Feedbacks of windthrow for Norway spruce and Scots pine stands under changing climate

Wind damage is one of the major natural disturbances that can occur worldwide in most types of forests. Enhanced management using adequate decision support systems (DSS) can considerably reduce the risk of windthrow. The decision support system 'Forest and Climate Change' (DSS-WuK) which is currently being developed at Göttingen University aims at providing a tool for the quantitative assessment of biotic and abiotic risks for forest ecosystems under the conditions of changing climate.

In order to assess the future risks of wind damage the system employs a coupled modelling approach combining the turbulence model SCAlar DIStribution (SCADIS) with the soil–vegetation–atmosphere-transfer (SVAT) model BROOK 90. The present study investigates projections of wind damage in Solling, Germany under climate scenarios A1B and B1, taking into account the windthrow feedbacks—changes of microclimate as a result of tree fall and consequent stabilization or destabilization of a forest stand.

The results of the study indicate that in Solling the risk of windthrow for spruce and pine forest stands is likely to increase considerably during the 21st century. The general tendencies indicate that under A1B the probability of damage would be higher than under B1 and that under the same climate and soil conditions the risk for spruce stands would be higher than for pine stands of equal age.

The degree of damage and feedback contribution as well as a sign of feedback in each particular case will strongly depend on the particular local or regional combination of climatic and soil factors with tree species, age and structure. For Solling the positive feedback to local climatic forcing is found.

The feedback contributes considerably (up to 6% under given conditions) to the projected forest damage and cannot be neglected. Therefore, the adequate projection of future damage probabilities can be performed only with a process-based coupled soil–atmosphere model with corresponding high spatial and temporal resolution.