Forests disturbance by tropical cyclones is mostly
documented by field studies of exceptionally strong cyclones and
satellite-based approaches attributing decreases in leaf area. By starting
their analysis from the observed damage, these studies are biased and may,
therefore, limit our understanding of the impact of cyclones in general.
This study overcomes such biases by jointly analyzing the cyclone tracks,
climate reanalysis, and changes in satellite-based leaf area following the
passage of 140 ± 41 cyclones. Sixty days following their passage,
18 ± 8 % of the cyclones resulted in a decrease and 48 ± 18 % showed no change in leaf area compared to nearby forest outside the storm
track. For a surprising 34 ± 7 % of the cyclones, an increase in leaf
area was observed. Cyclones resulting in higher leaf area in their affected
compared to their reference area coincided with an atmospheric pressure
dipole steering the cyclone towards a region experiencing a dry spell caused
by the same dipole. When the dipole was present, the destructive power of
cyclones was offset by their abundant precipitation enabling forest canopies
in the affected area to recover faster from the dry spell than canopies in
the reference area. This study documents previously undocumented widespread antagonist interactions on forest leaf area between tropical cyclones and
droughts.
Introduction
Each year almost 30 cyclones, about one-third of the world's tropical
cyclones, develop over the Pacific Ocean north of the Equator
(Landsea, 2000), where a subtropical ridge steers them mainly
west and northwest towards East Asia, where 90 % make landfall. The
majority of the tropical cyclones in the northwestern Pacific basin develop
between June and November (Bushnell et al., 2018) and more than
half acquire typhoon strength (WMO, 2017). Although natural
ecosystems, such as forests, have adapted to recurring high wind speeds
(Eloy
et al., 2017; Louf et al., 2018; Curran et al., 2008), stem breakage is
almost unavoidable at wind speeds above 40 m s-1
(Virot et al., 2016) but has been widely reported
at wind speeds well below this threshold together with other damage
(Tang
et al., 2003; Chiu et al., 2018; Chang et al., 2020).
By jointly analyzing cyclone tracks (The Joint Typhoon Warning
Center (JTWC), 2019), climate reanalysis data (ERA5-Land;
ECMWF, 2019), satellite-based proxies of soil dryness
(SPEIbase v2.6; Beguería et al.,
2014), land cover (ESA CCI, the European Space Agency's Climate Change Initiative; ESA, 2017), and leaf area
(Martins et al., 2020), we estimated (a) the impact of tropical cyclones on leaf area, and (b) the main drivers
of this impact. Previous studies attributed decreases in leaf area or
related satellite-based indices to different disturbance agents (Ozdogan et
al., 2014; Honkavaara et al., 2013; Forzieri et al., 2020), including
cyclones
(Takao et
al., 2014). A damage-based approach is designed to identify only decreases
in leaf area, thus failing to identify events in which tropical cyclones
left the leaf area unaltered or even increased it. In contrast, this study
starts the analysis from the actual storm tracks, which allows for an
unbiased assessment of the impact of cyclones on forests
(Blanc and Strobl, 2016).
Impact on leaf area
The land area affected was identified for each of the 580 tropical cyclones
that occurred in the study region between 1999 and 2018, considering that
cyclone-driven damage could only occur within the storm track at locations
that experienced high wind speeds or high precipitation. Pixels within the
storm track defined as 2, 3, or 4 times the diameter of the cyclone
for which threshold values for wind or precipitation were exceeded were
classified as affected areas (Fig. A1), the remaining pixels in the
track served as a cyclone-specific reference area. The uncertainty derived
from defining the width of the storm track
(Willoughby and Rahn, 2004) and
determining which wind speeds and amounts of precipitation could result in
damage are accounted for by an ensemble of nine related definitions with
different threshold values (Table A1). In this study uncertainties
represent the standard deviation across the nine definitions for the
affected area and are shown in Figs. 1, A1, and A3.
The impact of a tropical cyclone on leaf area was calculated based on the
adjusted Hedge's effect size by comparing the change in leaf area before and
after the cyclone in the affected area with the change before and after the
cyclone in the reference area for each individual cyclone (Eq. A1).
Using a reference area that is specific to each cyclone means that seasonal
dynamics related to leaf phenology and seasonal monsoons are accounted for
in the effect size, which is a unitless description of the mean change in
leaf area normalized by its standard deviation (Eq. A1). Hence, a
positive effect size denotes a faster increase (or a slower decrease) in
leaf area in the affected area compared to the reference area following the
passage of a tropical cyclone.
A total of 316 ± 22 tropical cyclones or 54 ± 4 % of the storm
events under study could not be further analyzed (Table A1) because
leaf area index observations were missing from either the affected area, the
reference area, or both, thus violating the requirements for calculating the
effect size (Eq. A1). Of the remaining 264 ± 22 tropical
cyclones, only 140 ± 41 passed the additional quality check necessary
to be retained for further analysis in this study, i.e., the difference in
the leaf area between the reference and affected area prior to the passage
of a storm should be less than 10 % of the leaf area in the reference
area. In other words, prior to the storm, the leaf area in the reference
area had to be similar to the leaf area in what will become the affected
area once the storm passed.
Changes in standardized precipitation and
evapotranspiration index following the precipitation brought by tropical
cyclones. (a) Response in standardized precipitation and
evapotranspiration index following the passage of a tropical cycle that
resulted in a decrease (orange), no change (gray), or increase (green) in
leaf area. Increasing leaf area was observed in forests that experienced a
dry spell prior to the passage of a cyclone that brought sufficient
precipitation to end the dry spell. (b–d) Response in standardized
precipitation and evapotranspiration index following the passage of a
tropical cycle that resulted in no change (gray; b) an increase
(green; c), and a decrease (orange; d) in leaf area for
the three cyclone groups (Table 1). Similar responses hint at
similar mechanisms underlying the responses in leaf area irrespective of the
cyclone group. The dashed line indicates the pathway moving from the
condition prior to the condition after the passage of the cyclones.
Spatial distribution of cyclone frequency, frequency of
dry spells with a standardized precipitation and evaporation index below -1,
and their correlation. (a) Return frequency (yr-1) of tropical
cyclones between 1999 and 2018 following a combined wind–precipitation
definition considering three diameters to define the width of the storm
track (definition 3a in Table A1). (b) Return frequency
(yr-1) of dry spells between 1999 and 2018 following the same
definition. (c) Smoothed density plot of the relationship (r∼ 0.11) between the return frequency of cyclones and dry
spells. High-density regions are shown in warm colors compared to the cold
colors used to indicate low-density regions. The density plot is based on
all nine definitions for affected areas (Table A1).
Of the 580 cyclones, 31 % were less than class
I, 14 % were classified as class I, 11 % as class II, 10 % as class
III, 21 % as class IV, and 13 % as class V. The distribution of the
intensity classes of the sample of 140 ± 41 cyclones that could be
further analyzed was similar to the census of the 580 cyclones
(Fig. A3). Despite the loss of around 75 % of the events, the
sample analyzed in this study was unbiased in terms of cyclone intensity
classes (Fig. A3).
Tropical cyclones have been widely observed to defoliate and disturb forests
(Wang
et al., 2013; Uriarte et al., 2019; Chambers et al., 2007; Douglas, 1999;
Lin et al., 2011). Nevertheless, in this study, only 18 ± 8 % of the
observed cyclones resulted in a detectable reduction in leaf area 60 d
after their passage as a direct effect of limb breaking, uprooting, stem
breakage, and landslides following high wind speeds and heavy precipitation.
For 48 ± 18 % of the cyclones, the change in leaf area 60 d after
a cyclone passed was so small that it could not be distinguished from the
threshold representing no change. Ecological theory predicts forest dwarfing
in regions with high cyclone frequencies compared to the longevity of a
tree, directly through gradual removal of taller trees over many generations
(Lin et al., 2020;
McDowell et al., 2020) and indirectly through the loss of nutrients
(Tang et al., 2003; Lin et
al., 2011). Where forest dwarfing has occurred, it might be hard to observe
the short-term effects of an individual tropical cyclone on forest structure
and function (Mabry et al., 1998).
Pressure fields (Pa) and changes therein in the month of
the passage of a tropical cyclone for cyclones that had a neutral, positive,
or negative impact on the leaf area (m2 m-2) of forests. Effect
sizes are based on the definition that uses 3 times the cyclone diameter
and wind speed to identify the affected and reference areas (definition 3a
in Table A1). (a) Mean atmospheric pressure and leaf area
prior to the passage of a tropical cyclone that had a neutral impact on
forest leaf area. (b) Changes in mean atmospheric pressure and leaf
area between cyclones with a neutral and positive effect on leaf area.
(c) Changes in mean atmospheric pressure and leaf area between
cyclones with a neutral and negative effect on leaf area.
Interactions between dry spells and cyclones
For a surprising 34 ± 7 % of the cyclones an increase or, given the
way the effect size was calculated, a reduced decrease in leaf area was
observed, leading to the question of which conditions could explain such an
increase (or reduced decrease). Following Liebig's law of the minimum
(Chapin et al., 2011), the observed increase
(or reduced decrease) in leaf area implies that about one-third of the
cyclones alleviated one or more growth factors that were limiting leaf area
prior to the passage of the cyclones. We hypothesize that a dry spell could
be the growth limiting factor prior to the cyclone, whereas the
precipitation brought by the cyclone could enhance plant growth through
mitigating soil dryness.
To test this hypothesis, the standardized precipitation and
evapotranspiration index prior to 60 d following the passage of the
cyclone, the accumulated precipitation prior to the cyclone, and the
accumulated precipitation brought by the cyclone were determined for each of
the 140 ± 41 tropical cyclones that passed the quality checks. An
increase (or reduced decrease) in leaf area was observed for cyclones that
made landfall during a dry spell and brought sufficient precipitation to
increase the standardized precipitation and evapotranspiration index
(Fig. 1a) supporting our hypothesis. The hypothesis was further
supported by no observed change in leaf area for cyclones making landfall when plant
water demand was satisfied by soil moisture availability shown by the
standardized precipitation and evapotranspiration index approaching zero
(Fig. 1a). Furthermore, decreases in leaf area 60 d following
the cyclone were observed for cyclones making landfall when there was an
excess in plant-available water (Fig. 1d).
Median and standard deviation for five cyclone
characteristics and six surface characteristics mainly prior to the passage
of the 140 ± 41 tropical cyclones that passed the quality checks.
Cyclone groups 1 to 3 were the outcome of a decision tree (Fig. A4)
that classified the four main factors of factorial analysis of the land
surface characteristics, cyclone characteristics, and effect sizes to
identify collinearity (Table A2). The column labeled with ANOVA shows the p value of an ANOVA test to test for significant differences
between cyclone groups.
CharacteristicCyclone group 1Cyclone group 2Cyclone group 3ANOVATropical cyclonecharacteristicsLatitude of landfall (degrees)33.6 ± 4.223.3 ± 6.922.9 ± 8.7<0.05Affected area during passageover land (km2)65 008 ± 19 0105944 ± 532415 960 ± 11 598<0.05Accumulated rainfall duringpassage over land (mm)41.7 ± 33.9100.8 ± 22.923.0 ± 31.2<0.05Maximum wind speed duringpassage over land (m s-1)12.5 ± 2.07.2 ± 2.812.1 ± 2.7<0.05Intensity of the tropicalcyclone, gusts (m s-1)29.2 ± 9.920.8 ± 9.525.0 ± 10.3<0.05Surface conditionsprior to the cyclonePacific Japan index (Pa Pa-1)-0.24 ± 0.09-0.15 ± 0.11-0.05 ± 0.12<0.05Prior accumulated rainfall(30 d prior to landfall (mm))30.1 ± 23.354.7 ± 38.016.5 ± 17.2<0.05Month of landfall8.0 ± 1.18.0 ± 2.08.0 ± 2.70.42Prior leaf area index (30 d priorto landfall (m2 m-2))4.50 ± 0.94.02 ± 0.823.56 ± 0.96<0.05Drought state (SPEI, 30 d priorto landfall (mm mm-1))-0.12 ± 0.600.06 ± 0.71-0.13 ± 0.64<0.05Delta SPEI (mm mm-1)0.13 ± 0.530.32 ± 0.620.04 ± 0.40<0.05Effect on forestleaf areaPositive effect size (%)624819Negative effect size (%)10824Neutral effect size (%)284457Share in tropical cyclones (%) 231859
Where a dry spell prior to the cyclone in combination with the precipitation
brought by the cyclone provides a mechanistic explanation for increased
plant growth following the passage of a tropical cyclone, the abundance of
such events (i.e., 34 ± 7%) suggests a non-random relationship
between the location and timing of dry spells and cyclones (Fig. 2c). For the midlatitudes, dry summers do indeed see an increase in the
number of tropical cyclones making landfall, which often ends the summer
drought (Yoo et al., 2015). In South Korea, for
example, at least 43 % but possibly as much as 90 % of the summer
droughts in coastal regions were abruptly ended by a tropical cyclone
(Yoo et al., 2015). The co-occurrence of dry
spells and tropical cyclones has been linked to a meridional dipole system
in the midlatitude regions of East Asia with a high-pressure system in the
region of 40–50∘ N and 150–160∘ E, where it causes the dry spell and the
low-pressure system in the region of 20–30∘ N and 120–150∘ E.
To confirm the relationship between dry spells and the occurrence of
cyclones, the meta-data for each of the 140 ± 41 tropical cyclones were extended, resulting in the first group of meta-data of six characteristics
describing the land surface mainly before the passage of a cyclone and a
second group containing five characteristics of the cyclone itself.
Following combined factorial analysis to identify collinearity between the
land surface characteristics, cyclone characteristics, and effect sizes
(Table A2), the four main factors which explained 58 % of the
variance were used in a decision tree (Fig. A4) to create three
cyclone groups (Table 1).
Sixty-two percent of the cyclones which were generated when the meridional
dipole was present (indicated by a negative Pacific Japan index
(Nitta, 1987)), making landfall at midlatitudes during a dry
spell and bringing sufficient precipitation to rewet the soil and end the
dry episode, increased the leaf area (or reduced the decrease) in the
affected compared to the reference area (cyclone group 1; Table 1).
When the dipole is in place, tropical cyclones generated from the monsoon
trough over the western Pacific Ocean are steered through the trough in between
the high- and low-pressure systems towards and then along the coast of East
Asia (Choi et al., 2010). While
traveling along the edges of the high-pressure system, the tropical cyclone
may disturb the circulation, resulting in an unfavorable environment to
sustain the dipole (Choi et al.,
2011; Kubota et al., 2016) and bringing precipitation to the dry region that
was under the high-pressure system.
Group 2 cyclones made landfall at low latitudes when the meridional dipole
was in place and brought abundant precipitation which increased soil wetness
(Table 1). Given that under the meridional dipole, the dry spell
occurs under the high-pressure system typically located between 40 and 50∘ N
but that many of the group 2 cyclones made landfall at lower latitudes
(i.e., 23.3 ± 6.9∘ N), chances to end a dry spell were lower which was
reflected in the almost equal chance to increase the leaf area (48 %) or
had an effect that could not be detected by our method (44 %;
Table 1). Nevertheless, the mechanistic relationship between soil
dryness, precipitation, and change in leaf area was confirmed also for this
group (Fig. 1b–d).
Almost 60 % of the tropical cyclones studied were classified as group 3
cyclones, making them the most abundant type of cyclone in the study region.
Although 57 % of the cyclones in this group resulted in no effect on leaf
area (Table 1), this group contained about one-third of the
cyclones resulting in a positive effect on leaf area (Table 1), which occurred when the soil was dry and the cyclone brought sufficient
precipitation to rewet the soil (Fig. 1b–d).
Analyzing the atmospheric pressure separately for cyclones that resulted in
no change, an increase, or a decrease in leaf area (Fig. 3) showed
that tropical cyclones that were followed by an increase (or reduced
decrease) in leaf area coincided with a meridional dipole (Fig. 3b). Moreover, the genesis of tropical cyclones that were followed by a
decrease in leaf area occurred under very different atmospheric conditions
compared to cyclones followed by an increasing leaf area (Fig. 3c).
A relationship between the atmospheric system causing dry spells, tropical
cyclones, and their subsequent impact on leaf area suggests that whether
more drought damage is to be expected in the future will not only depend on
an increase in drought frequency and intensity but will in part be
determined by the weather system that is causing the drought. Although the
co-occurrence of droughts and cyclones has previously been demonstrated
(Choi et al., 2011; Kubota et al.,
2016), we believe this study to be the first to document its large-scale
antagonist effect on forest leaf area.
Implications for disturbance ecology
By studying a representative sample of tropical cyclones in terms of storm
intensity, we showed that almost half of the tropical cyclones, i.e.,
48 ± 18 %, caused little to no damage to forest leaf area, suggesting
that forest dwarfing is a general structural adaption in the study region.
Moreover, a third, i.e., 34 ± 7 % of the cyclones in East Asia
resulted in an increase (or reduced decrease) in forest growth because
these storms relieved water stress within their track or even ended dry
spells. Remarkably, precipitation brought by a cyclone appeared as a more
powerful predictor than cyclone intensity when it comes to the vegetation
response (Table 1; Fig. A3). The observed frequency of
positive vegetation responses to cyclones suggests that the present-day
vision of cyclones as agents of destruction
(Altman
et al., 2018; Negrón-Juárez et al., 2010, 2014) should be refined
toward a recognition that, depending on the environmental conditions prior
to the storm and the atmospheric conditions leading to the genesis of the
tropical cyclone, cyclones frequently facilitate the recovery of forest leaf
area and as such dampen the effects of dry spells.
Materials and methodsCyclone track and track diameter
Since 1945, tropical cyclones in the western North Pacific Ocean have been
tracked and their intensity recorded by the Joint Typhoon Warning Center
(JTWC). The track data shared by the Joint Typhoon Warning Center consist of
quality-controlled 6-hourly geolocation observations of the center of the
storm with the diameter of the storm being a proxy for its intensity
(JTWC, 2019). For the period under consideration, from
1999 to 2018, the geolocations and diameters are the output of the Dvorak
model (Dvorak, 1984; Dvorak et al.,
1990) derived from visible and infrared satellite imagery. Storm diameters
are available starting from January 2003. Prior to this date a generic
diameter of 100 km (Lin et al., 2020) is used in
this study. Linear interpolation of the 6-hourly track data resulted in
hourly track data to fill in any gaps in the mapping of the cyclone track.
In this study, we focus on East Asia which, given the absence of natural
boundaries, is defined as the land contained within the northwestern
Pacific basin that, according to the Joint Typhoon Warning Center stretches
from 0 to 60∘ N and 100 to 150∘ E. The Joint Typhoon
Warning Center (2019) compiled track and intensity data for 580 tropical cyclones
between 1999 and 2018 in the northwestern Pacific basin. A shorter time
series (1999 to 2018) than the entire length of time available (1945 to
2018) was analyzed due to the more limited availability of the leaf area
index data, which had to be jointly analyzed with the track and intensity
data to quantify the impact of cyclones on natural ecosystems.
Criteria for distinguishing between the affected and
reference areas following the passage of an individual cyclone and the
number of events according to each specific definition. Group 1 groups
definitions are based on wind speed, group 2 definitions are based on
precipitation, and group 3 definitions are based on both wind speed and
precipitation. All three definitions include an estimate of the storm path
based on a multiple of the reported storm diameter. Column A denotes the
number of events for which data were lacking so that the effect size could
not be calculated; column B denotes the number of events for which all
required data were available; column C denotes the subset of B for which the
data passed the quality control; ES refers to effect size. A total of 580
unique tropical cyclones were considered in this study.
GroupAffected areaReference areaABCNegativeNeutralPositiveeffecteffecteffectsizesizesize1a>8 m s-1 and <2 diameters<8 m s-1 and <2 diameters3422381052251321b>10 m s-1 and <3 diameters<10 m s-1 and <3 diameters3052751823897471c>12 m s-1 and <4 diameters<12 m s-1 and <4 diameters2912891833192602a>60 mm and <2 diameters<60 mm and <2 diameters3382421151951452b>80 mm and <3 diameters<80 mm and <3 diameters3152651291159592c>100 mm and <4 diameters<100 mm and <4 diameters31126986932453a(>8 m s-1 or >60 mm) and <2 diameters(<8 m s-1 or <60 mm) and <2 diameters3522281032545333b(>10 m s-1 or >80 mm) and <3 diameters(<10 m s-1 or <80 mm) and <3 diameters3042761883895553c(>12 m s-1 or >100 mm) and <4 diameters(<12 m s-1 or <100 mm) and <4 diameters288292171358353Mean316264140256748SD222241112510Mean (%)544624184834SD (%)4478187Area affected by individual cyclones
The land area thought to be affected by a specific cyclone as well as the
reference area for each of the 580 cyclones that occurred in the study area
between 1999 and 2018 were identified based on nine different but related
definitions (Table A1). Each definition comprises a combination of
at least two out of three criteria, e.g., the diameter of the cyclone, the
maximum wind speed at each location during the passage of the cyclone, and
accumulated precipitation at each location during the passage of the
cyclone. Each forested pixel within each individual storm track was
classified as either an affected area or a reference area based on these
nine definitions. Differences in the results coming from differences in the
definitions were used throughout the analysis to estimate semantic
uncertainties. Uncertainties related to the estimated diameter of the
cyclone, wind speed, and precipitation data were not accounted for in the
calculation of the affected and reference areas because they were thought to
be smaller than the uncertainty coming from differences in the definitions
themselves.
The underlying assumption behind the definitions is that forests can only be
affected by a specific cyclone if they are located along its storm track.
The minimum width of each storm track is the diameter of the cyclone as
reported by the Joint Typhoon Warning Center. Following the observation that
over the ocean, the actual wind speed exceeds the critical wind speed for
stem breakage or uprooting (i.e., 17 m s-1,
Chen et al., 2018) over a
distance of at least 3 times the diameter of the cyclone
(Willoughby and Rahn, 2004), the
minimum width of a storm track in which cyclone-related forest damage could
occur is defined as 3 times the diameter recorded by the Joint Typhoon
Warning Center although wind speeds drop dramatically when cyclones make
landfall (Kaplan and Demaria,
2001). The minimum width of a storm track over land should, therefore, be
reduced compared to the observations over the ocean. This study used three
different widths to define a storm track, i.e., 2, 3, or 4 times
the recorded diameter (Table A1).
Spatial and temporal patterns of potential forest damage
by tropical cyclones in East Asia. (a) Return frequency (yr-1)
of tropical cyclones between 1999 and 2018 following a combined
wind–precipitation definition considering three diameters to define the
width of the storm track (definition 3a in Table A1). Since 1999,
2 240 000 ± 690 000 km2 of forest in the study region experienced
conditions that may have resulted in cyclone-driven damage, at least once
every decade. No less than 540 000 ± 260 000 km2, including 70 % of the tropical forest in the region, experienced potentially damaging
conditions at least once per year and are thus classified as being under
chronic wind stress. Forests unlikely to have experienced a tropical cyclone
between 1999 and 2018 are shaded in gray. For land locations shown in white,
the forest is not the dominant land cover. The dot–dashed lines show the
cyclone tracks between 1999 and 2018. The black lines indicate the events
that passed the quality control criteria used in this study. (b)
Latitudinal gradients of potentially damaged forest area (km2 yr-1) between 1999 to 2018 for all nine definitions of affected area.
Damage potential is the outcome of an interplay between cyclone frequency,
cyclone intensity, and the presence of forests. The different definitions of
affected area (Table A1) consistently show a high potential for
forest damage over island and coastal regions located between 10 and 35∘ N. This high potential is largely driven by the frequency of
tropical cyclones (Fig. A2), i.e., two or more cyclones making
landfall per year. Depending on how the affected area is defined, there is a
second region located between 40 and 50∘ N with a high potential
for storm damage. In this region, the potential damage is the outcome of the
high forest cover resulting in a strong dependency on the assumed width of
the storm track (Fig. A2). (c) Temporal dynamics of the
total potentially damaged forest area (km2 yr-1) for all nine
definitions of affected area. Irrespective of the definition of the affected
area, the coefficient of variation in the between-year variation in
potentially damaged areas ranged from 15 % to 20 %. Excluding the four most
powerful typhoons that occurred in the region since 1999 changed the average
coefficient of variation from 17 % to 16 %. This suggests that the most
powerful typhoons make only a small contribution to the total annually
potentially affected area in the region. Likewise, a recent literature
review reported that 66 % of the research papers in this area have
examined the effects of only about 6 % of the most powerful cyclones
(Lin et al., 2020). The relatively small
contribution of those events to the potential damage area suggests that in
regions with frequent tropical storms, disturbance ecology would benefit
from broadening its scope by examining the effects and recovery of a
representative sample of tropical cyclones rather than focusing on the most
devastating events.
Being located within the track of a specific cyclone is essential but not
sufficient for damage to occur. Within a storm track, only forested pixels
that experienced high wind speeds or high precipitation were counted as in
the potentially affected area. Forest pixels that were located within the
storm track but did not experience high wind speeds or high precipitation
were counted as in the reference area. Note that to better account for the
uncertainties arising from this approach, the threshold values for wind
speed and precipitation were increased as the track diameter increased
(Table A1). For a narrow storm track, it is reasonable to assume
that there would be damage shown in all pixels except those where wind speed
or precipitation did not exceed a relatively low threshold value. For wide
storm tracks the opposite applies; it is reasonable to assume that a few of
the pixels would show damage except where wind speed or precipitation
exceeded relatively high threshold values.
Wind speed and precipitation data were extracted from the ERA5-Land
reanalysis data for land (ECMWF, 2019). The ERA5-Land
reanalysis dataset has a spatial resolution of 9 km × 9 km and a time step
of 1 h. It is the product of a data assimilation study conducted with the
H-TESSEL scheme by ERA5 IFS Cy45r1 and nudged by climatological observations
(ECMWF, 2018). The Cy45r1 reanalysis dataset shows
statistically neutral results for the position error of individual cyclones
(ECMWF Confluence Wiki: Implementation of IFS cycle 45r1, https://confluence.ecmwf.int/display/FCST/, last access: 25 June 2019). The
spatial representation of the reanalysis data is reported to compare
favorably with observational data (Chen et al., 2021)
outside the domain of this study. No reports on similar tests for the
current study domain, i.e., East Asia, were found. Furthermore, land cover
maps released through the European Space Agency's Climate Change Initiative
(ESA, 2017) were used to restrict the analysis to forests. The
Climate Change Initiative maps integrate observations from several
space-borne sensors, including MERIS, SPOT-VGT, AVHRR, and PROBA-V, into a
continuous map with a 300 m resolution from 1994 onwards.
Wind speed and precipitation data were spatially disaggregated and
temporally aggregated to match the spatial and temporal resolution of the
leaf area index product (see below). Maximum wind speed and accumulative
precipitation were aggregated over time steps to match the 10 d resolution
of the leaf area index product. We preserved the temporal resolution of the
land cover map but aggregated its spatial resolution from 300 m to 1 km to
match the resolution of the leaf area index product. During aggregation, the
majority of land cover at the 300 m resolution was assigned to the 1 km
pixel resolution.
Loadings of each characteristic on four principal axes
and collinearity between variables within the same group. Given the
exploratory nature of this analysis, a factor loading of 0.6 was used as a
cutoff, and those values exceeding that level are highlighted in boldface.
CharacteristicPC1PC2PC3PC4Tropical cyclonecharacteristicsLatitude of landfall (degrees)-0.620.180.480.00Affected area during passageover land (km2)0.820.020.150.11Accumulated rainfall duringpassage over land (mm)-0.150.860.140.07Maximum wind speed duringpassage over land (m s-1)-0.320.240.050.22Intensity of the tropicalcyclone, gusts (m s-1)-0.340.60-0.450.08Surface conditionsprior to the cyclonePacific Japan index (Pa Pa-1)0.010.11-0.54-0.03Prior accumulated rainfall(30 d prior to landfall (mm))0.730.060.21-0.10Month of landfall0.290.110.76-0.02Prior leaf area index (30 d priorto landfall (m2 m-2))-0.30-0.750.130.06Drought state (SPEI, 30 d priorto landfall (mm mm-1))0.22-0.010.02-0.81Delta SPEI (mm mm-1)0.280.070.050.77Effect size0.410.370.120.16The proportion of totalvariance19 %16 %12 %11 %Impact on leaf area of an individual cyclone
Version 2 of European Space Agency's Climate Change Initiative product was
used to calculate leaf area in this study. The product has a 1 km spatial
resolution and a 10 d temporal resolution and is available from 1999
onwards. The default leaf area index product is distributed as a composite
image using at least six valid observations on a pixel within a 30 d
moving window (Verger et
al., 2014). The composite image is drawn from satellite-based observations
of the surface reflectance in the red, near-infrared, and shortwave infrared
from SPOT-VGT (from 1999 to May 2014) and PROBA-V (from June 2014 to
present). Gaps in missing observations are filled by the application of a
relationship between local weather and leaf area index dynamics. Gap filling
resulted in errors on the leaf area index estimates of less than 0.18
(Martins et al., 2017). The spatiotemporal
resolution of the leaf area index products was the coarsest of all data
products used and therefore determined the spatiotemporal resolution of the
analysis as a whole. Moreover, the availability of the leaf area index
product determined the starting date for the study.
The impact of cyclones on leaf area was calculated by comparing the change
in leaf area before and after the cyclone in the affected area with changes
before and after the cyclone in the reference area for each individual
cyclone. In this approach, the reference area serves as the control for the
affected area, given that the reference area and the affected area may have
a different size, the adjusted Hedge's effect size
(Rustad et al.,
2001) can be used to calculate the effect size of an individual cyclone on
leaf area (Eq. A1). Using a reference area that is specific to each
cyclone, seasonal dynamics such as leaf phenology are accounted for in the
effect size. Effect size is thus a unitless quantifier that describes the
mean change in state, obtained by normalizing the mean difference in leaf
area with the standard deviation (Eq. A1). A positive or negative
effect size value indicates, respectively, an increase or decrease in leaf
area (LAI, leaf area index) following the passage of a cyclone:
ES=LAI‾bef-LAI‾aftaff-LAI‾bef-LAI‾aftrefσ,
where ES is the event-based effect size for leaf area. The upper bar
represents the mean of leaf area index in the reference (ref) or the affected
(aff) area. The subscripts bef and aft denote the observation dates before and after
the cyclone; σ denotes the standard deviation of all observations
within the storm track. Given the 10 d frequency of the ESA leaf area
index product, two leaf area index maps are used for the calculation of the
effect size: one to characterize the leaf area index 1 to 10 d before the
cyclone and the other to characterize the leaf area index 60 to 70 d
after the cyclone. To distinguish between the affected and reference areas
the effect sizes were calculated for each event using the nine definitions.
After applying the quality control criteria (see below) a different number
of events was available for each definition (Table A1).
Contribution of return frequency and forest cover to the affected
area: (a) the zonal average of forest coverage (dotted line; km2) and
the return frequency (dashed line; yr-1) of tropical cyclones from 0 to
60∘ N averaged over East Asia, as defined in this study; (b) zonal
average of the interaction between return frequency and forest cover,
calculated by multiplying the return frequency with the forest cover
(dot–dash line; km2 yr-1) and the estimated zonal average of the
annual affected forest area (full line; km2 yr-1). Correlations
between return frequency and affected area (Pearson correlation coefficient =-0.35; p value < 0.01; n=60), forest cover and affected area
(Pearson correlation coefficient = 0.089; p value = 0.5; n= 60), and
frequency times cover and affected area (Pearson correlation coefficient = 0.44; p value < 0.01; n= 60). The latter thus correlates best
with the zonal variation in the affected area and was therefore shown in
panel (b). Results are shown for affected areas defined as locations within
an area extending to 3 times the cyclone width for which the wind
exceeded a threshold (definition 3a in Table A1).
Starting the analysis from the actual storm tracks, as was the case in this
study, allows for an unbiased assessment of the impact of cyclones on
forests (Blanc and Strobl, 2016), in contrast to
studies that attribute decreases in leaf area or related satellite-based
indices to different disturbance agents
(Ozdogan et
al., 2014; Honkavaara et al., 2013; Forzieri et al., 2020) including
cyclones
(Takao et
al., 2014). By design, the latter approach is not capable of identifying
neutral or positive impacts of cyclones on leaf area. As positive effects
were not limited to the cyclones from a low-intensity class (Fig. A3), the intensity class had little explanatory power (Table 1)
making a systematic bias towards positive effect sizes caused by low-intensity cyclones unlikely. Given the 60 d time window, our method is
more likely to be biased towards detecting no changes in leaf area than
detecting positive or negative changes in leaf area.
A meaningful effect size relies on the change in the reference area to
evaluate whether the change in leaf area in the affected area is faster,
similar, or slower. The way the effect size is calculated thus accounts for
phenological changes in leaf area. If the reference area were not used
in the calculation of the effect size, the change in leaf area over the
affected area would mostly represent leaf phenology especially if the 60 d
window includes the start or the end of the growing season, and it would thus
be unsuitable to address the question at hand.
Cumulative distribution of tropical cyclones as a
function of their maximum intensity for the nine definitions of affected
area used in this study. The cumulative distribution for the census of 580
tropical cyclones recorded for the study period is shown left of the y axis
for class I (31 %), class II (45 %), class III (55 %), class IV
(66 %), and class V (87 %) cyclones. The numbers shown on the right of
the y axis represent the cumulative distribution of the sample of the 580
events following a specific definition. Panel (a) shows wind only for two diameters, (b) wind only for three diameters, (c) wind only for four diameters, (d)
rain only for two diameters, (e) rain only for three diameters, (f) rain only for
four diameters, (g) wind or rain for two diameters, (h) wind or rain for three diameters, and (i) wind or rain for four diameters as detailed in Table A1. The
intensity distribution for tropical cyclones with a negative effect size is
shown in orange, for tropical cyclones with a neutral effect size in blue, and for tropical cyclones with a positive effect size in green. The
black solid line shows the distribution for the specific definition (n= 140 ± 41 cyclones depending on the definition). The gray solid line
shows the distribution of the 580 events that occurred between 1999 to 2018.
Small deviations between the gray and the black line suggest that the sample represented the 580 cyclones well in terms of their intensity class. The
maximum wind speed of category I cyclones is between 32 and
42 m s-1, between 42 and 49 m s-1 for category II, between
49 and 58 m s-1 for category III, between 58 and
69 m s-1 for category IV, and exceeding 69 m s-1 for category V. In
East Asia, tropical cyclones of intensity class III or higher are called
typhoons.
Decision tree proposing three groups of cyclones based
on cyclone characteristics, surface properties mainly prior to the passage
of the cyclone, and its effect on leaf area in the affected compared to the
reference area. Each box shows the fractions of negative (right), neutral
(middle), and positive (left) effect sizes (see also Table 1). The
number of events is listed as the percentage of the total number of events
in the random tree (n=1262). The first two principal components PC1 and
PC2 (Table A2) were used to create a two-layer decision tree.
As this study aims to quantify changes in leaf area index, it could not make
use of gap-filled leaf area index values which would level off such changes.
Furthermore, calculating the effect size required leaf area index estimates
before the passage of the cyclone in the reference and soon-to-be-affected
area and leaf area index estimates after the passage of the cyclone in the
reference and affected area. The 60 d time frame was a compromise to avoid
excessive data gaps in the leaf area index product when using the composite
leaf area index product. Because the leaf area index product reports leaf
area index values within a 60 d window, the analysis had to be refined so
that this 60 d window never included the cyclone. The offset between the
cyclone and a leaf area index observation from the composite leaf area index
product was calculated by subtracting the date of the cyclone from the last
observation date of the leaf area index composite data before the cyclone or
the first observation date of the leaf area index composite data after the
cyclone. Pixels with a negative offset indicated that the composite data
were likely to include observations from both before and after the cyclone
and were therefore discarded in the calculations of the effect size.
The calculation of the effect size assumes having a similar leaf area index
between the area that will become the affected area and the area that will
become the reference area after the passage of a cyclone. If the absolute
difference in leaf area index between the reference and the affected area
was less than 10 %, the effect size calculated for this event was
included in subsequent analyses. This can be formalized as
LAI‾bef,affLAI‾bef,ref-1<0.1,
where the 0.1 represents the 10 % threshold that was guided by the
observed relationship between the remotely sensed leaf area and its
deviation to ground truth data for leaf areas of 5 m2 m-2 or
below (Fig. 26 in Jorge, 2020). This quality control
criterion reflects the idea that prior to the passage of a tropical cyclone,
the LAI needs to be similar in what will become the reference and affected
area. If not, changes in leaf area following the passage of the cyclone
cannot be assigned to its passage.
Following the passage of a tropical cyclone, a change in LAI of less than
10 % before and after the passage of the cyclone was, in line with the
quality control criterion, considered to be too small to be considered
substantial. Such events were classified as cyclones with a neutral effect
size. This classification was formalized as
|LAI‾bef-LAI‾aftaff-LAI‾bef-LAI‾aftref|<0.1⋅LAI‾befref.
Multivariate analysis
Each tropical cyclone was characterized by some cyclone characteristics: (1) latitude of landfall (degrees); (2) intensity of the tropical cyclone (m s-1); (3) month of landfall; (4) maximum wind speed during passage over
land (m s-1); (5) affected area during passage over land (km2).
Likewise, the area affected by the cyclone was characterized by (6) accumulated rainfall on land 30 d prior to landfall of the cyclone (mm);
(7) accumulated rainfall during passage over land (mm); (8) leaf area 30 d prior to landfall (m2 m-2); (9) standardized precipitation
evapotranspiration index (mm mm-1) as a drought proxy; (10) change in
standardized precipitation evapotranspiration index (mm mm-1); and (11) Pacific Japan index the month of landfall (Pa Pa-1). These
characteristics were calculated as the average along the trajectory of the
cyclone.
Characteristics 1 to 4 were retrieved from the Joint Typhoon Warning Center
database as detailed in “Cyclone track and track diameter” (Sect. A1). Characteristics
5 and 7 were quantified from the analysis combining cyclone track, cyclone
diameter, and ERA5-Land reanalysis, as explained in “Area affected by
individual cyclones” (Sect. A2). Characteristics 6 and 7 were retrieved from the
ERA5-Land reanalysis data for land (ECMWF, 2019).
Characteristic 8 was taken from the leaf area index analysis as explained in
“Impact on leaf area of an individual cyclone” (Sect. A3). For characteristics 9 and
10, the standardized precipitation evapotranspiration index was used and
combined with the cyclone masks created in the “Area affected by individual cyclones” (Sect. A2). Characteristic 11, the Pacific Japan index, was
calculated from ERA5 hourly reanalysis
(Hersbach et al., 2018). Details on
the calculation of characteristics 9, 10, and 11 are provided in subsequent
sections.
Factor analysis (Grice, 2001) was used to reveal
the collinearity among the selected variables in the prior conditions,
tropical cyclone characteristic group, and effect size (Table A2).
The four main factors which explained 58 % of the variance, were
classified into three groups (Table 1) using a decision tree
(Fig. A4). Note that only the first and second axis were used in
the decision tree. The decision tree was created by means of the recursive
partitioning approach with a maximum of two levels and a minimum of 20
samples in each node provided by the R rpart package
(Therneau et al., 2019).
Drought analysis
The standardized precipitation evapotranspiration index, is a proxy index
for a drought that represents the climatic water balance and was used to
assess the drought of a forest soil before and after the passage of an
individual tropical cyclone. The standardized precipitation
evapotranspiration index data between 1999 and 2018 were retrieved from the
Global Standardized Precipitation and Evapotranspiration Index Database
(SPEIbase v2.6, Beguería et al.,
2014), which is based on the CRU TS v.4.03 dataset
(Harris et al., 2020). In this study, the
temporal resolution of the data was preserved, but the spatial resolution was
regridded from the original half-degree to 1 km to match the resolution of
the ESA leaf area index product. The contribution of an individual tropical
cyclone to ending a drought was evaluated by comparing the standardized
precipitation and evapotranspiration index from affected and reference areas
through the following equation:
δSPEI=SPEIimonaff-SPEIimonref,
where δSPEI is the event-based change in standardized precipitation
and evapotranspiration index. A positive or negative δSPEI,
respectively, denotes an increase or decrease in available water resources
following the passage of a tropical cyclone. The subscript imon represents
the integration time of available water resources in the calculation of the
standardized precipitation and evapotranspiration index either in the
reference (ref) or the affected (aff) area which are defined in the previous
section. The same time window, i.e., 60 d, was applied for the
calculation of δSPEI and event-based effect size for leaf area
index. The surface state was considered to experience a dry spell when the
standardized precipitation and evapotranspiration index dropped below -1.0
in this study.
Atmospheric analysis
The Pacific Japan index was calculated by comparing the difference of the
3-month running mean atmospheric pressure anomaly from Yokohama in Japan
(35∘ N, 155∘ E) with Hengchun in Taiwan (22.5∘ N, 125∘ E)
(Kubota et al., 2016) with the 20-year
climatology from 1999 to 2019. A monthly Pacific Japan index was used in
this study and the pressure data were retrieved from ERA5
(Hersbach et al., 2018). The
Pacific Japan index for the month of the passage of each tropical cyclone
were stratified according to the impact (given by the effect size) of the
cyclone on forest leaf area. Mean absolute atmospheric pressure field and
leaf area were calculated for those cyclones with a neutral effect size on
leaf area (Fig. 3a). Changes in pressure field and leaf area were
calculated for both cyclones with a positive and negative impact on leaf
area (Fig. 3b and c).
Data availability
Data table, R scripts, and data for performing the analysis and creating the plots can be found at
10.5281/zenodo.7511040 (Chen, 2023).
Author contributions
YYC and SL designed the study. YYC investigated and visualized the
results. YYC and SL contributed to the interpretation of the results.
SL wrote the original draft. SL and YYC reviewed and edited the
paper.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Biogeosciences. 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.
Acknowledgements
We acknowledge the reviewers and the editors for advice on the methodology, which improved the overall scientific quality of this study. We thank Matthew J. McGrath, Chao Yue, and Barry Gardiner for providing their comments on the result
of this study during the early phase of this study. Yi-Ying Chen thanks Huang-Hsiung Hsu
for the discussion of analyzing atmospheric conditions and Chi-Hua Wu for the
comments on considering the uncertainty in remote-sensing data. Yi-Ying Chen would like to thank the National Center for
High-performance Computing (NCHC) for sharing its computational resources
and data storage facilities.
Financial support
Yi-Ying Chen was funded through the Ministry of
Science and Technology (grant nos. MOST 109-2111-M-001-011 and grant MOST
110-2111-M-001-011). Sebastiaan Luyssaert was partly funded through the H2020 project
HoliSoils (SEP-210673589) and the HE project INFORMA (101060309).
Review statement
This paper was edited by Martin De Kauwe and Sara Vicca, and reviewed by two anonymous referees.
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