Fire-BGC (FIRE BioGeoChemical succession model)

Fire-BGC is a mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains.

ecological processfire successionconiferous forest landscapes



Initial contribute: 2020-01-03


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Application-focused categoriesNatural-perspectiveLand regions
Application-focused categoriesNatural-perspectiveAtmospheric regions

Detailed Description

English {{currentDetailLanguage}} English

Quoted from: Fire-BGC: A mechanistic ecological process model for simulating fire succession on coniferous forest landscapes of the northern Rocky Mountains. Forest Service research paper. 

      FIRE-BGC is the union of two ecosystem models that were developed using different approaches. The process-based,gap-replacement model FIRESUM (Keane and others 1989,1990a,b) was merged with the mechanistic bio-geochemical simulation model FOREST-BGC (Runningand Coughlan 1988; Running and Gower 1991) to predict changes in species composition in response to various ecosystem processes over long periods. The mechanistic approach of FOREST-BGC improved the level of detail needed to understand those ecosystem processes that gov-ern successional dynamics. FIRESUM's comprehensive simulation of forest dynamics in multispecies stands, and its integration of fire interactions with ecosystem components, allow FIRE-BGC to simulate changes in species composition and abundance as a consequence of multiple disturbances over long periods.

      FIRESUM is derived from the JABOWA (Botkin and others 1972; Kercher and Axelrod 1984; Shugart and West1980; Urban 1990) family of models where individual trees are grown deterministically using an annual time-step difference equation. Tree growth is affected by several site factors including available light, water stress, and growing season warmth. These environmental factors are represented in FIRESUM as scalars (values between 0 and 1)that reduce an optimal tree diameter annual growth increment. Tree establishment and mortality are modeled stochastically using Monte Carlo techniques. Fuel accumulation and decomposition are empirically calculated yearly. Fires are simulated at stochastic or fixed intervals and effects of each fire are simulated by reduction of litter, duff, and down woody fuels; and by tree mortality and by post-fire tree regeneration and growth.

      FOREST-BGC(a FOREST BioGeoChemical process model) is a process-level ecosystem simulation model that calculates the cycling of carbon, water, and nitrogen through forest ecosystems (Running and Coughlan 1988). It dynamically calculates the flow of energy between ecosystem components at a daily time step using fundamental physical and ecophysiological relationships. The model simulates these fluxes on a forest stand usually 0.1 ha in area.FOREST-BGC has mixed time resolution with carbon and nitrogen allocation and flow simulated at a yearly time step, and key forest canopy processes such as transpiration, photosynthesis, and respiration simulated at a daily time step. The ecosystem is represented by a set of compartments called state variables and the flow of carbon, water, and nitrogen to and from these compartments is simulated through the quantification of pro-cess relationships.

      FOREST-BGC does not identify individual trees in its simulation approach(Running and Coughlan 1988). Rather, it treats the whole forest as one "large tree” with one "big" leaf whose thickness is proportional to leaf area index. This"tree” is defined in terms of the carbon and nitrogen within the stems, leaves, and roots representing all trees that make up the stand.FOREST-BGC assumes horizontal homogeneity (uniform stand conditions) and models most ecosystem fluxes in the vertical dimension(Running and Coughlan 1988; Friend and others 1993). Korol and others(1991) successfully used FOREST-BGC to create an individual tree model of stand dynamics called TREE-BGC. However, TREE-BGC only recognized one tree species (Douglas-fir) and does not simulate landscape interactions.

      Creation of FIRE-BGC used the mechanistic design of FOREST-BGC as the framework and engine for ecosystem simulation. Then importantFIRESUM algorithms were added to the framework to simulate multi-species forest succession over long periods. These FIRESUM routines were then refined to utilize the detailed information generated by the mechanistic FOREST-BGC routines. Finally, this modeling framework was implemented in a landscape context by recognizing the spatial distribution of these processes across a simulation area(Busing 1991; Shugartand Seagle 1985; Urban and others 1991). This allowed a detailed simulation of ecosystem processes that act across several spatial scales (Bonanand Shugart 1989). Kimmins (1993) used a similar approach in the development of his FORSYTE-11 succession model.

Model Design

      FIRE-BGC models the flow of carbon, nitrogen, and water across various ecosystem components to calculate tree growth. The major compartments and processes simulated by the model are diagrammed in figure 1and illustrated in figure 2.Carbon is fixed by tree leaves (needles via photosynthesis using solar radiation, temperature, and precipitation, and this carbon is then distributed to leaves, stems, and roots. A portion of the leaves, stems, and roots are lost each year and accumulate on the forest, floor in the litter, duff, and soil (fig.1). These forest floor compartments lose carbon through decomposition. Nitrogen is cycled through the system from the available nitrogen pool.

      Spatial Scales-Two spatial scales are explicitly implemented in FIRE-BGC. Ecosystem processes that occur at the landscape level, such as seed dispersal and fire, are modeled in a spatial domain using raster data layers (Hunsaker and others 1993). Stand-level processes, such as tree growth and regeneration, are modeled independently of the spatial environment. Dynamic databases provide the linkage between the spatial simulation and the stand-level process simulation. Information recorded in these databases is always summarized by stand. For instance, fire behavior is predicted for each pixel in the landscape then the behavior estimates are averaged across all pixels within the stand boundaries for use in predicting tree mortality and fuel consumption.