Introduction
Poplar (Populus sp.) plantations are the most dominant
broadleaf forest ecosystems throughout northern and central China,
due to their rapid growth rates, high productivity and wide
adaptability (Gielen and Ceulemans, 2001; Wilske et al., 2009;
Zhang et al., 2011). Since the late-1970s, with the implementation
of the “Three-North Shelterbelt Program” (1978), the “Combating
Desertification Project” (1991), and the “Grain for Green Program
(1999; Wilske et al., 2009), poplar plantations have been playing
a vital role in timber production, bioenergy, urban greening,
desertification control, and carbon sequestration
(Martín-García et al., 2011; Zhou et al., 2013). By
2007, China had the largest poplar plantation area in the world
(over 7.0 million ha, Fang, 2008). However, indiscriminate use of
poplar species beyond their native range and habitats may result in
unanticipated consequences. For example, the use of poplars in
water-limited regions may increase the risk of environmental
degradation, soil moisture deficit, hydrologic and vegetation
changes (Gao et al., 2014).
Poplars require large quantities of water throughout the growing season, and
may experience water limitation even on the mesic sites (Kim
et al., 2008; Stanturf and Oosten, 2014). For example, poplar
plantations could cause the transformation of wetlands into drylands due to
the water-pumping effect on groundwater (Li et al., 2014;
Migliavacca et al., 2009). Thus, poplar plantations have
higher productivity, but also higher water use (Zhou et al., 2013) than other tree species.
The intensive land use practices in northern China over the past 50 years,
supported by irrigation, are thought to have triggered the decline in its
water table, land degradation, and increases in surface air temperature and
severe droughts (Ding et al., 2007; Qiu et al.,
2012; Wang et al., 2008). Therefore, understanding the
contribution of current land cover, including the effect of the poplar plantations on
regional water resources is essential for long-term sustainability of
ecosystem services and human wellbeing in this region. To date, most
researchers have concentrated primarily on the water balance of forest
ecosystems, with less emphasis on the relationship of forest ecosystems to
their environmental setting. Much can be learned from exploring the
partitioning of available energy and ecosystem responses to meteorological
forcing such as droughts. Not only are these of central importance for
understanding the water and carbon balance (Guo et al.,
2010; Jamiyansharav et al., 2011; Sun et al.,
2010; Takagi et al., 2009; Wu et al., 2007), but
they also help elucidate the degree to which forest water use is in balance
with supply from precipitation, and hence the degree to which plantations
located in water-limited regions are sustainable in the long-term.
To investigate the variations of energy partitioning and associated
evapotranspiration of poplar plantation under different climate conditions
and to highlight the management strategies for such plantation forests in water-
limited regions, we evaluated energy partitioning at different water
availabilities in a 10-year-old poplar (Populus euramericana cv. “74/76”) plantation on sandy
soil in northern China. We hypothesized that drought would trigger
significant increases in the surface resistance and affect energy
partitioning via increasing the Bowen ratio. Specifically, the objectives of
this study were to: (1) quantify the seasonal and inter-annual variability
of energy partitioning and bulk resistance parameters; (2) partition the
control of energy partitioning to biological and climatological components;
and (3) evaluate the long-term potential impact of poplar plantations on the
availability of water for adjacent ecosystems and livelihoods in
water-limited regions.
Materials and methods
Study site
The study was carried out in a managed poplar (Populus euramericana cv. “74/76”) plantation at
the Daxing Forest Farm, which is located in the southern suburbs of Beijing,
China (116∘15′07′′ E, 39∘31′50′′ N, 30 m a.s.l.). The trees were planted in 1998 with 3 m × 2 m
spacing; dead or low-vigor trees were replaced with new saplings in 2001 and 2003.
The stand characteristics over the 4 years of study are provided in Table 1. At the end of 2009, the average height of the trees was 16.2 ± 1.6 m
(mean ± SD), and the diameter at breast height (DBH) was 14.1 ± 1.6 cm. The average leaf area index (LAI) of the stand increased over time.
During the growing season, understory shrubs were kept at low density by
manual removal. Perennial herbs included Chenopodium glaucum Linn., Medicago sativa L.,
Melilotus officinalis (L.) Lam., Salsola collina Pall., and Tribulus terrestris L.
The stand characteristics of 4 years from 2006 to 2009, including
the minimum, maximum, and mean temperature (T), the annual precipitation (P),
evapotranspiration (ET), irrigation (I), canopy height (H), breast height
diameter (DBH), and leaf area index (LAI). The error estimates are standard
deviation (SD).
Tmin
Tmax
Tmean
P
ET
I
H
DBH
LAI
(∘C)
(mm)
(mm)
(mm)
(m)
(cm)
(m2 m-2)
2006
-10.6
29.7
12.5 ± 0.73
482
599
86
11.5 ± 1.1
10.8 ± 1.5
1.6 ± 0.3
2007
-9.8
29.5
13.0 ± 0.55
667
560
–
13.0 ± 1.3
12.2 ± 1.8
2.1 ± 0.4
2008
-7.4
28.8
13.3 ± 0.54
662
653
–
14.8 ± 1.2
13.8 ± 1.8
2.2 ± 0.7
2009
-10.2
30.5
12.5 ± 0.60
428
511
195
16.2 ± 1.6
14.5 ± 1.6
2.9 ± 0.4
The local climate is classified as a sub-humid warm temperate zone, with a
mean (1990–2009) annual temperature of 11.6 ∘C; maximum and
minimum temperatures are 40.6 and -27.4 ∘C,
respectively. The annual precipitation ranges from 262 to 1058 mm
(1952–2000), with an average of 556 mm, of which 60–70 % falls from
July to September (Daxing Weather Station, 116∘19′56 E,
39∘43′24 N). The annual frost-free period lasts 209
days, and the total hours of sunshine reaches 2772 h per year with 15.5 MJ m-2 d-1 of incoming solar radiation.
The average wind speed is 2.6 m s-1and it mostly comes from the southeast (during the growing season)
and the northwest (outside of the growing season).
The study area is on the alluvial plain of the Yongding River, and is flat
with an average slope of < 5∘. The top 2 m of the
soil is mostly composed of well-drained fluvial sand with a bulk density of
1.43–1.47 g cm-3, and a pH of 8.25–8.39. The soil porosity is
about 40 % and capillary porosity is 32 %. The mean groundwater depth
over the past 9 years (2001–2009) was 16.5 ± 0.2 m, and declined at
an average rate of 0.6 m per year. The maximum pan evaporation occurs from
May through June, exceeding precipitation for the same period. Severe
drought during the beginning of the growing season (from April to June) in
northern China is common. The site was irrigated using pumped
groundwater, and the amount of water supplied was estimated from the water
meter records at the three adjacent wells on a weekly basis from 2006
through 2009. Other management practices have included tilling and weeding
since the establishment of the plantations.
Eddy covariance system
The micrometeorological and eddy flux measurements were conducted at a 32 m
tower in the center of the study site, which was established in June 2005.
The footprint of the eddy flux covariance system, was about 1 km × 1 km in
size, with a fetch of at least 300 m in all directions. Fluxes of CO2,
sensible heat, and latent heat were calculated based on eddy-covariance
(EC) principles. The sensors included a CO2/H2O infrared analyzer
(Li-7500; LI-COR, Inc., Lincoln, NE, USA) and a three-dimensional sonic
anemometer (CSAT-3; Campbell Scientific, Inc., CSI, UT, USA). The anemometer
head was installed towards a predominant wind direction (southeast), and the
IRGA was installed at a slight vertical angle tilted northward (< 20∘) between the sonic path and anemometer body. The IRGA was calibrated
every year. The EC sensors were mounted initially at a height of 16 m in
2006. This was increased to about 18 m before the start of the growing
season in 2007, and again to 20 m in February 2009 to ensure that the
sensors remained well above the tree canopy.
Net radiation was measured with net radiometers (Q7.1, REBS, Seattle, WA,
USA) and (CNR-1; Kipp and Zonen, Delft, Netherlands) at 26 m above the
ground. Photosynthetically active radiation (PAR) was measured with a PAR
quantum sensor (LI-190SB; LI-COR, Inc.) mounted at 20 m. The atmospheric
pressure was measured by a barometric pressure sensor (CS105, CSI) at a height
of 21 m. Air temperatures and humidity were measured with a temperature and
relative humidity probe (HMP45C; Vaisala, Helsinki, Finland) at 5, 10, 15,
and 20 m above ground. Precipitation was measured with a tipping bucket rain
gauge (TE525-L; Texas Electronics, USA) at 22.5 m. Soil heat flux and soil
temperatures were each measured with three soil heat transducers
(HFT3, CSI) and three thermocouples (TCAV107; CSI) located at depths of 5,
10, and 20 cm below the soil surface. Soil water content was measured with
TDR sensors (CS616; CSI) buried at 20 and 50 cm.
With the exception of the rain gauge, all microclimatic data were recorded
with a data-logger (CR23X; CSI) at 30 min intervals and the fluctuations in
wind speed, sonic temperature and CO2 and H2O concentrations were
sampled at 10 Hz and recorded by a CR5000 data-logger (CSI).
2.3. Data processing and QA/QC
The 30 min mean fluxes were calculated from raw 10 Hz data with an EC
Processor software, version 2.3 (Noormets et al., 2010).
The program is designed for reprocessing EC flux data and can calculate
half-hour mean eddy-covariance fluxes of carbon, water, and energy. The wind
coordinates were rotated using the planar fit method (Paw U
et al., 2000; Wilczak et al., 2001). Fluxes were corrected for
additional sensor heating (Burba et al.,
2008) and fluctuations in air density (Webb et
al., 1980). The data quality controls included: screening of the 30 min
mean eddy-covariance fluxes based on instrument quality flag, integral
turbulence characteristics (Foken and Wichura,
1996), flux stationarity, atmospheric stability, and adequate turbulent
mixing (Goulden et al., 1996). The threshold
of friction velocity (μ*) below which flux loss occurred was
determined from the seasonal binned relationship between nighttime turbulent
flux of CO2 and friction velocity (μ*; Schmid et al., 2003). The threshold was consistent across different
seasons, but differed slightly between years: 0.18 (2006), 0.12 (2007), 0.14 (2008) and 0.13 m s-1 (2009). Data
gaps shorter than 2 h were filled using linear regressions between the
flux of interest and net radiation (Rn), gaps between 2 h and 7 days
in length were filled using the mean diurnal variation (MDV) method
(Falge et al., 2001), and gaps longer than 7 days were not
filled.
The 4-year study period was distinctively classified into “wet” and “dry” years. A dry year
referred to a year with annual precipitation less than 85 % of the 20-year average
according to the National Standard of the People's Republic of China (GB/T 20481-2006; China, 2006)
and “wet” when above this average. Years 2007 and 2008 were classified as `wet'
while 2006 and 2009 were `dry' years. We focused on the growing
season when the environmental forcing (e.g., solar radiation and
temperature) for energy and water fluxes and the physiological response of
vegetation were usually strong. In this study, the strongest forcing days
occurred approximately between day 100 (mid-April) and day 300 (late
October). The daytime was defined as the period between sunrise and
sunset with PAR > 4 µmol m-2 s-1. The regulation of
surface energy and gas exchange is often different during nocturnal periods
(Mahrt, 1999), with heat fluxes at night typically
weaker and markedly less stationary than during the daytime (Wilson et al., 2002b). Midday was defined as the period from 10:00 to
15:00 LST when the coupling between vegetation and the atmosphere was the
strongest.
Biophysical characteristics
The availability of relative extractable water (REW) content was calculated
to analyze the ecosystem response to drought stress. According to
Granier et al. (2007), soil water stress was assumed to occur when the
REW dropped below the threshold of 0.4. Daily REW is calculated as
REW=VWC-VWCminVWCmax-VWCmin,
where VWCmin and VWCmax are
the minimum and maximum soil volumetric water content, respectively, across the 4
years.
The Bowen ratio (β) reflects the influence of microclimate and the
hydrological cycle on the energy partitioning and water use of the ecosystem
(Perez et al., 2008). The midday β is calculated as Eq. (2):
β=HLE.
Based on the daytime half-hourly and daytime totals of turbulent energy
fluxes, the energy balance ratio (EBR) is calculated as Eq. (3):
EBR=∑(H+LE)∑(Rn-G-S),
where S is the latent and sensible heat storage in the air-column below the
EC system and is calculated as in Eq. (4) (Dou et al.,
2006):
S=∫0hcρcp∂T∂tdz+∫0hcρcpγ∂e∂tdz,
where hc is the height of the eddy flux system measurement (32 m), T is air
temperature in the air-column below hc, and e is water vapor pressure.
During midday periods (10:00–15:00 LST), the Penman–Monteith approximation was inverted to
calculate the surface resistance (Rs) in Eq. (5) (Kumagai
et al., 2004):
Rs=ρcp(δe/LE)γ+Δγβ-1Ra,
where Rs is the surface resistance to water vapor transport (s m-1), representing four components: bulk stomatal resistance of the
canopy, bulk boundary layer resistance of the vegetation, bulk ground
resistance, and bulk boundary layer resistance of the ground (Admiral et al., 2006; Cho et al., 2012; Perez et
al., 2008; Wilson et al., 2002b).
Ri is the climatological resistance (s m-1) indicating the
atmospheric demand (Wilson et al., 2002b) and is calculated
as
Ri=ρcpδeγA,
where A is the available energy (Rn-G); ρ is air density (kg m-3), cp is the specific heat of the air
(1005 J kg-1 K-1); δe is the atmospheric vapor pressure deficit (Pa);
LE is the latent heat flux; Δ is the change of saturation vapor
pressure with temperature (Pa K-1); γ is the psychrometric
constant (≈ 67 Pa K-1); and β is the Bowen ratio.
Ra is the aerodynamic resistance of the air layer between the canopy
and the flux measurement height (s m-1), that reflects the aerodynamic
properties of turbulent transport in the near-surface boundary layer
(Holwerda et al., 2012; Zhang et al., 2007).
Ra is calculated following Hossen et al. (2011)
and Migliavacca et al. (2009):
Ra=ra, m+rb=μμ∗2+6.2μ∗-2/3,
where ra, m is the aerodynamic resistance for momentum transfer, rb is
the quasi-laminar boundary-layer resistance, μ is the wind speed, and
μ* is the friction velocity.
The decoupling coefficient (Ω) explains the degree of coupling
between the atmosphere and the vegetation, and describes the relative
control of evapotranspiration by surface resistance and net radiation
(Pereira, 2004). The Ω value ranges from 0
to 1, with values approaching zero indicating that LE is highly sensitive to
surface resistance and ambient humidity deficit. The Ω value
approaching 1 indicates that LE or evapotranspiration is mostly controlled
by net radiation (Jarvis and McNaughton, 1986):
Ω=Δ+γΔ+γ(1+RsRa).
The equilibrium evaporation (LEeq) is the climatologically determined
evaporation (atmospheric demand) over an extensive wet surface and is
dependent only on Rn and temperature. It is calculated as
LEeq=Δ(Rn-G)Δ+γ.
The ratio LE / LEeq, which is also known as the
Priestley–Taylor α, reflects the control of evaporation by
atmospheric and physiological factors. LE / LEeq
characterizes the surface dryness of an ecosystem, indicating
whether soil water supply for evapotranspiration of an ecosystem was
limited. An LE / LEeq of < 1 indicates
water stress and suppressed evapotranspiration. Conversely,
LE / LEeq > 1.26 indicates unrestricted
water supply, and only available energy limits evapotranspiration
(Arain et al., 2003). The LE / LEeq is dependent
on the leaf area index (LAI), soil water content, meteorological
conditions (e.g., wind speed, solar radiation, VPD (vapor pressure
deficit, air stratification stability, convection, and advection
surface resistance), vegetation types, and altitude (Guo et al.,
2008).
Statistical analysis
Repeated ANOVA (SPSS) measurements were used for quantifying the changes of
all the biophysical variables, energy fluxes, and bulk parameters across
years. The t test was used to compare the differences of biophysical
variables among different studies. The partial correlation analysis was used
to distinguish the impacts of each of the three resistance parameters
(Rs,Ri, and Ra) on the Bowen ratios. All analyses were accessed at
α= 0.05.
Results
Environmental conditions
The annual precipitation rates in the 4 years of study differed from
the long-term (i.e., 1990–2009) average (556 mm yr-1). Thus,
years 2006 and 2009 were drier and 2007 and 2008 were wetter than
the mean (Table 1). The interannual contrast was exaggerated by the
seasonality of rainfall. Generally, over 90 % precipitation of
each year occurred in April–October, but with different timing and
magnitude among the years. The study site was irrigated during the
dry years of 2006 and 2009 to mitigate drought conditions (Fig. 1).
Seasonal drought stress (REW < 0.4) occurred during
periods in the late growing season of 2006 and 2009, the spring of
2007 and 2009, but not at all in 2008 (Fig. 2a–d). In 2006,
precipitation during the growing season reached 467 mm, of which
51 % had occurred by July. The amount of irrigation was 35 mm
in April, 21 mm in May, and 30 mm in September. The two separate
seasonal drought periods were #1_06 (from DOY – day of year –
164 to 192) and #2_06 (from DOY 231 to 300). The total rainfall
in 2007 and 2008 was similar, but more evenly distributed throughout
the year in 2008. In 2007, drought stress occurred during DOY
110–143 (#1_07) and 151–200 (#2_07). A single rain event
in late May (57 mm) and a few large precipitation events
(> 25 mm d-1) in July were recorded. The amount
of rainfall in 2009 was the smallest among the 4 years, during which
195mm of irrigation was applied from March to September. There were
several short and scattered droughts across the growing season in
2009 (Fig. 2d). Despite the higher than normal rainfall in the 2 wet
years, there was no flooding or overland runoff.
The cumulative precipitation (P) and periodic irrigation during
2006–2009; irrigation in 2006 and 2009 were separately represented by the
solid and dotted brackets, respectively.
The growing season Ta in 2008 was significantly lower than in 2007
and 2009 (dT = 1.3 ∘C, p < 0.05, Fig. 2e–h). The years
differed in the spring warm-up and the timing of peak temperature (by up to
35.9 ∘C). The maximum air temperature occurred in June 2006 and
2009, and in July 2007 and 2008. The warmest month was June 2006 (27.1 ± 2.4 ∘C).
The daytime average VPD of the four growing seasons (Fig. 2e–h) was 1.3 ± 0.7 kPa. The mean VPD in wet years
(i.e., 2007 and 2008) was 1.2 ± 0.7 kPa, which was significantly lower (F= 6.093, p < 0.01)
than in dry years (i.e., 2006 and 2009, 1.3 ± 0.8 kPa). The VPD of
the growing season in 2008 (i.e., 1.1 ± 0.5 kPa) was lower than in the other years
(p < 0.05). Higher Ta and lower precipitation
in May 2007 led to higher VPD compared to the same period in 2006 and 2008
(p < 0.001). Furthermore, the VPD was the highest in June 2009 (i.e.,
2.3 ± 1.1 kPa, p < 0.05) and the lowest in 2008 (i.e., 1.0 ± 0.5 kPa, p < 0.01).
The seasonal variation of environmental conditions during
2006–2009, (a–d) the relative extractable water (REW; drought periods longer
than 20 days are shaded), daily sum of precipitation (P); (e–h) daytime mean
air temperature (Ta), daytime mean air vapor deficit (VPD).
Seasonal changes in energy partitioning and β
The energy partitioning trends of daytime total net radiation (Rn) into
latent, sensible heat fluxes (LE and H), soil heat fluxes (G), and heat storage
of canopy (S) for the years 2006–2009 were presented in Fig. 3. Among these
years, Rn varied with solar radiation (R > 0.95, α
= 0.01 level), reached the maximum in July, and gradually decreased until
late October (in dry years) or November (in wet years). During the
growing season, there were no significant differences in average daytime
total Rn between wet and dry years. The average daytime total G during
the growing season displayed great seasonal and annual differences among
the years (p < 0.05), with a lower value in wet years (2.1 % in
2007) than in the dry years (4.9 % in 2006; p < 0.001). Moreover,
the average values of daytime total S among the four growing seasons were
0.46, 0.49, 0.51, and 0.54 MJ m-2,
respectively. S/Rn varied from 6.0 % in 2007 to 6.8 % in 2009,
showing no differences between the wet and dry years.
Partitioning of Rn into LE and H differed significantly between the wet
and dry years (Table 3; F= 17.599, p < 0.001). The dominant
turbulent energy flux during the early growing season was sensible heat flux
(H) with or without drought stress, except in 2006 when the irrigation was
applied (Table 3). Then LE was the dominant driver of energy partitioning
during the middle and late growing seasons under drought stress. The average
daytime total LE was about 20 % greater in wet years (6.77 MJ m-2)
than in dry years (5.72 MJ m-2, p < 0.01). The timing of peak LE
was weakly related to drought, peaking in July in 2006, 2008, and 2009, and
in August in 2007. The peak values of daytime total LE were 16.61,
17.01, 19.72, and 16.27 MJ m-2, in 2006–2009
respectively. The daily evaporative fraction (LE/(Rn-G)) was significantly
higher in wet years (60.3 and 64.8 % in 2007 and 2008, respectively) than in dry years (57.1 and 50.4 % in 2006 and 2009,
respectively; p < 0.05).
Seasonal patterns of daytime energy components (5-day running
average) during the growing seasons from 2006 to 2009, including net
radiation (Rn), latent heat (LE), sensible heat (H), soil heat flux
(G), and heat storage term (S).
The seasonal variation of the midday Bowen ratio (β) displayed a
rapid and significant trend across the growing season, especially at the
beginning (April–June) and the end (September–October) of the growing
season (Fig. 4). The Bowen ratios during the middle of the growing seasons
were all smaller than 1, and approximately lasted from DOY 180–250 in the
dry year and from DOY 180–290 in the wet years. The average midday β
in the dry years was greater (1.57) than in the wet years (0.83;
F= 19.176, p < 0.001). The Bowen ratio showed differences in response
to drought stress across the four growing seasons (Table 3); with much
higher values (> 1) during the drought periods in 2007 and 2009,
but not in 2006. The Bowen ratio was smaller than 1 during drought-stressed
periods in 2008.
Biophysical controls of energy partitioning
The Rs varied widely at the beginning and the end of growing season, but
changed steadily within a low range during the middle of growing season by
comparison. Moreover, these lower Rs values in the dry years lasted for a shorter
period (DOY 190–250) than in the wet years (Fig. 5a). A significantly
negative relationship was found between the Rs and LAI during the wet
years (Fig. 6). Overall, the seasonal average of Rs normalized by leaf
area index (LAI; i.e., Rs:LAI) was lowest during the wettest year
(2008, 54.1 s m-1 LAI-1; p < 0.05). The Rs:LAI in the
dry years (106.8 s m-1 LAI-1) was 50 % higher than in the wet
years (71.2 s m-1 LAI-1; p < 0.001). The Rs:LAI in the
seasonal drought-stressed periods in 2006, 2007, and 2009 were much higher
than those during unstressed periods (p < 0.001, Table 3).
The average midday Ri peaked in June and decreased in July/August
before reaching a second peak in October (Fig. 5b). The seasonal average
Ri during the growing season ranged from 68.3 to 77.9 s m-1, with a mean value of 74.4 s m-1, and showed no difference
among the four growing seasons (p > 0.05). Figure 5c presents the
seasonal and annual variations of midday Ra. The average Ra for
the four growing seasons was 23.2 ± 8.5 s m-1, ranging from 10.6
to 43.5, 9.7 to 52.5, 6.5 to 43.1, and 9.7
to 74.5 s m-1, from 2006 to 2009, respectively. Ra in 2007 was
significantly higher than in the dry years (p < 0.01), while Ra in
2008 was smaller than in the dry years (p < 0.001). However, there
were no significant differences between dry and wet years Ra (p > 0.05).
The seasonal changes of LE / LEeq values varied between 0.4 and 1.0
during most of the growing seasons (Fig. 5d). The average LE / LEeq of
the 4 years were 0.76, 0.73, 0.89, and 0.63, respectively. The mean
LE / LEeq of the dry years (0.68) was lower than that of wet years
(0.81; p < 0.001). Specifically, the value of LE / LEeq in
drought periods of 2007 and 2009 were much smaller. A significantly
exponential relationship existed between the LE / LEeq and Rs during the growing season (Fig. 7).
Seasonal and inter-annual variability of the midday (10:00–15:00 LST) mean Bowen ratio
(β; 5-day running average) across the growing
seasons, with detailed β during DOY 185–255 represented in the small pane.
The decoupling coefficient (Ω) across the growing seasons peaked in
mid-July in 2008 and in early August in the other years (Fig. 5e). The mean
Ω for the 4 years was 0.41, 0.46, 0.43, and 0.39 (Table 3),
respectively, and was significantly higher in wet years (0.45) than in
dry years (0.40; F= 9.460, p < 0.01). Compared to the value during unstressed
periods, the decoupling coefficient during the seasonal drought periods
(#1_06, #2_06; #1_07,
#2_07 and #1_09, #2_
09, #3_09) was much lower in value.
Seasonal dynamics of the midday (10:00–15:00 LST) mean surface
resistance (Rs), climatological resistance (Ri), aerodynamic
resistance (Ra), LE / LEeq, and decoupling coefficient (Ω)
(5-day running average) across the growing seasons from 2006 to 2009.
Discussion
Energy partitioning and Bowen ratio
The energy balance ratio (EBR) in the current study was 0.88 based on
daytime 30-min fluxes and > 0.96 based on daytime totals
(Table 2). The annual mean EBR at our site was similar to the values
of eight ChinaFlux sites, which averaged 0.83 and ranged from 0.58 to 1.00
(Li et al., 2005). The energy budget is also consistent with
the 50 site-years of flux data from 22 FLUXNET sites, which had energy
closure of 0.34–1.69 (Mean = 0.84, Wilson et
al., 2002a). A recent analysis of 173 FLUXNET sites also found an average
closure of 0.84 (Stoy et al., 2013), although the authors
also detected consistent differences among the biomes based on metrics
of landscape heterogeneity. In addition to the known reasons for decreasing
energy balance closure (Hernandez-Ramirez et al., 2010;
Li et al., 2005; Nakai et al., 2006;
Stoy et al., 2013), management operations at our site (e.g., irrigation,
tilling, and partial felling) may also affect the energy balance. Although
the causes of surface energy balance closure continue to be debated
(Stoy et al., 2013) and will not be conclusively answered in
the current study, the results reported here are similar to other FLUXNET
sites.
The relationship between leaf area index (LAI) and surface
resistance (Rs) during the growing season of the wet and dry years.
The relationships between surface resistance (Rs) and
LE / LEeq (Priestley–Taylor coefficient) during growing season of the wet
(a) and dry (b) year.
Energy balance closure statistics using half-hourly and daytime
totals during growing seasons from 2006 to 2009.
Daytime
Daytime sum
2006
2007
2008
2009
2006
2007
2008
2009
Slope
0.92
0.87
0.92
0.82
1.07
0.91
1.04
0.84
Intercept
20.50
17.24
10.72
13.08
-0.63
-0.09
-0.79
-0.30
R2
0.81
0.80
0.81
0.82
0.88
0.81
0.92
0.82
Daytime was defined as the period between sunrise and
sunset with PAR > 4 µmol m-2 s-1; the unit of intercept for half-hourly
values and the daytime sum value were W m-2 and MJ m-2, respectively.
The surface energy partitioning to sensible and latent heat depends
on water potential gradient and surface resistance (Arain et al.,
2003; Baldocchi et al., 2000; Chen et al., 2009). Canopy
development (Guo et al., 2010), rainfall dynamics and irrigation
(Ozdogan et al., 2010) affect these properties to some extent and
could directly lead to a change in soil moisture and the evaporation
component of LE, thereby impacting energy partitioning and β
(Chen et al., 2009; Ozdogan et al., 2010). However, the impact
of precipitation on the Bowen ratio may vary, even at the same site (Tang et al., 2014). In our study,
detectable responses of LE/(Rn-G) and the Bowen
ratio to drought stress and non-stress periods were observed in
response to soil water supply (Table 3) with a 50 mm threshold on
average (Fig. 8). The variability of energy partitioning during the
growing season was highly sensitive to water availability from
precipitation and irrigation. On an annual scale, the Bowen ratio
appeared linearly related to the total growing season precipitation
(R2= 0.89, p < 0.05). Thus, the Bowen ratio is
very responsive to the site water supply. A similar finding was
reported by Grünwald and Bernhofer (2007) in a temperate
spruce forest.
The value of the soil water supply (WS), energy partitioning ratios,
and biophysical variables in the different growing seasons during 2006–2009.
Year
Periods
WS
LE/(Rn-G)
H/(Rn-G)
β
Rs
Ri
Ra
α
Ω
(DOY)
(mm)
(%)
(%)
(s m-1)
(s m-1)
(s m-1)
100–163
76.2+56
50.5 (23.4)
45.9 (19.7)
3.48 (6.37)
418.7 (528.7)
87.8 (30.2)
20.0 (6.3)
0.64 (0.35)
0.25 (0.13)
2006
164–192d
127.8
68.0 (13.3)
33.2 (11.1)
0.66 (0.35)
184.0 (94.7)
94.9 (45.2)
23.8 (5.1)
0.79 (0.19)
0.42 (0.14)
193–230
219.6
77.7 (11.9)
13.8 (6.7)
0.19 (0.13)
50.4 (29.9)
51.5 (16.4)
27.8 (8.6)
1.01 (0.24)
0.70 (0.12)
231–300d
43
51.9 (12.7)
31.7 (11.6)
0.94 (0.52)
178.5 (68.8)
77.4 (27.5)
25.6 (6.8)
0.69 (0.23)
0.36 (0.14)
100–143d
61.8
35.2 (6.4)
57.8 (8.3)
2.37 (0.66)
426.9 (148.8)
96.1 (29.4)
18.1 (5.4)
0.41 (0.13)
0.16 (0.07)
2007
151–200d
146.8
49.5 (18.2)
37.0 (17.7)
1.41 (1.06)
314.1 (225.6)
91.7 (42.8)
25.3 (7.1)
0.58 (0.23)
0.35 (0.16)
200–300
396.8
66.0 (16.3)
15.5 (8.5)
0.35 (0.32)
74.1 (27.3)
61.1 (22.7)
30.4 (9.2)
0.87 (0.20)
0.60 (0.15)
100–117
53.4
16.3 (14.1)
71.8 (9.7)
1.86 (1.12)
206.9 (102.0)
60.7 (22.9)
13.6 (4.1)
0.59 (0.35)
0.21 (0.14)
118–155d
15.6
58.8 (12.3)
39.5 (10.7)
0.71 (0.36)
130.8 (48.6)
81.1 (32.3)
14.7 (4.2)
0.81 (0.23)
0.31 (0.11)
156–188
212.7
68.1 (14.6)
33.3 (10.7)
0.35 (0.23)
70.2 (33.4)
56.1 (20.6)
19.3 (5.9)
0.94 (0.23)
0.53 (0.14)
2008
189–212d
26
73.5 (12.7)
20.4 (7.5)
0.18 (0.15)
59.3 (27.1)
67.4 (41.1)
27.8 (6.8)
1.07 (0.25)
0.68 (0.11)
213–239
173.4
74.8 (11.9)
11.8 (6.2)
0.24 (0.16)
61.5 (23.7)
55.8 (14.3)
19.3 (5.2)
0.92 (0.14)
0.57 (0.10)
240–251d
19.2
60.4 (12.6)
23.4 (9.9)
0.42 (0.22)
88.7 (34.6)
60.4 (15.3)
18.0 (4.1)
0.87 (0.21)
0.46 (0.10)
252–300
116.2
47.2 (5.7)
39.2 (3.6)
0.41 (0.22)
72.1 (17.8)
57.3 (28.9)
18.4 (4.4)
0.85 (0.23)
0.48 (0.10)
100–158d
37.6+52
36.0 (16.5)
48.8 (13.4)
1.90 (0.83)
298.9 (150.8)
84.2 (39.3)
18.2 (3.8)
0.43 (0.19)
0.21 (0.08)
2009
165–186d
1.2
47.8 (15.6)
38.1 (14.8)
1.32 (0.78)
360.5 (139.8)
137.4 (43.8)
21.2 (5.9)
0.53 (0.28)
0.24 (0.10)
187–235
265+32
65.9 (12.8)
12.4 (6.7)
0.28 (0.18)
61.2 (30.9)
53.0 (22.8)
27.4 (6.6)
0.82 (0.18)
0.66 (0.13)
236–300d
20.4+20
50.4 (20.5)
33.1 (18.4)
1.28 (1.31)
208.3 (194.3)
72.3 (26.5)
26.9 (10.7)
0.64 (0.28)
0.39 (0.21)
2006
growing season
466+86
59.1 (18.9)
31.8 (16.4)
1.60 (3.94)
231.4 (338.3)
77.9 (33.6)
24.0 (7.4)
0.76 (0.30)
0.41 (0.21)
2007
growing season
630
56.6 (19.5)
28.7 (19.6)
0.93 (0.98)
192.2 (190.7)
75.4 (34.0)
26.9 (9.3)
0.73 (0.44)
0.46 (0.22)
2008
growing season
630
66.1 (15.2)
22.1 (13.4)
0.73 (1.04)
118.1 (115.3)
68.3 (44.9)
18.5 (6.3)
0.89 (0.59)
0.43 (0.19)
2009
growing season
400+195
48.5 (21.9)
34.6 (18.5)
1.54 (2.19)
248.9 (273.3)
77.1 (39.1)
23.8 (8.5)
0.63 (0.38)
0.39 (0.24)
Dry years
(2006, 2009)
growing season
–
52.6 (22.3)
33.0 (18.4)
1.57 (3.17)
240.3 (306.9)
77.5 (36.5)
23.9 (8.0)
0.68 (0.31)
0.40 (0.22)
Wet years
(2007, 2008)
growing season
–
61.5 (18.1)
25.1 (17.0)
0.83 (1.01)
153.1 (159.7)
71.6 (40.3)
22.5 (8.9)
0.81 (0.29)
0.45 (0.20)
WS is the soil water supply of a period (sum of precipitation and irrigation);
β, Bowen ratio; Rs, the surface resistance; Ri, the climatological resistance;
Ra, the aerodynamic resistance; α, the Priestley–Taylor coefficient; and Ω, the
decoupling coefficient. d indicates the drought-stressed periods. The values in the table
represent mean (SD).
By contrast, β varied from 0.18 to 0.71, with a mean of 0.35 ± 0.15 during most of the growing season in 2008 and in the non-stressed
periods of the other 3 years. This variation was close to 0.42 for deciduous
forests (Wilson et al., 2002b) and 0.55 in a temperate
Douglas fir (Humphreys et al., 2003), which is also similar
to the variations in a ponderosa pine forest in the western United States
(Goldstein et al., 2000) and a
deciduous broadleaved forest in the southern United States
(Wilson and Baldocchi, 2000). Seasonal drought stress
had a discernible impact on the Bowen ratio of this poplar plantation.
However, compared to the reported β values such as 0.74 in a
temperate mixed forest (Wu et al., 2007), 0.81 in a boreal
Scots pine forest (Launiainen, 2010), and 0.89 in a loblolly
pine plantation (Sun et al., 2010), the average β values in
wet years were close to the above values. β was higher in seasonal
drought periods and dry years than most temperate coniferous forests (Mean
= 1.07, Wilson et al., 2002b), which typically had higher
β values. The high β value in this study reflects the semiarid
conditions, and suggests a low tree water supply which might be resulted
from the combination of low rainfall, the sandy soil's low water-holding
capacity and the high plant and atmospheric water demand. It has been
suggested that the large-scale establishment of poplar plantations in sandy
semiarid regions of northern China could have an adverse impact on the
region's groundwater reserves (Li et al., 2014;
Petzold et al., 2011). Our findings corroborate the hypothesis that
drought would trigger significant changes in the energy partitioning of
water-demanding poplar species in a water-stressed region.
Biophysical control on Bowen ratio
The Bowen ratio is dependent on the interactions of climatic and biological
factors (Perez et al., 2008; Wilson and
Baldocchi, 2000). Ri quantifies the climatic control on energy
partitioning and tends to decrease the Bowen ratio. A higher Ri implies
a warm and dry climate in continental regions (Raupach,
2000; Wilson et al., 2002b). Rs reflects the physiological
control on surface energy exchange of an ecosystem (Costa et
al., 2010; Launiainen, 2010; Zhou et al., 2010)
and generally increases the Bowen ratio.
In this study, Rs similarly varied seasonally with plant phenology and
showed similar seasonal characteristics to other deciduous forests during
the course of the growing season (Cabral et al., 2010;
Kutsch et al., 2008; Li et al., 2012). As reported
by Tchebakova et al. (2002), Rs in seasonal
drought-stressed periods was much higher than in non-stressed periods.
It has been shown that drought stress during the canopy development affects
leaf area and may have lasting effects on canopy gas exchange through the
entire growing season, even after the moisture limitation is removed
(Noormets et al., 2008), which may explain significant
differences in Rs between the wet years of 2007 and 2008 (Fig. 9). Compared with
the Rs in other studies, the Rs:LAI in dry years in the current study
was close to that of the Euphrates Poplar (Populus euphratica Oliv.; 130.2 s m-1 LAI-1)
and smaller than that of the Gansu Poplar (Populus gansuensis Wang et Yang; 189.4 s m-1 LAI-1)
in semiarid regions (Chen et al., 2004). In wet years
it was similar to that of poplar (58.6 s m-1 LAI-1; Wilson et al., 2002b) and boreal aspen during the full-leaf period (51.8 s m-1 LAI-1; Blanken et al.,
1997) in mesic temperate regions. Rs is primarily driven by solar
radiation, moisture availability, and VPD (Fernández et
al., 2009; Li et al., 2012), and modulated by leaf area and
stomatal resistance, which in turn changes as a function of the above
factors (Wilson and Baldocchi, 2000). Compared to the
strong correlation between Rs and LAI in wet years, the increased scatter
in the Rs–LAI relationship during dry years (Fig. 6) suggests that
Rs in dry years was also influenced by other physiological and
non-physiological (e.g., soil evaporation, canopy structure, and turbulence)
factors (Wilson et al., 2002b). The mean Ri in the
current study was higher than the mean Ri reported for temperate forests
in Wilson et al. (2002b; t= 5.91, df= 741, p < 0.001), but was ∼ 50 % lower than the value reported by
Li et al. (2009) in a vineyard in Gansu province in China
(t= -29.87, df= 741, p < 0.001), as might be expected given the
predominant climatic conditions.
The response of Bowen ratio and LE/(Rn-G) to water supply (WS; including precipitation (P) and irrigation (I) during each individual period) of
the different periods across the four growing seasons.
On the seasonal scale, the Bowen ratio and Rs of this poplar plantation
were correlated and consistent with Wilson et al. (2002b)
and Li et al. (2009), but differed in wet and dry years (Fig. 10). The water limitation during the dry years manifested in disproportional
increase in Rs than the Bowen ratio; this response may serve as an
indicator as to when water reserves are being depleted. At the extremes, the
relationship converges, but as water becomes limiting, stomatal closure and
increased Rs do not appear to be able to affect the seasonal dynamics of
the Bowen ratio. The partial correlation analysis indicated that Rsand Ri had strong positive and negative effects, respectively, on
β in both wet and dry years (Table 4), which could not be detected
through correlation analysis (e.g., the impact of Ri and Ra on
β). Furthermore, the regulation of the Bowen ratio by Rs and
Ri seemed stronger in dry than in wet years. Ra had a significant
negative impact on the Bowen ratio in wet years, but not in dry years.
Seasonal variations of monthly average LAI and Rs during the
growing seasons in the wet years of 2007 and 2008.
The correlation analysis between the Bowen ratio (β) and
Rs,Ri, and Ra.
Partial correlation analysis*
Correlation analysis
SOCC
p
df
Pearson
p
df
β & Rs
0.965
< 0.001
0.939
< 0.001
Dry year
β & Ri
-0.667
< 0.001
347
-0.042
=0.436
349
β & Ra
0.037
=0.496
-0.221
< 0.001
β & Rs
0.905
< 0.001
0.85
< 0.001
Wet year
β & Ri
-0.614
< 0.001
383
0.64
=0.006
385
β & Ra
-0.217
< 0.001
-0.286
< 0.001
* Partial correlation analysis was carried out between the Bowen ratio and each of the three resistance
parameters (Rs,Ri, and Ra) with the other two as controlling variables.
SOCC is the abbreviation of ”second-order correlation coefficient”.
Response of monthly average Bowen ratio (β) on surface
resistance (Rs) in the wet and dry years.
The average LE / LEeq in the growing season was 0.74 at our site, which
is similar to deciduous forests (0.72; Wilson et al.,
2002b), but smaller than at a temperate broad-leaved forest (0.82; Komatsu, 2005). The average Ω value of 0.42 ± 0.22
(0.39–0.46) was close to the other forests (0.26–0.4,
Wilson and Baldocchi (2000); 0.25–0.43,
Motzer et al. (2005)). As essentially implied by the
Penman–Monteith equation, LE / LEeq exponentially related to Rs during the growing
season (Fig. 7), which is equivalent to the logarithmic relationship between
LE / LEeq and Gs (surface conductance) reported by other studies
(Chen et al., 2009; Hossen et al., 2011;
Zhu et al., 2014). The asymptotic value of LE / LEeq in dry years (0.89) and wet years (0.96) were both lower than the 1.1–1.4
range typical in temperate deciduous forest reported by
Monteith (1995), indicating that our study site was drier than these
reference sites during both dry and wet years. The low LE / LEeq values
under dry surface conditions of the ecosystem in this study may also be
related to the high porosity of the sandy soil and the low ground water
table (Zhao et al., 2013). Overall, as indicated
by the lower Ω values and the significant correlation coefficients
between LE / LEeq and Rs, Rs was the major factor controlling
the LE during the growing season, which was consistent with the relations
between Rs and the Bowen ratio. In addition, LE was more coupled to the
atmosphere during the dry years and seasonal drought periods across the
growing seasons, as reported in other studies (Bagayoko et
al., 2007; Bracho et al., 2008; Zha et al.,
2013).
Implications for poplar plantation establishments
As forestry is a long-term endeavor, with the economic payback decades from
stand establishment, the availability of resources for the stand to prosper
should come naturally to natural resource managers. Supplementing limited
resources directly (fertilization, irrigation) or indirectly (competition
control, site preparation, thinning) is commonplace in commercial forestry,
but it has to be sustainable in the broader context of the region's
ecosystems and livelihoods. As we show in a forthcoming study, the water
needs of poplar plantation exceed the annual precipitation in the region, and
plant survival during dry years depends on irrigation from groundwater. In
the current study, energy partitioning to latent and sensible heat and
surface resistance was sensitive to climatological drought – even under
irrigation – as indicated by low LE / LEeq (< 1) and low values
of the decoupling coefficient (Ω; Zhu et al.,
2014); the dry surface conditions dominated the poplar plantation in both
wet and dry years. In wet years, the plantation itself is in hydrologic
balance with the water that arrives as precipitation, with
evapotranspiration consuming nearly all of the precipitation. The same is
true in dry years, but irrigation increases ET even further by depleting
groundwater. Even if the plantations were in hydrologic balance with water
delivered as precipitation, their existence and operation could be a threat
to adjacent ecosystems and livelihoods if those rely on runoff or
groundwater recharge from the areas where the plantation has been sited. In
the absence of the plantations it is likely that groundwater recharge would
increase, especially given the sandy textured soil that tends to allow rapid
infiltration and percolation as well as limit moisture delivery to the
atmosphere directly from the soil surface itself. While poplar plantation
growth in this water-limited location might be sustained by the modest
precipitation in the region, it could still be unsustainable for the broader
context of the region's ecosystems and livelihoods. However, further study
to truly access these effects is needed by comparing the surface water
balance and/or spatial and temporal variations of groundwater levels at an
adjacent, similar site without a plantation.