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The BIOME-BGC Terrestrial Ecosystem Process Model. BIOME-BGC estimates fluxes and storage of energy, water, carbon , and nitrogen for the vegetation and soil components of terrestrial ecosystems.
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The BIOME-BGC Terrestrial Ecosystem Process Model • BIOME-BGC estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. • Model algorithms represent physical and biological processes that control fluxes of energy and mass: • New leaf growth and old leaf litterfall • Sunlight interception by leaves, and penetration to the ground • Precipitation routing to leaves and soil • Snow (SWE) accumulation and melting • Drainage and runoff of soil water • Evaporation of water from soil and wet leaves • Transpiration of soil water through leaf stomata • Photosynthetic fixation of carbon from CO2 in the air • N uptake from the soil • Distribution of C and N to growing plant parts • Decomposition of fresh plant litter and old soil organic matter • Plant mortality • Plant phenology • Fire/disturbance • The model uses a daily time-step with daily updating of vegetation, litter, and soil components.
BIOME-BGC Major Features: • Daily time step (day/night partitioning based on daily information); • Single, uniform soil layer hydrology (bucket model); • 1 uniform snow layer of SWE (no canopy snow interception/losses); • 1 canopy layer (sunlit/shaded leaf partitioning); • Dynamic phenology and C/N allocation (e.g. LAI, biomass, soil and litter) • Disturbance (fire) and mortality functions • Variable litter and soil C decomposition rates (3 litter and 4 soil C pools)
BIOME-BGC Eco-physiological Parameters Biome-BGC uses a list of 43parameters to differentiate biomes. These parameters define the general ecophysiological characteristics of the dominant vegetation type and must be specified prior to each model simulation. These parameters can be measured in the field, obtained from the literature or derived from other measurements. Default Biome types with defined parameters • Deciduous Broadleaf Forest (temperate) • Deciduous Needleleaf forest (larch) • Evergreen Broadleaf Forest (subtropical/tropical) • Evergreen Needleleaf Forest • C3 Grassland • C4 Grassland • Evergreen Shrubland
Biome-BGC Default Eco-physiological Parameters: Evergreen Needle-leaf Forest
BIOME-BGC Environmental Controls on Canopy Conductance (Walker Branch Site) M_total,sun,shade = (MPPFD,sun,shade * MTmin * MVPD * MPSI) where multipliers range from 0 (full Gs reduction) to 1 (no effect) Gs, sun,shade = Gs,max * M_total, sun,shade
BIOME-BGC Example Initialization File
BIOME-BGC Example Initialization File Cont.
Soil Class Silt loam Silt Loam β-value -4.625 -3.84 -5.275 VWC_sat 0.48 0.48 0.41 PSI_sat -0.0073 -0.0078 -0.0013 BIOME-BGC 1Soil Water – Soil Water Potential Curves (MPa) (%) 1after Cosby et al., 1984
Mature Aspen Stand (SSA-OA BERMS site) Mature Black Spruce Stand (NSA-OBS Ameriflux site) Verification of BIOME-BGC Daily and Seasonal Dynamics: Comparisons with Tower Eddy-flux Measurements ET ET NEP NEP Kimball et al., 1997a,b
Regional Extrapolation through remote sensing, field measurements and ecological models (BOREAS SSA) NPP ( Mg C ha-1) Water £ 0.2 LANDCOVER ³ 3.0 Measurements GIS Input Layers Modeling Kimball et al., 2000
Documentation of BIOME-BGC Updates • MODEL LOGIC: • Thornton, P. E. (1998). Description of a numerical simulation model for predicting the dynamics of energy, water, carbon, and nitrogen in a terrestrial ecosystem. Ph.D. dissertation, University of Montana, Missoula, MT, 280pp. [Available from Mansfield Library, University of Montana, Missoula, MT 59812]. • Thornton, P. E., B. E. Law, et al. (2002) Modeling the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology (in press). • BIOME ECOPHYSIOLOGICAL PARAMETERIZATION: • White, M. A., P. E. Thornton, and S. W. Running (2000). Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: Net primary production controls. Earth Interactions 4(3): 1-85. • PHENOLOGY: • White, M.A., P.E. Thornton, and S.W. Running (1997). A continental phenology model for monitoring vegetation responses to inter-annual climatic variability. Global Biogeochemical Cycles 11(2): 217-234 [Available online at NTSG website].