Wind and windstorms cause severe damage to natural and
human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.
Bundesministerium für Bildung und ForschungClimXtremeInstitute for Basic ScienceIBS-R028-D1Bundesministerium für Wirtschaft und EnergieQuWind1000325940ABundesministerium für Verkehr und Digitale Infrastruktur4818DWDP3AAXA Research FundAXA Research FundNational Research Foundation of KoreaNRF-2022M3K3A1097082Pusan National University2021General introduction
Wind is a common feature of our day-to-day weather just like air temperature and precipitation. Wind is per definition a sustained air movement in the atmosphere, which can range from still conditions to extraordinary values, from very local to global spatial scales, and has a wide range of temporal scales from seconds to decades. Winds can have both a beneficial and detrimental effect on society, infrastructure and agriculture. On the one hand,
storms, which have very strong winds, can lead to considerable damage in
infrastructure and forestry, e.g. Storm Kyrill in 2007 (Fink et al., 2009),
contribute to widespread forest fires, e.g. Australia 2020 (van Oldenborgh
et al., 2021) or enhance evaporation, thus drying out the soil (Bittelli
et al., 2008). We view damage as a disadvantageous change in the quantities,
quality or function of an object. On the other hand, moderately strong
winds can have positive effects on wind energy production and cause a
stronger mixing in the boundary layer (cancelling detrimental thermal
inversions to agriculture) or – in the case of nightly slope winds –
alleviate summer heat conditions in valleys and cities (Ganbat et al., 2015).
The damage associated with strong winds is primarily due to short-term wind
gusts and leads to a substantial increase in wind speed (Brasseur, 2001).
Wind gusts are sudden increases in wind speed, which last typically less than
20 s, while strong winds refer to sustained wind speed over longer
time periods. Strong wind gusts often lead to uprooting or breaking of
trees, damage to crops in fields (Gardiner et al., 2016), lifting of roofs
and damaging critical infrastructure like bridges and roads (Klawa and
Ulbrich, 2003; Mitchell-Wallace et al., 2017). In coastal areas, strong winds
and wind gusts may lead to storm surges and coastal flooding (Flather,
2001). The exact impacts of strong winds depend also on other factors
besides wind speed thresholds. For example, damage to forests depends on
many other factors like precipitation and topography (Gardiner, 2021). Thus,
to predict damage or identify areas at risk of wind or storm damage, indices
are a vital tool in assessing the likelihood and magnitude of damage in a
given sector or environment. For example, Merz et al. (2020) explore in
their review the current state of knowledge on skilful forecasts of impacts
for many hazards, for which indices are very useful. With storm damage we
refer to damage, mainly to properties and forests, caused by severe wind
storms, while wind damage is more general and includes all adverse effects
of wind, including storm damage. We define risk as the likelihood here that
wind causes some damage, and their consequences and risk can be quantified
as the function of hazard probability, exposure and vulnerability (e.g.
Kelman, 2003; Hoeppe, 2016; Franzke, 2017).
For wind indices and wind impact models different wind parameters are in
use. These are often derived from modelled data like reanalysis data sets.
While these model parameters are strongly related to observed wind
parameters, they are not the same and their definitions cannot be used
interchangeably. Since observational data are rare and it is more common to
work with modelled data, the following parameter definitions focus on
parameters derived from models. It is often assumed that the maximum daily
or hourly gust speed (metres per second) at 10 m height relates strongest to damage. The WMO defines a wind gust as the maximum of the wind averaged over 3 s intervals, which is in most cases shorter than the model time step. Thus, many models rely on parametrization for gust speed. For example, the ECMWF Integrated Forecasting System deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Other common parameters in use are daily or hourly mean or maximum wind speeds at 10 m height, which express the mean or maximum
values of all model time steps in an hour or a day. The parameterized gust
speed as well as mean wind speeds in a model grid cell can deviate widely
from local observations.
Indices can be used to predict damage caused directly by wind, or to
quantify how the wind modulates the damage caused by another process such as
fire or drought. Furthermore, the choice of indices depends also on land use
as it influences the interaction between land surfaces and the wind; tree
species and forest structures can have considerable influence on the damage
probability (Gardiner, 2021). The understanding of wind and storm dynamics, and
the ability to predict the damage they cause, requires an interdisciplinary
approach. However, much of the relevant literature is in specialized
journals. Here, we aim to bring these different disciplines together to
provide an interdisciplinary synthesis of the topic. To bridge the gap
between the different communities, within the ClimXtreme consortium, we
created a work group and invited specialists from outside the consortium to
broaden our research expertise. During regular joint meetings we identified
the following sectors: forests, urban areas, transport, agriculture and
energy as the most relevant terrestrial environments that could be impacted
by wind and storm damage. We focused on literature resources stemming mainly
from Europe, but in cases of relevance and to further expand the scope of
the review we also incorporated examples from other regions.
We provide a basic background on wind and storm formation and intra-seasonal
variability in Sect. 2. Section 3 focuses on the interactions between wind
and surface structures which are prone to wind damage. Section 4 focuses on
wind- and storm-related indices and thresholds. In particular, we cover the
following sectors: forests, urban areas, transport, agriculture and energy.
Additionally, we discuss compound indices and thresholds used by national
weather services. Finally, in Sect. 5 we provide an outlook and discuss open research questions. Due to the location of the authors, we provide mainly a European perspective on this topic but believe our synthesis is more widely applicable.
Wind and storm formation – mechanisms and conceptsThe general circulation and wind generation
The general circulation of the atmosphere is driven by the differential
heating of the Earth (Held, 2019); the equatorial regions receive more solar
radiation than higher latitudes, while in the polar regions the atmosphere
is losing heat into space. This differential heating of the Earths' surface
causes pressure differences in the atmosphere. As a result, a pressure
gradient force acts on the air masses, leading to a movement from high- to
low-pressure centres to alleviate this pressure difference. Since the
atmosphere moves toward an equilibrium, it causes a meridional heat
transport towards the poles through the atmosphere and ocean, which takes
place mainly through the movements of circulation systems and storms
(Bjerknes, 1922; Schultz et al., 2019; Ma et al., 2021).
Mid-latitude weather systems include both cyclones and anticyclones, but
strong wind situations are primarily associated with intense cyclones. The
main paths that weather systems and storms take are called storm tracks
(Hoskins and Valdez, 1990; Blender et al., 1997; Chang et al., 2002; Ulbrich et al., 2009). Storm tracks form over the major ocean basins of the Northern Hemisphere and Southern Hemisphere and are closely related to atmospheric jet streams,
which are areas of maximum upper-level wind speed and determine the areas
that are prone to storms as discussed below in Sect. 2.4. These regimes set the propensity with which weather systems take a more poleward or equatorward path on intra-seasonal timescales, thus offering potential
predictability.
In its most basic form, atmospheric jet streams (Feldstein and Franzke, 2017)
are a product of the pressure gradient force, induced by the above-mentioned
latitudinal air temperature gradients, and the Coriolis force. For
large-scale movements in the atmosphere, the wind is diverted to the right
(left) in the Northern (Southern) Hemisphere due to the Coriolis force. The
resulting winds in the free atmosphere, above the boundary layer, blow
parallel to lines of equal pressure, in a balance between the pressure
gradient and the Coriolis force, also called geostrophic wind. The strength
of the dominant westerly winds over western Europe is determined by the
pressure difference between the subpolar and subtropical regions over the
eastern North Atlantic. The stronger the pressure difference, the stronger
the mid-latitude westerlies.
Under hypothetical unperturbed conditions, the bands of maximum wind speed
sit at 30 and 60∘ latitude in either hemisphere at upper levels of the troposphere, due to surface friction. However, differential diabatic heating over land and the ocean, or orographic surface features, such as mountains, do perturb the jet stream in multiple ways. As
a result, in the extratropics of the Northern Hemisphere the jet stream is
commonly split into a subtropical and mid-latitudinal branch. While the
former is mainly driven by angular momentum transport by the thermally
direct Hadley circulation (Held and Hou, 1980), the latter is primarily
driven by the eddy momentum flux convergence provided by short waves that
form in regions of enhanced baroclinicity (Held, 1975). Accordingly, the
mid-latitudinal jet stream is referred to as an eddy-driven or polar
jet stream due to its proximity to polar latitudes.
In the atmosphere unstable conditions are needed for weather systems to form
(Holton and Hakim, 2012). So-called baroclinically unstable conditions occur
where we find strong horizontal and vertical air temperature gradients. For
example, the North Atlantic is an ideal source region for baroclinically
unstable conditions as very cold polar air is advected over moderately warm
ocean waters, leading to excessive air temperature gradients and, thus,
pressure gradients, which – under the influence of the Coriolis force –
generate enhanced baroclinicity.
In the boundary layer, the pressure gradient and Coriolis forces are not in
balance, because the surface characteristics, local conditions, vertical
stability, and other effects play crucial roles in modifying the winds.
Under the influence of surface friction, the air movements are not parallel
to the lines of equal pressure but have a tangential component from high- to
low-pressure centres. On the regional to local scale, wind systems like the
land–sea breeze and mountain-valley wind systems develop due to
differential heating conditions within comparatively small distances, which
vary between day- and nighttime.
How do cyclones form?
While anti-cyclones are primarily associated with low wind conditions in
their centre and strong winds are only found around its edges (i.e.
co-located with another pressure system), cyclones feature typically strong
pressure gradients and are thus associated with strong winds and wind gusts.
Many extratropical cyclones develop under the influence of the mid-latitude
jet stream, its associated baroclinicity and upper-air flow divergence.
Other cyclones develop as secondary cyclones in the trailing cold fronts of
pre-existing systems and are more influenced by lower-level processes such
as latent heat release (Parker, 1998; Dacre and Gray, 2009). Another large
group of cyclones develop by the interaction of atmospheric waves with
topography (McGinley, 1982; Radinovic, 1987). Focusing on the North Atlantic
sector for a European perspective, baroclinically driven (primary) cyclones
develop typically over the North Atlantic (Dacre and Gray, 2009), secondary
cyclones develop further downstream often close to the eastern North
Atlantic (Priestley et al., 2020b), and the orographically driven cyclones
dominate in the Mediterranean basin (Trigo et al., 1999).
The most common conceptual models to describe extratropical cyclone
development are the Norwegian and the Shapiro–Keyser models (Bjerknes, 1922;
Schultz et al., 2019; Dacre, 2020). According to the Norwegian model, a
stationary front forms between cold and warm air, initiating strong vertical
wind shear within the troposphere. A front is a density discontinuity and,
hence, separates cold and warm air masses. Typically triggered by an
upper-level trough, a cyclone begins to grow along this front where it
develops a warm and a cold front. As the cyclone deepens, both fronts become
better defined and a warm sector develops. When the cold front catches up to
the warm front, the so-called occlusion process starts. At this stage, the
cyclone reaches its most intense period (Bjerknes, 1922), followed by cyclone
decay. In the Shapiro–Keyser model, the initial development is similar, but
the cold front does not overtake the warm front, but rather it builds a T-bone structure (see Fig. 16–24 of Schultz et al., 2019) instead of a narrowing warm sector during occlusion as in the classical model (Shapiro and Keyser, 1990).
Windstorms produce winds which are strong enough to cause damage; they
typically have wind speeds in excess of 15 m s-1 (Wallace and Hobbs, 2006). In order to quantify the impact of windstorms, it is important to know the parts of a storm where the strongest wind speed typically occurs. There are three zones where strong winds can occur: the warm jet, the cold jet and
the sting jet (Clark and Gray, 2018). Hewson and Neu (2015; see their Fig. 1) have developed a conceptual windstorm model to describe how strong winds
may develop associated with the passage of a cyclone during different stages
of its development. In most cases, the strongest winds are often associated
with the passage of the cold jet at the cold front. However, Shapiro–Keyser
cyclones may on occasion feature sting jets, which, if they reach the surface, may lead to even more damaging wind speed (Clark and Gray, 2018).
The potentially most damaging events affecting Europe are commonly assigned
to slow movers, rapid developers or serial storms (Mailier et al., 2006).
Slow-mover cyclones lead to large accumulations of precipitation in the same
area, often triggering severe flooding (Grams et al., 2014). Rapid
developers are fast deepening cyclones, often fulfilling the conditions for
a “bomb” (Gyakum and Danielson, 2000). When occurring close to Europe,
many of these are secondary cyclones. Finally, serial storms (also known as
cyclone families) indicate that multiple and related cyclones affect the
same area within a comparatively short period of time, leading, potentially,
to severe cumulative losses (Mailier et al., 2006; Pinto et al., 2014). In
these clustering periods, the passage of storms occurs more frequently than
may be expected if they would occur independently from each other (Vitolo et
al., 2009; Franzke, 2013; Blender et al., 2015). Two physical reasons are
given in the literature (Economou et al., 2015; Dacre and Gray, 2020): (i) the steering through the large-scale flow, typically characterized by an
intensified, quasi-stationary jet stream extending towards Europe and (ii) the occurrence of secondary cyclogenesis.
Spatial characteristics of storms
To analyse cyclones and storms, objective identification and tracking
methods are needed (Ulbrich et al., 2009; Neu et al., 2013). This leads to a
Lagrangian perspective where certain properties during the life cycle of the
cyclone can be defined by, for example, radius, propagation speed, and spatial wind
distribution. Various objective methods for the identification and tracking
of extratropical cyclones have been used to investigate their
characteristics (Neu et al., 2013; Priestley et al., 2020b).
In the North Atlantic–European region, cyclone track densities show maximum
values over the western North Atlantic with a second maximum over the
Mediterranean (Ulbrich et al., 2009; Pinto et al., 2005). North Atlantic
cyclone activity shows a tilt towards the northern North Atlantic. While
this can be found in different reanalysis products, Coupled Model
Intercomparison Project phase 5 (CMIP5) simulations are characterized by a
bias of the maximum and tilt in the North Atlantic, leading to more zonally
oriented storm tracks (Zappa et al., 2013). While many cyclones can be
identified in the extratropics, only a subset of strong cyclones lead to a
high wind speed. See Sect. 4.1 for related storm indices.
Large-scale circulation characteristics and their impact on wind
Storms and their related wind gusts are local in nature. Nonetheless, the
large-scale background circulation can still provide information in which
areas strong winds are likely to occur. Here, we apply the concept of
atmospheric weather regimes (Hannachi et al., 2017) to determine the
characteristics of the large-scale circulation. Atmospheric weather regimes
are recurrent, dynamically relevant circulation patterns and allow the
description of low-frequency variability due to transitions between distinct
regimes. Because of their preferred occurrence locations, they potentially
provide prediction and downscaling possibilities for smaller-scale weather
events and extremes (Cassou et al., 2005).
To demonstrate the relation of specific regimes to preferred jet stream
patterns and storms, we show weather regimes based on sea level pressure
fields from the latest European Centre for Medium-Range Weather Forecasts
Reanalysis (ERA5) (Hersbach et al., 2020) over the North Atlantic–Eurasian
region (30–90∘ N, 90∘ W–90∘ E) for winters (December through March, DJFM) from 1979–2020. The details of the applied regime analysis are described in Crasemann et al. (2017). We identify five regime states (Fig. 1a): (1) Scandinavian–Ural blocking (SCA-URAL BL), (2) the North Atlantic Oscillation in the positive phase (NAO+), (3) blocking over the North Atlantic (ATL BL), (4) North Atlantic trough (ATL TR) and (5) the NAO in its negative phase (NAO-).
(a) Weather regimes determined from ERA5 reanalysis data for December to March (DJFM). Shown are the regime patterns in terms of
sea level pressure anomalies (shading); black contours indicate the
climatology for DJFM. Shown are isolines at 1000, 1005, 1010 and 1015 hPa.
(b) The jet stream patterns are associated with the individual weather regimes, obtained by composites of zonal wind anomalies at 250 hPa (shading). Black contours indicate the zonal wind climatology for DJFM. Shown are isolines at 20, 30 and 40 m s-1. (c) eXtreme Wind Storms XWS database (Roberts et al., 2014) track for Storm Klaus based on ERA5 data, identified with the method of Leckebusch et al. (2008). Storms are defined by the exceedance of the local 98th percentile of near-surface wind speed. Each dot represents the position of the wind field centre of Storm Klaus for 6 h time steps from 22 January 2009 at 06:00 local time (LT) to 26 January 2009 at 00:00 LT. The colour of the dots shows the weather regime of that date.
The characteristic patterns for the jet stream associated with these weather
regimes have been obtained by compositing the zonal wind anomalies at 250 hPa over the days assigned to each regime. The five jet stream patterns
(Fig. 1b) are very similar to those obtained by previous studies (Dorrington
and Strommen, 2020; Woollings et al., 2010; Franzke et al., 2011).
The regime analysis assigns the atmospheric circulation of each day over the
period 1979–2020 to one specific cluster and enables a characterization of
the large-scale background for specific windstorm events. As one example,
Fig. 1c shows the eastward movement of the extreme storm Klaus from 22
to 26 January 2009 along an unusual southerly path. The storm Klaus was
characterized by strong and record-breaking wind speed over northern Iberia
and southern France. During the formation, intensification, and eastward
movement of Klaus, the Atlantic trough weather regime associated with the
central jet stream configuration prevails (Fig. 1c). This central jet stream
pattern sets the necessary large-scale background flow for the development
and movement of this extreme storm (Liberato et al., 2011).
The concept of weather regimes enables the characterization of the
large-scale atmospheric circulation, in particular the jet stream pattern,
during extreme storm events. If changes in the occurrence of these extremes
can be related to an anomalous frequency of occurrence of a specific weather
regime, the use of these regime states offers potential predictability of
large- as well as small-scale wind impacts.
Temporal characteristics of storms and seasonal variability
The occurrence of extreme wind speed and storms is subject to a strong
seasonal pattern in Europe. According to Young et al. (1999), windstorms
occur 30 % more frequently in winter than in summer (see also Fig. S1 in the Supplement). We compared the wind gusts from three reanalysis products (ERA5, Hersbach et al., 2020; COSMO-REA6, Bollmeyer et al., 2015; and COSMO-REA2, Wahl et al., 2017) to 145 German station observations (Kaspar et al., 2013). While a direct comparison is difficult, qualitative statements on seasonality can be made with all data sets. The number of occurrences of wind gusts is determined for certain wind speed intervals, which are shown against the warning levels (WLs) of the Deutscher Wetterdienst (DWD). The warning levels are defined by six different wind speed thresholds: 14, 18, 25, 29, 33 and 39 m s-1 (Primo, 2016), referring to four WLs (WL1–WL4), with WL2 and WL3 being divided into two intervals (DWD, 2021). Compared to observations, wind gust frequencies are underestimated in reanalyses. The higher the wind gusts, the higher the underestimation. Therefore, COSMO-REA2 shows a significantly better agreement with the reference, especially for WL3 in summer and WL4 in winter. The benefit of the higher resolution provided by regional reanalyses compared to their global counterparts is well documented for near-surface wind speed (Niermann et al., 2019). Results shown in Fig. S1 emphasize the importance of using high-resolution models to represent extreme wind gusts in reanalysis products.
Above 25 m s-1 there is a clear difference between summer and winter months,
which becomes stronger the higher wind speed is considered. In summer, wind
speed over 30 m s-1 does not appear in the coarser reanalysis products ERA5 (∼30 km) and COSMO-REA6 (∼6 km) at all, and for the high-resolution reanalysis COSMO-REA2 (2 km) and the point observations
the occurrence of wind gusts of WL3 or WL4 in summer is smaller than in
winter by a factor of 10 to 100.
The intra-annual variability is not only visible in meteorological data but
also in loss data from insurance companies (Hoeppe, 2016; Franzke, 2017),
which shows the strong impact of storms and especially winter storms on
society and economic areas (Klawa and Ulbrich, 2003). The energy sector is
strongly affected by the occurrence of windstorms, especially their
seasonal variability. Due to the worldwide effort to convert the energy
system to renewable sources, the industry will have to deal more with
seasonal fluctuations in energy availability. The interest and the need for
precise knowledge of the wind conditions in various regions is therefore
growing, as energy production directly depends on it; for more details about
wind-based energy production please see Sect. 4.6.
Winds induced by convective activity
Most of the wind damage in temperate latitudes is due to extratropical
cyclones. However, damage can also occur to structures, crops and forests
from winds produced by convective storms (Gatzen et al., 2020; Parodi et
al., 2019); since our focus is more on extratropical storms, we keep this
part rather brief. The following conditions need to be met for convection to
occur (Wallace and Hobbs, 2006): (1) the atmosphere needs to be conditionally
unstable, (2) there needs to be a reservoir of substantial moisture in the
boundary layer, and (3) there needs to be sufficient lifting due to low
level convergence to cross the threshold to start the instability.
Convective systems and storms can lead to severe wind speed connected to
tornadoes, gust fronts and downbursts (Wallace and Hobbs, 2006). Tornadoes
are rapidly rotating air systems which connect with the ground and can lead
to devastatingly strong winds. Downbursts are downward directed winds due to
the negative buoyancy of the downdraught air. Convective storms can also have
gust fronts. The gust fronts form due to downdraughts in the convective storm
forming a pool of cold, dense air which replaces the warmer, buoyant air of
the environment.
These downdraughts can lead to severe wind gust speeds at the surface (Bunkers
and Hjelmfelt, 2021) with speeds of up to 42 m s-1. So far, relatively little attention has been paid to wind damage to infrastructure, forests and
agriculture from such events besides the studies by Jim and Liu (1997) and Peterson (2000). Forest damage from thunderstorms in areas which previously were rarely affected, such as eastern parts of Europe (Nosnikau et
al., 2018; Sulik and Kejna, 2020), has experienced an increase in
convectively available potential energy and near-surface moisture which can
cause more thunderstorm activity (Taszarek et al., 2021). It is expected that
anthropogenic global warming will lead to an increase of convective storms
(Lepore et al., 2021; Taszarek et al., 2021; Diffenbaugh et al., 2013).
Another type of convective storm is derechos, which are a clustering of
downbursts, organized by a line of thunderstorms (also called a squall line) that lead to widespread straight-line winds, and can cause damaging
winds. They occur frequently in the Great Plains area of the USA (Ashley and
Mote, 2005) but can occur around the world, including central and eastern
Europe (Gatzen et al., 2020). Some examples of the devastating impact of
derechos on forests are described in Goff et al. (2021) and
Negrón-Juárez et al. (2010).
Wind–surface interactionThe physics of fine-scale interactions between surfaces and wind
The characteristics of the wind speed and gustiness in a given environment
are dependent on surface characteristics, such as its roughness, all of
which are highly influential on the levels of damage caused. The momentum of
the mean horizontal wind is vertically transferred by turbulence; i.e. near
the surface, large whirling air packages break up into smaller ones and
their momentum dissipates into thermal energy or is absorbed by roughness
elements, such as trees and buildings. The strength of the wind is altered
by topography and the roughness of the surface (Stull, 2017; Kaimal and
Finnigan, 1994; Finnigan et al., 2020). Thus, the damage level can vary
dramatically at small scales (Gromke and Ruck, 2018; Forzieri et al., 2020).
Typically, the boundary layer above the Earth's surface is subdivided into
three sublayers: (1) a roughness sublayer that is characterized by the flow
around obstacles and varies locally and where mechanical turbulence
dominates; (2) one or more inertial sublayers, where the influence of the
individual obstacles and surfaces is blended together and the vertical
energy fluxes are constant with height; and (3) a mixing layer above, where
the Coriolis force gains influence and is often separated from the free
atmosphere by a capping inversion and an entrainment zone (Stull, 1988;
Kaimal and Finnigan, 1994). The effect of buoyancy and thermal stability is
very important for the formation of strong winds, i.e. for cyclones and
thunderstorms. During storm events, high wind speed increases friction
within the lower boundary layer and also increases form drag by obstacles.
The instability of the shear in the flow created by the drag of the surface
leads to turbulence, which affects the vertical exchange of mass, momentum
and scalars. Thermal gradients near the surface are reduced or disappear due
to this mixing, which results in neutral stratification near the surface;
i.e. thermal stability need not be considered in the equations of the
vertical wind profile (Stull, 1988).
As turbulent movements play a major role in the momentum transfer to the
surface, it is important to regard shear forces and gustiness as the damaging
characteristics of the wind field (Gromke and Ruck, 2018). For example, in
forest ecosystems trees are blown down at a mean wind speed considerably
lower than those estimated by pulling experiments (Oliver and Mayhead, 1974; Milne, 1991). Boundary layer eddies create a local
increase in wind speed and wind shear close to the surface (Romanic and Hangan, 2020) and leading to
coherent eddies (Raupach et al., 1996). The loading due to these turbulent
structures with higher energy and momentum can be accounted for in a gust
factor (Hale et al., 2015; Chen et al., 2018; Holland et al., 2006; Usbeck et al., 2010).
Since trees react to gusts like damped harmonic oscillators (Mayer, 1987;
Gardiner, 1992), there has been considerable debate about whether the arrival
frequencies of these coherent eddies could lead to resonant failure
(Gardiner, 1995; Peltola, 1996); however, this does not happen (Schindler
and Mohr, 2019; Schindler and Kolbe, 2020; Kamimura et al., 2022), probably
due to the efficient damping of trees (Spatz and Theckes, 2013). Besides the
drag force of a plant (Rudnicki et al., 2004; Queck et al., 2012; Vollsinger
et al., 2005), the level of damage depends also on the acclimation of plants
to the wind (Telewski, 1995; Nicoll et al., 2019), which is a function of
the maximum wind speed (Bonnesoeur et al., 2016; Dèfossez et al., 2022).
They are adapted to wind forces and build stronger roots and wood structures
depending on the main wind direction and magnitude (Nicoll and Ray, 1996;
Tomczak et al., 2020).
Furthermore, the development of turbulence above and within the canopy is
different between naturally uneven aged woods and managed forests or
plantations. Experiments showed that the inflection of the wind profile
(i.e. maximum gradient of wind speed) is weaker in heterogeneous compared to
homogeneous canopies and that it occurs deeper within the canopy; i.e. the
displacement height is lower (Cionco, 1972; Belcher et al., 2012; Queck et
al., 2016). Furthermore, homogeneous forests are more vulnerable than
naturally uneven aged woods (Everham and Brokaw, 1996; Mitchell, 2013).
Obviously, the adaptation to wind stress is not restricted to single trees
but extends to the structure of natural mixed woods too. The characteristics
of the tree (height, diameter, canopy size, wood properties) and the tree
resistance to uprooting and breakage are all affected by the level of wind
exposure (Gardiner et al., 2016). Recent experimental measurements of tree
damage during a super typhoon (Kamimura et al., 2022) have also shown that
collisions between the crowns of individual trees and the crowns of their
neighbours are extremely important in reducing tree movement during strong
winds and contributing to their overall stability. These adaptations of
plants to living in a windy environment must be considered when modelling
the risk of wind damage to tree stands.
Large eddy simulations (LESs) are used to better understand the complex
current patterns and the acting wind forces near heterogeneous surfaces
(Stoll et al., 2020; Takemi et al., 2020). These turbulence-resolving models
include all the basic physical equations; however, they require considerable
computer resources and are therefore unsuitable for operational use.
Simplified mechanistic models (Holland et al., 2006; Gross, 2018;
Duperat et al., 2021) parameterize the turbulence spectrum and operate on a
larger spatial scale and thus need fewer computational resources. Statistical
approaches (Jung and Schindler, 2015; Dupont, 2016) focus on predicting
critical thresholds at which wind damage occurs and are therefore efficient
for operational damage prediction. The indices discussed in Sect. 4 are
based on empirical observations and have proven useful in a wide range of
applications.
Mean wind and gust rates for different landscapes
The gustiness of the wind is critically important for assessing the likely
impact of strong winds on forests, agriculture and structures (Usbeck et
al., 2010; Gardiner et al., 2016). The level of gustiness is known to be
influenced by surface roughness (Table 1), the height above the ground and
wind speed (Ashcroft, 1994; Verkaik, 2000). Gust ratios are also affected by
wind speed (see Born et al., 2012; their Fig. 2) and by the type of storm
(Krayer and Marshall, 1992; Harper et al., 2008).
Wind rate (mean/gusts) for different landscapes; 3 s gust to 10 and
60 min mean wind at 10 m height, by terrain category. From Ashcroft
(1994). Roughness classes: 1 is off-sea wind onto flat coastal areas; 2 is level
grass plains (e.g. marsh); 3 is standard category (fairly level terrain– mostly
open fields with a few houses and buildings); 4 is fairly level terrain with
more hedges, trees and villages, farm buildings; 5 is many trees and hedges,
or fairly level wooded country or more open suburban areas.
RoughnessAerodynamicGust ratioGust ratioclassroughness(3 to 10 min)(3 to 60 min)length (m)10.0031.361.4420.011.421.4930.031.481.5640.11.581.6650.31.741.85Wind- and storm-related indices and critical thresholds
Wind- and storm-related indices and thresholds are a vital tool in assessing
the likelihood and magnitude of damage. While there are many definitions for
indices and thresholds, here we define an index as a number or a category,
serving as an aggregated measure of a quality, which can be reached by means
of observation, arithmetic calculation or different modelling techniques. A
threshold is defined here as a value taken or calculated from a numerical or
a categorical range, and when the threshold value is crossed, it indicates a
significant increase in the probability for an event to take place or for a
certain condition to be fulfilled. Indices can be used to predict damage
caused directly by wind or a storm or when wind modulates the damage caused
by another process such as fire or drought. Since indices and thresholds can
be as effective as complex mechanistic models but more cost-effective, it is
of no surprise that there is a plethora of indices. There are general
indices that are not bound to a given sector or environment, but many of the
indices and thresholds available require a careful selection process
according to the target ecosystem. Below we provide an extensive review of
available indices, focusing on five key terrestrial sectors.
General storm indices: scale and severity indices
Classical wind scales are defined by phenomena caused by the interactions
between wind and the surface. A very prominent example is given by the
Beaufort scale (Stull, 2017). It classifies the effect of wind on wave
generation, tree movement and the damage of buildings. Similar scales exist
for tornados, e.g. the Fujita scale and the Torro scale (Kirk, 2014), which
relate the tornado intensity to damage description. As short gusts and
shear forces are very important factors of storm risk, the Enhanced Fujita
scale includes further information on derived maximal tangential 3 s gust
speeds (Fujita, 1981). Recently an improved wind speed scale and damage
description has been suggested for central Europe (Feuerstein et al., 2011).
Finally, the Saffir–Simpson hurricane wind scale (Ellis et al., 2020) is
based on the highest wind speed averaged over a 1 min interval 10 m
above the surface. It can provide some indication of the potential damage a
hurricane will cause upon landfall.
Several storm severity indices have been developed to quantify the severity
of a windstorm regardless of the land cover. These indices are used to
identify severe winter storms and analyse their impacts and to investigate
storm trends in past and future climate conditions. They often include the
cube of the wind speed, assuming a proportionality of the dissipation rate
of the wind kinetic energy to damage. A selection of these indices is
presented in Table S1 in the Supplement.
From an historical context, one of the earliest storm severity indices was
developed by Lamb (1991) to grade and rank storms based on the greatest
observed wind speed over land, the area affected by damaging winds and the
overall duration of occurrence of damaging winds. Later, in a study by Klawa
and Ulbrich (2003), the wind speed values were scaled with the local 98th
percentile. Based on this approach, Leckebusch et al. (2008) identified and
tracked windstorms in time and space and computed an event-based storm
severity index that quantifies the potential impact of a storm. This index
considers the relation of the maximum daily wind speed to a certain local
percentile of maximum daily wind speed (e.g. the 95th or 98th) as well as
the affected area. For example, in their study they found a trend for an
increase in severity of storms during 1960–2000 and for 2070–2100 under
anthropogenic climate change conditions. Pinto et al. (2012) extended this
approach by taking into account the exposure and including local population
levels in a loss index, resulting in the finding that the maximum storm
losses for current climate conditions are likely to be exceeded in the
future. Additionally, Haylock (2011) used a storm severity index to identify
the severest storms for 72 h storm footprints. This index considers the
latitude and the excess of the maximum wind speed over a 72 h period
taken from six-hourly values over a threshold (e.g. the local 90th
percentile of wind speed).
ForestsTopographic indices
Many topographic indices have been used for assessing the risk of wind
damage to forests (see Table S2). These indices can be based on elevation,
slope characteristics such as compass angle, aspect, and curvature, or are
more complex such as TOPEX (Topographic exposure; Quine and White, 1998),
which was developed as part of a risk assessment method (Windthrow Hazard
Classification) to predict the height at which trees could be expected to be
first damaged (Miller, 1986). TOPEX is the sum of the angle to the horizon
in the eight principal points of the compass and can be calculated for
different distances from the point of interest. Furthermore, such indices
can be used to create even more complex predictive systems. For instance,
when TOPEX is combined with elevation and aspect it produces a system called
DAMS (Detailed Aspect Method of Scoring; Quine and White, 1993) for
predicting wind speed variation in the landscape. This system is entirely
based on topographic measures and compares favourably with modelling systems
based on solutions of the fluid equations (Suárez et al., 1999).
The actual variation of wind speed with height above the ground is a
function of the surface roughness and the topography. Predicting variations
of wind speed across flat surfaces is relatively straightforward,
especially for strong winds by using a measure of the aerodynamic roughness
of the surface and a logarithmic wind profile (Garratt, 1980; Stull, 1988).
Even in stable or unstable conditions the profile can be modified with the
addition of the diabatic term ψm (Kaimal and Finnigan, 1994;
Panofsky and Dutton, 1984). Often the roughness of the surface is simplified
into different roughness classes (Troen and Petersen, 1989) to allow for
easier estimation of the surface roughness. However, when even strong winds
flow over topography the simple logarithmic profile breaks down and the
shape of the wind profile strongly varies between the upwind slope, the
crest of the hill and the downwind slope, where the flow may even separate
(Belcher et al., 2012). Thus, one should not only calculate topographic
indices for the target locations but calculate also for the neighbouring
areas and assess the change in value between the target location and its
surroundings (Ruel et al., 1997; Schindler et al., 2012; Murshed and Reed,
2016).
We reviewed the literature, focusing on studies using topographic indices to
assess and predict damage caused by strong winds, as topographic indices are
a common feature in modelling wind damage in forests (Table S3). The most
commonly used variables were (Fig. 2) elevation, slope, aspect and TOPEX.
We assessed the usefulness of the four most used topographic indices in
modelling forest damage according to their inclusion in final models and
according to the importance/influence metrics reported. We note that most
studies employed a multivariate modelling approach; thus, a certain variable
may appear less useful due to overlap in the variance explained with another
variable, but not necessarily due to the variable's lack of explanatory
power (Scott and Mitchell, 2005). Furthermore, there are other topographic
indices that have not been tested so far for their contribution in forest damage
prediction (see Florinsky, 2017).
An assessment of the usefulness of the most commonly used
topographic indices.
Elevation was useful in about a third of the studies and was particularly
useful when the study area was very large, encompassing an entire region,
state or country (Díaz-Yáñez et al., 2019; Kramer et al., 2001;
Torun and Altunel, 2020; Mayer et al., 2005) or when there was a strong
gradient of elevation, preferably reaching above 900 m above sea level
(Krejci et al., 2018; Pasztor et al., 2015; Torun and Altunel, 2020; Kramer
et al., 2001; Mayer et al., 2005). The trend in the correlation between
elevation and forest damage was found to be inconclusive, to be both
positive (Díaz-Yáñez et al., 2019; Krejci et al., 2018; Pasztor
et al., 2015) and negative (Mayer et al., 2005; Albrecht et al., 2013), or
only present for a certain range of elevation (Albrecht et al., 2013; Torun
and Altunel, 2020). While there is an expectation for an increase in forest
damage with higher elevation due to an increase in wind speed (Machar et
al., 2014), diversity of trends can stem from the involvement of other
topographic indices that may contain similar information (e.g. slope or
TOPEX), and also due to varying levels of acclimation of trees to the wind
conditions present at different elevations (Gardiner, 2021).
The slope was shown to be useful in about half of all articles; however it
is difficult to observe a clear relation to forest damage. In articles that
identified a contribution of slope, the relation of damage with slope was
found to be either positive (Díaz-Yáñez et al., 2019) or
negative (Mayer et al., 2005; Morimoto et al., 2019; Schütz et al., 2006). But an important deciding factor can be the aspect of the slope
(useful in about 40 % of all articles) as there is often an interaction
between the two (Suvanto et al., 2018, 2016; Díaz-Yáñez et al., 2019; Hanewinkel et al., 2014). In this sense, the aspect likely indicates the forest's susceptibility to wind coming from a certain direction, as in most cases of usefulness of aspect, the slope was also useful. Finally, TOPEX was found by 77 % of articles as useful, and when a trend was
reported, all studies reported higher damage or probabilities for forest
damage being associated with more exposed locations (Albrecht et al., 2012,
2013; Jung et al., 2016; Morimoto et al., 2019; Mitchell et al., 2001;
Taylor et al., 2019). One of the reasons for TOPEX's usefulness is that it
does not strongly overlap with the information contained in other wind-based
variables (Albrecht et al., 2019; Schindler et al., 2012). However, when
TOPEX is calculated only for a certain cardinal direction (e.g. west) it
contains information that is very similar to aspect.
Fine-scale wind and surface interactions
Interactions between the surface and the wind field are controlled by
surface roughness, absolute wind velocity and atmospheric stability. The
commonly used index related to surface interactions that is relevant for
wind and storm damage is the critical wind speed (CWS). The CWS defines the
threshold wind speed for overcoming the maximum resistance to stem breakage
or uprooting of a tree (Gardiner et al., 2016; Peterson et al., 2019; Hale
et al., 2015; Chen et al., 2018; Holland et al., 2006). CWS is a standard
term in forest ecology. The typical averaging interval for CWS is a period
of a few minutes, e.g. 3 min (Peltola and Kellomäki, 1993), 10 min (Dupont et al., 2015; Peltola et al., 1999) or 60 min (Hale et al., 2015). CWS is estimated either at a height of 10 m above the canopy or
at the tree top at the stand edge.
One of the governing quantities to describe the interactions between wind
forces and stem breakage or uprooting is the applied maximum bending moment
(BMmax) (Quine et al., 2020), which is the sum of wind forces in the tree
crown and the additional turning moment due to stem bending and deflection
of the stem and crown of a tree (Peltola, 2006). BMmax calculation refers
typically to the mean bending moment (BMmean) and a gust factor (see e.g.
Gardiner et al., 1997). A tree uproots if its BMmax at the ground level
exceeds the resistance of the root–soil plate, and a tree breaks if its
BMmax at breast height (1.3 m) exceeds the critical value of the stem's
modulus of rupture (Peltola et al., 1999; Quine et al., 2020). The gust
factor is parameterized by wind measurements (field or wind tunnel) and
depends on the spacing / height ratio of tree stands and the location relative
to the forest or stand edge (Gardiner et al., 1997; Quine et al., 2020). The
wind measurements are taken from the top of the canopy, and the bending
moment is typically determined from the level of zero-plane displacement
(e.g. 0.8 of the tree height; Gardiner et al., 1997). Nevertheless,
measurements of the effects (Gardiner et al., 1997) as well as directly
solved finite element models of the crown architecture (Ruy et al., 2022)
have shown the influence of crown architecture on the maximum bending
moment. Therefore, the gust factor used in the calculation of BMmax may need
to be varied according to stand composition and tree type.
The probability of occurrence of CWS, as a measure of storm damage risk for
specific forest stands, depends on the statistics of wind velocity, e.g. on
hourly maximum synoptic winds (umax: Usbeck et al., 2010, Chen et al., 2018) or maximum geostrophic wind speed (Blennow and Olofsson, 2008). CWS is used to parameterize impact models for the estimation of storm risk in forests such as ORCHIDEE-CAN (Chen et al., 2018), SWAN+ADCIRC (Akbar et al., 2017), GALES and HWIND (Peltola et al., 1999; Gardiner et al., 2000, 2008).
The key parameter in the calculation of CWS is the diameter at breast height
(DBH), which is a standard parameter in forest inventories. DBH is commonly
defined as the stem diameter at 1.3 m above the ground (Peterson et al., 2019; Gardiner, 2021; Hale et al., 2015; Chen et al., 2018; Holland et al., 2006; Hanewinkel et al., 2014; Beck and Dotzek, 2010; Gardiner et al., 2008; Peltola, 2006). DBH is the most used structural parameter due to its easy and practicable measurement and due to its widespread application in forest management (Liu et al., 2018). DBH is also used to derive other structural parameters like tree height and leaf area index (LAI), which can also be derived from normalized difference vegetation index (NDVI) as a standard product of satellite remote sensing. These structural quantities are
important both for statistical analysis and for the parameterization of
storm risk models.
Other important parameters for calculating CWS are the mean drag coefficient
(cd), which is part of the equation of the drag force (Vogel, 1989; Akbar et al., 2017; Dupont et al., 2015), turbulence intensity, gust duration (Hale et al., 2015; Chen et al., 2018), tree density (Peterson et al., 2019; Albrecht et al., 2015), tree height, crown projection area and crown volume (Peterson et al., 2019; Gardiner et al., 2010, 2021; Albrecht et al., 2015; Hale et al., 2015; Chen et al., 2018; Dupont et al., 2015; Peltola, 2006), and tree
species (Hanewinkel et al., 2014). Additionally, the edge factor index
describes the influence of a tree's position relative to a forest edge, the
shape of the forest edge and the width of any upwind gap (Chen et al., 2018;
Gardiner et al., 2010; Peltola, 2006).
The severity of storm damage depends on the ability of a tree to resist the
applied bending moment from the wind and on the stability of the root soil
complex (Nicoll et al., 2006). If soil water content is close to saturation
the critical resistive moment of trees (BMcrit) can be reduced significantly
during storm events, which could become increasingly important with the
increasing frequency of heavy winter rain in temperate forests in the
context of regional climate change (Défossez et al., 2021).
The uncertainty of CWS results from the consecutive solving of analytic
equations including accumulated uncertainties of the different input
quantities. Additional uncertainties result from the differences in the
models used. Sensitivity tests using GALES (Locatelli et al., 2017) and
HWIND with a variation of the input parameters of ±20 % lead to a more than 20 % change in CWS. For example, CWS is especially sensitive to changes in DBH. The measurement uncertainty of the DBH ranges between 2 % and 10 % depending on the absolute diameter (Qin et al., 2019). Applied in HWIND and GALES the variation of DBH of ±20 % leads to changes of CWS of +30 % and -46 % (Gardiner et al., 2000). The most comprehensive analysis of wind risk model uncertainty was made by Locatelli et al. (2017), who found that tree DBH, tree height and inter-tree spacing were the most critical factors.
Urban areasThe urban boundary layer
The small-scale interactions of the wind field with urban surfaces are
significantly different from natural surfaces due to high three-dimensional (3D) variability of impermeable artificial obstacles (buildings). These
differences lead to a higher mean surface roughness of the urban surface
(Grimmond and Oke, 1999; Oke et al., 2017) combined with a general
attenuation of the mean wind speed, the wind speed averaged over some time
period (Chen et al., 2020), as compared with more natural surfaces. The
level of increase in roughness depends on the morphology – density, size
and composition – of the obstacles along the flow direction. The height of
the roughness layer is 2–3 times the mean height of the buildings. Within
this layer, mechanical turbulence generation dominates, and average wind
profiles can only be assumed above the roughness layer, within the inertial
sublayer. The averaged roughness of an urban surface is described by
roughness length z0 within equations for vertical wind profiles. This
parameter serves as a useful index for the prediction of turbulent impulse
transfer and for damage prediction, which are derived based on building
height, areal fraction and frontal area index (Grimmond and Oke, 1999). At
finer scales, wind speed shows high spatio-temporal variability. Thus, when
using indices based on averaged wind speed, it is also important to consider
that due to the small-scale aerodynamic and thermal heterogeneities of urban
infrastructure (buildings and trees), the local magnitude of the wind speed
is temporarily larger than under rural conditions (Droste et al., 2018). The
reasons for this anomaly are again the inflexibility and impermeability of
technical structures and buildings. These features cause canalization of
flows and stronger turbulence generation compared to natural surfaces. There
is also a diurnal–nocturnal distinction in the formation of local thermal
wind systems, with street canyon wind during the day and a nocturnal inflow
to the urban heat island (Droste et al., 2018; Lindén and Holmer, 2011).
Thus, indices in urban areas should account for both spatial and temporal
heterogeneities.
Indices for estimating damage to individual buildings
Damage occurs either directly by wind pressure or indirectly by the impact
at high speed of objects and debris moved by the wind (Tamura, 2009). At the
level of individual buildings, air movement results in wind pressure on the
building surface and an applied force. Damage to buildings caused by extreme
wind loads includes resonance- and vibration-induced damage, damage to roof
tiles or sheet roofing, roof lift-off, and the collapse of walls or entire
houses.
The occurrence and type of damage depend on the level of exposure as well as
the structural vulnerability of the individual buildings to severe local
winds. The European wind loading code EN 1991-1-4 regulates how to adapt the
structural design of buildings to the local wind climate. The code defines
basic wind velocities for different geographical wind zones based on the
50-year return level of 10 min wind speed at a 10 m height. In Germany, for
example, the basic wind velocities range from 22.5 m s-1 in wind zone 1 (inland areas in southern Germany) up to 30 m s-1 in wind zone 4 (coastal areas). The basic wind velocities are further adjusted based on the height above ground and the terrain roughness to account for short-term wind fluctuations. Terrain roughness is classified in five categories ranging
from coastal areas to cities with a high building density. Additionally,
where topography (e.g. hills, cliffs) increases wind velocities by more
than 5 %, the effect is taken into account using a topographic index, as
the ratio of the mean wind velocity at the height above the terrain to the
mean wind velocity above flat terrain. Finally, the wind speed is used to
compute the local peak velocity pressure, which is a fundamental index for
the determination of all wind loads for a specific building (Schmidt, 2019).
Nonetheless, assigning critical wind speed thresholds to building damage is
rather difficult given the heterogeneity of buildings, topography and
land cover.
Storm loss models: estimating damage on a district level
Often there is little to no information on the actual damage to individual
buildings or small-scale urban structures. Instead, storm loss models come
into play, and they relate wind speed to actual building damage data,
usually by applying statistical modelling techniques. In some cases, these
models rely on the use of wind indices like the exceedance of local wind
speed over a critical threshold to calculate monetary loss. In other cases,
the model itself calculates a damage index. The purpose of storm loss models
is, among other things, to assess current risk to residential structures or
to estimate expected losses in future climate conditions. It is often
assumed that the maximum daily gust speed (24 h maximum) is the most influential factor compared to other wind parameters like daily mean wind
speed or wind direction and is commonly used in indices as well as in loss
models (Donat et al., 2011; Klawa and Ulbrich, 2003; Koks and Haer 2020;
Leckebusch et al., 2008; Pardowitz et al., 2016; Welker et al., 2021).
Building damage data on a district level are usually provided by insurance
companies and are analysed in the form of the loss ratio, which is the amount
of insured loss per day and district, divided by the corresponding sum of
insured value, or claim ratio, which is the number of affected insurance
contracts per day and district, divided by the corresponding total number of
insurance contracts (Prahl et al., 2015).
The functional relationships between wind and damage are usually referred to
as damage functions. As the relationship between damage and wind depends
strongly on local conditions like building or city structure, there is no
universal function or model, and instead a variety of different damage
function formulations are in use. A detailed overview can be found in Prahl
et al. (2015). Power-law damage functions are common. Different exponents
for these functions can be found in the literature ranging from 2 to 12
(Münchener Rückversicherungs-Gesellschaft, 1999; Heneka et al., 2006;
Prahl et al., 2012). Some damage functions also assume an exponential form
(Prahl et al., 2015).
Another type of model is probabilistic models which calculate the probability that a certain loss threshold is exceeded (Pardowitz et al., 2016; Prahl et al., 2012). Some examples of existing models are shown in
Table 2. Most models still need to be fitted to local conditions and
validated with existing damage data. Model selection depends on the
available data.
A selection of damage functions including exponential damage
relationships (Dorland et al., 1999), power law damage functions (Klawa and
Ulbrich, 2003; Heneka et al., 2006) and probabilistic damage functions
(Pardowitz et al., 2016). a and b denote coefficients, D a damage index, f(vcrit) a normal distribution of the critical wind speed, G a damage ratio, L a loss, LR a loss ratio, P(LR>th) a probability that a certain loss threshold will be exceeded, th a loss threshold, v a mean daily wind speed, v98 the 98th percentile of the local wind speed, vcrit a critical wind speed at which buildings are assumed to suffer damage (comparable to the CWS used for trees), vmax the maximum daily gust speed, and vtot the building total wind speed at which maximum damage is reached.
Lvmax=2.48×107×exp(0.48v)Dorland et al. (1999)D(v)=vmaxv98-13Klawa and Ulbrich (2003)LRv,fvcrit,Δv=∫-∞vfvcritG(v)dvcritHeneka et al. (2006)G(v)=0,v<vcritD(v),vcrit≤v≤vtot1,vtot≤vD(v)=v-vcritvtot-vcrit2PLR>th=expa+b×v1+expa+b×vPardowitz et al. (2016)Transport
Transport systems are the backbones of modern societies. Disruptions within
the transport systems can have serious cascading effects that can cause
large costs. Weather in general, and windstorms in particular, can affect
all aspects and functions of transport systems (Leviäkangas et al., 2011). However, relevant thresholds of wind speed and their impacts are
different depending on the mode of transport. Vajda et al. (2014) identify
three wind gust thresholds of increasing magnitude, which they relate to
general impacts and consequences within different parts of the European
transport system:
During wind gusts >17 m s-1, adverse impacts on the transport system may start to occur, especially if the resilience of the
exposed part of the system is low, but disruptions are rather local. For
example, some windthrow of trees can occur along railways and roads, leading
to local problems with road and rail traffic. Furthermore, operation of
smaller boats could be suspended due to reduced manoeuvrability.
During wind gusts >25 m s-1, some adverse impacts can be expected, such as
windthrow and electricity cuts occurring on a larger scale. In addition,
delays and cancellations in air, rail, road traffic and disturbances of
ferry traffic can be expected.
During wind gusts >32 m s-1,
adverse impacts are very likely to occur, and windthrow of trees can be expected
on a large scale, leading to long-lasting power failures and delays, and
cancellation of rail and road traffic. Furthermore, damage to traffic
control devices and structures can occur, airports can be closed, and
ferries stay in harbour due to reduced visibility and high waves.
The effect of wind on road safety is not extensively explored in the
literature (Theofilatos and Yannis, 2014). In general, the number of road
vehicle crashes caused by strong wind is small compared to the total number
of crashes (Edwards, 1998). However, studies have identified specific types
of crashes which typically occur under strong wind conditions: overturning,
side slip and rotation crashes (Baker, 1986), with trucks, vans or buses
being particularly affected (Becker et al., 2022; Baker and Reynolds, 1992). A critical
rollover wind velocity of 20 m s-1 was found for high-sided lorries in
crosswind situations (Snaebjornsson et al., 2007). Particularly dangerous
situations with strong crosswinds can occur on bridges (Wang et al., 2014;
Charuvisit et al., 2004). A vehicle overturning model is applied by the
UK Met Office (Hemingway and Robbins, 2020). It estimates the risk
of overturning based on wind gust thresholds ranging from 23 to 45 m s-1, depending on vehicle type, loading, driving speed and wind direction. In
addition to direct effects of high wind speed on road vehicles, indirect
effects like blocked roads due to falling trees or drifting snow can affect
road transport (Leviäkangas et al., 2011).
The most frequent impact of high wind speed on railway transport is the
blockage of tracks due to windthrow of trees or drifting snow, as well as
loss of electricity due to damaged overhead lines (Leviäkangas et al., 2011), an example of a compound event. Only in rare cases can extreme gusts
exceeding 40 m s-1 blow trains off the track (Sprenger et al., 2018). Mean winds above 17 m s-1 or wind gusts above 30 m s-1 have been identified as thresholds relevant for wind-induced damage to railway transport (Thornes and Davis, 2002). Shaking of overhead cables can cause damage to masts and pantographs on trains. Consequences of windthrow can be collisions of trains with fallen trees. Precursory measures to prevent collisions are reduced travelling speeds or cancelling/limiting train services, commonly leading to widespread delays.
The most common impacts on ports are delays due to the disruption of loading
and unloading procedures, as well as direct damage to infrastructure. For
example, maximum wind speed recommended for crane operations is around 18 m s-1, depending on the design of the crane (TT Club et al., 2011). This can have effects on the overall efficiency of ports (Garcia-Alonso et al., 2020). From 88 disruptive events affecting ports and their surrounding seas
in the UK between 1950 and 2014, 36 % were attributed to wind storms and
12 % to storm surges, while the others were mainly related to human error
and mechanical faults (Adam et al., 2016).
In the case of inland waterway transport, there is generally no large impact
of wind on vessels, since they are sufficiently wide and stable
(Leviäkangas et al., 2011). However, at specific locations with high
local wind speed due to topography or at locations which are difficult to
navigate, navigation of pushed convoys without bow thrusters may be
suspended in case of high wind speed. In addition to location-specific
issues, the vulnerability of vessels to strong wind is strongly dependent on
the vessel's characteristics (Schweighofer, 2014). For specific types of
inland container vessels, a mean wind speed of 18 m s-1 can lead to flooding of
open cargo holds due to heeling and rolling (Hofman and Bačkalov, 2010)
and increase the risk of sliding of empty containers on the upper tiers.
In the case of deep-sea shipping, vessels like large container ships are
rarely lost at sea. However, high wind speed impose the danger of container
losses (Allianz, 2019). The global average annual loss of containers is
estimated to be up to 10 000 yr-1 (Frey and DeVogelaere, 2014). These
numbers are low compared to a total number of more than 200 million
containers transported per year, but each container lost at sea can lead to
a significant safety and environmental hazard. In contrast to container
ships, losses of dry bulk carriers are often related to heavy weather
conditions (INTERCARGO, 2018). Forecasts of ocean surface conditions are
important for route planning to avoid areas affected by windstorms
(Kite-Powell, 2011).
Aeroplanes are affected by strong winds mainly during take-off and landing.
Dangerous situations related to wind are mainly caused by abrupt changes in
wind speed due to wind gusts, wind shear or microbursts (strong downward
movements of air within and below thunderstorms). In the USA, for example,
48 % of weather-related aviation accidents are due to adverse wind
conditions, and of those wind-related accidents 34 % are due to crosswinds and 29 % due to wind gusts (Jenamani and Kumar, 2013). Therefore, for safety reasons, separation distances between aeroplanes are increased under
high-wind conditions. Furthermore, depending on the wind direction, runways
may need to be closed. At London Heathrow, for example, tailwinds of more
than 2.6 m s-1 and crosswinds above 13 m s-1 are avoided by changing flight
direction or runways (Pejovic et al., 2009). This can lead to delays,
diversions and cancellations of flights. At London Heathrow Airport, an
increase in wind speed of 0.5 m s-1 above the mean increases the probability of delay by 8 % (Pejovic et al., 2009).
AgricultureWind damage in the agriculture
Agricultural production levels are crucial for the worldwide economy. Wind
leads to substantial environmental, social and economic losses and has
distinct impacts on agriculture: physical damage to crops and related
infrastructure, soil erosion including nutrient and soil carbon removal,
dust storms, higher evapotranspiration rates of plants, and negative
impacts on flowering, pollinators and fruits (Torshizi et al., 2020).
Wind can damage crops through various mechanisms. Most vegetables already
react to low wind speed of around 4 m s-1 with physiological adaptations that affect the quantity or quality of the harvest (Rouse and Hodges, 2004). Most kinds of crops can also be directly damaged by abrasion from windblown dust
particles or rubbing leaves (Brandle et al., 2004). In orchards, wind can
cause a considerable loss by breaking branches or damaging the fruit set
(Gardiner et al., 2016). For cereals, lodging (i.e. flattening) is probably
the most important impact of wind. For instance, wheat
yield is usually reduced about 25 % when fields are lodged (Baker et al., 2014), but the loss can reach up to 50 %–68 % (Berry and Spink, 2012) and the yield of other cereals can decrease by 35 %–50 % under these conditions (Rajkumara, 2008). In most cases lodging is caused by strong wind accompanied by heavy rain, whereby the maximum wind speed is the critical parameter (Mohammadi et al., 2020; Niu et al., 2016). The vulnerability of plants to lodging depends on many factors; for example, excessive usage of nitrogen fertilizers increases lodging vulnerability of wheat (Berry et al., 2019). It is therefore difficult to determine general threshold values for a
critical wind speed. However, typical lodging threshold wind speeds at 10 m
above the ground for maize, oilseed rape, oats and wheat can be assumed to
be 11.5, 14.8, 15.1 and 16.5 m s-1 respectively (Joseph et al., 2020; Baker et al., 2014).
In general, plants exposed to wind are shorter and have thicker leaves, and
mature plants are less vulnerable to wind stress than younger plants
(Brandle et al., 2004). Therefore, land users must carefully balance between
the investment in wind adaptation measures and yields (Wiréhn et al., 2020). However, the careful selection of wind resistant varieties with short stems (Berry et al., 2014), climate resilient plants or the use of cultivar mixtures can significantly improve wind lodging stress resistance, as demonstrated in wheat (Kong et al., 2022). Field fruits react differently to wind exposure: vegetables in general have a very low tolerance to wind
stress. Cucumber, pepper and cabbage for example can be damaged by even a
low wind speed of around 5 m s-1; corn and cotton are a bit more resistant than most vegetables but also susceptible to wind damage when wind speed exceeds 6 m s-1 (Rouse and Hodges, 2004). Overall, critical thresholds for damage linked to wind speed vary substantially.
Whether or not wind-related agricultural damage will increase under
continued warming is unclear. Peña-Angulo et al. (2020) found that none
of the five metrics linked to wind speed show a significant trend in either
direction. However, the results are subject to considerable uncertainty
given that convective events, which are associated with downbursts and
straight-line winds, are poorly simulated in the current generation of
global circulation models.
Wind erosion, dust storms and agricultural drought
In regions with open and sandy arable land, wind can cause wind erosion and
dust storms. Wind erosion refers to the loss of fertile topsoil, whereas
dust storms are singular events where strong winds displace huge amounts of
soil in a short time. Dust storms are particularly frequent in the so-called
dust belt reaching from the north of Africa through the Middle East to
central Asia (Gholizadeh et al., 2021). However, soil loss due to wind
erosion is also an important issue in less erosion-prone areas such as
Europe (Borrelli et al., 2017). While wind is the main forcing factor, there
are other climatic factors such as precipitation, soil moisture and
radiation, which affect the soil surface and thus influence soil erosion
(Bärring et al., 2003).
The threshold values for the mean wind speed at which soil particles start
to be dislodged vary greatly depending on the type and condition of the soil
(Shahabinejad et al., 2019). According to Rouse and Hodges (2004) the
minimum mean wind speed to create erosion is normally about 5–6 m s-1 at 30 cm above the ground. Shahabinejad et al. (2019) found CWS values of 5.7–8.9 m s-1 at 10 m height for soils in Iran. Plants can suffer from dust storms due to loss of plant tissue through abrasion resulting in reduced photosynthesis
and burial of seedlings (Stefanski and Sivakumar, 2009). This can result in
considerable economic losses for farmers. For example, Gholizadeh et al. (2021) demonstrate that a dust storm lasting 1 h can reduce the annual
income of farmers by up to 1.2 %. Erosion reduces soil fertility for long
periods due to removal of soil containing essential nutrients. In many
cases, extreme drought conditions precede dust storms (Sivakumar, 2005;
Sissakian et al., 2013), as dry soil disaggregates faster and thus dislodges
more easily enhancing erosion. Wind erosion is thereby closely related to
land use practices.
Physiological water stress can be enhanced by increased evapotranspiration,
due to high wind speed. The longer such wind conditions last, the more
severe the risk as exemplified by a recent drought event in India (Masroor
et al., 2020). Thus, wind can exacerbate drought conditions and lead to crop
failure. While wind speed is not expected to increase as a global average
(McVicar et al., 2012), evapotranspiration likely will increase in many regions due
to the increased evaporative demand caused by higher air temperatures
(Tomas-Burguera et al., 2020) and a reduced number of days with rainfall.
The fact that some plants react to hot and windy weather conditions by
closing their stomata may balance some of the enhanced evapotranspiration
deficit. However, this is at the expense of plant growth.
Protection measures against wind
Because of the direct wind damage in agriculture, it is necessary or even
indispensable to take countermeasures to minimize the risks. Such measures
can be a better choice of location according to topographic features or
using windbreaks. Windbreaks usually consist of natural barriers such as
tree rows. The most important aspect of a windbreak is its height (Brandle
et al., 2004). Indeed, windbreak effects on adjacent crops result in a yield
reduction due to water and light competition up to a distance of one to two
windbreak heights, which is followed by a yield increase up to a distance of
8–12 heights (Weninger et al., 2021). To moderate effects of wind flow
around the windbreak, it should be at least 10 times as broad as it is high
(Brandle et al., 2004).
Wind-based energy production
Wind indices are of interest for estimating the wind potential and wind
energy. Extreme wind events on different spatial and temporal scales (e.g.
storms, gustiness or low-level jets) affect the energy production, the
structural integrity and operational safety of wind turbines. Microscale
variability in the wind field occurs temporally (e.g. gustiness) and
spatially (e.g. vertical wind shear). These variations of the wind field
depend on the time of day and thus on the stability of the atmospheric
stratification. There is also a dependence on the characteristics of the
wind turbine site (land use, terrain). Microscale variations of the wind
field influence both the wind potential and the operational reliability of a
wind turbine.
Wind indices are typically defined as the ratio of the current values of a
variable to the long-term mean. The variable is either related to the wind
speed or to the wind energy production. Extreme wind events are directly
related to wind-speed-based indices. To identify the energy potential at a
site, the power density wind index can be used (Katinas et al., 2018; Celik,
2003). It is based on parameters of wind speed frequency distribution. The
power density index results in significantly higher variations than the real
energy production of the wind turbine at the location and should be applied
carefully. In practice, both the current values of the wind speed are needed
(control of the turbine) and the evaluation of the annual energy yield
compared to the long-term average using wind indices (planning of turbines,
financing).
When addressing wind climate at a location, including the occurrence of
strong wind events, which includes both productive and destructive events,
much attention was given to the connection between the wind climate and the
wind energy potential (Carta and Mentado, 2007). In comparison to the wind
speed-based indices, the production-based indices use the energy yield of
turbines as input data. The wind energy production index can be based on a
wind speed index (Ritter et al., 2015) calculated from wind speed data by an
additional application of a power curve (Hahn and Rohrig, 2003; Ding et al., 2005). Another possibility is the use of energy yield data of a wind turbine
directly. The BDB index (BDB, 2021) describes the ratio of monthly reported
energy yields from wind turbines in a region to the long-term mean yields of
these wind turbines. High wind speed or wind shear due to storms or
low-level jets need to be taken into account when calculating wind speed
indices. However, the energy-production-based indices contain the effects of
such events only when the wind turbine is working, i.e. until reaching the
turbine cut-out wind speed. Due to their design, most systems switch off at
a wind speed above 25 m s-1 (Christakos et al., 2016), but there are also
slightly higher and lower shutdown wind speed values for different system
types (Chauhan and Saini, 2014). An analysis showed that storms had a
positive effect on the wind energy production for southwestern Europe and
the Iberian Peninsula (Gonçalves et al., 2020, 2021). As such, the
highest values of wind energy production result for stormy weather
conditions (Petrović and Bottasso, 2014). Climate change impacts on wind
energy have been investigated for a few years (Pryor and Barthelmie, 2010;
Moemken et al., 2018). The studies are mostly in agreement on a minimal
effect of climate change on the wind energy production (Jung and Schindler,
2020).
Topographic effects are another example of small-scale effects on the wind
field, leading to a local wind speed-up, separation and reattachment. These
processes can be studied by numerical models (Uchida and Ohya, 2003, 2008,
2011; Uchida and Li, 2018; Uchida, and Sugitani, 2020). Uchida and Kawashima
(2019) defined two indices to evaluate the terrain-induced turbulence and
the fatigue damage based on the measurement data and the design value. These
studies indicated the need for further development of standards. A commonly
used turbulence index is the effective turbulence for site-specific fatigue
assessment of wind turbines (Slot et al., 2019). Additionally, the usage of
the effective turbulence index significantly reduces the number of
aero-elastic simulations needed for checking the loads on major components
of the wind turbine.
Compound indices
Strong winds often co-occur with other phenomena, and their co-occurrence
affects the damage levels observed. This is an integral part of the compound
event concept in which multiple phenomena or hazards form a complex causal
chain of events that can lead to a more extreme impact than each phenomenon
by itself (Zscheischler et al., 2018). A compound event is often associated
with one driver (e.g. an extreme cyclone) which may cause multiple hazards
(e.g. strong wind and heavy precipitation), but it can have more complex
characteristics (Zscheischler et al., 2020). For example, strong wind can
also serve as a modulator for hazards like drought and wildfire. A full
typology of compound events can be found in Zscheischler et al. (2020).
Precipitation
Strong wind speed often co-occurs with heavy precipitation (Martius et al., 2016), causing multivariate compound events. Additionally, it is argued that wind and precipitation enhance the impact by extratropical cyclones, since cyclones with extreme precipitation often have a longer lifetime than
cyclones with only extreme wind speed (Messmer and Simmonds, 2021). Furthermore, the impact of such multivariate compound events is much higher
than a hazard containing only wind or precipitation (Martius et al., 2016).
In coastal areas, even when wind is not considered as a hazard itself, wind
together with heavy precipitation can cause storm surges and coastal
flooding (Wahl et al., 2015; Couasnon et al., 2020). Furthermore,
precipitation is important when saturating the soil prior to the occurrence
of a windstorm. Soil water content is an index that governs the stability of
the root sector of trees during storm events (Everham and Brokaw, 1996;
Défossez et al., 2021).
Air temperature
Wind and low air temperatures are both drivers, causing windchill as human
health and agricultural hazards among other risks. Each driver, when acting
by itself, would have caused less of an impact than the compound effect
(Danielsson, 1996). Windchill is a threat mainly in cold climates, where
enhanced wind speed increases the heat transfer from an object. Such heat
loss can cause injuries and mortality both in animals and plants. Windchill
can be calculated as the windchill temperature, also called windchill
factor (Quayle and Steadman, 1998; Bluestein and Zecher, 1999), which is
usually taken as the air temperature at which there would be an equivalent
rate of heat loss. Also, low air temperatures can lead to the freezing of
soil and enhances the stability of trees against windthrow during windstorms
(Pasztor et al., 2015). In contrast, trees in frozen soil are more likely to
undergo stem breakage than uprooting (Everham and Brokaw, 1996; Peltola,
2006).
Drought
The impact of wind on drought is comparatively small
compared with other drivers like temperature and (lack of) precipitation,
but it has an effect in terms of the evapotranspiration. Wind is thus
included in some drought indices through evapotranspiration in the Penman or
Penman–Monteith equation, such as in the Baumgartner index (Baumgartner et
al., 1967). These indices are therefore short-term indices that operate on a
scale of days and typically do not take into account the long-term impacts
of drought on the risk of wind damage to forests. Drought can be considered
as a pre-condition, which potentially amplifies the impact of winds.
Csilléry et al. (2017) showed that long-term drought can increase the
risk of wind damage on sites where drought can lead to a weakening of trees
but can also decrease the risk of damage on normally extremely wet sites.
Fire
Indices used for assessing fire risk include often wind and topography to determine the rate of spread and damage caused by a
wildfire. Wind and slope are viewed as the major factors influencing fire
development (Byram 1959a, b; Sharples 2008). The most used indices for
fire risk are based on the Canadian Forest Fire Weather Index (FWI) system
(Van Wagner and Forest, 1987) that uses information on fuel loading and meteorological
conditions (rainfall, temperature, humidity and wind speed) to predict the
probability of a fire starting and then the probable spread of the fire.
Humidity, wind speed and air temperature are used to calculate the
day-to-day drying of the fuel load. The initial spread index is then used to
adjust the FWI as an exponential function of wind speed (doubles the FWI for
every increase of wind speed by 19 km h-1 or 5.3 m s-1). The spread of the fire will also be affected by the topography and, in particular, how the topography modifies the wind speed and direction.
Wind can alter the angle of the fire toward unburnt fuel, extending the
preheating range and increasing the rate of spread. Slope has a similar
effect by affecting the distance between the flames and the fuel. Thus,
typically the greatest rate of spread is found when an upslope is combined
with upward winds and vice versa (Sharples, 2008). Since topography
influences wind traits, it can create a channelling effect enhancing fire
intensity, but with the strength of the effect depending on the overlap
between wind direction and landscape orientations (Barros et al., 2012; Mansuy et al., 2014). Kushla and Ripple (1997) found in a review that a higher relative elevation,
proximity to ridges and increased exposure to wind all led to greater fire
damage in forests. Additionally, aspects that are associated with greater
exposure to dry winds increased fire damage in forests, and damage was lower
in aspects with cold and moist winds. There is an index combining slope,
aspect and wind speed (wind-topo), but it had a rather low importance for
the final model chosen for statistical interpretation (Masoudvaziri et al., 2022).
Wind speed warning levels used at national meteorological services and sector-related critical thresholds
Advanced storm warnings are crucial for the protection of property and
lives. Meteorological services operate a structured warning system for
windstorms and recommend appropriate protective measures and rules of
conduct depending on the warning level (e.g. Germany: DWD, 2021; Ireland: MetEireann, 2023; or Sweden: SMHI, 2023). The warnings will be published
when the event reaches a certain probability level to occur. It can be well
spatially located, especially when the warning criterion is met, such as
wind speed or precipitation exceeding a certain threshold value. These
threshold values are set individually by all meteorological services. In
some cases, the weather services already indicate possible consequences due
to the wind speed, by warning of damage to infrastructure, forests or
energy systems at differing warning levels. There are even variants of
weather forecasting systems that follow a more risk-based approach; i.e. the
probabilities and consequences of extreme events are integrated into the
forecasting system in order to achieve an improved warning management (Neal
et al., 2014 or Kaltenberger et al., 2020). A Europe-wide overview of warnings and, in part, possible impacts are provided by Meteoalarm
(https://www.meteoalarm.org, last access: 27 April 2023), developed by EUMETNET (European Meteorological Network), which provides relevant information on extreme weather events from 37 national meteorological services.
We collected many critical thresholds from the literature for the five
sectors which are the focus of this article. The vulnerability of each
sector to wind speed is illustrated in Table S1. Figure 3 provides a
synthesis and comparison of thresholds from the five sectors. The
agriculture sector seems to be the most sensitive to wind, as negative
effects are already noticeable at mean wind speed well below the first
official warning level of the DWD (WL1 =14 m s-1; Rouse and Hodges, 2004).
Critical threshold ranges of wind speed (mean wind speed (averaging interval 1 h) and wind gusts) for five affected sectors. Warning levels (WL1-4 at speeds of 14, 18, 29 and 39 m s-1, respectively) of the DWD for wind gusts (Primo, 2016) are marked on the right axis. For each sector different ranges of critical mean wind speed from the literature are plotted, to show the mean wind speed (or gust), where impacts are expected. Thresholds in the first three sectors (forest, urban areas and transport) refer to wind gusts at 10 m height. For agriculture we present the mean wind speed at 10 m height, and the shown thresholds for the energy sector refer to the mean wind speed measured at the height of the wind turbines (wind in hub height). The upper bar (e.g. “National impact”) is left open as damage will occur also at higher wind speeds. The threshold ranges mean the following: (1) forest local – limited area of damage; forest regional – damage level is meaningful for the affected forest and short-term forest planning and timber price; forest national – damage can occur across several countries. (2) Urban marginal: light objects, tiles can be lifted or come loose. Urban noticeable: heavier objects are lifted, and first damage to individual building components is possible. Urban major: large vehicles overturn; roofs are severely damaged. (3) Transport local disruption: blocked roads through windthrow or sliding containers at ships. Transport delays: cancellation through electricity cuts and increasing number of wind-related accidents. Transport traffic stop: damage of overhead cables and longer power failures as well as airport and harbour closures. (4) Agriculture erosion: soil loss to wind erosion. Agriculture yield loss: damage to leaves and yield loss due to lodged fields. (5) Energy cut-in speed: start of energy production. Energy cut-out speed: automatic shutdown of wind turbines.
At WL2 (18–29 m s-1), initial restrictions must already be expected in all five sectors, but these are initially localized. In the forest, individual trees and areas may be affected (Gardiner et al., 2010, 2013, 2016). Buildings may show slight roof damage but no structural damage yet (Feuerstein et al., 2011). In road traffic there may be some accidents, and there may be delays in train and air traffic (Vajda et al., 2014). Concerning wind energy, no damage is expected yet, but depending on the type of turbine, precautionary shutdowns of turbines may occur. For WL3 (29–39 m s-1), the literature describes
significant impacts in the forest, building and transportation sectors.
Damage will be significant, and impacts are already affecting regional
areas. The influence of storms on transportation can quickly impact society
at regional to national levels.
Severe damage is described at the national level from WL4 onwards, including
damage to wind turbines (Quaschning, 2016), massive building damage
(Feuerstein et al., 2011), or even the shutdown of entire transport sectors
(air and railway) (Vajda et al., 2014). While forest, urban areas and
transport are affected by wind speed of the same order of magnitude (i.e.
consequences for society are mostly locally at WL2, regionally at WL3 and
nationally at WL4), the energy production from wind is impacted at a much
higher wind speed (when only damage is considered), while agricultural
productivity is impacted at a much lower wind speed.
Outlook and open research questions
In this review we covered a wide range of topics dealing with wind damage to
terrestrial ecosystems with an emphasis on studies dealing with central
Europe. To conclude, we address issues of importance in the near future and
topics that require further research. The most intriguing question in this
field is how wind-related damage levels may change in future decades, given
the strong dominance of decadal variability (Feser et al., 2015). Therefore,
attention was given to identifying drivers of future changes in windstorms
and cyclone characteristics which are particularly important for the
predictability of present-day and long-term trends in socio-economic damage
(Koks and Haer, 2020; Hoeppe, 2016; Franzke, 2021). The key current drivers
that contribute to future changes in storms are well known; many studies
assume that the atmospheric moisture content will increase due to global
warming (IPCC, 2021). Idealized studies suggest that this increase in
moisture will lead to a stronger circulation, more intense storms (including
stronger winds and more rainfall) and, thus, to an expansion of the
windstorm footprint (Catto et al., 2019). Additionally, studies show that the
lower-tropospheric meridional air temperature gradient will decrease due to
Arctic amplification, whereas the upper-tropospheric meridional air
temperature gradient will increase due to the warming of the tropical upper
troposphere and the cooling of the polar lower stratosphere (Lee et al., 2019). However, it is still uncertain how these contrasting forcing
mechanisms will contribute to the future changes in storms quantitatively
(Catto et al., 2019; their Fig. 2). The recently extended ERA5 reanalysis
product could enable further studies to deal with wind-related damage in the
past, present and future, reducing uncertainties. Indeed, increasing the
resolution of climate models may improve their capacity to quantify
statistical storm properties. CMIP6 models already indicate a general
improvement in future storm tracking (Priestley et al., 2020a; Harvey et al., 2020). As a result, more accurate projections of wind and storm damage based
on future emission scenarios and climate change may be attainable in the
future. According to a recent study, winter storm-related wind gusts could
increase towards the second half of the 21st century in Germany (Jung and
Schindler, 2021). This demonstrates the need for more studies in damage
analysis.
Methodologically, the usage of the indices described here in damage analysis
has many advantages, but their creation can be time-consuming, and their
usage may lead to statistical pitfalls. For example, it is important to
choose indices while trying to avoid an overlap in variability explained by
different topographic indices (Mitchell et al., 2001). In this sense, we are lacking
a clear methodology that can select the most suitable indices in advance,
especially when many indices are easily available. There are three main
approaches: (1) a hypothesis-based approach, where typically only few
variables are used in the analyses because these variables can be well
explained and justified due to past research, incorporation of expert
knowledge in the development of indices using co-design and familiarity with
the study site (Gebhardt et al., 2019; Merz et al., 2020); (2) a computational approach, where a feature-selection algorithm (e.g. genetic algorithm) is first used to trim down the number of independent variables before performing an analysis; and (3) an exploratory approach with little
limitation on the number of independent variables used, where one can
examine, for example, if a certain group of indices is more useful than
another (e.g. gust-related indices vs. topographic indices) in achieving
accurate models according to a given evaluation metric (e.g. coefficient of
determination or area under the curve). The choice of method is dependent not only on
the specific research goals, but also on the skill set and computational
resources available. For instance, an exploratory analysis including many
variables on a large area may demand access to high-performance computing.
Furthermore, when modelling on a large spatial scale, it is important to
choose analysis tools that test and quantify the homogeneity of the relation
between indices and damage variables across the different subregions at the
study site. Thus, taking into account that key parameters may change within
the study area, such as the topography or the vegetation structure, altering
the relations between an independent variable and storm damage. Finally,
when analysing socio-economic impacts, the availability of data is often a
limiting factor, and these limitations shape the selection and analysis
approach.
We identify that the area most in need of new indices for wind-related
damage analysis is compound events. Damage from extreme climatic events
most commonly occurs through interactions between different hazards
(Zscheischler et al., 2020). The main challenge is to handle the different
timescales of each factor; for example, a storm may last from hours to
days, but drought can last years. Therefore, we require indices that
incorporate a multitude of factors that are very site specific, as both the
topography and the land cover can strongly modify these interactions.
Another important challenge is the inclusion of non-climate drivers related
to exposure and vulnerability in the compound indices. Concerning the five
sectors dealt with here, we present sector-specific outlooks.
Forest
In a forest setting, there are very few measurements of
tree damage due to storms (Kamimura et al., 2022) and very few studies of
the dynamic nature of damage at the timescale of a storm. Such studies are
required to understand damage initiation and propagation during storms
(Dupont et al., 2015). In addition, predicting airflow over complex terrain
is still difficult when there are steep slopes and multiple changes in
vegetation height (Finnigan et al., 2020). Similarly, there is a need for
improvement of land surface information and, in particular, the acquirement
of highly resolved 3D distributions of vegetation elements at the landscape
scale to enable the creation of fine-scale maps for risk assessment. To this
end, it is often difficult to assess damage or risk at the most relevant
spatial scale. The recent developments in remote sensing techniques
(terrestrial and airborne laser scanning) promise effective assessments of
surfaces structures (Favorskaya and Jain, 2017) and may prove useful for many
of these issues. However, it may take much time to achieve a sufficient
level of data collection. For example, terrestrial laser scanning is
accurate but confined to small areas. Furthermore, using airborne laser scanning and satellite data does not
allow an effective assessment, since these techniques do not provide a sufficient resolution and, thus, need
further development. Another consequence is
that we still lack in monitoring and modelling the small-scale variability
in the interactions of the wind field with the surface. The main research
questions for the future are as follows: how does the structure of a forest canopy
influence the turbulence within and above the canopy? And, as they grow,
stems, roots and canopies acclimate to the wind forces, so what is the
optimal cultivation and canopy structure to reduce damage (Dèfossez et
al., 2022)?
Urban
In urban settings, storm and loss indices as well as
damage functions do not usually consider differences in the exposure and
vulnerability of different building types or types of urban areas. To
further assess wind damage risks on a smaller spatial scale, investigations
of individual building damage or damage to specific types of neighbourhoods
are needed, together with modelling of urban areas. However, damage data at
a fine spatial scale are difficult to obtain, and it is a priority to improve
the documentation of urban damage to support the development of new indices.
The availability of data, such as the spatial extent of wind damage to
individual buildings or green spaces, is key in developing mitigation
strategies. For example, wind channelling as a function of wind speed and
direction needs to be reliably simulated during the development phase of new
building projects.
Transport
Studies addressing wind effects on transport usually
focus on direct effects in a particular part of the transport system.
Results from such studies can strongly depend on the region, data and
methodologies used for the study. Studies with a more unified approach
addressing wind effects on transport on a broader scale could lead to more
comparable results. Furthermore, little research is available that takes
into account cascading effects that propagate through different parts of the
transport system. In general, it remains unclear how climate change and
resulting changes in the wind extremes will affect the transport system.
Studies addressing this question should consider not only future changes in
wind extremes, but also potential changes of the transport system as part of
climate change mitigation measures. Such measures could make the transport
system more vulnerable to extreme winds. For example, a shift from road to
rail transport to reduce CO2 emissions could lead to a higher
vulnerability to wind-related tree fall, because single storm events can
lead to a collapse of rail transport over whole countries for periods of
several days.
Agriculture
The future of the agriculture sector is closely linked to the global challenge of feeding a still growing population, which is expected to reach 9.7 billion people by 2050 (United Nations Department of Economic and Social Affairs, 2022). In response to this challenge, the awareness for
sustainable and efficient agricultural practices has been gradually
increasing. Wind damage in agriculture landscapes is thereby a growing
concern due to the potential change in frequency and intensity of wind
events as the climate continues to warm (Seneviratne et al., 2021). In order
to optimize crop yields and reduce waste, the relationship between wind
damage and crop yields needs to be investigated in more detail to quantify
this impact. For example, understanding and better short-term prediction of
wind events are key to improved crop management. Furthermore, there is a
lack of simple indices incorporating soil properties and their tendency to
lead to soil erosion and nutrient loss, or wind erosion, as such events can
be a major challenge for farmers. There is much space to develop new
practices to mitigate wind damage in agriculture by using vegetation and
cover crops reduce wind damage. Since the positioning of vegetation (e.g.
trees as windbreaks) alters the small-scale interactions of the wind field
with the soil and crops, a more accurate positioning of vegetation would be
supported by the creation or adaptation of existing compound indices or
modelling platforms. Such manipulations of the surface cover can be highly
flexible in the spatial scale of the wind-field modification, thus providing
a good counter measure to different types of vulnerability in agricultural
sector.
Renewable energy
It is important to follow the influence of climate change projections on wind energy production. With the increase in the reliance on renewable energy, it will be important to reduce uncertainties in wind potential and the risk for technical and safety issues in the operation of the wind turbines. Furthermore, while we know where turbines are located and their characteristics, it is important that
the data on the turbines wind field and the energy generated are accessible
for scientific projects and to the private sector. Currently much of the
data are not made publicly available. For instance, we especially lack wind
data at hub height for the evaluation of numerical models. Other key
challenges that are similar to other sectors are the acquirement of high-spatial-resolution measurements and past and future modelling of the wind
field over heterogeneous surfaces and complex terrains.
In conclusion, predicting and assessing the damage caused by wind and storms
is a complex matter, but there are effective and simple methodologies to
support assessment and decision making. In the light of future
uncertainties, it is vital to continue developing tools to prepare for the
next calamities that are bound to occur.
Code availability
The calculation of the circulation regimes with k-means clustering has been done with R (R Core Team, 2020; https://www.R-project.org/) using the R function “kmeans”.
Data availability
The data used for Fig. 1a and b comprise ERA5 daily data for sea level pressure (Hersbach et al., 2018, 10.24381/cds.bd0915c6) and were accessed at DKRZ.
The data used were accessed via the collaborative evaluation system XCES, based on Freva technology (Kadow et al., 2021, 10.5334/jors.253), hosted at DKRZ. The reanalysis data are publicly and freely available from the producers ERA5 at the Copernicus Climate Data store (CDS; 10.24381/cds.adbb2d47, Hersbach et al., 2023), COSMO-Rea6 at DWD Open Data Server (https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/, German Meteorological Service, DWD, 2023a), and COSMO-REA2 at University of Bonn (https://reanalysis.meteo.uni-bonn.de/?Download_Data___COSMO-REA2, University of Bonn, 2023). Also, the station observations used are freely available under the DWD Open Data Server (https://opendata.dwd.de, German Meteorological Service, DWD, 2023b).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-23-2171-2023-supplement.
Author contributions
DG and CLEF designed the outline of the review paper. All authors contributed to writing of the manuscript.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences, and at least one of the (co-)authors is a guest member of the editorial board of Natural Hazards and Earth System Sciences for the special issue “Past and future European atmospheric extreme events under climate change”. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Past and future European atmospheric extreme events under climate change”. It is not associated with a conference.
Acknowledgements
We thank the two anonymous reviewers for their comments which helped to improve this paper.
Financial support
This publication is the outcome of a working group of the project ClimXtreme, funded by the German Bundesministerium für Bildung und Forschung (Alexia Karwat, Daniel Gliksman, Dörthe Handorf, Deborah Niermann, Hilke S. Lentink, Jens Grieger). Astrid Ziemann was funded by Project QuWind100 (German Federal Ministry for Economic Affairs and Energy, BMWi, based on a decision by the German Bundestag, funding code 0325940A). Christian L. E. Franzke was supported by the Institute for Basic Science (IBS), Republic of Korea, under IBS-R028-D1 and by the National Research Foundation of Korea (NRF-2022M3K3A1097082). Joaquim G. Pinto was supported by the AXA Research Fund. Nico Becker’s work was carried out within the framework of the Hans Ertel Centre for Weather Research. This research network of universities, research institutes, and the Deutscher Wetterdienst is funded by the Bundesministerium für Digitales und Verkehr (grant no. 4818DWDP3A).
Review statement
This paper was edited by Christian Ohlwein and reviewed by two anonymous referees.
ReferencesAdam, E. F., Brown, S., Nicholls, R. J., and Tsimplis, M.: A systematic
assessment of maritime disruptions affecting UK ports, coastal areas and
surrounding seas from 1950 to 2014, Nat. Hazards, 83, 691–713,
10.1007/s11069-016-2347-4, 2016.Akbar, M. K., Kanjanda, S., and Musinguzi, A.: Effect of Bottom Friction,
Wind Drag Coefficient, and Meteorological Forcing in Hindcast of Hurricane
Rita Storm Surge Using SWAN + ADCIRC Model, J. Marine Sci. Eng., 5, 38,
10.3390/jmse5030038, 2017.Albrecht, A., Hanewinkel, M., Bauhus, J., and Kohnle, U.: How does
silviculture affect storm damage in forests of south-western Germany?
Results from empirical modeling based on long-term observations, Eur. J.
Forest Res., 131, 229–247, 10.1007/s10342-010-0432-x, 2012.Albrecht, A., Kohnle, U., Hanewinkel, M., and Bauhus, J.: Storm damage of
Douglas-fir unexpectedly high compared to Norway spruce, Ann. Forest Sci.,
70, 195–207, 10.1007/s13595-012-0244-x, 2013.
Albrecht, A. T., Fortin, M., Kohnle, U., and Ningre, F.: Coupling a tree
growth model with storm damage modeling – conceptual approach and results of
scenario simulations, Environ. Modell. Softw., 69, 63–76, 2015.Albrecht, A. T., Jung, C., and Schindler, D.: Improving empirical storm
damage models by coupling with high-resolution gust speed data, Agr.
Forest Meteorol., 268, 23–31, 10.1016/j.agrformet.2018.12.017, 2019.Allianz: Safety and shipping review 2019 – An annual review of trends and
developments in shipping losses and safety, Allianz Global Corporate &
Specialty, Munich, Germany,
https://www.agcs.allianz.com/content/dam/onemarketing/agcs/agcs/reports/AGCS-Safety-Shipping-Review-2019.pdf
(last access: 14 December 2021), 2019.Ashcroft, J.: The relationship between the gust ratio, terrain roughness,
gust duration and the hourly mean wind speed, J. Wind Eng. Ind. Aerod., 53, 331–355, 10.1016/0167-6105(94)90090-6, 1994.Ashley, W. S. and Mote, T. L.: Derecho hazards in the United States, B.
Am. Meteorol. Soc., 86, 1577–1592, 10.1175/BAMS-86-11-1577, 2005.Baker, C. J.: A simplified analysis of various types of wind-induced road
vehicle accidents, J. Wind Eng. Ind. Aerodyn., 22, 69–85,
10.1016/0167-6105(86)90012-7, 1986.Baker, C. J. and Reynolds, S.: Wind-induced accidents of road vehicles,
Accident Anal. Prev., 24, 559–575, 10.1016/0001-4575(92)90009-8, 1992.Baker, C. J., Sterling, M., and Berry, P.: A generalised model of crop lodging, J. Theor. Biol., 363, 1–12. 10.1016/j.jtbi.2014.07.032, 2014.
Bärring, L., Jönsson, P., Mattsson, J. O., and Åhman, R.:
Wind erosion on arable land in Scania, Sweden and the relation to the wind
climate – a review, Catena, 52, 173–190, 2003.Barros, A. M. G., Pereira, J. M. C., and Lund, U. J.: Identifying
geographical patterns of wildfire orientation: A watershed-based analysis,
Forest Ecol. Manag., 264, 98–107, 10.1016/j.foreco.2011.09.027, 2012.
Baumgartner, A., Klemmer, L., Raschke, E., and Waldmann, G.: Waldbrände
in Bayern 1950 bis 1959, Mitteilungen aus der Staatsforstverwaltung Bayerns, 36, 57-79, 1967.BDB: WEA-Standortdaten, Betreiber-Datenbasis (BDB), http://www.btrdb.de/sto.html, last access: 21 December 2021.Beck, V. and Dotzek, N.: Reconstruction of Near-Surface Tornado Wind Fields
from Forest Damage, J. Appl. Meteorol. Clim., 49, 1517–1537,
10.1175/2010JAMC2254.1, 2010.Becker, N., Rust, H. W., and Ulbrich, U.: Weather impacts on various types of
road crashes: a quantitative analysis using generalized additive models, Eur. Transp. Res. Rev., 14, 37, 10.1186/s12544-022-00561-2, 2022.Belcher, S. E., Harman, I. N., and Finnigan, J. J.: The Wind in the Willows:
Flows in Forest Canopies in Complex Terrain, Annu. Rev. Fluid. Mech., 44,
479–504, 10.1146/annurev-fluid-120710-101036, 2012.Berry, P., Kendall, S., Rutterford, Z., Orford, S., and Griffiths, S.: Historical analysis of the effects of breeding on the height of winter wheat (Triticum aestivum) and consequences for lodging, Euphytica, 203, 375–383, 10.1007/s10681-014-1286-y, 2014.Berry, P., Blackburn, A., Sterling, M., Miao, Y., Hatley, D., Gullick, D., Joseph, G., Whyatt, D., Soper, D., Murray, J., and Baker, C.: A multi-disciplinary approach for the precision management of lodging risk, in: Precision agriculture '19 – Papers presented at the 12th European Conference on Precision Agriculture, Montpellier, France, 8–11 July 2019, edited by: Stafford, J. V., Wageningen Academic Publishers, Wageningen, 969–975, 10.3920/978-90-8686-888-9, 2019.Berry, P. M. and Spink, J.: Predicting yield losses caused by lodging in wheat, Field Crop. Res., 137, 19–26, 10.1016/j.fcr.2012.07.019, 2012.Berry, P. M., Baker, C. J., Hatley, D., Dong, R., Wang, X., Blackburn, G. A., Miao, Y., Sterling, M., and Whyatt, J. D.: Development and application of a model for calculating the risk of
stem and root lodging in maize, Field Crop. Res., 262, 108037, 10.1016/j.fcr.2020.108037, 2021.Bittelli, M., Ventura, F., Campbell, G., Snyder, R., Gallegati, F., and
Pisa, P.: Coupling of heat, water vapor, and liquid water fluxes to compute
evaporation in bare soils, J. Hydrol., 362, 191–205,
10.1016/j.jhydrol.2008.08.014, 2008.
Bjerknes, J.: Life cycle of cyclones and the polar front theory of
atmospheric circulation. Geophys. Publik., 3, 1–18, 1922.
Blender, R., Fraedrich, K., and Lunkeit, F.: Identification of cyclone-track
regimes in the North Atlantic, Q. J. Roy. Meteor. Soc., 123, 727–741, 1997.
Blender, R., Raible, C., and Lunkeit, F.: Non-exponential return time
distributions for vorticity extremes explained by fractional Poisson
processes, Q. J. Roy. Meteor. Soc., 141, 249–257, 2015.Blennow, K. and Olofsson, E.: The probability of wind damage in forestry
under a changed wind climate, Climatic Change, 87, 347–360,
10.1007/s10584-007-9290-z, 2008.Bluestein, M. and Zecher, J.: A New Approach to an Accurate Wind Chill Factor, B. Am. Meteorol. Soc., 80, 1893–1900,
10.1175/1520-0477(1999)080<1893:ANATAA>2.0.CO;2, 1999.Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl,
S., and Steinke, S.: Towards a high-resolution regional reanalysis for the
European CORDEX domain, Q. J. Roy. Meteor. Soc., 141, 1–15,
10.1002/qj.2486, 2015.Bonnesoeur, V., Constant, T., Moulia, B., and Fournier, M.: Forest trees
filter chronic wind-signals to acclimate to high winds, New Phytol., 210, 850–860, 10.1111/nph.13836, 2016.Born, K., Ludwig, P., and Pinto, J. G.: Wind gust estimation for Mid-European
winter storms: Towards a probabilistic view, Tellus A, 64, 17471,
10.3402/tellusa.v64i0.17471, 2012.Borrelli, P., Lugato, E., Montanarella, L., and Panagos, P.: A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a
Quantitative Spatially Distributed Modelling Approach, Land Degrad. Dev., 28, 335–344, 10.1002/ldr.2588, 2017.Brandle, J. R., Hodges, L., and Zhou, X. H.: Windbreaks in North American agricultural systems, in: New Vistas in Agroforestry: A Compendium for 1st World Congress of Agroforestry, Springer Netherlands, 65–78, 10.1007/978-94-017-2424-1_5, 2004.
Brasseur, O.: Development and application of a physical approach to
estimating wind gusts, Mon. Weather Rev., 129, 5–25, 2001.Bunkers, M. and Hjelmfelt, M.: Chapter Three – Thunderstorm downbursts:
Windstorms and blowdowns, in: Plant Disturbance Ecology, 2nd edn., edited by: Johnson, E. and Miyanishi, K., Academic Press, San Diego, 65–115, 10.1016/B978-0-12-818813-2.00003-4, 2021.
Byram, G.: Combustion of forest fuels, in Forest fire: control and use,
edited by: Davis, K. P., McGraw-Hill, New York, 61–89, 1959a.
Byram, G.: Forest fire behaviour, in: Forest Fire: Control and Use, edited by: Davis, K., McGraw-Hill, New York, 584 pp., 1959b.Carta, J. and Mentado, D.: A continuous bivariate model for wind power
density and wind turbine energy output estimations, Energ. Convers. Manage., 48, 420–432, 10.1016/J.ENCONMAN.2006.06.019, 2007.Cassou, C., Terray, L., and Phillips, A.: Tropical Atlantic influence on
European heat waves, J. Climate, 18, 2805–2811, 10.1175/JCLI3506.1, 2005.Catto, J., Ackerley, D., Booth, J. Champion, A. J., Colle, B. A., Pfahl, S., Pinto, J. G., Quinting, J. F., and Seiler, C.: The Future of Midlatitude Cyclones, Current Climate Change Reports, 5, 407–420, 10.1007/s40641-019-00149-4, 2019.Celik, A.: Assessing the suitability of wind speed probability distribution
functions based on wind power density, Renew. Energ., 28, 1563–1574, 10.1016/S0960-1481(03)00018-1, 2003.Charuvisit, S., Kimura, K., and Fujino, Y.: Effects of wind barrier on a
vehicle passing in the wake of a bridge tower in cross wind and its
response, J. Wind Eng. Ind. Aerodyn., 92, 609–639, 10.1016/j.jweia.2004.03.006, 2004.Chauhan, A. and Saini, R.: Statistical analysis of wind speed data using
Weibull distribution parameters, in: 2014 1st International Conference on
Non-Conventional Energy (ICONCE 2014), Kalyani, India, 16–17 January 2014, IEEE, 160–163, 10.1109/ICONCE.2014.6808712, 2014.
Chang, E., Lee, S., and Swanson, K.: Storm track dynamics, J. Climate, 15,
2163–2183, 2002.Chen, X., Jeong, S., Park, H., Kim, J., and Park, C.-R.: Urbanization has
stronger impacts than regional climate change on wind stilling: a lesson
from South Korea, Environ. Res. Lett., 15, 054016,
10.1088/1748-9326/ab7e51, 2020.Chen, Y.-Y., Gardiner, B., Pasztor, F., Blennow, K., Ryder, J., Valade, A., Naudts, K., Otto, J., McGrath, M. J., Planque, C., and Luyssaert, S.: Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262), Geosci. Model Dev., 11, 771–791, 10.5194/gmd-11-771-2018, 2018.Christakos, K., Cheliotis, I., Varlas, G., and Steeneveld, G.-J.: Offshore
Wind Energy Analysis of Cyclone Xaver over North Europe, Energy Proced., 94, 37–44, 10.1016/j.egypro.2016.09.187, 2016.Cionco, R.: A wind-profile index for canopy flow, Bound.-Lay. Meteorol., 3,
255–263, 10.1007/BF02033923, 1972.
Clark, P. and Gray, S.: Sting jets in extratropical cyclones: A review, Q.
J. Roy. Meteor. Soc., 144, 943–969, 2018.Crasemann, B., Handorf, D., Jaiser, R., Dethloff, K., Nakamura, T., Ukita,
J., and Yamazaki, K.: Can preferred atmospheric circulation patterns over
the North-Atlantic-Eurasian region be associated with Arctic sea ice loss?,
Polar Sci., 14, 9–20, 10.1016/j.polar.2017.09.002, 2017.Csilléry, K., Kunstler, G., Courbaud, B., Allard, D., Lassegues, P.,
Haslinger, K., Gardiner, B., Lassègues, P., Haslinger, K., and Gardiner,
B.: Coupled effects of wind-storms and drought on tree mortality across 115
forest stands from the Western Alps and the Jura mountains, Glob. Change
Biol., 23, 5092–5107, 10.1111/gcb.13773, 2017.Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, 10.5194/nhess-20-489-2020, 2020.
Dacre, H.: A review of extratropical cyclones: observations and conceptual
models over the past 100 years, Weather, 75, 4–7, 2020.
Dacre, H. and Gray, S.: The spatial distribution and evolution
characteristics of North Atlantic cyclones, Mon. Weather Rev., 137, 99–115,
2009.Dacre, H. and Pinto, J. G.: Serial clustering of extratropical cyclones: a
review of where, when and why it occurs, npj Climate and Atmospheric Science, 3, 48, 10.1038/s41612-020-00152-9, 2020.Danielsson, U.: Windchill and the risk of tissue freezing, J. Appl. Physiol., 81, 2666–2673, 10.1152/jappl.1996.81.6.2666, 1996.Dèfossez, P., Veylon, G., Yang, M., Bonnefond, J., Garrigou, D., Trichet, P., and Danjon, F.: Impact of soil water content on the overturning resistance of young Pinus Pinaster in sandy soil, Forest Ecol. Manag., 480, 118614, 10.1016/j.foreco.2020.118614, 2021.Dèfossez, P., Rajaonalison, F., and Bosc, A.: How wind acclimation impacts Pinus pinaster growth in comparison to resource availability, Forestry: An International Journal of Forest Research, 95, 118–129, 10.1093/forestry/cpab028, 2022.Díaz-Yáñez, O., Mola-Yudego, B., and González-Olabarria, J.: Modelling damage occurrence by snow and wind in forest ecosystems, Ecol. Model., 408, 108741, 10.1016/j.ecolmodel.2019.108741, 2019.
Diffenbaugh, N., Scherer, M., and Trapp, R.: Robust increases in severe
thunderstorm environments in response to greenhouse forcing, P. Natl.
Acad. Sci. USA, 110, 16361–16366, 2013.
Ding, M., Wu, Y.-C., and Zhang, L.-J.: Study on the algorithm to the
probabilistic distribution parameters of wind speed in wind farms. Proc.
Chinese Soc. Elec. Eng., 25, 107–110, 2005.Donat, M. G., Pardowitz, T., Leckebusch, G. C., Ulbrich, U., and Burghoff, O.: High-resolution refinement of a storm loss model and estimation of return periods of loss-intensive storms over Germany, Nat. Hazards Earth Syst. Sci., 11, 2821–2833, 10.5194/nhess-11-2821-2011, 2011.Dorland, C., Tol, R., and Palutikof, J.: Vulnerability of the Netherlands
and Northwest Europe to storm damage under climate change, Climatic Change, 43, 513–535, 10.1023/A:1005492126814, 1999.Dorrington, J. and Strommen, K.: Jet speed variability obscures
Euro-Atlantic regime structure, Geophys. Res. Lett., 47, e2020GL087907,
10.1029/2020GL087907, 2020.Droste, A., Steeneveld, G., and Holtslag, A.: Introducing the urban wind
island effect, Environ. Res. Lett., 13, 094007,
10.1088/1748-9326/aad8ef, 2018.Duperat, M., Gardiner, B., and Ruel, J.-C.: Testing an individual tree wind
damage risk model in a naturally regenerated balsam fir stand: potential
impact of thinning on the level of risk, Forestry: An International Journal
of Forest Research, 94, 141–150, 10.1093/forestry/cpaa023, 2021.Dupont, S.: A simple wind–tree interaction model predicting the probability
of wind damage at stand level, Agr. Forest Meteorol., 224, 49–63, 10.1016/j.agrformet.2016.04.014, 2016.Dupont, S., Ikonen, V.-P., Väisänen,, H., and Peltola, H.: Predicting tree damage in fragmented landscapes using a wind risk model coupled with an airflow model, Can. J. Forest Res., 45, 1065–1076, 10.1139/cjfr-2015-0066, 2015.DWD: Warnkriterien, Deutscher Wetterdienst (DWD),
https://www.dwd.de/DE/wetter/warnungen_aktuell/kriterien/warnkriterien.html, last access: 15 December 2021.
Economou, T., Stephenson, D., Pinto, J. G., Shaffrey, L., and Zappa, G.:
Serial clustering of extratropical cyclones in a multi-model ensemble of
historical and future simulations, Q. J. Roy. Meteor. Soc., 141, 3076–3087, 2015.Edwards, J.: The relationship between road accident severity and recorded
weather, J. Safety Res., 29, 249–262, 10.1016/S0022-4375(98)00051-6, 1998.Ellis, J., Harris, M., Román-Rivera, M., Ferguson, J., Tereszkiewicz,
P., and McGill, S.: Application of the Saffir-Simpson Hurricane Wind Scale
to Assess Sand Dune Response to Tropical Storms, Journal of Marine Science and Engineering, 8, 670, 10.3390/jmse8090670, 2020.Everham, E. and Brokaw, N.: Forest damage and recovery from catastrophic
wind, Bot. Rev., 62, 113–185, 10.1007/BF02857920, 1996.Favorskaya, M. N. and Jain, L. C.: Innovations in Remote Sensing of Forests, in: Handbook on Advances in Remote Sensing and Geographic Information Systems, Intelligent Systems Reference Library, vol. 122, Springer, Cham,
10.1007/978-3-319-52308-8_1, 2017.Feldstein, S. and Franzke, C.: Atmospheric teleconnection patterns, in:
Nonlinear and stochastic climate dynamics, Cambridge University Press, 54–104, 10.1017/9781316339251, ISBN: 9781316339251, 2017.
Feser, F., Barcikowska, M., Krueger, O., Schenk, F., Weisse, R., and Xia, L:
Storminess over the North Atlantic and Northwestern Europe – a review, Q. J.
Roy. Meteor. Soc., 141, 350–382, 2015.Feuerstein, B., Groenemeijer, P., Dirksen, E., Hubrig, M., Holzer, A., and
Dotzek, N.: Towards an improved wind speed scale and damage description
adapted for Central Europe, Atmos. Res., 100, 547–564,
10.1016/j.atmosres.2010.12.026, 2011.Fink, A. H., Brücher, T., Ermert, V., Krüger, A., and Pinto, J. G.: The European storm Kyrill in January 2007: synoptic evolution, meteorological impacts and some considerations with respect to climate change, Nat. Hazards Earth Syst. Sci., 9, 405–423, 10.5194/nhess-9-405-2009, 2009.Finnigan, J., Ayotte, K., Harman, I., Katul, G., Oldroyd, H., Patton, E.,
Poggi, D., Ross, A., and Taylor, P.: Boundary-Layer Flow Over Complex
Topography, Bound.-Lay. Meteorol., 177, 247–313, 10.1007/s10546-020-00564-3, 2020.
Flather, R.: Storm surges, in: Encyclopedia of Ocean Sciences, edited by:
Steele, J., Thorpe, S., and Turekian, K., Academic, San Diego, California,
2882–2892, ISBN 978-0-12-813082-7, 2001.
Florinsky, I.: An illustrated introduction to general geomorphometry, Prog.
Phys. Geog., 41, 723–752, 2017.Forzieri, G., Pecchi, M., Girardello, M., Mauri, A., Klaus, M., Nikolov, C., Rüetschi, M., Gardiner, B., Tomaštík, J., Small, D., Nistor, C., Jonikavicius, D., Spinoni, J., Feyen, L., Giannetti, F., Comino, R., Wolynski, A., Pirotti, F., Maistrelli, F., Savulescu, I., Wurpillot-Lucas, S., Karlsson, S., Zieba-Kulawik, K., Strejczek-Jazwinska, P., Mokroš, M., Franz, S., Krejci, L., Haidu, I., Nilsson, M., Wezyk, P., Catani, F., Chen, Y.-Y., Luyssaert, S., Chirici, G., Cescatti, A., and Beck, P. S. A.: A spatially explicit database of wind disturbances in European forests over the period 2000–2018, Earth Syst. Sci. Data, 12, 257–276, 10.5194/essd-12-257-2020, 2020.Franzke, C.: Persistent regimes and extreme events of the North Atlantic
atmospheric circulation, Philos. T. Roy. Soc. A, 371, 20110471, 10.1098/rsta.2011.0471, 2013.Franzke, C.: Impacts of a Changing Climate on Economic Damages and
Insurance, Economics of Disasters and Climate Change, 1, 95–110,
10.1007/s41885-017-0004-3, 2017.Franzke, C.: Towards the development of economic damage functions for
weather and climate extremes, Ecol. Econ., 189, 107172, 10.1016/j.ecolecon.2021.107172, 2021.
Franzke, C., Woollings, T., and Martius, O.: Persistent circulation regimes
and preferred regime transitions in the North Atlantic, J. Atmos. Sci., 68,
2809–2825, 2011.Frey, O. and DeVogelaere, A.: The Containerized Shipping Industry and the
Phenomenon of Containers Lost at Sea, Marine Sanctuaries Conservation
Series, ONMS-14, 1–51, https://sanctuaries.noaa.gov/science/conservation/lostcontainers.html (last
access: 14 December 2021), 2014.Fujita, T.: Tornadoes and Downbursts in the Context of Generalized Planetary
Scales, J. Atmos. Sci., 38, 1511–1534,
10.1175/1520-0469(1981)038<1511:TADITC>2.0.CO;2, 1981.Ganbat, G., Seo, J., Han, J., and Baik, J.-J.: A theoretical study of the interactions of urban breeze circulation with mountain slope winds, Theor. Appl. Clim., 121, 545–555, 10.1007/s00704-014-1252-6, 2015.Garcia-Alonso, L., Moura, T. G. Z., and Roibas, D.: The effect of weather
conditions on port technical efficiency, Mar. Policy, 113, 103816,
10.1016/j.marpol.2020.103816, 2020.
Gardiner, B.: Mathematical modelling of the static and dynamic
characteristics of plantation trees, in: Mathematical Modelling of Forest Ecosystems, edited by: Franke, J. and Roeder, A., Sauerländers Verlag,
40–61, ISBN 3793908003, 1992.Gardiner, B.: The interactions of wind and tree movement in forest canopies,
in: Wind and Trees, edited by: Coutts, M. and Grace, J., Cambridge
University Press, 10.1017/CBO9780511600425, ISBN 9780511600425, 1995.Gardiner, B.: Wind damage to forests and trees: a review with an emphasis on
planted and managed forests, J. Forest Res., 26, 248–266, 10.1080/13416979.2021.1940665, 2021.Gardiner, B. A., Stacey, G. R., Belcher, R. E., and Wood, C. J.: Field and wind tunnel assessments of the implications of respacing on tree stability, Forestry, 70, 233–252, 10.1093/forestry/70.3.233, 1997.Gardiner, B., Peltola, H., and Kellomäki, S.: Comparison of two models
for predicting the critical wind speed required to damage coniferous trees,
Ecol. Model., 129, 1–23, 10.1016/S0304-3800(00)00220-9, 2000.Gardiner, B., Byrne, K., Hale, S., Kamimura, K., Mitchell, S., Peltola, H.,
and Ruel, J.-C.: A review of mechanistic modelling of wind damage risk to
forests, Forestry: An International Journal of Forest Research, 81, 447–463, 10.1093/forestry/cpn022, 2008.Gardiner, B., Blennow, K., Carnus, J. M., Fleischer, P., Ingemarson, F.,
Landmann, G., Lindner, M., Marzano, M., Nicoll, B., Orazio, C., Peyron, J.,
Reviron, M., Schelhaas, M., Schuck, A., Spielmann, M., and Usbeck, T.:
Destructive storms in European forests: past and forthcoming impacts, Final
report to European Commission-DG Environment, European Forest Institute,
https://library.wur.nl/WebQuery/wurpubs/fulltext/162053 (last access: May 2023), 2010.
Gardiner, B., Schuck, A., Schelhaas, M.-J., Orazio, C., Blennow, K., and
Nicoll, B.: Living with Storm Damage to Forests: What Science Can Tell Us,
edited by: Gardiner, B., Schuck, A., Schelhaas, M.-J., Orazio, C., Blennow, K., and Nicoll, B., European Forest Institute, Joensuu, ISBN: 978-952-5980-08-0, 2013.Gardiner, B., Berry, P., and Moulia, B.: Review: Wind impacts on plant
growth, mechanics and damage, Plant Sci., 245, 94–118, 10.1016/j.plantsci.2016.01.006, 2016.Garratt, J.: Surface influence upon vertical profiles in the atmospheric
near-surface layer, Q. J. Roy. Meteor. Soc., 106, 803–819, 10.1256/smsqj.45010, 1980.Gatzen, C. P., Fink, A. H., Schultz, D. M., and Pinto, J. G.: An 18-year climatology of derechos in Germany, Nat. Hazards Earth Syst. Sci., 20, 1335–1351, 10.5194/nhess-20-1335-2020, 2020.Gebhardt, O., Kuhlicke, C., Wolf, L., Vitolo, C., Duo, E., van Lanen, H., Rohrer, M., Sutanto, S., and Stoffel, M.: Results of the co-evaluation of the ANYWHERE tools, products and services at the pilot sites (Deliverable 1.4), UFZ, Leipzig, Germany, http://anywhere-h2020.eu/docs/ (last access: 30 May 2023), 2019.German Meteorological Service (DWD): COSMO-REA6 data set, DWD [data set], https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/, last access: 31 May 2023a.German Meteorological Service (DWD): DWD station network, https://opendata.dwd.de/, last access: 31 May 2023b.Gholizadeh, H., Zoghipour, M., Torshizi, M.; Nazari, M., and Moradkhani, N.:
Gone with the wind: Impact of soil-dust storms on farm income, Ecol. Econ.,
188, 107133, 10.1016/j.ecolecon.2021.107133, 2021.Goff, T., Nelson, M., Liknes, G., Feeley, T., Pugh, S., and Morin, R.: Rapid
assessment of tree damage resulting from a 2020 windstorm in Iowa, USA,
Forests, 12, 1–13, 10.3390/f12050555, 2021.Gonçalves, A., Liberato, M., and Nieto, R.: Wind Energy Assessment
during High-Impact Winter Storms in the Iberian Peninsula, Environ. Sci. Proc., 4, 28, 10.3390/ecas2020-08132, 2020.Gonçalves, A., Liberato, M., and Nieto, R.: Wind Energy Assessment during High-Impact Winter Storms in Southwestern Europe, Atmosphere, 12, 509, 10.3390/atmos12040509, 2021.Grams, C. M., Binder, H., Pfahl, S., Piaget, N., and Wernli, H.: Atmospheric processes triggering the central European floods in June 2013, Nat. Hazards Earth Syst. Sci., 14, 1691–1702, 10.5194/nhess-14-1691-2014, 2014.Grimmond, C. and Oke, T.: Aerodynamic Properties of Urban Areas Derived from
Analysis of Surface Form, J. App. Meteorol. Clim., 38, 1262–1292,
10.1175/1520-0450(1999)038<1262:APOUAD>2.0.CO;2, 1999.Gromke, C. and Ruck, B.: On Wind Forces in the Forest-Edge Region During
Extreme-Gust Passages and Their Implications for Damage Patterns,
Bound.-Lay. Meteorol., 168, 269–288, 10.1007/s10546-018-0348-4, 2018.Gross, G.: A windthrow model for urban trees with application to storm
“Xavier”, Meteorol. Z., 27, 299–308, 10.15488/4901, 2018.
Gyakum, J. and Danielson, R.: Analysis of meteorological precursors to
ordinary and explosive cyclogenesis in the western North Pacific, Mon. Weather Rev., 128, 851–863, 2000.Hahn, B. and Rohrig, K.: ISET-Wind-Index Assessment of the Annual Available
Wind Energy, Proc. Europ. Wind Energy Conf. Exh. EWEC2003, Madrid, Spain,
16–19 June 2003,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.1675&rep=rep1&type=pdf (last access: 21 December 2021), 2003.Hale, S., Gardiner, B., Peace, A., Nicoll, B., Taylor, P., and Pizzirani,
S.: Comparison and validation of three versions of a forest wind risk model,
Environ. Modell. Softw., 68, 27–41, 10.1016/j.envsoft.2015.01.016, 2015.Hanewinkel, M., Kuhn, T., Bugmann, H., Lanz, A., and Brang, P.:
Vulnerability of uneven-aged forests to storm damage, Forestry, 87,
525–534, 10.1093/forestry/cpu008, 2014.Hannachi, A., Straus, D., Franzke, C., Corti, S., and Woollings, T.:
Low-frequency nonlinearity and regime behavior in the Northern Hemisphere
extratropical atmosphere, Rev. Geophys., 55, 199–234,
10.1175/2009JCLI3087.1, 2017.
Harper, B., Kepert, J., and Ginger, J.: Wind speed time averaging conversions
for tropical cyclone conditions, in: AMS 28th Conference on Hurricanes and
Tropical Meteorology, Orlando, FL, 28 April–2 May 2008, 1–2, 2008.Harvey, B., Cook, P., Shaffrey, L., and Schiemann, R.: The response of the
northern hemisphere storm tracks and jet streams to climate change in the
CMIP3, CMIP5, and CMIP6 climate models. J. Geophys. Res.-Atmos., 125,
e2020JD032701, 10.1029/2020JD032701, 2020.Haylock, M. R.: European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models, Nat. Hazards Earth Syst. Sci., 11, 2847–2857, 10.5194/nhess-11-2847-2011, 2011.
Held, I.: 100 years of progress in understanding the general circulation of
the atmosphere, Meteor. Mon., 59, 6.1–6.23, 2019.
Held, I. M.: Momentum transport by quasi-geostrophic eddies, J. Atmos. Sci.,
32, 1494–1497, 1975.
Held, I. M. and Hou, A. Y.: Nonlinear axially symmetric circulations in a
nearly inviscid atmosphere, J. Atmos. Sci., 37, 515–533, 1980.Hemingway, R. and Robbins, J.: Developing a hazard-impact model to support
impact-based forecasts and warnings: The Vehicle OverTurning (VOT) Model,
Meteorol. Appl., 27, e1819, 10.1002/met.1819, 2020.Heneka, P., Hofherr, T., Ruck, B., and Kottmeier, C.: Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg, Nat. Hazards Earth Syst. Sci., 6, 721–733, 10.5194/nhess-6-721-2006, 2006.Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], 10.24381/cds.bd0915c6, 2018.Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I.,
Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 10.1002/qj.3803, 2020.Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], 10.24381/cds.adbb2d47, 2023.Hewson, T. and Neu, U.: Cyclones, windstorms and the IMILAST project, Tellus A, 67, 27128, 10.3402/tellusa.v67.27128, 2015.
Hoeppe, P.: Trends in weather related disasters–Consequences for insurers
and society, Weather and Climate Extremes, 11, 70–79, 2016.Hofman, M. and Bačkalov, I.: Risk-based analysis of inland vessel
stability, in: Proceedings of the 11th International Ship Stability Workshop,
Wageningen, 21–23 June 2010, 67–72, http://www.shipstab.org/files/Proceedings/ISSW/ISSW_2010_Wageningen_The_Netherlands/ISSW_2010_s03-p2.pdf (last access: 14 December 2021), 2010.Holland, A., Riordan, A., and Franklin, E.: A Simple Model for Simulating
Tornado Damage in Forests, J. App. Meteorol. Clim., 45, 1597–1611,
10.1175/JAM2413.1, 2006.
Holton, J. and Hakim, G.: An Introduction to Dynamic Meteorology, Wiley, ISBN: 9780123848666, 2012.
Hoskins, B. and Valdes, P.: On the existence of storm tracks, J. Atmos. Sci.,
47, 1854–1864, 1990.INTERCARGO: Bulk Carrier Casualty Report – Years 2008 to 2017 and the
trends, International Organization of Dry Cargo Shipowners, London, United
Kingdom,
https://www.intercargo.org/wp-content/uploads/2018/05/bulk-carrier-casualty-report-2017.pdf (last access: 14 December 2021), 2018.IPCC: Summary for Policymakers, in: Climate Change 2021: The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by:
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/report/ar6/wg1/#SPM (last access: 1 May 2023), 2021.Jenamani, R. and Kumar, A.: Bad weather and aircraft accidents–global
vis-à-vis Indian scenario, Curr. Sci. India, 104, 316–325, https://www.jstor.org/stable/24089632 (last access: 14 December 2021), 2013.Jim, C. and Liu, H.: Storm damage on urban trees in Guangzhou, China,
Landscape Urban Plan., 38, 45–59, 10.1016/S0169-2046(97)00018-2, 1997.Joseph, G., Mohammadi, M., Sterling, M., Baker, C., Gillmeier, S., Soper, D.,
Jesson, M., Blackburn, G. A., Whyatt, J. D., Gullick, D., Murray, J., Berry, P., Hartley, D., and Finnan, J.: Determination of crop dynamic and aerodynamic parameters for lodging prediction, J. Wind Eng. Ind. Aerod., 202, 104169, 10.1016/j.jweia.2020.104169, 2020.
Jung, C. and Schindler, D.: Statistical Modeling of Near-surface Wind Speed: A Case Study from Baden-Wuerttemberg (Southwest Germany), Austin J. Earth Sci., 2, 1006, ISSN: 2380-0771, 2015.Jung, C. and Schindler, D.: Introducing a new approach for wind energy
potential assessment under climate change at the wind turbine scale, Energ.
Convers. Manage., 225, 113425, 10.1016/j.enconman.2020.113425, 2020.Jung, C. and Schindler, D.: Does the winter storm-related wind gust
intensity in Germany increase under warming climate? – A high-resolution
assessment, Weather and Climate Extremes, 33, 100360. 10.1016/j.wace.2021.100360, 2021.Jung, C., Schindler, D., Albrecht, A., and Buchholz, A.: The role of
highly-resolved gust speed in simulations of storm damage in forests at the
landscape scale: A case study from southwest Germany, Atmosphere, 7, 7, 10.3390/atmos7010007, 2016.Kadow, C., Illing, S., Lucio-Eceiza, E. E., Bergemann, M., Ramadoss, M., Sommer, P. S., Kunst, O., Schartner, T., Pankatz, K., Grieger, J., Schuster, M., Richling, A., Thiemann, H., Kirchner, I., Rust, H. W., Ludwig, T., Cubasch, U., and Ulbrich, U.: Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling, Journal of Open Research Software, 9, p. 13, 10.5334/jors.253, 2021.
Kaimal, J. and Finnigan, J.: Atmospheric boundary layer flows: Their
structure and measurement, Oxford University Press, ISBN: 9780197560167, 1994.Kaltenberger, R., Schaffhauser, A., and Staudinger, M.: “What the weather will do” – results of a survey on impact-oriented and impact-based warnings in European NMHSs, Adv. Sci. Res., 17, 29–38, 10.5194/asr-17-29-2020, 2020.
Kamimura, K., Nanko, K., Matsumoto, A., Ueno, S., and Gardiner, B.: Tree
dynamic response and survival in a category-5 tropical cyclone: The case of
super typhoon Trami, Science Advances, 8, 1–11, 2022.Kaspar, F., Müller-Westermeier, G., Penda, E., Mächel, H., Zimmermann, K., Kaiser-Weiss, A., and Deutschländer, T.: Monitoring of climate change in Germany – data, products and services of Germany's National Climate Data Centre, Adv. Sci. Res., 10, 99–106, 10.5194/asr-10-99-2013, 2013.Katinas, V., Gecevicius, G., and Marciukaitis, M.: An investigation of wind
power density distribution at location with low and high wind speed using
statistical model, Appl. Energ., 218, 442–451, 10.1016/j.apenergy.2018.02.163, 2018.Kelman, I.: Defining Risk, FloodRiskNet Newsletter, Issue 2, 3 pp., http://www.ilankelman.org/abstracts/kelman2003frn.pdf (last access: 1 May 2023), 2003.Kirk, P.: An updated tornado climatology for the UK: 1981–2010, Weather,
69, 171–175, 10.1002/wea.2247, 2014.Kite-Powell, H.: The value of ocean surface wind information for maritime
commerce, Mar. Technol. Soc. J., 45, 75–84, 10.4031/MTSJ.45.1.13, 2011.Klawa, M. and Ulbrich, U.: A model for the estimation of storm losses and the identification of severe winter storms in Germany, Nat. Hazards Earth Syst. Sci., 3, 725–732, 10.5194/nhess-3-725-2003, 2003.Koks, E. and Haer, T.: A high-resolution wind damage model for Europe, Sci.
Rep.-UK, 10, 6866, 10.1038/s41598-020-63580-w, 2020.Kong, X., Peng, P., Li, L., Zhang, K., Hu, Z., Wang, X., and Zhao, G.: Wind lodging-associated yield loss is reduced by wheat genetic diversity, Eur. J. Agron., 133, 126441, 10.1016/j.eja.2021.126441, 2022.
Kramer, M., Hansen, A., Taper, M., and Kissinger, E.: Abiotic controls on
long-term windthrow disturbance and temperate rain forest dynamics in
southeast Alaska, Ecology, 82, 2749–2768, 2001.Krayer, W. and Marshall, R.: Gust factors applied to hurricane winds, B. Am. Meteorol. Soc., 73, 613–617, 10.1175/1520-0477(1992)073<0613:gfathw>2.0.co;2, 1992.Krejci, L., Kolejka, J., Vozenilek, V., and Machar, I.: Application of GIS to
empirical windthrow risk model in mountain forested landscapes, Forests, 9,
96, 10.3390/f9020096, 2018.Kushla, J. and Ripple, W.: The role of terrain in a fire mosaic of a
temperate coniferous forest, Forest Ecol. Manag., 95, 97–107,
10.1016/S0378-1127(97)82929-5, 1997.
Lamb, H.: Historic storms of the North Sea, British Isles, and Northwest
Europe, Cambridge University Press, ISBN 10: 0521619319, 1991.Leckebusch, G., Renggli, D., and Ulbrich, U.: Development and application of
an objective storm severity measure for the Northeast Atlantic region, Meteorol. Z., 17, 575–587, 10.1127/0941-2948/2008/0323, 2008.Lee, S., Furtado, J., and Charlton-Perez, A.: Wintertime North American
weather regimes and the Arctic stratospheric polar vortex, Geophys. Res. Lett., 46, 14892–14900, 10.1029/2019GL085592, 2019.Lepore, C., Abernathey, R., Henderson, N., Allen, J. T., and Tippett, M. K.: Future global convective environments in CMIP6 models, Earth's Future, 9, e2021EF002277, 10.1029/2021EF002277, 2021.Leviäkangas, P., Tuominen, A., Molarius, R., Kojo, H., Schabel, J., Toivonen, S., Keränen, J., Ludvigsen, J., Vajda, A., Tuomenvirta, H., Juga, I., Nurmi, P., Rauhala, J., Rehm, F., Gerz, T., Muehlhausen, T., Schweighofer, J., Michaelides, S., Papadakis, M., Dotzek, N., and Groenemeijer, P.: Extreme weather impacts on transport systems, VTT Working Papers 168, VTT Technical Research Centre of Finland,
https://www.vttresearch.com/sites/default/files/pdf/workingpapers/2011/W168.pdf,
(last access: 14 December 2021), 2011.Liberato, M., Pinto, J. G., Trigo, I., and Trigo, R.: Klaus – an exceptional
winter storm over Northern Iberia and Southern France, Weather, 66, 330–334,
10.1002/wea.755, 2011.Lindén, J. and Holmer, B.: Thermally induced wind patterns in the
Sahelian city of Ouagadougou, Burkina Faso, Theor. Appl. Climatol., 105,
229–241, 10.1007/s00704-010-0383-7, 2011.Liu, R., Liu, S., Yang, X., Lu, H., Pan, X., Xu, Z., Ma, Y., and Xu, T.:
Wind Dynamics Over a Highly Heterogeneous Oasis Area: An Experimental and
Numerical Study, J. Geophys, Res.-Atmos., 123, 8418–8440, 10.1029/2018JD028397, 2018.Locatelli, T., Tarantola, S., Gardiner, B., and Patenaude, G.: Variance-based
sensitivity analysis of a wind risk model - Model behaviour and lessons for
forest modelling, Environ. Modell. Softw., 87, 84–109, 10.1016/j.envsoft.2016.10.010, 2017.
Ma, Q., Lembo, V., and Franzke, C.: The Lorenz energy cycle: trends and the
impact of modes of climate variability, Tellus A, 73, 1–15, 2021.
Machar, I., Pechanec, V., Brus, J., Kilianova, H., and Kirchner, K.: Forest
management at the upper treeline in Jeseniky Mountains (Czech Republic), in:
International Multidisciplinary Scientific GeoConference – SGEM 2014, Albena, Bulgaria, 17–26 June 2014, Albena, Bulgaria, STEF92 Technology Ltd., Sofia, Bulgaria, 361–366, ISBN 978-619-7105-17-9, 2014.
Mailier, P., Stephenson, D., Ferro, C., and Hodges, K.: Serial clustering of
extratropical cyclones, Mon. Weather Rev., 134, 2224–2240, 2006.Mansuy, N., Boulanger, Y., Terrier, A., Gauthier, S., Robitaille, A., and Bergeron, Y.: Spatial attributes of fire regime in eastern Canada: influences of regional landscape physiography and climate, Landscape Ecol., 29, 1157–1170, 10.1007/s10980-014-0049-4, 2014.Martius, O., Pfahl, S., and Chevalier, C.: A global quantification of
compound precipitation and wind extremes, Geophys. Res. Lett., 43,
7709–7717, 10.1002/2016GL070017, 2016.Masoudvaziri, N., Ganguly, P., Mukherjee, S., and Sun, K.: Impact of
geophysical and anthropogenic factors on wildfire size: a spatiotemporal
data-driven risk assessment approach using statistical learning, Stoch.
Env. Res. Risk A., 36, 1103–1129, 10.1007/s00477-021-02087-w, 2022.Masroor, M., Rehman, S., Avtar, R., Sahana, M., Ahmed, R., and Sajjad, H.:
Exploring climate variability and its impact on drought occurrence: evidence
from Godavari Middle sub-basin, India, Weather and Climate Extremes, 30, 100277, 10.1016/j.wace.2020.100277, 2020.Mayer, H.: Wind-induced tree sways, Trees, 1, 195–206,
10.1007/BF01816816, 1987.
Mayer, P., Brang, P., Dobbertin, M., Hallenbarter, D., Renaud, J., Walthert,
L., and Zimmermann, S.: Forest storm damage is more frequent on acidic soils,
Ann. Forest Sci., 62, 303–311, 2005.
McGinley, J.: A Diagnosis of Alpine Lee Cyclogenesis, Mon. Weather Rev., 110,
1271–1287, 1982.McVicar, T., Roderick, M., Donohue, R., Li, L., van Niel, T., Thomas, A., Grieser, J., Jhajharia, D., Himri, Y., Mahowald, N. M., Mescherskaya, A. V., Kruger, A. C., Rehman, S., and Dinpashoh, Y.: Global review and synthesis of trends in observed terrestrial
near-surface wind speed: Implications for evaporation, J. Hydrol., 416–417, 182–205, 10.1016/j.jhydrol.2011.10.024, 2012.Merz, B., Kuhlicke, C., Kunz, M., Pittore, M., Babeyko, A., Bresch, D. N.,
Domeisen, D. I., Feser, F., Koszalka, I., Kreibich, H., and Pantillon, F.:
Impact forecasting to support emergency management of natural hazards,
Rev. Geophys., 58, e2020RG000704, 10.1029/2020RG000704, 2020.Messmer, M. and Simmonds, I.: Global analysis of cyclone-induced compound
precipitation and wind extreme events, Weather and Climate Extremes, 32, 100324, 10.1016/j.wace.2021.100324, 2021.MetEireann warning levels:
https://www.met.ie/met-eireann-warning-system-explained, last access:
10 January 2023.
Miller, K. F.: Windthrow hazard in conifer plantations, Irish Forestry, 43,
66–78, 1986.Milne, R.: Dynamics of swaying of Picea sitchensis, Tree Physiol., 9, 383–399, 10.1093/treephys/9.3.383, 1991.Mitchell, S.: Wind as a natural disturbance agent in forests: a synthesis,
Forestry, 86, 147–157, 10.1093/forestry/cps058, 2013.
Mitchell, S., Hailemariam, T., and Kulis, Y.: Empirical modeling of cutblock
edge windthrow risk on Vancouver Island, Canada, using stand level information, Forest Ecol. Manag., 154, 117–130, 2001.Mitchell-Wallace, K., Jones, M., Hillier, J., and Foote, M.: Natural
catastrophe risk management and modelling: A practitioner's guide, John
Wiley & Sons, 10.1002/9781118906057, ISBN: 9781118906040, ISBN: 9781118906057, 2017.Moemken, J., Reyers, M., Feldmann, H., and Pinto, J. G.: Future Changes of
Wind Speed and Wind Energy Potentials in EURO-CORDEX Ensemble Simulations,
J. Geophys. Res., 123, 6373–6389, 10.1029/2018JD028473, 2018.Mohammadi, M., Finnan, J., Sterling, M., and Baker, C.: A calibrated oat lodging model compared with agronomic measurements, Field Crop. Res., 255, 107784, 10.1016/j.fcr.2020.107784, 2020.Morimoto, J., Nakagawa, K., Takano, K., Aiba, M., Oguro, M., Furukawa, Y.,
Mishima, Y., Ogawa, K., Ito, R., Takemi, T., Nakamura, F., and Peterson, C.:
Comparison of vulnerability to catastrophic wind between Abies plantation forests and natural mixed forests in northern Japan, Forestry, 92, 436–443, 10.1093/forestry/cpy045, 2019.
Münchener Rückversicherungs-Gesellschaft: Munich Re:
Winterstürme in Europa (II) – Schadenanalyse 1999 –
Schadenspotentiale, Münchener Rück, 1999.
Murshed, S. and Reed, E.: Mapping of the vulnerability of forest resources
due to extreme winter storms in the state of Baden-Württemberg in
Germany, AutoCarto, 128–140, 2016.Negrón-Juárez, R., Chambers, J., Guimaraes, G., Zeng, H., Raupp, C., Marra, D. M., Ribeiro, G., Saatchi, S., Nelson, B., and Higuchi, N.: Widespread Amazon forest tree mortality from a single cross-basin squall line event, Geophys. Res. Lett., 37, L16701, 10.1029/2010GL043733, 2010.Neal, R. A., Boyle, P., Grahame, N., Mylne, K., and Sharpe, M.: Ensemble based first guess support towards a risk-based severe weather warning service: ensemble severe weather forecasts, Meteorol. Appl., 21, 563–577, 10.1002/met.1377, 2014.
Neu, U., Akperov, M., Bellenbaum, N., Benestad, R., Blender, R., Caballero,
R., Cocozza, A., Dacre, H., Feng, Y., Fraedrich, K., Grieger, J., Gulev, S.,
Hanley, J., Hewson, T., Inatsu, M., Keay, K., Kew, S., Kindem, I.,
Leckebusch, G., Liberato, M., Lionello, P., Mokhov, I., Pinto, J. G.,
Raible, C., Reale, M., Rudeva, I., Schuster, M., Simmonds, I., Sinclair, M.,
Sprenger, M., Tilinina, N., Trigo, I., Ulbrich, S., Ulbrich, U., Wang, X., and Wernli, H.: IMILAST: a community effort to intercompare extratropical
cyclone detection and tracking algorithms, B. Am. Meteorol. Soc., 94,
529–547, 2013.Nicoll, B. and Ray, D.: Adaptive growth of tree root systems in response to
wind action and site conditions, Tree Physiol., 16, 891–898,
10.1093/treephys/16.11-12.891, 1996.Nicoll, B., Gardiner, B., Rayner, B., and Peace, A.: Anchorage of coniferous
trees in relation to species, soil type, and rooting depth, Can. J. Forest
Res., 36, 1871–1883, 10.1139/x06-072, 2006.Nicoll, B., Connolly, T., and Gardiner, B.: Changes in Spruce Growth and
Biomass Allocation Following Thinning and Guying Treatments, Forests, 10,
253, 10.3390/f10030253, 2019.Niermann, D., Borsche, M., Kaiser-Weiss, A., and Kaspar, F.: Evaluating
renewable-energy-relevant parameters of COSMO-REA6 by comparison with
satellite data, station observations and other reanalyses, Meteorol. Z., 28,
347–360, 10.1127/metz/2019/0945, 2019.Niu, L., Feng, S., Ding, W., and Li, G.: Influence of Speed and Rainfall on
Large-Scale Wheat Lodging from 2007 to 2014 in China, PloS ONE, 11, e0157677, 10.1371/journal.pone.0157677, 2016.Nosnikau, V., Gardiner, B., Donis, J., and Fedarovich, L.: The Overview of
Existing Silvicultural Approaches Under the Influence of Adverse Weather
Phenomena and Recommendations for Increasing the Forest Resilience to High
Wind Events in Belarus, World Bank: GFDRR, Minsk, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/874711556620591536/the-overview-of-existing-silvicultural-approaches-under-the-influence-of-adverse-weather-phenomena-and-recommendations-for-increasing-the-forest-resilience-to-high-wind-events-in-belarus
(lats access: 1 May 2023), 2018.Oke, D., Mills, D., Christen, D., and Voogt, J.: Urban Climates, Cambridge
University Press, Cambridge, 582 pp., 10.1017/9781139016476, ISBN: 9781139016476, 2017.Oliver, H. and Mayhead, G.: Wind Measurements in a Pine Forest During a
Destructive Gale, Forestry: An International Journal of Forest Research, 47, 185–194, 10.1093/forestry/47.2.185, 1974.
Panofsky, H. and Dutton, J.: Atmospheric Turbulence, Wiley, New York, ISBN 10: 0471057142, 1984.Pardowitz, T., Osinski, R., Kruschke, T., and Ulbrich, U.: An analysis of uncertainties and skill in forecasts of winter storm losses, Nat. Hazards Earth Syst. Sci., 16, 2391–2402, 10.5194/nhess-16-2391-2016, 2016.
Parker, D.: Secondary frontal waves in the North Atlantic region: a
dynamical perspective of current ideas, Q. J. Roy. Meteor. Soc., 124,
829–856, 1998.Parodi, A., Lagasio, M., Maugeri, M., Turato, B., and Gallus, W.:
Observational and modelling study of a major downburst event in Liguria: The
14 October 2016 case, Atmosphere, 10, 1–17, 10.3390/ATMOS10120788,
2019.Pasztor, F., Matulla, C., Zuvela-Aloise, M., Rammer, W., and Lexer, M.:
Developing predictive models of wind damage in Austrian forests, Ann. Forest
Sci., 72, 289–301, 10.1007/s13595-014-0386-0, 2015.Pejovic, T., Williams, V. A., Noland, R. B., and Toumi, R.: Factors
Affecting the Frequency and Severity of Airport Weather Delays and the
Implications of Climate Change for Future Delays, Transp. Res. Record,
2139, 97–106, 10.3141/2139-12, 2009.Peltola, H.: Swaying of trees in response to wind and thinning in a stand of
Scots pine, Bound.-Lay. Meteorol., 77, 285–304, 10.1007/BF00123529, 1996.Peltola, H.: Mechanical stability of trees under static loads, Am. J. Bot.,
93, 1501–1511, 10.3732/ajb.93.10.1501, 2006.
Peltola, H. and Kellomaki, S.: A mechanistic model for calculating windthrow and stem breakage of Scots pines at stand edge, Silva Fenn., 27, 99–111, 1993.Peltola, H., Kellomäki, S., Väisänen, H., and Ikonen, V.-P.: A
mechanistic model for assessing the risk of wind and snow damage to single
trees and stands of Scots pine, Norway spruce, and birch, Can. J. Forest Res., 29, 647–661, 10.1139/x99-029, 1999.Peña-Angulo, D., Reig-Gracia, F., Domínguez-Castro, F., Revuelto, J., Aguilar, E., van der Schrier, G., and Vicente-Serrano, S. M.: ECTACI: European Climatology and Trend Atlas of Climate Indices (1979–2017), J. Geophys. Res.-Atmos., 125, e2020JD032798, 10.1029/2020JD032798, 2020.Peterson, C.: Catastrophic wind damage to North American forests and the
potential impact of climate change, Sci. Total Environ., 262, 287–311,
10.1016/S0048-9697(00)00529-5, 2000.
Peterson, C., Ribeiro, G., Negrón-Juárez, R., Marra, D., Chambers,
J., Higuchi, N., Lima, A., and Cannon, J.: Critical wind speed suggest wind
could be an important disturbance agent in Amazonian forests, Forestry: An International Journal of Forest Research, 92, 444–459, 2019.Petrović, V. and Bottasso, C.: Wind turbine optimal control during storms, J. Phys. Conf. Ser., 524, 012052, 10.1088/1742-6596/524/1/012052, 2014.Pinto, J. G., Spangehl, T., Ulbrich, U., and Speth, P.: Sensitivities of a
cyclone detection and tracking algorithm: individual tracks and climatology,
Meteorol. Z., 14, 823–838, 10.1127/0941-2948/2005/0068, 2005.Pinto, J. G., Karremann, M., Born, K., Della-Marta, P., and Klawa, M.: Loss
potentials associated with European windstorms under future climate
conditions, Clim. Res., 54, 1–20, 10.3354/cr01111, 2012.Pinto, J. G., Gómara, I., Masato, G., Dacre, H., Woollings, T., and
Caballero, R.: Large-scale dynamics associated with clustering of
extratropical cyclones affecting Western Europe, J. Geophys. Res., 119,
13704–13719, 10.1002/2014JD022305, 2014.Prahl, B. F., Rybski, D., Kropp, J., Burghoff, O., and Held, H.: Applying
stochastic small-scale damage functions to German winter storms: Damage
functions for German storm loss, Geophys. Res. Lett., 39, L06806,
10.1029/2012GL050961, 2012.Prahl, B. F., Rybski, D., Burghoff, O., and Kropp, J. P.: Comparison of storm damage functions and their performance, Nat. Hazards Earth Syst. Sci., 15, 769–788, 10.5194/nhess-15-769-2015, 2015.
Priestley, M., Ackerley, D., Catto, J., Hodges, K., McDonald, R., and Lee,
R.: An Overview of the Extratropical Storm Tracks in CMIP6 Historical
Simulations, J. Climate, 33, 6315–6343, 2020a.Priestley, M., Dacre, H., Shaffrey, L., Schemm, S., and Pinto, J. G.: The
role of secondary cyclones and cyclone families for the North Atlantic storm
track and clustering over western Europe, Q. J. Roy. Meteor. Soc., 146,
1184–1205, 10.1002/qj.3733, 2020b.Primo, C.: Wind gust warning verification, Adv. Sci. Res., 13, 113–120, 10.5194/asr-13-113-2016, 2016.Pryor, S. and Barthelmie, R.: Climate change impacts on wind energy: A
review, Renewable and Sustainable Energy Reviews, 14, 430–437,
10.1016/j.rser.2009.07.028, 2010.Quaschning, V.: Understanding renewable energy systems, Routledge, 10.4324/9781315769431, 2016.Quayle, R. and Steadman, R.: The Steadman Wind Chill: An Improvement over Present Scales, Weather Forecast., 13, 1187–1193,
10.1175/1520-0434(1998)013<1187:TSWCAI>2.0.CO;2, 1998.Queck, R., Bienert, A., Maas, H.-G., Harmansa, S., Goldberg, V., and
Bernhofer, C.: Wind fields in heterogeneous conifer canopies:
parameterisation of momentum absorption using high-resolution 3D vegetation
scans, Eur. J. Forest Res., 131, 165–176, 10.1007/s10342-011-0550-0, 2012.Queck, R., Bernhofer, C., Bienert, A., and Schlegel, F.: The TurbEFA Field
Experiment – Measuring the Influence of a Forest Clearing on the Turbulent
Wind Field, Bound.-Lay. Meteorol., 160, 397–423, 10.1007/s10546-016-0151-z, 2016.Qin, L., Liu, Q., Zhang, M., and Saeed, S.: Effect of measurement errors on
the estimation of tree biomass, Can. J. Forest Res., 49, 1371–1378,
10.1139/cjfr-2019-0034, 2019.Quine, C. and White, I.: Revised windiness scores for the windthrow hazard
classification: the revised scoring method, Research Information Note 230, Research Division of the Forestry Authority, https://cdn.forestresearch.gov.uk/2022/02/rin230.pdf (31 May 2023), 1993.
Quine, C. and White, I.: The potential of distance-limited topex in the
prediction of site windiness, Forestry: An International Journal of Forest Research, 71, 325–332, 1998.Quine, C., Gardiner, B., and Moore, J.: Chapter Four - Wind disturbance in forests: The process of wind created gaps, tree overturning, and stem breakage, in: Plant Disturbance Ecology, Elsevier, New York, 117–184, 10.1016/b978-0-12-818813-2.00004-6, 2020.
Radinovic, D.: Mediterranean cyclones and their influence on the weather and
climate, PSMP Rep Series 24, WMO, Geneva, Switzerland, 131 pp., 1987.
Rajkumara, S.: Lodging in cereals – a review, Agricultural Reviews, 29, 55–60, 2008.
Raupach, M., Finnigan, J., and Brunet, Y.: Coherent eddies and turbulence in vegetation canopies: the mixing-layer analogy, Bound.-Lay. Meteorol., 78,
351–382, 1996.R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 1 May 2023), 2020.Ritter, M., Shen, Z., López Cabrera, B., Odening, M., and Deckert, L.:
Designing an index for assessing wind energy potential, Renew. Energ., 83, 416–424, 10.1016/j.renene.2015.04.038, 2015.Roberts, J. F., Champion, A. J., Dawkins, L. C., Hodges, K. I., Shaffrey, L. C., Stephenson, D. B., Stringer, M. A., Thornton, H. E., and Youngman, B. D.: The XWS open access catalogue of extreme European windstorms from 1979 to 2012, Nat. Hazards Earth Syst. Sci., 14, 2487–2501, 10.5194/nhess-14-2487-2014, 2014.Romanic, D. and Hangan, H.: Experimental investigation of the interaction
between near-surface atmospheric boundary layer winds and downburst outflows, J. Wind Eng. Ind. Aerod., 205, 104323, 10.1016/j.jweia.2020.104323, 2020.
Rouse, R. and Hodges, L.: Windbreaks, Agronomy and Horticulture Department, Agronomy and Horticulture – Faculty Publications, ISBN: 0935817875, 2004.Rudnicki, M., Mitchell, S., and Novak, M.: Wind tunnel measurements of crown
streamlining and drag relationships for three conifer species, Can. J. Forest Res., 34, 666–676, 10.1139/x03-233, 2004.Ruel, J., Pin, D., Spacek, L., Cooper, K., and Benoit, R.: The estimation of
wind exposure for windthrow hazard rating: comparison between Strongblow,
MC2, Topex and a wind tunnel study, Forestry: An International Journal of Forest Research, 70, 253–266, 10.1093/forestry/70.3.253, 1997.Ruy, M., Gonçalves, R., and Vicente, W.: Effect of dimensional variables on the behavior of trees for biomechanical studies, Appl. Sci., 12, 3815, 10.3390/app12083815, 2022.Schindler, D. and Kolbe, S.: Assessment of the Response of a Scots Pine Tree
to Effective Wind Loading, Forests, 11, 145, 10.3390/f11020145, 2020.Schindler, D. and Mohr, M.: No resonant response of Scots pine trees to wind
excitation, Agr. Forest Meteorol., 265, 227–244,
10.1016/j.agrformet.2018.11.021, 2019.Schindler, D., Grebhan, K., Albrecht, A., Schönborn, J., and Kohnle, U.:
GIS-based estimation of the winter storm damage probability in forests: A
case study from Baden-Wuerttemberg (Southwest Germany), Int. J. Biometeorol.,
56, 57–69, 10.1007/s00484-010-0397-y, 2012.
Schmidt, P.: Lastannahmen – Einwirkungen auf Tragwerke: Grundlagen und
Anwendung nach EC 1, Springer Vieweg, ISBN: 978-3-8348-9855-5, 2019.
Schultz, D., Bosart, L., Colle, B., Davies, H., Dearden, C., Keyser, D.,
Martius, O., Roebber, P., Steenburgh, W., Volkert, H., and Winters, A.:
Extratropical cyclones: a century of research on meteorology's centerpiece.
Meteor. Mon., 59, 16–1, 2019.Schütz, J., Götz, M., Schmid, W., and Mandallaz, D.: Vulnerability
of spruce (Picea abies) and beech (Fagus sylvatica) forest stands to storms and consequences for silviculture, Eur. J. Forest Res., 125, 291–302, 10.1007/s10342-006-0111-0, 2006.Schweighofer, J.: The impact of extreme weather and climate change on inland
waterway transport, Nat. Hazards, 72, 23–40, 10.1007/s11069-012-0541-6, 2014.Scott, R. and Mitchell, S.: Empirical modelling of windthrow risk in
partially harvested stands using tree, neighbourhood, and stand attributes,
Forest Ecol. Manag., 218, 193–209, 10.1016/j.foreco.2005.07.012, 2005.Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., and Zhou, B.: Weather and Climate Extreme Events in a Changing Climate, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1513–1766, https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/ (last access: 1 May 2023), 2021.Shahabinejad, N., Mahmoodabadi, M., Jalalian, A., and Chavoshi, E.: In situ
field measurement of wind erosion and threshold velocity in relation to soil
properties in arid and semiarid environments, Environ. Earth Sci., 78, 10.1007/s12665-019-8508-5, 2019.Shapiro, M. and Keyser, D.: Fronts, jet-streams and the tropopause, in:
Extratropical cyclones, American Meteorological Society, Boston, MA, 167–191, 10.1007/978-1-944970-33-8_10, 1990.
Sharples, J.: Review of formal methodologies for wind-slope correction of
wildfire rate of spread, Int. J. Wildland Fire, 17, 179–193, 2008.
Sissakian, V., Al-Ansari, N., and Knutsson, S.: Sand and dust storm events in
Iraq, Natural Science, 5, 1084–1094, 2013.Sivakumar, M.: Impacts of sand storms/dust storms on agriculture, in: Natural
Disasters and Extreme Events in Agriculture, Springer, Berlin, Heidelberg,
159–177, 10.1007/3-540-28307-2_10, 2005.Slot, R. M. M., Sørensen, J. D., Svenningsen, L., Moser, W., and Thøgersen, M. L.: Effective turbulence and its implications in wind turbine fatigue assessment, Wind Energy, 22, 1699–1715, 10.1002/we.2397, 2019.SMHI: SMHI Warnings and advisories, https://www.smhi.se/en/weather/sweden-weather/warnings,
last access: 10 January 2023.Snæbjörnsson, J., Baker, C., and Sigbjörnsson, R.: Probabilistic
assessment of road vehicle safety in windy environments, J. Wind Eng. Ind.
Aerodyn., 95, 1445–1462, 10.1016/j.jweia.2007.02.020, 2007.Spatz, H.-C. and Theckes, B.: Oscillation damping in trees, Plant Sci., 207, 66–71, 10.1016/j.plantsci.2013.02.015, 2013.Sprenger, M., Schmidli, J., and Egloff, L.: The Laseyer wind storm-case
studies and a climatology, Meteorol. Z., 27, 15–32,
10.3929/ethz-b-000239023, 2018.Stefanski, R. and Sivakumar, M.: Impacts of sand and dust storms on agriculture and potential agricultural applications of a SDSWS, IOP C. Ser. Earth Env., 7, p. 12016, 10.1088/1755-1307/7/1/012016, 2009.Stoll, R., Gibbs, J. A., Salesky, S. T., Anderson, W., and Calaf, M.: Large-Eddy Simulation of the Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 177, 541–581, 10.1007/s10546-020-00556-3, 2020.Stull, R.: An introduction to boundary layer meteorology, Springer Dordrecht,
10.1007/978-94-009-3027-8, 1988.
Stull, R.: Practical Meteorology: An Algebra-based Survey of Atmospheric
Science, Black and White edn., Sundog Publishing, LLC, Vancouver, 942 pp., ISBN 978-0-88865-283-6, 2017.
Suárez, J., Gardiner, B., and Quine, C.: A comparison of three methods
for predicting wind speed in complex forested terrain, Meteorol. Appl., 6,
329–342, 1999.Sulik, S. and Kejna, M.: The origin and course of severe thunderstorm
outbreaks in Poland on 10 and 11 August, 2017, Bulletin of Geography, Physical Geography Series, 18, 25–39, 10.2478/bgeo-2020-0003, 2020.Suvanto, S., Henttonen, H., Nöjd, P., and Mäkinen, H.: Forest
susceptibility to storm damage is affected by similar factors regardless of
storm type: Comparison of thunder storms and autumn extra-tropical cyclones
in Finland, Forest Ecol. Manag., 381, 17–28, 10.1016/j.foreco.2016.09.005, 2016.Suvanto, S., Henttonen, H., Nöjd, P., and Mäkinen, H.: High-resolution topographical information improves tree-level storm damage
models, Can. J. Forest Res., 48, 721–728, 10.1139/cjfr-2017-0315, 2018.Takemi, T., Yoshida, T., Horiguchi, M., and Vanderbauwhede, W.:
Large-Eddy-simulation analysis of airflows and strong wind hazards in urban
areas, Urban Climate, 32, 100625, 10.1016/j.uclim.2020.100625, 2020.
Tamura, Y.: Wind-induced damage to buildings and disaster risk reduction, in: The Seventh Asia-Pacific Conference on Wind Engineering, Taipei, Taiwan, 8–12 November 2009, 23 pp., 2009.Taszarek, M., Allen, J. T., Brooks, H. E., Pilguj, N., and Czernecki, B.:
Differing trends in United States and European severe thunderstorm
environments in a warming climate, B. Am. Meteorol. Soc., 102, E296–E322, 10.1175/BAMS-D-20-0004.1, 2021.Taylor, A., Dracup, E., MacLean, D., Boulanger, Y., and Endicott, S.: Forest
structure more important than topography in determining windthrow during
Hurricane Juan in Canada's Acadian Forest, Forest Ecol. Manag., 434,
255–263, 10.1016/j.foreco.2018.12.026, 2019.Telewski, F.: 14 - Wind-induced physiological and developmental responses in
trees, in: Wind and Trees, edited by: Grace, J. and Coutts, M., Cambridge University Press, 237–263, 10.1017/CBO9780511600425.015, 1995.Theofilatos, A. and Yannis, G.: A review of the effect of traffic and
weather characteristics on road safety, Accident Anal. Prev., 72, 244–256,
10.1016/j.aap.2014.06.017, 2014.Thornes, J. and Davis, B.: Mitigating the impact of weather and climate on
railway operations in the UK, in: ASME/IEEE Joint Railroad Conference, Washington, DC, USA, 23–25 April 2002, IEEE, 29–38, 10.1109/RRCON.2002.1000089, 2002.Tomas-Burguera, M., Vicente-Serrano, S. M., Peña-Angulo, D., Domínguez-Castro, F., Noguera, I., and El Kenawy, A.: Global Characterization of the Varying Responses of the Standardized Precipitation Evapotranspiration Index to Atmospheric Evaporative Demand, J. Geophys. Res.-Atmos, 125, e2020JD033017, 10.1029/2020JD033017, 2020.Tomczak, A., Jelonek, T., Grzywiński, W., Mania, P., Tomczak, K., and
Pazdrowski, W.: The Effects of Wind Exposure on Scots Pine Trees:
Within-Stem Variability of Wood Density and Mechanical Properties, Forests,
11, 1095, 10.3390/f11101095, 2020.Torshizi, M., Miri, A., Shahriari, A., Dong, Z., and Davidson-Arnott, R.:
The effectiveness of a multi-row Tamarix windbreak in reducing aeolian
erosion and sediment flux, Niatak area, Iran, J. Environ. Manage., 265,
110486, 10.1016/j.jenvman.2020.110486, 2020.Torun, P. and Altunel, A.: Effects of environmental factors and forest
management on landscape-scale forest storm damage in Turkey, Ann. Forest
Sci., 77, 1–13, 10.1007/s13595-020-00945-1, 2020.
Trigo, I., Davies, T., and Bigg, G.: Objective climatology of cyclones in the
Mediterranean region, J. Climate, 12, 1685–1696, 1999.
Troen, I. and Petersen, E.: European Wind Atlas, 1st ed., Risø National
Laboratory, Roskilde, ISBN: 87-550-1482-8, 1989.TT Club, ICHCA International, and Port Equipment Manufacturers Association:
Recommended minimum safety specifications for quay container cranes, TT Club
(in collaboration with ICHCA International and PEMA),
https://www.ttclub.com/-/media/files/tt-club/port-equipment-information-papers/recommended-minimum-safety-features-for-quay-container-cranes.pdf
(last access: 14 December 2021), 2011.Uchida, T. and Kawashima, Y.: New Assessment Scales for Evaluating the
Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced
Turbulence, Energies, 12, 2624, 10.3390/en12132624, 2019.Uchida, T. and Li, G.: Comparison of RANS and LES in the Prediction of
Airflow Field over Steep Complex Terrain, Open Journal of Fluid Dynamics, 8, 286, 10.4236/ojfd.2018.83018, 2018.Uchida, T. and Ohya, Y.: Large-eddy simulation of turbulent airflow over
complex terrain, J. Wind Eng. Ind. Aerod., 91, 219–229,
10.1016/S0167-6105(02)00347-1, 2003.Uchida, T. and Ohya, Y.: Micro-siting technique for wind turbine generators
by using large-eddy simulation, J. Wind Eng. Ind. Aerodyn., 96,
2121–2138, 10.1016/j.jweia.2008.02.047, 2008.Uchida, T. and Ohya, Y.: Latest Developments in Numerical Wind Synopsis
Prediction Using the RIAM-COMPACT® CFD Model – Design Wind Speed Evaluation and Wind Risk (Terrain-Induced Turbulence) Diagnostics in Japan, Energies, 4, 458–474, 10.3390/en4030458, 2011.Uchida, T. and Sugitani, K.: Numerical and Experimental Study of Topographic
Speed-Up Effects in Complex Terrain, Energies, 13, 3896,
10.3390/en13153896, 2020.Ulbrich, U., Leckebusch, G., and Pinto, J. G.: Extra-tropical cyclones in the
present and future climate: a review, Theor. Appl. Climatol., 96, 117–131,
10.1007/s00704-008-0083-8, 2009.United Nations Department of Economic and Social Affairs: World Population Prospects 2022: Summary of Results, United Nations Department of Economic and Social Affairs, UN DESA/POP/2022/TR/NO. 3, Population Division, New York, https://population.un.org/wpp/Publications/ (last access: 1 May 2023), 2022.University of Bonn: COSMO-REA2 data set, University of Bonn [data set], https://reanalysis.meteo.uni-bonn.de/?Download_Data___COSMO-REA2, last access 31 May 2023.Usbeck, T., Wohlgemuth, T., Pfister, C., Volz, R., Beniston, M., and
Dobbertin, M.: Wind speed measurements and forest damage in Canton Zurich
(Central Europe) from 1891 to winter 2007: Wind Speed Measurements and
Forest Damage in Canton Zurich, Int. J. Climatol., 30, 347–358,
10.1002/joc.1895, 2010.Vajda, A., Tuomenvirta, H., Juga, I., Nurmi, P., Jokinen, P., and Rauhala,
J.: Severe weather affecting European transport systems: the identification,
classification and frequencies of events, Nat. Hazard., 72, 169–188,
10.1007/s11069-013-0895-4, 2014.van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., and Otto, F. E. L.: Attribution of the Australian bushfire risk to anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 21, 941–960, 10.5194/nhess-21-941-2021, 2021.Van Wagner, C. and Forest, P.: Development and structure of the Canadian
forest fire weather index system, Canadian Forestry Service, Forestry Tech. Rep., 35 pp., https://cfs.nrcan.gc.ca/publications?id=19927 (last access: 1 May 2023), 1987.Verkaik, J. W.: Evaluation of Two Gustiness Models for Exposure Correction
Calculations, J. Appl. Meteorol., 39, 1613–1626,
10.1175/1520-0450(2000)039<1613:EOTGMF>2.0.CO;2, 2000.
Vitolo, R., Stephenson, D., Cook, I., and Mitchell-Wallace, K.: Serial
clustering of intense European storms, Meteorol. Z, 18, 411–424, 2009.
Vogel, S.: Drag and Reconfiguration of Broad Leaves in High Winds, J. Exp.
Bot., 40, 941–948, 1989.Vollsinger, S., Mitchell, S. J., Byrne, K. E., Novak, M. D., and Rudnicki,
M.: Wind tunnel measurements of crown streamlining and drag relationships
for several hardwood species, Can. J. Forest Res., 35, 1238–1249,
10.1139/x05-051, 2005.Wahl, S., Bollmeyer, C., Crewell, S., Figura, C., Friederichs, P., Hense,
A., Keller, J. D., and Ohlwein, C.: A novel convective-scale regional
reanalysis COSMO-REA2: Improving the representation of precipitation,
Meteorol. Z., 26, 345–361, 10.1127/metz/2017/0824, 2017.Wahl, T., Jain, S., Bender, J., Meyers, S., and Luther, M.: Increasing risk
of compound flooding from storm surge and rainfall for major US cities,
Nat. Clim. Change, 5, 1093–1097, 10.1038/nclimate2736, 2015.Wang, B., Xu, Y., Zhu, L., and Li, Y.: Crosswind effect studies on road vehicle passing by bridge tower using computational fluid dynamics, Eng.
Appl. Comp. Fluid, 8, 330–344, 10.1080/19942060.2014.11015519, 2014.Wallace, J. and Hobbs, P.: Atmospheric science: an introductory survey, Vol. 92, Elsevier, ISBN: 9780127329512, 2006.
Welker, C., Röösli, T., and Bresch, D. N.: Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland, Nat. Hazards Earth Syst. Sci., 21, 279–299, 10.5194/nhess-21-279-2021, 2021.Weninger, T., Scheper, S., Lackóová, L., Kitzler, B., Gartner, K., King, N. W., Cornelis, W., Strauss, P., and Michel, K.: Ecosystem services of tree
windbreaks in rural landscapes–a systematic review, Environ. Res. Lett., 16, 103002, 10.1088/1748-9326/ac1d0d, 2021.
Wiréhn, L., Käyhkö, J., Neset, T. S., and Juhola, S.: Analysing trade-offs in adaptation decision-making – agricultural management under climate change in Finland and Sweden, Reg. Environ. Change, 20, 1–14, 2020.Woollings, T., Hannachi, A., and Hoskins, B.: Variability of the North
Atlantic eddy-driven jet-stream, Q. J. Roy. Meteor. Soc., 136, 856–868,
10.1002/qj.625, 2010.Young, I.: Seasonal variability of the global ocean wind and wave climate,
Int. J. Climatol., 19, 931–950, 10.1002/(SICI)1097-0088(199907)19:9<931::AID-JOC412>3.0.CO;2-O, 1999.
Zappa, G., Shaffrey, L., Hodges, K., Sansom, P., and Stephenson, D.: A multimodel assessment of future projections of North Atlantic and European
extratropical cyclones in the CMIP5 climate models, J. Climate, 26, 5846–5862, 2013.Zscheischler, J., Westra, S., van den Hurk, B., Seneviratne, S., Ward, P.,
Pitman, A., AghaKouchak, A., Bresch, D., Leonard, M., Wahl, T., and Zhang,
X.: Future climate risk from compound events, Nat. Clim. Change, 8,
469–477, 10.1038/s41558-018-0156-3, 2018.Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C.,
Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A.,
Mahecha, M. D., Maraun, D., Ramos, A., Ridder, N., Thiery, W., and Vignotto,
E.: A typology of compound weather and climate events, Nature Reviews Earth & Environment, 1, 333–347, 10.1038/s43017-020-0060-z, 2020.