Strong winter wind storms can lead to billions of euros in forestry losses, disrupt train services and necessitate millions of euros in spending on vegetation management along the German railway system. Therefore, understanding the link between tree fall and wind is crucial.
Existing tree fall studies often emphasize tree and soil factors more than meteorology. Using a tree fall dataset from Deutsche Bahn (DB; 2017–2021) and meteorological data from the ERA5 reanalysis and RADOLAN (Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer)) radar, we employed stepwise model selection to build a logistic regression model predicting the risk of a tree falling on a railway line in a 31 km grid cell.
While the daily maximum gust speed (the maximum wind speed in a model time step at 10 m height) is the strongest risk factor, we also found that the duration of strong wind speeds (wind speeds above the local 90th percentile), the gust factor (the ratio of the maximum daily gust wind speed to the mean daily gust speed), precipitation, soil water volume, air density and the precipitation sum of the previous year are impactful. Therefore, our findings suggest that high wind speeds, a low gust factor and a prolonged duration of strong winds, especially in combination with wet conditions (high precipitation and high soil moisture) and high air density, increase tree fall risk. Incorporating meteorological parameters linked to local climatological conditions (through anomalies or in relation to local percentiles) improved the model accuracy. This indicates the importance of considering tree adaptation to the environment.
