Model-based analysis of climate change impacts on the productivity of oak-pine forests in Brandenburg

The relationship between climate and forest productivity is an intensively studied subject in forest science. This thesis is embedded within the general framework of future forest growth under climate change and its implications for the ongoing forest conversion of pure pine forests into mixed oak-pine forests. My objective is to investigate future forest productivity at different spatial scales (from a single specific forest stand up to aggregated information for Germany as a whole) with a focus on oak-pine forests in the federal state of Brandenburg. The overarching question is: how are the oak-pine forests affected by climate change described by a variety of climate scenarios? I answer this question by using a model-based analysis of tree growth processes and responses to different climate scenarios with an emphasis on drought events. In addition, a method is developed which considers climate change uncertainty of forest management planning. As a first ’screening’ of climate change impacts on forest productivity, I calculated the change in net primary production (NPP) on the basis of a large set of climate scenarios for different tree species and the total area of Germany. To account for uncertainties associated with climate change projections, it is necessary to use a broad variety of climate change scenarios to derive probabilities of the expected impacts. In process-based models this comes at the cost of large amounts of computing time. These limitations can be circumvented by applying static reduced models (SRM). The term static reduced