Articles | Volume 11, issue 7
https://doi.org/10.5194/gmd-11-3027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-11-3027-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
27 Jul 2018
Development and technical paper |
| 27 Jul 2018
Parameter calibration in global soil carbon models using surrogate-based optimization
Haoyu Xu
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Tao Zhang
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Yiqi Luo
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Center for Ecosystem Science and Society, Northern Arizona
University, Flagstaff, AZ, USA
Xin Huang
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
Wei Xue
CORRESPONDING AUTHOR
Department of Computer Science and Technology, Tsinghua University,
Beijing 100084, China
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084,
China
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Cited
14 citations as recorded by crossref.
- Comparative Analysis of Evolutionary Algorithms for PID Controller Optimization in Pneumatic Soft Robotic Systems: A Simulation and Experimental Study M. Massoud & J. Libby https://doi.org/10.1109/ACCESS.2024.3480834
- Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data E. Berryman et al. https://doi.org/10.1029/2018JG004613
- Optimization of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT) using surrogate machine learning methods P. Buchanan et al. https://doi.org/10.5194/bg-22-5349-2025
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. https://doi.org/10.5194/gmd-15-6637-2022
- Optimization of PI-Cascaded Controller’s Parameters for Linear Servo Mechanism: A Comparative Study of Multiple Algorithms M. Abdelbar et al. https://doi.org/10.1109/ACCESS.2023.3304333
- Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions S. Chadburn et al. https://doi.org/10.1029/2020GB006678
- Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. https://doi.org/10.5194/gmd-16-2037-2023
- Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models L. Wang et al. https://doi.org/10.5194/bg-22-7845-2025
- Automatic calibration method for the CycLiq constitutive model with focus on liquefaction behaviour B. Hu-Yan et al. https://doi.org/10.1016/j.soildyn.2026.110324
- Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency X. He et al. https://doi.org/10.1038/s41559-025-02961-8
- Coupling process-based models with machine learning for the prediction of soil carbon and nitrogen cycling X. Zhang et al. https://doi.org/10.1016/j.envsoft.2026.107089
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. https://doi.org/10.3390/rs17030408
- An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model R. Zhao et al. https://doi.org/10.1016/j.geoderma.2022.116283
- A surrogate model-based ESM parameter tuning scientific workflow management framework for HPC L. Hu et al. https://doi.org/10.1007/s12145-024-01460-x
14 citations as recorded by crossref.
- Comparative Analysis of Evolutionary Algorithms for PID Controller Optimization in Pneumatic Soft Robotic Systems: A Simulation and Experimental Study M. Massoud & J. Libby https://doi.org/10.1109/ACCESS.2024.3480834
- Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data E. Berryman et al. https://doi.org/10.1029/2018JG004613
- Optimization of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT) using surrogate machine learning methods P. Buchanan et al. https://doi.org/10.5194/bg-22-5349-2025
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. https://doi.org/10.5194/gmd-15-6637-2022
- Optimization of PI-Cascaded Controller’s Parameters for Linear Servo Mechanism: A Comparative Study of Multiple Algorithms M. Abdelbar et al. https://doi.org/10.1109/ACCESS.2023.3304333
- Modeled Microbial Dynamics Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions S. Chadburn et al. https://doi.org/10.1029/2020GB006678
- Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. https://doi.org/10.5194/gmd-16-2037-2023
- Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models L. Wang et al. https://doi.org/10.5194/bg-22-7845-2025
- Automatic calibration method for the CycLiq constitutive model with focus on liquefaction behaviour B. Hu-Yan et al. https://doi.org/10.1016/j.soildyn.2026.110324
- Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency X. He et al. https://doi.org/10.1038/s41559-025-02961-8
- Coupling process-based models with machine learning for the prediction of soil carbon and nitrogen cycling X. Zhang et al. https://doi.org/10.1016/j.envsoft.2026.107089
- A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation X. Wu et al. https://doi.org/10.3390/rs17030408
- An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model R. Zhao et al. https://doi.org/10.1016/j.geoderma.2022.116283
- A surrogate model-based ESM parameter tuning scientific workflow management framework for HPC L. Hu et al. https://doi.org/10.1007/s12145-024-01460-x
Saved (final revised paper)
Latest update: 01 Jul 2026
Short summary
This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of soil organic carbon. Experiments on three popular global soil carbon cycle models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. This research would help develop and improve the parameterization schemes of Earth climate systems.
This study proposes a new parameter calibration method based on surrogate optimization...
