Vegetation regulates the exchange of water, energy, and carbon fluxes
between the land and the atmosphere. This regulation of surface fluxes
differs with vegetation type and climate, but the effect of vegetation on
surface fluxes is not well understood. A better knowledge of how and when
vegetation influences surface fluxes could improve climate models and the
extrapolation of ground-based water, energy, and carbon fluxes. We aim to
study the link between vegetation and surface fluxes by combining the yearly
average MODIS leaf area index (LAI) with flux tower measurements of water
(latent heat), energy (sensible heat), and carbon (gross primary
productivity and net ecosystem exchange). We show that the correlation
of the LAI with water and energy fluxes depends on the vegetation type and
aridity. Under water-limited conditions, the link between the LAI and the water and
energy fluxes is strong, which is in line with a strong stomatal or
vegetation control found in earlier studies. In energy-limited forest we
found no link between the LAI and water and energy fluxes. In contrast to water
and energy fluxes, we found a strong spatial correlation between the LAI and
gross primary productivity that was independent of vegetation type and
aridity. This study provides insight into the link between vegetation and
surface fluxes. It indicates that for modelling or extrapolating surface
fluxes, the LAI can be useful in savanna and grassland, but it is only of
limited use in deciduous broadleaf forest and evergreen needleleaf forest to
model variability in water and energy fluxes.
Introduction
Vegetation and water, energy, and carbon fluxes are tightly coupled.
Large-scale vegetation patterns are driven by the long-term memory of water
and energy availability (Köppen, 1936; Prentice et al., 1992; Cramer
et al., 2001). Recent climate change has led to shifts in the spatial
distribution of vegetation as well as shifts in the timing of the growing
season (Jeong et al., 2011; Rosenzweig et al., 2008; Fei et al., 2017).
Additionally, vegetation plays a crucial role in the exchange of water,
energy, and carbon between the land surface and the atmosphere, mainly
through its effects on evapotranspiration, turbulence, the redistribution of
water, and surface heating (Shao et al., 2015; Jia et al., 2014; Esau and
Lyons, 2002). Large-scale reforestation and afforestation has increased
evapotranspiration over most of Europe
(Teuling et al., 2019), and large-scale
deforestation has increased the air temperature in tropical regions and
decreased air temperature in boreal regions (Perugini
et al., 2017). This two-way interaction between vegetation and terrestrial
surface fluxes has been known for a long time (e.g. Bates and Henry,
1928; Woodwell et al., 1978), but it is still a very relevant research topic
today (Forkel et al., 2019; Lu et al., 2019; Teuling and Hoek van Dijke,
2020; Kirchner et al., 2020; Evaristo and McDonnell, 2019) given the
importance of understanding the impacts of climate change on vegetation as
well as the effects of land cover change on climate.
Plants regulate the exchange of water, energy, and carbon with the
atmosphere through their stomata. The stomatal regulation of these fluxes
depends on available energy, the transpiration demand, and the available soil
moisture in the root zone. When both the available energy and soil moisture
are abundant, stomata open and water and carbon can freely move in and out:
the stomatal control on surface fluxes is low. When the available energy is
high but soil moisture is limiting, stomata tend to close and exert a large
control on water and carbon fluxes (Mallick et al., 2016; O'Toole and
Cruz, 1980). Zooming out from the stomatal to canopy scale, there are several
other ways in which vegetation influences surface fluxes. Soil and crown
mutual shadowing and deep ground water uptake by vegetation influence the
latent heat flux, whereas soil moisture influences ecosystem respiration and, in turn, carbon exchange (Chen et al., 2019; Schmitt et al., 2010). The
vegetation control of ecosystem fluxes has been shown by different data or
modelling studies and depends on the climate and vegetation type (Williams et
al., 2012; Xu et al., 2013; Wagle et al., 2015). Williams and
Torn (2015) found a strong vegetation control on surface heat flux
partitioning in both arid and humid grassland, cropland, and forest, but
Padrón et al. (2017) concluded that, globally, vegetation
control on evapotranspiration was low or even absent in the equatorial
regions. Chen et al. (2019) showed that
temperature, precipitation, and vegetation leaf area explained 91 % of the
mean annual variability in vegetation carbon uptake for wetland sites. Mallick et
al. (2018) showed that vegetation control on evapotranspiration was stronger
in arid ecosystems compared with the mesic ecosystems. Similar results were
found for dry and wet Amazonian forest (Costa et al., 2010; Mallick et
al., 2016) and dry and wet grassland (De Kauwe et al.,
2017). Ferguson et al. (2012) studied land–atmosphere coupling
of fluxes, which includes the effect of vegetation as well as other factors such as soil wetness, soil texture, and surface temperature. From remote sensing
data and model output, they concluded that transitional zones between arid
and humid climates (shrublands, grasslands, and savannas) tend to have a
strong land–atmosphere coupling, whereas land–atmosphere coupling is weak in the energy-limited regions.
Vegetation is coupled to the atmosphere through its leaves. The leaf area
index (LAI) is an important vegetation characteristic and is indicative of
the total amount of foliage that intercepts light and assimilates carbon.
Furthermore, both rainfall interception and canopy conductance increase with the
LAI (Van Heerwaarden and Teuling, 2014; Gómez et al., 2001). Therefore, a high
LAI is related to high vegetation carbon uptake and high canopy
evapotranspiration of water (Lindroth et al., 2008; Duursma et al.,
2009). The highest mean yearly LAI is found in tropical and temperate forests,
whereas a low LAI is found in cold and in arid climate zones (Fig. 1; Iio et al.,
2014; Asner et al., 2003). This global
LAI pattern closely resembles large-scale patterns in estimates of water,
energy, and carbon exchange (Miralles et al., 2011; Jung et al., 2011).
With the increasing availability of remotely sensed LAI data, the LAI – in addition to
its usage in many remote sensing applications (e.g. Si et al., 2012; Zheng and Moskal, 2009) – has become a frequently used variable to represent
vegetation in land surface models (Williams et al., 2016; Sellers et al.,
1997; Lawrence and Chase, 2010 amongst many others) or to estimate or
extrapolate regional or global water and carbon fluxes (Beer et al.,
2007; Yan et al., 2012; Turner et al., 2003; Xie et al., 2019). The
algorithms to retrieve the LAI from remotely sensed data have improved over the
past few decades, thereby increasing the accuracy of LAI products (Shabanov et al.,
2005; Yan et al., 2016). Nevertheless, it is important to be aware of the
product uncertainties, especially over dense forest, where saturated
reflectance and canopy clumping can only provide limited information for LAI
retrievals (Shabanov et al., 2005; Xu et al., 2018), and at high
latitudes, where the solar zenith angle is low (Fang et al.,
2019).
Global distribution of vegetation leaf area index (LAI). The mean
LAI, at 5 km resolution, is derived from the MODIS data product MCD15A3H.006
(Myneni et al., 2015).
The interaction between the vegetation LAI and surface fluxes on the larger scale is
not yet well understood, and vegetation is not well represented in many
land–atmosphere and climate models (Williams et al.,
2016). A small-scale study in temperate deciduous forest, for instance,
revealed that the correlation between sap flow and the normalized difference
vegetation index (NDVI) can change from positive to negative depending on
the season and soil moisture availability (Hoek
van Dijke et al., 2019). A detailed knowledge of how and when the vegetation LAI
is linked to the surface fluxes is required to improve global climate
modelling and extrapolation of water and carbon fluxes from canopy to
ecosystems. The high availability of remote sensing LAI products, recent
developments in cloud-based platforms for geospatial analysis
(Mutanga and Kumar, 2019), and the availability of publicly
available eddy covariance data from FLUXNET (Baldocchi et al., 2001)
allows for an analysis of the link between vegetation characteristics and
surface fluxes. Thus, the objective of our study is to gain insight into the
link between the vegetation LAI and surface fluxes for different vegetation
types along an aridity gradient. We address the following research
questions:
What is the link between LAI and respective water, energy, and carbon
fluxes in different vegetation types?
How is the interaction between LAI
and respective water, energy, and carbon fluxes governed by climatological aridity?
We hypothesize that the link between the LAI and surface fluxes is strong in
semi-arid and arid climates, owing to the strong stomatal control, whereas the
link is weak in humid climates.
In our study we focus on five metrics of water, energy, and carbon fluxes
measured by flux towers. Latent heat (LE), a measure for the
evapotranspiration of water, and sensible heat (H), represent the exchange
of water and energy between the Earth's surface and the atmosphere. LE and H
are linked through the evaporative fraction (EF). The EF is the ratio of
latent heat to the sum of LE and H and is a useful measure of the
partitioning of total available energy between the evapotranspiration of
water and surface heating. Net ecosystem exchange (NEE) is the net exchange
of carbon between the land and the atmosphere, which is directly measured by
flux towers. Gross primary productivity (GPP) is derived from the NEE and is the
gross uptake of atmospheric carbon by the vegetation.
Data and methodologyDataData selection
This study includes five vegetation types: savanna (SAV), grassland (GRA),
deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and
evergreen needleleaf forest (ENF). The SAV sites include the two classes
“savanna” and “woody savanna”. These vegetation types follow the
International Geosphere-Biosphere Programme (IGBP) classification (Loveland et
al., 2000). These five vegetation types were selected because of the
availability of a high number of flux tower sites. For some “site-years” (a term used to refer to the yearly averaged values for
every site), the LAI
flux or meteorological measurements were not available. These site-years
were included in each of the analyses for which the required metrics were
available.
Within the FLUXNET2015 dataset (Baldocchi et al., 2001), we selected all
Tier 1 sites (open and free for scientific purposes; Pastorello et al., 2020) within the five studied
vegetation types. We completed the dataset with two sites from the OzFlux
network to increase the number of sites in the EBF class (Liddell, 2013a,
b). Two forest sites were excluded from the analyses because they were
affected by a beetle outbreak that resulted in high tree mortality, and one
heavily managed grassland site was excluded from the analysis. For each
site, only years with good-quality data were selected; this was carried out following the quality
selection procedure explained below. This site selection procedure,
in combination with the quality check, resulted in a dataset of 545 site-years spread over 93 sites (Fig. 2,
Table 1).
Location and vegetation type of the 93 included flux tower sites.
Data averaging and aggregation
We studied the yearly averaged LAI and surface fluxes for different vegetation
types. For most vegetation types, the LAI and surface fluxes showed seasonal
variability, with high values during the growing season and lower or zero
LAI and surface fluxes during the cold or dry season. The non-growing season
might not be relevant for finding the link between the LAI and surface fluxes, but selecting growing season values alone led to difficulties. The
vegetation types differ with respect to the timing, number, and length of the growing
seasons, and procedures such as time-series analysis did not successfully select
the growing seasons. To be consistent in the methodology, yearly averaged
fluxes were used for all flux tower sites. Using yearly averaged values for
every site (site-years) has a few implications: (1) we study
both spatial (site-to-site) variability and temporal (year-to-year)
variability simultaneously, and (2) the averaged flux and meteorological
measurements might not represent similar conditions. The latter occurs, for
example, when a site-year receives plenty of precipitation in December,
increasing the site-year's aridity index, while this precipitation mainly
impacts the next site-year's fluxes or LAI values. To test the effect of
using site-year data, we also studied spatial and temporal variability
separately. For these analyses, the data were aggregated in three ways: (1) site-year data with one average value per site per year; (2) multi-year
data with one multi-year average LAI and flux value per site, which were used to study the
spatial correlation; and (3) yearly average data for a few sites, which were used to study
the temporal correlation. Sites were included in the multi-year data if at
least 3 years of data were available. The three aggregation methods led
to similar conclusions for water and energy but slightly different results
for carbon, as is shown in the paper.
Flux measurements
Within the FLUXNET2015 database, LE, H, NEE, and GPP measurements are
gap-filled using the MDS (marginal distribution sampling) method
(Reichstein et al., 2005), and LE and H are corrected by an energy
balance closure correction factor. The MDS method uses the correlation of
fluxes with the driver variables (incoming radiation, temperature, and
vapour pressure deficit) to estimate flux values during gap periods. The
energy balance closure corrects LE and H for the total incoming radiation,
assuming that the Bowen ratio (the ratio of the sensible heat flux to the
latent heat flux) is correct. A similar energy balance closure correction
was applied to the LE and H measurements of the OzFlux sites. Monthly
averaged flux values were discarded if the percentage of measured and good-quality gap-fill data was below 50 %. Yearly average fluxes were calculated
if measurements for each month were available. The evaporative fraction
(EF), the ratio between LE and the total energy available at Earth's surface,
was calculated using Eq. (1) as follows:
EF=LELE+H,
where LE is the latent heat flux and H is the sensible heat flux.
Meteorological measurements
Meteorological measurements are delivered with the flux tower data.
Precipitation data are downscaled from the ERA-Interim reanalysis data
(Vuichard and Papale, 2015). Net radiation and air
temperature are measured at the flux tower and gap-filled using the MDS
method (Reichstein et al., 2005). Yearly
potential evaporation (Ep) was calculated from mean daily air temperature
and net radiation using the Priestley–Taylor formulation
(Priestley and Taylor, 1972). The Priestley–Taylor
equation is a modification of the Penman equation and requires less
measurements. The aridity index (AI), an indicator of dryness, was
calculated according to Eq. (2):
AI=PEp,
where P is precipitation and Ep is the potential evaporation. An aridity
value of one indicates that, on a yearly scale, precipitation equals
potential evaporation, whereas values below one indicate site-years that
received less precipitation than their potential evaporation.
Leaf area index
The leaf area index (LAI) is the ratio of green leaf area to ground area (unitless). We used the LAI derived from the MODIS data product
MCD15A3H.006 (Myneni et al., 2015). This algorithm derives 4 d
composite LAI values at a 500 m spatial resolution from the Terra and Aqua
satellites and is available for 2003 onwards. Within this 4 d period, the
best pixel is selected from the MODIS sensors located on the Terra and Aqua
satellite for the calculation of the LAI. The LAI calculation algorithm uses a
lookup table that was generated using a 3D radiative transfer equation
(Myneni et al., 2015). Heinsch et al. (2006) compared the MODIS
data product with ground measurements at FLUXNET sites and concluded that
62.5 % of the MODIS LAI was well estimated but that MODIS LAI
overestimated ground-measured LAI for the other sites. Despite this
overestimation, MODIS LAI was used because it has a long record length,
good (and free) data availability, good spatial coverage, and high temporal
resolution. The overestimation and saturation of the signal at high LAI
could introduce noise in the LAI data. However, we do not expect this noise
to change the conclusions of our analysis. The resolution of the LAI data
product is 500 m, compared with a typical flux tower footprint length of 100
to 1000 m (Kim et al., 2006). The exact size and
location of the footprint of flux towers, however, varies with factors such as
wind direction and wind speed, surface roughness, and flux measurement
height (Kim et al., 2006; Barcza et al., 2009). For our analyses, we
selected the one nearest LAI pixel for each flux tower. Data were filtered
to remove clouds, using the product's delivered quality label. To
smoothen outliers, the moving mean LAI was calculated for three consecutive
data points. Monthly mean values were calculated if a maximum of one data point
was missing. The site-year average LAI was calculated when no monthly data were
missing.
Illustration of the applied methodology. The correlation
coefficient between leaf area index (LAI) and evaporative fraction (EF) is
calculated for 30 site-years for grassland over a moving window of aridity
index. In the illustration, the correlation has a significant positive slope
at p= 0.056 for the 30 most arid grassland sites, whereas the slope is nearly flat and is not significant (p= 0.49) for the 30 most
humid grassland sites.
Methodology
To study the link between the LAI and surface fluxes, we performed a linear
regression between the LAI and the surface fluxes. We calculated the correlation
coefficient for (1) site-year data, (2) multi-year average data (spatial
variability), and (3) yearly data for a few specific sites (temporal
variability). Afterwards, to study if the link between the LAI and fluxes
changed with aridity, all site-years within one vegetation type were ranked
by aridity, from most arid to most humid. For each consecutive 30 site-years
in this ranking, we performed a linear regression between the LAI and the
fluxes. For some site-years, part of the data was missing that was needed to
calculate the regression. Within each window of 30 site-years, the slope of
the regression was calculated if at least 15 complete site-years were
available (Fig. 3).
A list of all included site-years for the 93 sites. For each site, the yearly average leaf area index (LAI) and aridity index (AI) are calculated
for all years included in the dataset. Studied vegetation types include the following: savanna (SAV), woody savanna (woody SAV; savanna and woody savanna sites are combined into one class, “savanna”), grassland (GRA) deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF).
LAI and LE were positively correlated in SAV, GRA, and EBF
(Fig. 4, Table 2). The
slope of the correlation between the different vegetation types is
different; the slope was steepest for SAV (slope = 46.1 W m-2): a
twofold increase in the LAI (1 to 2) was associated with an almost twofold increase in LE (51 to
97 W m-2), compared with a flatter slope in GRA (9.80 W m-2) and EBF
(13.0 W m-2). In ENF and DBF, the LAI and LE were not significantly
correlated. LAI and H were negatively correlated in SAV, GRA, and EBF, whereas
there was no significant correlation in ENF and DBF. The LAI and the EF were
positively correlated in SAV, GRA, and EBF, whereas no correlation was found in
ENF and DBF. A positive slope indicates that, for a higher LAI, a higher
fraction of the available energy is used for the evapotranspiration of water,
compared with surface heating. The slope between the LAI and EF was steeper in SAV
and GRA (slope = 0.27 for both) than in EBF (slope = 0.08). A positive
correlation between LAI and GPP was found in all vegetation types (r= 0.47–0.97), with a very strong correlation coefficient for SAV (r=0.97). The correlation followed a steep slope for SAV (slope = 3.37 gC m-2 d-1) and GRA (slope = 2.17 gC m-2 d-1), a
similar slope in EBF (slope = 1.71 gC m-2 d-1) and ENF (slope = 1.81 gC m-2 d-1), and a less steep slope in DBF (slope = 0.76 gC m-2 d-1). The correlation between the LAI and NEE was
negative in SAV, EBF, and ENF. This indicates that the net carbon uptake
increases with the LAI. Among the different fluxes, GPP showed the strongest
correlation with the LAI for all vegetation types. Comparing the different
vegetation types, the correlation between the LAI and fluxes was strongest in
SAV.
The spatio-temporal correlation between surface fluxes and leaf
area index (LAI). Panels show (a) the latent heat flux (LE), (b) the
sensible heat flux (H), (c) the evaporative fraction (EF), (d) gross primary
productivity (GPP), and (e) net ecosystem exchange (NEE). A line indicates a
significant correlation at p<0.05.
Using multi-year average data reduced the number of data points to only 5 to
16 sites per vegetation type. Nevertheless, the spatial correlation
(site-to-site variability) between the LAI and surface fluxes is very similar to
the spatio-temporal correlation (Fig. 5, Table 2). For SAV, GRA, and ENF, the slope and
strength of the correlation were similar when compared with the site-year
data. For the EBF, for the site-year data, the correlation with LE and EF
was only significant at p≤0.1, and the correlation was not significant
for H and NEE.
The spatial correlation between surface fluxes and the leaf area index
(LAI). Panels show (a) the latent heat flux (LE), (b) the sensible heat flux
(H), (c) the evaporative fraction (EF), (d) gross primary productivity
(GPP), and (e) net ecosystem exchange (NEE). All sites are included that
have at least 3 years of LAI and flux data available. A line indicates a
significant correlation at p<0.05, and a dashed line indicates a
significant correlation at p<0.1.
Strength and significance of the correlation between the LAI and
surface fluxes for site-year and multi-year average data. The correlation
coefficients are shown for significant correlations at *p≤0.05 or
at **p≤0.1. “–” indicates that the correlation was not
significant.
Site-years Multi-year average LEHEFGPPNEELEHEFGPPNEESavanna0.88*-0.72*0.89*0.97*-0.89*0.94*-0.96*0.95*0.99*-0.90*Grassland0.65*-0.71*0.74*0.86*–0.68*-0.80*0.79*0.84*–Evergreen broadleaf forest0.84*-0.69*0.83*0.88*-0.51*0.87**–0.87**0.96*–Evergreen needleleaf forest–––0.84*-0.58*–––0.89*-0.57*Deciduous broadleaf forest–––0.47*-0.33*–––0.65**–
LAI and surface fluxes were low, and the variability in fluxes was not
significantly correlated with variability in the LAI.
The effect of climatological aridity on the link between LAI and surface
fluxes
Figure 7 shows the steepness and significance of the
correlation between the LAI and surface fluxes for different aridity values. In
dry vegetation types or regions, the correlation between the LAI and fluxes was
significant and had a steeper slope, whereas in the more humid vegetation
types or regions, the slope was relatively horizontal and the correlation
was often not significant. In SAV, GRA, and EBF, the correlation between the LAI
and LE was significant for the whole range of aridity values. In arid GRA,
the correlation had a steeper slope compared with humid GRA. With respect to the LAI
versus H and LAI versus EF, the slope was steep and significant for SAV. For
GRA, the correlation was strong and significant in the arid regions and
insignificant in the humid regions. For EBF, the slope and the significance of
the correlation did not change with aridity. For LAI and GPP, the slope and the
significance of the correlation did not change with aridity for SAV, GRA,
EBF, and ENF. For DBF, the correlation between the LAI and GPP was negative at
higher aridity, but these results were strongly influenced by one site with
an above average LAI for all site-years. For the LAI versus NEE, a steep
slope with a negative correlation was found in arid SAV and humid ENF. In
other humid regions, the correlation was less steep.
An illustration of the temporal correlation between the yearly average
surface fluxes and the leaf area index (LAI). For each land cover type, two
sites were selected that had the highest number of available data. The
colours of the symbols indicate the land cover type as shown in Figs. 4 and 5.
Panels show (a) the latent heat flux (LE), (b) the sensible heat flux (H),
(c) the evaporative fraction (EF), (d) gross primary productivity (GPP), and
(e) net ecosystem exchange (NEE). A line indicates a significant correlation
at p<0.05, and a dashed line indicates a significant correlation at
p<0.1.
The effect of aridity on the relation between surface fluxes and the
leaf area index (LAI). The slope of the correlation between the LAI and surface
fluxes is shown for different aridity values for (a) the latent heat flux
(LE), (b) the sensible heat flux (H), (c) the evaporative fraction (EF), (d) gross primary productivity (GPP), and (e) net ecosystem exchange (NEE). Each
dot indicates the slope value for the 30 closest aridity values. The filled
symbols indicate that the correlation was significant at p<0.05,
whereas the hollow symbols indicate a non-significant correlation.
To study how the correlations varied with climatic drivers of surface
fluxes, we calculated the correlation coefficient of the fluxes versus
precipitation (P) and incoming shortwave radiation (Rg)
(Fig. 8). In SAV, GRA, and EBF, the water fluxes
showed a strong correlation with P, indicating that water availability
partly explained the spatio-temporal variability in surface fluxes. In ENF
and DBF, there was a weak or non-existing correlation between LE and P, but there was a strong
correlation with Rg. This indicates that available radiation was the primary
driver of water and energy fluxes at these sites.
Water and energy control on surface fluxes. The correlation coefficient (r) of site-year surface fluxes versus (a) the mean yearly precipitation (P) and (b) incoming shortwave radiation (Rg). Each bar indicates a significant correlation at p<0.05.
Discussion
The EBF site-years span a wide range of LAI values (LAI of 0.9–6.1) and
aridity conditions (AI of 0.3–9.3), and both are a potential limitation
of our analysis for the EBF vegetation type. The uncertainty of the LAI
retrieval in dense vegetation is higher than in other vegetation types
due to saturation of the remotely sensed signal. The large range of climatic
conditions indicates that our EBF site-years range from arid, water-limited
conditions to humid conditions. Despite this high variability in site-years,
the sites fell within one vegetation type.
The correlation between the LAI and respective water and energy fluxes (LE, H, and EF)
varied with vegetation type and aridity. For the spatio-temporal and spatial
variability, we found (1) strong (positive or negative) correlations and
(partly) steep slopes for SAV and GRA, (2) a significant correlation but
less steep slope for EBF, and (3) no significant correlations for ENF and
DBF. With respect to the temporal variability, this pattern was similar for LE, but
almost no significant correlations were found between the LAI and the respective H and EF for
SAV and GRA. Evapotranspiration is the sum of transpiration, soil
evaporation, and interception evaporation, and the magnitude of each component
depends on the LAI. Transpiration increases with LAI at the cost of soil
evaporation when there is sufficient moisture available (Gu et al., 2018;
Wang et al., 2014). In arid climates, the transpiration component is higher
than in wetter climates (Gu et al., 2018), and the link
between transpiration and the LAI is particularly strong in these arid climates
(Sun et al., 2019). When soil moisture is deficient and
vegetation encounters a high evaporative demand, stomatal control is
stronger (Mallick et al., 2016).
This accelerates a strong stomatal coupling between the LAI and LE and could
explain the strong correlation between the LAI and the respective LE, H, and EF that was
found in SAV and arid GRA. Soil water deficiency and high evaporative demand
leads to a high increase in LE, for a small increase in LAI, which could
explain the steep(er) slope in arid GRA and SAV vegetation.
In forests, soil evaporation is low, whereas interception evaporation is
high. The elevated interception evaporation is due to the large leaf area (both
green leaves included in the LAI and brown leaves after leaf senescence)
with a high canopy water storage capacity and a high turbulence, enhancing
fast evaporation (De Jong and Jetten, 2007). In EBF,
interception evaporation contributes to up to 30 % of the total
evapotranspiration (Wei et al., 2017; Gu et al., 2018). This could
explain the strong correlation between the LAI and the respective water and energy fluxes in
EBF. A high interception evaporation was, however, also reported for temperate
and boreal forest (Miralles et al., 2011); however,
for the latter forest types, we found no correlation between the LAI and water and
energy fluxes. The ENF and DBF sites were found in humid regions, and fluxes
were primarily energy-limited. At these energy-limited sites, the LAI
played a weak or non-existent role in controlling surface fluxes. This indicates a
weak or non-existent vegetation control on surface water and energy fluxes at
energy-limited sites. This is in line with a low land–atmosphere coupling at
energy-limited sites (Ferguson et al., 2012).
In contrast to the results for water and energy fluxes, the spatio-temporal
and spatial correlation between GPP and LAI was strong across all
vegetation types and (almost) all aridity gradients. A strong link between the
LAI and carbon uptake on a yearly timescale over all vegetation types is
expected, as plants try to optimize carbon gain and would generally not
display leaves with a negative carbon balance. A strong link between the LAI and
the mean yearly GPP was also shown by Hashimoto et al. (2012). However, other studies found a weak link between the LAI and GPP on
annual timescales (Law et al., 2002). In contrast to the spatial
variability, year-to-year variability in GPP was only
correlated with LAI at some sites. Water availability is an important driver of temporal
variability in GPP (Williams and Albertson, 2004; Kutsch et al., 2008),
and GPP is strongly reduced under drought conditions (Vicca et
al., 2016). The effect of drought is also visible in the reduced LAI, although on a
longer timescale of 1 or 2 years in forests (Le Dantec et al., 2000;
Kim et al., 2017). This different response time to water availability for
forest LAI and GPP could partly explain the absence of a temporal
correlation for some of the sites. The spatial correlation between the LAI and
NEE was less strong compared with the GPP, which is in agreement with the results
of Chen et al. (2019). The NEE is the sum of carbon uptake by the
vegetation (GPP) and carbon loss by ecosystem respiration. Ecosystem
respiration varies with climate and soil carbon storage, which are not
directly related to the LAI. This could explain the absence of a correlation
between the LAI and NEE.
These results partly confirmed our hypothesis. As hypothesized, the
correlation between the LAI and surface fluxes was strong in arid regions for
water and energy fluxes, and the correlation was absent in humid ENF and
DBF. For humid EBF, however, we found a strong correlation between the LAI and
water and energy fluxes, and the correlation with the LAI was strong
across all aridity gradients for GPP. While carbon uptake is the primary goal of
vegetation, independent of the aridity gradient, ecosystem water loss inevitably comes with carbon uptake but also depends on the vapour pressure deficit,
available radiation, and soil moisture, which are not directly linked to the
LAI.
Our statistical analysis cannot be used to study causality between the LAI and
surface fluxes or to study vegetation control on the surface fluxes. The
correlation between the LAI and water fluxes is confounded by the effect of soil
moisture, especially in arid and semi-arid ecosystems, where both canopy
development and LE increase with water availability (Kergoat, 1998;
Mallick et al., 2018). Similarly, precipitation is the main controller for
spatial variability in both vegetation and GPP (Koster
et al., 2014). Furthermore, the LAI is related to vegetation properties, but it is not
a direct measure of canopy conductance; despite this, there are similarities, with
previous studies showing the stomatal or vegetation control on surface
fluxes. A strong vegetation control on water and energy fluxes in arid and
semi-arid regions has been shown on timescales of days or shorter (e.g.
Mallick et al., 2016, 2018); our study also shows that,
on large spatio-temporal scale, the correlation between LAI and respective water and energy fluxes is
strongest in arid regions. For EBF, however, we found a strong
spatial correlation of vegetation versus the respective water and energy fluxes,
whereas Padrón et al. (2017) showed that vegetation control
in equatorial regions was absent. An interesting follow-up study would be to
link stomatal control for different vegetation types (De
Kauwe et al., 2017) to the canopy-scale pattern investigated in this study.
Our analyses provide insight into how and when the vegetation LAI is related to
surface fluxes. The results show that the LAI is a good predictor of spatial
variability in GPP across different vegetation types and aridity gradients.
Furthermore, the analysis suggests that the LAI could be
used to describe canopy-scale spatio-temporal variability in water and energy
fluxes in SAV, GRA, and EBF. However, the LAI is not a good predictor for water and energy fluxes in
ENF and DBF nor for NEE. It is important to be aware of these limitations
when using the LAI to describe or estimate water, energy, and carbon fluxes in
climate models or extrapolation methods. This study provides insight into the
link between surface fluxes and the LAI and could be used to improve predictions
of the effects of land cover change on surface fluxes.
Conclusions
The objective of this study was to gain insight into the link between the
vegetation LAI and land–atmosphere fluxes for different vegetation types
along an aridity gradient. We studied this link at a large spatio-temporal
scale using flux tower measurements of water, energy, and carbon, combined
with satellite-derived LAI data. The data analysis led to the following
conclusions.
The link between the LAI and the respective water and energy fluxes depends on vegetation
type and aridity. The correlation of the LAI with water and energy fluxes
is strong in SAV, GRA, and EBF. In DBF and ENF, however, no significant
correlation was found. Contrary to water and energy fluxes, the spatial
correlation between the LAI and GPP was strong and independent of the vegetation
type and aridity. This suggests that using the LAI to model or extrapolate
surface fluxes of water and energy is very possible in SAV, GRA, and EBF,
but it is limited in DBF and ENF.
As hypothesized, the link between the LAI and water and energy fluxes was strong
in arid, water-limited conditions and was absent or weak for humid,
radiation-limited conditions. EBF, which was found over a high range of
aridity conditions, although mostly in humid environments, forms an exception:
the spatial correlation between the LAI and the respective water and energy fluxes was
strong, despite the overall humid conditions.
This research – facilitated by the recent availability of large global
datasets of remotely sensed LAI, flux tower data, and cloud-computing
platforms – has added to the understanding of the LAI interaction with surface
fluxes and could help to improve modelling or extrapolating surface fluxes.
Data availability
The FLUXNET2015 dataset is available from
https://fluxnet.org/data/fluxnet2015-dataset/ (Lawrence Berkeley National Laboratory, last access: January 2019).
Flux measurements for the two OzFlux sites are available from
http://data.ozflux.org.au/portal/pub/listPubCollections.jspx (James Cook University, last access: February 2019).
Leaf area index (LAI) data (the MCD15A3H data product,
https://lpdaac.usgs.gov/products/mcd15a3hv006/, LP DAAC, last access: August 2019) were acquired from
https://code.earthengine.google.com/ (last access: August 2019, Myneni et al., 2015).
Author contributions
The data analyses were carried out by AJHvD in close consultation with KM, MS, MM,
MH, and AJT. AJHvD prepared the draft of the paper; all authors contributed
to discussions and were involved in writing the final paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We also acknowledge Michael Liddell for
providing the data from two OzFlux research sites. We further acknowledge the
FLUXNET community for acquiring and sharing the eddy covariance data
including the following networks: AmeriFlux, AfriFlux, AsiaFlux, CARBOAFRICA,
CarboEuropeIP, CARBOITALY, CARBOMONT, ChinaFlux, Fluxnet-Canada, GreenGrass,
ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET
eddy covariance data processing and harmonization was carried out by the
European Fluxes Database Cluster, the AmeriFlux Management Project, and the Fluxdata
project of FLUXNET, with support from the CDIAC and the ICOS Ecosystem Thematic
Centre, and the OzFlux, ChinaFlux, and AsiaFlux offices. The ERA-Interim
reanalysis data are provided by ECMWF and processed by LSCE.
Financial support
This research has been supported by the Luxembourg National Research Fund (FNR; grant no. PRIDE15/10623093/HYDROCSI).
Review statement
This paper was edited by Eyal Rotenberg and reviewed by two anonymous referees.
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