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Location of the studied federal states Northrhine-Westphalia and Thuringia within Germany. The white lines indicate the railway tracks, the color transition the mean annual wind speed. The inserted column chart shows the mean number of tree fall events along the German-wide rail network for the three year period 2015–2017 on a monthly basis (Datasource: Deutscher Bundestag 2018)
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Trees along railway networks represent a high risk due to their potential to fall during extreme weather events. The identification of locations along railway tracks with highest tree fall hazard is an important part of a proactive natural hazard management. A new user-friendly GIS tool (as ArcGIS toolbox) was developed that provides the opportunit...
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Citations
... As high temperatures and flooding are key risks to railway assets around the world these dominate the academic research in this area (Palin, Stipanovic Oslakovic, Gavin, et al., 2021). However, other risks, such as low temperatures (Stenström, Famurewa, Parida, et al., 2012), snow and ice (Stenström, Famurewa, Parida, et al., 2012), lightning strikes (Maduranga, Edirisinghe, Alahacoon, et al., 2022) and wind and storms have also been quantified (Fu & Easton, 2018;Szymczak, Bott, Babeck, et al., 2022). Much of the research is asset specific, with weather impacts to track (Dobney, Baker, Chapman, et al., 2009 and embankment assets (Powrie & Smethurst, 2019) identified as priorities for industry and research. ...
... Analysis of heat-related infrastructure failures in the Southeast of England has concluded that the majority of heat-related failures occur during early/midsummer then and reduced significantly, despite extremely high temperatures due to a process known as failure harvesting (Ferranti, Chapman, Lowe, et al., 2016). Wind-related infrastructure failures can result in windblown objects such as trees blocking tracks or damaging catenaries (Palin et al., 2021), and predicting the risk of wind-related infrastructure failures has been explored (Fu & Easton, 2018;Szymczak, Bott, Babeck, et al., 2022). ...
In this paper, we estimate the vulnerability of railway infrastructure, switches, signals, tracks, and catenaries to different weather conditions, temperature, precipitation, snow depth, and wind speed across the entire Swedish railway network between 2006-2020. Using a method to quantify the fault rate we establish thresholds that can be useful for identifying areas of concern for operations. Results reveal that high or low temperatures have a noticeable impact on the fault rates for switches, tracks, and catenaries. High levels of precipitation are associated with higher fault rates across tracks and catenaries. Snow depth has an influence on fault rates for switches and tracks, and high wind speeds are associated with higher fault rates for tracks and catenaries. Finally, signals were found to be the most resilient asset. When comparing two dominant climate zones, notable differences were only found for track asset vulnerability.
... Despite such measurements there were on average 3062 tree fall events per year in the years from 2017 to 2021, causing disruptions and delay in the railway service as well as damage to the infrastructure. In recent years the interest in the topic has increased and a number of studies on tree fall hazards appeared, showing that this not only a problem for the German railway network (Bíl et al., 2017;Koks et al., 2019;Kučera and Dobesova, 2021;Szymczak et al., 2022). Therefore, it is vital to study the connection of tree fall and wind. ...
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.
... Although tree fall onto railway lines has serious consequences not only for train operations but also human safety there is little work in the published literature on predicting the level of risk or attempting to map where the risk is highest along railway networks. 1 Some of the work that has been published on the topic includes Bíl et al. (2017) in the Czech Republic, Gullick et al. (2019) in the UK, Szymczak et al. (2022) in Germany, and an attempt at a global analysis by Koks et al. (2019). The Bíl et al. (2017) paper is an empirical modelling approach that relies on records of past events to identify railway sections at risk of tree fall. ...
... Remedial action currently employed by DB is to identify the most vulnerable trees and cut them down (Messenzehl, 2019). Szymczak et al. (2022) have developed a GIS tool that offers the possibility to detect individual trees along railway lines and to estimate the hazard of tree fall, using different parameters to describe meteorological, site and topographic conditions as well as tree characteristics. The Koks et al. (2019) approach relies on a fixed threshold of 42 m/s for wind damage to occur, which does not account for variations in tree characteristics and wood properties (see debate in Albrecht et al., 2016) and therefore their approach is unlikely to be useable at an individual tree level. ...
... The first step was to test the ability of the ForestGALES model to identify those sections of the railway line that suffered from past tree fall. The model used LiDAR and ortho-photo derived data of tree height (height values accurate to ± 0.15 m), crown width and type (broadleaf/ conifer) derived by Szymczak et al. (2022) along the railway network in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH), to predict the wind speeds at which trees were expected to fail. The calculated wind speed values were compared between 500 m long railway sections with and without recorded tree fall events derived from a damage database of DB Netz AG (tree fall includes stem fall and branch fall in the database). ...
Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train
services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large
number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined.
In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify
the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia
(NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast
models, could predict where tree fall occurred during a damaging windstorm. We used information on tree
characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to
calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a
function of its size and species, and the underlying soil. The railway network was then divided into 500 m
sections and the statistics of the CWS, tree height, and species composition (broadleaf/conifer mix) within each
section were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there
was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had
recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind
speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on
18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the
storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of
74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify
the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm
tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most
probable sections of the railway network to experience tree fall during an approaching damaging storm. Such
information could be of value in firstly planning remedial work along railway lines, and secondly preparing the
railway network ahead of a major storm.
... In Czechia, for example, forests are closer than 50 m for 30% of the overall rail network length (Bíl et al. 2017). Identification of the most hazardous locations that was performed by Nyberg and Johansson (2013), Bíl et al. (2017) or Szymczak et al. (2022), can be an alternative approach to large-scale cutting of trees. Several types of trees do not pose a high risk to rail traffic and can be planted near railway lines. ...
Storms (thunderstorms or windstorms) are common meteorological phenomena in Europe. They produce rain, lightning, strong winds or hail and are capable of causing serious failures in rail transport. We identified 14,786 incidents associated with storms, which took place on the Czech rail network between 2002 and 2021. This represented 5% of all reported rail incidents in Czechia over the monitored period. Using the exact binomial test and a grid covering each combination of month and hour, we found that these incidents were concentrated between May and August in the early morning and afternoon hours. Most of the rail problems were concentrated on only a small number of days, indicating the significant impact of severe storms. The incidents (82%) were associated with rail device failures and/or power outages, such as malfunctions of switches, signals or communication and safety devices. In addition, tree falls on rail infrastructure were also common. The total train delay time exceeded 3917 h, and in some cases rail traffic was even completely disrupted. Based on the findings, which provide unique insight into the impacts of storms on railways, mitigation measures are discussed. This is particularly important given climate change, which may lead to an increased likelihood of severe weather and associated damage in the future.
... A few papers investigated uncommon themes with respect to forest disturbances. Among them, Szymczak et al. [101] used airborne RGB and LiDAR data to create a GIS (geographic information system) tool for detecting fallen trees along railway lines as well as calculating risk of treefall for individual trees. The European Forest Condition Monitor is a web-based tool created by Buras et al. [49] for monitoring forests across Europe, characterizing forest condition and highlighting an increase in forest decline. ...
... Most of the studies on the individual tree level aimed at characterizing individual trees and deadwood in terms of their exact location and dimensions (tree height, diameter at breast height, and crown diameter and volume) to enable an improved area-wide inventory to support sustainable forest management [54,73,115] or strategic management regarding urban climate, human well-being, and climate change adaptation in urban areas [83]. Additionally, the detection and parameterization of individual trees was also used in a targeted natural hazard management for damage prevention, as shown by Steffen et al. [100] and Szymczak et al. [101]. Both studies proposed an approach to identify trees with the potential to damage infrastructural elements (e.g., roads and railroads) in the case of a hazard event such as windthrow. ...
One-third of Germany’s land surface area is covered by forest (around 11.4 million hectares), and thus, it characterizes the landscape. The forest is a habitat for a large number of animal and plant species, a source of raw materials, important for climate protection, and a well-being refuge for people, to name just a few of its many functions. During the annual forest condition surveys, the crown condition of German forests is assessed on the basis of field samples at fixed locations, as the crown condition of forest trees is considered an important indicator of their vitality. Since the start of the surveys in 1984, the mean crown defoliation of all tree species has increased, now averaging about 25% for all tree species. Additionally, it shows a strong rise in the rate of dieback. In 2019, the most significant changes were observed. Due to the drastic changes in recent years, efforts are being made to assess the situation of the forest using different remote sensing methods. There are now a number of freely available products provided to the public, and more will follow as a result of numerous projects in the context of earth-observation (EO)-based monitoring and mapping of the forests in Germany. In 2020, the situation regarding the use of remote sensing for the German forest was already investigated in more detail. However, these results no longer reflect the current situation. The changes of the last 3 years are the content of this publication. For this study, 84 citable research publications were thoroughly analyzed and compared with the situation in 2020. As a major result, we found a shift in the research focus towards disturbance monitoring and a tendency to cover larger areas, including national-scale studies. In addition to the review of the scientific literature, we also reviewed current research projects and related products. In congruence to the recent developments in terms of publications in scientific journals, these projects and products reflect the need for comprehensive, timely, large-area, and complementary EO-based information around forests expressed in multiple political programs. With this review, we provide an update of previous work and link it to current research activities. We conclude that there are still gaps between the information needs of forest managers who usually rely on information from field perspectives and the EO-based information products.
... In this paper we first analyse the ability of the hybrid-mechanistic model ForestGALES (Hale et al., 2015), which can calculate the wind risk to forest stands and individual trees in forest stands, to identify those sections of the railway line that suffered from tree fall. The model used LiDAR and ortho-photo derived data of tree height (height values accurate to ±0.15 90 m), crown width and type (broadleaf/conifer) derived by Szymczak et al. (2022) along the railway network in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH), to predict the wind speeds at which trees were expected to fail. In a first validation phase, the calculated wind speed values were compared between sections with and without recorded tree fall events derived from a damage database of DB Netz AG. ...
... Data Sources 120 The principal data sets used in this paper were German Federal LiDAR data and downscaled ERA5 wind speed data using the COSMO Climate Limited-Area Model (CLM) (Rockel et al., 2008). The LiDAR data were derived from rasterized digital terrain data and surface models at 1 m resolution and analysed by the German Centre for Rail Traffic Research (Frick et al., 2021;Szymczak et al., 2022) for a 100 m wide path along all the Deutsch Bahn (DB) railway lines. Analysis took place in the Northrhine-Westphalia (NRW) and Thuringia (TH) federal states where the data had been made publicly 125 available. ...
... The analysis provided individual tree height, individual tree canopy shapes, and the distance from the railway track for every tree along these 100 m wide paths. Assignment of trees as either conifer or deciduous was made using a supervised classification based on Sentinel-2 data (for details see Frick et al. (2021) and Szymczak et al. (2022)). Other data sets required in the analysis include the National Forest Inventory (NFI) data from 2012 (https://bwi.info), ...
Rails are a fundamental part of rail transport ensuring smooth movement of rail vehicles. Rails may, however, break or buckle, which threatens the safety and continuity of rail traffic. In this study, we focused on broken and buckled rail incidents in Czechia between 2002 and 2022 in order to investigate their spatiotemporal patterns, the most hazardous locations, and the factors influencing both issues. Broken rails were often reported in the cold months (November–March; 78.3%). Buckled rails, on the contrary, occurred in the summer (June–August; 83.1%). From a logistic regression model that included factors such as daily air temperature, rail traffic intensity, railway line geometry, share of freight traffic, maximum axle load or maximum allowed train speed, a strong influence of daily air temperature on the occurrence of these problems was found. This is particularly important given the increase in air temperature due to climate change. The most hazardous locations on the Czech rail network were identified using the KDE+ method. The results provide infrastructure managers and other stakeholders with valuable insights into the occurrence of broken and buckled rails and can support mitigation efforts.
In the northern hemisphere, snow accumulating on trees and overhead lines causes widespread outages in the electricity distribution networks. Accurate outage risk models are an essential element in improving the resilience of modern distribution networks. In this paper, a Random Forest-based model for estimating the susceptibility of overhead lines to outages caused by tree crown snow loads is proposed. The model uses a novel combination of an aerial inspection outage risk dataset, an advanced forest crown snow load risk map, a canopy height model, and forest characteristics data. All predictor variables used in the study are available as open data. As a result, outage risk probability in 50 m overhead line sections for a distribution network was generated. Cross-validation of the model showed a good predictive performance with a receiver operating characteristic area under curve (ROC AUC) of 0.75 and an accuracy of 0.74. The impact of the predictor variables was investigated by using Shapley additive explanations (SHAP) values. The most impactful variables were the forest crown snow load risk, the number of nearby canopy height model pixels, and the birch tree volume. The outage risk probability model developed in this paper could be similarly applied to assess the crown snow load risk in other distribution networks or even in other types of networks, such as roads and railways.
