Articles | Volume 8, issue 4
https://doi.org/10.5194/gmd-8-1071-2015
© Author(s) 2015. 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-8-1071-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
15 Apr 2015
Development and technical paper |
| 15 Apr 2015
Crop physiology calibration in the CLM
I. Bilionis
Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
B. A. Drewniak
Environmental Science Division, Argonne National Laboratory, Argonne, IL, USA
E. M. Constantinescu
CORRESPONDING AUTHOR
Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
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Cited
28 citations as recorded by crossref.
- Strategic information revelation in collaborative design A. Dachowicz et al. 10.1016/j.aei.2018.04.004
- Simulating county‐level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization G. Leng et al. 10.1002/2016MS000645
- Advances in Bayesian Probabilistic Modeling for Industrial Applications S. Ghosh et al. 10.1115/1.4046747
- Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques D. Lu & D. Ricciuto 10.5194/gmd-12-1791-2019
- Machine learning for high-dimensional dynamic stochastic economies S. Scheidegger & I. Bilionis 10.1016/j.jocs.2019.03.004
- County level calibration strategy to evaluate peanut irrigation water use under different climate change scenarios X. Zhen et al. 10.1016/j.eja.2022.126693
- Machine Learning for High-Dimensional Dynamic Stochastic Economies S. Scheidegger & I. Bilionis 10.2139/ssrn.2927400
- Simulating the net ecosystem CO2 exchange and its components over winter wheat cultivation sites across a large climate gradient in Europe using the ORCHIDEE-STICS generic model N. Vuichard et al. 10.1016/j.agee.2016.04.017
- Model Selection and Uncertainty Quantification of Seismic Fragility Functions F. Peña et al. 10.1061/AJRUA6.0001014
- Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation R. Tripathy et al. 10.1016/j.jcp.2016.05.039
- Global parameters sensitivity analysis of modeling water, energy and carbon exchange of an arid agricultural ecosystem M. Wu et al. 10.1016/j.agrformet.2019.03.007
- On the applicability of surrogate‐based Markov chain Monte Carlo‐Bayesian inversion to the Community Land Model: Case studies at flux tower sites M. Huang et al. 10.1002/2015JD024339
- Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications P. Pandita et al. 10.1115/1.4050246
- Impact assessment of climate change on rice yields using the ORYZA model in the Sichuan Basin, China C. Xu et al. 10.1002/joc.5473
- Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites H. Post et al. 10.1002/2015JG003297
- Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM) M. Chen et al. 10.1016/j.agrformet.2018.03.012
- Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides S. Karumuri & I. Bilionis 10.1016/j.jcp.2024.113117
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification R. Tripathy & I. Bilionis 10.1016/j.jcp.2018.08.036
- The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022JG007187
- Evaluation of CLM-Crop for maize growth simulation over Northeast China M. Sheng et al. 10.1016/j.ecolmodel.2018.03.005
- Conditional interval reduction method: A possible new direction for the optimization of process based models R. Hollós et al. 10.1016/j.envsoft.2022.105556
- Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. 10.1016/j.agrformet.2017.11.012
- Computationally Efficient Variational Approximations for Bayesian Inverse Problems P. Tsilifis et al. 10.1115/1.4034102
- Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. 10.5194/gmd-10-1291-2017
- Representing agriculture in Earth System Models: Approaches and priorities for development S. McDermid et al. 10.1002/2016MS000749
- Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. 10.1029/2020AV000310
- Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation F. Wang et al. 10.1016/j.heliyon.2024.e27549
- Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 1991–2012 X. Wang et al. 10.1016/j.agrformet.2016.10.016
27 citations as recorded by crossref.
- Strategic information revelation in collaborative design A. Dachowicz et al. 10.1016/j.aei.2018.04.004
- Simulating county‐level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization G. Leng et al. 10.1002/2016MS000645
- Advances in Bayesian Probabilistic Modeling for Industrial Applications S. Ghosh et al. 10.1115/1.4046747
- Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques D. Lu & D. Ricciuto 10.5194/gmd-12-1791-2019
- Machine learning for high-dimensional dynamic stochastic economies S. Scheidegger & I. Bilionis 10.1016/j.jocs.2019.03.004
- County level calibration strategy to evaluate peanut irrigation water use under different climate change scenarios X. Zhen et al. 10.1016/j.eja.2022.126693
- Machine Learning for High-Dimensional Dynamic Stochastic Economies S. Scheidegger & I. Bilionis 10.2139/ssrn.2927400
- Simulating the net ecosystem CO2 exchange and its components over winter wheat cultivation sites across a large climate gradient in Europe using the ORCHIDEE-STICS generic model N. Vuichard et al. 10.1016/j.agee.2016.04.017
- Model Selection and Uncertainty Quantification of Seismic Fragility Functions F. Peña et al. 10.1061/AJRUA6.0001014
- Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation R. Tripathy et al. 10.1016/j.jcp.2016.05.039
- Global parameters sensitivity analysis of modeling water, energy and carbon exchange of an arid agricultural ecosystem M. Wu et al. 10.1016/j.agrformet.2019.03.007
- On the applicability of surrogate‐based Markov chain Monte Carlo‐Bayesian inversion to the Community Land Model: Case studies at flux tower sites M. Huang et al. 10.1002/2015JD024339
- Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications P. Pandita et al. 10.1115/1.4050246
- Impact assessment of climate change on rice yields using the ORYZA model in the Sichuan Basin, China C. Xu et al. 10.1002/joc.5473
- Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites H. Post et al. 10.1002/2015JG003297
- Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM) M. Chen et al. 10.1016/j.agrformet.2018.03.012
- Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides S. Karumuri & I. Bilionis 10.1016/j.jcp.2024.113117
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification R. Tripathy & I. Bilionis 10.1016/j.jcp.2018.08.036
- The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2) E. Sinha et al. 10.1029/2022JG007187
- Evaluation of CLM-Crop for maize growth simulation over Northeast China M. Sheng et al. 10.1016/j.ecolmodel.2018.03.005
- Conditional interval reduction method: A possible new direction for the optimization of process based models R. Hollós et al. 10.1016/j.envsoft.2022.105556
- Improving maize growth processes in the community land model: Implementation and evaluation B. Peng et al. 10.1016/j.agrformet.2017.11.012
- Computationally Efficient Variational Approximations for Bayesian Inverse Problems P. Tsilifis et al. 10.1115/1.4034102
- Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska K. Williams et al. 10.5194/gmd-10-1291-2017
- Representing agriculture in Earth System Models: Approaches and priorities for development S. McDermid et al. 10.1002/2016MS000749
- Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. 10.1029/2020AV000310
- Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation F. Wang et al. 10.1016/j.heliyon.2024.e27549
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Short summary
Farming is using more of the land surface terrestrial ground and this expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, we calibrate the parametric models within CLM-Crop (part of the Community Land Model (CLM)). The agreement between AmeriFlux observations and model projections is greatly improved for soybean, which is the focus of this study.
Farming is using more of the land surface terrestrial ground and this expansion exerts an...
