Parameter calibration in global soil carbon models using surrogate-based optimization
<p>Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/11/3027/2018/gmd-11-3027-2018.pdf |
| _version_ | 1818501973348974592 |
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| author | H. Xu T. Zhang T. Zhang Y. Luo Y. Luo X. Huang X. Huang W. Xue W. Xue |
| author_facet | H. Xu T. Zhang T. Zhang Y. Luo Y. Luo X. Huang X. Huang W. Xue W. Xue |
| author_sort | H. Xu |
| collection | DOAJ |
| description | <p>Soil organic carbon (SOC)
has a significant effect on carbon emissions and climate change. However, the
current SOC prediction accuracy of most models is very low. Most evaluation
studies indicate that the prediction error mainly comes from parameter
uncertainties, which can be improved by parameter calibration. Data
assimilation techniques have been successfully employed for the parameter
calibration of SOC models. However, data assimilation algorithms, such as the
sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high
computation costs and are not appropriate for complex global land models.
This study proposes a new parameter calibration method based on surrogate
optimization techniques to improve the prediction accuracy of SOC.
Experiments on three types of soil carbon cycle models, including the
Community Land Model with the Carnegie–Ames–Stanford Approach
biogeochemistry submodel (CLM-CASA') and two microbial models show that the
surrogate-based optimization method is effective and efficient in terms of
both accuracy and cost. Compared to predictions using the tuned parameter
values through Bayesian MCMC, the root mean squared errors (RMSEs) between
the predictions using the calibrated parameter values with surrogate-base
optimization and the observations could be reduced by up to 12 % for
different SOC models. Meanwhile, the corresponding computational cost is
lower than other global optimization algorithms.</p> |
| first_indexed | 2024-12-10T21:03:36Z |
| format | Article |
| id | doaj.art-353ffba41d25426495b6a6753f0218a1 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-10T21:03:36Z |
| publishDate | 2018-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-353ffba41d25426495b6a6753f0218a12022-12-22T01:33:42ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032018-07-01113027304410.5194/gmd-11-3027-2018Parameter calibration in global soil carbon models using surrogate-based optimizationH. Xu0T. Zhang1T. Zhang2Y. Luo3Y. Luo4X. Huang5X. Huang6W. Xue7W. Xue8Department of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084, ChinaCenter for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USADepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing 100084, China<p>Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.</p>https://www.geosci-model-dev.net/11/3027/2018/gmd-11-3027-2018.pdf |
| spellingShingle | H. Xu T. Zhang T. Zhang Y. Luo Y. Luo X. Huang X. Huang W. Xue W. Xue Parameter calibration in global soil carbon models using surrogate-based optimization Geoscientific Model Development |
| title | Parameter calibration in global soil carbon models using surrogate-based optimization |
| title_full | Parameter calibration in global soil carbon models using surrogate-based optimization |
| title_fullStr | Parameter calibration in global soil carbon models using surrogate-based optimization |
| title_full_unstemmed | Parameter calibration in global soil carbon models using surrogate-based optimization |
| title_short | Parameter calibration in global soil carbon models using surrogate-based optimization |
| title_sort | parameter calibration in global soil carbon models using surrogate based optimization |
| url | https://www.geosci-model-dev.net/11/3027/2018/gmd-11-3027-2018.pdf |
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