Articles | Volume 18, issue 1
Biogeosciences, 18, 95–112, 2021
Biogeosciences, 18, 95–112, 2021

Research article 07 Jan 2021

Research article | 07 Jan 2021

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Peter Horvath et al.

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Cited articles

Ahti, T., Hämet-Ahti, L., and Jalas, J.: Vegetation zones and their sections in northwestern Europe, Ann. Bot. Fenn., 5, 169–211, 1968. 
Alexander, R. and Millington, A. C.: Vegetation mapping: From Patch to Planet, in: Vegetation Mapping, John Wiley and Sons, LTD, Chichester, England, 321–331, 2000. 
Álvarez-Martínez, J. M., Jiménez-Alfaro, B., Barquín, J., Ondiviela, B., Recio, M., Silió-Calzada, A., and Juanes, J. A.: Modelling the area of occupancy of habitat types with remote sensing, Methods Ecol. Evol., 9, 580–593,, 2018. 
Assal, T. J., Anderson, P. J., and Sibold, J.: Mapping forest functional type in a forest-shrubland ecotone using SPOT imagery and predictive habitat distribution modelling, Remote Sens. Lett., 6, 755–764,, 2015. 
Bakkestuen, V., Erikstad, L., and Halvorsen, R.: Step-less models for regional environmental variation in Norway, J. Biogeogr., 35, 1906–1922,, 2008. 
Short summary
We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
Final-revised paper