Articles | Volume 18, issue 1
https://doi.org/10.5194/bg-18-95-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-95-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2021
Research article |
| 07 Jan 2021
| 07 Jan 2021
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Rune Halvorsen
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Frode Stordal
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Lena Merete Tallaksen
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Section for Geography and Hydrology, Department of
Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Terje Koren Berntsen
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Anders Bryn
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Division of Survey and Statistics, Norwegian Institute of
Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
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Cited
9 citations as recorded by crossref.
- Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes R. Beigaitė et al. 10.1111/gcb.16110
- A comparison of three ways to assemble wall-to-wall maps from distribution models of vegetation types P. Horvath et al. 10.1080/15481603.2021.1996313
- A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model H. Zhou et al. 10.1002/hyp.15152
- Mapping the future afforestation distribution of China constrained by a national afforestation plan and climate change S. Song et al. 10.5194/bg-21-2839-2024
- Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2 X. Gao et al. 10.1007/s00376-021-1138-3
- Variability in frost occurrence under climate change and consequent risk of damage to trees of western Quebec, Canada B. Marquis et al. 10.1038/s41598-022-11105-y
- CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic C. Liu et al. 10.5194/essd-15-133-2023
- Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice A. Bryn et al. 10.3389/fevo.2021.658713
- Integration of a Frost Mortality Scheme Into the Demographic Vegetation Model FATES M. Lambert et al. 10.1029/2022MS003333
9 citations as recorded by crossref.
- Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes R. Beigaitė et al. 10.1111/gcb.16110
- A comparison of three ways to assemble wall-to-wall maps from distribution models of vegetation types P. Horvath et al. 10.1080/15481603.2021.1996313
- A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model H. Zhou et al. 10.1002/hyp.15152
- Mapping the future afforestation distribution of China constrained by a national afforestation plan and climate change S. Song et al. 10.5194/bg-21-2839-2024
- Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2 X. Gao et al. 10.1007/s00376-021-1138-3
- Variability in frost occurrence under climate change and consequent risk of damage to trees of western Quebec, Canada B. Marquis et al. 10.1038/s41598-022-11105-y
- CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic C. Liu et al. 10.5194/essd-15-133-2023
- Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice A. Bryn et al. 10.3389/fevo.2021.658713
- Integration of a Frost Mortality Scheme Into the Demographic Vegetation Model FATES M. Lambert et al. 10.1029/2022MS003333
Latest update: 18 Nov 2024
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.
We evaluated the performance of three methods for representing vegetation cover. Remote sensing...
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