Dynamic Global Vegetation Models: Searching for the balance between demographic process representation and computational tractability

Vegetation is subject to multiple pressures in the 21st century, including changes in climate, atmospheric composition and human land-use. Changes in vegetation type, structure, and function also feed back to the climate through their impact on the surface-atmosphere fluxes of carbon and water. Dynamic Global Vegetation Models (DGVMs), are therefore key component of the latest Earth System Models (ESMs). Model projections for the future land carbon sink still span a wide range, in part due to the difficulty of representing complex ecosystem and biogeochemical processes at large scales (i.e. grid lengths ≈ 100km). The challenge for developers of DGVMs is therefore to find an optimal balance between detailed process representation and the ability to scale-up.

In this study funded by the 4C Climate Carbon H2020 project, authors categorise DGVMs into four groups; Individual, Average Area, Two Dimensional Cohort and One Dimensional Cohort models. From this they review popular methods used to represent dynamic vegetation within the context of Earth System modelling. Authors argue that the minimum level of complexity required to effectively model changes in carbon storage under changing climate and disturbance regimes, requires a representation of tree size distributions within forests. Furthermore, they find that observed size distributions are consistent with Demographic Equilibrium Theory, suggesting that One Dimensional Cohort models with a focus on tree size, offer the best balance between computational tractability and realism for ESM applications.

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Dynamic Global Vegetation Models: Searching for the balance between demographic process representation and computational tractability
Arthur P. K. Argles , Jonathan R. Moore, Peter M. Cox