Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3519-2017
© Author(s) 2017. 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-10-3519-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
25 Sep 2017
Development and technical paper |
| 25 Sep 2017
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
Peter Dittrich
Bio Systems Analysis Group, Institute of Computer Science, Jena Centre for Bioinformatics and Friedrich Schiller University, 07745 Jena, Germany
Michael Stifel Center Jena for Data-Driven and Simulation Science, 07745 Jena, Germany
Nuno Carvalhais
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
CENSE, Departamento de Ciéncias e Engenharia do Ambiente, Faculdade de Ciéncias e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
Martin Jung
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
Andreas Heinemeyer
Department of Environment, Stockholm Environment Institute, University of York, York, YO105NG, UK
Mirco Migliavacca
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
James I. L. Morison
Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK
Sebastian Sippel
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
Jens-Arne Subke
Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling, UK
Matthew Wilkinson
Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK
Miguel D. Mahecha
CORRESPONDING AUTHOR
Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany
Michael Stifel Center Jena for Data-Driven and Simulation Science, 07745 Jena, Germany
German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
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Cited
10 citations as recorded by crossref.
- Performance Prediction of Hybrid Bamboo-Reinforced Concrete Beams Using Gene Expression Programming for Sustainable Construction H. Waqas et al. 10.3390/ma16206788
- Modeling of bed-to-wall heat transfer coefficient in fluidized adsorption bed by gene expression programming approach J. Krzywanski et al. 10.1016/j.powtec.2024.120392
- Ten challenges for the future of pedometrics A. Wadoux et al. 10.1016/j.geoderma.2021.115155
- Predicting the Response of RC Beam from a Drop-Weight Using Gene Expression Programming M. Tariq et al. 10.3390/ma15196910
- Warming homogenizes apparent temperature sensitivity of ecosystem respiration B. Niu et al. 10.1126/sciadv.abc7358
- Weekly carbon dioxide exchange trend predictions in deciduous broadleaf forests from site-specific influencing variables D. Wood 10.1016/j.ecoinf.2023.101996
- Earth System Data Cubes: Avenues for advancing Earth system research D. Montero et al. 10.1017/eds.2024.22
- Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction A. Azarpour et al. 10.1021/acsomega.4c06610
- From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
- Improved shear strength model for exterior reinforced concrete beam-column joints using gene expression programming I. Mansouri et al. 10.1016/j.engstruct.2020.111563
10 citations as recorded by crossref.
- Performance Prediction of Hybrid Bamboo-Reinforced Concrete Beams Using Gene Expression Programming for Sustainable Construction H. Waqas et al. 10.3390/ma16206788
- Modeling of bed-to-wall heat transfer coefficient in fluidized adsorption bed by gene expression programming approach J. Krzywanski et al. 10.1016/j.powtec.2024.120392
- Ten challenges for the future of pedometrics A. Wadoux et al. 10.1016/j.geoderma.2021.115155
- Predicting the Response of RC Beam from a Drop-Weight Using Gene Expression Programming M. Tariq et al. 10.3390/ma15196910
- Warming homogenizes apparent temperature sensitivity of ecosystem respiration B. Niu et al. 10.1126/sciadv.abc7358
- Weekly carbon dioxide exchange trend predictions in deciduous broadleaf forests from site-specific influencing variables D. Wood 10.1016/j.ecoinf.2023.101996
- Earth System Data Cubes: Avenues for advancing Earth system research D. Montero et al. 10.1017/eds.2024.22
- Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction A. Azarpour et al. 10.1021/acsomega.4c06610
- From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
- Improved shear strength model for exterior reinforced concrete beam-column joints using gene expression programming I. Mansouri et al. 10.1016/j.engstruct.2020.111563
Discussed (final revised paper)
Latest update: 08 Mar 2025
Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Accurate representation of land-atmosphere carbon fluxes is essential for future climate...
