Book Description

Forest Growth and Yield (G&Y) modelling is essential for timber supply analysis and sustainable forest management. Empirical G& Y models are typically developed based on historical data obtained from permanent sample plots (PSP) with an assumption that forest will grow back at the same rate as in the past. With the predicted trend of global warming, growth and yield projections with traditional G& Y models would become invalid because the projected climate change will likely affect forest growth in the future. Therefore, new and innovative approaches are required to account for the potential impacts of climate change on tree growth and yield predictions. In this study, process based and statistical models were used to assess climate change on forest growth and yield in the province of Newfoundland and Labrador, Canada. A process based model (JABOWAIII) was used to assess the impact of climate change on forest G& Y. The model was calibrated with seventy PSPs that cover the entire spectrum of soil and weather conditions. The model results were used to estimate species specific climate change modifiers, which can be used to adjust the existing yield curves to account for the effects of climate change. A multiple regression and an Artificial Neural Network (ANN) models that take into account biophysical conditions and historical climate factors were developed to predict the G& Y of individual trees of forest stands. Thirteen independent variables were included in the statistical based G&Y model. Stand and tree-level independent variables included species distribution, stand age, tree height, tree diameter, stand basal area. Biophysical and climate variables including growing degree days, potential solar radiation, and annual precipitation were also included in the new model as input variables. First- and second-level auto-correlations of growth in individual trees were also considered in the models. Results from the ANN-model were compared with those produced with linear-regression models. Both JABOWAHIII model and statistical model predicted that forest G& Y will be negatively affected by warmer temperature. Uncertainties related to both process-based models and statistical models are also discussed in the thesis.