Skip to main content
ORNL DAAC HomeNASA Home

DAAC Home > Get Data > Model Archive > Model Archive > Dataset Documentation
BIOME-BGC: TERRESTRIAL ECOSYSTEM PROCESS MODEL, VERSION 4.1.1
Get Data

Summary:

Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles.

Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include the following:

The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days the program updates its memory of the mass stored in different components of the vegetation, litter, and soil.

Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions.

Biome-BGC Version 4.1.1 was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, The University of Montana, Missoula, Montana, USA. Additional information can be found on there web site at:http://www.ntsg.umt.edu/.

Data Citation:

Cite this model product as follows:

Thornton, P. E., S. W. Running, and E. R. Hunt. 2005. Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/805.

References:

Hunt, E. R. Jr., F. C. Martin, S.W. Running. (1991) Simulating the effects of climatic variation on stem carbon accumulation of a ponderosa pine stand: comparison with annual growth increment data. Tree Physiology 9: 161-172.

Kimball, J. S., M. A. White, S. W. Running.(1997) BIOME-BGC simulations of stand hydrologic processes for BOREAS. Journal of Geophysical Research 102(D24): 29,043-29,051.

Knight, D. H., T. J. Fahey, S.W. Running. (1985) Water and nutrient outflow from contrasting lodgepole pine forests in Wyoming. Ecological Monographs 55: 29.48

Korol, R. L., S.W. Running, K. S. Milner, E. R. Hunt, Jr. (1991) Testing a mechanistic carbon balance model against observed tree growth. Canadian Journal of Forest Research 21: 1098-1105.

McLeod, S., S.W. Running. (1988) Comparing site quality indices and productivity of ponderosa pine stands in western Montana. Canadian Journal of Forest Research 18: 346-352.

Nemani, R.R., S.W. Running. (1989) Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation. Agriculture and Forest Meteorology 44: 245-260.

Pierce, L.L. et al., 1993. Ecohydrological changes in the Murray-Darling Basin. III. A simulation of regional hydrological changes. Journal of Applied Ecology, 30: 283-294.

Running, S.W., 1994. Testing FOREST-BGC ecosystem process simulations across a climatic gradient in Oregon. Ecological Applications, 4(2): 238-247.

Running, S.W. and Coughlan, J.C., 1988. A general model of forest ecosystem processes for regional applications I. Hydrological balance, canopy gas exchange and primary production processes. Ecological Modelling, 42: 125-154.

Running, S.W. and Gower, S.T., 1991. FOREST BGC, A general model of forest ecosystem processes for regional applications II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology, 9: 147-160.

Running, S.W. and Hunt, E.R., Jr., 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an applicationfor global-scale models. In: J.R. Ehleringer and C. Field (Editors), Scaling Physiological Processes: Leaf to Globe. Academic Press, San Diego, CA, pp. 141-158.

Thornton, P.E., Law, B.E., Gholz, H.L., Clark, K.L., Falge, E., Ellsworth, D.S., Goldstein, A.H., Monson, R.K., Hollinger, D., Falk, M., Chen, J. and Sparks, J.P., 2002. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology, 113, 185-222.

White, J.D., S.W. Running. (1994) Testing scale dependent assumptions in regional ecosystem simulations. Journal of Vegetation Science 5: 687-702.

White, M.A., P. E. Thornton, S. W. Running, R. R. Nemani. (2000) Parameterization and sensitivity analysis of the BIOME-BGC terrestirial ecosystem model: net primary production controls. Earth Interactions 4, Paper No. 3.

Vitousek, P. M., T. Fahey, D. W. Johnson, and M. J. Swift. 1988. Element interactions in forest ecosystems: succession, allometry and input-output budgets. Biogeochemistry 5:7-34.

Model Product Description:

Model Documentation and User's Guide

The complete users guide is available at ftp://daac.ornl.gov/../data/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/bgc_users_guide_411.pdf. There is also a companion file of model release documentation, http://daac.ornl.gov/daacdata/model_archive/BIOME_BGC/biome_bgc_4.1.1/comp/BiomeBGC_v411_release.pdf.

Source Code

All of the source code for the Biome-BGC model, Version 4.1.1, released July 2000, including operating instructions and example input and output files, is available.

Model code files:

Model Scale and Resolution

Biome-BGC has a daily time-step and no explicit spatial scale. The model has an intermediate number of vegetation (4) and litter/soil (3) pools.

Precursors

Biome-BGC is a multi-biome generalization of FOREST-BGC, a model originally developed to simulate a forest stand development through a life cycle (Running and Coughlan, 1988; Running and Gower, 1991). Biome-BGC combines Forest-BGC with MT-CLIM, which extrapolates meteorological driving variables from valleys to different slopes, aspects, and elevations (Running and Hunt 1993).