BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-14-4435-2017Ideas and perspectives: how coupled is the vegetation to the boundary layer?De KauweMartin G.mdekauwe@gmail.comhttps://orcid.org/0000-0002-3399-9098MedlynBelinda E.KnauerJürgenhttps://orcid.org/0000-0002-4947-7067WilliamsChristopher A.https://orcid.org/0000-0002-5047-0639ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, AustraliaDepartment of Biological Science, Macquarie University, North Ryde, NSW 2109, AustraliaHawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, AustraliaDepartment of Biogeochemical Integration, Max Planck Institute for
Biogeochemistry, 07745 Jena, GermanyGraduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01602, USAMartin G. De Kauwe (mdekauwe@gmail.com)9October201714194435445311May201717May20171September20176September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/14/4435/2017/bg-14-4435-2017.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/14/4435/2017/bg-14-4435-2017.pdf
Understanding the sensitivity of transpiration to stomatal
conductance is critical to simulating the water cycle. This sensitivity is a
function of the degree of coupling between the vegetation and the atmosphere
and is commonly expressed by the decoupling factor. The degree of coupling
assumed by models varies considerably and has previously been shown to be a
major cause of model disagreement when simulating changes in transpiration
in response to elevated CO2. The degree of coupling also offers us
insight into how different vegetation types control transpiration fluxes,
which is fundamental to our understanding of land–atmosphere interactions. To explore
this issue, we combined an extensive literature summary from 41 studies with
estimates of the decoupling coefficient estimated from FLUXNET data. We found
some notable departures from the values previously reported in single-site
studies. There was large variability in estimated decoupling coefficients
(range 0.05–0.51) for evergreen needleleaf forests. This is a result that was
broadly supported by our literature review but contrasts with the early
literature which suggests that evergreen needleleaf forests are generally
well coupled. Estimates from FLUXNET indicated that evergreen broadleaved
forests were the most tightly coupled, differing from our literature review
and
instead suggesting that it was evergreen needleleaf forests. We also found
that the assumption that grasses would be strongly decoupled (due to
vegetation stature) was only true for high precipitation sites. These results
were robust to assumptions about aerodynamic conductance and, to a lesser
extent, energy balance closure. Thus, these data form a benchmarking metric
against which to test model assumptions about coupling. Our results identify
a clear need to improve the quantification of the processes involved in
scaling from the leaf to the whole ecosystem. Progress could be made with
targeted measurement campaigns at flux sites and greater site
characteristic information across the FLUXNET network.
Introduction
Predicting the response of transpiration to global change and the subsequent
feedback to climate remains a major challenge for Earth system models
. Improving our understanding of how stomatal controls on
transpiration vary between vegetation types is fundamental to simulating
land–atmosphere interactions. Experimental evidence strongly indicates that
stomatal conductance (Gs) is generally reduced in response to
elevated CO2 due to either a decrease in
stomatal aperture with the reduced photosynthetic demand for CO2 and/or
a change in stomatal density . In models, incorporating
this leaf-level reduction in Gs commonly results in predictions
of decreased transpiration and increased run-off at global scales
. However, the magnitude of this effect varies
strongly among models because the sensitivity of transpiration to a change
in Gs depends on the assumption made about the strength of
coupling of the vegetation to the surrounding boundary layer
. identified differences in the
degree of coupling to be a major cause of disagreement among 11 model
predictions of transpiration in response to elevated CO2 at two forest
Free-Air CO2 Enrichment (FACE) experiments in the USA. Consequently,
resolving this discrepancy among models in their assumptions of
vegetation–atmosphere coupling is key to reducing model uncertainty in future
predictions of changes in transpiration.
The degree of coupling between vegetation and the atmosphere is commonly
expressed by the decoupling factor (Ω; Jarvis and McNaughton, 1986).
If the decoupling factor is high, transpiration is more strongly controlled
by incoming radiation and less by changes in Gs. Low-stature canopies and species with large leaves are expected to be more
decoupled (high Ω) than tall-stature canopies and species with small
leaves. This occurs because (i) small-stature canopies decrease the surface
roughness, and hence the aerodynamic conductance, and (ii) large leaves
decrease the leaf boundary layer conductance. Both act to diminish the rate
of exchange between the vegetation surface and the atmosphere. Other
characteristics of the vegetation, including foliage clumping, leaf shape,
canopy density, and the vertical canopy distribution, will also alter the
coupling. Values given in the literature for coniferous forests are typically
low at Ω=∼ 0.1–0.2 . Values are
typically higher for deciduous broadleaved species at Ω=0.2–0.4
, evergreen broadleaved species at Ω=0.4–0.9
, grasses at Ω=0.8, and crops at
Ω = 0.2-0.9 . These literature
estimates of the degree of coupling are wide and thus do not offer a clear
constraint to models. Furthermore, methods to estimate Ω often differ
across studies, which complicates interpretations about variation across
plant functional types. Single studies that have employed a consistent
method to estimate Ω across multiple species are rare
e.g..
There has been considerable recent effort to develop better global datasets
of stomatal behaviour for use by the modelling community
. However, constraining the coupling between stomatal
conductance and transpiration is equally important. For example,
demonstrated modest changes in transpiration when using the
dataset to constrain the parameterization of Gs in
the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model.
The CABLE model assumes a relatively weak level of coupling . It
is likely that models that assume stronger coupling e.g. the Joint UK
Land Environment Simulator, JULES; would obtain different results.
To shed new light on this important question of vegetation–atmosphere
coupling, we used eddy covariance data from FLUXNET to estimate the Ω
coefficient for different plant functional types (PFTs). We aimed to
(i) examine whether decoupling coefficients estimated from FLUXNET were consistent
with literature values and (ii) develop a benchmark metric against which to
test model assumptions about coupling.
Materials and methodsFlux data
Half-hourly eddy covariance measurements of the exchange of carbon dioxide,
energy, and water vapour were obtained from the FLUXNET “La Thuile” Free and
Fair dataset (http://www.fluxdata.org). We estimated the degree of
decoupling as
Ω=1+ϵ1+ϵ+GaGs,
where ϵ=s/γ, s is the slope of the saturation vapour
pressure curve at air temperature (Pa K-1), γ is the
psychrometric constant (Pa K-1), and Ga
(mol m-2 s-1) is the aerodynamic conductance.
We estimated values of Gs by inverting the Penman–Monteith
equation using measured latent heat (LE) flux for all datasets in which the net
radiation (Rn; W m-2) and the frictional velocity
(u*; m s-1) were available:
Gs=GaγλEs(Rn-G)-(s+γ)λE+GaMacpD,
where E (mol m-2 s-1) is the canopy transpiration, λ is
the latent heat of vaporization (J mol-1), D (Pa) is the vapour
pressure deficit, G (W m-2) is the soil heat flux, Ma
(kg mol-1) is molar mass of air, and cp is the heat capacity of
air (J kg-1 K-1). At sites where values of G were not
available, G was set to zero.
Ga was calculated following :
Ga=cuu*2+6.2u*-23,
where the first term in the denominator of Eq. (3) represents the turbulent
aerodynamic resistance (Gam), the second term represents the canopy
boundary layer component (Gb), c=P/(RgasTk) is a conversion factor from units of m s-1 to mol m-2 s-1, P is atmospheric pressure (Pa), Rgas is
the gas constant (J mol-1 K-1), Tk is the air
temperature in Kelvin, and u (m s-1) is the wind speed.
In our analysis we derived the average (three most productive months)
decoupling coefficient as the focus of our paper was on the spatial
variability in coupling across FLUXNET. This is likely a metric that
can be readily exploited to assess existing coupling assumptions in
models. In the future, researchers may wish to explore the temporal variability in this
metric.
The approach we have taken similar to ignores differences
between canopy and air temperature (radiative coupling) within the canopy
see. However, correcting for the longwave radiative
conductance (Gr) mostly impacts vegetation with the weakest control
on transpiration, and as a result this assumption has little impact on the
degree of coupling range for forest species but may be a factor for other
species.
Flux data were first screened as follows: (i) data flagged as “good”
quality control flag “fqcOK” = 1;; (ii) data from the
three most productive months to account for the different timing of summer
in the Northern and Southern hemispheres; (iii) daylight hours between 08:00 am
and 04:00 pm to account for periods when the vegetation is
photosynthesizing; (iv) half hours with precipitation and the subsequent 48
half hours were excluded to minimize the influence of soil evaporation
; and (v) data with u* < 0.25 were
excluded to avoid conditions of low turbulence . We also
excluded sites classified as mixed forest, permanent wetlands, or those where
the PFT was unclassified.
Box and whisker plot (line, median; box, inter-quartile range)
showing the estimated coupling coefficient (Ω) from FLUXNET data
grouped by plant functional type. Whiskers extend to 1.5 times the
inter-quartile range, with dots outside of the whiskers showing outliers.
Plant functional types are defined as ENF – evergreen needleleaf forest,
EBF – evergreen broadleaved forest, DBF – deciduous broadleaved forest, TRF
– tropical rain forest, SAV – savanna, SHB – shrub, GRA – grasses, C3C –
C3 crops, and C4C – C4 crops. Values of n indicate the number of
site years for FLUXNET.
Pressure was estimated using the hypsometric equation based on site elevation
data. Where site elevation information was missing, values were gap-filled
using the 30 arcsec (∼ 1 km) global digital elevation model GTOPO30
data from the United States Geological Survey (USGS). After filtering,
164 sites and 592 site years remained.
We also tested the sensitivity of estimated values to (i) errors in
Ga and (ii) errors due to a lack of energy balance closure.
Firstly, we increased and decreased estimated values of Ga by 30 %
to examine the sensitivity of Gs values inverted from the
Penman–Monteith equation. Secondly, following the recommendations by
, we tested the sensitivity of our results to energy balance
closure by correcting fluxes using the Bowen-ratio method (each half-hourly
LE and H flux) based on the available energy (Rn-G) on a longer timescale
(three most productive months).
We also replicated our analysis using eddy covariance data taken from the
FLUXNET2015 dataset
(http://fluxnet.fluxdata.org/data/fluxnet2015-dataset). Figure A1 in
the Appendix is a replicate of Fig. 1 and shows that the patterns we derived are
robust across flux releases.
Literature summary of decoupling coefficients; see Table A2 for
summaries of individual studies. Plant functional types (PFT) are defined as
ENF – evergreen needleleaf forest, EBF – evergreen broadleaved forest,
DBF – deciduous broadleaved forest, TRF – tropical rain forest, SAV –
savanna, SHB – shrub, GRA – grasses, C3C – C3 crops, and C4C – C4
crops.
Values of the coupling coefficient (Ω) for sites from
the evergreen needleleaf forest (ENF) plant functional type. Estimated
values of Ω have been split into (a) sites where the
coefficient of variation (COV) is < 20 %, (b) sites
where the COV is > 20 %, and (c) sites with only
2 years of data. Site error bars represent 1 standard deviation (site year
variation) in estimated Ω values. Solid horizontal grey lines show
overall mean coupling coefficients.
Results
We summarized previously reported estimates of the decoupling coefficient
from 41 studies in Tables 1 and A2 in the Appendix. Broadly speaking, estimated
decoupling coefficients from FLUXNET (Fig. 1) differed among PFTs in line
with literature values (Tables 1 and A2) and in line with expectations
related to vegetation roughness and/or stature. Evergreen needleleaf forests
(ENFs), which have small leaves, were in general tightly coupled (low
Ω), while deciduous broadleaved forests, tropical rain forest (large
leaves), and grasses and crops (small stature) had a lower degree of coupling
(higher Ω). However, there were some notable departures from
expectations. Estimates derived from FLUXNET indicated that evergreen
broadleaf forests were the most coupled PFT (mean Ω=0.21) as opposed
to the literature review, which suggested that ENF PFTs were the most coupled
(mean Ω=0.19). The FLUXNET data also indicated unexpectedly wide
ranges for Ω within PFTs. For grasses, Ω ranged from 0.02–0.8;
the number of low values was particular surprising given the expectation
that shorter-stature vegetation would be more decoupled.
The wide range in estimated values for ENF sites was also striking; Ω
extended from 0.05 to 0.51. To attempt to better understand this range, we
first separated ENF sites into the following: (i) sites with a low inter-annual coefficient
of variation (20 %), indicating consistent year-to-year estimates of the
degree of coupling; (ii) sites with a coefficient of variation
> 20 %, indicating sites with year-to-year variability in
coupling; and (iii) sites with only 2 years of data. This separation was
intended to rule out sampling issues. Figure 2 shows that the variability in
the estimated decoupling coefficient cannot be explained by sampling bias;
there is
significant site-to-site variability regardless of the inter-annual
variability.
Values of the estimated coupling coefficient (Ω) for
forest (ENF, EBF, DBF, TRF) vegetation and grasses as a function of
precipitation in the three most productive months. Only data for 90 % of
the three most productive months were flagged as “good” and are shown. Lines
indicate statistically significant regressions (P<0.05). Plant
functional types are defined as GRA – grasses, ENF – evergreen needleleaf forest, EBF – evergreen broadleaved forest, DBF – deciduous
broadleaved forest, and TRF – tropical rain forest.
We then probed these results for relationships with site variables by
testing to see if (i) sites with higher precipitation (in the three most
productive months) were more decoupled when precipitation was assumed to be
a proxy for leaf area index (LAI) and productivity or (ii) windy sites were more
coupled. For grasses we found a significant relationship between the degree
of coupling and precipitation (Fig. 3). The data suggest that for sites that
are likely to be more open grasslands (i.e. sites with low precipitation),
the vegetation is very coupled to the atmosphere, with a high level of
stomatal control. This relationship between the degree of coupling and
precipitation (r= 0.46) explains the high variability in estimated
decoupling coefficients for grasses as shown in Fig. 1. The prediction that
grasses would be weakly coupled due to small vegetation stature only holds
true at sites with high 3-month precipitation, which are presumably sites
with high LAI. We also found a significant relationship for ENF sites
(r= 0.40) and deciduous broadleaved forests (r= 0.64),
suggesting that the degree of coupling declined with canopy density. We also
found evidence of a weak negative relationship (r=-0.21) between
wind speed and the degree of coupling for forest sites, i.e. windier sites
tended to be more coupled (Fig. 4). For non-forest PFTs, we did not find a
significant relationship between wind speed and coupling.
Values of the estimated coupling coefficient (Ω) for forest
(ENF, EBF, DBF, TRF) vegetation as a function of wind speed. Line indicates
statistically significant regression (P<0.05), and r is the
correlation coefficient. Plant functional types are defined as ENF –
evergreen needleleaf forest, EBF – evergreen broadleaved forest, DBF –
deciduous broadleaved forest, and TRF – tropical rain forest.
Finally, we examined the sensitivity of our results to potential errors. We
tested whether our results were sensitive to different estimates of
Ga and whether our estimates of Gs were sensitive to
energy imbalance. We found that the broad pattern of our results in Fig. 1
was insensitive to errors in Ga. Increasing or decreasing
Ga by 30 % led to the median decoupling coefficient
decreasing or increasing by roughly 0.05 for evergreen broadleaf forest (EBF)
sites, for example. However, we did find that our results were sensitive to a
correction for the lack of energy balance closure. Figure A2 shows that
attempting to correct for a lack of closure leads to sites becoming less
coupled, but it does not shift the between-PFT differences in the degree of
coupling. The largest changes were for C3 crops (Ω changed from
∼ 0.44 to ∼ 0.6) and deciduous broadleaved forests (Ω
changed from ∼ 0.31 to ∼ 0.41).
Discussion
Correctly characterizing the sensitivity of transpiration to Gs
is critical for simulating the water cycle, particularly for future
projections of the terrestrial biosphere where it is widely expected that
Gs will decrease in response to increasing atmospheric CO2.
The parameterization of this crucial link between leaf- and canopy-scale
water fluxes has been largely ignored in model studies addressing the impact
of elevated CO2. Resulting projections of
changes in transpiration and associated fluxes (e.g. run-off, precipitation)
are likely to be model specific, with large uncertainty among models
. Model studies rarely provide information about the degree of
coupling assumed within the model. The range of assumptions commonly
incorporated in models includes the following: (i) coupling as a function of roughness
length (determined by vegetation height), e.g. JULES; (ii) coupling as a
function of leaf size, e.g. CLM the Community Land Model;;
(iii) coupling as affected by within-canopy turbulence, e.g. CABLE
; (iv) some combination of all three, e.g. CABLE/CLM;
(v) coupling that is not sensitive to wind speed (i.e. wind speed is fixed to
5 m s-1), e.g. SDGVM Sheffield Dynamic Global Vegetation
Model;; or (vi) models that use an alternative to the
Penman–Monteith equation, e.g. LPJ Lund–Potsdam–Jena family of
models;. This family of models uses an empirically calibrated
hyperbolic function of canopy conductance and the implied level
of coupling depends on how this function is parameterized.
Understandably, the pioneering work of is widely cited when
issues of coupling are discussed in the literature. However, many of the
earlier estimates of coupling they summarized were taken from single sites
and thus do not necessarily reflect the diversity of global vegetation. In
this study we have summarized 41 literature studies in combination with
estimates of the decoupling coefficient from 164 sites and 592 site years
from FLUXNET. Our literature summary (Tables 1 and A2) highlights the
diversity of approaches used to determine Ω. In contrast, we have
applied a consistent methodology across all 164 FLUXNET sites. For forest
PFTs, our results point to a weaker level of coupling than is often assumed.
Notably, ENF species were found to be less coupled (mean Ω=0.21;
range 0.05–0.51) across FLUXNET than summarized in
(Ω=0.1). Our estimate derived from FLUXNET was supported by our
wider literature summary (n=13). We found that the often-assumed low degree
of coupling for grasses is likely to only be true for high precipitation (and
presumably high LAI) sites; low precipitation sites were strongly coupled. A
further plausible explanation is that these drier sites are limited by
available soil moisture, with lower Gs resulting in a high degree
of coupling. We could not easily explain the coupling values estimated for
evergreen broadleaf forests, which were estimated to be more coupled than
evergreen needleleaf forests; this is a break from theoretical understanding
developed from vegetation roughness and/or stature. Finally, grouping the
data by PFTs also highlighted marked within-PFT variation in the degree of
coupling.
As land models move towards more realistic representations of the variability
in stomatal conductance informed by leaf-level syntheses
, it is also important that they accurately simulate the
coupling between vegetation and the atmosphere. Without this focus, any
efforts to improve realism at the leaf scale will not be reflected in
improvements in simulated transpiration at the canopy scale.
Caveats
One criticism of the approach taken here is that we have assumed a big-leaf
approximation to estimate the vegetation degree of coupling
see. It is of course likely that variation within a canopy
in terms of micro-climate (i.e. vapour pressure deficit, irradiance,
temperature), as well as how stomata respond, may invalidate this approach.
The use of a big-leaf approximation could be a possible explanation for the
surprisingly high level of coupling found in evergreen broadleaf forests,
although it would appear unlikely given the weaker level of coupling found
for deciduous broadleaved and tropical rainforest species.
We found high variation in the estimated coupling factor both across sites
and within sites. Two assumptions we make with respect to the flux data could
explain this variation. Firstly, we excluded data following rainfall (48 h)
to minimize the effects of soil evaporation.
Clearly, if soil evaporation is still a component of the LE flux after this
point it would introduce error to our estimates. This assumption may also
vary with PFT. Secondly, flux towers commonly do not close the energy balance
. Our use of the inverted Penman–Monteith equation implies
that we are attributing any errors due to energy imbalance to the sensible
heat flux see. Additionally, where data on the soil heat
flux were missing, we assumed there was no storage. Correcting for these
issues is not straightforward as it requires determining which flux is the
source of the error seefor a detailed discussion. We
followed the recommendations by and tested the sensitivity of our
results to energy balance closure by correcting using the Bowen-ratio method
based on the available energy (Rn-G). Whilst we did find some sensitivity in
our results (particularly for C3 crops and deciduous broadleaved forests), it
did not change the ordering of coupling factors between PFTs or explain the
unexpectedly high level of coupling for EBF sites.
Finally, we estimated the canopy aerodynamic conductance (Ga)
using an empirical equation following . tested
the impact of different methods of estimating Ga from flux data
on estimates of the stomatal slope parameter (the sensitivity of stomatal
conductance to assimilation). They found that a more physically based
representation of Ga led to a lower estimate of
Ga at two EBF flux sites and higher estimates of Ga
at another EBF and a deciduous broadleaved site. We tested the sensitivity of
our results to a change in Ga as shown by
and found the patterns in coupling to be robust across PFTs.
Route forward
Estimates of coupling from ecosystem-scale flux data are directly relevant
for models. We previously speculated that discrepancies among
models in coupling might be resolved by examining eddy covariance data. The
range in coupling factors we have estimated from the FLUXNET data provides an
overall constraint on the degree of coupling that should be assumed in
models and an indication of the appropriate degree of variability in
coupling across PFTs and rainfall regimes. The next steps involve determining
what models currently assume about the degree of coupling and then to
determine how flux-derived estimates of coupling would change model
predictions.
In this study we examined the long-term average coupling factor. It may also
be instructive to consider how estimated coupling factors change across the
course of a day or within a season. However, it is likely that such an
approach may be more sensitive to noise in the fluxes and events such
as drought.
Our results also identify a clear need to better understand
leaf-to-atmosphere coupling. We need to better understand why coupling
factors vary within PFTs. There are a number of plausible explanations, such
as drought, diversity of vegetation within a flux footprint, and data issues, and
it is likely that more detailed site-specific insight will be required to
move forward. To assist in better understanding patterns, we will need
greater detail in terms of ancillary data from FLUXNET sites. We attempted to
probe our results with respect to canopy height and LAI, but for many sites
this information was not available. Other potentially useful information
would include leaf size, stem density and crown length, and whether canopy
height is static or increasing. These data would facilitate more
sophisticated approaches to be explored: for example, estimates of
Gb based on leaf size . A more fundamental process
understanding will require targeted Gs measurements throughout
the canopy alongside corresponding sap flux measurements in forests and
chamber measurements in grasslands. Recently, compared
estimates of plant water-use efficiency derived from leaf gas exchange data
and eddy flux data for eight sites where these measurements were acquired at
the same point in time. They found similarities for DBF and TRF PFTs but
differences for EBF and ENF PFTs. The authors were unable to explain these
scaling discrepancies. Further targeted measurement campaigns at flux sites
could lead to new knowledge, which would advance our understanding of the
processes involved in scaling from the leaf to the canopy.
All code is freely available from
https://github.com/mdekauwe/flux_decoupling.
All eddy covariance data are available from
http://fluxnet.fluxdata.org/data/la-thuile-dataset/.
Box and whisker plot (line, median; box, inter-quartile range)
showing the estimated coupling coefficient (Ω) from FLUXNET2015 data
grouped by plant functional type. Whiskers extend to 1.5 times the
inter-quartile range, with dots outside of the whiskers showing outliers.
Plant functional types are defined as ENF – evergreen needleleaf forest,
EBF – evergreen broadleaved forest, DBF – deciduous broadleaved forest, TRF
– tropical rain forest, SAV – savanna, SHB – shrub, GRA – grasses, C3C –
C3 crops, and C4C – C4 crops. Values of n indicate the number of
site years for FLUXNET.
Box and whisker plot (line, median; box, inter-quartile range)
showing the estimated coupling coefficient (Ω) from FLUXNET data
grouped by plant functional type. These data have been corrected for energy
imbalance by adjusting the Bowen-ratio method for the imbalance across the three
most productive months. Whiskers extend to 1.5 times the inter-quartile
range, with dots outside of the whiskers showing outliers. Plant functional
types are defined as ENF – evergreen needleleaf forest, EBF – evergreen
broadleaved forest, DBF – deciduous broadleaved forest, TRF – tropical rain
forest, SAV – savanna, SHB – shrub, GRA – grasses, C3C – C3 crops, and C4C
– C4 crops. Values of n indicate the number of site years for FLUXNET.
Literature summary of decoupling coefficients. Where possible we
have summarized data from the growing season during daylight hours. E
is transpiration, Ga is the total aerodynamic conductance
(Ga=Gam+Gb), Gam is the
turbulent aerodynamic resistance, Gb is the canopy boundary layer
conductance, Gs is the stomatal conductance, u* is the
frictional velocity, EC is eddy covariance, PM is Penman–Monteith,
Rtot is the total resistance, Ra is the aerodynamic
resistance, PAR is photosynthetically active radiation, and TC is the total
conductance. The simple gradient approach refers to an estimation of
Gs from vapour pressure deficit, pressure, and transpiration.
Method refers to the approach to estimating Ω: (1) default, as in this
manuscript (amphistomatous vegetation); (2) hypostomatous vegetation; and
(3) accounting for radiative coupling following . Plant
functional types (PFT) are defined as ENF – evergreen needleleaf forest,
EBF – evergreen broadleaved forest, DBF – deciduous broadleaved forest, TRF
– tropical rain forest, SAV – savanna, SHB – shrub, GRA – grasses, C3C –
C3 crops, and C4C – C4 crops.
PFTDominant speciesΩScaleMethodGaGbGsReferenceENFAbies amabilis0.18Stand2f(wind, roughness,radiation)f(wind)Inverted PM withsap flowENFCallitrisglaucophylla J. Thompson0.15Canopy1f(wind, roughness)–Inverted PM, E from sap flowENFChamaecyparis obtusa0.21Canopy1f(wind, u*)–Inverted PM, E from ECENFPicea glauca0.4Stand1f(wind, u*)–Simplified PM with sap flowENFPicea abies0.19Canopy1Inversion bulk transfer of sensible heat (EC)–Inversion bulk transferof sensible and latent heat (EC)ENFPicea crassifolia0.06Canopy1f(wind, u*)–Inverted PM, E from ECENFPinus elliottii0.43Canopy1f(wind, roughness)f(u*)Inverted PM, E from ECENFPinus pinaster0.08Stand1Emp. relation between Ga and wind–Inverted PM, E from sap flowENFPinus sylvestris0.1Stand1f(wind, height)–Leaf gas exchangeENFPinus sylvestris0.32Canopy1f(wind, u*)–Inverted PM, E from ECENFPinus taeda0.25Canopy1f(roughness, u*)f(characteristic leafdimension, wind)Bottom-up model: f(VPD, LAI, andradiation)ENFPseudotsuga menziesii0.26Canopy1f(wind, u*)–Inverted PM, E from ECENFPseudotsuga menziesii0.15Canopy1f(wind, u*)–Inverted PM, E from EC
Continued.
PFTDominant speciesΩScaleMethodGaGbGsReferenceEBFAcacia ampliceps0.28Stand3Empirical relationshipbetween Ga andwind speed–Inverted (simplified)PM, E from sap flowEBFAzadirachta indica0.28Tree2f(leaf temperature)–Inverted (Simplified)PM from sap flowEBFCitrus limon0.12Stand1f(wind, u*)–Rtot – Ra, Rtot fromsimplified PM, E fromsap flowEBFEucalyptus camaldulensis0.33Stand3Empirical relationshipbetween Ga andwind speed–Inverted (simplified)PM, E from sap flowEBFEucalyptus crebra F. Muell.0.19Canopy1f(wind, roughness)–Inverted PM, E fromsap flowEBFEucalyptus globulus0.63Stand1TC – Gs–Simple gradientapproach, E fromsap flowEBFEucalyptus grandis0.28Stand1f(wind, u*)–Rtot – Ra, Rtot fromsimplified PM, E from sap flowEBFEucalyptus urophylla0.1Stand1f(wind, roughness)–Inverted PM, E from sap flowEBFNothofagus fusca0.24Stand1f(wind, roughness)–Stomatal conductancefrom gas exchangeupscaled by leaf areaEBFQuercus0.3Canopy1f(roughness, u*)f(characteristic leafdimension, wind)Bottom-up model: f(VPD, LAI, and PAR)EBFSchima superba0.22Stand1f(wind, roughness)–Inverted PM, E from sap flowEBFVaccinium vitis-vidaea0.2Canopy (understorey only)1f(wind, u*)–Inverted PM, E from EC
Continued.
PFTDominant speciesΩScaleMethodGaGbGsReferenceDBFAcer rubrum0.23Stand1f(wind, u*)f(u*)Inverted PM, E from sap flowDBFBetula papyrifera0.36Stand1f(wind, u*)–Simplified PM fromsap flowDBFFagus sylvatica0.28Canopy1f(wind, roughness)f(leaf size)Bottom-up model: f(VPD, maximum Gs, temperature, andradiation)DBFFagus crenata0.3Stand1f(wind, roughness)f(characteristic leafdimension, wind)Inverted PM, E from sap flowDBFFagus sylvatica0.2Canopy1f(wind, roughness)–Inverted PM, E fromthe Bowen ratioDBFJuglans regia0.37Tree2–f(leaf temperature,roughness)Modelled following and upscaledDBFPopulus balsamifera0.4Stand1f(wind, u*)–Simplified PM withsap flowDBFPopulus trichocarpa×P. deltoides0.66Canopy3TC – Gs–Simple gradientapproach, E fromsap flowDBFQuercus petraea0.1Canopy1f(wind, roughness)–Inverted PM, E from ECDBFSalix viminalis0.7Canopy1Inverted PM when the canopy is wet–Inverted PM, E from sap flowDBF–0.41Canopy1Surface layer similarityf(u*)Inverted PM, E usingthe Bowen ratio (EC)TRFAnacardium excelsum0.75Tree2TC – Gs–Leaf gas exchangeTRFCecropia longipes0.9Tree2TC – Gs andstomatal conductance–Leaf gas exchangeTRFFicus insipida0.82Tree2TC – Gs–Leaf gas exchangeTRFHedyosmumanisodorum Todzia0.37Leaf1–f(wind, leaf extension)Leaf gas exchangeTRFLuehea seemannii0.88Tree2TC – Gs–Leaf gas exchangeTRFNaucleopsis sp.0.27Leaf1–f(wind, leaf extension)Leaf gas exchangeTRFPsychotria brachiataRuiz & Pav.0.27Leaf1–f(wind, leaf extension)Leaf gas exchangeTRFRuagea cf. pubescens H. Karst.0.25Leaf1–f(wind, leaf extension)Leaf gas exchangeTRFSpondias mombin0.9Tree2TC – Gs–Leaf gas exchangeTRFTrichilia guianensis Klotzsch0.43Leaf1–f(wind, leaf extension)Leaf gas exchangeTRF–0.43Canopy1TC – Ga – f(wind, u*)–Inverted PM, E from EC
Continued.
PFTDominant speciesΩScaleMethodGaGbGsReferenceSAV–0.14Stand1f(wind, roughness)–Inverted PM, E from ECSHBProsopis juliflora0.13Stand3Empirical relationshipbetween Ga andwind speed–Inverted (simplified)PM, E from sap flowSHBQuercus sp.0.4Canopy1f(wind, roughness)f(u*)Inverted PM, E from ECGRABrachiaria brizantha0.5Canopy1f(wind, roughness)f(u*)Inverted PM, E from ECGRAFestuca arundinaria Shreb.0.34Canopy1f(roughness, u*)f(characteristic leafdimension, wind)Bottom-up model: f(VPD, LAI, and radiation)GRAPhragmites australis0.48Canopy1f(wind, u*)f(u*)Inverted PM, E from ECGRA–0.45Canopy1f(wind, u*)–Inversion bulk transferof sensible and latentheat (EC)GRA–0.49Canopy1f(wind, u*)f(u*)Inverted PM, E from ECGRA–0.31Canopy1f(wind, u*)f(u*)Inverted PM, E from ECGRA–0.21Canopy1f(wind, u*)f(u*)Inverted PM, E from ECGRA–0.8–1–––C3CCrotalaria juncea0.59Canopy1f(wind, roughness)–Inverted PM, E fromBowen ratio energybalance methodC3CMusa sp.0.2Stand1f(wind, characteristicleaf dimension, LAI)–Inverted PM, E fromsap flow ratio energybalance methodC4CZea mays0.58Canopy1f(wind, roughness)f(u*)Inverted PM, E fromBowen ratio energybalance method
The authors declare that they have no conflict of
interest.
Acknowledgements
Martin G. De Kauwe was supported by an Australian Research Council (ARC)
Linkage grant LP140100232 and acknowledges support from the ARC Centre of
Excellence for Climate System Science CE110001028. This work used eddy
covariance data acquired by the FLUXNET community and in particular by the
following networks: AmeriFlux (US Department of Energy, Biological and
Environmental Research, Terrestrial Carbon Program; DE-FG02-04ER63917 and
DE-FG02-04ER63911), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP,
CarboItaly, CarboMont, ChinaFlux, FLUXNET Canada (supported by CFCAS, NSERC,
BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC,
OzFlux, TCOS Siberia, and USCCC. We acknowledge financial support for the
eddy covariance data harmonization provided by CarboEuropeIP, FAO–GTOS–TCO,
iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science
Foundation, the University of Tuscia, Université Laval and Environment
Canada,
and the US Department of Energy. We also acknowledge contributions for the database development and technical
support from the Berkeley Water Center, Lawrence Berkeley National Laboratory,
Microsoft Research eScience, Oak Ridge National Laboratory, the University of
California, and the University of Virginia. Finally, we thank the two anonymous
reviewers for their constructive criticisms that improved this work.
Edited by: David Bowling
Reviewed by: two anonymous referees
ReferencesAinsworth, E. and Rogers, A.: The response of photosynthesis and stomatal
conductance to rising [CO2]: mechanisms and environmental
interactions, Plant Cell Environ., 30, 258–270,
10.1111/j.1365-3040.2007.01641.x, 2007.Aires, L., Pio, C., and Pereira, J.: The effect of drought on energy and
water
vapour exchange above a mediterranean C3/C4 grassland in Southern Portugal,
Agr. Forest Meteorol., 148, 565–579, 2008.Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H.,
Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N.,
Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C.
S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES),
model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4,
677–699, 10.5194/gmd-4-677-2011, 2011.
Betts, R., Boucher, O., Collins, M., Cox, P., Falloon, P., Gedney, N.,
Hemming,
D., Huntingford, C., Jones, C., Sexton, D., and Webb, M. J.: Projected
increase in continental runoff due to plant responses to increasing carbon
dioxide, Nature, 448, 1037–1041, 2007.
Black, T. A., Tanner, C., and Gardner, W.: Evapotranspiration from a snap
bean
crop, Agron. J., 62, 66–69, 1970.
Bladon, K. D., Silins, U., Landhäusser, S. M., and Lieffers, V. J.:
Differential transpiration by three boreal tree species in response to
increased evaporative demand after variable retention harvesting,
Agr. Forest Meteorol., 138, 104–119, 2006.Bracho, R., Powell, T. L., Dore, S., Li, J., Hinkle, C. R., and Drake, B. G.:
Environmental and biological controls on water and energy exchange in Florida
scrub oak and pine flatwoods ecosystems, J. Geophys. Res.-Biogeo., 113, G02004, 10.1029/2007JG000469, 2008.
Brown, K. W.: Vegetation and the Atmosphere, Sugar beet and potatoes,
Academic Press, London, 1976.Cao, L., Bala, G., Caldeira, K., Nemani, R., and Ban-Weiss, G.: Importance of
carbon dioxide physiological forcing to future climate change, Proc. Natl. Acad. Sci. USA, 107,
9513–9518, 10.1073/pnas.0913000107, 2010.
Daudet, F., Le Roux, X., Sinoquet, H., and Adam, B.: Wind speed and leaf
boundary layer conductance variation within tree crown: consequences on
leaf-to-atmosphere coupling and tree functions, Agr. Forest
Meteorol., 97, 171–185, 1999.De Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C.,
Hickler, T., Jain, A. K., Luo, Y., Parton, W. J., Prentice, I. C., Smith, B.,
Thornton, P. E., Wang, S., Wang, Y.-P., Wårlind, D., Weng, E., Crous,
K. Y., Ellsworth, D. S., Hanson, P. J., Seok Kim, H., Warren, J. M., Oren,
R., and Norby, R. J.: Forest water use and water use efficiency at elevated
CO2: a model-data intercomparison at two contrasting temperate
forest FACE sites, Glob. Change Biol., 19, 1759–1779,
10.1111/gcb.12164, 2013.De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma,
R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal
stomatal conductance scheme within the CABLE land surface model,
Geosci. Model Dev., 8, 431–452, 10.5194/gmd-8-431-2015,
2015.Dekker, S. C., Groenendijk, M., Booth, B. B. B., Huntingford, C., and Cox, P.
M.: Spatial and temporal variations in plant water-use efficiency inferred
from tree-ring, eddy covariance and atmospheric observations, Earth Syst.
Dynam., 7, 525–533, 10.5194/esd-7-525-2016, 2016.
Foken, T.: The energy balance closure problem: an overview, Ecol.
Appl., 18, 1351–1367, 2008.
Gaofeng, Z., Ling, L., Yonghong, S., Xufeng, W., Xia, C., Jinzhu, M.,
Jianhua,
H., Kun, Z., and Changbin, L.: Energy flux partitioning and
evapotranspiration in a sub-alpine spruce forest ecosystem, Hydrol.
Proc., 28, 5093–5104, 2014.Gedney, N., Cox, P., Betts, R., Boucher, O., Huntingford, C., and Stott, P.:
Detection of a direct carbon dioxide effect in continental river runoff
records, Nature, 439, 835–838, 10.1038/nature04504, 2006.Goldberg, V. and Bernhofer, C.: Testing different decoupling coefficients
with measurements and models of contrasting canopies and soil water
conditions, Ann. Geophys., 26, 1977–1992, 10.5194/angeo-26-1977-2008,
2008.
Granier, A. and Bréda, N.: Modelling canopy conductance and stand
transpiration of an oak forest from sap flow measurements, Ann.
Sci. Forest., 53, 537–546, 1996.
Groenendijk, M., Dolman, A., van der Molen, M., Leuning, R., Arneth, A.,
Delpierre, N., Gash, J., Lindroth, A., Richardson, A., Verbeeck, H., and Wohlfahrt, G.:
Assessing parameter variability in a photosynthesis model within and between
plant functional types using global Fluxnet eddy covariance data,
Agr. Forest Meteorol., 151, 22–38, 2011.
Haijun, L., Cohen, S., Lemcoff, J. H., Israeli, Y., and Tanny, J.: Sap flow,
canopy conductance and microclimate in a banana screenhouse, Agr.
Forest Meteorol., 201, 165–175, 2015.
Hao, Y., Wang, Y., Huang, X., Cui, X., Zhou, X., Wang, S., Niu, H., and
Jiang,
G.: Seasonal and interannual variation in water vapor and energy exchange
over a typical steppe in Inner Mongolia, China, Agr. Forest
Meteorol., 146, 57–69, 2007.
Herbst, M.: Stomatal behaviour in a beech canopy: an analysis of Bowen ratio
measurements compared with porometer data, Plant Cell Environ., 18,
1010–1018, 1995.
Hinckley, T., Brooks, J., Čermák, J., Ceulemans, R., Kučera,
J., Meinzer, F., and Roberts, D.: Water flux in a hybrid poplar stand, Tree
Physiol., 14, 1005–1018, 1994.
Huntingford, C. and Monteith, J.: The behaviour of a mixed-layer model of the
convective boundary layer coupled to a big leaf model of surface energy
partitioning, Bound.-Lay. Meteorol., 88, 87–101, 1998.
Iida, S., Ohta, T., Matsumoto, K., Nakai, T., Kuwada, T., Kononov, A. V.,
Maximov, T. C., van der Molen, M. K., Dolman, H., Tanaka, H., and Yabuki, H.:
Evapotranspiration from understory vegetation in an eastern Siberian boreal
larch forest, Agr. Forest Meteorol., 149, 1129–1139, 2009.
Jacobs, C. and De Bruin, H.: The sensitivity of regional transpiration to
land-surface characteristics: significance of feedback, J. Clim.,
5, 683–698, 1992.
Jarvis, P.: The interpretation of the variations in leaf water potential and
stomatal conductance found in canopies in the field, Philos.
T. R. Soc. Lond.,
273, 593–610, 1976.
Jarvis, P. and McNaughton, K.: Stomatal control of transpiration: Scaling up
from leaf to region, Adv. Ecol. Res., 15, 1–49, 1986.
Jarvis, P. G.: Attributes of Trees as Crop Plants, Transpiration and
assimilation of tree and agricultural crops: the omega factor, 460–480,
Institute of Terrestrial Ecology, 1985.
Jassal, R. S., Black, T. A., Spittlehouse, D. L., Brümmer, C., and Nesic,
Z.: Evapotranspiration and water use efficiency in different-aged Pacific
Northwest Douglas-fir stands, Agr. Forest Meteorol., 149,
1168–1178, 2009.
Khatun, R., Ohta, T., Kotani, A., Asanuma, J., Gamo, M., Han, S., Hirano, T.,
Nakai, Y., Saigusa, N., Takagi, K., Wang, H., and Yoshifuji, N.: Spatial variations in
evapotranspiration over East Asian forest sites, I. Evapotranspiration and
decoupling coefficient, Hydrol. Res. Lett., 5, 83–87, 2011.Knauer, J., Zaehle, S., Medlyn, B., Reichstein, M., Williams, C. A.,
Migliavacca, M., De Kauwe, M. G., Werner, C., Keitel, C., Kolari, P.,
Limousin, J.-M., and Linderson, M.-J.: Towards physiologically meaningful
water-use efficiency estimates from eddy covariance data, Glob. Change
Biol., 10.1111/gcb.13893, 2017.
Köstner, B., Schulze, E.-D., Kelliher, F., Hollinger, D., Byers, J.,
Hunt,
J., McSeveny, T., Meserth, R., and Weir, P.: Transpiration and canopy
conductance in a pristine broad-leaved forest of Nothofagus: an analysis of
xylem sap flow and eddy correlation measurements, Oecologia, 91, 350–359,
1992.
Kosugi, Y., Takanashi, S., Tanaka, H., Ohkubo, S., Tani, M., Yano, M., and
Katayama, T.: Evapotranspiration over a Japanese cypress forest, I. Eddy
covariance fluxes and surface conductance characteristics for 3 years, J.
Hydrol., 337, 269–283, 2007.
Kowalczyk, E. A., Wang, Y. P., Wang, P., Law, R. H., and Davies, H. L.: The
CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate
models and as an offline model, Tech. Rep. CSIRO Marine and Atmospheric
Research paper 013, CSIRO, CABLE model, 2006.
Kumagai, T., Saitoh, T. M., Sato, Y., Morooka, T., Manfroi, O. J., Kuraji,
K.,
and Suzuki, M.: Transpiration, canopy conductance and the decoupling
coefficient of a lowland mixed dipterocarp forest in Sarawak, Borneo: dry
spell effects, J. Hydrol., 287, 237–251, 2004.Launiainen, S.: Seasonal and inter-annual variability of energy exchange
above a boreal Scots pine forest, Biogeosciences, 7, 3921–3940,
10.5194/bg-7-3921-2010, 2010.
Law, B., Falge, E., Gu, L. v., Baldocchi, D., Bakwin, P., Berbigier, P.,
Davis, K., Dolman, A., Falk, M., Fuentes, J., Goldstein, A., Granier, A.,
Grelle, A., Hollinger, D., Janssens, I. A., Jarvis, P., Jensen, N. O., Katul,
G., Mahli, Y., Matteucci, G., Meyers, T., Monson, R., Munger, W., Oechel, W.,
Olson, R., Pilegaard, K., Paw, U. K. T., Thorgeirsson, H., Valentini, R.,
Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Environmental controls over
carbon dioxide and water vapor exchange of terrestrial vegetation,
Agr. Forest Meteorol., 113, 97–120, 2002.
Lee, X. and Black, T. A.: Atmospheric turbulence within and above a
Douglas-fir
stand. Part II: Eddy fluxes of sensible heat and water vapour, Bound.-Lay.
Meteorol., 64, 369–389, 1993.
Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig,
S.,
Eamus, D., de Dios, V. R., Mitchell, P., Ellsworth, D. S., de Beeck, M. O.,
Wallin, G., Uddling, J., Tarvainen, L., Linderson, M.-L., Cernusak, L. A.,
Nippert, J. B., Ocheltree, T. W., Tissue, D. T., Martin-StPaul, N. K.,
Rogers, A., Warren, J. M., De Angelis, P., Hikosaka, K., Han, Q., Onoda, Y.,
Gimeno, T. E., Barton, C. V. M., Bennie, J., Bonal, D., Bosc, A., Low, M.,
Macinins-Ng, C., Rey, A., Rowland, L., Setterfield, S. A., Tausz-Posch, S.,
Zaragoza-Castells, J., Broadmeadow, M. S. J., Drake, J. E., Freeman, M.,
Ghannoum, O., Hutley, L. B., Kelly, J. W., Kikuzawa, K., Kolari, P., Koyama,
K., Limousin, J.-M., Meir, P., Lola da Costa, A. C., Mikkelsen, T. N.,
Salinas, N., Sun, W., and Wingate, L.: Optimal stomatal behaviour around the
world, Nature Climate Change, 5, 459–464, 2015.
Lindroth, A.: Aerodynamic and canopy resistance of short-rotation forest in
relation to leaf area index and climate, Bound.-Lay. Meteorol., 66,
265–279, 1993.
Loustau, D., Berbigier, P., Roumagnac, P., Arruda-Pacheco, C., David, J.,
Ferreira, M., Pereira, J., and Tavares, R.: Transpiration of a 64-year-old
maritime pine stand in Portugal, Oecologia, 107, 33–42, 1996.
Magnani, F., Leonardi, S., Tognetti, R., Grace, J., and Borghetti, M.:
Modelling the surface conductance of a broad-leaf canopy: effects of partial
decoupling from the atmosphere, Plant Cell Environ., 21, 867–879,
1998.
Mahmood, K., Morris, J., Collopy, J., and Slavich, P.: Groundwater uptake and
sustainability of farm plantations on saline sites in Punjab province,
Pakistan, Agr. Water Manage., 48, 1–20, 2001.
Martin, P.: The significance of radiative coupling between vegetation and the
atmosphere, Agr. Forest Meteorol., 49, 45–53, 1989.
Martin, T., Brown, K., Kučera, J., Meinzer, F., Sprugel, D., and
Hinckley, T.: Control of transpiration in a 220-year-old Abies amabilis
forest, Forest Ecol. Manag., 152, 211–224, 2001.
McElwain, J. C. and Chaloner, W. G.: Stomatal density and index of fossil
plants track atmospheric carbon dioxide in the Palaeozoic, Ann. Bot.-London,
76, 389–395, 1995.
McNaughton, K. and Jarvis, P.: Water Deficits and Plant Growth,
Predicting effects of vegetation changes on transpiration and evaporation,
Academic Press, San Diego, Vol. VII, 1–47, 1983.
McNaughton, K. and Jarvis, P.: Effects of spatial scale on stomatal control
of
transpiration, Agr. Forest Meteorol., 54, 279–302, 1991.Medlyn, B., Barton, C., Broadmeadow, M., Ceulemans, R., De Angelis, P.,
Forstreuter, M., Freeman, M., Jackson, S., Kellomaki, S., Laitat, E., Rey,
A., Roberntz, P., Sigurdsson, B., Strassemeyer, J., Wang, K., Curtis, P., and
Jarvis, P.: Stomatal conductance of forest species after long-term exposure
to elevated CO2 concentration: a synthesis, New Phytol., 149,
247–264, 2001.Medlyn, B. E., De Kauwe, M. G., Lin, Y.-S., Knauer, J., Duursma, R. A.,
Williams, C. A., Arneth, A., Clement, R., Isaac, P., Limousin, J.-M.,
Linderson, M.-L., Meir, P., Martin-StPaul, N., and Wingate, L.: How do leaf
and ecosystem measures of water-use efficiency compare?, New
Phytol., 10.1111/nph.14626,
2017.
Meinzer, F., Goldstein, G., Holbrook, N., Jackson, P., and Cavelier, J.:
Stomatal and environmental control of transpiration in a lowland tropical
forest tree, Plant Cell Environ., 16, 429–436, 1993.
Meinzer, F., Andrade, J., Goldstein, G., Holbrook, N., Cavelier, J., and
Jackson, P.: Control of transpiration from the upper canopy of a tropical
forest: the role of stomatal, boundary layer and hydraulic architecture
components, Plant Cell Environ., 20, 1242–1252, 1997.
Meirelles, M., Franco, A., Farias, S., and Bracho, R.: Evapotranspiration and
plant–atmospheric coupling in a Brachiaria brizantha pasture in the
Brazilian savannah region, Grass and Forage Science, 66, 206–213, 2011.
Mielke, M. S., Oliva, M., de Barros, N. F., Penchel, R. M., Martinez, C. A.,
and de Almeida, A. C.: Stomatal control of transpiration in the canopy of a
clonal Eucalyptus grandis plantation, Trees-Struct. Funct., 13,
152–160, 1999.
Miner, G. L., Bauerle, W. L., and Baldocchi, D. D.: Estimating the
sensitivity
of stomatal conductance to photosynthesis: A review, Plant Cell
Environ., 40, 1214–1238, 2017.Morison, J. I. L.: Sensitivity of stomata and water use efficiency to high
CO2, Plant Cell Environ., 8, 467–474, 1985.
Motzer, T., Munz, N., Küppers, M., Schmitt, D., and Anhuf, D.: Stomatal
conductance, transpiration and sap flow of tropical montane rain forest trees
in the southern Ecuadorian Andes, Tree Physiol., 25, 1283–1293, 2005.
Nicolás, E., Barradas, V., Ortuño, M., Navarro, A., Torrecillas, A.,
and Alarcón, J.: Environmental and stomatal control of transpiration,
canopy conductance and decoupling coefficient in young lemon trees under
shading net, Environ. Exp. Bot., 63, 200–206, 2008.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E.,
Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical
Description of version 4.5 of the Community Land Model (CLM), NCAR
Technical Note NCAR/TN-503+STR, National Center for Atmospheric Research.
Climate and Global Dynamics Division, National Center for Atmospheric
Research, P.O. Box 3000, Boulder, Colarado, 2013.
Raupach, M. and Finnigan, J.: Single-layer models of evaporation from plant
canopies are incorrect but useful, whereas multilayer models are correct but
useless, Funct. Plant Biol., 15, 705–716, 1988.
Raupach, M., Finkele, K., and Zhang, L.: SCAM (Soil-Canopy-Atmosphere Model):
Description and comparison with field data, Aspendale, Australia: CSIRO CEM
Technical Report, p. 81, 1997.
San José, J. J., Montes, R. A., and Florentino, A.: Water flux through a
semi-deciduous forest grove of the Orinoco savannas, Oecologia, 101,
141–150, 1995.Sánchez, J. M., Caselles, V., and Rubio, E. M.: Analysis of the energy
balance closure over a FLUXNET boreal forest in Finland, Hydrol. Earth Syst.
Sci., 14, 1487–1497, 10.5194/hess-14-1487-2010, 2010.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J., Levis, S., Lucht, W., Sykes, M., Thonicke, K., and Venevski, S.:
Evaluation of ecosystem dynamics, plant geography and terrestrial carbon
cycling in the LPJ Dynamic Vegetation Model, Glob. Change Biol., 9,
161–185, 2003.
Smith, D., Jarvis, P., and Odongo, J.: Management of windbreaks in the Sahel:
the strategic implications of tree water use, Agroforest. Syst., 40,
83–96, 1998.
Steduto, P. and Hsiao, T. C.: Maize canopies under two soil water regimes:
III.
Variation in coupling with the atmosphere and the role of leaf area index,
Agr. Forest Meteorol., 89, 201–213, 1998.
Stoy, P., Katul, G., Siqueira, M., Juang, J., Novick, K., Mccarthy, H.,
Christopher Oishi, A., Uebelherr, J., Kim, H., and Oren, R.: Separating the
effects of climate and vegetation on evapotranspiration along a successional
chronosequence in the southeastern US, Glob. Change Biol., 12, 2115–2135,
2006.
Su, Z., Schmugge, T., Kustas, W., and Massman, W.: An evaluation of two
models
for estimation of the roughness height for heat transfer between the land
surface and the atmosphere, J. Appl. Meteorol., 40, 1933–1951,
2001.
Takagi, K., Kimura, R., and Şaylan, L.: Variability of surface
characteristics and energy flux patterns of sunn hemp (Crotalaria juncea L.)
under well-watered conditions, Theor. Appl. Climatol., 96,
261–273, 2009.
Tateishi, M., Kumagai, T., Suyama, Y., and Hiura, T.: Differences in
transpiration characteristics of Japanese beech trees, Fagus crenata, in
Japan, Tree Physiol., 30, 748–760, 2010.
Thom, A. S.: Vegetation and the Atmosphere, chap. Momentum, Mass and Heat
Exchange of Plant Communities, Academic Press, London, 57–109, 1975.
Wever, L. A., Flanagan, L. B., and Carlson, P. J.: Seasonal and interannual
variation in evapotranspiration, energy balance and surface conductance in a
northern temperate grassland, Agr. Forest Meteorol., 112,
31–49, 2002.
White, D., Beadle, C., and Worledge, D.: Control of transpiration in an
irrigated Eucalyptus globulus Labill. plantation, Plant Cell Environ.,
23, 123–134, 2000.
Whitehead, D., Jarvis, P. G., and Waring, R. H.: Stomatal conductance,
transpiration, and resistance to water uptake in a Pinus sylvestris spacing
experiment, Can. J. Forest Res., 14, 692–700, 1984.Williams, C. A., Reichstein, M., Buchmann, N., Baldocchi, D., Beer, C.,
Schwalm, C., Wohlfahrt, G., Hasler, N., Bernhofer, C., Foken, T., Papale, D., Schymanski, S., and Schaefer, K.:
Climate and vegetation controls on the surface water balance: Synthesis of
evapotranspiration measured across a global network of flux towers, Water
Resour. Res., 48, W06523, 10.1029/2011WR011586, 2012.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier,
P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom,
A., Law, B. E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel,
W., Tenhunen, J., Valentini, R., and Verma, S.: Energy
balance closure at FLUXNET sites, Agr. Forest Meteorol., 113,
223–243, 2002.
Wilson, K. B. and Baldocchi, D. D.: Seasonal and interannual variability of
energy fluxes over a broadleaved temperate deciduous forest in North America,
Agr. Forest Meteorol., 100, 1–18, 2000.
Wohlfahrt, G., Haslwanter, A., Hörtnagl, L., Jasoni, R. L., Fenstermaker,
L. F., Arnone, J. A., and Hammerle, A.: On the consequences of the energy
imbalance for calculating surface conductance to water vapour, Agr.
Forest Meteorol., 149, 1556–1559, 2009.Woodward, F. and Kelly, C.: The influence of CO2 concentration on stomatal
density, New Phytol., 131, 311–327, 1995.
Woodward, F. I., Smith, T. M., and Emanuel, W. R.: A global land primary
productivity and phytogeography model, Global Biogeochem. Cy., 9,
471–490, 1995.Wullschleger, S. D., Meinzer, F., and Vertessy, R.: A review of whole-plant
water use studies in tree, Tree Physiol., 18, 499–512, 1998.
Wullschleger, S. D., Wilson, K. B., and Hanson, P. J.: Environmental control
of
whole-plant transpiration, canopy conductance and estimates of the decoupling
coefficient for large red maple trees, Agr. Forest Meteorol.,
104, 157–168, 2000.
Zeppel, M. and Eamus, D.: Coordination of leaf area, sapwood area and canopy
conductance leads to species convergence of tree water use in a remnant
evergreen woodland, Aust. J. Bot., 56, 97–108, 2008.
Zhang, Z. Z., Zhao, P., McCarthy, H. R., Zhao, X. H., Niu, J. F., Zhu, L. W.,
Ni, G. Y., Ouyang, L., and Huang, Y. Q.: Influence of the decoupling degree
on the estimation of canopy stomatal conductance for two broadleaf tree
species, Agr. Forest Meteorol., 221, 230–241, 2016.
Zhou, L., Zhou, G., Liu, S., and Sui, X.: Seasonal contribution and
interannual
variation of evapotranspiration over a reed marsh (Phragmites australis) in
Northeast China from 3-year eddy covariance data, Hydrol. Process., 24,
1039–1047, 2010.Zhu, P., Zhuang, Q., Ciais, P., Welp, L., Li, W., and Xin, Q.: Elevated
atmospheric CO2 negatively impacts photosynthesis through radiative forcing
and physiology-mediated climate feedback, Geophys. Res. Lett., 44, 1956–1963, 0.1002/2016GL071733, 2017.