Evaluating wind damage vulnerability in the Alps: A new wind risk model parametrisation

Costa, Maximiliano; Gardiner, Barry; Locatelli, Tommaso; Marchi, Luca; Marchi, Niccolò; Lingua, Emanuele

doi:10.1016/j.agrformet.2023.109660

Cited by 8 publications

(1 citation statement)

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“…There are many studies which investigate the impact of wind speed on tree fall, including tree motion measurements and tree pulling experiments (Peltola et al, 2000;Kamimura et al, 2012;Schindler and Kolbe, 2020;Jackson et al, 2021), mechanistic modelling (Gardiner et al, 2008;Hale et al, 2015;Kamimura et al, 2016;Costa et al, 2023) as well as statistical and machine learning approaches (Schindler et al, 2009;Schmidt et al, 2010;Hanewinkel et al, 2014;Hale et al, 2015;Jung et al, 2016;Kamimura et al, 2016;Kamo, Konoshima and Yoshimoto, 2016;Hart et al, 2019;Zeppenfeld et al, 2023). Among the statistical approaches, logistic regression models are very common and are also used in our study.…”

Section: Introductionmentioning

confidence: 99%

Storm damage beyond wind speed – Impacts of wind characteristics and other meteorological factors on tree fall along railway lines

Lorenz,

Becker,

Gardiner

et al. 2024

Preprint

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Abstract. Strong winter wind storms can lead to billions in forestry losses, disrupt train services and amount to millions of Euro spend on vegetation management alongside 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 dataset from Deutsche Bahn (2017–2021) and meteorological data from ERA5 reanalysis and RADOLAN 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 daily maximum gust speed is the strongest risk factor, we also found that daily duration of strong wind speeds, precipitation, soil water volume, air density and the precipitation sum of the previous year increase tree fall risk. A high daily gust factor decreases the risk. Using interaction terms between maximum gust speed and duration of strong wind speeds as well as gust factor improves the model performance. Therefore, our findings suggest that high and prolonged wind speeds, especially in combination with wet conditions (high precipitation and high soil moisture) and a 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 taking tree adaptation to the environment into account.

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