Progress of vegetation modelling and future research prospects

Li, Siqi; Zhang, Xu ORCID: https://orcid.org/0000-0003-1833-9689; Lu, Zhengyao; Ni, Jian; Lu, Jianhua. 2024 Progress of vegetation modelling and future research prospects. Science China Earth Sciences, 67. 2718-2738. https://doi.org/10.1007/s11430-023-1367-1

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Official URL: https://link.springer.com/article/10.1007/s11430-0...

Abstract/Summary

Terrestrial vegetation is a crucial component of the Earth system, and its changes not only represent one of the most distinct aspects of climate change but also exert significant feedback within the climate system by exchanging energy, moisture, and carbon dioxide. To quantitatively and mechanistically study climate-vegetation feedback, numerical vegetation models have been developed on the theory of ecophysiological constraints on plant functional types. The models eventually can simulate vegetation distribution and succession across different spatial and temporal scales, and associated terrestrial carbon cycle processes by categorizing vegetation into biomes according different plant functional types and their associated environmental factors. Here we review the developing history of vegetation models and provide recent advances and future directions. Before 21st century, static vegetation models, as developed statistical models, can only simulate equilibrated characteristics of vegetation distribution. In last several decades, Dynamic Global Vegetation Models (DGVMs) have been developed to simulate instantaneous responses of vegetation to climate change and associated dynamics, and can be coupled with Earth system models to investigate interactions among atmosphere, ocean, and land. DGVMs are also widely applied to investigate the dynamics accounting for changes in the geographic distribution patterns of land surface vegetation at different spatial and temporal scales and to assess the impacts of terrestrial carbon and water fluxes and land use changes. We suggest that future vegetation modeling could integrate with machine learning, and explore vegetation transient response and feedback as well as impacts of process hierarchies and human activities on climate and ecosystem.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s11430-023-1367-1
ISSN:	1674-7313
Additional Keywords:	Vegetation models, DGVMs, Ecosystems, Plant functional types, Plant functional traits
Date made live:	05 Aug 2024 12:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537798

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1007/s11430-023-1367-1

ISSN:

1674-7313

Additional Keywords:

Vegetation models, DGVMs, Ecosystems, Plant functional types, Plant functional traits

Date made live:

05 Aug 2024 12:58 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/537798