Appendix. Description of the Sheffield Dynamic Global Vegetation Model (SDGVM)
The SDGVM is a generalised global-scale model that predicts vegetation structure and dynamics from input data of climate, [CO2] and soil texture. The basic processes and assumptions in the SDGVM are outlined in Cramer et al. (2001). The SDGVM requires input data of climate, [CO2] and soil texture. The climate data are monthly mean and minimum temperatures, water vapour pressure deficit, precipitation and cloudiness. The physiology and biophysical module simulates carbon and water fluxes from vegetation (Woodward et al., 1995) with water and nutrient supply defined by the water and nutrient flux module. The soil module incorporates the Century soil model of carbon and nitrogen dynamics (Parton et al., 1993), with a model of plant water uptake. Eight soil carbon and nitrogen pools are modeled: surface and soil structural material, active soil organic matter, surface microbes, surface and soil metabolic material, slow and passive soil organic material.
The carbon and nitrogen dynamics are described by linear autonomous differential equations with parameters that depend on soil texture, temperature, precipitation, humidity, soil moisture, water flow, potential evapotranspiration and litter. These variables are held constant over a given period and the differential equations are solved by standard means for these conditions. The set of parameters is updated at each successive period, and the carbon calculation is advanced using the final state in the previous period as the initial state in the current period. These equations are solved each month. The organic nitrogen flows are equal to the product of the carbon flow and the nitrogen to carbon ratio of the state variable that receives the carbon (Parton et al., 1993). The carbon to nitrogen ratios of the soil state variables receiving the flow of carbon, are linear functions of the mineral nitrogen pool. The mineral nitrogen pool is an additional pool, which stores surplus nitrogen. The dynamics imposed by the linear functions ensure that this pool is always positive.
Water fluxes are modelled using a ‘bucket’ model. The model is composed of four buckets: one thin (5 cm) layer at the surface and three buckets of equal depth, which make up the remainder of the soil layer. The depth of the total soil layer is set to a default of 1 m. The effects of bare soil evaporation, sublimation, transpiration and interception (each of which represents a loss of water available to the vegetation system) are incorporated into the model.
The primary productivity model simulates canopy CO2 and water vapour exchange and nitrogen uptake and partitioning within the canopy. Nitrogen uptake is linked directly with the Century soil model, which simulates the turnover of carbon and nitrogen in plant litter, of differing ages and depths within the soil, in addition to soil water status.
The primary productivity model determines the assimilated carbon available for the growth of plant leaves, stems and roots. The plant structure and phenology module defines the vegetation leaf area index and the vegetation phenology. Leaf phenology is defined by temperature thresholds for cold deciduous vegetation and by drought duration for drought deciduous vegetation (Cramer et al., 2001).
The vegetation dynamics module (Cramer et al., 2001; Woodward et al., 2001) simulates the establishment, growth, competition and mortality of plant functional types (evergreen and deciduous broad leaved and needle leaved trees, grasses with the C4 photosynthetic metabolisms and C3 grasses and shrubs). Functional types of plants compete for light and soil water and all suffer random mortality that increases with age. The densities (plants per unit area), heights and ages of all of the functional types, except grasses, are simulated at the finest spatial resolution (pixel) of the model. A fire module, based on temperature and precipitation, burns a fraction of the smallest pixel of study (Woodward et al., 2001). The fire model simulates disturbance by fire for a small fraction of the pixel. It is assumed that 80% of above-ground carbon and nitrogen are lost as a consequence of the fire and fire only occurs when, in effect, leaf litter reaches a critical point of dryness, at which point fire will occur at a random time and for a random subset of the pixel (Woodward et al., 2001).
The SDGVM simulations start from a soil, defined by texture and depth, climate and atmospheric CO2 concentration. Therefore there is a necessary initialisation stage in which the soil carbon and nitrogen storage of the soil is determined, with the appropriate vegetation for the simulated climate. The model initialisation is determined by running with a repeated and random selection of annual climates from 1901 to 1920. The soil carbon and nitrogen values are first determined by solving Century analytically. Then the model is run until the vegetation structure is at equilibrium, typically after, at most 500 yr. When initialisation is completed the SDGVM then simulates vegetation for the whole climate series. Fire was simulated from the same initial values as the fire-off simulation for the 20th century.