Forest Ecology and Management 203 (2004) 77–88
www.elsevier.com/locate/foreco
Influence of clear-cutting on the risk of wind damage at
forest edges
Hongcheng Zenga,*, Heli Peltolaa, Ari Talkkaria, Ari Venäläinenb,
Harri Strandmana, Seppo Kellomäkia, Kaiyun Wangc
a
Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland
b
Finnish Meteorological Institute, P.O. Box 503, FIN-00101 Helsinki, Finland
c
Chengdu Insitute of Biology, Chinese Academy of Sciences, 610041 Chengdu, China
Received 22 July 2003; received in revised form 25 May 2004; accepted 21 July 2004
Abstract
The objective was to predict the risk of wind damage at forest edges in Scots pine (Pinus sylvestris), Norway spruce (Picea
abies), and birch (Betula spp.) dominated stands in Central Finland. The method used here was to integrate a mechanistic wind
damage model and an airflow model with forest database containing information at the tree, stand, and regional levels. Analyses
were made for the current forest edges (Case I) and for situations in which new forest edges might be created through clearcutting: (i) whenever a stand were to reach the minimum acceptable mean diameter and/or stand age (Case II); or (ii) whenever
the stand age were to exceed 100 years (Case III). These case studies were used to analyse the number of stands and total area at
risk, and length of vulnerable edges for different critical wind speeds and risk probabilities.
It was evident that new clear-cuttings did increase the high wind speeds at forest edges, especially in Case II, as compared
with the wind speeds at current forest edges (Case I). This local effect was however compensated at regional level by the more
intensive cuttings in Case II, since the old stands, which were more vulnerable, were cut and the average tree size at the regional
level decreased relative to Case I or III. The overall risk of wind damage at the regional level therefore decreased in Case II in
terms of the number of stands and the total area at risk, and also in terms of the length of vulnerable edges. On the other hand, the
risk increased at the regional level in Case III relative to Case I or II, because there were still a lot of vulnerable old stands left at
the newly created edges. The sensitivity analyses of wind conditions also showed that Norway spruce was more vulnerable in
overall than Scots pine under current conditions and birch the least vulnerable (i.e. Norway spruce has risks even in slow wind
speeds, while Scots pine and birch have risks at higher wind speeds). Furthermore, Norway spruce was more sensitive than Scots
pine under more windy conditions.
# 2004 Elsevier B.V. All rights reserved.
Keywords: Airflow model; Clear-cutting; GIS; Mechanistic model; Wind damage
* Corresponding author. Tel.: +358 13251 4439; fax: +358 13251 4444.
E-mail address: [email protected] (H. Zeng).
0378-1127/$ – see front matter # 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2004.07.057
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H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
1. Introduction
The structure and functioning of a forest ecosystem
is greatly affected by wind, and wind-induced damage
is a continuous cause of economic loss in forests.
Approximately, 175 million m3 of timber was blown
down in Europe during storms in December 1999, for
example (Bomersheim, 2000), and over 7 million m3
in Finland in November 2001. The economic impact
of wind damage is particularly severe in managed
forests, on account of the reduction in the yield of
recoverable timber, the increased costs of unscheduled
thinnings, and general problems in forestry planning.
In principle, the susceptibility of a stand to wind
damage is controlled by tree and stand characteristics
such as tree species, tree height, tree diameter, crown
area, rooting depth and width, and stand density,
which are in turn determined by forest management
(Coutts, 1986; Gardiner, 1995; Gardiner et al., 1997;
Lee and Black, 1993; Kerzenmacher and Gardiner,
1998; Peltola et al., 1999a, 2000; Dunham and
Cameron, 2000; Zhu et al., 2000). Furthermore, large
differences in the risk of wind damage can be observed
between regions and locations that differ in their
topography and/or climate (Copeland et al., 1996;
Peltola et al., 1999b; Quine, 2000; Proe et al., 2001).
The highest risk of wind damage is most likely to
be found where there are sudden changes in wind
loading to which the trees are not acclimated, as in
stands adjacent to recently clear-felled areas or in
stands that have recently been thinned intensively
(Neustein, 1965; Kolstock and Lockow, 1981; Laiho,
1987; Lohmander and Helles, 1987; Peltola, 1996a,b;
Gardiner et al., 1997, 2000; Peltola et al., 1999a).
Therefore, the most basic questions in the planning of
forest management are how new clear-cuttings and/or
thinnings may affect the speed and direction of local
airflow and how the wind hits the downwind forest
(Peltola, 1996b; Peltola et al., 1999a; Talkkari et al.,
2000).
Neustein (1965), for example, has suggested that
small clearings can reduce the sweep of wind within
them. On the other hand, the extent of susceptible
edges per hectare of felled area increases rapidly as the
size of clear-cutting areas diminishes. Thus, the
reduced amount of wind damage at the edges of small
clearings that might be expected from the observed
reduction in wind speed compared with larger ones
can be outweighed by the much greater length of the
perimeter at risk (Alexander, 1964, 1967; Neustein,
1965; Elling and Verry, 1978).
Because clear-cuttings may increase wind speeds,
they may increase the risk of wind damage locally, and
especially at forest edges, but the risk of damage does
not necessarily increase at the regional level in as
straightforward a manner. This is because the average
size of trees in stands will decrease at the regional
level when old stands are cut. Therefore, at regional
level, a better understanding is needed of facts such as
how clear-cutting may affect local wind conditions
and consequently the risk of damage.
PC-based airflow models such as Wind Atlas and
Application Program (WAsP) (Troen and Petersen,
1989; Mortensen et al., 2000; Achberger et al., 2002;
Venäläinen et al., 2003) and MS-Micro/3 (Walmsey et
al., 1993) have become available in the last few years
for studying the speed and direction of local airflow.
Mechanistic models (Peltola et al., 1999a; Gardiner et
al., 2000) have correspondingly been developed for
predicting the critical wind speed and/or probability of
a given tree in a stand being uprooted or broken under
a range of silvicultural and wind conditions.
Only a few attempts have been made so far,
however, to integrate these mechanistic models with
airflow models in order to assess the likelihood of
wind damage in forests. Talkkari et al. (2000), for
example, integrated the mechanistic wind damage
model HWIND (Peltola et al., 1999a) with the airflow
model MS-Micro/3 (Walmsey et al., 1993) under
Finnish conditions, and Blennow and Sallnäs (2000)
integrated HWIND with the WAsP airflow model
(Mortensen et al., 2000) under Swedish conditions to
test the ability to identify actually damaged stands at
current forest edges. In both cases the integration of
these component models seemed to have great
potential for identifying the stands likely to suffer
actual damage under given wind conditions. For
similar purposes, Suárez et al. (2001) developed the
probabilistic ForestGALES model, which, like
HWIND, calculates the critical wind speed required
for damage to occur. This is combined with the
probability of the wind speed being exceeded derived
from a wind zone and terrain-based measure of wind
exposure (DAMS) linked to Weibull distributions of
wind speed (Quine and White, 1993; Quine, 2000;
Gardiner and Quine, 2000; Gardiner et al., 2000).
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
In the above context, the aim of this study was to
predict the risk of wind damage at forest edges in Scots
pine (Pinus sylvestris), Norway spruce (Picea abies),
and birch (Betula spp.) dominated stands in Central
Finland. Analyses were made for the current forest
edges (Case I), and for situations in which new edges
might be created through clear-cutting, employing two
clear-cutting criteria as produced for under current
Finnish forest management regulations (Tapio, 2001).
Analyses were made of the number of risk stands (i.e.
the stands with height greater than 10 m and adjoining
gaps endure risks), total area at risk, and length of
vulnerable edges at different critical wind speeds and
risk probabilities. The method was to integrate the
mechanistic wind damage model HWIND (Peltola et
al., 1999a), the WAsP airflow model (Mortensen et al.,
2000), and information at the tree, stand, and regional
levels.
2. Material and methods
2.1. Outlines for integration of the component
models and data flow
Component models at the tree, stand, and regional
levels were integrated here in order to produce a
means of assessing the risk of wind damage at forest
margins as affected by forest management, in terms of
certain clear-cutting options. This was done by
employing; (i) the mechanistic wind damage model
HWIND, which predicts the critical wind speeds
needed to cause damage, (ii) a regional airflow model
WAsP, which simulates the distribution of wind
conditions for sites defined by their orography,
roughness, and obstacles (Fig. 1), (iii) geographical
databases on forest stand parameters, topography, and
surface roughness in the area concerned, and (iv) the
probability distribution of long-term extremes in wind
speed at the sites. The forest stand data and digital
orographic data, expressed as coverages, were stored
in ArcInfo (ESRI, 2002), which was also used for
identification of forest gaps and the stands adjoining
them, and the digitizing of roughness classes. The
component models and data flow needed for the study
will be described in more detail below.
79
Fig. 1. Outlines of the data flow and model structure.
2.2. Component models
2.2.1. The mechanistic wind damage model
HWIND
The HWIND model developed by Peltola et al.
(1999a) describes the mechanistic behaviour of trees
under wind (and snow) loading and predicts the mean
wind speed lasting 1 h at 10 m above ground level (and
at the canopy top) at which trees at forest margins will
be uprooted or broken. The model needs as its input
information stand by not only tree and stand characters
(i.e. tree species, tree height, diameter at breast height,
stand density, and snow load if present), but also gap
features (including distance from stand edge, and gap
size). The wind is speeded up along the gaps and
reaches the highest speed at the edges and will slow
down when penetrating the forest. However, the wind
will reach the maximum speed at the distance of 10
tree heights if the gap length in wind direction is
longer than 10 tree heights.
To provide a measure of the maximum bending
moment on the tree, the model calculates the wind
force and the gravitational force caused by the mass of
the stem and crown (including snow). Trees are
assumed to deflect to a point of no return when acted
upon by a sufficient wind of constant mean speed and
direction. This makes it possible to calculate the
maximum wind speed that a tree can withstand. It is
possible to use either the wind speed needed for
uprooting or that needed for stem breakage (we use the
smaller of the two critical speeds). In order to derive
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H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
the resistive bending moment of anchorage, the
resistance to uprooting is calculated using an estimate
of the root–soil plate mass (Coutts, 1986). A tree is
assumed to be uprooted if the maximum bending
moment exceeds the support provided by the root–soil
plate (Peltola and Kellomäki, 1993; Peltola et al.,
1999a). Correspondingly, the resistance to stem
breakage relies on values for the modulus of rupture
determined for different species of timber (Jones,
1983; Morgan and Cannell, 1994). A tree is assumed
to break if the breaking stress acting on the stem
exceeds the critical value of the modulus of rupture
(Sunley, 1968; Petty and Worrel, 1981; Petty and
Swain, 1985; Peltola et al., 1999a).
Furthermore, as HWIND uses 1-h average wind
speeds for calculating the critical wind speeds, while
wind measurements are usually available as 10 min
averages, these critical wind speeds can be multiplied
by 1.1 to obtain critical wind speeds lasting only
10 min if needed (Tammelin, 1991). The properties of
the HWIND model, its parameters, inputs, and the
validity of its outputs for podzol soil conditions in
Finland have been discussed in detail earlier by Peltola
et al. (1999a), Gardiner et al. (2000) and Talkkari et al.
(2000).
2.2.2. The airflow model WAsP
The PC-based airflow model WAsP (Wind Atlas
and Application Program, see Troen and Petersen,
1989; Mortensen et al., 2000, 2002) provides a means
of simulating airflow over a forested area from data
on local wind conditions, stand location, topography
(terrain), and surface roughness. WAsP contains submodels for orographic flow perturbations, roughness
changes, and the influence of obstacles on the wind
field (Troen, 1990; Mortensen et al., 2000, 2002).
Based on a polar grid terrain representation, the
orographic flow perturbations are evaluated as the
sum of spectral potential flow solutions using a
Fourier-Bessel expansion. Clear-cutting changes the
cover of the fields and consequently reduces the
roughness values, which speeds up the wind for a
certain degree (Venäläinen et al., 2004). For vertical
extrapolation to a new height above the surface in flat
terrain with homogeneous roughness, a logarithmic
wind profile is assumed, although this needs to be
perturbed to account for the influence of non-neutral
stratification. The homogeneous-terrain wind speed
is then modified by the above-mentioned terrain
perturbations.
Wind speeds and their frequency are simulated
in WAsP by a Weibull distribution in which A is
the scale parameter related to mean wind speed and K
the shape parameter describing the form of the
distribution function. Thus, wind speed has a close
relationship to the Weibull parameter A (Tammelin,
1991):
1
u¼AG 1þ
K
(1)
where u is the wind speed (m s1) and gamma (G) is a
function of the Weibull parameter K.
Correspondingly, the frequency of a given wind
speed is calculated by the following equation
(Achberger et al., 2002):
f ðuÞ ¼
k u k1
u k
exp A A
A
(2)
where f(u) is the frequency of occurrence of wind
speed u.
Moreover, the total risk probability of wind damage
is assessed from the cumulative frequency of critical
wind speeds higher than u
u k
FðuÞ ¼ exp A
(3)
where F(u) is the cumulative frequency of wind speeds
greater than u.
The resource grid squares used here for representing the distribution of wind speed and frequency were
of size 50 m 50 m. In addition to coordinates and
elevation data, every grid square was assigned a mean
wind speed (lasting 10 min) and Weibull parameters A
and K calculated at a height of 10 m above ground
level (corresponding to the HWIND calculations of
critical wind speeds).
The properties of the WAsP model, its parameters,
inputs, and the validity of its outputs as assessed
against other models and field measurements of wind
speed have been discussed in detail earlier by
Walmsley et al. (1990), Mortensen and Petersen
(1997), Suárez et al. (1999), Mortensen et al. (2000,
2002), Achberger et al. (2002).
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
2.3. Site and geographical databases
2.3.1. Site and stand data
The area examined, of about 7 km2, was located in
Central Finland (638010 N; 278480 E) and represented a
typical boreal forest, mostly dominated by Scots pine
and Norway spruce stands, but with some birch stands
also present. The current forest stands of 441 ha
surveyed in the area in 2001 included 266 stands and
clear-cut areas (45.5 ha), comprising 76 Scots pine
stands (142 ha), 158 Norway spruce stands (225 ha),
27 birch stands (26 ha) and 5 stands of other broadleaved species (2.5 ha). The area has an undulating
terrain, with some small hills, and altitudes range
between 90 and 170 m.
Since some stands were old enough to be harvested
according to Finnish forest management regulations
(Tapio, 2001), two different clear-cutting scenarios
were applied. Separate stand database were created for
all three case studies (I–III), i.e. for the current forest
edges in 2001 (Case I) and for situations where
additional edges had been created through clearcutting: (i) whenever stands reached the minimum
acceptable species-specific mean diameter (DBH)
and/or stand age (Case II) and (ii) whenever the stand
age exceeded 100 years (Case III). In Case II, the DBH
criterion was 28 cm for all species and the age criteria
were 100, 90 and 80 years for Scots pine, Norway
spruce and birch, respectively. Within each scenario,
the cuttings were assumed to be carried out
simultaneously in all stands fulfilling the criteria at
the beginning of simulations. New clear-cuttings in
these situations may be expected to increase wind
Fig. 2. Relative areal coverage of the various terrain types in the
7.04 km2 area studied, in the middle of which new clear-cutting took
place in a smaller area of 441 ha.
81
Table 1
Stand statistics for the current forest structure and the new clearcutting options
Case I
Case II
Case III
Age (year)
Pine
Spruce
73.6 (9.8)
67.0 (3.5)
49.5 (4.8)
48.1 (3.3)
52.0 (5.2)
63.8 (3.5)
DBH (cm)
Pine
Spruce
20.7 (1.4)
25.1 (0.8)
18.8 (1.1)
20.3 (0.7)
20.3 (1.5)
24.6 (0.8)
Height (m)
Pine
Spruce
15.5 (0.8)
20.1 (0.5)
15.0 (0.7)
16.9 (0.5)
15.9 (0.9)
19.8 (0.5)
0.013 (0)
0.012 (0)
0.013 (0)
0.012 (0)
0.013 (0)
0.012 (0)
DBH/height
Pine
Spruce
Stand density (stems/ha)
Pine
1298 (186)
Spruce
946 (120)
Basal area (m2/ha)
Pine
19.2 (1.3)
Spruce
23.1 (0.6)
1540 (173)
1427 (149)
19.7 (1.4)
20.2 (0.9)
1493 (178)
999 (127)
20.8 (1.5)
22.9 (0.7)
*
Values in brackets are standard errors.
speeds relative to the current situation (Case I) and
increase the risk of wind damage at forest edges
because of changes in the forest structure (Fig. 2,
Table 1). Unfortunately, there no information was
available on stands actually damaged by wind in the
area concerned (because no significant wind damage
had been detected in the latest stand inventory in
2001).
2.3.2. Wind conditions
The inputs to WAsP regarding local wind conditions consisted of measurements made at the
Ritoniemi meteorological station in Vehmersalmi
(628480 N, 278550 E), situated about 30 km north of
the area concerned, during 1990–2001. The altitude of
the station is 86 m, and average wind speeds and
directions for 10-min periods were recorded at a
height of 27 m above the ground every 3 h, i.e. to
represent the wind direction and speed for the entire 3h period. Since it was obvious that some gusts would
exist during the unobserved periods, the observed
wind speeds were multiplied by 1.5 in order to include
the effects of these gusts. This was done because the
maximum speeds of gusts are usually estimated to be
1.2–1.7 times of the average wind speed recorded over
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H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
10 min (Tammelin, 1991). Wind directions (measured
to an accuracy of 108) were grouped into eight sectors
for the WAsP simulations. Although the most frequent
directions are 180 and 225, most of the stormy winds
blew from the direction 270, which was therefore
selected to represent the whole body of wind data in
order to simplify the calculations. Furthmore, most
stormy winds happened during winter time, so the
birch was simulated without leaves, which reduced the
critical wind speeds greatly comparing with that with
leaves (Peltola et al., 1999a).
Separate cumulative probabilities for critical wind
speeds were calculated for each stand in the database
for each Cases I–III, based on the critical wind speeds
(calculated using the HWIND model) and the local
mean distribution of wind speeds (based on the
Weibull parameters A and K, calculated for individual
grid squares in WAsP). In addition, speculative
simulations were also carried out for Case I in which
the mean wind speed was increased and decreased by
20%, to find out how this would affect the probability
of wind damage to the forests. As the wind speed
distribution was mainly determined by Weibull parameter A, any change in wind speed can be represented
by a small difference in parameter A. Furthermore, a
Wind Probability Model was developed here for
ArcInfo using Visual Basic for Applications (VBA),
in which the critical wind speeds and the Weibull
parameters A and K were used to calculate the
cumulative probability of wind speeds greater than the
critical ones. Thus, the output was the frequency of
possible wind damage events in a year.
2.3.3. Topography and surface roughness data
Since wind direction and wind speed are influenced
by both surface roughnesses (the height above ground
at which the wind speed is 0) and topography, the
Digital Elevation Model (DEM) with a pixel size of
25 m was obtained from the National Land Survey of
Finland. Contour lines at 5 m intervals were derived
from this using the 3D Analyst extension to ArcGIS
(Booth, 2000).
Four roughness classes were recognised separately
for Cases I–III; lakes and other water areas (0), clearcut areas, small seedling stands (<2 m) and fields
(0.3), young forests less than 15 m tall, or mature
forests of small basal area (0.5), and dense, mature
forests over 15 m tall (0.7). This is because gaps, forest
margins, surface roughness, and critical wind speeds
all change after new clear-cutting due to the changes in
gap sizes and forest structure. Since the roughness
changes, local wind conditions will also be affected
(Venäläinen et al., 2004). New roughness values were
therefore also digitized and transferred to WAsP in
order to calculate the resource grids separately for
each Cases I–III.
Because uprooting and stem breakage mostly occur
at forest margins, only those stands adjoining gaps
were considered here. In ArcInfo, data on such stands
and the edges between stands and gaps were extracted
in the form of a polygon map and polyline map,
respectively. The attributes of the stands were
appended to the corresponding attribute tables for
the polylines to ensure that the edges had the same
stand characteristics as the relative forest stands.
Furthermore, the sizes of the open gaps (here clear-cut
areas, small seedling stands and open fields) were also
fixed in ArcInfo, i.e. if some polygons representing
gaps were adjacent, they were merged into one
polygon and taken as one gap.
Furthermore, HWIND uses gap sizes defined by the
length of the gap in the direction of the wind. Since
most of the gaps were irregular in shape (i.e. the gap
diameter varying in different directions), the shape
was simplified here by assuming gaps to be circular,
with their diameters determined by their areas. The
gap sizes were calculated in tree height and all the
gaps were categorized into 2, 4, 6, 8 and 10 tree
height groups in this context in order to simplify the
computations.
These roughness classes were determined from
digital aerial ortho-photos (with 2 m resolution, based
on data from 2000) using visual interpretation and
digitized as a polyline layer in ArcInfo (ESRI, 2002).
The forest stand data were used as ground-truth
information for interpretation and digitization purposes where available within the geographical extents
of the aerial photos. Also, the roughness lines were
converted to WAsP map format from the Arcview
shape file using an application developed here, and
further imported into WAsP and merged with the
contour lines. The conversion tool (DataConvert)
developed here transfers the data from the resource
grids to another data file that can be used by another
software, e.g. ArcInfo. These transferred results could
not be used directly, however, as the values were not
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
83
related to stands, but to the grids. Grid squares located
in the same stand were therefore further merged
together in ArcInfo and the maximum values for the
Weibull parameters A and K were chosen to represent
the values for the stands. Furthermore, some small
stands occupying less than a grid square had to be
assigned the closest grid values.
3. Results
3.1. Number of risk stands, area, and length of
vulnerable edges
Three cases were analysed here: the current forests
and two different clear-cutting scenarios. In Case I
(current forest structure) there were 43 open areas
(including clear-cut areas and seedling stands),
amounting to 85 ha. When new clear-cut areas were
created in Case II, 53 new clear-cut areas resulted
(109 ha), comprising 11 Scots pine stands, 41 Norway
spruce stands and one birch stand. In Case III, 12 new
clear-cutting areas emerged (32 ha), 7 Scots pine and 5
Norway spruce stands. The clear-cuttings achieved in
Case II were thus much more intensive than that in
Case III.
The number of risk stands (the stands with height
greater than 10 m locate adjoining gaps), area at risk,
and length of vulnerable edges generally decreased for
critical wind speeds of less than 20 m s1 in Case II
relative to the current risk (Case I) and to Case III,
while it increased for critical wind speeds greater than
20 m s1 (Fig. 3).
As a result of the increment of critical wind speeds
in Case II, although the risk increased locally for some
stands because of the higher wind speeds at the edges
of clear-cut areas, the overall risk in terms of both the
number and area of risk stands and the length of
vulnerable edges (at different critical speeds and
different risk probabilities 0.1%) decreased regionally in Case II relative to the current forest structure
(Case I) and to Case III (Fig. 3, Table 2).
Only 4 Scots pine stands and 26 Norway spruce
stands had a risk probability 0.1% (Table 3) in Case
I, the corresponding figures in Case II being 3 and 16
and those in Case III 8 and 28, respectively (Table 3).
This could be explained by the fact that the average
tree sizes of the stands decreased at the regional level
Fig. 3. Numbers of risk stands, their area and the length of vulnerable edges at given critical wind speeds for each case. The three
adjacent bars for each class of critical wind speeds represent Cases
I–III, respectively. The total values for each cases in each critical
wind speed class are tagged at the top of each bar.
(old stands were cut down) and critical wind speeds
increased in Case II relative to Cases I and III (Table 1,
Fig. 3). On the other hand, the risk increased at the
regional level in Case III relative to Cases I and II,
because more vulnerable forest edges were created
and a lot of vulnerable old stands still existed at
these edges. Furthermore, as the clear-cut areas
differed in shape, the percentage changes in risk area
and length of vulnerable edges brought about by the
new clear-cuttings may not correlate well with each
other (Table 3).
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H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
Table 2
Numbers of risk stands, their total area, and the length of vulnerable edges, by risk probability classes
Case I
Case II
Stands
Area (ha)
Case III
Edges (m)
Stands
Area (ha)
Edges (m)
Stands
Area (ha)
Edges (m)
No damage (stand height <10 m)
Pine
13
33.5
Spruce
26
28.4
Birch
7
8.4
2024
3712
692
26
32
8
57.7
30.7
8.8
6941
6960
1791
18
27
7
46.4
28.5
8.4
3410
3904
692
<0.001
Pine
Spruce
Birch
21
36
3
42.0
63.7
1.3
4276
6617
476
21
39
7
31.6
61.7
7.6
7549
9170
1187
15
31
3
20.7
42.4
1.3
4108
4778
476
0.001–0.01
Pine
Spruce
Birch
4
22
0
5.0
40.0
0
565
2639
0
3
13
0
2.8
24.3
0
682
2144
0
8
25
0
14.7
58.4
0
2160
5623
0
0.01–0.12
Pine
Spruce
Birch
0
4
0
0
8.3
0
0
804
0
0
3
0
0
4.5
0
0
553
0
0
3
0
0
3.6
0
0
269
0
3.2. Species differences and sensitivity analyses of
wind conditions
Overall, the mean critical wind speed was between
15 and 30 m s1, depending on the species and case
studies concerned (Fig. 3). However, as the critical
wind speeds for birch stands were calculated without
leaves, they were usually much higher than those for
Scots pine or Norway spruce stands. Altogether, 11%
of the total Scots pine stands in the current forests had
a risk probability higher than 0.1%, 30% stands of
total Norway spruce stands (Case I), implying that
Norway spruce was more suspect to damage than
Scots pine (i.e. Norway spruce has risks even in slow
wind speeds, while Scots pine and birch have risks at
higher wind speeds). The same is also true of Cases II
and III, since the percentages of Scots pine and
Norway spruce stands were 6 and 18% in Case II and
20 and 33% in Case III, respectively. This may be due
to larger average taper (DBH/height) and stand density
and smaller height of Scots pine stands than those of
Norway spruce stands (Table 1).
With risk probability greater than 0.1% (Table 3),
the number of risk stands, the total area at risk, and
the length of vulnerable edges of Norway spruce
decreased in Case II, while in the Scots pine the
number of risk stands and total area decreased, but the
length of vulnerable edges increased relative to the
Table 3
Differences in risk probabilities larger than 0.1% for current forests and the clear-cutting options
Scots pine
Norway spruce
Stands
Area (ha)
Edges (m)
Stands
Area (ha)
Total
Edges (m)
Stands
Area (ha)
Edges (m)
Case I
Case II
After change
Difference (%)
4
5.0
565
26
48.3
3443
30
53.3
4008
3
25
2.8
44
682
21
16
39
28.8
40
2696
22
19
37
31.6
41
3378
16
Case III
After change
Difference (%)
8
100
14.7
197
2160
282
28
8
62
29
5891
71
36
20
76.7
44
8051
101
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
85
Table 4
Differences in the total number of risk stands, their area, and the length of vulnerable edges upon a change in wind speed (risk probability larger
than 0.1%)
Scots pine
Stands
Original
After change
Difference (%)
Speed +20%
After change
Difference compared with original (%)
Norway spruce
Area (ha)
4
0
100
4.95
0
100
7
75
current forest structure (Case study I). In Case III
the number of risk stands, total area at risk, and
length of vulnerable edges increased for both species
(Table 3).
The results also demonstrate that both species were
sensitive to the change in wind conditions (Table 4).
Norway spruce was more sensitive in more windy
conditions (+20%) than Scots pine, while less
sensitive in less windy conditions (20%). Furthermore, if the critical wind speeds were low enough or
the observed wind speeds high enough (so that the risk
probability was high enough, 1%), the risk probability would not change as much as in the stands with
a lower initial risk probability (<0.1%).
4. Discussion and conclusion
The component models were integrated here in
order to study the regional level effect of forest
management, and more specifically of different clearcutting options, on the number of risk stands (the
stands with height greater than 10 m and located
adjoining the gaps), their area, and the length of
vulnerable edges at different critical wind speeds and
wind damage risk probabilities. By comparison, it
may be noted that Talkkari et al. (2000) and Blennow
and Sallnäs (2000) tested the same approach and
showed that these components had great potential for
identifying actually damaged stands under given wind
conditions. Talkkari et al. (2000) also analysed the
total area at risk at certain critical wind speeds (and the
probability of wind damage). So far, few attempts have
been made to study the effect of alternative forest
8.53
72.3
Edges (m)
Stands
565.0
26
Area (ha)
48.3
Edges (m)
3443.2
0
100
4
84.6
8.34
82.7
803.9
76.7
1061.5
87.9
54
107.7
94.72
96.1
8386.8
143.6
management options (e.g. clear-cutting options) on the
risk of damage as was done here.
Most of the gusts implied in the present material
could not be observed, and the measured 10-min
average wind speeds were not very high, either. Thus,
the risk of wind damage was very small or marginal
(for all the Cases I–III), although critical wind speeds
(10-min average speeds) predicted at around 15 m s1
could cause actual damage under Finnish conditions
(Laiho, 1987; Talkkari et al., 2000). So, the stands with
risk probability larger than 0.1% were analysed in
order to include all the possible damages.
By comparison, Venäläinen et al. (2004) found in
this same area that new clear-cuttings did increase
high wind speeds at forest edges relative to the current
forest structure (Case I), this being most obvious in
Case II. This was because the number and size of the
gaps and the length of vulnerable forest edges, and
also the surface roughness (as affected by forest
structure), changed following the new clear-cutting
operations (Fig. 2), consequently affecting local wind
conditions as well. Besides the negative effect of
increasing wind speeds, clear-cutting has another
positive effect reflected by Case II in this study: the old
stands, which were most vulnerable to be damaged,
had been cut so that the average tree sizes were
reduced at regional level, the critical wind speeds
increased, and consequently the risk of wind damage
was lessened. There were still some old stands in Case
III so that more risks were induced comparing with
current forests (Case I) and Case II.
Furthermore, because of the difference of shapes
(Fig. 4), the percentage changes in risk area and length
of vulnerable edges, which were induced by the new
86
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
Fig. 4. The risk probability of wind damage, by species, in the
current forest structure (Case I (a)) and the new clear-cutting options
(Cases II (b) and III (c)).
clear-cuttings, may not correlate with each other. As
the risk area in the present case was calculated using
whole stands rather than only their edges, where
damage is most likely to occur, these numbers can be
taken as denoting the potential area for wind damage
and the risk edges can be taken as representing the first
occurrences of damage.
Oragraphy plays another important role on the risk
of wind damage on forests except the roughness values
affected by clear-cuttings (sometimes even more
important than roughness for mountain areas).
Talkkari et al. (2000), for example, found the forests
located at the top of hills endure more risks than those
in other areas. The effects of the clear-cutting
alternatives analysed here may be changed in other
areas if the landscape changes. The effect of forest
management, e.g. clear-cuttings, however, cannot be
ignored, as it may affect the risks of wind damage
greatly like this study suggested.
In principle, the reason that Norway spruce
was more suspect to damage than Scots pine could
be expected in view of the tree and stand characteristics that Norway spruce stands have lower critical
wind speeds on average and a higher risk of damage
than Scots pine, which are in accordance with
the earlier results of Laiho (1987) and Peltola et al.
(2000), for example. The susceptibility of a stand to
wind damage has also been found to be controlled by
these trees and stand characteristics in other studies
(Coutts, 1986; Gardiner et al., 1997; Peltola et al.,
1999a).
The differences of Scots pine and Norway spruce
among the three case studies demonstrated that the
current forest structure (e.g. age and size distribution
of trees/stands and tree species dominance) affects the
risk of damage at the regional level and explains the
species differences (risk) in addition to different forest
management options (e.g. the creation of new clearcut areas with more vulnerable edges). Unfortunately,
no comparative information was available on stands
actually damaged by wind in this area.
This study represents a first attempts at studying the
effect of alternative forest management options (clearcutting options) on the risk of wind damage at the
regional level. The results show that the component
models may have considerable potential for providing
a better understanding of how clear-cutting may affect
local wind conditions, for example, and consequently
of the risk of damage in terms of number of risk stands,
the sizes of risk areas, and length of vulnerable edges
at different critical wind speeds, and also of the risk
probabilities. They may be helpful in identifying
specific parts of forested areas subject to particular
levels of wind damage risk (Fig. 4) and in assisting in
the selection of site-specific silvicultural methods (e.g.
patterns of clear-cutting related to determinations of
rotation length, thinning standards, etc.) and their
proper timing for reducing the risk of wind damage.
Thus, the reduction of risk of wind damage depends on
the forest managers making appropriate silvicultural
decisions.
H. Zeng et al. / Forest Ecology and Management 203 (2004) 77–88
Acknowledgements
This study was funded through the SUNARE
Research Programme promoted by the Academy of
Finland (2001–2004), under the project ‘‘Silvicultural
strategies for managing wind and snow-induced risks
in forestry’’ (SilviRisks, Project No. 52724), led by
Academy Prof. Seppo Kellomäki, University of
Joensuu, Faculty of Forestry. It is also a part of the
Finnish-Chinese cooperation project ‘‘Response of
ecosystem processes in high-frigid coniferous forests
to climate change: a comparative study of coniferous
forests in the boreal region and the subalpine region of
western China’’ (Project no. 200013) and is related to
the work carried out under the Finnish Centre of
Excellence Programme (2000–2005) at the Centre of
Excellence for Forest Ecology and Management
(Project No. 64308), co-ordinated by Academy Prof.
Seppo Kellomäki, University of Joensuu, Faculty of
Forestry. Support from the Academy of Finland, the
National Technology Agency (Tekes), and the University of Joensuu are gratefully acknowledged.
The authors would also like to thank Mr. Matti
Lemettinen and Alpo Hassinen of the Mekrijärvi
Research Station, University of Joensuu, for help with
the measurements of wind speed at the site, Mr. Juha
Hiltunen of the Northern Savo Forest Centre for providing
the stand-level data (X-forest-data) and Mr Malcolm
Hicks for revising the English of the manuscript.
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