Winter storm risk assessment in forests with high resolution gust speed data

Winter storms pose a major threat to forest management in Central Europe. They affect forests at a large spatial scale and produce large losses in standing and merchantable timber within few hours. The assessment of winter storm vulnerability by statistical modelling serves as an important tool to tackle uncertainities about the damage risk and to inform management decision processes. This study made use of an extensive forest inventory data set from South-West Germany before and after winter storm Lothar in 1999, one of the most severe storm events in Germany over the last decades. Hierarchical logistic models were fitted to relate storm damage probability on individual tree level to features of dendrometry, site, orography, and storm-specific high resolution data of maximum gust speed. We developed two different approaches to implement gust speed as a predictor and compared them to a baseline model with a structured spatial effect function with no gust speed information. Regional and local variability which could not be described by the predictors was modelled by multi-level group effects. Generalisation performance was tested with a spatially and temporally independent data set on storm separation between explicit spatial gust speeds and unknown variability achieved with the parametric multi-level approach led to a higher degree of transparency and utilisability.

Automated summary

Winter storms in Central Europe pose a major threat to forest management, causing significant losses in timber. Statistical modelling is a valuable tool in assessing the vulnerability of forests to winter storms. This study used forest inventory data before and after a severe storm in Germany to develop models that relate storm damage probability to various tree and environmental factors. The models incorporating gust speed information led to a higher degree of transparency and usability.