The model

    The Forest-BGC (Bio Geochemical Cycles) is an ecophysiological model developed by RUNNING, S.W. & COUGHLAN, J.C, 1988; RUNNING, S.W. & GOWER, S.T., 1991, which calculates key processes involved in the carbon, water and nitrogen cycles in forest ecosystems. The model treats canopy interception and evaporation, transpiration, photosynthesis, growth and maintenance respiration, carbon allocation above and below ground, litterfall, decomposition and nitrogen mineralization mechanistically; but, in a general way, incorporating minimal species-specific data.

    The model has a mixed-time resolution. Hydrologic, photosynthetic and maintenance respiration processes are computed daily. The other carbon processes and all nitrogen processes are computed yearly . Figure 1 shows a flow diagram of Forest-BGC model showing the submodels corresponding to daily and yearly time resolutions. The paper RUNNING, S.W. & COUGHLAN, J.C, 1988 presents a complete description of the daily half part of the model, while the dynamic carbon allocation and nitrogen budgets ruling the yearly half part of the model are presented in RUNNING, S.W. & GOWER, S.T., 1991.

    Hydrologic balances, plant water availability and canoppy gas exchange processes are treated daily because metereological data are routinaly summarized as daily averages or totals, and these processes react on a daily basis to enviromental conditions RUNNING, S.W. & COUGHLAN, J.C, 1988. The model requires so, daily climate input data of incident shortwave radiation, maximun and minimum air temperature, dew point and precipitation. Carbon allocation, litterfall and decomposition processes cannot be meaningfully calculated daily because the minimum routine measurement frecuency of these processes is each month (RUNNING, S.W. & COUGHLAN, J.C, 1988). Forest-BGC splits time resolution in daily and yearly processes because these time scales seem to be optimal for the model dynamics. Definition of important site and vegetation constants such as soil water capacity and LAI must be provided as inputs to the model. The full model requires the meassurement of more than a hundred of input variables to work. Forest-BGC was conceived so most of these variables could be injected eventually to the model from remote sensing data adquisition methods.

    LAI represents the ratio of leaf area per unit ground area and it's used by the model for measuring vegetation structure over large areas (for application at regional/global scale), and relating it to energy and mass exchange. It's also used for calculating canopy interception, transpiration, respiration, photosynthesis, carbon allocation and litterfall. Most ecosystem process models that simulate carbon and hydrologic cycles require LAI as an input variable.

    In the Forest-BGC, LAI is calculated by the following equation:

    where Specific Leaf Area, Leaf Carbon and Ground Surface Area are input parameters introduced to the model.

    However, according to the same authors, LAI can be easily obtained by remote sensing. As an example, MOD15A2 is the level-4 MODIS global Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product, composited every 8 days at 1-kilometer resolution by the MODIS instrument operating on the Terra and Aqua NASA's spacecrafts . LOPES, M.M.D., 2005, provides more different ways to estimate LAI values from remote sensing data, i.e. from LandSat ETM+ imagery. According to this, LAI is a key simplification for regional/global applications, targeting Forest-BGC as a clear potential model for the study of forest ecosystem processes at regional and global scales.

    The Forest-BGC model provides different outputs, where the more relevants are the Gross Primary Production (GPP), which represents the carbon fixed during photosynthesis; and, the Autotrophic Respiration (Ra) . Net Primary Production (NPP), which can be defined as the time integral of positive increments to plant biomass, is the central carbon related variable summarizing the interface between plant and other processes, and it can be meassured in terms of CO2 exchange as:

    when GPP and Ra have opposite signs. It is broadly accepted that NPP is a key variable for monitoring climate changes. Species/soil interrelationships, climate and stand age influence NPP of forests. The importance of NPP creates a great need for process models that accurately estimate this value. In a global scale, terrestrial NPP is one of the most modelled ecological parameters, with models that differ markedly in approach and complexity and often yielding comparable estimates. NPP is sensitive to many controls, including aspects of climate, topography, soils, plant and microbial characteristics, disturbance regime and anthropogenic impacts; and these mechanisms operate over different time scales. A number of global models have been developed, particularly for global carbon, but they are primarily static estimates that do not provide dynamical explanations of the mechanisms underlaying the observed processes, and which are not driven by data for specific sites and conditions . Forest-BGC allows the accurate estimate of NPP values for stands and regional/global areas, on a yearly time scale, providing a mechanistic process that uses minimal species-specific information. Because of this, Forest-BGC can be a very useful tool for climate changing monitoring at global scale .