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Journal of Hydrology 481 (2013) 85–95
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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
How might Australian rainforest cloud interception respond to climate change?
Jim Wallace a,b,⇑, Dave McJannet c
a
Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER), James Cook University, Townsville, QLD 4811, Australia
CSIRO Land and Water, Australian Tropical Science and Innovation Precinct, Townsville, QLD 4814, Australia
c
CSIRO Land and Water, EcoSciences Precinct, Dutton Park, QLD 4120, Australia
b
a r t i c l e
i n f o
Article history:
Available online 5 January 2013
This manuscript was handled by K.
Georgakakos, Editor-in-Chief, with the
assistance of Matthew McCabe, Associate
Editor.
Keywords:
Rainforest
Climate change
Cloud interception
Canopy wetness
s u m m a r y
The lower and upper montane rainforests in northern Queensland receive significant amounts of cloud
interception that affect both in situ canopy wetness and downstream runoff. Cloud interception contributes 5–30% of the annual water input to the canopy and this increases to 40–70% of the monthly water
input during the dry season. This occult water is therefore an important input to the canopy, sustaining
the epiphytes, mosses and other species that depend on wet canopy conditions. The potential effect of
climate change on cloud interception was examined using the relationship between cloud interception
and cloud frequency derived from measurements made at four different rainforest locations. Any given
change in cloud frequency produces a greater change in cloud interception and this ‘amplification’
increases from 1.1 to 1.7 as cloud frequency increases from 5% to 70%. This means that any changes in
cloud frequency will have the greatest relative effects at the higher altitude sites where cloud interception is greatest. As cloud frequency is also a major factor affecting canopy wetness, any given change in
cloud frequency will therefore have a greater impact on canopy wetness at the higher altitude sites. These
changes in wetness duration will augment those due to changes in rainfall and may have important
implications for the fauna and flora that depend on wet canopy conditions. We also found that the Australian rainforests may be more efficient (by 50% on average) in intercepting cloud water than American
coniferous forests, which may be due to differences in canopy structure and exposure at the different
sites.
Ó 2013 Published by Elsevier B.V.
1. Introduction
Australia contains some unique tropical rainforests in the Wet
Tropics region of northern Queensland. These forests vary in structure and composition as the soils and climate change with increasing distance from the coast (Tracey, 1982; McJannet et al.,
2007d,b). There is also a marked effect of altitude as the terrain
rises rapidly from the coastal plain (50–100 m ASL) to the ‘Tablelands’ plateau (1000 m ASL) and further to the mountains of the
Great Dividing Range (up to 1600 m ASL). On the seaward side of
these mountains, rainfall increases with altitude and this has a major effect on the forest water balance and the duration of wet conditions in the canopy (McJannet et al., 2007a,b; Wallace and
McJannet, 2012). However, the rainforests above 1000 m also receive significant inputs of water as cloud interception (McJannet
et al., 2007c), which constitutes between 4% and 29% of the annual
water input. More importantly, the above studies have also shown
that the contribution of cloud interception during dry season
⇑ Corresponding author at: Centre for Tropical Water & Aquatic Ecosystem
Research (TropWATER), James Cook University, Townsville, QLD 4811, Australia.
Tel.: +44 1635 770 634.
E-mail address: [email protected] (J. Wallace).
0022-1694/$ - see front matter Ó 2013 Published by Elsevier B.V.
http://dx.doi.org/10.1016/j.jhydrol.2012.12.028
months can be as high as 65%, when it becomes the major water
input sustaining in situ canopy wetness and downstream runoff.
If climate change alters the frequency and amount of cloud interception there could therefore be important hydrological and ecological consequences.
The potential impacts of climate change on the water balance of
these forests have been reported by Wallace and McJannet (2012).
A range of climate simulations for north Queensland predict future
(2050) rainfall and temperature changes of ±20% and 1–3 K respectively (see Section 2.5), and using these figures they found that the
water balance was primarily affected by rainfall changes rather
than temperature. Runoff downstream of the forests changed by
a greater amount than rainfall, especially in the dry season, where
there were important effects on the onset and duration of the period when there is no runoff. They also found potential in situ impacts of climate change that affect how long the rainforest
canopy is wet, which may have important implications for the epiphytes, mosses and other species that depend on these wet canopy
conditions (Hölscher et al., 2004; Köhler et al., 2006). However, the
climate impacts on canopy wetness analysis presented by Wallace
and McJannet (2012) only dealt with possible changes in rainfall
and no account was taken of how climate change might influence
cloud interception. As this is an important input to the higher alti-
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tude rainforests in Australia, this paper presents an analysis of how
cloud interception might change under future climate scenarios
and in turn how this might affect the seasonality of canopy wetness and downstream runoff.
2. Methods
2.1. Locations and weather
The cloud interception and climate impact analyses were performed for four rainforest locations where significant cloud interception has been recorded by McJannet et al. (2007a,b), Fig. 1.
Detailed descriptions of the forest sites and methods used to measure rainfall and cloud interception are given by McJannet et al.
(2007a,b), so only brief site descriptions are given below.
The first site, Upper Barron (UB), is a lower montane rainforest
on the Atherton Tablelands (1050 m ASL). The two sites at Mt Lewis are located in the Mt Lewis Forest Reserve and are also classified as lower montane rainforest. The lower site (ML1; 1100 m ASL)
is in a shallow valley while the second site (ML2; 1160 m ASL) is
located on a more exposed hill slope. The forest at ML2 is much
more stunted because of this exposure, with a canopy height of
12 m, compared to 32 m at ML1. The fourth site is located in upper
montane cloud forest near the summit of Bellenden Ker (BK)
(1560 m ASL), which is the second highest mountain in Queensland. The rainforests types vary from complex notophyll vine forest
on the Tablelands to simple microphyll vine fern thicket at the top
of the mountains (Tracey, 1982), Table 1. The rainforests generally
form closed canopies and are up to 30 m tall. The upper montane
cloud forest at the top of Bellenden Ker is much more stunted
(8 m) and has the lowest leaf area index (3.3). Further details the
forest types are given by McJannet et al. (2007b,c).
The Australian Wet Tropics region has high annual rainfall, but
with distinct wet and dry seasons. The dry season (six driest
months) runs from June to November with an average of 20% of
the annual rainfall, while the wet season (six wettest months) generally occurs from December to May with 80% of annual rainfall.
The rainforest furthest from the coast (UB) has the highest radia-
tion and warmest air and all sites have very high humidity (Table 1). The upper montane forest (BK) grows in a much cloudier
and cooler climate and is exposed to the highest wind speeds.
Long term weather records for the four sites were obtained
from the Australian SILO meteorological database (Jeffrey et al.,
2001). Locations within a few kilometres of the rainforest sites
were chosen and daily rainfall, temperature, solar radiation and vapour pressure extracted for the period 1950–2008. For the periods
where weather data (618–1113 days) were also measured at the
rainforest sites by McJannet et al. (2007a) correlations were established between the actual site and the SILO weather data (see Wallace and McJannet, 2012). Site rainfalls, solar radiation and
temperature were significantly different from those in SILO, so in
subsequent calculations of canopy wetness duration and downstream runoff using SILO long term data we therefore applied the
regressions given by Wallace and McJannet (2012) for rainfall,
temperature, solar radiation and vapour pressure.
2.2. Rainforest water balance
The water balance of a rainforest has been described by McJannet et al. (2007c) as:
ðPg þ Pc Þ ¼ I þ Et þ Es þ R þ D þ dh;
ð1Þ
where (Pg + Pc) represents total precipitation input to the forest as
rainfall, Pg and cloud interception, Pc. Water losses from the canopy
are given by the sum of the canopy interception I, and transpiration,
Et. Direct evaporation from the forest floor, Es, is usually small and R
and D represent the processes of runoff (i.e. lateral flows whether
overland or subsurface) and drainage, respectively. Over short time
periods (days to months) changes in soil moisture storage (dh) can
be a significant part of the water balance. The difference between
the rainfall, Pg and the total evaporation (I + Et + Es) is referred to
as ‘excess water’ by Wallace and McJannet (2012), and is given by
(R + D + dh). When the change in dh is small compared to the other
water balance components (e.g. high rainfall months and/or annual
periods) the excess water gives a good measure of (R + D).
Fig. 1. Location map showing Wet Tropics region and location of the four rainforest sites that have significant cloud interception (D). Altitudes of field sites are given in
brackets. Reproduced from McJannet et al. (2007c).
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Table 1
Details of the location, forest type and structure and weather characteristics for the four rainforest sites that receive significant cloud interception. Further details of the size
classes used to determine stem density and basal area are given by McJannet et al. (2007a).
Lat. (S)
Long. (E)
Altitude (m)
Forest type
LAI (m2 m2)
Basal area (m2 ha1)
Stem density (ha1)
Canopy height (m)
Average precipitation (mm year1)a
Average potential evaporation
(mm year1)
Average daily solar radiation (MJ m2)
Average daily temperature (°C)
Average daily relative humidity (%)
Average daily wind speed (m s1)
a
Upper Barron
Mount Lewis 1
Mount Lewis 2
Bellenden Ker
17°27.1
145°29.7
1050
Complex Notophyll Vine
Forest
4.1
69
925
25
2087
1237
16°31.7
145°16.7
1100
Simple Notophyll Vine
Forest
4.5
62
650
32
3471
982
16°31.8
145°16.3
1160
Simple Notophyll Vine
Forest
4.1
75
2150
12
2673
N/A
17°16.0
145°51.0
1560
Simple Microphyll Vine Fern
Thicket
3.3
74
2019
8
7386
1073
17.2
18.3
94
2.9
14.4
16.8
90
1.1
N/A
16.3
93
N/A
12.6
14.2
88
3.2
Corrected rainfall from McJannet et al. (2007b).
2.3. Cloud interception and frequency
Estimates of cloud interception were made at all four field sites
using a combination of cloud interception gauges and the canopy
water balance during periods when clouds interception was detected (McJannet et al., 2007a). This method overcomes problems
associated with the difference in the cloud droplet interception
efficiency between the gauge and the forest canopy.
Cloud frequency (the % of time when clouds are present within
the rainforest canopy) can be derived from the times when the
cloud interception gauges recorded events. However, as cloud
could also be passing through the forest canopy before the first recorded event, in any gaps between events and after the final event,
the true cloud frequency may be significantly longer than simply
the sum of when gauged events occurred. Several estimates of
cloud frequency were therefore made (see Section 3) using a combination of cloud interception and high relative humidity data,
measured using a Vaisala capacitance sensor located in clearing
near the forest (for ML1, ML2 and UB) and above the forest canopy
at BK.
2.4. Canopy wetness duration
Rainforest canopies can be wetted by rain and/or cloud interception which may or may not occur simultaneously. The total
duration (% of time) of canopy wetness is therefore given by;
W total ¼ fcnr þ fcr þ T nc
r Wr;
ð2Þ
where the total cloud frequency duration (fc) is split into fractions
that occurred when it was (fcr ) and was not (fcnr ) raining. Total
monthly durations of canopy wetness due to cloud interception
were estimated from mean monthly cloud frequency (as described
above) and the split into fcr and fcnr was made using 30 min recordings of rainfall from each site. Monthly wetness durations due to
rain in the absence of canopy cloud were derived from the product
of the total wetness duration due to rainfall (W r ) and the fraction of
time rain occurred in the absence of cloud interception (T nc
r ). W r
was calculated using a modification of Gash et al. (1995) model described by Wallace and McJannet (2012). Wetness durations during
rainfall are given by the amount of precipitation (Pg) that fell divided by the mean rainfall rate (given by Wallace and McJannet
(2008)). Additional wetness durations after rainfall were derived
by dividing the canopy storage by the mean potential evaporation
rate after rain, Ep, derived using 30 min weather data recorded at
each site (see Wallace and McJannet, 2012). The mean monthly
fraction of time rain occurred in the absence of cloud interception
(T nc
r ) was calculated using 30 min recordings of rainfall and cloud
interception from each site.
2.5. Climate scenarios
Wallace and McJannet (2012) have pointed out that general circulation model (GCM) simulations of climate change for the north
coast of Queensland are highly uncertain (Cai et al., 2003; CSIRO,
2007; Li et al., 2009; Suppiah et al., 2010; QCCCE, 2010; McGregor
and Nguyen, 2010) with different GCM’s predicting either decreases (up to 20%), increases (up to 20%) or no change in rainfall
by 2050 on a medium (SRES A1B) CO2 emissions scenario (see Suppiah et al., 2010). To examine the potential effects of climate
change on lower and upper montane rainforest canopy wetness
we have therefore perturbed rainfall by ±20% (as in Wallace and
McJannet (2012)). GCM’s also include predictions of how the cloud
condensation level will change; however, these are not very accurate in individual grid cells, especially near steep topography (Still
et al., 1999). Furthermore, information on the change in cloud base
alone is not sufficient to provide an estimate of change in cloud
interception as this is not well correlated with altitude alone. In
the absence of any better information we have therefore estimated
a range of potential effects of climate change on rainforest cloud
interception (Pc) by assuming that cloud frequency (fc) changes
by a similar percentage as rainfall. We chose to perturb fc by
±20% rather than Pc, since much of the cloud interception occurs
when it is not raining and, as we shall see later, there is a good relationship between Pc and fc.
The above GCM climate change studies include temperature
changes, predicted to be between 1 and 3 K by 2050 (Suppiah
et al., 2010), so our perturbation fc by ±20% should also include
the effects of temperature change. However, we also discuss how
climate induced temperature change alone might affect the cloud
condensation level.
3. Results and discussion
3.1. Canopy water inputs
The contribution of cloud interception (Pc) to the total water input to the forest canopy (Pg + Pc) is shown in Fig. 2 for the four rainforest locations. The number of months averaged at each site (from
1 to 3.5) is determined by the length of each data set (Table 2) and
is shown on the x-axis. Most of the rainfall and much of the cloud
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J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
Fig. 2. Seasonal variation in monthly rainfall (
) and cloud interception (
) for four rainforest locations. Also shown is the percentage of total water input occurring
as cloud interception (h). The number of months averaged is shown in brackets on the x-axis for each site. Note the y-axis scale for Bellenden Ker is different from the other
three sites.
interception occurs during the wet season (December to May).
However, significant cloud interception continues during the dry
season and this becomes an increasing fraction of the total water
input to the canopy at this time. The least cloud interception occurred at Mount Lewis 1, reaching a monthly maximum of 15%
of the canopy water input and averaging 5% over the entire year
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Table 2
Annual average rainfall (Pg), cloud interception (Pc) and total water input (Pg + Pc) to the forest canopy at four rainforest locations. Also shown are the start and end dates of the
different periods of data availability at each site.
Observation period
Mount Lewis 1
Mount Lewis 2
Upper Barron
Bellenden Ker
01-June-2001
01-June-2001
06-July-2001
03-October-2002
17-June-2004
17-June-2004
11-June-2004
11-June-2004
Duration (days)
Pg (mm)
Pc (mm)
Pg + Pc (mm)
Pc (%)
1113
1113
1072
618
3172
2633
1675
6880
181
390
715
2090
3353
3023
2390
8970
5
13
30
23
from 69% at BK to 19% at ML1. However, as cloud may not be present for the entire day these values will be overestimates, or at best
an upper limit. Several studies of cloud interception have estimated cloud frequency by assuming cloud presence when the relative humidity is >95% (e.g. Dasch, 1988; Saxena and Lin, 1990).
We estimated fc from our 30 min relative humidity data for periods
of 618, 1072 and 1113 days at BK, UB and ML1 and 2 respectively,
Table 3. It is clear that this method can grossly overestimate fc,
especially at the lower montane sites UB, ML1 and ML2, since
the values are well in excess of the upper limit derived in the first
method. The third estimate of fc was made using 30 min recordings
of cloud interception and this yielded much lower values of cloud
frequency. These values can be regarded as minimum values of fc
for each site, since they are based on the sum of the 30 min occasions when the cloud gauge recorded a tip (or more) on its tipping
bucket. Cloud interception will have been occurring before the first
tip, in gaps between tips as the gauge collected enough water for
the next tip and again for a period after the last tip. A fourth estimate of fc was therefore made that included an allowance for these
periods by including 30 min periods where the relative humidity
was >95% for periods up to 6 h after the last tip. The period after
the last tip was chosen as 6 h as this was the maximum time taken
for the cloud interception tipping bucket to fill (derived by dividing
the bucket size (mm) by the average cloud interception minus the
wet canopy evaporation rate). Sensitivity analysis revealed that fc
was not very sensitive to the threshold value of relative humidity
(95%), i.e. a 4% point change in RH gives a 5–10% change in fc. Similarly, fc is not too sensitive to the post-last tip duration value,
where a ± 50% change only leads to a 5–20% change in fc. The values
of fc derived using the fourth method above are therefore considered the best estimates, noting they are approximately half way
between the alternative method minimum and maximum values
(Table 3). Comparison of the two similar altitude Mount Lewis forests illustrates the effect of site exposure on cloud frequency, with
the forest on the exposed (windward) hill slope (ML2) experiencing twice the cloud frequency as the nearby forest in a shallow valley on the leeward side of the hill (ML1). Clouds were present for
over 50% of the time at the highest and most exposed site (BK);
3–6 times as long as the other forest sites.
The seasonal pattern of cloud frequency derived using the
fourth method above at the four rainforest sites is shown in
Fig. 3. Also shown are the monthly mean values of cloud interception (Pc) and wind speed recorded during the interception periods.
The number of months averaged at each site is the same as in Fig. 2.
At all four sites cloud frequency and interception are highly seasonal with maximum values between March and July and minimum rates in October. Cloud frequency varied greatly between
(Table 2). This site is in a shallow valley and is less exposed to the
prevailing winds than the nearby Mount Lewis 2 site on an exposed ridge, where much more cloud interception occurred. For
example, the monthly Pc at ML2 reached 30% in June and July
and the annual average Pc was 13%. The relative contribution of
Pc was even greater at Upper Barron, peaking at close to 70% of
the water input in June and averaging 30% of the annual water input (Fig. 2;Table 2). The largest amounts of cloud interception occurred at Bellenden Ker at just over 2000 mm per annum; again
dry season cloud water inputs at this site were between 40% and
50% of the total.
The cloud interception rates at ML1 (0.5 mm day1), ML2
(1.1 mm day1) and UB (1.4 mm day1) are very similar to the typical rates of interception of passing cloud reported by Bruijnzeel
(2005) and Bruijnzeel et al. (2012) in their reviews of tropical montane cloud forests (range 0.2–4 mm day1). The Pc rate for BK
(5.7 mm day1) is greater than the range given by Bruijnzeel
(2005), but there have been other studies of cloud interception in
rainforests that found high rates between 5 and 6 mm day1, e.g.
by Cavelier et al. (1996), Brown et al. (1996) and Holder (1998).
It is also possible that the Pc values at BK were enhanced by wind
blown rain, although corrections for this have been made in the
rainfall estimate (see McJannet et al., 2007b).
3.2. Cloud frequency and interception
The amount of water intercepted by a forest is determined by a
number of factors including wind speed, cloud liquid water content
and the geometry of the forest leaves and branches (Bruijnzeel,
2005). A number of process models have been developed to describe cloud interception to forest canopies (e.g. Shuttleworth,
1977; Lovett, 1984; Slinn, 1982), but their practical application is
limited by lack of information on key parameters such as droplet
size distribution and liquid water content. Although cloud interception is a complex process, Vong et al. (1991) have presented
data from a number of studies in the Appalachian Mountains that
indicate a positive relationship between annual cloud interception
and cloud frequency (fc). This kind of relationship allows estimates
of climate impacts on cloud interception to be made if we know
how fc might vary with climate.
The frequencies with which the Australian rainforest locations,
UB, ML1, Ml2 and BK are immersed in cloud were derived from
measurements of the occurrence of cloud interception and high
relative humidity. The four methods used to estimate fc are summarised in Table 3. The first estimate of fc is made from daily data,
where any day when cloud interception is detected by the cloud
gauge is designated as a cloud day. This gives values for fc ranging
Table 3
A summary of four different estimates of cloud frequency at the Australian rainforest sites, Mount Lewis 1 and 2, Upper Barron and Bellenden Ker.
1.
2.
3.
4.
%
%
%
%
of days with cloud interception
time when relative humidity (RH) > 95%
of time when cloud interception recorded
of time when cloud interception recorded and afterwards if RH > 95%
Mount Lewis 1
Mount Lewis 2
Upper Barron
Bellenden Ker
19
68
1
8
30
68
3
16
35
63
6
18
69
62
35
51
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J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
Fig. 3. Seasonal variations in monthly average cloud frequency (h), cloud interception () and wind speed (+) at four rainforest locations. Note the vertical axes for Bellenden
Ker have different scales to those for the other three sites.
the different sites; with the lowest values at ML1 and the highest at
BK. Within individual sites there was little or no correlation between cloud interception (Pc) and wind speed during the interception periods (r2 values ranged from 0.00 to 0.34). However, the
highest Pc rates were observed at the windiest site (BK; mean wind
speed 5.1 m s1) and the lowest Pc rates at the calmest site (ML1;
mean wind speed 1.1 m s1).
Cloud interception was highly correlated with cloud frequency
(fc) at all four sites, where the individual linear regressions
(through the origin) for each of the sites (not shown in Fig. 4 for
clarity) had slopes that increased from 1.9 (r2 = 0.99) at ML1, 2.1
at ML2 (r2 = 0.81), 3.5 at UB (r2 = 0.78) and 3.6 at BK (r2 = 0.64).
The lower correlation coefficient at BK may, in part, be due to
the smaller number of months averaged for the shorter data set
at this site (see Table 2 and Fig. 2). The best fit line to all the Australian data points in Fig. 4a is curvilinear, as this allows for the
combined effects of cloud frequency and wind speed on cloud
interception. The physical explanation for this may be twofold;
J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
91
Fig. 4. (a) The relationship between monthly cloud interception (Pc) and cloud frequency (fc) for four Australian rainforest sites (Mount Lewis 1 (h), ML1; Mount Lewis 2 (s),
ML2; Upper Barron (N), UB and Bellenden Ker (), BK). The fitted line has the form Pc = 0.039 fc2 + 1.30 fc; r2 = 0.87. For comparison the linear regression through the origin
Pc = 3.44 fc; r2 = 0.79 is also shown. (b) equivalent data from a number of forested sites in the Appalachian Mountains (Vong et al., 1991;Bruijnzeel, 2005) along with the
curvilinear regression from Fig. 4a.
(1) for a given amount of time more cloud interception occurs at
the higher altitude sites where the higher wind speeds force more
cloud droplets per unit time past the condensation structures in
the forest and (2) theory suggests that the liquid water content
within clouds increases with altitude (e.g. Korolev et al., 2007).
For comparison, cloud interception and frequency data from a
number of studies of cloud interception in the Appalachian Mountains in the eastern United States (US) of America by Vong et al.
(1991) (with additional data reported by Bruijnzeel (2005)) are
shown in Fig. 4b. It should be noted that the US data uses different
methods and periods to estimate cloud interception and there are
considerable uncertainties in the estimation of cloud frequency.
For example, many of the Appalachian Mountain studies were
quite short (1–2 months) and fc was derived from relative humidity
(e.g. Dasch, 1988; Saxena and Lin, 1990), shown here (Table 3) to
be an overestimate, or inferred from nearby studies at different
times (Reiners and Lang, 1979). However, despite these uncertainties it appears that the Australian rainforests may be more efficient
(by 50% on average) in intercepting cloud water than the Ameri-
can coniferous forests, which may be due to differences in canopy
structure and exposure at the different sites.
The fitted curve in Fig. 4 can be used to derive predictions of
how cloud interception may change if the change in cloud frequency can be predicted. In the absence of any better information
(discussed further below), cloud frequency is assumed to change
by the same amount as future rainfall change, which is ±20% for
the Australian rainforest locations. A 20% change in fc produces a
larger relative change in cloud interception because of the increasing slope of the best fit curve in Fig. 4a. Furthermore, as fc increases,
the same relative change in fc produces a larger relative change in
cloud interception. This amplification (ratio of the relative change
in Pc to the relative change in fc) is around 1.1 when fc is 5% and
grows to 1.7 when fc is 70%. This implies that changes in cloud frequency will be amplified in cloud interception at all rainforest
sites, with the greatest amplification at the higher altitude sites
such as Upper Barron and Bellenden Ker. Again this is consistent
with what would be expected as both wind speed and/or cloud liquid water content increase with altitude.
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J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
A number of studies have examined the potential impacts of climate change on tropical forests that are exposed to cloud interception (e.g. Pounds et al., 1999; Still et al., 1999; Foster, 2001). Foster
(2001) reports global and regional climate modelling studies that
indicate that there would be an increase in dry days due to a rise
in the cloud level. For a 2 CO2 scenario she reported that absolute
humidity surfaces would move upslope by 200–300 m, similar to
values reported by Still et al. (1999). Other studies have reported
higher estimates of the shift in cloud base height, i.e. up to
500 m for a 3 K rise in temperature (Peters and Darling, 1985; Halpin, 1994), which is very close to the moist adiabatic lapse rate
(Held and Soden, 2000). However, as the actual amount of cloud
interception is not well correlated with altitude alone (Vong
et al., 1991; McJannet et al., 2007a), it is difficult to use this approach to estimate the quantitative change in cloud interception
as the climate temperature changes.
Climate change predictions are made using General Circulation
Models (GCM’s) that represent the key climate processes in a multi-layered atmosphere. Their most common outputs are predictions of rainfall and temperature change that occur as the
concentration of atmospheric CO2 is increased. However, these
models (or their nested higher resolution meso-scale models) also
include predictions of how the cloud condensation level will
change, although Still et al. (1999) point out that these models
are not expected to produce highly accurate predictions of the
amount of water vapour in individual grid cells, especially near
steep topography. This location specific uncertainty is also recognised in the recent GCM study by Foster (2010) who report small
and/or insignificant changes in the cloud condensation level in
northern Queensland up to 2050. Their work highlights the importance of knowing how relative humidity will change as the atmosphere warms, since where this is small (as suggested by Held
and Soden (2000)) then changes in the cloud condensation level
will also be small. Even if it were possible to more accurately predict climate induced changes in cloud condensation level, without
any commensurate indication of how cloud frequency might
change, this information is of limited use in predicting quantitative
changes in cloud interception as climate temperature changes.
Much of the biodiversity of tropical montane rainforests depends on the availability of water in the forest canopy, which is
highly regulated by cloud interception. For example, in their study
of birds, lizards and frogs in the highland rainforests at Monteverde
in Costa Rica, Pounds et al. (1999) reported that altitudinal shifts in
these faunal populations were most highly correlated with variations in cloud frequency, as indicted by total dry days. They found
an increase in the frequency of dry days, which they ascribe to a
raising of the cloud level due to atmospheric warming. They also
found that changes in bird populations and a decline in lizard
and frog numbers were all associated with changes in cloud
frequency.
3.3. Canopy wetness duration
The seasonal variation in canopy wetness duration due to rainfall and cloud interception is shown in Fig. 5. Wetness due to rain
(in the absence of cloud interception), cloud (in the absence of
rainfall) and the simultaneous occurrence of cloud and rain are
shown separately. At the lower montane sites (ML1, ML2 and
UB) wetness durations follow the pattern of long term rainfall
and are highest around February/March (40–50%) and lowest in
September/October (10–20%). Wetness due to cloud interception
follows the different seasonal pattern of cloud frequency (see
Fig. 3) and makes the smallest contribution in the summer months,
but becomes the dominant wetting mechanism in winter. For
example, throughout the months from May to September canopy
wetness at ML2 and UB is more influenced by cloud interception
than by rainfall alone. The pattern of canopy wetness at the highest
altitude site (BK) is quite different. Here wetness durations are
much higher, 60–80% for over 9 months and are dominated by
cloud interception throughout the year. In the winter months
(June, July and August) canopy wetness is almost entirely due to
cloud passing through the canopy, even though it only constitutes
less than half of the water input to the canopy. This is because for a
given amount of water input to the canopy, wetness durations due
to cloud interception are much longer than those due to rainfall as
the rate of cloud interception is so much slower (one fifth) than
typical rainfall rates (Wallace and McJannet, 2008).
Potential climate impacts on canopy wetness duration at Mount
Lewis 1 and Bellenden Ker is shown in Fig. 6. Climate change has
less effect on wetness duration at ML1 as wetness is more dependent on rainfall at this site. This is because changes in rainfall
(of ±20%) can reduce the duration of wetness during rain, but not
after rain; also discussed in Wallace and McJannet (2012), who
found that depending on the relative contributions of these two
periods, wetness duration due to rainfall changed by between
10% and 15% for a 20% change in rainfall. On the other hand, since
wetness durations at Bellenden Ker are much more dominated by
cloud interception, a 20% change in this produces a similar change
in canopy wetness duration. Any given change in rainfall and cloud
interception will therefore have a greater impact on canopy wetness at the higher altitude sites where cloud interception is a larger
fraction of total canopy water input. However, the ecological implications of wetness duration changes at higher and lower altitude
sites will depend on how close the species are to their desiccation
limits.
3.4. Downstream impacts
The seasonal variation in excess water (R + D + dh) generated by
both rainfall and cloud interception is shown in Fig. 7. Excess water
was calculated on a monthly basis as the difference between total
water input (Pg + Pc) and total evaporation (I + Et + Es) using the
SILO long term rainfall data for ML1 and BK. Interception (I) and
transpiration (Et) were calculated from monthly (Pg + Pc) using
the regression equations derived for these sites by Wallace and
McJannet (2012). Es is a small component and was assumed to be
fixed at 0.2 mm day1. Cloud interception (Pc) was derived for each
month using the seasonally varying cloud frequencies (fc) shown in
Fig. 3 in the Pc versus fc relationship shown in Fig. 4. These calculations were repeated for normal, +20% and 20% rainfall and
cloud frequency.
At the lower montane Mount Lewis 1 site excess water is dominated by rainfall during the summer and as this declines in the
winter months there is a period when total evaporation exceeds
rainfall and so (R + D + dh) becomes negative, implying that the forest is dependent on soil moisture and/or groundwater at this time.
Under normal rainfall conditions the duration of this period is
96 days and this changes by 44 and + 23 days respectively as
rainfall and cloud frequency are altered by +20% and 20%. As reported by Wallace and McJannet (2012) for other rainforest sites,
the climate induced shift in the start of the zero excess water period is greater than the shift in the end date. Excess water is always
positive at Bellenden Ker (Fig. 7b), but there are large changes
associated with the ±20% rainfall and cloud frequency climate
change scenarios. As cloud interception is very large at BK
(Fig. 3), the contribution that the 20% change in cloud frequency
makes to the change in excess water becomes quite significant in
the winter months, accounting for up to 30% of the total change
in (R + D + dh).
A given change in rainfall and cloud frequency produces a larger
change in (R + D + dh), similar to that reported by Wallace and
McJannet (2012) for rainfall alone. The greatest amplification (ratio
93
J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
Canopy wetness duration (%)
100
(a) Mount Lewis 1
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
Canopy wetness duration (%)
100
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sep
Oct
Nov
Dec
(b) Mount Lewis 2
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
Canopy wetness duration (%)
100
May
Jun
Jul
Aug
(c) Upper Barron
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
Canopy wetness duration (%)
100
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
(d) Bellenden Ker
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Fig. 5. Seasonal variation in canopy wetness duration at four rainforest locations. Wetness due to rain with no canopy cloud (black), rain and cloud occurring together (mid
grey) and cloud occurring in the absence of rain (light grey) are shown separately for each site.
of the % change in (R + D + dh) over the % change in Pg and fc) occurs
at ML1, where the ratio is 3.4 in June and 1.7 for the entire year.
The amplification at BK is smaller, around 1.6 in winter and 1.3
as an annual average. The amount of amplification is greater when
rainfall is lower because of the highly non-linear relationship between (R + D + dh) and rainfall (see Wallace and McJannet, 2012).
Similarly, non-linearity in the relationship between cloud intercep-
tion and cloud frequency (Fig. 4) also contributes to amplification
of changes in (R + D + dh).
4. Conclusions
This paper confirms that cloud interception is a very significant
water input to rainforests in north Queensland, especially during
94
J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
100
Canopy wetness duration (%)
90
Bellenden Ker
80
70
60
50
40
30
Mount Lewis 1
20
10
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Fig. 6. Seasonal variation in monthly mean canopy wetness duration at Mount Lewis 1 and Bellenden Ker under normal rainfall and cloud frequency (h, j), 20% more rainfall
and cloud (D,N) and 20% less rainfall and cloud (e, ).
600
(a) Mount Lewis 1
500
400
300
200
100
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
-100
1800
60%
(b) Bellenden Ker
1600
1200
40%
1000
30%
800
600
20%
400
10%
Cloud frequency change
contribution
50%
1400
200
0
0%
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Fig. 7. The seasonal variation in excess water (R + D + dh) at (a) Mount Lewis 1 and (b) Bellenden Ker under normal rainfall and cloud frequency (), 20% more rainfall and
cloud frequency (j) and 20% less rainfall and cloud frequency (N). The contribution that cloud frequency change makes to the overall change in (R + D + dh) is also shown for
Bellenden Ker.
the dry season when it can exceed the rainfall input. Cloud interception also makes a major contribution to the length of time the
rainforest canopy is wet, again becoming the dominant wetting
mechanism during the dry season at all of the locations studied.
At the upper montane rainforest (BK), cloud interception is the
dominant wetting mechanism throughout the year. The much
slower rate of cloud interception (0.8 mm h1; Wallace and
McJannet, 2008) compared with rainfall (3.4 mm h1) means that
a given amount of cloud interception will produce a wetness duration that is over four times that produced by the same amount of
rainfall. This is the main reason why cloud interception has a greater impact on canopy wetness duration.
There may be important impacts on cloud interception (Pc) at
these higher altitude rainforests due to climate change, especially
as any given change in cloud frequency (fc) produces a greater
change in cloud interception. This ‘amplification’ (ratio of the %
change in Pc to the % change in fc) increases with fc growing from
1.1 when fc is 5% to 1.7% when fc is 70%. This means that any
changes in cloud frequency will have an amplified impact on cloud
interception at all rainforest sites, with the greatest amplification
at the highest altitude sites such as Bellenden Ker where cloud
J. Wallace, D. McJannet / Journal of Hydrology 481 (2013) 85–95
interception is a large fraction of total canopy water input. As cloud
frequency is also a major factor affecting canopy wetness, any given change in cloud frequency will therefore have a greater impact
on canopy wetness at the higher altitude sites. These changes in
wetness duration will augment those due to changes in rainfall
and may have important implications for the epiphytes, mosses
and other species that depend on these wet canopy conditions
(Hölscher et al., 2004; Köhler et al., 2006). More combined hydro-ecological studies of canopy wetness and rainforest flora and
fauna are needed to address this question.
Downstream freshwater species may also be impacted by climate induced changes in rainfall and cloud frequency where again
any given climate change will produce a greater change in runoff
by up to a factor of 3.4 (seasonally) and 1.7 (annually). Large shifts
may also occur in the timing and duration of the winter period
when there is no excess water for downstream runoff and any
freshwater species that respond to flows at this time may be affected. More certain estimation of these flow regime changes on
aquatic biota require better estimates of how climate change will
affect not only rainfall, but also condensation levels and the frequency of cloud formation at rainforest canopy levels.
Acknowledgements
The analysis in this paper are based on extensive field data collected with the help of Mark Disher, Andrew Ford, Peter Richardson, Trevor Parker, Adam McKeown, Trudi Prideaux, Jenny
Holmes and Pepper Brown. We are also grateful to the internal
CSIRO reviewers and the external referees for their helpful comments and advice on the draft manuscript. Funding for this research was provided by the Cooperative Research Centre for
Tropical Rainforest Ecology and Management.
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