Mapping the probability of forest snow disturbances in Finland

The changing forest disturbance regimes emphasize the need for improved damage risk information. In this study, our aim was to improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on tree crowns, versus forest properties in predicting the probability of snow damage, producing a snow damage probability map for Finland and test its performance, and comparing the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports in forest use declarations were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model. The statistical models were also able to identify the snow damage stands more accurately for the heavy snow load winter. The two tested statistical modelling methods. The snow damage model and the derived wall-to-wall probability map were able to discriminate between the damage and no-damage cases on a good level. The model and the damage probability mapping approach identifies the drivers of and susceptibility factors to snow damage across forest landscapes. Moreover, it can be used to estimate the concurrent and future snow damage risks in forests, which informs practical forestry and decision-making regarding climate change mitigation and adaptation of forestry.