Agricultural and Forest Meteorology 150 (2010) 1297–1306
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Agricultural and Forest Meteorology
journal homepage: www.elsevier.com/locate/agrformet
The response of sap flow in shrubs to rainfall pulses in
the desert region of China
Wenzhi Zhao ∗ , Bing Liu
Linze Inland River Basin Research Station, Key Laboratory of Inland River Basin Ecohydrology, Cold and Arid Regions Environmental and Engineering Research Institute,
Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China
a r t i c l e
i n f o
Article history:
Received 17 September 2009
Received in revised form 14 May 2010
Accepted 31 May 2010
Keywords:
Rainfall pulse
Desert shrubs
Sap velocity
Rainfall response
Threshold-delay model
a b s t r a c t
Rainfall pulses can significantly drive the evolution of the structure and function of desert ecosystems,
and understanding the mechanisms that underlie the response of desert plants to rainfall is the key to
understanding the responses of desert ecosystems to global climatic change. The present study was carried out at the desert-oasis ecotone in the middle of China’s Heihe River Basin. We measured sap flow in
the branches and stems of desert shrubs (Nitraria sphaerocarpa and Elaeagnus angustifolia) using sap flow
gauges, and studied the response of sap velocity to rainfall pulses using the “threshold-delay” model. The
results showed that the response of sap flow began about 1 h earlier after rainfall, and that sap velocity
increased two to threefold, compared to its pre-rainfall value. The sap velocity increased significantly,
then decreased gradually, with increasing rainfall. The response of sap flow differed significantly between
rainfall, species, position within species during pulse duration, and the interactive effects also differed
significantly (P < 0.0001). The response pattern followed the threshold-delay model, with lower thresholds of ≤5.2 and 1 mm of rainfall for the stems and branches, respectively, demonstrating the importance
of small rainfall events (<5 mm) for plant growth and survival in desert regions.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
In desert regions, rainfall events can be characterized as rainfall
pulses with discontinuous, highly variable, and largely unpredictable frequency and intensity (Noy-Meir, 1973; Weltzin and
Tissue, 2002; Schwinning and Sala, 2004). Small events (≤5 mm) are
most common, whereas large events (≥10 mm) are infrequent (Sala
and Lauenroth, 1982; Loik et al., 2004). For example, rainfall events
of 5 mm or less accounted for 25% of total rainfall and 70% of the
events in a North American shortgrass steppe (Sala and Lauenroth,
1982), whereas rainfall events of 5 mm or less comprised 82% of
the events in the Heihe River basin (Zhang and Zhao, 2008). Similar
patterns occur in quite different arid and semiarid regions across
the globe, from the cold and warm deserts of Utah and Arizona to
the cold deserts of Patagonia (Golluscio et al., 1998; Schwinning et
al., 2002; Loik et al., 2004; Cheng et al., 2006).
Rainfall is a major driver of biological processes in arid ecosystems (Noy-Meir, 1973). Sap flow significantly accelerates, and plant
transpiration and respiration increase, after plants absorb the water
provided by large rainfall events (Schwinning and Sala, 2004),
and this triggers a cascade of responses that affect plant growth,
reproduction, and net ecosystem productivity (Stephenson, 1990;
∗ Corresponding author. Tel.: +86 931 4967137.
E-mail addresses: [email protected] (W. Zhao), [email protected] (B. Liu).
0168-1923/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.agrformet.2010.05.012
Reynolds et al., 1999). Consequently, these events alter the carbon and water balance of the desert ecosystem (Huxman et al.,
2004), and rainfall pulses could significantly drive the evolution
of the structure and function of desert ecosystems (Noy-Meir,
1973). Understanding the responses of sap flow to rainfall pulses is
therefore the basis for comprehending the physiological responses
of desert plants and how these responses determine the ecophysiological patterns of adaptation of desert plants to their
habitats.
Global climate change appears increasingly likely to increase the
variability of precipitation patterns. As a result, desert plants will
be forced to endure repetitive cycles of water scarcity followed by
rainfall response (Smith and Nowak, 1990; Jackson et al., 2001).
Understanding the mechanisms that underlie the responses of
desert plants to rainfall is a key problem in studies of the responses
of desert ecosystems to global climate change (Ehleringer et al.,
1999; Puigdefábregas and Pugnaire, 1999; Jiang, 2001; Yuan and
Deng, 2004). Since the 1970s, the two-layer partitioning hypothesis for soil water (Walter, 1971) and the pulse-reserve hypothesis
(Noy-Meir, 1973) have become the two major paradigms for understanding the responses of water-limited ecosystems to rainfall
(Ogle and Reynolds, 2004), but the mechanisms underlying these
responses have remained ambiguous. Ogle and Reynolds (2004)
developed a composite model that draws on earlier paradigms of
resource partitioning (Walter, 1971), thresholds (Beatley, 1974),
and pulse responses (Noy-Meir, 1973) to describe the response of
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W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
Fig. 1. The map of the Heihe River Basin and location in China.
different plants to rainfall in terms of a “threshold-delay” model.
This model permits analysis of the effects of individual pulses of rain
based on the physiological responses of plants (Ogle and Reynolds,
2004). A weakness of the model is that it is empirical rather than
mechanistic. Thus, no single parameterized threshold-delay model
can be expected to describe every system, and the model must be
parameterized for each site. However, many scientists have investigated the responses of the woody, shrub and herbage to rainfall
pulses using this model (Ogle and Reynolds, 2004; Robertson et al.,
2009; Ivans et al., 2006; Sponseller, 2007), and their results provide
a useful framework for evaluating plant responses to rainfall pulses
(Zeppel et al., 2008).
Rainfall pulses can rapidly increase sap velocity and alter the
water-use efficiency of vegetation (Zeppel et al., 2008). Some studies have indicated that the response of different plant functional
types to rainfall pulses and plant water-use strategies would vary
widely within a given area (Noy-Meir, 1973; Reynolds et al., 2004).
For example, shrub species in the Proteaceae, such as Isopogon gardneri rapidly increased their sap flow (by up to 5 times) after 34 mm
of rainfall in southern Australia, whereas deep-rooted eucalyptus species (Myrtaceae; e.g., Eucalyptus wandoo) were sufficiently
reliant on antecedent soil water that they did not respond to summer precipitation (Engel et al., 2005; Stewart and Burgess, 2006).
These results agreed with those reported by Xu and Li (2006) based
on studies of the photosynthesis of Haloxylon ammodendron and
Tamarix ramosissima. However, the results for the same species may
vary widely within a given area. For example, sap flow of eucalyptus species (e.g., Eucalyptus crebra) increased rapidly as rainfall
increased from 5 to 20 mm in eastern Australia (Morgan and Barton,
2008; Zeppel et al., 2008), but did not increase under comparable
conditions in southern Australia (Stewart and Burgess, 2006). In
addition, sap flow varies with respect to water availability in different soil layers (Nadezhdina and Čermák, 2003; Nadezhdina et al.,
2006, 2007), and in one study, the responses of sap flow to localized irrigation were more pronounced in the stem than in branches
(Nadezhdina et al., 2007). Studies of the response of sap flow to rainfall pulses are scarce around the world, and previous studies have
not quantified the response parameters and have only qualitatively
described differences between species. In general, desert shrubs
are dominant species in desert regions (Schwinning and Ehleringer,
2001), and Nitraria sphaerocarpa and Elaeagnus angustifolia are representative dominant shrubs in desert regions of China’s Heihe
River Basin. Therefore, we investigated the response of these shrubs
to rainfall events by measuring sap flow in their branches and
stems using stem flow gauges to confirm whether the thresholddelay model could be parameterized for these species and used to
assess various response variables (including the rainfall threshold
required to initiate a response). Our goal was to provide a scientific basis for understanding the mechanisms that underlie the
responses of these desert plants to global climatic change.
2. Materials and methods
2.1. Study area
The study area (Fig. 1) is located in a desert-oasis ecotone in
the middle of China’s Heihe River Basin, in Linze County of Gansu
province (between 39◦ 22 N and 39◦ 23 N, and between 100◦ 07 E
and 100◦ 08 E). The environment is dominated by a continental
arid temperate climate. The annual rainfall averages 116.8 mm,
and about 65% of the total rainfall falls, with low rainfall intensity,
between July and September; only 3% falls during the winter. The
potential evaporation is 2390 mm year−1 , and the dryness index
(evaporation divided by precipitation) is 20.54. The annual temperature averages 7.6 ◦ C, and the lowest and highest temperatures
are about −27.3 ◦ C in January and 39.1 ◦ C in July. The growing season is from May to October, and the frost-free period is about 165
days. The wind direction is mainly from the northwest, and the
wind speed averages 3.2 m s−1 , with frequent gales (wind speed
≥21 m s−1 ).
The typical zonal soil is characterized as a desert soil, and
highly susceptible to wind erosion due to its coarse texture (grains
between 0.05 and 0.25 mm in diameter account for 80–90% of the
total) and low vegetation cover (which ranges from 5 to 7%). The
W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
1299
Fig. 2. Installation of the sap flow gauges on the branches and stems of (A) N. sphaerocarpa and (B) E. angustifolia.
organic matter content ranges from 0.12 to 0.83%, and the soluble
salt content is less than 0.1% of the total ionic content. The landscape includes fixed, semi-fixed, semi-mobile, and mobile dunes,
as well as inter-dune lowlands. Desert shrubs are found on fixed
and semi-fixed dunes, including H. ammodendron, E. angustifolia, T.
ramosissima, and N. sphaerocarpa, as are annual herbaceous species
such as Bassia dasyphylla, Halogeton arachnoideus, Suaeda glauca,
and Agriophyllum squarrosum.
2.2. Measurements
2.2.1. Sap flow measurements
The experiments were carried out in the desert-oasis ecotone
from June to October 2008. A representative sample site on the
top of a fixed dune, 100 m × 100 m in area (1 ha), was selected,
and all vegetation and sap flow measurements were conducted
inside this plot. N. sphaerocarpa and E. angustifolia are representative dominant shrub which grow on sandy soils in study
site. The morphological parameters of N. sphaerocarpa except for
the LAI were smaller than those of E. angustifolia. E. angustifolia (mean height 1.35 ± 0.21) was at least 0.7 m taller than N.
sphaerocarpa. Basal diameter of N. sphaerocarpa and E. angustifolia were 6.26 ± 2.53 and 12.39 ± 6.60 mm, and crown length both
were 0.39 ± 0.14 and 0.61 ± 0.21 m, respectively. The root distribution of N. sphaerocarpa was shallower than 50 cm in soil, whereas
the roots of E. angustifolia ranged from 80 to 150 cm. Leaves are
needle, white tomentose on both surfaces, which provide high leaf
reflectance.
We used stem flow gauges (Flow32, Dynamax Inc., Houston, TX,
USA) with the energy balance method to measure sap flow in the
branches and stems of N. sphaerocarpa and E. angustifolia (Fig. 2). We
selected sample branches and stems without lateral ramifications,
and smoothed them to remove any superficial bark roughness using
a razor blade. To determine the influence of the environment on sap
flow differences between species and positions (stem vs. branch)
within a species, we attached gauges to branches and the basal stem
at least 40 cm above the soil surface. Based on the measurement
scale of the gauges and the characteristics of the shrubs, we used
model SGB3 and SGB5 gauges for the branches for N. sphaerocarpa
and model SGB9 gauges for its stems. We used model SGB9, SGB13,
and SGB19 gauges for the branches of E. angustifolia, and model
SGB25 and SGB35 gauges for its stems. We used three replicates for
each model gauge in each position. Table 1 presents the diameters
of the sampled shrubs.
The theoretical method and methodology of sap flow gauging
have been described previously by Smith and Allen (1996) and
Yue et al. (2008), and we carefully installed the gauges following the manufacturer’s instructions. We prepared the branch and
stem surfaces by sanding, and installed the gauges with a layer of
G4 silicon grease between the gauge and the bark. We wrapped
the gauges in aluminum foil to shield them from rain and direct
solar radiation, so as to reduce extraneous thermal gradients across
the heated section. Shelters were attached above the gauges, and
the joints were sealed with wax to prevent water from flowing
down the branches and stems into the gauges (Yue et al., 2008).
The data were recorded at 10-s intervals and stored as 30-min
averages using a CR1000 datalogger (Campbell Scientific, Logan,
UT, USA).
This study used the stem heat balance basics described in detail
by Kigalu (2007), and the energy balance is expressed as:
Qf = Pin − Qcd − Qr + Qs
(1)
where Qf is the amount of heat (W) transported in the moving
sap; Pin is the heater power input (W); Qcd is the heat conduction loss along the stem up and down stream (W); Qr is the
radial heat conduction loss (W); Qs is the heat stored in the stem
section (W).
Table 1
Diameters of the sample shrubs used for the sap flow measurements. Values represent means, with the standard deviation in parentheses.
Gauge type
Diameter (mm)
N. sphaerocarpa
E. angustifolia
SGB3 (branch)
SGB5 (branch)
SGB9 (stem)
SGB9 (branch)
SGB13 (branch)
SGB19 (branch)
SGB25 (stem)
SGB35 (stem)
3.11 ± 0.20
4.82 ± 0.19
8.84 ± 0.28
8.83 ± 0.29
13.15 ± 0.17
19.78 ± 0.92
25.35 ± 0.35
35.03 ± 0.30
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W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
Fig. 3. The frequency distributions on the rainfall and rainfall events.
The sap velocity (kg m−2 h−1 ) was calculated from the energy
balance across the sap flow meter which states that:
F=
Pin − Qcd − Qr + Qs
Cs × dTsap
(2)
where Cs is the specific heat capacity of the sap or water
(4.186 J g−1 C−1 ); dTsap is the temperature differential between the
heater and the stem section (◦ C).
conditions, and precipitation thresholds (Zeppel et al., 2008). The
model incorporates how quickly a plant responds, the magnitude
of the response, the duration of the response, and the thresholds below or above which no response, or no further response,
is evident. The resulting dynamic model of plant responses to
rainfall pulses can be expressed as follows (Ogle and Reynolds,
2004):
yt = kyt−1 + ıt
2.2.2. Meteorological measurements
We measured meteorological data so that we could analyze the
response of sap flow to rainfall pulses and other environmental factors during the study period. We installed a meteorological tower in
the experimental field, surrounded by a large area of desert shrubs.
The meteorological variables were wind speed, air temperature,
relative humidity, net and photosynthetically active radiation, soil
temperature, soil moisture, soil heat flux, rainfall, and atmospheric
pressure. These parameters were measured using an AG1000 automatic weather station (Onset Computer Corporation, Pocasset, MA,
USA). The sensors were installed at two levels above the ground (2
and 3 m). Rainfall was measured with a tipping-bucket rain gauge
(model TE525, metric; Texas Electronics, Dallas, TX). Volumetric
soil moisture was measured by means of probes (Decagon Devices,
Pullman, WA, USA), installed at eight depths below the soil surface
(10, 20, 30, 40, 50, 60, 80, and 100 cm). The meteorological data
were measured at a frequency of 10 Hz and recorded every 5 min
using a CR1000 datalogger (Campbell Scientific Inc., Logan, UT),
then stored as the 30-min mean data, whereas rainfall and wind
data were stored as the 10-min mean data. Soil water content was
measured every 2 days by means of oven-drying to validate the soil
moisture data provided by the automatic weather station during
the study period. We calculated the potential evapotranspiration
(PET) according to the Penman–Monteith model of Vörösmarty et
al. (1998).
2.3. Statistical analysis
2.3.1. The threshold-delay model
The threshold-delay model analyzes the effect of individual
pulses of rain based on the physiological responses of the plants
(Ogle and Reynolds, 2004). It is based on six parameters that capture the nonlinear nature of plant responses to rainfall pulses. The
rate of a plant’s response to rainfall pulses can vary based on species
or plant functional types, the delay in the timing of physiological responses, the effect of antecedent moisture and physiological
(3)
ıt = Min ymax (1 − k), ı∗t 1 −
ı∗t =
yt−1
ymax
⎧
ımax
L
L
U
⎪
⎪
⎨ RU − RL (Rt− − R ) R < Rt− < R
0
⎪
⎪
⎩
ımax
Rt− ≤ RU
(4)
(5)
Rt− ≥ RU
where yt is the rate variable (e.g., photosynthetic rate, transpiration
rate), yt−1 is the antecedent value of this variable, ymax is the maximum response value, ıt is the response variable, ı∗t is the potential
response variable, ımax is the maximum potential response variable, RL is the lower threshold of rainfall, RU is the upper threshold
of rainfall, Rt− is the effective rainfall, is the time lag, t is the
response time, and k is the reduction rate.
2.3.2. Data analysis
We analyzed the significance in response of sap flow to rainfall pulses using repeated-measures ANOVA to compare the main
effects and the interactive effects of rainfall, species, position (stem
vs. branch), and pulse duration using version 13.0 of the SPSS software (SPSS Inc., Chicago, USA). We identified the rainfall threshold
using ANOVA and Tukey’s HSD test (after testing for homogeneity
of variance and a normal distribution), and identified the threshold as the lowest rainfall event that differed significantly from the
antecedent value. We determined the parameters of the thresholddelay model by means of multiple linear regression. In addition, we
used the gap-filling approach of Falge et al. (2001), which involves
linear interpolation between the mean diurnal values when the differences between data were large, and removed low-quality data.
Simultaneously, the data were excluded for the following situations, to comply with the characteristics of a rainfall pulse: when
rainfall events lasted longer than 5 days or interpulse periods lasted
less than 1 week.
W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
1301
Fig. 4. The dynamic variation of rainfall pulses and soil water content (%). R is rainfall (mm); SWC10, SWC20, SWC30, and SWC40 are soil water content (%) at 10, 20, 30, and
40 cm below the soil surface, respectively.
3. Results and analyses
3.1. Rainfall and soil moisture characteristics
Rainfall data for the study region were collected from 1967
to 2008. The results indicated that the annual rainfall averaged
113.3 mm, with the following distribution of rainfall: ≤5 mm, 46.7%
of annual rainfall amount and 56.1% of the events; 5.1–10 mm, 27.7
and 18.0%, respectively; 10.1–15 mm, 13.0 and 3.6%; 15.1–20 mm,
4.8 and 2.7%; 20.1–25 mm, 2.8 and 0.9%; and >25 mm, 5.0 and 18.7%
(Fig. 3). The percentages of the total amount and frequency of events
decreased with increasing rainfall, and the trends during the experimental period (from June to October 2008) were similar to those
reported from 1967 to 2008. Small events (≤5 mm) were most frequent, whereas larger events (≥10 mm) were infrequent but had a
greater influence on total rainfall. Therefore, the rainfall pattern in
Fig. 5. The diurnal variation in the response of sap velocity to rainfall pulses. (A) Branches and (B) stems of N. sphaerocarpa. (C) Branches and (D) stems of E. angustifolia.
Before rainfall represent the day before a rainfall event, and after rainfall represent the day from the rainfall day to the time when the maximum occurred after the rain.
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W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
Table 2
Statistical analysis of the changes in sap velocity in response to rainfall pulses.
Fig. 6. The response of sap velocity to rainfall class. Values represent the mean ± SD
change in sap velocity from the first day before a rainfall event to the time when the
maximum sap velocity occurred after the rain (Tukey’s HSD test, P < 0.05).
Factor
df
F statistic
Rainfall
Species
Position (stem vs. branch)
Pulse duration
Rainfall × species
Rainfall × position
Species × position
Rainfall × species × position
Rainfall × pulse duration
Species × pulse duration
Rainfall × species × pulse duration
Position × pulse duration
Rainfall × position × pulse duration
Species × position × pulse duration
Rainfall × species × position × pulse duration
6
1
1
12
6
6
1
6
42
12
42
12
42
12
42
17.01***
50.62***
93.63***
16.13***
4.73***
3.72**
546.74***
15.63***
2.32***
1.45
1.19
1.16
0.90
5.41***
1.55*
*
the study area during the study period can be characterized as the
kind that is typical in arid regions.
During the experimental period, there were 14 rainfall events,
which produced a total rainfall of 67.9 mm and an average rainfall of 4.9 mm, with individual events ranging from 0.1 to 21.6 mm.
Soil moisture depended significantly on rainfall. Soil water content increased with increasing rainfall amount, and the dynamic
variation of soil water content increased intensely after rainfall,
then gradually decreased as a result of the evaporation (Fig. 4). Soil
water content in the upper 10 cm averaged 5.1%, with 32.1% of the
variation coefficient, which indicated that soil moisture fluctuated
significantly with increasing rainfall. Soil water content from 10 to
20 cm was the highest, with a maximum value of 5.2%. However,
soil water content and variation coefficient in deep-layer both were
very small.
**
***
P < 0.05.
P < 0.01.
P < 0.001.
3.2. Response of sap velocity to rainfall pulses
3.2.1. Diurnal variation in sap velocity responses
The diurnal variation in sap velocity in branches was best
described by a bimodal curve, whereas sap velocity in the stems
followed a unimodal curve (Fig. 5). Sap flow in the branches of
N. sphaerocarpa began between 07:00 and 08:00, and averaged
31.8 kg m−2 h−1 before rainfall, but began about 1 h earlier after
rainfall, with sap velocity as a daytime average increasing to 2.26
times its pre-rainfall value (Fig. 5A). Sap flow in the stems began
about 2 h earlier after rainfall, and the mean sap velocity increased
to 2.26 times its pre-rainfall value of 92.9 kg m−2 h−1 (Fig. 5B). How-
Fig. 7. Patterns in the threshold-delay data for sap velocity in response to rainfall pulses. (A) Branches and (B) stems of N. sphaerocarpa. (C) Branches and (D) stems of E.
angustifolia. R, rainfall (mm); Vs, sap velocity (g cm−2 day−1 ).
W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
1303
Table 3
The parameters of the threshold-delay model for changes in sap velocity in response to rainfall pulses. RL and RU are the lower and upper thresholds of rainfall (respectively),
yt−1 is the antecedent value of the variable (sap velocity), ıt is the response variable, k is the reduction rate in the absence of significant rainfall, and is the time lag.
Species
Position
RL (mm)
RU (mm)
yt−1 (kg m−2 day−1 )
ıt (kg m−2 day−1 )
k
(day)
N. sphaerocarpa
Branch
Stem
Branch
Stem
1
4.8
1
5.2
3.8
12
5.4
12
392.6
1111.5
603.3
358.6
499.8
527.5
1256.1
424.8
1.16
0.80
1.12
0.73
4.38
4.17
5.0
5.13
E. angustifolia
ever, sap flow in the branches of E. angustifolia began between
06:00 and 07:00 before rainfall, at a velocity of 34.4 kg m−2 h−1 ,
but began slowly increasing about 1 h earlier after rainfall and
sap velocity as a daytime average increased to 3.06 times its prerainfall value (Fig. 5C). Sap flow in the stems of E. angustifolia
followed a similar pattern to that of N. sphaerocarpa branches, but
sap velocity increased to only 2.05 times its pre-rainfall value after
rainfall. Based on these results, the response to rainfall was larger
in branches and occurred sooner than in stems, and the response
appeared to vary widely both between and within species. Sap flow
in the branches of E. angustifolia showed the largest response to
rainfall pulses, followed by sap flow in N. sphaerocarpa stems, N.
sphaerocarpa branches, and E. angustifolia stems.
after 3.8 and 5.2 mm of rainfall, respectively. The lag times ranged
from 4.17 to 5.13 days, and the values of E. angustifolia were larger
than that of N. sphaerocarpa.
3.2.2. Dynamic variation in the response of sap velocity
The percentage response of sap velocity increased significantly with increasing rainfall classes (P < 0.05), then gradually
decreased (Fig. 6). The response differed significantly among rainfall, species, positions (stem vs. branch), and pulse durations
(P < 0.0001; Table 2). The species × position × pulse duration and
rainfall × species × position × pulse duration interactions were significant. This means that sap flow in these desert shrubs responds
significantly to rainfall pulses.
The results indicated that the response pattern of sap flow
agreed with the description proposed by the threshold-delay
model. Sap flow in the branches responded significantly after only
0.6 mm of rainfall, and the differences in sap velocity were significant, but the difference was smaller than 5% in the stem, and
was nonsignificant. Sap flow in the branches increased rapidly after
3.8 mm of rainfall at August 28, until the velocity reached its maximum (1108.9 kg m−2 day−1 ) of N. sphaerocarpa at September 6
(Fig. 7A). Sap flow in the stem increased and the increase became
significant after 5.2 mm of rainfall (P < 0.05), and the sap velocity for E. angustifolia achieved its maximum (3167.3 kg m−2 day−1 )
through 10 day after 5.4 mm of rainfall (August 7) (Fig. 7C). However, the response variable of sap velocity and the time lag both
reached their maximum after 12 mm of rainfall. The sap velocity in the branches and stems of N.sphaerocarpa were 773.8 and
1791.2 kg m−2 day−1 , which represent increases to 4.38 and 4.76
times of the pre-rainfall values. In contrast, the response variables for the branches and stems of E. angustifolia were 1923.1
and 555.8 kg m−2 day−1 , and the sap velocity increased to 3.95 and
2.24 times, respectively, the pre-rainfall values. Sap flows in the
stems and branches reached their maximum velocities (2365.6 and
1003.2 kg m−2 day−1 , respectively; Fig. 7C and D). However, sap
velocity and the response variable gradually decreased as rainfall
increased beyond 21.6 mm.
We also determined the rainfall threshold by means of ANOVA
and Tukey’s HSD test, and determined the parameters of the
threshold-delay model by means of multiple linear regression
(Table 3). The lower thresholds of rainfall for branch of N. sphaerocarpa and E. angustifolia appeared after 1 mm of rainfall, while
the lower thresholds for stem in two species occurred after 4.8 and
5.2 mm of rainfall, respectively. The upper thresholds for stem in
two species were same, with a value of 10 mm rainfall. But, the
upper thresholds of branch were different, and the value occurred
4. Discussion
3.3. Response of sap velocity to soil moisture
The results revealed that the variation in sap velocity could be
expressed as a polynomial function of soil moisture (Fig. 8), and the
regression equation had a high coefficient of determination (R2 ).
Moreover, the R2 values were higher for the stems than for the
branches, which suggest that the response of stems to soil moisture
availability was more direct than the response of branches.
4.1. Response of sap velocity to soil moisture
Rainfall input at the soil surface triggers an infiltration pulse of
soil moisture, particularly in arid regions (Eltahir, 1998; Reynolds
et al., 2004). Generally, large rainfall events lead to larger pulses,
whereas small events may only be able to wet the uppermost
soil layers, where a large fraction of the soil moisture is lost by
direct evaporation (Beatley, 1974). Rainfall of 1 mm or less had little
impact on soil moisture (Fig. 4) as a result of the strong evaporative
demand in the study area, which agrees with the results reported
by Wei et al. (2008). There is considerable debate regarding the
importance of these small events (<5 mm) to plant growth and survival (Beatley, 1974; Sala and Lauenroth, 1982; Schwinning et al.,
2003); some have suggested that small rainfall events do not even
reach the roots of plants (e.g., Dougherty et al., 1996). However, in
a study on the Colorado Plateau by Schwinning et al. (2003), every
addition of approximately 1 mm increased the pulse duration for
infiltration amounts between 2 and 20 mm. In the North American
shortgrass steppe, a 5-mm event increased soil water potential in
the upper 5 cm for a 2-day period (Sala and Lauenroth, 1982).
Sala and Lauenroth (1982, 1985) also showed that small
amounts of rain stimulated the growth of grasses in semiarid
regions and concluded that these small events may provide a shallow source of moisture that is available to some plants. Desert
shrub for Caragana korshinskii also showed greater potential to use
stem flow water in the arid conditions (Li et al., 2008). Our results
showed that soil water content increased in the upper 20 cm of
the soil for 2–3 days (Fig. 4). Sap flow in the branches increased
rapidly after 3.8 mm of rain, but its maximum of sap velocity for N.
sphaerocarpa reached after a week. This suggests that even small
events (<5 mm) are not only important for plant growth and survival in desert regions, but also that the response are characterized
by a lag. Moreover, Sala et al. (1982) found that a large rainfall
event wetted the soil profile to a depth of 100 cm and improved
the soil water status for several weeks in a North American shortgrass steppe. Our results showed that large events are most likely
to produce soil moisture recharge at sufficient depth to induce and
maintain a significant soil moisture response (Fig. 4), leading to a
rapid response by sap flow in the stem. This finding validates the
biological significance of rainfall events >5 mm for desert shrubs.
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W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
Fig. 8. Changes in sap velocity in response to changes in soil moisture at 0–20 cm below the soil surface. Curves represent the results of polynomial regression.
Resource pulses induce a hierarchy of ecological responses to
rainfall events (Schwinning and Sala, 2004). A larger cumulative
effect is produced when soil moisture responds to frequent, small
events; as a result, the response amount and pulse duration for
soil moisture after consecutive rainfall of 3.3, 3.2, and 5.4 mm was
considerably higher than the corresponding changes after a single
rainfall of 12 mm (Fig. 4). Accordingly, sap velocity in the stem after
the 21.6 mm rainfall was lower than that after the 5.4 mm rainfall
(Fig. 7), since the latter event was preceded by two smaller events.
Plant responses to “biologically important” rainfall events is related
to the water sources exploited by the plants and to the importance
of seasonality, plant phenology, plant age, and antecedent conditions (Reynolds et al., 2004). For example, the sap response was not
significant after a 5.4 mm rainfall on 26 September (Fig. 7) because
that the antecedent water availability was higher owing to consecutive rainfall of 3.3 and 3.2 mm on 21 and 22 September, and because
the physiological function of the plants was reduced because this
rain fell during a late growth stage.
4.2. Response of sap velocity to rainfall pulses
The response of plants to rainfall is intimately correlated with
the characteristics of the rainfall event (Stewart and Burgess, 2006;
Fravolini et al., 2005); that is, the “pulse-reserve” paradigm for
desert ecosystems suggests a strong linear relationship between
pulses of rainfall and plant productivity (Reynolds et al., 2004). The
Fig. 9. Patterns of potential evapotranspiration in response to rainfall pulses. R, rainfall (mm); PET, potential evapotranspiration (mm day−1 ).
W. Zhao, B. Liu / Agricultural and Forest Meteorology 150 (2010) 1297–1306
rainfall threshold is generally governed by the plant’s water-use
patterns (Beatley, 1974; Ewers and Oren, 2000); the water source
on which plant survival depends appears to vary widely between
shallow- and deep-rooted plants, so the rainfall threshold may vary
among species and regions (Ewers and Oren, 2000). Time lags occur
in the plant response to rainfall pulses (Ogle and Reynolds, 2004);
for example, Artemisia tridentata and Purshia tridentata in the Great
Basin Desert responded within about 2 days to a range of summer
rainfall pulse magnitudes (Loik, 2007), whereas the hemi-parasitic
species Nuytsia floribunda (Loranthaceae) in southern Australia
required more than 2 weeks to fully respond to rainfall events
(Stewart and Burgess, 2006). Rainfall events ≤5 mm accounted for
56.1% of the events in the study region (Fig. 3). The lower stem and
branch rainfall thresholds were ≤5.2 and 1 mm for N. sphaerocarpa
and E. angustifolia, with lag times ranging from 4.17 to 5.13 days
(Table 3), which also indicated that these small rainfall events are
important for plant growth and survival. Therefore, the majority
of rainfall events in desert regions appear to be important for the
survival and growth of these desert shrubs.
The pulse-reserve paradigm suggests that desert plants do not
respond directly to rainfall, but rather respond to soil water availability (Reynolds et al., 2004; Robertson et al., 2009). Small rainfall
events can wet the plant surface in desert regions, and desert
plants can absorb rainwater adhering to their leaves and branches
(through lenticels) and use the water to increase sap velocity.
Large amounts of rainfall can effectively supply soil moisture and
improve soil water availability (Reynolds et al., 2004), leading to a
sap flow response in the stem after the rainfall. However, rainfall
will significantly increase the air’s relative humidity and decrease
air temperature when rainfall is higher than the upper threshold
for a species, which will result in a decrease in evaporation and
decrease sap velocity.
The rooting depth will also have a strong influence on the
response of desert plants to rainfall (Noy-Meir, 1973; Walter, 1971).
In desert regions, the root systems of N. sphaerocarpa have similar
horizontal and vertical distribution in sands shallower than 50 cm.
In contrast, the roots of E. angustifolia generally extend deeper.
Shallow-rooted plants will depend more dynamically on shallow
moisture, whereas deep-rooted plants that are more coupled to
deep soil water should be able to maintain their physiological
activity and growth for longer intervals following a moderate to
large rain event (Ogle and Reynolds, 2004). This suggests that
the difference in the rainfall threshold, response variables, and
time lag between N. sphaerocarpa and E. angustifolia resulted from
differences in their ability to exploit moisture at different rooting depths, which agrees with the pulse-reserve hypothesis. The
rainfall threshold for the branches of N. sphaerocarpa was lower
than that for those of E. angustifolia, possibly because the leaf
and bark texture of the former are coarser than those of the latter, which will effectively enhance their adsorption capacity and
increase soil water availability. The desert plants responded to
summer rainfall pulses by regulating their water potential and
photosynthesis; for example, Loik (2007) found that stem water
potential initially increased by about 2.00 MPa for A. tridentata and
1.00 MPa for P. tridentata following an 11.5 mm rainfall pulse in
the Great Basin Desert. In addition, stomatal conductance increased
by 0.3 mol m−2 s−1 and photosynthetic CO2 assimilation increased
eightfold for A. tridentata and sixfold for P. tridentate 2–3 days.
The variation in sap flow in plants is related not only to the
physiological characteristics, but also to environmental variables
(McDowell et al., 2008; Yue et al., 2008). The potential evapotranspiration (PET) is driven by meteorological variables and vegetation
characteristics, and is related to actual evapotranspiration, i.e. situations when available water is limited (Vörösmarty et al., 1998).
Fig. 9 showed that potential evapotranspiration responded to rainfall pulses, and the average values after rainfall were 30% more than
1305
those found before rainfall. However, at the same time sap velocity
increased two to threefold, compared to its pre-rainfall value. This
suggested that the differences in transpiration before and after rainfall were not caused by differences in potential evapotranspiration,
but by rainfall pulses. Soil moisture and nutrients are more readily
available to plants after a rainfall, which will enhance the ability of
the plants to respond to light conditions (i.e., to begin transpiration
and photosynthesis) and thereby allow sap flow to begin earlier.
Many authors have reported time lags between sap flow measured
at the base of trees and transpiration in the crown (Goldstein et
al., 1998; Ewers and Oren, 2000). Our results also showed that sap
flow in the stem in response to light was delayed more than sap
flow in the branches in the morning, which might be explained
by a slow stomatal response to light and to water conduction in
the stem (O’Brien et al., 2004). The photo-inhibition that occurs
in desert plants at noon (Liu and Zhao, 2008) would decrease the
transpiration rate in the crown and sap velocity in the branches.
However, the lag arises during the course of sap flow transmission
from the roots to the crown (Ewers and Oren, 2000), which would
not induce a “mid-afternoon depression” phenomenon for sap flow
in the stem.
5. Conclusions
The majority of rainfall events are small in desert regions. Small
rainfall events can wet the plant surface to increase sap velocity
in the branches. Large events can effectively supply soil moisture
and improve soil water availability, leading to a sap response in the
stems after the rainfall. The lower stem and branch rainfall thresholds were ≤5.2 and 1 mm for N. sphaerocarpa and E. angustifolia.
Therefore, small events are important for the survival and growth
of desert shrubs, and the greater frequency of smaller events may
give them a greater impact than less-frequent larger events on plant
physiological responses.
The magnitude of the responses to rainfall depends on the rooting pattern of the species. The results suggest that the difference in
the rainfall threshold, response variables, and time lag between N.
sphaerocarpa and E. angustifolia resulted from differences in their
ability to exploit moisture at different rooting depths, which agrees
with the pulse-reserve hypothesis. On the other hand, the magnitude of the responses also depends on the response of soil water to
rainfall, variation in sap flow velocity was expressed as a polynomial function of soil moisture.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (No. 30771767) and the key project of the National
Natural Science Foundation of China (No. 40930634). We thank
all participants in the vegetation and environmental surveys conducted at the Linze Inland River Basin Research Station, Cold and
Arid Regions Environmental and Engineering Research Institute,
Chinese Academy of Sciences. We also gratefully acknowledge the
journal’s anonymous reviewers for their valuable comments on an
earlier version of our manuscript.
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