Forestry
An International Journal of Forest Research
Forestry 2013; 86, 411 – 420, doi:10.1093/forestry/cpt011
Advance Access publication 18 April 2013
Analysis of wind damage caused by multiple tropical storm events
in Japanese Cryptomeria japonica forests
Kana Kamimura1,2*, Satoshi Saito1, Hiroko Kinoshita3, Kenji Kitagawa4, Takanori Uchida5 and Hiromi Mizunaga6
1
Forestry and Forest Products Research Institute, Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
2
Present address: INRA, UR 1263 EPHYSE, F-33140 Villenave d’Ornon, France
3
Kyushu Rinsan Corporation, 815-1 Nakagawa, Yufuincho, Oita 879-5104, Japan
4
The United Graduate School of Agriculture Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
5
Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasugakoen, Kasuga, Fukuoka 816-8580, Japan
6
Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka 422-8529, Japan
*Corresponding author. Telephone: +33 5 57 12 24 54; Fax: +33 5 57 12 24 20; E-mail: [email protected]
Received 8 August 2012
This study analyzed wind damage caused by tropical storms from 1991 to 2007 to Japanese forests mainly
consisting of Cryptomeria japonica. Statistical analyses based on logistic regression and Cox regression
models were conducted in relation to conditions at the forest and stand level. Known damage to forests
managed by Kyusyu Rinsan Corporation (KR forests), located on the Kyushu Island, was analyzed at the
forest level, using tropical storm characteristics such as air pressure, precipitation and periods when the
forests were within the storm zone as predictors. Wind damage was also examined at the stand level (150 analysis points) using Cox regression models, according to stand age, site index, terrain conditions, management
practices and wind velocity indicators (horizontal and vertical velocity vectors). The results indicated that at the
forest level, higher maximum hourly wind speed and longer periods of .15 m s21 of wind speed were significantly correlated to damage occurrence. At the stand level, indicators of upward vertical velocity, thinning
treatments and site index were positively associated with the probability of wind damage. For instance,
stands receiving higher upward vertical velocities and thinning treatment within 2 years were more likely to
have reduced stability against tropical storms. Stands with higher and lower site index than average also
showed lower stability in our analysis.
Introduction
Catastrophic wind causes serious environmental and economic
damage in the commercial coniferous forests in Japan. From
1990 to 2003, 17.6 million m3 of timber was damaged, resulting in a loss of USD 1.5 –3 billion (Suzuki et al., 2009). Wind
damage in Japanese forests is often caused by intense tropical
storms, and the location and degree of damage is strongly
related to the storm path and intensity. In addition, damage
from tropical storms might increase due to climate change
because tropical storms (i.e. tropical cyclones) are expected to
increase in intensity with stronger winds and heavier rainfall
(Oouchi et al., 2006; Yoshimura et al., 2006). Furthermore,
global warming might extend the tropical storm season to November, whereas currently it typically ends in October in the
western North Pacific (Oouchi et al., 2006). This implies that
the critical period when forests are exposed to strong winds
and heavy rainfall would be longer than that under the current
conditions, and therefore adaptive forest management strategies need to be considered.
There have been a number of studies of wind damage in
forests based on observations of actual wind damage. Several
factors have been found to be correlated with wind damage, including tree species, stand age, stand density, stand height,
diameter at breast height (d.b.h.), tree height to d.b.h. ratio
(‘slenderness’, h d.b.h.21), crown diameter and weight, soil-water
logging, soil depth, elevation and aspect (e.g. Shichiri, 1987;
Fukunaga et al., 1993; Valinger et al., 1993; Cameron and
Dunham, 1999; Dunham and Cameron, 2000; Ruel, 2000; Dobbertin, 2002; Evans et al., 2007; Chapman et al., 2008). It has
also been found that interventions in forests such as thinning
can lead to windthrow due to the creation of open spaces in
the canopy, allowing wind penetration within stands (e.g.
Cremer et al., 1982; Albrecht et al., 2012). A reduction in stem
quality caused by breakage has also been found (e.g. Ruel,
2000; Achim et al., 2005; Mitchell, 2012). Understanding site
and management factors that increase windthrow susceptibility
is important for developing forest management plans that
reduce damage risk. However, these previous studies were
based on damage caused by only one or two storm events.
Peterson (2000) found different damage phenomena caused by
two tornadoes for similar forest conditions, and Albrecht et al.,
(2012) found that contributing factors vary from event to
event. Furthermore, it is not clear whether these identified
# Institute of Chartered Foresters, 2013. All rights reserved. For Permissions, please e-mail: [email protected].
411
Forestry
factors apply under all forest conditions, in particular in regions
receiving strong and unstable wind from tropical storms.
This study aims to clarify whether or not there are any specific
indicators of wind damage occurrence using observational and
climate data related to multiple tropical storm events from
1991 to 2007 in forests dominated by Cryptomeria japonica
stands, on Kyushu Island, Japan. In light of the patterns
observed, forest management strategies that may reduce the
future probability of tropical storm damage in the studied
forests are also briefly discussed.
Materials and methods
Analyses were conducted at two spatial levels, forest and stand, based
on available data for the period 1991 –2007 (Figure 1). All available
data were integrated into two databases (i.e. a storm track and a
stand condition database) using basic stand and meteorological data,
geographic information system (GIS), a growth-yield table and a computational fluid dynamics (CFD) model. Statistical analyses were then conducted to determine which indicators were related to damage
occurrence.
Study site
The forests analyzed in this study are located in the town of Yufuin, Oita
Prefecture on the island of Kyushu, Japan (Figure 2). These forests, which
Figure 1 The procedural flow of database creation and statistical analysis.
412
have an approximate area of 4040 ha, are privately owned by the Kyushu
Electric Power Company and managed by its subsidiary company, Kyushu
Rinsan Corporation (KR forests). In this paper, the study forests are identified as the KR forests. The KR forests consist mainly of planted commercial coniferous tree species such as C. japonica and Chamaecyparis
obtuse, and have suffered tropical storm damage on 13 occasions
between 1991 and 2007. Silvicultural management for timber production
has been intensively carried out in these forests. The rotation period for
harvesting is usually 40 –45 years, with thinning carried out every 5
years between ages 15 and 40. The average ratio of stem removal
during each thinning is 18%. Only C. japonica stands were used for analysis in this study because of the high commercial importance of this
species in Kyushu and the fact that it occupies most of the planted
area. The average elevation is 600 m, with an average temperature of
138C, mean hourly wind speed of 1.6 m s21 and annual precipitation
of 1974 mm calculated from meteorological data recorded at the
Yufuin Automated Meteorological Data Acquisition System (AMeDAS;
33815′ 14.00′′ N, 131820′ 49.99′′ E; 453 m a.s.l.; 6.5 m of anemometer
height) between 1980 and 2011. (This information is available from the
Japan Meteorological Agency website at http://www.data.jma.go.jp/
obd/stats/etrn/index.php.) Andosols (also called Andisols) are the main
type of soil in the KR forests, as determined from the soil map provided
by the Japanese Society of Forest Environment (http://ritchi.ac.affrc.go.
jp/Dojouzu.jpg, accessed on 14 January 2012). Andosols are volcanic
soils characterized by high permeability and a black-colored surface
with ,8% organic matter (Bridges et al., 2002) and are common
in forested landscapes in Japan. The organic matter leads to the
Analysis of wind damage caused by multiple tropical storm events
10-m height above the mean stand height using the following equation
(Okuma et al., 1996):
U(z) = U(z1 )∗ (z/z1 )a ,
Figure 2 Position of the study forests (KR forests) and the tropical storm
tracking route in the storm zone (.15 m s21 wind speed predicted) since
1977. According to the tropical storm tracking data, 25.5 storms
occurred every year and 3.3 storms hit the KR forests (i.e. the forests
within the storm zone) every year.
aggregation of soil structure and a high natural water content (Maeda
and Soma, 1986).
where U(z) denoted the wind speed (m s21) at height z (¼ 23 m), U(z1)
the wind speed at anemometer height z1 (¼ 6.5 m) and a the exponent
related to roughness length. The AMeDAS station is located in a slightly
open area with low shrubs; thus, a ¼ 0.24 was used as the roughness
length for calculation (Okuma et al., 1996).
The time periods (h) during which the KR forests were within the
storm zone were calculated using the eye positions and storm zone
data. Precipitation duration and intensity are dependent on tropical
storm characteristics. Some tropical storms have longer periods of precipitation and others do not have continuous rain. Kamimura et al.
(2012) showed that precipitation immediately before the strongest
wind is related to a decrease in root anchorage strength. Yufuin
AMeDAS station also provides the cumulative precipitation data over
10-min, hourly, daily, monthly and annual periods. Using the hourly precipitation data, we summed the precipitation recorded for a 6-h period
prior to the time of WSmax for comparing the effect of precipitation
among tropical storms. The occurrence of damage was determined
based on field observations to estimate the percentage of damaged
stems at stand-level after catastrophic tropical storms by the foresters
of the Kyushu Rinsan Corporation.
Preparation of a stand condition and management
database
Field survey data, information on stand damage and records of management practices (i.e. thinning, pruning, harvesting, planting) were available
from 1991 to 2007 (Table 1). In addition to the field survey, data for
Preparation of a tropical storm track database
The tracking database of tropical storms between 1951 and 2010 is
available on the Japan Meteorological Agency website (http://www.
jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/trackarchives.html;
accessed on 14 January 2012). The database contains all tropical storms
having .17 m s21 wind speed near their center. The tracking data consist
of the storm identification numbers, storm eye positions (i.e. latitude and
longitude), time from tropical storm development to disappearance,
central atmospheric pressure, maximum wind speed and wind directions.
The data also show two radii of the storm zone. One shows the area receiving a wind speed of .15 m s21 and the other a wind speed
.25 m s21 estimated using storm conditions such as air pressure. The
eye of the storm was plotted using a GIS with storm zone digital data
(Figure 2). A steel tower (33810′ 42.96′′ N, 131816′ 10.92′′ E) was used to represent the whole of the KR forests for the forest level analysis. If the
lower storm zone (.15 m s21) for a given tropical storm touched the representative point, the storm was considered to have affected (although
not necessary have caused damage in) the KR forests. From 1991 to
2007 when the wind damage data are available, the KR forests were
part of the storm zone (.15 m s21) for 69 tropical storms and 13
damage events were observed. The tower also records wind speed and
direction, but the wind data were not used for the analysis of forest
damage in this study due to some missing storm data. The position
was also used to calculate relative wind speed velocity for the analysis
points.
Wind climate data for the KR forests were added to the tropical storm
track database. The weather conditions, including 10-min mean wind
speeds, directions, precipitation and temperature, were recorded at the
Yufuin AMeDAS station (10 km in northeast from the representative
point). The maximum hourly wind speed (WSmax) at the station was
extracted from every tropical storm event from 1977 to 2010. The
wind speed was adjusted to a height of 23 m (WSmax23) which was
Table 1 Available stand data from 1991 to 2007 and tropical storms
numbers leading to damage in the KR forests
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Typhoon no. causing damage
Survey1
19
10
7, 13
Management record2
Yes
Yes
Yes
Yes
18
16, 18, 21, 23
14
10
4, 5
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1
Stand survey consisted on tree height and diameter at breast height
(d.b.h.) for all trees in the managed forests. It took 3 years (1994–
1996) to complete the survey.
2
Forestry management records contain silvicultural activities such as harvesting, thinning and pruning with the information of the number of
target trees, its tree height and d.b.h.
413
Forestry
single trees including tree height and d.b.h. were also collected before
undertaking the management actions such as thinning and harvesting.
As several kinds of data were missing for economic and technical
reasons, only reliable stand data consisting of age and stem height,
were selected.
A growth and yield table of C. japonica, used for the KR forests and
originally created by Oita Prefecture, was applied to provide the missing
stand condition information. Based on the stand height at a reference
age of 40 years, this table has data for five site classes by site index
(SI); SI 1 (23 m), 2 (21 m), 3 (18 m), 4 (16 m) and 5 (13 m). As there is
no detailed information on SI and limited information on other stand
characteristics for these study sites, the SI of stands within the KR
forests was determined using available tree height and age data.
Several stands were not included for the analysis when information
was insufficient for SI estimation, or when including trees of multiple
ages in a stand. As a result, 21 subcompartments (984.8 ha) were
selected for this study. Subsequently, analysis points were plotted at
50-m intervals over the study areas in stands with reliable data, giving
a total of 150 analysis points (Figure 3). Although some farmland
divides the study areas, the analysis points were all used for analysis,
because no change in land use occurred over the time of the study.
The elevation (m), slope (8) and aspect (8 directions) at each analysis
point were determined based on a digital elevation model (DEM, 10 m×
10 m resolution) using the ArcGIS Data Collection Premium Series for
Oita Prefecture (ESRI Japan Co., Tokyo, Japan). The relative number of
grid cells (10 m×10 m) contributing the rainwater flow to the sample
point was also calculated to identify areas of high water accumulation.
Each grid cell contained the sum of the number of the neighboring
grid cells providing the water flow to the cell was calculated using the
flow direction based on a surface elevation model (Kennedy, 2006).
For instance, if the aspect of grid cell A is southerly, rain water in cell
A will flow to the adjacent grid cell B. If no other cells have water
flowing into grid cell B, the water accumulation score (i.e. sum of the
number of grid cells) of cell B is 1. The distance from the forest edge
formed from harvested stands (including stands having trees ,5 years
old), logging roads, farm lands and several structures, such as the
steel tower, was also estimated using DEM. The distance from nearby
roads, existing and newly constructed (including logging roads), was
Figure 3 Damaged area from 1991 to 2007 and the 150 analysis points in the subcompartments analyzed in this study.
414
Analysis of wind damage caused by multiple tropical storm events
previously digitized and the distance between the analysis points and the
roads computed.
The airflow condition above the KR forests was estimated using a CFD
model, RIAM-COMAPCT (Uchida and Ohya, 2003). This model calculates
three-dimensional velocity vectors at given positions in the vertical, parallel and orthogonal directions relative to the flow streamlines based on
large eddy simulation and direct simulation using a finite difference
method. In this study, RIAM-COMAPCT was used to calculate the mean
horizontal and vertical velocities at an approximate height of 23 m
using 100 m×100 m grid data for north, northeast, east, southeast,
southwest and west, which were the wind direction observed during
the tropical storms causing damage from 1991 to 2007. The horizontal
and vertical velocities were individually used to represent the wind condition. The estimated velocities for each wind direction were generalized
by dividing by the equivalent velocity at the representative point (a steel
tower) and summed for all directions. The velocities for each direction
were given equal weighting because there was insufficient wind climatologically data to indicate the relative probabilities. Consequently, three
kinds of wind indicators were calculated at each of the 150 analysis
points: horizontal wind (Wh), upward wind (Wup) and downward wind
(Wdown) indicators.
Finally, the analysis points were overlaid with the stand, terrain and
airflow information. In addition, the distances from the roads and open
areas were averaged to create single indices over 17 years for analysis.
Each point also had associated stand conditions such as damage occurrence (DApoints), stand age (AGE) in 1991 (the baseline year), SI, mean distances from roads (Droad) and distance from the nearest open area (i.e.
forest edge; Dopen) for the observational period, occurrence of thinning
(THIN) and/or pruning (PRUN) treatments in the same year or the year
before damage, terrain conditions including elevation (ELEV), slope
(SLOPE) and aspect (ASPECT) and airflow information (Wh, Wup and
Wdown). Finally, years from the baseline year (1991) to the date of
damage or harvesting events were added to the database. If no disturbance events happened at the analysis points, then a value of 17 years
was used (Table 2).
WSmax (WD), path of tropical storm eye position in west of east of the
KR forests (EP), the period of rainfall before WSmax was recorded (Train),
6 h of cumulative precipitation before WSmax was recorded (P6), the
period that the KR forests were within the 25 m s21 area of the storm
zone (T25), the period that the KR forests were within the 15 m s21
area of the storm zone (T15), the period of .10 m s21 wind recorded
(Tws10) and atmospheric pressure at WSmax (PRE). No inter-correlation
was found among the predictor variables. Then, the expected probability
of tropical storm damage at the forest level (Pts) over the 17-year study
period was described by using a logit transformation as follows:
logit(Pts ) = b0 + b1 x1 + b2 x2 + · · · + bn xn ,
where x denotes predictor variables such as tropical storm characteristics, terrain conditions, stand characteristics, and management experience and b denotes coefficients with n covariates used for both
analyses. To fit the logistic regression models in this study, maximum
likelihood estimation was applied for maximizing probability by choosing
the proper variables (Hosmer and Lemeshow, 2000), and the forward
method was used to objectively select those variables.
For the analysis at stand-level, the Cox regression model (Cox, 1972),
which is one of the logistic regression models, was used to find specific
indicators and damage probability instead of applying a general logistic
regression procedure. Tango et al. (1996) pointed out that the results
from logistic regression might be problematic in cases where the information contained censored data (e.g. varying time periods for individual
data points). Our data also included censored data due to damage or
harvesting during the study period. In other words, the overall risk of
damage for forest management might be different depending on how
long the stands remained without being harvested, in addition to the
stand conditions. When t denotes the time to an event from the baseline
year (regarding damage occurrence, harvesting or some stands remaining without harvesting or damage until the end of the observation
period), the hazard function l(t, x) for the occurrence of damage
during the 17-year period is expressed as follows:
l(t, x) = l0 (t) exp(b1 x1 + b2 x2 + · · · + bn xn ),
Statistical analysis
Two logistic regression analyses were conducted for the two scales, and
the dependent variables were tested to avoid strong inter-correlation
within the variables before creating regression models. Any predictor variables having .0.7 of bivariate correlation were removed from further
analyses (e.g. Pallant, 2010). For the tropical storm track data, logistic regression analysis was conducted to identify the significant indicators
leading to wind damage in the KR forests. Damage occurrence caused
by storms in the KR forests (DAall) was the dependent variable and predictor variables included storm attributes: WSmax23, wind direction at
where, l0(t) is the baseline hazard function related to t. More specifically,
the dependent variables were DApoints and the time period of data, and
the predictor variables were terrain conditions, stand characteristics,
management practices and airflow characteristics, specifically: ELEV,
SLOPE, ASPECT, accumulated water flow index (FLOW), AGE, Dopen,
Droad, SI, THIN, PRUN, Wh, Wup and Wdown. Stand height was not used
in this analysis because it was inter-correlated to SI (Table 3).
In addition, positive prediction values were calculated as the number
of analysis points for which damage was correctly predicted divided by
the total number of analysis points with predicted damage. This shows
Table 2 Mean of stand and terrain characteristics of the 150 analysis plots
Characteristics
Stand age (year)
Stand height (m)
Site index (class 1 to 5)
Slope (8)
Elevation (m)
Distance from roads (m)
Distance from open area (m)
All
Undamaged
Damaged
n
Mean
SD
n
Mean
SD
n
Mean
SD
150
150
150
150
150
150
150
38.5
16.4
3.1
16.2
909.5
58.6
205.9
13.9
4.7
0.8
7.9
104.6
37.8
89.1
93
93
93
93
93
93
93
40.2
16.8
3.0
16.3
909.0
63.5
203.3
12.2
3.8
0.6
8.1
103.9
40.0
78.6
57
57
57
57
57
57
57
35.6
15.8
3.5
15.9
910.4
50.8
210.2
16.1
5.8
2.8
7.5
106.7
32.7
104.7
415
Forestry
Table 3 Abbreviations (variables) with the description used in this paper
Group
Abbreviation
Description
Codes/values
Type1
Failure
DAall
0 ¼ No, 1 ¼ Yes
ca
0 ¼ No, 1 ¼ Yes
ca
WSmax23
WD
Damage occurrence at the representative point in the KR forests
from 1991 to 2007
Damage occurrence at the representative point in the KR forests
from 1991 to 2007
Max hourly wind speed (WSmax) at 23m height
Wind direction of the WSmax
co
ca
EP
Train
P6
T25
T15
TWS10
PRE
ELEV
SLOPE
ASPECT
Tropical storm eye position toward the KR forests at the max WS
Periods of raining before the WSmax recorded
Cumulative precipitation for 6-h at the WSmax recorded
Periods of the KR forests within the 25 m s21 storm area
Periods of the KR forests within the 15 m s21 storm area
Periods with more than 10 m s21 wind speed
Atmospheric pressure at the WSmax recorded
Elevation (above sea level)
Slope
Slope aspect
FLOW
AGE
Dopen
Droad
SI
Number of cells of accumulated water flow
Stand age
Mean distance from the open area
Mean distance from the roads
Site index
THIN
Thinning at the same or the previous year of the damage
occurrence
Pruning at the same or the previous year of the damage occurrence
Normalized horizontal wind velocity vector
Normalized upward velocity vector
Normalized downward velocity vector
(m s21)
1 ¼ North, 2 ¼ Northeast,
3 ¼ East, 4 ¼ Southeast,
5 ¼ South, 6 ¼ Southwest,
7 ¼ West, 8 ¼ Northwest
1 ¼ East, 2 ¼ West
Precipitation (mm)
Precipitation (mm)
Time (h)
Time (h)
Time (h)
Pressure (hP)
(m)
Degree (m)
1 ¼ North, 2 ¼ Northeast,
3 ¼ East, 4 ¼ Southeast,
5 ¼ South, 6 ¼ Southwest,
7 ¼ West, 8 ¼ Northwest
Unit less
(year)
(m)
(m)
1 ¼ 23 m, 2 ¼ 21 m,
3 ¼ 18 m, 4 ¼ 16 m,
5 ¼ 13 m of stand
height at 40-year old
0 ¼ No, 1 ¼ Yes
DApoint
Tropical storm
phenomenon
Terrain condition
Stand characteristics
and
managements
Airflow
characteristics
1
PRUN
Wh
Wup
Wdown
1 ¼ No, 0 ¼ Yes
Unit less
Unit less
Unit less
ca
co
co
co
co
co
co
co
co
co
co
co
co
co
ca
ca
ca
co
co
co
Variable types indicates that ‘co’ is continuous and ‘ca’ is categorical variable.
the percentage of cases that the model correctly selects damaged points
(Pallant, 2010).
ArcGIS9.3 (ESRI Co., Redlands, USA) and PASW Statistics 18.0 (IBM,
Inc., New York, USA) were used to prepare all data, and to conduct statistical analysis, respectively.
Results
Forest-level analysis with tropical storm track database
WSmax23, T25 and T15 were positively related to the occurrence of
damage (P , 0.05; Table 4), indicating that, as expected, the
forests which received stronger maximum hourly wind speeds
over a longer period within the storm zone were more likely to
suffer damage. The model correctly predicted damage in
61.5% of the damaged stands (analysis points). Between 1991
416
and 2010, there were 13 tropical storms (red and green dots in
Figure 4) that had a predicted damage occurrence probability
of .50% in the KR forests (i.e. at the representative point) and
eight tropical storms (red dots) which actually produced
damage. Most tropical storms not leading to damage had predicted probabilities of ,20%. Tropical storms having .50% of
the probability of damage occurrence increased in number in
each decade over the study period (two damaging tropical
storms in the 1980s, five in the 1990s and eight in the 2000s).
Stand-level analysis with forest condition
and management database
The Cox regression model determined that SI, THIN and WSup
were associated with higher damage probabilities (Table 5).
Analysis of wind damage caused by multiple tropical storm events
Table 4 Significant indicators (P , 0.05) with regression coefficients as
the result of logistic regression analysis of tropical storm track
Table 5 Indicators (P , 0.05) with regression coefficients as the result of
the Cox regression analysis for 17-year period
Variables
Regression coefficient
WSmax23
T25
T15
0.201
0.153
0.049
SE
Sig.
Variables
Regression coefficient
0.096
0.074
0.022
0.037
0.039
0.027
SI1
SI (2)
SI (3)
SI (4)
SI (5)
THIN
Wup
20.521
21.316
21.935
0.883
0.844
5.981
Constant is 26.436.
SE
Sig.
0.655
0.572
0.827
0.634
0.333
2.694
0.000
0.426
0.022
0.019
0.164
0.011
0.026
1
SI (1) was selected as the baseline category for setting the dummy
variables.
Figure 4 Predicted probability of damage due to tropical storm events
from 1977 to 2010 (n ¼ 114) based on maximum hourly wind speed
and periods within the storm zones. Damage data of the KR forests
were available from 1991 to 2007 (n ¼ 71).
Stands with SIs 2 –4 were more likely to be stable against tropical
storms than other stands, whereas those with SI 5 seemed to be
susceptible. Newly thinned stands were more likely to be associated with a high probability of damage occurrence. In addition,
higher upward wind velocity led to damage in the KR forests.
Figure 5 shows the cumulative hazard function over 17 years.
Stands with SIs of 2, 3 and 4 had a lower probability of
damage, whereas those with SIs of 1 and 5 had an increased
probability of damage with the management.
Discussion
Predictors at the forest level
Tropical storms with the highest maximum hourly wind speeds
and longest periods of strong wind most commonly caused
damage to the KR forests. The frequency of such tropical storm
Figure 5 Cumulative hazard function depending on the yield classes
from 1991 to 2007.
events has increased over the past 30 years (Figure 4), thereby
increasing the probability of damage. This trend is in agreement
with a broader analysis of tropical cyclones from 1975 to 2005,
which showed higher intensity and longer duration compared
with previous periods, partly due to rising sea temperatures
(Emanuel, 2005). On the other hand, some tropical storm
events, such as tropical storms 2003-10, 2006-13 and 2007-5,
did not directly lead to damage in our study area, even though
the predicted probabilities were slightly higher. In comparing
the tropical storms that did or did not lead to damage in the
KR forests, characteristics such as wind direction, precipitation
and season did not vary significantly between these groups. It
is difficult to fully explain why tropical storms with strong wind
do not always lead to damage and why those with moderate
wind sometimes cause damage. Nevertheless, several plausible
reasons can be proposed. Tropical storms 2006-13 and 2007-5
417
Forestry
may not have led to damage, because severe damage to vulnerable stands had already occurred in the previous years (i.e. 2004
and 2005). Tropical storm 1991-12 caused damage at low hourly
mean wind speeds (12 m s21 observed at the Yufuin AMeDAS),
and this may been because this tropical storm had a very long
duration in the KR forests (93 h with wind speed of 15 m s21).
This was the longest period among the tropical storm analyzed
in this study. Previous studies have showed a relationship
between stem swaying and mechanical phenomena of tree. A
longer period of tree swaying during a tropical storm could
lead to a progressive decrease in the anchorage of the soil –
root plate (e.g. Coutts, 1986; O’sullivan and Ritchie, 1993; Ray
and Nicoll, 1998). O’Sullivan and Ritchie (1993) also found that
anchorage was reduced by 25% due to repeated loading of
a stem.
Regarding the other predictors of damage, we previously postulated that the amount of precipitation and its duration were
also strongly linked to the occurrence of damage, as the water
content in the soil would reduce the stability of the root–soil
plate (Kamimura et al., 2012). However, we found no direct relation with precipitation in the KR forests. It is not clear how many
hours of precipitation should be included as an indicator of precipitation intensity, and this requires further investigation. Precipitation may also be a secondary predictor when compared
with the main predictors (i.e. strong winds, long periods of oscillation). Nevertheless, heavy rainfall should be included in future
studies of forest damage because the intensity of heavy rainfall
and/or interaction of wind and rainfall can trigger other destructive events in the forest, particularly landslides (Larsen and
Simon, 1993; Guthrie et al., 2010). Further investigation is
required to clarify the relationship between wind damage and
precipitation and to better understand the mechanics of tropical
storm damage.
Previous observations of damage in the KR forests caused by
the tropical storms 1991-17 and 1991-19 revealed that the
slope aspect (southwest) was a key predictor of damage due
to wind direction (Fukunaga et al., 1993; Kaga, 1995). In addition,
several empirical studies found the slope aspect as an indicator
of wind damage occurrence caused by a single storm event (e.g.
Boose et al., 1994; Kupfer et al., 2008). However, we found no significant relationship between wind direction and the occurrence
of damage, presumably because wind directions change within
and during each storm event. Our results confirm that caution
is necessary when evaluating predictive factors using single
event data sets.
Predictors at the stand level
At the stand level, two significant predictors, Wup and SI, were
found. Wup had a positive regression coefficient (a greater probability of wind damage could be expected with increasing Wup).
It was surprising that horizontal velocity vectors were not
selected as a predictor. However, terrain conditions in the KR
forests are complex, and variable wind velocities (including separation bubbles) are expected such terrain (e.g. Finnigan and
Brunet, 1995). Active turbulence events initiated by surface heterogeneities enhance vertical rather than stream-wise wind velocity (Fesquet et al., 2009). Uchida et al. (2010) simulated
temporal and spatial conditions of tropical storm 2004-18
using RIAM-COMPACT and a meso-scale climate model with
418
Table 6. Averaged characteristics of tree at 1991 and wind velocity
vectors based on the site index
SI n
Age Tree height (m) Elev. (m) Slope (8) Wh
Wup
Wdown
1
2
3
4
5
33
59
38
24
38
0.16
0.12
0.14
0.25
0.09
0.01
0.04
0.03
0.09
0.03
6
18
96
19
11
21.8
23.6
16.7
9.8
10.7
719.0
865.5
896.8
1003.0
1035.4
17.6
13.8
15.5
26.1
8.0
1.88
1.66
2.06
1.82
1.89
detailed surface digital data and meteorological information in
the KR forests. They found that the stands were damaged not
only by the strength of wind enhanced by terrain conditions,
but also by the fluctuations in the wind velocity. Tropical
storms typically generate winds from several directions over a
short term, and the direction at a given location and time is
strongly dependent on the tropical storm’s path.
With regard to SI, our results showed that the stands with SIs
of 2, 3 and 4 were more likely to be stable in a tropical storm
than those with SIs of 1 and 5, although differences of age
and tree height need to be carefully considered (Table 6).
Although it is difficult to explain the effect of SI in this study,
high damage probability in the stand with SI 1 might be due
to fast height growth compared with diameter growth (e.g. Gardiner et al., 1997; Cucchi et al., 2005). We also found that stands
with SI 5 showed the least stability of all stands. Stands with SI 5
were located on higher elevations than other stands. Soil water
content tends to be lower on higher slope positions, and this
likely contributes to lower productivity of C. japonica (Tsurita,
2008; Karisumi, 2010). One might also expect these sites to be
more wind exposed, although little difference in wind velocity
vectors was found between the stands (Table 6).
Thinning treatments in the KR forests over the 17-year period
reduced the stability against tropical storms. This finding is in
good agreement with previous studies (e.g. Cremer et al., 1982;
Gardiner et al., 1997; Albrecht et al., 2012). However, it is not
clear how the number and timing of thinning treatments
should be accounted for in the wind damage analysis. As a thinning indicator, we used whether there was thinning or not in the
same or 1 year before the damage occurred. Kitagawa et al.
(2010) suggested that thinning could improve stand stability,
yet only after a period of time when increased stem diameter
growth was sufficient to provide enhanced support. Although
recent thinnings are more likely to be associated with damage
occurrence, positive effects from thinning need to be considered
in the longer term.
Conclusions
This paper describes the relationship between tropical storms,
stand characteristics and the occurrence of damage in
C. japonica forests –for multiple storm events. Based on our findings, two recommendations can be made for managing the KR
forests to minimize the damage risk from frequent tropical
storms. First, it is better to use short rotation periods and/or
limited thinning for C. japonica in areas where they would be
exposed to strong vertical wind velocities during tropical
Analysis of wind damage caused by multiple tropical storm events
storms. Second, because we found that the stands with SI 1
have lower stability, certain management strategies need to be
considered for timber production on these highly productive
sites. For instance, a shorter rotation and limited thinning
treatments would be preferable level to reduce the probability
of long-term damage, although it may reduce the timber
supply. In addition, when trees are planted on site with highprobability of damage, lower planting densities with no subsequent thinning, or thinning stands only at a younger ages
should be considered.
This study used limited data and its accuracy was not always
certain because these forests were not intended for experimental purposes. In addition, our analysis was based on data over
only 17 years, although there was a high frequency of tropical
storms during this period. More data, including detailed stand
data and historical information for longer periods, would thus
be necessary to fully understand the indicators for, and mechanisms of wind damage from tropical storms.
Acknowledgements
We are grateful to managers and foresters in Kyushu Rinsan Corporation,
who made a great effort of field observations of tropical storm damage
and supported our field experiments. We also would like to thank Dr Barry
Gardiner, Institut National de la Recherche Agronomique (INRA)
Bordeaux, France, and Prof. Stephen Mitchell, University of British
Colombia, Canada, who kindly provided us very helpful comments on
this paper.
Funding
The work was supported by the Japan Society for the Promotion of
Science as a Grant-in-Aid for Scientific Research for ‘Effects on wind
climate in forests caused by complexity of geographical and stand conditions’ (23580202)
References
Achim, A., Ruel, J.C., Gardiner, B.A., Laflamme, G. and Meunier, S. 2005
Modelling the vulnerability of balsam fir forests to wind damage. For.
Ecol. Manag. 204, 35–50.
Albrecht, A., Hanewinkel, M., Bauhus, J. and Kohnle, U. 2012 How does
silviculture affect storm damage in forests of south-western Germany?
Results from empirical modeling based on long-term observations.
Eur. J. For. Res. 131, 229– 247.
Boose, E.R., Foster, D.R. and Fluet, M. 1994 Hurricane impacts to tropical
and temperate forest landscapes. Ecol. Monogr. 64, 369– 400.
Bridges, E.M., Batjets, N.H. and Nachtergaele, F.O. 2002 World Reference
Base for Soil Resources, ATLAS (translated in Japanese), Kokon Shoin,
Tokyo.
Cameron, A.D. and Dunham, R.A. 1999 Strength properties of wind- and
snow-damaged stems of Picea sitchensis and Pinus sylvestris in
comparison with undamaged trees. Can. J. For. Res. 29, 595– 599.
Cremer, K.W., Borough, C.J., McKinnell, F.H. and Carter, P.R. 1982 Effects of
stocking and thinning on wind damage in plantations. New Zeal. J. For.
Sci. 12(2), 244– 268.
Cucchi, V., Meredieu, C., Stokes, A., de Coligny, F., Suarez, J. and Gardiner,
B.A. 2005 Modelling the windthrow risk for simulated forest stands of
Maritime pine (Pinus pinaster Ait.). For. Ecol. Manag. 213, 184–196.
Dobbertin, M. 2002 Influence of stand structure and site factors on wind
damage comparing the storms Vivian and Lothar. For. Snow Landsc. Res.
77, 187–205.
Dunham, R.A. and Cameron, A.D. 2000 Crown, stem and wood properties of
wind-damaged and undamaged Sitka spruce. For. Ecol. Manag. 135, 73–81.
Emanuel, K. 2005 Increasing destructiveness of tropical cyclones over the
past 30 years. Nature 436, 686– 688.
Evans, A.M., Camp, A.E., Tyrrell, M.L. and Riely, C.C. 2007 Biotic and abiotic
influences on wind disturbance in forests of NW Pennsylvania, USA. For.
Ecol. Manag. 245, 44–53.
Fesquet, C., Dupont, S., Drobinski, P., Dubos, T. and Barthlott, C. 2009
Impact of terrain heterogeneity on coherent structure properties:
numerical approach. Bound.-Layer Meteorol. 133, 71– 92.
Finnigan, J.J. and Brunet, Y. 1995 Turbulent airflow in forests on flat and
hilly terrain. In: Wind and Trees–Coutts MP. Grace, J. (ed). Cambridge
University Press, Cambridge, pp. 3 –40.
Fukunaga, H., Matsutani, T., Imada, M. and Kaga, H. 1993 The influence of
topographic factors and stand structure on wind damage occurence (in
Japanese with English summary). Bull. Kyushu Univ. For. 69, 13 –30.
Gardiner, B.A., Stacey, G.R., Belcher, R.E. and Wood, C.J. 1997 Field and
wind tunnel assessments of the implications of respacing and thinning
for tree stability. Forestry 70, 233–252.
Guthrie, R.H., Mitchell, S.J., Lanquaye-Opoku, N. and Evans, S.G. 2010
Extreme weather and landslide initiation in coastal British Colombia.
Q. J. Eng. Geol. Hydroge. 43, 417–428.
Hosmer, D.W. and Lemeshow, S. 2000 Applied Logistic Regression, 2nd
edn. Wiley, New York.
Kaga, H. 1995 The influence of typhoon disaster on forestry management a case of Kyusyurinsan Co., Inc. (in Japanese). Jpn J. For. Plann. 24, 51–57.
Kamimura, K., Kitagawa, K., Saito, S. and Mizunaga, H. 2012 Root
anchorage of hinoki (Chamaecyparis obtuse (Sieb. Et Zucc.) Endl.) under
the combined loading of wind and rapidly supplied water on soil:
analyses based on tree-pulling experiments. Eur. J. For. Res. 131, 219–227.
Karisumi, N. 2010 The Latest Illustrations of Tree Roots (in Japanese).
Siubundo-Shinkosha, Tokyo.
Kennedy, M. 2006 Introducing Geographic Information Systems with
ArcGIS. Wiley, Hoboken.
Kitagawa, K., Kamimura, K., Saito, S., Uchida, T. and Mizunaga, H. 2010
Wind profiles and mechanical resistance of uprooted trees in a
Japanese cypress (Chamaecyparis obtusa) plantation slightly damaged
by Typhoon Melor 0918 at Kamiatago Experimental Forest, Tenryu,
Japan: Validity of mechanistic models for wind damage risks. Jpn J. For.
Environ 52(2), 57– 66.
Kupfer, J.A., Myers, A.T., McLane, S.E. and Melton, G.N. 2008 Patterns of
forest damage in a Southern Mississippi landscape caused by Hurricane
Katrina. Ecosystems 11, 45– 60.
Chapman, E.L. et al. 2008 Hurricane Katrina impacts on forest trees of
Louisiana’s Pearl River basin. For. Ecol. Manag. 256, 883–889.
Larsen, M.C. and Simon, A. 1993 A rainfall intensity-duration threshold for
landslides in a humid-tropical environment, Puerto Rico. Geogr. Ann. 75A,
13– 23.
Coutts, M.P. 1986 Components of tree stability in sitka spruce on peaty
gley soil. Forestry 59, 173–197.
Maeda, T. and Soma, K. 1986 Physical properties. In Ando Soils in Japan.
Wada, K. (ed). Kyusyu University Press, Fukuoka, pp. 99 –111.
Cox, D.R. 1972 Regression models and life-tables. J. Roy. Stat. Soc. B 34(2),
187–220.
Mitchell, S.J. 2013 Wind as a natural disturbance agent in forests: a
synthesis. Forestry, 84, 147–157.
419
Forestry
Okuma, T., Kanda, J. and Tamura, Y. 1996 Wind-Resistance Design of
Buildings (in Japanese). Kajima Institute Publishing Co., Ltd, Tokyo.
Oouchi, K., Yoshimura, J., Yoshimura, H., Mizuta, R., Kusunoki, S. and Noda,
A. 2006 Tropical cyclone climatology in a global-warming climate as
simulated in a 20 km-mesh global atmospheric model: frequency and
wind intensity analyses. J. Meteorol. Soc. Jpn 84(2), 259– 276.
Suzuki, S., Yoshitake, T. and Goto, Y. 2009 Values for forest damage
caused by strong wind, heavy rain, snow and forest fire based on
statistics compiled in Japan from fiscal year 1954 to 2003. Bull. FFPRI
8(1), 71– 100.
Tango, T., Yamaoka, K. and Takagi, H. 1996 Logistic Regression Analysis—
Statistical Analysis Using SAS (in Japanese). Asakura Publishing Co, Tokyo.
O’sullivan, M.F. and Ritchie, R.M. 1993 Tree stability in relation to cyclic
loading. Forestry 66(1), 69–82.
Tsurita, T. 2008 Difference of moisture conditions of forest soils in
different slope positions (in Japanese). Kyushu J. For. Res. 61, 137–139.
Pallant, J. 2010 SPSS Survival Manual, 4th edn. MacGraw-Hill, Berkshire.
Uchida, T. and Ohya, Y. 2003 Large-eddy simulation of turbulent airflow
over complex terrain. J. Wind Eng. Ind. Aerod. 91, 219– 229.
Peterson, C.J. 2000 Damage and recovery of tree species after two
different tornados in the same old growth forest: a comparison of
infrequent wind disturbances. For. Ecol. Manag. 135, 237– 252.
Ray, D. and Nicoll, B.C. 1998 The effect of soil water-table depth on root-plate
development and stability of Sitka Spruce. Forestry 71(2), 169–182.
Uchida, T., Mizunaga, H., Saito, S., Kamimura, K., Kinoshita, H. and
Maruyama, T. 2010 Wind damage simulation in forest and forestry field
by CFD technique (in Japanese with English summary). Jpn J. For.
Environ. 52(2), 67 –77.
Ruel, J.C. 2000 Factors influencing windthrow in balsam fir forests: from
landscape studies to individual tree studies. For. Ecol. Manag. 135,
169–178.
Valinger, E., Lundqvist, L. and Bondesson, L. 1993 Assessing the risk of
snow and wind damage from tree physical characteristics. Forestry
66(3), 249– 260.
Shichiri, S. 1987 On the analysis of wind-damage to forests by typhoon
No. 12 in 1987 (In Japanese with English summary). Bull. Nagasaki
Agri. For. Experiment. Sta., For. Div. 20, 1 –15.
Yoshimura, J., Sugi, M. and Noda, A. 2006 Influence of greenhouse
warming on tropical cyclone frequency. J. Meteorol. Soc. Jpn 84(2),
405–428.
420