Impacts of climate change on forest growth: a modelling approach with application to management

Abstract

The aim of this thesis was to modify and apply a statistical growth and yield model for analysing forest resources and optimal management under a changing climate in Finland. Initially, the structural and functional properties of physiological and statistical growth and yield models were compared under the current climate to assess whether the physiological model could be utilised in the modification of the statistical model (I). Thereafter, the impacts of elevated temperature and CO2 on tree growth were introduced into a statistical growth and yield model with species-specific transfer functions, which were formulated based on data simulated with a physiological model (II-III). These functions were created separately for three main tree species and they described the increase in stem volume growth of trees as a function of elevated temperature and CO2, stand density, competition status of a tree in a stand, geographical location and site fertility type of a stand. This method allowed the internal dynamics of the statistical model to be followed when the impacts of climate change were applied to the volume growth, allocated between diameter and height growth. Finally, this methodology was applied to derive an optimal management solution for a forest region located in eastern Finland under a changing climate by using the large-scale forestry scenario model and National Forest Inventory sample plot data (IV). In model comparisons, it was found that the physiological and statistical models agreed well in terms of relative growth rates regardless of tree species (I). This implies that both models predicted in a similar way the competition within a stand and the effect of position on tree growth. However, the statistical model was less sensitive to initial stand conditions and management than the physiological model. The transfer functions worked reasonably well in the statistical model and the model predictions were logical as regards the differences in productivity between species, sites and locations under current and changing climate (II, III). In these simulations, the volume growth was enhanced less in southern than in northern Finland, where currently low summer temperatures are more limiting to growth. In a regional forestry scenario analyses (IV), the accelerating tree growth under a changing climate increased the maximum sustainable removal of timber at regional level. Changes in optimal forest management were also detected: the proportion of thinnings increased because the stands fulfilled thinning requirements earlier, and the optimisation allocated more cuttings on mineral soils where extraction of wood was cheaper than on peatlands. Altogether, this study presents an attempt to integrate the capabilities of physiological and statistical growth and yield modelling approaches in order to make the latter more responsive to changing environmental conditions. As a result, the statistical model system can be expected to provide more precise predictions for a regional forestry scenario analyses by solving endogenously optimal forest management under a changing climate in