1. General Model Information

Name: Biome model - BioGeochemical Cycles

Acronym: BIOME-BGC


Main medium: terrestrial
Main subject: biogeochemistry
Organization level: global
Type of model: not specified
Main application:
Keywords: carbon dynamics, global change, plant functional type, nitrogen, water, NPP

Contact:

Steven W. Running,
School of Forestry
University of Montana
Missoula, MT 59812
USA
Tel.: +1 406 243-6311
Fax: +1 406 243-4510
Email: swr@ntsg.umt.edu

Dr. Peter E. Thornton
Numerical Terradynamic Simulation Group
School of Forestry, The University of Montana
Missoula, MT 59812
Phone: (406) 243-4326
email: peter@ntsg.umt.edu

Author(s):

Steven W. Running, Ramakrishna R. Nemani, and Kathy A. Hibbard

Abstract:

A primary research focus for NTSG is the development and application of computer modeling tools to simulate the biological and physical processes controlling carbon, water, and nitrogen dynamics in terrestrial ecosystems.

Biome-BGC is the computational core of this research. The current code is the result of more than 15 years of development, testing, and re-development. The present model is a direct descendent of the Forest-BGC computer model (for details regarding the Forest-BGC code, see Running and Coughlan, 1988, and Running and Gower, 1992 in the Publications section of our website). The most important differences between the Biome-BGC and Forest-BGC models is that Biome-BGC is designed to simulate ecosystem processes in both forest and non-forest biomes, and Biome-BGC has a more sophisticated treatment of several fundamental processes, including photosynthesis, allocation, and soil carbon and nitrogen dynamics.
Source: NTSG http://www.forestry.umt.edu/ntsg/EcosystemModeling/BiomeBGC/

The BIOME-BGC (BioGeochemical Cycles, compartment flow diagram) model is a multi-biome generalization of FOREST-BGC, a model developed to simulate the development of forest carbon and nitrogen pools over time [Running and Coughlan, 1988; Running and Gower, 1991] . The model is driven by routinely available daily climate data (maximum and minimum air temperature, precipitation) and the definition of several key climate, vegetation, soil, and site conditions to estimate fluxes of carbon, nitrogen, and water through ecosystems. Component cycles of BIOME-BGC have previously undergone testing and validation, including the carbon [McLeod and Running, 1988; Korol et al, 1991; Hunt et al, 1991; Pierce, 1993], nitrogen [Running, 1994], and hydrologic cycles [Knight et al, 1985; Nemani and Running, 1989; White and Running, 1995].

The BIOME-BGC model routes daily precipitation (after removing a portion for canopy evaporation) to a soil water pool, whose depth is a function of the plant rooting depth (biome-dependent). Any water passing below this depth in the soil profile is assumed to contribute to stream outflow. Daily soil water potential is calculated from soil water content and assumed equal to pre-dawn leaf water potential. Scalars (0-1) for pre-dawn leaf water potential, night minimum air temperature, and atmospheric humidity and CO2 are used to reduce a biome-specific maximum leaf conductance for water vapor (gsH20) to an actual daily value. Actual gsH20 is scaled for elemental and stomatal differences between water vapor and CO2 and used to calculate daily leaf conductance for CO2 (gsCO2). gsCO2 is then passed into an adaptation of the Farquhar model (Leuning, 1990) which scales daily photosynthesis for rate limitations due to leaf nitrogen, incident radiation, and intercellular CO2. Leaf-level carbon and water fluxes are scaled to the canopy according to leaf area index. Daily maintenance respiration is calculated as a function of biomass and temperature using a Q10 of 2.0 (respiration rate doubles for a 10 C increase in temperature).

A new version of BIOME-BGC is currently being used to model the response of an annual grassland ecosystem to elevated CO2 as part of the Jasper Ridge CO2 Project at Stanford University (Pierce et al., 1996). Several improvements have been made to the model structure to facilitate the fast time response of grasslands (relative to forests) to changes in resource availability. Carbon and nitrogen are now allocated at a biome-dependent time step which ranges from daily to yearly. Grasslands can typically shift allocation patterns at approximately weekly to monthly time scales, while forests allocate at monthly to yearly time scales. Allocation to roots and shoots is controlled by the relative balance of uptake of carbon (through photosynthesis) and nitrogen (through root uptake) - excess C relative to N favors allocation of resources to acquiring N through additional root growth - excess N relative to C favors allocation of resources to acquiring C through additional leaf growth. Allocation to woody biomass is a function of leaf allocation. Growth respiration is calculated as a constant proportion of C allocated. Turnover of plant biomass is now controlled by a phenology logic (White et al. 1997) for grasses, deciduous, and evergreen trees. In addition, plant biomass turnover is controlled by the balance between maintenance respiration and photosynthesis. If photosynthesis exceeds maintenance respiration requirements, then C and N are allocated for growth. If maintenance respiration requirements exceed photosynthesis, then C and N must turnover from plant biomass until the maintenance respiration requirements are met. Turnover of leaf, fine root, and woody C and N is then routed to the litter pool, where decomposition occurs and C is released through respiration at a rate controlled by the litter pool C:N and temperature and water constraints. Leaf and fine root litter are routed into a "fast", and woody litter to "slow" decomposition pools. N can only be mineralized within the litter pool if the C:N of the litter pool falls below a critical C:N (which is a function of biome type). After decomposing for a specified number of years (biome-specific) any remaining litter C and/or N is passed to the soil pool. Turnover of the soil pool is much slower and is controlled by a user-definable maximum annual turnover rate and temperature and water constraints. Respiration / turnover of the litter and soil pools controls the availability of nitrogen in soils and consequent N uptake by plants. N uptake is controlled by a biome-dependent maximum root uptake rate (per unit of root biomass) and scalars for water, temperature, and soil fertility. N uptake can then feedback to control leaf N content and photosynthesis.

The response of BIOME-BGC to instantaneous increases in atmospheric CO2 (holding climate constant as per chambered CO2 field experiments) vary by time scales in much the same manner as other models being compared in CMEAL (McMurtrie and Comins, 1995; Rastetter et al., in press). Short term responses ( 1 yr) are controlled primarily by physiological increases in C uptake due to increased intercellular CO2. Intermediate time scale responses (10 years for grasslands, >10 years for forests) are controlled by soil N availability (litterfall vs. decomposition) and the C:N plasticity of plant and soil pools. Long term responses (>100 years) are controlled by the net amount of N input or leached from the ecosystem.
Source:: GCTE 1996


II. Technical Information

II.1 Executables:

Operating System(s): UNIX
http://www.forestry.umt.edu/ntsg/

II.2 Source-code:

Programming Language(s): Pascal and C
http://www.forestry.umt.edu/ntsg/

II.3 Manuals:



II.4 Data:



III. Mathematical Information


III.1 Mathematics


III.2 Quantities


III.2.1 Input

III.2.2 Output


IV. References

Hunt, E.R. Jr., Piper, S.C., Nemani, R., Keeling, C.D., Otto, R.D.,and S.W. Running (1996) Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model.
Global Biogeochemical Cycles 10(3) 431-456.

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

Knight, D.H., T.J. Fahey, and S.W. Running (1985) Factors affecting water and nutrient outflow from lodgepole pine forests in Wyoming.
Ecol.Monogr., 55, 29-48 .

Korol, R.L., S.W. Running, K.S. Milner, and E.R. Hunt J r (1991) Testing a mechanistic carbon balance model against observed tree growth.
Can.J.For.Res., 21, 1098-1105.

McLeod, S. and S.W. Running (1988) Comparing site quality indices and productivity of ponderosa pine standsin western Montana.
Can.J.For.Res., 18, 346-352.

McMurtrie, R.E. and H.N. Comins (in press) The temporal response of forest ecosystems to doubled atmospheric CO2.
Global Change Biology .

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

Rastetter, E.B., G.I. Agren, and G.R. Shaver (submitted) Responses to increased CO2 concentration in N-limited ecosystems: application of a balanced-nutrition, coupled-element- cycles model.
Ecological Applications.

Running, S.W. and J.C. Coughlan (1988) A general model of forest ecosystem processes for regional applications,I. Hydrologic balance, canopy gas exchange and primary production processes.
Ecol.Model., 42, 125-154.

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

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

White, J.D. and S.W. Running (1994) Testing scale dependent assumptions in regional ecosystem simulations.
J.Veg.Sci. 5 : 687-702.

White, M.A., Thornton, P.E., and S.W. Running (in press) A continental phenology model for monitoring vegetation responses to interannual climate variability
(in press to Global Biogeochemical Cycles)


V. Further information in the World-Wide-Web


VI. Additional remarks


Last review of this document by: Juergen Bierwirth: Nov, 15th 2000
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:39 CEST 2002

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