Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4017-2023
https://doi.org/10.5194/gmd-16-4017-2023
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

Related authors

Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin
Mingjie Shi, Nate McDowell, Huilin Huang, Faria Zahura, Lingcheng Li, and Xingyuan Chen
EGUsphere, https://doi.org/10.22541/au.171053013.30286044/v2,https://doi.org/10.22541/au.171053013.30286044/v2, 2024
Short summary
Global 1 km land surface parameters for kilometer-scale Earth system modeling
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024,https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024,https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023,https://doi.org/10.5194/essd-15-2755-2023, 2023
Short summary
Spatial heterogeneity effects on land surface modeling of water and energy partitioning
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022,https://doi.org/10.5194/gmd-15-5489-2022, 2022
Short summary

Related subject area

Climate and Earth system modeling
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024,https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024,https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024,https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024,https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024,https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary

Cited articles

Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q., and Levine, J. M.: Climate variability has a stabilizing effect on the coexistence of prairie grasses, P. Natl. Acad. Sci. USA, 103, 12793–12798, https://doi.org/10.1073/pnas.0600599103, 2006. 
Adler, P. B., Fajardo, A., Kleinhesselink, A. R., and Kraft, N. J. B.: Trait-based tests of coexistence mechanisms, Ecol. Lett., 16, 1294–1306, https://doi.org/10.1111/ele.12157, 2013. 
Angert, A. L., Huxman, T. E., Chesson, P., and Venable, D. L.: Functional tradeoffs determine species coexistence via the storage effect, P. Natl. Acad. Sci. USA, 106, 11641–11645, https://doi.org/10.1073/pnas.0904512106, 2009. 
Antoniadis, A., Lambert-Lacroix, S., and Poggi, J.-M.: Random forests for global sensitivity analysis: A selective review, Reliab. Eng. Syst. Safe., 206, 107312, https://doi.org/10.1016/j.ress.2020.107312, 2020. 
Download
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.