Articles | Volume 20, issue 13
https://doi.org/10.5194/bg-20-2671-2023
https://doi.org/10.5194/bg-20-2671-2023
Research article
 | 
06 Jul 2023
Research article |  | 06 Jul 2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen

Related authors

Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
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
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
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
Preprint under review for HESS
Short summary
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
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
Metamorphic testing of machine learning and conceptual hydrologic models
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
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
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

Related subject area

Biogeochemistry: Air - Land Exchange
Impact of meteorological conditions on the biogenic volatile organic compound (BVOC) emission rate from eastern Mediterranean vegetation under drought
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
Short summary
Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery
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
Short summary
Compound soil and atmospheric drought (CSAD) events and CO2 fluxes of a mixed deciduous forest: the occurrence, impact, and temporal contribution of main drivers
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
Short summary
Similar freezing spectra of particles on plant canopies as in air at a high-altitude site
Annika Einbock and Franz Conen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2067,https://doi.org/10.5194/egusphere-2024-2067, 2024
Short summary
The influence of plant water stress on vegetation–atmosphere exchanges: implications for ozone modelling
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
Short summary

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. 
Download
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.
Altmetrics
Final-revised paper
Preprint