Author:

Abstract:

To maximize impacts of forests on climate change mitigation strategies, forest resources should get a more prominent role in the new green economy. The harbor of Antwerp is the n°1 petrochemical hub of Europe with an annual import of 45Mt of petrol of which around 6% could be sustainably replaced with a biorefinery running on a forest woody biomass (FWB) feedstock. To evaluate the feasibility of this biorefinery, accurate predictions of future local FWB stocks are necessary. Current predictions of FWB availability in Flanders are based on the decision support systems Sim4Tree, which is using the traditional empirical growth curves. These empirical growth curves predict current FWB growth accurately, but fail to take into account new management strategies and the effects of climate change. Process-based models form a climate and management sensitive alternative but often lack the accuracy necessary to make economic evaluations. Therefore, the use of hybrid models, which combine both empirical and process-based models, has been suggested as an approach to predict future FWB availability in a climate and management sensitive manner. In our research, we define species and soil specific scaling factors, describing growth changes predicted using the mechanistic forest growth model 4C (FORESEE). These scaling factors can easily be incorporated in any empirical model describing forest growth to increase their sensitivity to different climate scenarios. Our results highlight the importance of combining mechanistic with empirical forest growth models, as well as pinpoint some of the most important points of improvement for mechanistic modelling techniques to ensure further adoption of these forest management tools in cross-sectoral feasibility analysis.