Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4017-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4017-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
17 Jul 2023
Development and technical paper |
| 17 Jul 2023
| 17 Jul 2023
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Atmospheric Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, WA, USA
Yilin Fang
Earth System and Science Division, Pacific Northwest National Laboratory,
Richland, WA, USA
Zhonghua Zheng
Department of Earth and Environmental Sciences, The University of
Manchester, Manchester, UK
Mingjie Shi
Atmospheric Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, WA, USA
Marcos Longo
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Charles D. Koven
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Jennifer A. Holm
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Rosie A. Fisher
CICERO Center for International Climate and Environmental Research, Oslo,
Norway
Nate G. McDowell
Atmospheric Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, WA, USA
School of Biological Sciences, Washington State University, P.O. Box 644236,
Pullman, WA, USA
Jeffrey Chambers
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
L. Ruby Leung
Atmospheric Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, WA, USA
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Cited
12 citations as recorded by crossref.
- A unified ensemble soil moisture dataset across the continental United States L. Li et al.
- CMIP7 data request: land and land ice priorities and opportunities Y. Li et al.
- Will thinning and fuel reduction treatments help dry conifer forests persist under climate change? A. Hanbury-Brown et al.
- Developing mobility zones based on heat exposure and vegetation condition: a tale of two transportation modes P. Wang et al.
- The UFLUX ensemble of multiple-scale carbon, water, and energy fluxes S. Zhu et al.
- Systematic evaluation of atmospheric forcing, surface datasets, and mesh effects on Kilometer-Scale land surface and river modeling L. Li et al.
- Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration S. Sun et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al.
- Representing lateral groundwater flow from land to river in Earth system models C. Liao et al.
- Preparing for tomorrow: A protocol for tree species parameterization in dynamic vegetation models G. Marano et al.
- Enhancing SWAT with mechanistic plant hydraulics: development and application in the Hanjiang River Basin X. Wei et al.
12 citations as recorded by crossref.
- A unified ensemble soil moisture dataset across the continental United States L. Li et al.
- CMIP7 data request: land and land ice priorities and opportunities Y. Li et al.
- Will thinning and fuel reduction treatments help dry conifer forests persist under climate change? A. Hanbury-Brown et al.
- Developing mobility zones based on heat exposure and vegetation condition: a tale of two transportation modes P. Wang et al.
- The UFLUX ensemble of multiple-scale carbon, water, and energy fluxes S. Zhu et al.
- Systematic evaluation of atmospheric forcing, surface datasets, and mesh effects on Kilometer-Scale land surface and river modeling L. Li et al.
- Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration S. Sun et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al.
- Representing lateral groundwater flow from land to river in Earth system models C. Liao et al.
- Preparing for tomorrow: A protocol for tree species parameterization in dynamic vegetation models G. Marano et al.
- Enhancing SWAT with mechanistic plant hydraulics: development and application in the Hanjiang River Basin X. Wei et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is...
