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• A dataset from 317´000 annual tree observations during 37 years is presented. • Drought and high N deposition increase the risk of uprooting and stem breakage. • Wind damage under drought and high N deposition endangers the sustainability of Picea abies. Damage by wind in forests has increased in recent decades, counteracting possible growth increases by nitrogen (N) deposition. This is also shown by the long-term forest monitoring data from this study. The main factors affecting uprooting and stem breakage in Fagus sylvatica (L.) (9′846 individual beech trees in 102 plots) and Picea abies (Karst.) (9′168 Norway spruce trees in 72 plots) have been analysed by binomial mixed regression models. Our long-term forest observations showed that drought increased uprooting of beech and increased both uprooting and stem breakage of Norway spruce. In beech, high N deposition interacted with drought, resulting in increased uprooting under drought. In Norway spruce, N deposition increased stem breakage directly. Foliar potassium (K) deficiency increased uprooting of both beech and Norway spruce. Moreover, we found an indirect effect of soil acidification in increasing uprooting of beech by decreasing foliar calcium (Ca) concentrations. In Norway spruce, the soil acidification effect was confounded by foliar K concentrations. In beech, stem breakage increased with higher temperature which can be explained by a temperature-mediated growth increase of white rot fungi. In contrast, temperature was negatively related with stem breakage in Norway spruce, which may be interpreted as an inhibition of the heartrot fungus Heterobasidion at higher temperatures.In summary, drought and high N deposition increase the risk of mechanical damage to both beech and Norway spruce and thus endanger sustainable growth rates. We therefore suggest that wind damage must be taken in account in productivity calculations and carbon budgets of temperate forests. [Display omitted] [ABSTRACT FROM AUTHOR]

• Wind damage can heavily impact montane forests. • Models can be used to produce wind vulnerability maps for montane forests. • New parametrisations can be validated and added to existing models. • Mapping vulnerability to wind damage can help forest managers and practitioners. The risk of wind damage to European forests is expected to increase due to the changed climate. Research efforts in forestry have been focussing on the development of analytical and modelling tools to aid with the evaluation and the prediction of forests' vulnerability to wind damage, and ultimately to inform forest management decisions aimed at promoting wind-resistance in forest stands. Recent catastrophic wind damage to European forests has shown that wind damage risk applies also to montane forests. These are of particular importance for the various ecosystem services they provide, including protection from gravitational hazards and defence against soil erosion. At present, the available forest wind risk models have been tested and used mainly on production or planted forests in different countries, but never in the complexity of mountainous terrains. The aim of this study is to introduce a methodology for the validation of a new parametrisation of the ForestGALES wind risk model, specifically designed for the alpine scenario. To do so, we used parameters from pulling tests in the field, and laboratory mechanical tests. We also developed a workflow that, starting from data obtained by LiDAR, aims at producing wind damage vulnerability maps. After validating this new parameterisation, we investigated the variations in wind vulnerability for a case study in the Italian Alps by introducing the Difference of Vulnerability (DoV) raster. Among our results, the DoV proves to be an innovative tool whose future use can increase understanding of the factors predisposing a forest stand to wind instability. [ABSTRACT FROM AUTHOR]

Highlights • The agent-based model was created using the basic knowledge of gust, wind, and tree. • The Agent-based model demonstrated wind damage similar to the observed damage in forests. • The model described damage propagation and forest edge effects. • The model showed some phenomena, which were similar to the previous research. • Possibilities of improving the agent-based model were discusses. Abstract Powerful storms, consisting of strong gusts and winds, damage forests. Therefore, foresters need forest management strategies to reduce the damage risk. This paper focused on the damage patterns within the forest as the final results of multiple tree-wind dynamic interactions in time and space during a storm. Recent developments in computer technology allow for the possibility of simulating the complex and dynamic phenomena of damage during a storm but are extremely time consuming. To simplify the simulations without losing the crucial aspects of wind damage in forests, we introduced a computer simulation model using the agent-based modelling (ABM) technique, which capture the phenomena and interactions of individuals called 'agents'. We created an ABM for forest wind damage simulation, coupling together an accepted understanding of wind gusts in forests, tree bending moments, and damage propagation. The model was tested with variations in three conditions: trees acclimated and unacclimated to their wind environment; three levels of gust strength; and three tree planting densities. The ABM was able to replicate damage patterns and demonstrate damage propagation within the forest and the effects of forest edges. The difference in the rate of damage in the forest between acclimated and unacclimated edges became similar with an increase in the gust intensity, and a decrease in tree density through a reduction in the shelter effect of the forest. The ABM could be improved in the future by parametrizing the variation in individual tree resistance, and the variation in gust and wind strength, as well as adding more information on local environmental conditions such as topography and soil variation, and storm characteristics such as duration and intensity. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We model the motion of trees in the wind using realistic 3D tree models derived from terrestrial laser scanning data. • We test the model against long-term field data collected in Wytham Woods, Oxford. • The model proved accurate and could be used in other natural forests or for open grown trees. Abstract Wind damage is an important driver of forest structure and dynamics, but it is poorly understood in natural broadleaf forests. This paper presents a new approach in the study of wind damage: combining terrestrial laser scanning (TLS) data and finite element analysis. Recent advances in tree reconstruction from TLS data allowed us to accurately represent the 3D geometry of a tree in a mechanical simulation, without the need for arduous manual mapping or simplifying assumptions about tree shape. We used this simulation to predict the mechanical strains produced on the trunks of 21 trees in Wytham Woods, UK, and validated it using strain data measured on these same trees. For a subset of five trees near the anemometer, the model predicted a five-minute time-series of strain with a mean cross-correlation coefficient of 0.71, when forced by the locally measured wind speed data. Additionally, the maximum strain associated with a 5 ms−1 or 15 ms-1 wind speed was well predicted by the model (N = 17, R2 = 0.81 and R2 = 0.79, respectively). We also predicted the critical wind speed at which the trees will break from both the field data and models and find a good overall agreement (N = 17, R2 = 0.40). Finally, the model predicted the correct trend in the fundamental frequencies of the trees (N = 20, R2 = 0.38) although there was a systematic underprediction, possibly due to the simplified treatment of material properties in the model. The current approach relies on local wind data, so must be combined with wind flow modelling to be applicable at the landscape-scale or over complex terrain. This approach is applicable at the plot level and could also be applied to open-grown trees, such as in cities or parks. [ABSTRACT FROM AUTHOR]

Highlights • Machine learning techniques were accurate in predicting wind damage to trees. • Random forests proved the most accurate and discriminating methodology. • Models were sensitive to removal of site and stand but not tree characteristics. • All models were able to accurately replicate a mechanistic wind risk model. • Machine learning techniques could help the management of wind damage to forests. Abstract This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory plots damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle-Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage. [ABSTRACT FROM AUTHOR]

Intensifying natural disturbance regimes are driving changes in turbulent forest‐atmosphere exchange of energy and carbon dioxide (CO 2 ), which are affecting the atmosphere at local to global scales. In the European Alps, windthrow and bark beetle infestations have been increasing in severity and intensity; however, studies employing eddy covariance to investigate net ecosystem‐atmosphere exchange of sensible ( H ) and latent heat ( LE ), and CO 2 ( NEE ) along forest disturbance chronosequences in this ecoregion have yet to emerge. This paper presents the first eddy covariance measurements at windthrow sites in the European Alps. Despite the complex terrain, turbulent flux data quality at the sites was high, as indicated by the near‐100% energy balance closures and the large proportion of data satisfying strict quality criteria. Seasonal datasets (15th May–15th October) from the two sites (disturbed in 2009 and 2007, respectively) were subsequently combined in a time series‐chronosequence approach to investigate H , LE , and NEE over the 2nd to 8th growing seasons post‐windthrow. While varying weather conditions strongly influenced energy partitioning, post-disturbance vegetation recovery coincided with a general decrease in median summer Bowen ratios ( H/LE ), although the decline was most pronounced between the 2nd and 3rd summers. Net CO 2 emissions on the other hand demonstrated a clear and consistent post‐disturbance decline, though the seasonal fluxes point toward large losses of ecosystem carbon (C) after disturbance. In the 3rd season post‐windthrow, a net total of 405 ± 15 g C m −2 was lost to the atmosphere, while the seasonal net ecosystem production of −4 ± 16 g C m −2 observed at the 2007 windthrow site indicates that 8 years after disturbance the site remains an annual net C source. This study therefore demonstrates that eddy covariance can provide robust and valuable insights into forest disturbance impacts on H , LE , and NEE in the European Alps. [ABSTRACT FROM AUTHOR]

Abstract: The most severe damage to forests in Central Europe occurs during winter storms caused by Northern Hemispheric mid-latitude cyclones. Storm events in the winter semesters of the past 150 years were investigated to quantify changes and evaluate whether damage rates, forest properties and climate had changed. Records of damage extent (wind throw/snap/breakage), forest area and growing stock in Switzerland were comparatively analysed. Storm damage (m3) was 17 times greater during the period 1958–2007 than during the period 1908–1957 and 22 times greater than in the period 1858–1907. Forest area in Switzerland has increased by 63% and growing stock by 292% over the past 150 years. The significant recent increase in storm damage could only partially be explained by increased growing stock. Weather reports prior to storms indicated that severe storm damage occurred almost always when soils were unfrozen (96%) and wet (96%). During the observation period mean winter temperature has increased by nearly 2°C and winter precipitation has increased by nearly 50% in the study region. In the Zurich region, daily maximum gust wind speed and storm damage were compared. Maximum gust wind speed above 35ms−1 was associated with extensive storm damage. Catastrophic storm damage and maximum gust wind speed measured during storms have increased during recent decades. In conclusion, increasing growing stock, warm winter temperature and high precipitation, and even more markedly, increasing maximum gust wind speed have all contributed to the recent increase in windstorm damage to forests. [Copyright &y& Elsevier]