Avalanche dynamics models are used for hazard zoning and engineering to predict runout distances and impact pressures of snow avalanche events. The effect of mountain forests as an effective biological protection measure against avalanches has rarely been addressed in this context. Avalanche runout distances of small to medium avalanches are strongly influenced by the structural conditions of forests in the avalanche path; however, this varying decelerating effect has not yet been implemented in avalanche models. Within the two-dimensional avalanche dynamics program RAMMS the standard Voellmy-Salm model can be applied to predict runout distances, flow velocities and impact pressures in complex three-dimensional terrain. Currently, the occurrence of forests is realized by increasing but constant friction parameters μ (dry-Coulomb type friction) and ξ (velocity squared friction) compared to open unforested terrain. Back-calculations of 41 well documented small avalanches which released in forests of the Swiss Alps emphasize the need for a further calibration dependent on differences in forest structure. Since the friction parameters are more conceptual than physical, they must be fitted by matching model results and recorded data which basically involves solving an inverse problem. A way of providing probabilistic statements about unobservable information is Bayesian inference. Therefore, we present a framework for a Bayesian probabilistic model calibration of the friction parameter ξ accounting for differences in forest structure in the avalanche path. Considering different forest characteristics within avalanche simulations will improve current applications for avalanche models, e.g. in mountain forest and natural hazard management.