This is an Accepted Article that has been peer-reviewed and approved for publication
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correction. Please cite this article as an “Accepted Article”; doi:
Received Date : 05-Dec-2011
Revised Date : 05-Dec-2011
Accepted Date : 31-Dec-2011
Article type : Primary Research Articles
Modeling biophysical controls on canopy foliage water 18O enrichment in wheat
Running title: Canopy foliage water 18O enrichment
Wei Xiao1, Xuhui Lee2*, Xuefa Wen3*, Xiaomin Sun3, and Shichun Zhang4
 Key Laboratory of Meteorological Disaster of Ministry of Education &
Yale-NUIST Center on Atmospheric Environment, Nanjing University of
Information Science & Technology, Nanjing 210044, China
 School of Forestry and Environmental Studies, Yale University, New Haven,
Connecticut 06511, USA
 Institute of Geographic Sciences and Natural Resources Research, Chinese
Academy of Sciences, Beijing 100101, China
 Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences,
Changchun 130012, China
* Corresponding authors
Dr. Xuhui Lee
School of Forestry and Environmental Studies, Yale University
21 Sachem Street, New Haven, Connecticut 06510, USA
Phone: 1-203-432-6271; Fax: 1-203-432-5023
Dr. Xuefa Wen
Institute of Geographical Sciences and Natural Resources Research, Chinese
Academy of Sciences
A11 Datun Road, Chaoyang District, Beijing 100101, China
Phone: 86-10-64889432; Fax: 86-10-64889399
Keywords: foliage water 18O enrichment; canopy-scale; biophysical factors; wheat;
Primary Research Articles
Leaf water 18O enrichment is an important factor controlling the H218O, C18OO and
O18O exchanges between the biosphere and the atmosphere. At present, there is
limited capacity to explain the enrichment mechanisms in field conditions. In this
study, three models of varying complexity were used to simulate the leaf water 18O
enrichment at the canopy scale. Comparisons were made among the models and with
high-frequency isotopic measurements of ecosystem water pools in wheat and corn.
The results show that the steady state assumption was a better approximation for
ecosystems with lower canopy resistance, that it is important to consider the effect of
leaf water turnover in modeling the enrichment and not necessary to deal with time
changes in leaf water content, and that the leaf-scale Péclet effect was incompatible
with the big-leaf modeling framework for canopy-air interactions. After turbulent
diffusion has been accounted for in an apparent kinetic factor parameterization, the
mean 18O composition of the canopy foliage water was a well-behaved property
predictable according to the principles established by leaf-scale studies, despite
substantial variations in the leaf water enrichment with leaf and canopy positions.
In the Appendices we provided a discussion on the observed variability of leaf water
18O composition with leaf and canopy positions and on the procedure for correcting
isotopic measurements for organic contamination.
In the process of transpiration, leaf water of terrestrial plants is more enriched in 18O
than the water taken up by their roots, due to the slower diffusion (the kinetic effect)
through the stomatal opening and the lower saturation vapor pressure (the equilibrium
effect) of the heavier H218O molecules than the lighter H216O molecules. The enriched
18O signal is an important factor controlling the H218O, C18OO and O18O exchanges
between the biosphere and the atmosphere and insights into its biophysical controls
are highly relevant to issues of interest to biologists and global change scientists. The
18O tracer provides unique constraints on water and carbon cycles (Farquhar et al.,
1993; Yakir et al., 1994; Farquhar et al., 2007), paleoclimate reconstruction (Gray &
Thompson, 1976; Libby et al., 1976; Epstein & Yapp, 1977; Roden et al., 2000;
Cullen et al., 2008) and the Earth’s Dole effect (Dole et al., 1954; Bender et al., 1994;
Hoffmann et al., 2004). During terrestrial photosynthesis, some of the CO2 entering
chloroplasts is assimilated after having exchanged oxygen atoms with the
18O-enriched water, a process that transfers the leaf water 18O signal to plant materials
(Farquhar et al., 1993). In recent years, an active area of research in global change
concerns the 18O content in plant biomarkers as proxies of environmental conditions
influencing the plant growth (Barbour et al., 2000; Evans & Schrag, 2004; Miller et
al., 2006; Treydte et al., 2006; Barbour, 2007; Helliker & Richter, 2008; Sternberg,
2009; Kahmen et al., 2011). Central to these and other related studies is a mechanistic
understanding on how biotic and abiotic factors influence the leaf water 18O/16O ratio
(δL, in delta notation in reference to VSMOW).
A distinction should be made between leaf- and canopy-scale processes. The
mechanistic models for foliage water 18O enrichment, reviewed below, are constructed
at the leaf scale, requiring input variables measured immediately outside the leaf
boundary layer. The above-mentioned applications, on the other hand, relate the
enrichment to environmental conditions occurring outside the plant canopy, in the
so-called atmospheric surface layer, and in this context, the appropriate modeling
framework should be aimed at the canopy scale. Similarly, knowledge of the
canopy-scale 18O enrichment is a prerequisite for flux partitioning studies involving
the C18OO and H218O tracers (Ogée et al., 2004; Lee et al., 2007). By providing the
lower boundary conditions for global modeling systems of atmospheric H218O, C18OO
and O18O (Farquhar et al., 1993; Ciais et al., 1997; Riley et al., 2002; Cuntz et al.,
2003; Hoffmann et al., 2004), models of the 18O leaf enrichment at the canopy scale
help to bridge field research, which generates local empirical knowledge, and a
mechanistic understanding of the changes of these isotopologues in the atmosphere at
the global scale.
Mathematical models for predicting δL are built on our understanding of
fractionations associated with phase change of water during transpiration. The first
model of the evaporative enrichment was based on the theory developed by Craig &
Gordon (1965, referred to as C65 hereafter) and calculates δL with the assumptions of
isotopic homogeneity in leaf water and the steady state, the latter of which states that
the isotopic concentration of water entering the leaf equals that leaving the leaf.
Dongmann et al. (1974, D74) employed C65 to calculate δL in non-steady state and
retained the assumption that 18O is evenly distributed in the leaf water pool. In the
non-steady state model of Farquhar & Cernusak (2005, F05), biotic controls are
included by considering the heterogeneous isotopic composition of leaf water and
temporal variations of leaf water content (W). Ogée et al. (2007) developed a
two-dimensional model for non-steady state and associated the microscale spatial
patterns of isotopic enrichment in the leaf with leaf geometry and non-uniform gas
exchange parameters. Few studies have evaluated these models over full growing
seasons and in field conditions (Wingate et al., 2010; Xiao et al., 2010; Griffis et al.,
The goal of this modeling study is to evaluate the performance of the models C65,
D74 and F05 against observations made in wheat and corn. Plant materials were
collected at high temporal frequencies and in spatial replicates to characterize the
canopy-scale δL. Data of this kind are rare because such a destructive sampling
scheme may not be acceptable for natural ecosystems. As two major food crops, the
areas of wheat and corn are over 31 and 24 million ha in China and over 230 and 160
million ha worldwide, respectively, according to the Food and Agriculture
Organization of the United Nation, and represent two different photosynthetic modes
(C3 and C4, respectively). Previous studies have shown that species with parallel vein
structures, such as grasses, display much larger variations in δL with leaf position than
species with reticulate venation such as cotton (Yakir et al., 1994; Wang & Yakir,
1995; Helliker & Ehleringer, 2000; Gan et al., 2002 & 2003). Having parallel vein
structures, wheat and corn may be good model systems for grassland ecosystems. By
comparing the three models of varying complexity, we wish to quantify the influences
of the Péclet effect, non-steady state and changes in leaf water content on the 18O
enrichment at the canopy scale. We will investigate biotic (such as stomatal behavior
and leaf water content) and abiotic controls (such as relative humidity and air
turbulence) on the isotopic enrichment. Finally, we will test the hypothesis that
despite substantial variations in δL with leaf and canopy positions (Appendix A), the
mean δ18O of foliage water at the canopy scale is a well-behaved property predictable
according to the simple big-leaf model of biosphere-atmosphere interactions. In
Appendix A, we present an analysis of the spatial variations with leaf position and
canopy position observed in the wheat and corn experiments.
2. Materials and methods
2.1 Site and Data
The experiment was conducted at the Luancheng Agro-Ecosystem Experimental
Station (37° 53´ N, 114° 41´ E, elevation 50 m), located in the North China Plain
(Wen et al. 2012). Wheat (Triticum aestivum L.) was planted in November of 2007
and harvested on June 18, 2008 (day of year or DOY 170), with a maximum LAI of
4.5 and a maximum height of 0.75 m observed on DOY 120. Corn (Zea mays L.) was
planted in the beginning of June before wheat harvest, with a maximum LAI of 4.2
and a maximum height of 2.77 m reached on August 16 (DOY 229). The wheat and
corn cultivation was 16 ha in size. The fetch of the measurement site was greater than
Isotopic measurements were made of ecosystem water pools. The O18/O16 ratio in
water vapor was measured continuously using a tunable diode laser (TDL) trace gas
analyzer (Model TGA100A, Campbell Scientific Inc., Utah, USA; Wen et al., 2012).
Air was sampled at two heights above the canopy; they were increased from 0.6/1.6 m
at the beginning to 1.1/2.1 m at the end of the wheat season and from 1.1/2.1 m at the
beginning to 3.2/4.2 m at the end of the corn season, to adjust for plant growth. In this
study, the measurement at the lower intake was used. Leaf, stem and soil samples
were collected from 4 sampling plots within 50 m of the gas intakes. Leaf samples
from the upper and lower canopy were archived separately, with the main leaf vein
removed. In the case of corn, the leaf samples were a mixture of small sections from
the upper, middle and bottom positions of the leaf. Soil samples were collected from
the depths of 0-5, 15-20 and 40-45 cm. Water in these solid samples was extracted
using a vacuum extraction system (West et al., 2006). Precipitation and dew water
were also collected on an event-by-event basis. Precipitation was collected using an
open container with mineral oil inside to block evaporation. During dew events, dew
water was removed before dawn from the leaf surface using clean cotton balls and
squeezed to sampling vials (Wen et al., 2012). Isotopic composition of the liquid
water was measured with an isotopic ratio infrared spectroscopy (IRIS, Model
DLT-100, Los Gatos Research, Mountain View, CA, USA) and corrected for organic
contaminants following the procedure of Schultz et al. (2011). The average correction
was 2.5‰ for leaf water samples and 1.1‰ for stem water samples. A subset of the
leaf water samples were also measured with an isotope ratio mass spectrometer
(IRMS, MAT 253, Finnigan Inc.) using a continuous flow method. The mean
difference (standard deviation) between the IRIS and the IRMS measurements was
-0.3‰ (± 0.7‰, number of samples = 163). More details on the IRIS correction
procedure are given in Appendix B.
For most of the experiment, collection of leaf, stem and soil samples was made at
midday between 12:00 and 14:00 LST every 2-4 days. On several days, additional
collection was made at 06:00 and 18:00 LST. During four intensive periods (DOY
134-137, 142-144, 236-237 and 244-246), the samples were collected every 3-4 h. On
DOY 256, 5 segments from the base to the tip of 4 corn leaves were sampled every 3
h from 6:00 to 18:00 LST.
Leaf water content (W, mol m-2 leaf area) was measured at the same time of leaf
sampling. For the canopy-scale model simulations, the canopy foliage water content
was computed as the product of W and LAI, in units of mol m-2 (ground area).
Supporting canopy-scale measurements included eddy covariance fluxes and
routine meteorological variables. The latent heat, sensible heat, momentum and CO2
fluxes were measured with a sonic anemometer (CSAT-3, Campbell Scientific Inc.)
and a CO2/H2O infrared analyzer (LI-7500, Licor Inc., Lincoln, NB, USA) which
were mounted at a 3-m height above the ground. Soil heat flux was measured at a
depth of 2 cm with three heat flux sensors (HFP01, Campbell Scientific Inc.). Air
temperature and humidity (HMP45C, Campbell Scientific Inc.) and wind speed
(A100R, Vector Instruments; Rhyl, North Wales, UK) were measured at 1.4 m and 3.9
m heights. Net radiation was measured with a 4-component radiometer (CNR-1, Kipp
& Zone, Delft, The Netherlands). Canopy temperature was measured with an infrared
radiometer (IRTS-P, Apogee Instruments Inc., Logan, UT, USA). Soil temperature
was measured with thermocouples (105T, Campbell Scientific Inc.) at 10, 20 and 50
cm depths. Soil water content was measured with water content reflectometers
(CS616-L, Campbell Scientific Inc.) at 5, 20 and 50 cm depths. Precipitation was
measured with a rain gauge (TE525MM, Campbell Scientific Inc.).
During the intensive periods, physiological measurement was conducted
simultaneously with the leaf and stem sampling using a photosynthesis system
(LI-6400, Licor Inc.). Stomatal resistance (rc), transpiration, photosynthesis, leaf
temperature (TL) and vapor pressure deficit (VPD) were measured at the same canopy
positions of leaf sampling.
2.2.1 Overall Model Structure
The simple isotopic land surface model (SiLSM) of Xiao et al. (2010) was modified
here to simulate the H218O composition of foliage water. SiLSM was originally
developed to simulate the isotopic exchanges of H218O and C18OO between
ecosystems and the atmosphere. It consists of three submodels, i.e., a parameterization
of the C18OO isoforcing, a submodel for H218O enrichment in foliage water and a
big-leaf land surface model. In this paper, the latter two submodels were employed.
The H218O submodel of SiLSM is adopted from Farquhar & Cernusak (2005, F05). In
this study, we also evaluated the performance of the leaf water enrichment submodels
of Craig & Cordon (1965, C65) and Dongmann et al. (1974, D74). Fig. 1 shows the
overall model structures, and Table 1 summarizes the key assumptions made by these
isotope submodels. A detailed description of these modeling components is provided
2.2.2 Isotopic Leaf Enrichment Submodels
The C65 submodel was originally developed to calculate the isotopic composition of
evaporation water vapor from a liquid water surface, as
] 1000/) 1 ()1 /[(]) 1 ([
kk eqaL eq
where subscripts E, L and a represent the evaporating water vapor, liquid water body
and atmospheric water vapor, respectively, αeq (> 1) is the equilibrium fractionation
factor (Majoube, 1971), εeq = (1 - 1/αeq)1000, εk is the kinetic fractionation factor, and
h is the relative humidity of the ambient air referenced to the water surface
temperature. This model is used to predict the leaf water enrichment (Dongmann et al.,
1974; Bariac et al., 1989; Walker et al., 1989; Flanagan et al., 1991; Flanagan &
Ehleringer, 1991). With the assumptions that the leaf water is isotopically well mixed
(that is, δL,b = δL,e, where δL,b is the 18O composition of bulk leaf water and δL,e is that
at the evaporating site in the leaf) and that transpiration is in isotopic steady state (that
is, δx = δE), we obtain
L b ,
where superscript s denotes the steady-state prediction, and subscript x represents
xylem water. In the canopy-scale application, δL,b is the mean foliage 18O composition
of the canopy layer, εeq and h are in reference to the canopy temperature Tc, δa is the
18O composition of water vapor in the surface layer over the canopy, and εk is the
canopy apparent kinetic fractionation factor given as
where ra, rb and rc are aerodynamic, boundary-layer and canopy resistance,
respectively. In this formulation, the molecular kinetic factor (28‰) and the kinetic
factor associated with the leaf boundary layer (19‰) are given by Merlivat (1978)
and Farquhar et al. (1989), respectively, and no fractionation occurs in turbulent
diffusion in the atmospheric surface layer (Lee et al., 2009).
Numerous modeling and experimental studies have shown that the assumption of
steady state is not fulfilled in field conditions (e.g. Dongmann et al., 1974; Cernusak
et al., 2005; Farquhar & Cernusak, 2005; Lai et al., 2006; Welp et al., 2008). D74
expresses the isotopic enrichment of leaf water in non-steady state, as
L b , b ,L
and δL,b are the 18O composite of bulk leaf water in steady and
non-steady state, respectively, wi is the mole fraction of water vapour in the
intercellular space, W is the leaf water content, rt is total resistance to the diffusion of
water vapor, αk is the fractionation factor for diffusion (αk = 1+ εk/1000). The solution
of Equation (4) at time t is
is δL,b at time zero, and τ is a time constant given by
In the canopy-scale framework, W is the canopy water content per unit ground area
(mol m-2). D74 makes two implicit assumptions. As with C65, it assumes that the leaf
water is well mixed. In addition, W is taken as a constant invariant with time (White,
In F05, no assumption is made about steady-state, a well-mixed leaf water pool,
or a constant W. Leaf-scale measurements have shown that leaf water is not
isotopically well mixed; its 18O composition is highest at the evaporation site in the
leaf and lowest near the xylem (Helliker & Ehleringer, 2000; Yakir & Sternberg, 2000;
Gan et al., 2002). The progressive enrichment from the xylem to the site of
evaporation maintains an isotopic gradient that drives the diffusion of H218O
molecules in the opposite direction of mass water flow in the leaf, a phenomenon
termed the Péclet effect (Farquhar & Lloyd, 1993). The F05 submodel has taken the
Péclet effect into account. The key equations of F05 are
where P is the Péclet number. In the limit of P ?0, Equations 7 and 8 reduce to δL,e =
δL,b or the well-mixed condition. F05 allows W to vary with time. If W is also held
constant, this model becomes identical to D74.
2.2.3 Big-leaf Model and Parameterizations
A big-leaf photosynthesis/transpiration model (Ronda et al., 2001) was employed to
calculate the canopy evapotranspiration (ET), canopy temperature (Tc) and the
resistance terms (ra , rb and rc). Different values were used for the C3 wheat and C4
corn for the plant physiological parameters including the mesophyll conductance, the
initial light use efficiency, the CO2 compensation point and the maximal primary
productivity. The relative humidity referenced to canopy temperature (RHc), the
equilibrium fractionation factor (εeq), and the apparent kinetic fractionation factor (εk),
input variables required by the three isotopic submodels (Fig. 1), were then
determined from the big-leaf model outputs.
The integrated model system was driven by observed meteorological and plant
morphological variables. The input data common to the three isotopic models are the
H218O composition of atmospheric water vapor (δa) and that of xylem water (δx). D74
and F05 also require data on W. A seasonal time series of W was established by linear
interpolation between the weekly W measurements. Superimposed on the seasonal
variation was a diurnal variation according to the diurnal composite W measured
during the intensive periods. The input data of the big-leaf model include
micrometeorological variables (air temperature, relative humidity, solar radiation, sky
long-wave radiation, atmospheric CO2 concentration, wind speed and friction
velocity), soil temperature and moisture, leaf area index (LAI) and canopy height.
Tunable parameters of the model were optimized according to the observed latent
heat flux and the foliage 18O content. The big-leaf model contains two tunable
parameters in the stomatal resistance parameterization, the vapor pressure deficit
constant D0 and the CO2 concentration constant a1 in Equation (11) of Ronda et al.
(2001). They were tuned by a nonlinear least squares method to minimize the
difference between the observed and the simulated latent heat flux. The optimization
results were D0 = 0.50 kPa and a1 = 11.9 for wheat and D0 = 0.74 kPa and a1 = 4.2 for
corn, using the complete dataset for each crop. The one free isotopic parameter Leff,
the effective length in the Péclet number in F05, was tuned with the observed δL,b; the
optimized value was essentially zero (Leff = 2.3 × 10-9 m) for both wheat and corn.
Leaf water content (W) is an important determinant of the non-steady state behavior
of δL. The midday W decreased continuously from the early to the late growth season
for both wheat and corn, and was low in midday and high in the early morning (Fig.
2). During the wheat season, the midday W decreased from 11.2 mol m-2 (mol water
per m2 leaf area) on DOY 97 to 7.5 mol m-2 (DOY 154), with a seasonal total decline
of 3.7 mol m-2. During the corn season, the midday W decreased from 12.3 mol m-2
(DOY 195) to 7.4 mol m-2 (DOY 246), with a change of 4.9 mol m-2. Measurements
during the four intensive periods show that the day-to-night variation of wheat W
ranged from 0.3 (DOY 144) to 1.1 mol m-2 (DOY 142), with a mean value of about
0.5 mol m-2, and that of corn W ranged from 0.8 (DOY 245) to1.5 mol m-2 (DOY 244),
with a mean value of 1.1 mol m-2. Thus both the seasonal and diurnal variations in W
were smaller for wheat than for corn.
A notable feature is that the diurnal variations in wheat δL,b were smaller than
those in corn δL,b (Fig. 3). The 24 h, midday (10:00 to 15:00 LST) and midnight
(22:00 to 3:00 LST) mean values of the observed δL,b during the intensive periods
were 2.7, 4.2 and 1.6‰ for wheat leaves, respectively, and 2.9, 8.0 and 0.1‰ for corn
leaves, respectively, showing that the canopy mean δL,b of wheat was lower in midday
and higher at night in comparison to that of corn, while the 24 h mean value of wheat
was lower. For reference, the mean values of δx and δa were -6.8 and -11.5‰ during
the wheat intensive periods, and -8.5 and -15.2‰ during the corn intensive periods,
The observed diurnal variations were reproduced reasonably well by the three
models (Figs. 3 and 4), both on days with strongly diurnal variations (e.g. DOY 142)
and on days that lacked variations (e.g. DOY 137 and 144). C65 slightly
outperformed F05 and D74 in the simulation of the nighttime δL,b. The three models
produced nearly identical results for the midday periods; the simulated midday δL,b
was 4.4‰, 4.3‰ and 4.2‰ for wheat and 10.5‰, 9.8‰ and 9.6‰ for corn according
to C65, D74 and F05, respectively. Larger differences were found in the simulated
midnight δL,b, with mean values of 2.1‰, 4.7‰ and 4.4‰ for wheat and -2.0‰, 1.7‰
and 1.3‰ for corn according to C65, D74 and F05, respectively.
The three models also captured the seasonal variations (Fig. 5). Our
measurement is among the few made on the seasonal time scale in the published
literature (Welp et al., 2008; Wingate et al., 2010; Griffis et al., 2011; Kim, 2011).
The mean model bias errors (ME) were less than 2.0‰, the root mean squares
deviation (RMSD) less than 2.6‰ and index of agreement (IA) better than 0.90
A summary of statistics on the model-predicted δL,b for the two crop seasons over
three averaging periods (24 h, midday and midnight) is shown in Table 3. Consistent
with the results shown in Fig. 3, the difference among the three models mainly
occurred at night, with the F05 and D74 predictions about 2.5‰ higher than the C65
The model-to-model biases occurred primarily at night (18:00 to 06:00 LST) and
between the steady-state model C65 and the non-steady state models D74 and F05
(Fig. 6). Since the turnover time of leaf water was much longer at night (midnight
average τ = 8.2 and 6.3 h for wheat and corn, respectively) than in the daytime (τ =
0.5 and 0.8 h), it took longer time for the leaf water to reach steady-state, and
therefore the effects of non-steady state on isotopic enrichment were more significant
at night than during the day. The bias between F05 and D74 was negligible.
Our model simulations confirm the need to consider turbulent diffusion when
calculating the canopy-scale foliage water 18O enrichment (Fig. 8). The apparent
kinetic factor described by Equation 3 is a canopy-scale property; it considers the
different fractionation strengths associated with molecular and turbulent diffusion
along the water vapor diffusion pathway (from the stomatal cavity to the atmospheric
surface layer outside the canopy; Lee et al., 2009). For wheat, if the aerodynamic
resistance ra was omitted in the apparent kinetic fractionation parameterization, the
simulated δL,b was obviously biased high for all the models. For example, the F05
midday mean value of δL,b increased to 9.6‰ from the original 4.9‰. A similar model
sensitivity has been shown for a soybean canopy (Xiao et al., 2010). Model
performance for corn was less sensitive to the apparent kinetic fractionation
formulation; omission of ra increased the midday F05 prediction by 1.1‰.
4.1 Differences among the Three Models
Comparison of the three models of varying complexity (Table 1) allows us to
differentiate the roles of various isotopic mechanisms underlying the observed
variations. The relevant mechanisms include kinetic fractionation, leaf water turnover,
time variations in W and the Péclet effect. With the inclusion of turbulent diffusion in
the apparent kinetic fractionation formulation, all the three models captured
reasonably well the observed seasonal variations of midday δL (Fig. 5 and Table 2).
Numerous leaf- and canopy-scale studies have documented the effect of non-steady
state (e.g. Farquhar & Cernusak, 2005; Lai et al., 2006; Seibt et al., 2006; Welp et al.,
2008; Wingate et al., 2010; Griffis et al., 2011), the Péclet effect (Gan et al., 2002,
2003; Barbour & Farquhar, 2004; Farquhar & Cernusak, 2005) and the effect of the
leaf water turnover rate (Griffis et al., 2011) on leaf water 18O enrichment. The
modeling study of Xiao et al. (2010) suggests that for soybean the Péclet effect is less
important to the leaf water 18O enrichment than the effect of non-steady state at the
canopy scale. In this study, the optimized value of the scaled effective length Leff in
the Péclet number was essentially zero for both the wheat and the corn ecosystem.
The small Leff implies that at the canopy scale the Pélect effect was negligible so the
only difference between D74 and F05 is that D74 did not consider the time variations
in W whereas F05 did (Table 1). A comparison between the two suggests that the W
time variations played a minor role on leaf water enrichment; their omission would
introduce errors of no more than 0.3‰ at night (Fig. 6) and no more than 0.1‰ when
averaged over the 24 h cycle (Table 3). The convergence of the F05 and the D74
predictions confirms the necessity to consider the effect of leaf water turnover in
modeling δL and supports the postulation that it is not necessary to deal with changes
in W in field conditions (Farquhar & Cernusak, 2005).
The roles of turbulent diffusion and leaf water turnover were not symmetrical
through the course of the day. Omission of turbulence in the apparent kinetic
fractionation parameterization would cause overestimation of δL,b primarily in the
daytime hours, whereas omission of the leaf water turnover would cause
underestimation of δL at night and with little consequence for the daytime. According
to our sensitivity test, if both were omitted (as in the leaf-scale Craig-Gordon model),
these errors would largely cancel out when averaged over periods of the diurnal cycle
or longer, with the seasonal mean δL changing by less than 1.1‰ in comparison to the
F05 predictions. However the mean values are misleading because the diurnal
amplitude of δL would increase by 1.3‰ and 1.6‰ for wheat and corn, respectively,
in comparison to the F05 predictions shown in Fig. 5.
The negligible difference between D74 and F05 indicates that the implicit
assumptions made by D74 – that the 18O content is well mixed in leaf water and that
W is invariant with time – are good approximations for canopy-scale applications.
D74 has been used in regional and global scale models of atmospheric C18OO and
H218O (Cuntz et al., 2003; Riley et al., 2002; Still et al., 2009). Although having a
more rigorous treatment than D74 of the mass balance of the leaf water pool and
having integrated the empirical knowledge about 18O diffusion in the pool, F05 is
more difficult to use in regional and global models because of the tunable parameter
Leff and the lack of information on the time variations in W. There are field
measurements of W to help constrain the isotopic turnover time in several ecosystems
(Lee et al., 2007; Xiao et al. 2010; Seibt et al., 2007; Lai et al., 2006; White, 1989;
Yakir, 1998). W can also be inferred from satellite imagery (Yilmaz et al., 2008).
However, few studies have reported the variations of W at fine enough time scales
required by F05 (Farquhar & Cernusak, 2005; Xiao et al., 2010; Fig. 2).
4.2 Biotic versus Abiotic Controls on δL
In the C65 model, abiotic influences on δL, such as RHc and δa, are dealt with
explicitly and biotic influences are realized through the alteration of the apparent
kinetic fractionation factor by stomatal resistance (Farquhar et al., 1989). In the limit
that kinetic fractionation is purely molecular, a situation equivalent to having either an
infinite stomatal resistance or a negligibly small aerodynamic resistance, δL is
constrained by two asymptotic values at low and high RH. Farquhar et al. (2007)
show that δL – δx should equal the sum of the molecular kinetic factor (28‰) and the
equilibrium factor (~9‰) at RHc = 0 and approximately zero at RHc = 1, giving a
theoretical humidity sensitivity of about -0.40‰/% RH change (see also Still et al.,
2009). The sensitivity observed in this study was about half of this theoretical value
(-0.20‰/% RH). Welp et al. (2008) also observed a low sensitivity (-0.27‰/% RH)
for a soybean system. According to the authors’ unpublished data, the sensitivity was
-0.33‰/%RH for a temperature deciduous forest (Kim, 2011) and -0.25‰/%RH for a
short steppe grassland (Hu et al., 2012). It appears that δL is more sensitive to RH for
natural ecosystems with larger stomatal resistance than for managed cropland
ecosystems. Calculations with an isotopic large-eddy simulation model shows that the
sensitivity may also depend on surface roughness (Lee et al., 2012).
The strong negative correlation with RH can explain the larger daily variations of
δL,b in corn than in wheat during the intensive campaigns (Fig. 3). The RHc diurnal
variation was in the range of 31% to 96% for corn and of 44% to 96% for wheat. The
larger RH variation in corn would result in a 3.7‰ larger variation in δL,b.
Because a weighting scheme was used to adjusting the apparent kinetic
fractionation factor (Equation 3), leaves with higher stomatal resistance are generally
more enriched in H218O under similar hydrological conditions (Wang & Yakir, 1995;
Helliker & Ehleringer, 2000; Barbour & Farquhar, 2000). C3 plants usually have
lower stomatal resistance than C4 plants (Pearcy & Ehleringer, 1984; Knapp, 1993). In
this study, the midday mean value of the canopy resistance rc was 0.8 m2 s mol-1 for
wheat and 2.8 m2 s mol-1 for corn, respectively. The apparent kinetic fractionation
factor εk of wheat was much lower than that of corn, with the midday mean values of
13.2 and 23.2‰, respectively. There were, however, negligible differences in the
modeled seasonal mean δL between wheat and corn (Table 3). In other words,
variations in abiotic conditions in the field can offset some or all of the stomatal
The difference in canopy resistance contributed to the contrasting non-steady
state behaviors between the two crops (Fig. 6). Largely because of the lower rc, wheat
had a shorter isotopic turnover time (0.5h) than corn (0.8h) in the midday periods. As
a result, its departure from steady state, as measured by the difference between the
F05 and C65 models, was lower than 0.5‰ from 08:00 to 20:30 LST. Using this
threshold, corn did not attain steady state except for a shorter duration in the afternoon
(12:30 – 16:30 LST). Our results suggest that the steady state assumption is a better
approximation in the daytime than at night and for ecosystems with lower canopy
resistance (Cernusak et al., 2002, 2008).
Leaf water content W is a prescribed biological parameter in D74 and F05. The W
value varies within the range of 3.4 to 18.4 mol m-2 (projected leaf area) in the
literature (Farquhar & Cernusak, 2005; Lee et al., 2007; Seibt et al., 2007; Xiao et al.,
2010), with a mean of 8.9 ± 4.2 mol m-2 (number of ecosystems = 7, including the two
crops in this study). These studies suggest that the deviation from steady state at night
should be greater for plants with larger W. For example, Seibt et al. (2007) compare
nocturnal δL between a beech and a sitka spruce ecosystem. Their beech leaves have a
low W value of 3.4 mol m-2 (the lowest among the studies cited above), and the
difference between the D74 and C65 predictions is about 2‰ at midnight. In
comparison, the difference between the two predictions is greater than 10‰ for the
sitka spruce leaves having a high W of 11.8 mol m-2. Our W values fell between the
values for their forests (Fig. 2) and the departure from steady state was approximately
3‰ at midnight (Fig. 6).
The modeled midday δL,b shows varying bias errors over the growing seasons
(Fig. 5). The prediction errors do not seem to have stemmed from errors in the
fractionation parameterizations because all the three submodels had similar biases.
The bias of canopy temperature Tc simulation introduced errors in the wheat δL,b
simulation (Fig. 7). The reader is reminded that Tc, an input variable to the isotopic
submodels (Fig. 1), was solved from the surface energy balance equation in the
standard LSM. A significant linear positive relationship was found between the bias of
the Tc simulation and the bias of the δL simulation by F05 for both wheat (linear
correlation r = 0.32, p < 0.05) and corn (r = 0.27, p < 0.05). Uncertainties in Tc is also
a source of errors in δL,b simulated with the same isotopic LSM for a soybean
ecosystem (Xiao et al., 2010). A low bias in Tc caused RH to increase which would
4.3 Leaf Scale versus Canopy Scale
The mean δ18O of foliage water at the canopy scale behaved in manners that were
mostly predictable according to the mechanistic knowledge established by leaf- and
plant-scale research. The large departure from steady state in the evening (Figs. 3 and
6), the dominant role of RH and good accuracy of the C65 model in midday (Figs. 3
and 5) are well-known features of δL at the leaf scale. However, this result is not
intuitive given the large spatial variations in δL within the canopy layer (Appendix A).
For example, it was common that the δL in the upper and lower canopy layers would
differ by 5‰ in midday. Still larger variations were seen with leaf position: the
base-to-tip gradient of the corn leaves could reach 15‰, a variation that was
comparable to the temporal variations of the canopy mean δL over the whole growing
season (Fig. 5). This last example is particularly noteworthy. Helliker & Ehleringer
(2000) show that the Craig-Gordon model does not work for grass leaves because of a
“string-of-lakes” effect or progressive enrichment along the parallel veins of these
leaves. These large micro-scale “noises” were filtered out by our field sampling
scheme (section on Materials and Methods) such that the δL values reported here
represented the algebraic mean across canopy and leaf positions. (Spatial replication
further reduced the variability.) That the canopy δL was mostly predictable according
to the Craig-Gordon model (Figs. 3 and 5) while the microscale δL is not (Helliker &
Ehleringer 2000) suggests some strong compensation mechanism at the canopy scale.
The success of the δL models was helped by the canopy-scale apparent kinetic
fractionation formulation (Fig. 8). A distinction between the canopy and the leaf scale
is that the former involves turbulent diffusion in the atmospheric surface layer
whereas the latter does not. Our results show that after turbulent diffusion has been
accounted for in the apparent kinetic factor parameterization (Equation 3), the
leaf-scale enrichment models can be applied to the canopy scale with good accuracy.
The effect of turbulent diffusion was stronger for wheat, which had lower canopy
resistance, than for corn. We postulate that in global modeling studies the
canopy-scale apparent kinetic formulation will improve model performance especially
for productive ecosystems which generally have low canopy resistance.
The notion of the Péclet effect is, however, not transferable from the leaf scale to
the canopy scale. At the leaf scale, this effect describes the process in which the H218O
molecules diffuse, in the opposite direction of the mass flow of water, from the site of
evaporation within the leaf to the less enriched water near the veins (Farquhar &
Lloyd, 1993). At the canopy scale, however, the H218O molecules in the more
enriched leaves (such as in the upper canopy at midday, Appendix A) cannot move via
molecular diffusion to the less enriched leaves in another part of the canopy. This
scale incomparability may be one reason for why the canopy-scale version of the F05
model was forced to have a very small Péclet number. Thus we are left with a
paradoxical situation: The model implies a well mixed foliage water pool whereas the
observations show that this pool was clearly stratified within the canopy (Appendix
A). Overcoming this paradox will require more sophisticated multilayer models
(Baldocchi, 1992; Leuning, 1995; De Pury and Farquhar, 1997; Ogée et al., 2003)
than the simple big leaf model used in this study.
The high model biases for corn (Figs 3 and 5) could have resulted from the
simple upscaling schemes used both in the field measurement and in the models. Our
leaf sampling strategy was meant to obtain the mean canopy 18O/16O ratio. In the case
of corn, the samples were even mixture of small leaf segments taken at the base,
middle and tip of a selected leaf. The modeling study of Farquhar & Gan (2003)
suggests that this method may have underestimated the true average because the
H218O distribution with leaf position is not linear. In the LSM, the leaf photosynthesis
and stomatal resistance were weighted by photosynthetically active radiation with a
fixed canopy light extinction coefficient of 0.6. Unlike multi-layer and two-leaf
models (De Pury & Farquhar, 1997), this scaling method is incapable of handling the
interaction between stomatal behavior and leaf-scale humidity variations in the
canopy. Strictly speaking, there is a fundamental difficulty in interfacing the two-leaf
model with the isotopic parameterizations. This is because mass balance requires that
we track the time rate of changes of both 18O and 16O masses in a fixed set of foliage
(as in Equation 7), but the fractions of sunlit and shaded leaves are variable with solar
elevation. Now that the results were found to be insensitive to the temporal variations
in W, it is not necessary to consider the time rate of changes in the two-leaf model.
In leaf-scale studies, a number of other biotic factors are proved to be important
in controlling the leaf water 18O enrichment. Helliker & Ehleringer (2000) report that
leaf water 18O enrichment of C4 grasses is greater than that of C3 grasses under
controlled conditions; They attribute this to interveinal differences between the two
functional groups. The modeling study of Ogée et al. (2007) suggests that it is the
difference in mesophyll tortuosity between C3 and C4 plants rather than in leaf length
or interveinal distance that contributes to the stronger enrichment in C4 leaves than in
C3 leaves. Our big leaf model does not consider these morphological traits through
4.4 Implications for Global Change Studies
The δ18O of foliage water is an important determinant of atmospheric O18O and
C18OO budgets. The classic theories of the Earth Dole effect (Bender et al., 1994) and
vegetation effects on atmospheric C18OO (Farquhar et al., 1993) have relied on the
simple Craig-Gordon model for the δL calculation. That C65 performed equally well
in daylight hours as the more sophisticated D74 and F05 suggests that the steady-state
assumption is acceptable for determining the photosynthetic O18O and C18OO fluxes
at the canopy scale. Perhaps these theories can be improved by incorporating a
variable, rather than a constant, kinetic fractionation factor. Since more productive
ecosystems generally have lower canopy resistance rc and the apparent kinetic factor
decreases with decreasing rc, this approach may alter the regional distributions of
O18O and C18OO by reducing the contributions of cropland and native vegetation in
warm and wet climates and increasing those in dry and cool climates.
18O is a useful tracer for partitioning the net ecosystem CO2 flux into its gross
component fluxes. Flux partitioning is of interest to global change scientists because
the eddy covariance technique can only measure the net flux but validation of carbon
flux models requires data on the component fluxes. The 18O content of foliage
respiration in the dark is extremely sensitive to δL and is much more enriched
(Cernusak et al., 2004) than that of soil respiration (e.g., Wingate et al., 2010; Santos
et al., 2011). Incorporating the non-steady behaviors in the δL calculation (Fig. 6) will
further increase the difference between the two isotopic end members.
We thank the three journal reviewers whose comments have improved this article.
This study was supported by the National Basic Research Program of China (grant
2010CB833501), the National Natural Science Foundation of China (grants 30970517,
31070408 and 31100359), the Strategic Program of Knowledge Innovation of the
Chinese Academy of Sciences (grant KZCX2-EW-QN305), the U. S. National
Science Foundation (grant ATM- 0914473), the Ministry of Education of China (grant
PCSIRT), NUIST scientific foundation (grants KLME1006 and 20100357) and the
Jiangsu Provincial Government (grant PAPD).
Baldocchi DD (1992) A Lagrangian random walk model for simulating water vapor,
CO2 and sensible heat flux densities and scalar profiles over and within a
soybean canopy. Boundary-layer Meteorology, 61:113-144.
Barbour MM (2007) Stable oxygen isotope composition of plant tissue: a review.
Functional Plant Biology, 34: 83-94.
Barbour MM, Farquhar GD (2000) Relative humidity- and ABA-induced variation in
carbon and oxygen isotope ratios of cotton leaves. Plant, Cell and Environment,
Barbour MM, Farquhar GD (2004) Do pathways of water movement and leaf
anatomical dimensions allow development of gradients in H218O between veins
and the sites of evaporation within leaves? Plant, Cell and Environment, 27,
Barbour MM, Fischer RA, Sayre KD, Farquhar GD (2000) Oxygen isotope ratio of
leaf and grain material correlates with stomatal conductance and grain yield in
irrigated wheat. Australian Journal of Plant Physiology, 27, 625-637.
Bariac T, Rambal S, Jusserand C, Berger A (1989) Evaluating water fluxes of
field-grown alfalfa from diurnal observations of natural isotope concentrations,
energy budget and ecophysiological parameters. Agricultural and Forest
Meteorology, 48, 263-283.
Bender M, Sowers T, Labeyrie L (1994) The Dole effect and its variations during the
last 130,000 years as measured in the Vostok ice core. Global Biogeochemical
Cycles, 8, 363-376.
Cernusak LA, Farquhar GD, Pate JS (2005) Environmental and physiological controls
over oxygen and carbon isotope composition of Tasmanian blue gum, Eucalyptus
globulus. Tree Physiology, 25, 129-146.
Cernusak LA, Farquhar GD, Wong SC, Stuart-Williams H (2004) Measurement and
interpretation of the oxygen isotope composition of carbon dioxide respired by
leaves in the dark. Plant Physiology, 136, 3350-3363.
Cernusak LA, Mejia-Chang M, Winter K, Griffiths H (2008) Oxygen isotope
composition of CAM and C3 Clusia species: non-steady-state dynamics control
leaf water 18O enrichment in succulent leaves. Plant, Cell and Environment, 31,
Cernusak LA, Pate JS, Farquhar GD (2002) Diurnal variation in the stable isotope
composition of water and dry matter in fruiting Lupinus angustifolius under field
Plant, Cell and Environment,
Ciais P, Denning AS, Tans PP et al. (1997) A three-dimensional synthesis study of
δ18O in atmospheric CO2 1. Surface fluxes. Journal of Geophysical Research,
102, D5, 5857-5872.
Craig H, Gordon LI (1965) Deuterium and oxygen-18 variations in the ocean and the
marine atmosphere. In: Proceedings of the Conference on Stable Isotopes in
Oceanographic Studies and Paleotemperatures (ed Tongiorgi E), pp 9-130.
Laboratory of Geology and Nuclear Science, Pisa.
Cullen LE, Adams MA, Anderson MJ, Grierson PF (2008) Analyses of δ13C and δ18O
in tree rings of Callitris columellaris provide evidence of a change in stomatal
control of photosynthesis in response to regional changes in climate. Tree
Physiology, 28, 1525-1533.
Cuntz M, Ciais P, Hoffmann G, Knorr W (2003) A comprehensive global
three-dimensional model of δ18O in atmospheric CO2: 1. Validation of surface
Journal of Geophysical Research,
108, 4527, doi:
De Pury DGG, Farquhar GD (1997) Simple scaling of photosynthesis from leaves to
canopies without the errors of big-leaf model. Plant, Cell and Environment, 20,
Dole M, Lane GA, Rudd DP, Zaukelies DA (1954) Isotopic composition of
atmospheric oxygen and nitrogen. Geochimica et Cosmochimica Acta, 6, 65-78.
Dongmann G, Nürnberg HW, Förstel H, Wagener K (1974) On the enrichment of
H218O in the leaves of transpiring plants. Radiation and Environmental
Biophysics, 11, 41-52.
Epstein S, Yapp C (1977) Isotope tree thermometers. Nature, 266, 477-478.
Evans MN, Schrag DP (2004) A stable isotope-based approach to tropical
dendroclimatology. Geochimica et Cosmochimica Acta, 68, 3295-3305.
Farquhar GD, Cernusak LA (2005) On the isotopic composition of leaf water in the
non-steady state. Functional plant biology, 32, 293-303.
Farquhar GD, Cernusak LA, Barnes B (2007) Heavy water fractionation during
transpiration. Plant Physiology, 143, 11-18.
Farquhar GD, Gan KS (2003) On the progressive enrichment of the oxygen isotopic
composition of water along a leaf. Plant, Cell and Environment, 26, 801-819.
Farquhar GD, Hubick KT, Condon AG, Richards RA (1989) Carbon isotope
discrimination and plant water-use efficiency. In: Stable Isotopes in Ecological
Research (eds Rundel PW, Ehlerginger JR, Nagy KA), pp 21-40. Springer-Verlag,
Farquhar GD, Lloyd J (1993) Carbon and oxygen isotope effects in the exchange of
carbon dioxide between terrestrial plants and the atmosphere. In: Stable Isotopes
and Plant Carbon-water Relations (eds Ehleringer JR, Hall AE, Farquhar GD),
pp 47-70. Academic Press, San Diego.
Farquhar GD, Lloyd J, Taylor JA, Flanagan LB, Syvertsen JP, Hubick KT, Wong SC,
Ehleringer JR (1993) Vegetation effects on the isotope composition of oxygen in
atmospheric CO2. Nature, 363, 439-443.
Flanagan LB, Comstock JP, Ehleringer JR (1991) Comparison of modeled and
observed environmental-influences on the stable oxygen and hydrogen isotope
composition of leaf water in Phaseolus-Vulgaris L. Plant Physiology, 96,
Flanagan LB, Ehleringer JR (1991) Effects of mild water-stress and diurnal changes
in temperature and humidity on the stable oxygen and hydrogen isotopic
composition of leaf water in Cornus-Stolonifera L.. Plant Physiology, 97,
Gan KS, Wong SC, Yong JWH, Farquhar GD (2002) 18O spatial patterns of vein
xylem water, leaf water and dry matter in cotton leaves. Plant Physiology, 130,
Gan KS, Wong SC, Yong JWH, Farquhar GD (2003) Evaluation of models of leaf
water 18O enrichment using measurements of spatial patterns of vein xylem water,
leaf water and dry matter in maize leaves. Plant, Cell and Environment, 26,
Gray J, Thompson P (1976) Climatic information from 18O/16O ratios of cellulose in
tree rings. Nature, 262, 481-482.
Griffis TJ, Lee X, Baker JM et al. (2011) Oxygen isotope composition of
evapotranspiration and its relation to C4 photosynthetic discrimination. Journal
of Geophysical Research, 116, G01035, doi:10.1029/2010JG001514.
Helliker BR, Ehleringer JR (2000) Establishing a grassland signature in veins: 18O in
the leaf water of C3 and C4 grasses. Proceedings of the National Academy of
Sciences of the United States of America, 97, 7894-7898.
Helliker BR, Richter SL (2008) Subtropical to boreal convergence of tree-leaf
temperatures. Nature, 454, 511-515, doi:10.1038/nature07031.
Hoffmann G, Cuntz M, Weber C et al. (2004) A model of the Earth's Dole effect.
Global Biogeochemical Cycles, 18, GB1008, doi:1010.1029/2003GB002059.
Hu Z, Li S, Sun X, Wen X, Lee X, Li L, Yu G (2012) Partitioning of
evapotranspiration in a temperate grassland through isotopic measurements of
18O-H2O. In review.
Kahmen A, Sachse D, Arndt SK, Tu KP, Farrington H, Vitousek PM, Dawson TE
(2011) Cellulose δ18O is an index of leaf-to-air vapor pressure difference (VPD)
in tropical plants. Proceedings of the National Academy of Sciences of the United
States of America, 108, 1981-1986.
Kim K (2011) Laboratory and field investigations of stable water isotopes in
ecosystems. Unpublished PhD thesis, Yale University, New Haven. Available at:
http://xleelab.sites.yale.edu/publications (accessed 8 September 2011)
Knapp AK (1993) Gas exchange dynamics in C3 and C4 grasses: consequence of
differences in stomatal conductance. Ecology, 74, 113-123.
Lai C, Ehleringer JR, Bond BJ, Paw UKT (2006) Contributions of evaporation,
isotopic non-steady state transpiration and atmospheric mixing on the δ18O of
water vapour in Pacific Northwest coniferous forest. Plant, Cell and
Environment, 29, 77-94.
Lee X, Griffis TJ, Baker JM, Billmark KA, Kim K, Welp LR (2009) Canopy-scale
kinetic fractionation of atmospheric carbon dioxide and water vapor isotopes.
Global Biogeochemical Cycles, 23, GB1002, doi:10.1029/2008GB003331.
Lee X, Huang J, EG Patton (2012) A large-eddy simulation study of water vapour and
carbon dioxide isotopes in the atmospheric boundary layer. Boundary-Layer
Meteorology, in press, doi: 10.1007/s10546-011-9631-3.
Lee X, Kim K, Smith R (2007) Temporal variations of the isotopic signal of the
whole-canopy transpiration in a temperate forest. Global Biogeochemical Cycles,
21, GB3013, doi:10.1029/2006GB002871.
Leuning R (1995) A critical appraisal of a combined stomatal-photosynthesis model
for C3 plants. Plant Cell Environment, 18: 339-355.
Libby Pandolfi LJ, Payton PH, Marshall III J, Becker B, Giertz-Sienbenlist V (1976)
Isotopic tree thermometers. Nature, 261, 284-288.
Majoube M (1971) Fractionnement en oxygene-18 et en deuterium entre l’eau et sa
vapeur. Journal de Chimie et Physique, 58, 1423-1436.
Merlivat L (1978) Molecular diffusivities of H216O, HD16O and H218O in gases.
Journal of Chemical Physics, 69, 2864-2871.
Miller DL, Mora CI, Grissino-Mayer HD, Mock CJ, Uhle ME, Sharp Z (2006)
Tree-ring isotope records of tropical cyclone activity. Proceedings of the
National Academy of Sciences of the United States of America, 103,
Ogée J, Brunet Y, Loustau D, Berbigier P, Delzon S (2003) MuSICA, a CO2, water and
energy multilayer, multileaf pine forest model: evaluation from hourly to yearly
time scales and sensitivity analysis. Global Change Biology, 9, 697-717.
Ogée J, Cuntz M, Peylin P, Bariac T (2007) Non-steady-state, non-uniform
transpiration rate and leaf anatomy effects on the progressive stable isotope
enrichment of leaf water along monocot leaves, Plant, Cell and Environment, 30,
Ogée J, Peylin P, Ciais P et al. (2004) Partitioning net ecosystem carbon exchange
into net assimilation and respiration with canopy-scale isotopic measurements:
An error propagation analysis with
13CO2 and CO18O data. Global
Biogeochemical Cycles, 18, doi:10.1029/2003GB002166.
Pearcy RW, Ehleringer J (1984) Comparative ecophysiology of C3 and C4 plants.
Plant, Cell and Environment, 7: 1-13.
Riley WJ, Still CJ, Torn MS, Berry JA (2002) A mechanistic model of H218O and
C18OO fluxes between ecosystems and the atmosphere: Model description and
Global Biogeochemical Cycles,
Roden JS, Lin G, Ehleringer JR (2000) A mechanistic model for interpretation of
hydrogen and oxygen isotope ratios in tree-ring cellulose. Geochimica et
Cosmochimica Acta, 64, 21-35.
Ronda RJ, de Bruin HAR, Holtslag AAM (2001) Representation of the canopy
conductance in modeling the surface energy budget for low vegetation. Journal
of Applied Meteorology, 40, 1431-1444.
Santos EA, Wagner-Riddle C, Lee X, Warland J, Brown S, Staebler R, Bartlett P, Kim
K (2011) Use of the isotope flux ratio approach to investigate the C18O16O and
13CO2 exchange near the floor of a temperate deciduous forest. Biogeosciences
Discussion, 8, 7671-7712, doi:10.5194/bgd-8-7671-2011.
Schultz NM, Griffis TJ, Lee X, Baker JM (2011) Identification and correction of
spectral contamination in 2H/1H and 18O/16O measured in leaf, stem, and soil
water. Rapid Communications in Mass Spectrometry, 25, 3360-3368.
Seibt U, Wingate L, Berry A (2007) Nocturnal stomatal conductance effects on the
δ18O signatures of foliage gas exchange observed in two forest ecosystem. Tree
Physiology, 27, 585-595.
Seibt U, Wingate L, Berry JA, Lloyd J (2006) Non-steady state effects in diurnal 18O
discrimination by Picea sitchensis branches in the field. Plant, Cell and
Environment, 29, 928-939.
Sternberg L da SL (2009) Oxygen stable isotope ratios of tree-ring cellulose: The next
phase of understanding. New Phytologist, 181, 553-562.
Still CJ, Riley WJ, Biraud SC et al. (2009) Influence of clouds and diffuse radiation
on ecosystem-atmosphere CO2 and CO18O exchanges. Journal of Geophysical
Research, 114, G01018, doi:10.1029/2007JG000675.
Treydte KS, Schleser GH, Helle G, Frank DC, Winiger M, Haug GH, Esper J (2006)
The twentieth century was the wettest period in northern Pakistan over the past
millennium. Nature, 440, 1179-1182.
Walker CD, Leaney FW, Dighton JC, Allison GB (1989) The influence of
transpiration on the equilibration of leaf water with atmospheric water-vapor.
Plant, Cell and Environment, 12, 221-234.
Wang X, Yakir D (1995) Temporal and spatial variations in the oxygen-18 content of
leaf water in different plant species. Plant, Cell and Environment, 18, 1377-1385.
Welp LR, Lee X, Kim K, Griffis TJ, Billmark KA, Baker JM (2008) δ18O of water
vapor, evapotranspiration and the sites of leaf water evaporation in a soybean
canopy. Plant, Cell and Environment, 31, 1214-1228, doi: 1210.1111/j.1365-
Wen X, Lee X, Sun X, Wang J, Z Hu, Li S, Yu G (2012) Dew water isotopic ratios
and their relations to ecosystem water pools and fluxes in a cropland and a
grassland in China. Oecologia, in press, doi:10.1007/s00442-011-2091-0.
West AG, Patrickson SJ, Ehleringer JR (2006) Water extraction times for plant and
soil materials used in stable isotope analysis. Rapid Communications in Mass
Spectrometry, 20, 1317-1321, doi:10.1002/rcm.2456.
White JWC (1989) Stable hydrogen isotope ratios in plants: a review of current theory
and some potential applications. In: Stable isotopes in ecological research (eds
Rundel PW, Ehleringer JR, Nagy KA), pp 142-162. Springer-Verlag, Berlin.
Wingate L, Ogée J, Burlett R, Bosc A (2010) Strong seasonal disequilibrium
measured between the oxygen isotope signals of leaf and soil CO2 exchange.
Global Change Biology, 16, 3048-3064, doi:10.1111/j.1365-2486.2010.02186.x.
Xiao W, Lee X, Griffis TJ, Kim K, Welp LR, Yu Q (2010) A modeling investigation of
canopy-air oxygen isotopic exchange of water vapor and carbon dioxide in a
soybean field. Journal of Geophysical Research, 115, G01004, doi:10.1029
Yakir D (1998) Oxygen-18 of leaf water: a crossroad for plant associated isotopic
signal. In: Stable Isotopes: Integration of Biological, Ecological and
Geochemical Processes (ed Griffiths H), pp 147-168. BIOS Scientific Publishers
Yakir D, Berry JA, Giles L, Osmond CB (1994) Isotopic heterogeneity of water in
transpiring leaves: identification of the components that controls the δ18O of
atmospheric O2 and CO2. Plant, Cell and Environment, 17, 73-80.
Yakir D, Sternberg LDL (2000) The use of stable isotopes to study ecosystem gas
exchange. Oecologia, 123: 297-311.
Yilmaz MT, Hunt Jr. ER, Jackson TJ (2008) Remote sensing of vegetation water
content from equivalent water thickness using satellite imagery. Remote Sensing
of Environment, 112, 2514-1522.
Table 1 Description of the isotopic submodels
W variation Non-steady state Péclet effect
Craig & Gordon (C65) No No No
Dongmann et al. (D74) No Yes No
Farquhar and Cernusak (F05) Yes Yes Yes
Table 2 The index of agreement (IA), root-mean-square deviation (RMSD), and mean
error (ME) of the three model schemes over the two crop seasons
C65 D74 F05
Whole-day 0.95 1.6 0.4 0.902.1 1.0 0.91 2.0 1.0 Wheat
Nighttime 0.88 1.8 1.0 0.733.6 3.3 0.75 3.3 3.0
Whole-day 0.92 2.6 1.6 0.912.6 2.0 0.92 2.5 1.9 Corn
Nighttime 0.83 2.4 -1.7 0.763.5 2.3 0.80 3.0 1.8
Table 3 A summary of statistics on the model-predicted δL,b (‰) for the two crop
seasons over three averaging periods [24 h, midday (10:00-15:00 LST) and midnight
(22:00-3:00 LST)]. For reference, the mean xylem and water vapor isotopic
compositions were -6.9 and -12.8‰ for wheat and -8.2 and -15.6‰ for corn,
C65 D74 F05 C65 D74 F05
24h 2.3 3.3 3.2 -0.1 1.1 1.0
Midday 4.3 4.2 4.1 4.6 4.1 4.0
Midnight 1.0 3.4 3.1 -3.4 -0.6 -0.9
Fig. 1 Schematic diagram of the model structures.
Fig. 2 Top panel: leaf water content (W, mol m-2 leaf area, circles) observed in midday
and leaf area index (LAI, solid lines). Bottom panel: diurnal composite W
observed during the intensive sampling periods for wheat (closed circles) and
corn (open circles) and sinusoidal curve fits. Error bars are ±1 standard deviation
of four spatial replicates.
Fig. 3 Time series (a: wheat; c: corn) and ensemble diurnal patterns (b: wheat; d: corn)
of the observed and simulated canopy mean values of δL,b during the intensive
periods (blue line, C65; green line, D74; red line, F05; closed circles,
observations). Mean values of the xylem and water vapor isotopic compositions
during the intensive periods were -6.9 and -11.5‰ for wheat and -8.5‰ and
-15.2‰ for corn. Error bars are ±1 standard deviation of four spatial replicates.
Fig. 4 Scatter plot of the modeled δL,b against the observations during wheat and corn
intensive periods (closed circles: C65; squares: D74; triangles: F05).
Fig. 5 Time series of the observed and simulated δL,b in wheat and corn canopy over
the growing seasons (blue line, C65; green line, D74; red line, F05 model; closed
circles, observation). Error bars are ±1 standard deviation of four spatial
Fig. 6 Top panels: diurnal composite of the modeled δL,b for wheat (a) and corn
growing season (b) (blue line: C65, green line: D74; red line: F05); Bottom
panels: difference among the three models for wheat (c) and corn (d) (blue line:
D74 versus C65; green line: F05 versus C65; red line: F05 versus D74).
Fig. 7 Bias of the midday foliage 18O composition (δL,b) simulated by F05 versus the
bias of the simulated canopy temperature (Tc).
Fig. 8 Measured δL,b versus δL,b simulated by F05 considering (closed circles) and
omitting turbulent diffusion (squares) in the apparent kinetic fractionation
Fig. A1 Spatial distribution of δL,b (‰) along the corn leaf at different times of the day,
Fig. A2 The correlation between δL,b gradient (tip to base) in the corn leaves and
Fig. A3 Observed midday (10:00 - 15:00 LST) δL,b in the upper (closed circles) and
lower (open circles) canopy in wheat and corn. Error bars are ±1 standard
deviation of four spatial replicates.
Fig. A4 Time variation of δL,b during four intensive observation periods in the upper
(closed circles) and lower layer of the canopy (open circles). Error bars are ±1
standard deviation of four spatial replicates.
Fig. A5 Ethanol (a) and methanol (b) contamination correction curves for IRIS δ18O
Fig. A6 Comparison of IRIS and IRMS δ18O measurements.
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