Beyond the perception of wind only as a meteorological hazard: importance of mechanobiology for biomass allocation, forest ecology and management

Annals of Forest Science, Journal Year: 2025, Volume and Issue: 82(1)

Published: Jan. 13, 2025

Abstract Key message Although global changes are expected to intensify the impact of wind as a hazard, recent studies have emphasized critical role plays in tree growth and development. Wind-induced swaying generates strains that perceives, triggering process known thigmomorphogenesis. This alters tree’s patterns wood properties enhance its mechanical stability. Thus, functions not only hazard but also factor, enabling acclimate loads reduce risk. Despite significant thigmomorphogenesis carbon allocation, this remains largely overlooked forest ecology management models. We strongly advocate for integration wind-induced strain sensing, primary driver thigmomorphogenesis, alongside established environmental factors models, well instrumented stands aimed at studying effects on growth. crucial step is essential comprehensive understanding dynamics informed decision-making management.

Language: Английский

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Antecedent rainfall, wind direction and seasonal effects may amplify the risk of wind-driven power outages in the UK

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 26, 2025

Language: Английский

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

Published: Feb. 23, 2024

Abstract. Strong winter wind storms can lead to billions in forestry losses, disrupt train services and amount millions of Euro spend on vegetation management alongside the German railway system. Therefore, understanding link between tree fall is crucial. Existing studies often emphasize soil factors more than meteorology. Using a dataset from Deutsche Bahn (2017–2021) meteorological data ERA5 reanalysis RADOLAN radar, we employed stepwise model selection build logistic regression predicting risk falling line 31 km grid cell. While daily maximum gust speed strongest factor, also found that duration strong speeds, precipitation, water volume, air density precipitation sum previous year increase risk. A high factor decreases interaction terms speeds as well improves performance. our findings suggest prolonged especially combination with wet conditions (high moisture) density, Incorporating parameters linked local climatological (through anomalies or relation percentiles) improved accuracy. This indicates importance taking adaptation environment into account.

Language: Английский

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Effect of repeated pulling loads on Norway spruce (Picea abies (L.) Karst.) trees

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 567, P. 122071 - 122071

Published: June 26, 2024

Language: Английский

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LiDAR insights on stand structure and topography in mountain forest wind extreme events: The Vaia case study

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 359, P. 110267 - 110267

Published: Oct. 31, 2024

Language: Английский

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Forest dynamics where typhoon winds blow

New Phytologist, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Summary Tropical cyclones (TCs) sporadically cause extensive damage to forests. However, little is known about how TCs affect forest dynamics in mountainous terrain, due difficulties modelling wind flows and quantifying structural changes. Typhoon Mangkhut (2018) was the strongest TC strike Hong Kong over 40 yr, with gusts > 250 km h −1 . Remarkably, event captured by a dense anemometer network repeated LiDAR surveys across natural forests plantations. We mapped long‐term mean extreme speeds using CFD models analysed corresponding changes canopy height, which uncovered TC‐forest at unprecedented scales (> 400 000 pixels, 1108 2 ). Forest height more strongly limited exposure than background topography, limitation attributable dynamic equilibrium between growth disproportionate taller Counterintuitively, wind‐sheltered also suffered heavy damage. As result, canopies of were rugged, contrasted flat‐topped wind‐exposed sites. Plantations susceptible compared rainforests similar stature (canopy change −0.86 m vs −0.39 m). Our findings highlight as important, often overlooked factor that fundamentally shapes structure dynamics.

Language: Английский

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Improving the Windthrow Risk Model Forestgales with Long-Term Monitoring Data – a Statistical Calibration Approach

Published: Jan. 1, 2024

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Language: Английский

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Improving the predictive capacity of the windthrow risk model ForestGALES with long-term monitoring data – A statistical calibration approach

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 576, P. 122389 - 122389

Published: Nov. 15, 2024

Language: Английский

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Comment on egusphere-2024-120

Published: April 11, 2024

Abstract. Strong winter wind storms can lead to billions in forestry losses, disrupt train services and amount millions of Euro spend on vegetation management alongside the German railway system. Therefore, understanding link between tree fall is crucial. Existing studies often emphasize soil factors more than meteorology. Using a dataset from Deutsche Bahn (2017–2021) meteorological data ERA5 reanalysis RADOLAN radar, we employed stepwise model selection build logistic regression predicting risk falling line 31 km grid cell. While daily maximum gust speed strongest factor, also found that duration strong speeds, precipitation, water volume, air density precipitation sum previous year increase risk. A high factor decreases interaction terms speeds as well improves performance. our findings suggest prolonged especially combination with wet conditions (high moisture) density, Incorporating parameters linked local climatological (through anomalies or relation percentiles) improved accuracy. This indicates importance taking adaptation environment into account.

Language: Английский

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Comments on egusphere-2024-120

Published: May 1, 2024

Abstract. Strong winter wind storms can lead to billions in forestry losses, disrupt train services and amount millions of Euro spend on vegetation management alongside the German railway system. Therefore, understanding link between tree fall is crucial. Existing studies often emphasize soil factors more than meteorology. Using a dataset from Deutsche Bahn (2017–2021) meteorological data ERA5 reanalysis RADOLAN radar, we employed stepwise model selection build logistic regression predicting risk falling line 31 km grid cell. While daily maximum gust speed strongest factor, also found that duration strong speeds, precipitation, water volume, air density precipitation sum previous year increase risk. A high factor decreases interaction terms speeds as well improves performance. our findings suggest prolonged especially combination with wet conditions (high moisture) density, Incorporating parameters linked local climatological (through anomalies or relation percentiles) improved accuracy. This indicates importance taking adaptation environment into account.

Language: Английский

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