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

Citation

Horvath, Peter et al. (2020), Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models, Dryad, Dataset, https://doi.org/10.5061/dryad.dfn2z34xn

Abstract

Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

Usage Notes

The dataset presented here consists of two raster maps displaying PFT coverage based on a Remote Sensing (RS) and a Distribution model (DM) product. The data is covering Norway at a resolution of 30m and 100m respectively. The first PFT map is derived from RS originally created by Johansen (2009) using aggregation based on a conversion table which is closely described in the accompanying article.  The second PFT map is created by first assembling vegetation type predictions from individual DMs carried out by Horvath et al. (2019), and then aggregating the types based on the same conversion table. The accompanying ".clr" file consists of RGB color codes used to display the PFT maps in a GIS software and was used to present the maps in the article. 

 

Horvath, P., Halvorsen, R., Stordal, F., Tallaksen, L. M., Tang, H., and Bryn, A.: Distribution modelling of vegetation types based on area frame survey data, Applied Vegetation Science, 22, 547-560, https://doi.org/10.1111/avsc.12451, 2019.

Johansen, B. E.: Satellittbasert vegetasjonskartlegging for Norge, Direktoratet for Naturforvaltning, Norsk Romsenter, 2009.

Funding

Universitetet i Oslo, Award: UiO/GEO103920