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 (Potter and Klooster. 1997). This DGVM includes seasonal phenology algorithms calibrated using global inter-annual data sets from the Advanced Very High Resolution (AVHRR) satellite "greenness" index (Potter and Brooks - 1998). The coupled CASA-DGVM design is based on a 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. The model generates global gridded estimates of primary production, above and below ground biomass, leaf area index (LAI), and trace gas fluxes.

Background

Our DGVM does not yet include regional disturbance influences of fire, flood, wind-throw, deforestation, and other perturbations to the process of vegetation development. Furthermore, we do not consider the potential effects of limiting seed sources, dispersal mechanisms, or plant recruitment rates on the outcome of dynamic vegetation cover. Rather, it is assumed that, without regard to individual species, seeds of all the general PFTs exist throughout the terrestrial biosphere in supply adequate to populate any given location under the proper environmental conditions.

Preliminary Results

The DGVM was then allowed to come to steady state based on mean (1931-60) climate conditions (Leemans and Cramer, 1990). In all cases, the predicted PFT matched closely with the category reported by DeFries and Townsend (1994) based on their analysis of seasonal NDVI data sets. In the process of running to a steady state PFT, 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.

In a first global application, the CASA-DGVM correctly predicts the presence of forest classes in about 75% to 95% of all cases worldwide, and grasslands in about 58% of all cases. Potential effects of hypothetical climate change scenarios are under evaluation.

Predicted Vegetation