Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2839-2022
https://doi.org/10.5194/gmd-15-2839-2022
Development and technical paper
 | 
07 Apr 2022
Development and technical paper |  | 07 Apr 2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments

Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin

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Cited articles

Barton, L., Wolf, B., Rowlings, D., Scheer, C., Kiese, R., Grace, P., Grace, P., Stefanova, K., and Butterbach-Bahl, K.: Sampling frequency affects estimates of annual nitrous oxide fluxes, Scientific Reports, 5, 15912, https://doi.org/10.1038/srep15912, 2015. 
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nature Computational Science, 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0, 2021. 
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving conservation of energy in neural network emulators for climate modeling, arXiv [preprint], arXiv:1906.06622, 2019. 
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing analytic constraints in neural networks emulating physical systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. 
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R., and Zechmeister-Boltenstern, S.: Nitrous oxide emissions from soils: how well do we understand the processes and their controls?, Philos. T. Roy. Soc. B, 368, 20130122, https://doi.org/10.1098/rstb.2013.0122, 2013. 
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Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
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