A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Li, Lingcheng; Fang, Yilin; Zheng, Zhonghua; Shi, Mingjie; Longo, Marcos; Koven, Charles D.; Holm, Jennifer A.; Fisher, Rosie A.; McDowell, Nate G.; Chambers, Jeffrey; Leung, L. Ruby

doi:https://doi.org/10.5194/gmd-16-4017-2023

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.

https://doi.org/10.5194/gmd-16-4017-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 14

Development and technical paper

|

17 Jul 2023

Development and technical paper |

| 17 Jul 2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung

Supplement

https://doi.org/10.5194/gmd-16-4017-2023-supplement

Data sets

LBA-ECO CD-32 Flux Tower Network Data Compilation, Brazilian Amazon: 1999-2006, V2 N. Restrepo-Coupe, H. R. da Rocha, L. R. Hutyra, A. C. de Araujo, L. S. Borma, B. Christoffersen, O. Cabral, P. B. de Camargo, F. L. Cardoso, A. C. L. Costa, D. R. Fitzjarrald, M. L. Goulden, B. Kruijt, J. M. F. Maia, Y. S. Malhi, A. O. Manzi, S. D. Miller, A. D. Nobre, C. von Randow, L. D. Abreu Safaj, R. K. Sakai, J. Tota, S. C. Wofsy, F. B. Zanchi, and S. R. Saleska https://doi.org/10.3334/ORNLDAAC/1842

Model code and software

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES Lingcheng Li https://doi.org/10.5281/zenodo.7730685

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.