Articles | Volume 11, issue 7
https://doi.org/10.5194/gmd-11-3027-2018
https://doi.org/10.5194/gmd-11-3027-2018
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, Tao Zhang, Yiqi Luo, Xin Huang, and Wei Xue

Related authors

An automatic and effective parameter optimization method for model tuning
T. Zhang, L. Li, Y. Lin, W. Xue, F. Xie, H. Xu, and X. Huang
Geosci. Model Dev., 8, 3579–3591, https://doi.org/10.5194/gmd-8-3579-2015,https://doi.org/10.5194/gmd-8-3579-2015, 2015
Short summary

Related subject area

Biogeosciences
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
Geosci. Model Dev., 18, 4915–4933, https://doi.org/10.5194/gmd-18-4915-2025,https://doi.org/10.5194/gmd-18-4915-2025, 2025
Short summary
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025,https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
pyVPRM: a next-generation vegetation photosynthesis and respiration model for the post-MODIS era
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
Geosci. Model Dev., 18, 4713–4742, https://doi.org/10.5194/gmd-18-4713-2025,https://doi.org/10.5194/gmd-18-4713-2025, 2025
Short summary
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025,https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025,https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary

Cited articles

Aleman, D. M., Romeijn, H. E., and Dempsey, J. F.: A response surface approach to beam orientation optimization in intensity-modulated radiation therapy treatment planning, INFORMS J. Comput., 21, 62–76, 2009. 
Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-carbon response to warming dependent on microbial physiology, Nat. Geosci., 3, 336–340, 2010. 
Behzad, M., Asghari, K., Eazi, M., and Palhang, M.: Generalization performance of support vector machines and neural networks in runoff modeling, Expert Syst. Appl., 36, 7624–7629, 2009. 
Booker, A. J., Dennis Jr., J. E., Frank, P. D., Serafini, D. B., Torczon, V., and Trosset, M. W.: A rigorous framework for optimization of expensive functions by surrogates, Struct. optimization, 17, 1–13, 1999. 
Breiman, L.: Statistical modeling: The two cultures (with comments and a rejoinder by the author), Stat. Sci., 16, 199–231, 2001. 
Download
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.
Share