Modeling forested ecosystem dynamics in the Upper Great Lakes

Forest growth modeling is moving away from description and towards explanation. The acceptance of global warming and effects related to climate change has reinforced this evolution. Shortcomings in individual tree forest ecosystem models concerning anthropogenic climate change indicate the relevance of mechanistic formulations of key physiological processes. Similarly, gaps in the understanding of tree physiology as well as operational considerations require the use of relevant empirical relationships. This paradigm is applied in the development of Forestv5.1, a distance-dependent, quasi-stochastic, highly mechanized model useable in both an operational and research context. The model predicts the growth of deciduous and coniferous species for the Great Lakes Region of North America. The model uses a daily time step and was written with two overriding design tenants in mind: (i) model drivers must mimic controls on plant growth as they exist in nature and (ii) model initialization must be achievable through the use of typical forest inventory field plot data. Forest v5.1 predicts the carbon, nutrient and water cycle as these influence tree growth and with particular emphasis on light interception and assimilation. Model outputs are both in dimension as well as biomass. Sensitivity analysis shows the importance of parameters that characterize maximum photosynthetic potential and scaling factors. A comparison of observed and predicted growth trajectories for 25 yr indicates tree diameter development exhibits useful levels of precision (−0.12 to 0.11 cm yr−1 ) relative to an empirical model. The model, using HadCM2-generated weather, projects that carbon and water use efficiency will increase as a result of climate change. Net basal area increment increases on average by 0.17 m2 ha−1 yr−1 relative to a projection of current climate. Shortcomings and avenue for further refinement of the modeling platform are discussed.