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Iterative integration of deep learning in hybrid Earth surface system modelling

Nature Reviews Earth & Environment volume 4, pages 568–581 (2023)Cite this article

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Abstract

Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations.

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Fig. 1: Answering the grand environmental challenges through integrating Earth system modelling and deep learning.
Fig. 2: Comparison between outputs from Earth surface system models and hybrid models.
Fig. 3: Computational logics of hybrid models.
Fig. 4: A framework for iterative integration of Earth surface system modelling and deep learning.

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Acknowledgements

The authors thank M. Reichstein for his comments on the manuscript. M.C. acknowledges funding from the National Key R&D Program of China under no. 2022YFF0711604. G.L. acknowledges funding by the National Natural Science Foundation of China under grant no. 41930648. Z.Y.M acknowledges funding from Key Scientific and Technological Project of the Ministry of Water Resources. P.R.C under no. SKS-2022001. N.B. acknowledges funding by the Volkswagen foundation, by the European Union’s Horizon 2020 research and innovation program under grant agreement no. 820970 and under the Marie Sklodowska-Curie grant agreement no. 956170, as well as by the German Federal Ministry of Education and Research under grant no. 01LS2001A.

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Author notes

  1. These authors contributed equally: Min Chen, Zhen Qian.

Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, Jiangsu, China

    Min Chen, Zhen Qian, Jian Liu, Teng Zhong, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhuo Sun, Zaiyang Ma, Yuanqing He, Kai Xu & Guonian Lü

  2. International Research Center of Big Data for Sustainable Development Goals, Beijing, China

    Min Chen

  3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu, China

    Min Chen, Zhen Qian, Jian Liu, Teng Zhong, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhuo Sun, Zaiyang Ma, Yuanqing He, Kai Xu & Guonian Lü

  4. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, Jiangsu, China

    Zhen Qian, Jian Liu, Teng Zhong, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhuo Sun, Zaiyang Ma, Yuanqing He, Kai Xu & Guonian Lü

  5. School of Engineering and Design, Technical University of Munich, Munich, Germany

    Niklas Boers

  6. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Niklas Boers

  7. Global Systems Institute and Department of Mathematics, University of Exeter, Exeter, UK

    Niklas Boers

  8. Fenner School of Environment and Society, Australian National University, Canberra, Australia

    Anthony J. Jakeman

  9. Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder, CO, USA

    Albert J. Kettner

  10. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Martin Brandt

  11. Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China

    Mei-Po Kwan & Jian Lin

  12. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China

    Mei-Po Kwan

  13. The Bartlett Centre for Advanced Spatial Analysis (CASA), University College London (UCL), London, UK

    Michael Batty

  14. The Alan Turing Institute, The British Library, London, UK

    Michael Batty

  15. School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, USA

    Wenwen Li

  16. Systems Science Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Rui Zhu

  17. Department of Geography, National University of Singapore, Singapore, Singapore

    Wei Luo

  18. Department of Civil and Construction Engineering, Brigham Young University, Provo, UT, USA

    Daniel P. Ames

  19. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

    C. Michael Barton

  20. CSIRO Environment, Canberra, Australian Capital Territory, Australia

    Susan M. Cuddy

  21. Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany

    Sujan Koirala

  22. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Fan Zhang

  23. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, USA

    Carlo Ratti

  24. Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, China

    Junzhi Liu

  25. College of Geography and Marine, Nanjing University, Nanjing, Jiangsu, China

    Zhixin Zhang

  26. School of Information Engineering, China University of Geosciences in Beijing, Beijing, China

    Chunxiao Zhang

  27. Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, Jiangxi, China

    Hui Lin

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Contributions

M.C. and Z.Q. initiated the writing and co-led the design and writing of the article. G.L. conceptualized and supervised this article. All co-authors provided input on the manuscript text, figures and discussion of scientific content.

Corresponding authors

Correspondence to Min Chen or Guonian Lü.

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Nature Reviews Earth & Environment thanks T. Wagener, G. Coco, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Community Surface Dynamics Modeling System (CSDMS): https://csdms.colorado.edu/

Open Geographic Modeling and Simulation (OpenGMS): https://geomodeling.njnu.edu.cn/

Open Modeling Foundation (OMF): https://www.openmodelingfoundation.org/

The Network for Computational Modeling in Social and Ecological Sciences (CoMSESNet): https://www.comses.net/

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Chen, M., Qian, Z., Boers, N. et al. Iterative integration of deep learning in hybrid Earth surface system modelling. Nat Rev Earth Environ 4, 568–581 (2023). https://doi.org/10.1038/s43017-023-00452-7

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