogeosciences, 21, 843–868, https://doi.org/10.5194/bg-21-843-2024, https://doi.org/10.5194/bg-21-843-2024, 2024
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The radiative effects of atmospheric particles are still unknown for a wide variety of species and types of vegetation present in Amazonian biomes. We examined the effects of aerosols on solar radiation and their impacts on photosynthesis in an area of semideciduous forest in the southern Amazon Basin. Under highly smoky-sky conditions, our results show substantial photosynthetic interruption (20–70 %), attributed specifically to the decrease in solar radiation and leaf canopy temperature.
Ruben B. Schulte, Jordi Vilà-Guerau de Arellano, Susanna Rutledge-Jonker, Shelley van der Graaf, Jun Zhang, and Margreet C. van Zanten
Biogeosciences, 21, 557–574, https://doi.org/10.5194/bg-21-557-2024, https://doi.org/10.5194/bg-21-557-2024, 2024
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We analyzed measurements with the aim of finding relations between the surface atmosphere exchange of NH3 and the CO2 uptake and transpiration by vegetation. We found a high correlation of daytime NH3 emissions with both latent heat flux and photosynthetically active radiation. Very few simultaneous measurements of NH3, CO2 fluxes and meteorological variables exist at sub-diurnal timescales. This study paves the way to finding more robust relations between the NH3 exchange flux and CO2 uptake.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
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We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
Yuhao Cui, Eri Tachibana, Kimitaka Kawamura, and Yuzo Miyazaki
Biogeosciences, 20, 4969–4980, https://doi.org/10.5194/bg-20-4969-2023, https://doi.org/10.5194/bg-20-4969-2023, 2023
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Fatty alcohols (FAs) are major components of surface lipids in plant leaves and serve as surface-active aerosols. Our study on the aerosol size distributions in a forest suggests that secondary FAs (SFAs) originated from plant waxes and that leaf senescence status is likely an important factor controlling the size distribution of SFAs. This study provides new insights into the sources of primary biological aerosol particles (PBAPs) and their effects on the aerosol ice nucleation activity.
Joyson Ahongshangbam, Liisa Kulmala, Jesse Soininen, Yasmin Frühauf, Esko Karvinen, Yann Salmon, Anna Lintunen, Anni Karvonen, and Leena Järvi
Biogeosciences, 20, 4455–4475, https://doi.org/10.5194/bg-20-4455-2023, https://doi.org/10.5194/bg-20-4455-2023, 2023
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Urban vegetation is important for removing urban CO2 emissions and cooling. We studied the response of urban trees' functions (photosynthesis and transpiration) to a heatwave and drought at four urban green areas in the city of Helsinki. We found that tree water use was increased during heatwave and drought periods, but there was no change in the photosynthesis rates. The heat and drought conditions were severe at the local scale but were not excessive enough to restrict urban trees' functions.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
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We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Nadav Bekin and Nurit Agam
Biogeosciences, 20, 3791–3802, https://doi.org/10.5194/bg-20-3791-2023, https://doi.org/10.5194/bg-20-3791-2023, 2023
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The mechanisms of soil CO2 flux in dry desert soils are not fully understood. Yet studies conducted in desert ecosystems rarely discuss potential errors related to using the commonly used flux chambers in dry and bare soils. In our study, the conventional deployment practice of the chambers underestimated the instantaneous CO2 flux by up to 50 % and the total daily CO2 uptake by 35 %. This suggests that desert soils are a larger carbon sink than previously reported.
Rosemary J. Eufemio, Ingrid de Almeida Ribeiro, Todd L. Sformo, Gary A. Laursen, Valeria Molinero, Janine Fröhlich-Nowoisky, Mischa Bonn, and Konrad Meister
Biogeosciences, 20, 2805–2812, https://doi.org/10.5194/bg-20-2805-2023, https://doi.org/10.5194/bg-20-2805-2023, 2023
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Lichens, the dominant vegetation in the Arctic, contain ice nucleators (INs) that enable freezing close to 0°C. Yet the abundance, diversity, and function of lichen INs is unknown. Our screening of lichens across Alaska reveal that most species have potent INs. We find that lichens contain two IN populations which retain activity under environmentally relevant conditions. The ubiquity and stability of lichen INs suggest that they may have considerable impacts on local atmospheric patterns.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
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We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Hamadou Balde, Gabriel Hmimina, Yves Goulas, Gwendal Latouche, and Kamel Soudani
Biogeosciences, 20, 1473–1490, https://doi.org/10.5194/bg-20-1473-2023, https://doi.org/10.5194/bg-20-1473-2023, 2023
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This study focuses on the relationship between sun-induced chlorophyll fluorescence (SIF) and ecosystem gross primary productivity (GPP) across the ICOS European flux tower network. It shows that SIF, coupled with reflectance observations, explains over 80 % of the GPP variability across diverse ecosystems but fails to bring new information compared to reflectance alone at coarse spatial scales (~5 km). These findings have applications in agriculture and ecophysiological studies.
John T. Walker, Xi Chen, Zhiyong Wu, Donna Schwede, Ryan Daly, Aleksandra Djurkovic, A. Christopher Oishi, Eric Edgerton, Jesse Bash, Jennifer Knoepp, Melissa Puchalski, John Iiames, and Chelcy F. Miniat
Biogeosciences, 20, 971–995, https://doi.org/10.5194/bg-20-971-2023, https://doi.org/10.5194/bg-20-971-2023, 2023
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Better estimates of atmospheric nitrogen (N) deposition are needed to accurately assess ecosystem risk and impacts from deposition of nutrients and acidity. Using measurements and modeling, we estimate total N deposition of 6.7 kg N ha−1 yr−1 at a forest site in the southern Appalachian Mountains, a region sensitive to atmospheric deposition. Reductions in deposition of reduced forms of N (ammonia and ammonium) will be needed to meet the lowest estimates of N critical loads for the region.
Yi-Ying Chen and Sebastiaan Luyssaert
Biogeosciences, 20, 349–363, https://doi.org/10.5194/bg-20-349-2023, https://doi.org/10.5194/bg-20-349-2023, 2023
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Tropical cyclones are typically assumed to be associated with ecosystem damage. This study challenges this assumption and suggests that instead of reducing leaf area, cyclones in East Asia may increase leaf area by alleviating water stress.
Deborah F. McGlynn, Graham Frazier, Laura E. R. Barry, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz
Biogeosciences, 20, 45–55, https://doi.org/10.5194/bg-20-45-2023, https://doi.org/10.5194/bg-20-45-2023, 2023
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Using a custom-made gas chromatography flame ionization detector, 2 years of speciated hourly biogenic volatile organic compound data were collected in a forest in central Virginia. We identify diurnal and seasonal variability in the data, which is shown to impact atmospheric oxidant budgets. A comparison with emission models identified discrepancies with implications for model outcomes. We suggest increased monitoring of speciated biogenic volatile organic compounds to improve modeled results.
Luke D. Schiferl, Jennifer D. Watts, Erik J. L. Larson, Kyle A. Arndt, Sébastien C. Biraud, Eugénie S. Euskirchen, Jordan P. Goodrich, John M. Henderson, Aram Kalhori, Kathryn McKain, Marikate E. Mountain, J. William Munger, Walter C. Oechel, Colm Sweeney, Yonghong Yi, Donatella Zona, and Róisín Commane
Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022, https://doi.org/10.5194/bg-19-5953-2022, 2022
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As the Arctic rapidly warms, vast stores of thawing permafrost could release carbon dioxide (CO2) into the atmosphere. We combined observations of atmospheric CO2 concentrations from aircraft and a tower with observed CO2 fluxes from tundra ecosystems and found that the Alaskan North Slope in not a consistent source nor sink of CO2. Our study shows the importance of using both site-level and atmospheric measurements to constrain regional net CO2 fluxes and improve biogenic processes in models.
Pascal Wintjen, Frederik Schrader, Martijn Schaap, Burkhard Beudert, Richard Kranenburg, and Christian Brümmer
Biogeosciences, 19, 5287–5311, https://doi.org/10.5194/bg-19-5287-2022, https://doi.org/10.5194/bg-19-5287-2022, 2022
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For the first time, we compared four methods for estimating the annual dry deposition of total reactive nitrogen into a low-polluted forest ecosystem. In our analysis, we used 2.5 years of flux measurements, an in situ modeling approach, a large-scale chemical transport model (CTM), and canopy budget models. Annual nitrogen dry deposition budgets ranged between 4.3 and 6.7 kg N ha−1 a−1, depending on the applied method.
Michael Staudt, Juliane Daussy, Joseph Ingabire, and Nafissa Dehimeche
Biogeosciences, 19, 4945–4963, https://doi.org/10.5194/bg-19-4945-2022, https://doi.org/10.5194/bg-19-4945-2022, 2022
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We studied the short- and long-term effects of CO2 as a function of temperature on monoterpene emissions from holm oak. Similarly to isoprene, emissions decreased non-linearly with increasing CO2, with no differences among compounds and chemotypes. The CO2 response was modulated by actual leaf and growth temperature but not by growth CO2. Estimates of annual monoterpene release under double CO2 suggest that CO2 inhibition does not offset the increase in emissions due to expected warming.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
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Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Valery A. Isidorov and Andrej A. Zaitsev
Biogeosciences, 19, 4715–4746, https://doi.org/10.5194/bg-19-4715-2022, https://doi.org/10.5194/bg-19-4715-2022, 2022
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Biogenic volatile organic compounds (VOCs) play a critical role in earth-system processes: they are
main playersin the formation of tropospheric O3 and secondary aerosols, which have a significant impact on climate, human health and crops. A complex mixture of VOCs, formed as a result of physicochemical and biological processes, is released into the atmosphere from the forest floor. This review presents data on the composition of VOCs and contribution of various processes to their emissions.
David S. McLagan, Harald Biester, Tomas Navrátil, Stephan M. Kraemer, and Lorenz Schwab
Biogeosciences, 19, 4415–4429, https://doi.org/10.5194/bg-19-4415-2022, https://doi.org/10.5194/bg-19-4415-2022, 2022
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Spruce and larch trees are effective archiving species for historical atmospheric mercury using growth rings of bole wood. Mercury stable isotope analysis proved an effective tool to characterise industrial mercury signals and assess mercury uptake pathways (leaf uptake for both wood and bark) and mercury cycling within the trees. These data detail important information for understanding the mercury biogeochemical cycle particularly in forest systems.
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.
Anders Lindroth, Norbert Pirk, Ingibjörg S. Jónsdóttir, Christian Stiegler, Leif Klemedtsson, and Mats B. Nilsson
Biogeosciences, 19, 3921–3934, https://doi.org/10.5194/bg-19-3921-2022, https://doi.org/10.5194/bg-19-3921-2022, 2022
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We measured the fluxes of carbon dioxide and methane between a moist moss tundra and the atmosphere on Svalbard in order to better understand how such ecosystems are affecting the climate and vice versa. We found that the system was a small sink of carbon dioxide and a small source of methane. These fluxes are small in comparison with other tundra ecosystems in the high Arctic. Analysis of temperature sensitivity showed that respiration was more sensitive than photosynthesis above about 6 ℃.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
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The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 19, 3739–3756, https://doi.org/10.5194/bg-19-3739-2022, https://doi.org/10.5194/bg-19-3739-2022, 2022
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A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
Siqi Li, Wei Zhang, Xunhua Zheng, Yong Li, Shenghui Han, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, and Chong Zhang
Biogeosciences, 19, 3001–3019, https://doi.org/10.5194/bg-19-3001-2022, https://doi.org/10.5194/bg-19-3001-2022, 2022
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The CNMM–DNDC model was modified to simulate ammonia volatilization (AV) from croplands. AV from cultivated uplands followed the first-order kinetics, which was jointly regulated by the factors of soil properties and meteorological conditions. AV simulation from rice paddy fields was improved by incorporating Jayaweera–Mikkelsen mechanisms. The modified model performed well in simulating the observed cumulative AV measured from 63 fertilization events in China.
Matthias Volk, Matthias Suter, Anne-Lena Wahl, and Seraina Bassin
Biogeosciences, 19, 2921–2937, https://doi.org/10.5194/bg-19-2921-2022, https://doi.org/10.5194/bg-19-2921-2022, 2022
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Because soils are an important sink for greenhouse gasses, we subjected sub-alpine grassland to a six-level climate change treatment.
Two independent methods showed that at warming > 1.5 °C the grassland ecosystem lost ca. 14 % or ca. 1 kg C m−2 in 5 years.
This shrinking of the terrestrial C sink implies a substantial positive feedback to the atmospheric greenhouse effect.
It is likely that this dramatic C loss is a transient effect before a new, climate-adjusted steady state is reached.
Johan A. Eckdahl, Jeppe A. Kristensen, and Daniel B. Metcalfe
Biogeosciences, 19, 2487–2506, https://doi.org/10.5194/bg-19-2487-2022, https://doi.org/10.5194/bg-19-2487-2022, 2022
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This study found climate to be a driving force for increasing per area emissions of greenhouse gases and removal of important nutrients from high-latitude forests due to wildfire. It used detailed direct measurements over a large area to uncover patterns and mechanisms of restructuring of forest carbon and nitrogen pools that are extrapolatable to larger regions. It also takes a step forward in filling gaps in global knowledge of northern forest response to climate-change-strengthened wildfires.
Sparkle L. Malone, Youmi Oh, Kyle A. Arndt, George Burba, Roisin Commane, Alexandra R. Contosta, Jordan P. Goodrich, Henry W. Loescher, Gregory Starr, and Ruth K. Varner
Biogeosciences, 19, 2507–2522, https://doi.org/10.5194/bg-19-2507-2022, https://doi.org/10.5194/bg-19-2507-2022, 2022
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To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover.
Bruna R. F. Oliveira, Jan J. Keizer, and Thomas Foken
Biogeosciences, 19, 2235–2243, https://doi.org/10.5194/bg-19-2235-2022, https://doi.org/10.5194/bg-19-2235-2022, 2022
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This study analyzes the impacts of this windthrow on the aerodynamic characteristics of zero-plane displacement and roughness length and, ultimately, their implications for the turbulent fluxes. The turbulent fluxes were only affected to a minor degree by the windthrow, but the footprint area of the flux tower changed markedly so that the target area of the measurements had to be redetermined.
Minttu Havu, Liisa Kulmala, Pasi Kolari, Timo Vesala, Anu Riikonen, and Leena Järvi
Biogeosciences, 19, 2121–2143, https://doi.org/10.5194/bg-19-2121-2022, https://doi.org/10.5194/bg-19-2121-2022, 2022
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The carbon sequestration potential of two street tree species and the soil beneath them was quantified with the urban land surface model SUEWS and the soil carbon model Yasso. The street tree plantings turned into a modest sink of carbon from the atmosphere after 14 years. Overall, the results indicate the importance of soil in urban carbon sequestration estimations, as soil respiration exceeded the carbon uptake in the early phase, due to the high initial carbon loss from the soil.
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, and Kai Puolamäki
Biogeosciences, 19, 2095–2099, https://doi.org/10.5194/bg-19-2095-2022, https://doi.org/10.5194/bg-19-2095-2022, 2022
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Causal structure discovery algorithms have been making headway into Earth system sciences, and they can be used to increase our understanding on biosphere–atmosphere interactions. In this paper we present a procedure on how to utilize prior knowledge of the domain experts together with these algorithms in order to find more robust causal structure models. We also demonstrate how to avoid pitfalls such as over-fitting and concept drift during this process.
Makoto Saito, Tomohiro Shiraishi, Ryuichi Hirata, Yosuke Niwa, Kazuyuki Saito, Martin Steinbacher, Doug Worthy, and Tsuneo Matsunaga
Biogeosciences, 19, 2059–2078, https://doi.org/10.5194/bg-19-2059-2022, https://doi.org/10.5194/bg-19-2059-2022, 2022
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This study tested combinations of two sources of AGB data and two sources of LCC data and used the same burned area satellite data to estimate BB CO emissions. Our analysis showed large discrepancies in annual mean CO emissions and explicit differences in the simulated CO concentrations among the BB emissions estimates. This study has confirmed that BB emissions estimates are sensitive to the land surface information on which they are based.
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
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We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Cited articles
Aboelyazeed, D., Xu, C., Hoffman, F. M., Liu, J., Jones, A. W., Rackauckas, C., Lawson, K. E., and Shen, C.: A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems, Zenodo [code], https://doi.org/10.5281/zenodo.8067204, 2023.
Ali, A. A., Xu, C., Rogers, A., McDowell, N. G., Medlyn, B. E., Fisher, R.
A., Wullschleger, S. D., Reich, P. B., Vrugt, J. A., Bauerle, W. L.,
Santiago, L. S., and Wilson, C. J.: Global-scale environmental control of
plant photosynthetic capacity., Ecol. Appl., 25, 2349–2365,
https://doi.org/10.1890/14-2111.1, 2015.
Ali, A. A., Xu, C., Rogers, A., Fisher, R. A., Wullschleger, S. D., Massoud, E. C., Vrugt, J. A., Muss, J. D., McDowell, N. G., Fisher, J. B., Reich, P. B., and Wilson, C. J.: A global scale mechanistic model of photosynthetic capacity (LUNA V1.0), Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, 2016.
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M.:
Automatic differentiation in machine learning: A survey, J. Mach.
Learn. Res., 18, 1–43, 2018.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol.,
320, 18–36, https://doi.org/10/ccx2ks, 2006.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29,
https://doi.org/10/fgmngv, 2001.
Bezanson, J., Karpinski, S., Shah, V. B., and Edelman, A.: Julia: A fast
dynamic language for technical computing, arXiv [preprint], https://doi.org/10.48550/arXiv.1209.5145, 24 September 2012.
Chen, J. M., Wang, R., Liu, Y., He, L., Croft, H., Luo, X., Wang, H., Smith, N. G., Keenan, T. F., Prentice, I. C., Zhang, Y., Ju, W., and Dong, N.: Global datasets of leaf photosynthetic capacity for ecological and earth system research, Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, 2022.
Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016.
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil
hydraulic properties, Water Resour. Res., 14, 601–604,
https://doi.org/10.1029/wr014i004p00601, 1978.
Collatz, G., Ribas-Carbo, M., and Berry, J.: Coupled Photosynthesis-Stomatal
Conductance Model for Leaves of C4 Plants, Aust. J. Plant
Physiol., 19, 519, https://doi.org/10/cw8rtn, 1992.
Croft, H., Chen, J. M., Luo, X., Bartlett, P., Chen, B., and Staebler, R.
M.: Leaf chlorophyll content as a proxy for leaf photosynthetic capacity,
Glob. Change Biol., 23, 3513–3524, https://doi.org/10.1111/gcb.13599,
2017.
Dusenge, M. E., Duarte, A. G., and Way, D. A.: Plant carbon metabolism and
climate change: elevated CO2 and temperature impacts on photosynthesis,
photorespiration and respiration, New Phytol., 221, 32–49,
https://doi.org/10.1111/nph.15283, 2019.
ElSaadani, M., Habib, E., Abdelhameed, A. M., and Bayoumi, M.: Assessment of
a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and
Filling the Gaps in Between Soil Moisture Observations, Fr.
Art. Int., 4, 636234, https://doi.org/10.3389/frai.2021.636234, 2021.
Fang, K. and Shen, C.: Near-real-time forecast of satellite-based soil
moisture using long short-term memory with an adaptive data integration
kernel, J. Hydrometeor., 21, 399–413,
https://doi.org/10.1175/jhm-d-19-0169.1, 2020.
Fang, K., Shen, C., Kifer, D., and Yang, X.: Prolongation of SMAP to
spatiotemporally seamless coverage of continental U.S. using a deep learning
neural network, Geophys. Res. Lett., 44, 11030–11039,
https://doi.org/10.1002/2017gl075619, 2017.
Fang, K., Kifer, D., Lawson, K., Feng, D., and Shen, C.: The data synergy
effects of time-series deep learning models in hydrology, Water Resour.
Res., 58, e2021WR029583, https://doi.org/10.1029/2021WR029583, 2022.
Farouki, O. T.: The thermal properties of soils in cold regions, Cold
Reg. Sci. Technol., 5, 67–75,
https://doi.org/10.1016/0165-232X(81)90041-0, 1981.
Farquhar, G. D. and von Caemmerer, S.: Modelling of Photosynthetic Response to Environmental Conditions, in: Physiological Plant Ecology II: Water Relations and Carbon Assimilation, edited by: Lange, O. L., Nobel, P. S., Osmond, C. B., and Ziegler, H., Springer Berlin Heidelberg, Berlin, Heidelberg, 549–587, https://doi.org/10.1007/978-3-642-68150-9_17, 1982.
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/fs9dpz, 1980.
Feng, D., Fang, K., and Shen, C.: Enhancing streamflow forecast and
extracting insights using long-short term memory networks with data
integration at continental scales, Water Resour. Res., 56,
e2019WR026793, https://doi.org/10.1029/2019WR026793, 2020.
Feng, D., Lawson, K., and Shen, C.: Mitigating prediction error of deep
learning streamflow models in large data-sparse regions with ensemble
modeling and soft data, Geophys. Res. Lett., 48, e2021GL092999,
https://doi.org/10.1029/2021GL092999, 2021.
Feng, D., Liu, J., Lawson, K., and Shen, C.: Differentiable, learnable,
regionalized process-based models with multiphysical outputs can approach
state-of-the-art hydrologic prediction accuracy, Water Resour. Res.,
58, e2022WR032404, https://doi.org/10.1029/2022WR032404, 2022a.
Feng, D., Beck, H., Lawson, K., and Shen, C.: The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-245, in review, 2022b.
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.
Gowda, S., Ma, Y., Cheli, A., Gwóÿzdÿ, M., Shah, V. B., Edelman,
A., and Rackauckas, C.: High-performance symbolic-numerics via multiple
dispatch, ACM Commun. Comput. Algebra, 55, 92–96,
https://doi.org/10.1145/3511528.3511535, 2022.
Hengl, T.: Sand content in % (kg/kg) at 6 standard depths (0, 10, 30,
60, 100 and 200 cm) at 250 m resolution (v0.2), Zenodo [data set],
https://doi.org/10.5281/ZENODO.2525662, 2018.
Hengl, T. and Wheeler, I.: Soil Organic Carbon Content In X 5 G/Kg At
6 Standard Depths (0, 10, 30, 60, 100 And 200 Cm) At 250 M Resolution, Zenodo [data set],
https://doi.org/10.5281/ZENODO.1475458, 2018.
Hossain, M. S., Al-Hammadi, M., and Muhammad, G.: Automatic fruit
classification using deep learning for industrial applications, IEEE
T. Ind. Inform., 15, 1027–1034,
https://doi.org/10.1109/TII.2018.2875149, 2019.
Hrnjica, B., Mehr, A. D., Jakupoviæ, E., Crnkiæ, A., and
Hasanagiæ, R.: Application of deep learning neural networks for nitrate
prediction in the Klokot River, Bosnia and Herzegovina, in: 2021 7th
International Conference on Control, Instrumentation and Automation (ICCIA),
2021 7th International Conference on Control, Instrumentation and Automation
(ICCIA), 1–6, https://doi.org/10.1109/ICCIA52082.2021.9403565, 2021.
Hüllermeier, E. and Waegeman, W.: Aleatoric and epistemic uncertainty in
machine learning: an introduction to concepts and methods, Mach. Learn.,
110, 457–506, https://doi.org/10.1007/s10994-021-05946-3, 2021.
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Engin., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007.
Innes, M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E., Shah, V. B.,
and Tebbutt, W.: A Differentiable Programming System to Bridge Machine
Learning and Scientific Computing, arXiv [preprint], https://doi.org/10.48550/arXiv.1907.07587, 18 July
2019.
Ji, J.: A climate-vegetation interaction model: Simulating physical and
biological processes at the surface, J. Biogeogr., 22, 445–451,
https://doi.org/10.2307/2845941, 1995.
Kirschbaum, M. U. F.: Direct and indirect climate change effects on
photosynthesis and transpiration, Plant Biol., 6, 242–253,
https://doi.org/10.1055/s-2004-820883, 2004.
Knauer, J., Zaehle, S., Medlyn, B. E., Reichstein, M., Williams, C. A.,
Migliavacca, M., De Kauwe, M. G., Werner, C., Keitel, C., Kolari, P.,
Limousin, J.-M., and Linderson, M.-L.: Towards physiologically meaningful
water-use efficiency estimates from eddy covariance data, Glob. Change
Biol., 24, 694–710, https://doi.org/10.1111/gcb.13893, 2018.
Knorr, W. and Heimann, M.: Uncertainties in global terrestrial biosphere
modeling, Part II: Global constraints for a process-based vegetation model,
Global Biogeochem. Cy., 15, 227–246,
https://doi.org/10.1029/1998GB001060, 2001.
Koven, C. D., Knox, R. G., Fisher, R. A., Chambers, J. Q., Christoffersen, B. O., Davies, S. J., Detto, M., Dietze, M. C., Faybishenko, B., Holm, J., Huang, M., Kovenock, M., Kueppers, L. M., Lemieux, G., Massoud, E., McDowell, N. G., Muller-Landau, H. C., Needham, J. F., Norby, R. J., Powell, T., Rogers, A., Serbin, S. P., Shuman, J. K., Swann, A. L. S., Varadharajan, C., Walker, A. P., Wright, S. J., and Xu, C.: Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama, Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, 2020.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D.,
Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J.,
Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A.,
Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P.,
Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B.,
Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox,
R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey,
A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M.,
Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val
Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of
New Features, Benchmarking, and Impact of Forcing Uncertainty, J.
Adv. Model. Earth Sy., 11, 4245–4287,
https://doi.org/10.1029/2018ms001583, 2019.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep Learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Leong, W. J. and Horgan, H. J.: DeepBedMap: a deep neural network for resolving the bed topography of Antarctica, The Cryosphere, 14, 3687–3705, https://doi.org/10.5194/tc-14-3687-2020, 2020.
Letts, M. G., Roulet, N. T., Comer, N. T., Skarupa, M. R., and Verseghy, D.
L.: Parametrization of peatland hydraulic properties for the Canadian land
surface scheme, Atmos. Ocean, 38, 141–160,
https://doi.org/10.1080/07055900.2000.9649643, 2000.
Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig,
S., Eamus, D., de Dios, V. R., Mitchell, P., Ellsworth, D. S., de Beeck, M.
O., Wallin, G., Uddling, J., Tarvainen, L., Linderson, M.-L., Cernusak, L.
A., Nippert, J. B., Ocheltree, T. W., Tissue, D. T., Martin-StPaul, N. K.,
Rogers, A., Warren, J. M., De Angelis, P., Hikosaka, K., Han, Q., Onoda, Y.,
Gimeno, T. E., Barton, C. V. M., Bennie, J., Bonal, D., Bosc, A., Löw,
M., Macinins-Ng, C., Rey, A., Rowland, L., Setterfield, S. A., Tausz-Posch,
S., Zaragoza-Castells, J., Broadmeadow, M. S. J., Drake, J. E., Freeman, M.,
Ghannoum, O., Hutley, L. B., Kelly, J. W., Kikuzawa, K., Kolari, P., Koyama,
K., Limousin, J.-M., Meir, P., Lola da Costa, A. C., Mikkelsen, T. N.,
Salinas, N., Sun, W., and Wingate, L.: Optimal stomatal behaviour around the
world, Nat. Clim. Change, 5, 459–464,
https://doi.org/10.1038/nclimate2550, 2015.
Lin, Y.-S., Medlyn, B., Duursma, R., and Knauer, J.: Leaf Gas Exchange Database, Bitbucket [data set], https://bitbucket.org/gsglobal/leafgasexchange/src/master/, 2017.
Liu, J., Rahmani, F., Lawson, K., and Shen, C.: A multiscale deep learning
model for soil moisture integrating satellite and in situ data, Geophys.
Res. Lett., 49, e2021GL096847, https://doi.org/10.1029/2021GL096847,
2022.
Liu, J., Hughes, D., Rahmani, F., Lawson, K., and Shen, C.: Evaluating a
global soil moisture dataset from a multitask model (GSM3 v1.0) with
potential applications for crop threats, Geoscientific Model Development,
16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, 2023.
Luo, X., Keenan, T., Chen, J., Croft, H., Prentice, I., Smith, N., Walker,
A., Wang, H., Wang, R., Xu, C., and Zhang, Y.: Global variation in the
fraction of leaf nitrogen allocated to photosynthesis, Nat.
Commun., 12, 4866, https://doi.org/10.1038/s41467-021-25163-9, 2021.
Ma, K., Feng, D., Lawson, K., Tsai, W.-P., Liang, C., Huang, X., Sharma, A.,
and Shen, C.: Transferring hydrologic data across continents – Leveraging
data-rich regions to improve hydrologic prediction in data-sparse regions,
Water Resour. Res., 57, e2020WR028600,
https://doi.org/10.1029/2020wr028600, 2021.
Ma, Y., Gowda, S., Anantharaman, R., Laughman, C., Shah, V. B., and
Rackauckas, C.: ModelingToolkit: A composable graph transformation system
for equation-based modeling, CoRR, arXiv [preprint], https://doi.org/10.48550/arXiv.2103.05244, 9 February 2022.
Mäkelä, J., Knauer, J., Aurela, M., Black, A., Heimann, M., Kobayashi, H., Lohila, A., Mammarella, I., Margolis, H., Markkanen, T., Susiluoto, J., Thum, T., Viskari, T., Zaehle, S., and Aalto, T.: Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH, Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019, 2019.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., de Angelis, P., Freeman, M., and Wingate,
L.: Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Glob. Change Biol., 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Meyer, F. H.: Encyclopedia of Plant Physiology, New Series. Editors: Pirson,
A.; Zimmermann, M.H., Vol. 12, Part A (in 4 parts) Physiological Plant
Ecology I. Responses to the Physical Environment, Editors: Lange, O.L.;
Nobel, P.S.; Osmond, C.B.; Ziegler, H., Springer-Verlag,
Berlin–Heidelberg–New York, 1981, 110 figs. XV, 625 pages. Cloth DM
239,–, Z. Pflanz. Bodenkunde, 146,
543–544, https://doi.org/10.1002/jpln.19831460417, 1983.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10,
282–290, https://doi.org/10/fbg9tm, 1970.
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), UCAR/NCAR, https://doi.org/10.5065/D6RR1W7M, 2013.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.,
Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A.,
Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B.,
Fang, L., Bai, J., and Chintala, S.: PyTorch: An imperative style,
high-performance deep learning library, Adv. Neur. In., 32, 8024–8035, 2019.
Qian, X., Liu, L., Croft, H., and Chen, J.: C3 plants converge on a universal relationship between leaf maximum carboxylation rate and chlorophyll content, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2019-228, 2019.
Quillet, A., Peng, C., and Garneau, M.: Toward dynamic global vegetation
models for simulating vegetation–climate interactions and feedbacks: recent
developments, limitations, and future challenges, Environ. Rev., 18,
333–353, https://doi.org/10.1139/A10-016, 2010.
Rahmani, F., Oliver, S., Ouyang, W., Appling, A., Lawson, K., and Shen, C.:
Developing and testing a long short-term memory stream temperature model in
daily and continental scale, AGU 2020 Fall Meeting, Earth and
Space Science Open Archive, https://doi.org/10.1002/essoar.10505077.1,
2020.
Rahmani, F., Shen, C., Oliver, S., Lawson, K., and Appling, A.: Deep
learning approaches for improving prediction of daily stream temperature in
data-scarce, unmonitored, and dammed basins, Hydrol. Process., 35,
e14400, https://doi.org/10.1002/hyp.14400, 2021.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rogers, A.: The use and misuse of V(c,max) in Earth System Models,
Photosynth. Res., 119, 15–29, https://doi.org/10.1007/s11120-013-9818-1,
2014.
Saha, G. K., Rahmani, F., Shen, C., Li, L., and Cibin, R.: A deep
learning-based novel approach to generate continuous daily stream nitrate
concentration for nitrate data-sparse watersheds, Sci. Total
Environ., 878, 162930, https://doi.org/10.1016/j.scitotenv.2023.162930,
2023.
Saleem, M. H., Potgieter, J., and Arif, K. M.: Plant disease detection and
classification by deep learning, Plants, 8, 468,
https://doi.org/10.3390/plants8110468, 2019.
Shen, C.: Deep Learning: A Next-Generation Big-Data Approach for Hydrology,
Eos, 99, https://doi.org/10.1029/2018EO095649, 2018.
Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F.-J., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X., and Tsai, W.-P.: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community, Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, 2018.
Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky,
A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng,
Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P.,
Aboelyazeed, D., Rahmani, F., Beck, H. E., Bindas, T., Dwivedi, D., Fang,
K., Höge, M., Rackauckas, C., Roy, T., Xu, C., and Lawson, K.:
Differentiable modeling to unify machine learning and physical models and
advance Geosciences, arXiv [preprint], https://doi.org/10.48550/arXiv.2301.04027, 10 January
2023.
Tang, J. and Zhuang, Q.: Equifinality in parameterization of process-based
biogeochemistry models: A significant uncertainty source to the estimation
of regional carbon dynamics, J. Geophys. Res., 113, G04010,
https://doi.org/10.1029/2008JG000757, 2008.
Thompson, M., Gamage, D., Hirotsu, N., Martin, A., and Seneweera, S.:
Effects of elevated carbon dioxide on photosynthesis and carbon
partitioning: A perspective on root sugar sensing and hormonal crosstalk,
Front. Physiol., 8, 578, https://doi.org/10.3389/fphys.2017.00578, 2017.
Tsai, W.-P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and
Shen, C.: From calibration to parameter learning: Harnessing the scaling
effects of big data in geoscientific modeling, Nat. Commun., 12, 5988,
https://doi.org/10.1038/s41467-021-26107-z, 2021.
Urban, L., Aarrouf, J., and Bidel, L.: Assessing the effects of water
deficit on photosynthesis using parameters derived from measurements of leaf
gas exchange and of chlorophyll A fluorescence, Front. Plant Sci.,
8, 2068, https://doi.org/10.3389/fpls.2017.02068, 2017.
Verheijen, L. M., Brovkin, V., Aerts, R., Bönisch, G., Cornelissen, J. H. C., Kattge, J., Reich, P. B., Wright, I. J., and van Bodegom, P. M.: Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis, Biogeosciences, 10, 5497–5515, https://doi.org/10.5194/bg-10-5497-2013, 2013.
Von Caemmerer, S.: C4 photosynthesis in a single C3 cell is theoretically
inefficient but may ameliorate internal CO2 diffusion limitations of C3
leaves, Plant Cell Environ., 26, 1191–1197,
https://doi.org/10.1046/j.0016-8025.2003.01061.x, 2003.
Von Caemmerer, S.: Steady-state models of photosynthesis, Plant Cell
Environ., 36, 1617–1630, https://doi.org/10.1111/pce.12098, 2013.
Wang, H. B., Ma, M. G., Xie, Y. M., Wang, X. F., and Wang, J.: Parameter
inversion estimation in photosynthetic models: Impact of different
simulation methods, Photosynthetica, 52, 233–246,
https://doi.org/10.1007/s11099-014-0027-8, 2014.
Wunsch, A., Liesch, T., and Broda, S.: Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX), Hydrol. Earth Syst. Sci., 25, 1671–1687, https://doi.org/10.5194/hess-25-1671-2021, 2021.
Xu, C., McDowell, N. G., Fisher, R. A., Wei, L., Sevanto, S.,
Christoffersen, B. O., Weng, E., and Middleton, R. S.: Increasing impacts of
extreme droughts on vegetation productivity under climate change, Nat.
Clim. Change, 9, 948–953, https://doi.org/10.1038/s41558-019-0630-6,
2019.
Yin, X. and Struik, P. C.: C3 and C4 photosynthesis models: An overview from
the perspective of crop modelling, NJAS: Wageningen Journal of Life
Sciences, 57, 27–38, https://doi.org/10.1016/j.njas.2009.07.001, 2009.
Zhang, E., Liu, L., and Huang, L.: Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach, The Cryosphere, 13, 1729–1741, https://doi.org/10.5194/tc-13-1729-2019, 2019.
Zhang, X.-Y., Huang, Z., Su, X., Siu, A., Song, Y., Zhang, D., and Fang, Q.:
Machine learning models for net photosynthetic rate prediction using poplar
leaf phenotype data., PLoS One, 15, e0228645,
https://doi.org/10.1371/journal.pone.0228645, 2020.
Zhang, Z., Xin, Q., and Li, W.: Machine learning-based modeling of
vegetation leaf area index and gross primary productivity across North
America and comparison with a process-based model, J. Adv.
Model. Earth Sy., 13, e2021MS002802,
https://doi.org/10.1029/2021MS002802, 2021.
Zhi, W., Feng, D., Tsai, W.-P., Sterle, G., Harpold, A., Shen, C., and Li,
L.: From hydrometeorology to river water quality: Can a deep learning model
predict dissolved oxygen at the continental scale?, Environ. Sci. Technol.,
55, 2357–2368, https://doi.org/10.1021/acs.est.0c06783, 2021.
Zhi, W., Ouyang, W., Shen, C., and Li, L.: Temperature outweighs light and
flow as the predominant driver of dissolved oxygen in US rivers, Nat. Water,
1, 249–260, https://doi.org/10.1038/s44221-023-00038-z, 2023.
Zhu, F., Li, X., Qin, J., Yang, K., Cuo, L., Tang, W., and Shen, C.:
Integration of multisource data to estimate downward longwave radiation
based on deep neural networks, IEEE T. Geosci. Remote
Sens., 1–15, https://doi.org/10.1109/TGRS.2021.3094321, 2021.
Short summary
Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Photosynthesis is critical for life and has been affected by the changing climate. Many...
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