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.
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
Related authors
No articles found.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
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., 29, 2361–2375, https://doi.org/10.5194/hess-29-2361-2025, https://doi.org/10.5194/hess-29-2361-2025, 2025
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly, and Forrest M. Hoffman
Biogeosciences, 20, 1829–1841, https://doi.org/10.5194/bg-20-1829-2023, https://doi.org/10.5194/bg-20-1829-2023, 2023
Short summary
Short summary
Rising atmospheric carbon dioxide increases vegetation growth and causes more heatwaves and droughts. The impact of such climate extremes is detrimental to terrestrial carbon uptake capacity. We found that due to overall climate warming, about 88 % of the world's regions towards the end of 2100 will show anomalous losses in net biospheric productivity (NBP) rather than gains. More than 50 % of all negative NBP extremes were driven by the compound effect of dry, hot, and fire conditions.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Martijn M. T. A. Pallandt, Jitendra Kumar, Marguerite Mauritz, Edward A. G. Schuur, Anna-Maria Virkkala, Gerardo Celis, Forrest M. Hoffman, and Mathias Göckede
Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, https://doi.org/10.5194/bg-19-559-2022, 2022
Short summary
Short summary
Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
Altmetrics
Final-revised paper
Preprint