Impact model: PnET-BGC

Sector
Forests
Region
regional

PnET-BGC is a deterministic dynamic ecosystem model that includes physiological and biogeochemical processes and uses generalized empirical relationships among water, nitrogen and carbon. It was developed by linking a forest-soil-water model PnET-CN [Aber et al., 1997], with a biogeochemical cycling sub-model BGC [Gbondo-Tugbawa et al., 2001]. Through this coupling, PnET-BGC accounts for both water and N limitations on forest productivity. A strength of this model is its ability to simultaneously simulate fluxes of energy, water and major elements (Ca2+, Mg2+, K+, Na+, C, N, P, S, Si, Al3+, Cl-, and F-) in forest ecosystems. The model considers major ecosystem processes, including atmospheric deposition, CO2 effects on vegetation, canopy interactions with precipitation and atmospheric deposition, root uptake, litterfall, soil organic matter dynamics, nitrification, mineral weathering, chemical reactions involving gas, solid and solution phases, and surface water processes [Gbondo-Tugbawa et al., 2001]. PnET-BGC has been used to assess the effects of climate change on hydrology [Campbell et al., 2011] and biogeochemistry [Pourmokhtarian et al., 2012] at Hubbard Brook, as well as atmospheric deposition and land disturbance on soil and surface waters in northern forest ecosystems at local and regional scales [Chen and Driscoll, 2004; Chen et al., 2004; Chen and Driscoll, 2005; Zhou et al., 2015]. One limitation of the PnET-BGC model is the assumption of a homogeneous distribution of vegetation at the watershed scale and constant forest composition during the simulation (i.e., no changes in species assemblages). Therefore, the model does not consider vegetation changes that may occur as climate changes, as well as other factors (e.g., pests, pathogens), which could alter hydrologic (e.g., transpiration) and biological (e.g., uptake, growth) processes.
PnET-BGC requires inputs of meteorological data, atmospheric deposition, forest disturbance history, vegetation, soil, and site parameters. Meteorological inputs include a time-series of monthly maximum and minimum air temperature (°C), photosynthetically active radiation (PAR; mmol m-2 s-1), precipitation (cm), and atmospheric CO2 concentration (ppm). Atmospheric deposition includes monthly wet and constant dry to wet ratios of major elements. PnET-BGC uses the dominant forest cover type (i.e., northern hardwood trees, spruce-fir, red oak/red maple, and red pine) and its associated generalized physiological characteristics. Soil information includes soil mass per unit area, cation exchange capacity, cation exchange and anion adsorption coefficients, water holding capacity, element weathering rates, and elemental stoichiometry. Site characteristics include historical land disturbance (e.g., forest harvesting, hurricane, ice storm, fire) as well as latitude, longitude and elevation [Chen and Driscoll, 2005; Gbondo-Tugbawa et al., 2001; Zhai et al., 2008]. The effects of atmospheric CO2 on vegetation, including response of stomatal conductance and a CO2 fertilization effect on biomass, were implemented by a multi-layered sub-model of photosynthesis and phenology developed by Ollinger et al. [1997, 2002] using a constant ratio of leaf internal to ambient concentration of CO2 (Ci/Ca). A detailed description of PnET-BGC is provided by Aber and Driscoll [1997] and Aber et al. [1997], and a detailed sensitivity analysis of parameters and state variables are provided by Gbondo-Tugbawa et al. [2001] and Pourmokhtarian et al. [2012]. Model simulations run on a monthly time step with an initiation period starting at year 1000 to allow for the soil and vegetation pools to reach steady-state. Model hydrological outputs include monthly streamflow, transpiration, evaporation, water use efficiency, soil moisture, water stress index (DWater), snow pack, and snowmelt.

Information for the model PnET-BGC is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.