Dynamic global vegetation modelling for prediction of plant functional types and biogenic trace gas fluxes
GCTE/LUCC RESEARCH ARTICLE
Article first published online: 25 DEC 2001
Global Ecology and Biogeography
Volume 8, Issue 6, pages 473–488, November 1999
How to Cite
Potter, C. S. and Klooster, S. A. (1999), Dynamic global vegetation modelling for prediction of plant functional types and biogenic trace gas fluxes. Global Ecology and Biogeography, 8: 473–488. doi: 10.1046/j.1365-2699.1999.00152.x
- Issue published online: 25 DEC 2001
- Article first published online: 25 DEC 2001
- Dynamic vegetation;
- global modelling;
- remote sensing;
- net primary production;
- plant biomass;
- biogenic trace gases;
- climate change
1. A Dynamic Global Vegetation Model (DGVM) has been developed as a new feature of the NASA-CASA (Carnegie Ames Stanford Approach) ecosystem production and trace gas model. This DGVM includes seasonal phenology algorithms calibrated using historical interannual data sets derived from the Advanced Very High Resolution (AVHRR) satellite ‘greenness’ index.
2. The coupled CASA-DGVM design is based conceptually on two main elements of Tilman's resource-ratio hypothesis of vegetation change, namely: 1) plant competition for resources (water and light) over relatively short time periods of months and seasons; and 2) the long-term pattern in the supply of growth-limiting resources such as water and nutrients, i.e. the resource-supply trajectory. This simulation model generates global gridded estimates of primary production, above and below ground biomass, leaf area index (LAI), and trace gas fluxes from soil.
3. Eight distributed test locations for the DGVM were evaluated initially to represent a variety of climate conditions ranging from Arctic (64°N Alaska) to tropical and subtropical (24°S southern Africa) latitude zones. At all test locations, the predicted plant functional type (PFT) matched closely with the actual reported PFT.
4. In the process of running the model to steady state PFTs, most forest locations showed a rapid progression of transient states, from bare ground to grassland, to grasses with shrub cover, and finally to the forest PFT. From this first global application, the DGVM correctly predicts the presence of forest classes in approximately 75–95% of all cases worldwide, and grasslands in approximately 58% of all cases.
5. The effects of two hypothetical climate change scenarios were evaluated. Scenario I was set by warming air surface temperatures linearly to 4 °C above average over a 25-year simulation period. Scenario II was set by decreasing annual rainfall amounts linearly to 50% below average over a 25-year simulation period.
6. The warming scenario I resulted in PFT at high-latitude forest and boreal forest sites changing to mixed coniferous forest, accompanied by increase in canopy LAI. The drought scenario II resulted in PFT at the boreal forest and savanna sites changing to grasslands. At locations where PFT did not change with climate, however, soil water and canopy LAI were predicted to decline progressively under the warming scenario, beginning from steady-state temperate and tropical zone PFTs. They also declined under the drought scenario beginning from practically any steady state PFT.