Actual evapotranspiration (ET) is an essential variable in the
hydrological process, linking carbon, water, and energy cycles. Global
ET has significantly changed in the warming climate. Although the increasing vapor pressure deficit (VPD) enhances atmospheric water demand due to global warming, it remains unclear how the dynamics of ET are affected. In this study, using multiple datasets, we disentangled the relative contributions of precipitation, net radiation, air temperature (T1), VPD, and wind speed on the annual ET linear trend using an advanced separation method that considers the Budyko framework. We found that the precipitation variability dominantly controls global ET in the dry climates, while the net radiation has substantial control over ET in the tropical regions, and VPD impacts ET trends in the boreal mid-latitude climate. The critical role of VPD in controlling ET trends is particularly emphasized due to its influence in controlling the carbon–water–energy cycle.
Introduction
Actual evapotranspiration (ET) is when water transforms from a liquid to a
gaseous state. Such transformation synchronously absorbs the air's energy,
making ET the largest terrestrial water flux component, accounting for
>60 % of global land precipitation (Trenberth et al., 2007). The ET directly affects hydrological processes at regional and global scales (Zhang et al., 2016) by linking water, energy, and carbon cycles. As a
result, ET plays a crucial role in land–atmosphere interactions amongst
various climatic variables, including precipitation, air temperature,
humidity, solar radiation, and wind speed (Koster et al., 2006; Wang et al.,
2011; Miralles et al., 2018), which consequently influence the climate at
regional and global scales. An accurately estimated ET can therefore provide
a comprehensive contribution to understanding the changes in hydrological
cycles and the associated extreme events, such as droughts and floods, as
well as their impacts on ecosystem productivity, water-use efficiency, and
irrigation (Sheffield et al., 2012; Sun et al., 2017; Jalilvand et al.,
2019).
However, the available ground ET measurements from traditional methods (e.g.,
eddy covariance, porometry and lysimeters, and scintillometry) have
shortcomings such as sparse observational sites and short time span (Allen
et al., 1991; Everson et al., 2009; Monteith and Unsworth, 1990). To overcome
these limitations, various spatially distributed ET products have been
developed and widely used, including those from remote sensing, land surface
models, and reanalysis, such as the Global Land Evaporation Amsterdam
Model (GLEAM) and the Global Land Data Assimilation System (GLDAS; Miralles et al., 2011a, b; Mu et al., 2011; Reichle et al., 2017; Loew et
al., 2016; Peng et al., 2020).
Studies of climate warming intensification on the global water cycle have
indicated the increasing importance of understanding ET changes in space and
time (e.g., Allen and Ingram, 2002; Wu et al., 2013; Pan et al., 2015). In
recent decades, ET has shown sudden increasing or decreasing trends across
the globe (Miralles et al., 2013), so an accurate attribution of the ET changes is urgently needed. The changing ET
over the longer time scale is jointly determined by climatic modes (e.g., El
Niño–Southern Oscillation) (Martens et al., 2018; Miralles et al., 2013)
and the long-term changes of climatic variables (Pan et al., 2020; Zeng and Cai, 2016; Rigden and Salvucci, 2016). For example, the upward trend of the global ET from 1982 to late 1990 was attributed to increased radiation and air temperature (Douville et al., 2013; Jung et al., 2010). Similarly, the
change of global ET during 1998–2008 lapsed due to limited soil moisture
supply in the Southern Hemisphere and transitions to the El Niño
condition (Jung et al., 2010; Miralles et al., 2013). Zhang et al. (2015)
demonstrated how the water supply, available energy, and atmospheric water
demand jointly affected the global ET changes from 1982 to 2013, accounting
for 49 %, 32 %, and 19 % of global ET changes, respectively.
However, different evapotranspiration algorithms, parameterizations, and
input climate forcing datasets can cause uncertainties when attributing
global ET changes (Vinukollu et al., 2011; Michel et al., 2016). For
example, Miralles et al. (2016) evaluated the performances of three models
using the same forcing data, finding that the GLEAM product was relatively
better than the other global ET products over most wet and dry conditions.
Similarly, Badgley et al. (2015) used 19 different combinations of input
forcing datasets to run the Priestly–Taylor Jet Propulsion Laboratory
(PT-JPL) ET model, indicating that the choice of forcing datasets accounted
for an average 20 % error. Those results have indicated that the
inappropriate choice of ET models and forcing data may add significant
uncertainties to the ET attributions.
Potential ET (PET) is determined by radiation, air temperature, vapor pressure deficit (VPD), and wind speed, and it reflects the magnitude of atmospheric demand on land ET.
Against the backdrop of a warming climate, rising air temperature has an increased atmospheric water demand, i.e., increased PET (Fu and Feng, 2014;
Feng and Fu, 2013; Dai et al., 2004). Studies have indicated that increased
VPD primarily determines the recent PET increase, which is a function of air
temperature and humidity (Dai and Zhao, 2017; Ficklin and Novick, 2017). However
increased VPD tends to make plants close their stomata to avoid water loss
and thus restrain transpiration (Novick et al., 2016; McAdam and Brodribb, 2015).
A high atmospheric water demand induced by VPD promotes PET, while the
increased surface resistance limits ET. Therefore, it is very important to
clarify how VPD affects long-term ET changes. Li et al. (2021) have found that VPD has dominated the increase of annual ET in energy-limited regions such as southeastern China. However, it's not clear how VPD affects global
long-term ET changes.
Furthermore, the above studies focused on the influences of climatic
variables on long-term ET changes. However, a distinct shortcoming in these
studies is that they only demonstrated the responses of long-term ET changes
(variance) on certain factors (e.g., climatic variables and surface
conductance). A few studies disentangle the contributions of relatively
complete climatic factors (mainly atmospheric), including precipitation, net
radiation, air temperature, VPD, and wind speed, to the annual ET linear
trend. Along these lines, Li et al. (2021) attempted to quantify the
contribution of those forcing variables to ET trends over China with the
Budyko theory. However, there are still unclear questions about the global
land ET mechanism. For example, how differently would the conclusions of
dominating ET factors over water-limited regions be for global dry lands?
The variable that controls ET over the global tropical zone is unclear, despite the results of VPD controlling ET over the energy-limited region of China. The variable that controls ET over the boreal region is unclear. For example, precipitation, air temperature, and radiation control Amazon's ET changes
(Pan et al., 2020), while significantly increased ET in the humid region
mostly results from increasing air temperature (Wang et al., 2022). For the boreal region, increasing air temperature is significantly correlated with
ET (Wang et al., 2022), while increasing VPD contributes to ET process
(Helbig et al., 2020). Therefore, it is necessary to assess global ET
mechanisms using the same attribution method for solving these problems.
In this study, we have adopted the Budyko theory to advance our
understanding of the response of global ET trends to climatic variables,
including precipitation (P), net radiation (Rn), air temperature
(T1), VPD, and wind speed (u). The Budyko theory investigates the
interactions between ET, PET, and P (Yokoo et al., 2008; Yang et al., 2008;
Liu et al., 2011). For example, Teuling et al. (2019) explored the dynamics
of ET in Europe at high resolution (1 km2) with the Budyko model and
key meteorological variables. Here we use multiple datasets such as
GLEAM3.0a, EartH2Observe ensemble (EartH2Observe-En), GLDAS2.0-Noah, and Modern Era Retrospective-Analysis for Research and
Application-Land (MERRA-Land). Using multiple datasets can reduce uncertainties of the forcing data to accurately attribute global ET changes over different land covers and climate regimes (S. J. Li et al., 2018).
Data and methodsData
We use multiple ET products and their respective forcing data, including the
remote sensing-based GLEAM product, the land surface model's ensembled product (EartH2Observe-En), and two reanalysis products (GLDAS2.0-Noah and
MERRA-Land). These products have different temporal lengths, and we have
used their overlapping period from 1980 to 2010. In the attribution method
with the Budyko framework, we use respective forcing data of each product
(please see detailed description in Sect. 2.2 “Forcing data”). To study the ET mechanism within different climatic conditions, we have divided the global
land into tropical, dry, mild temperate, snow, and polar zones,
respectively, using the Köppen climate classification (Kottek et al., 2006; Fig. 1). The Köppen climate classification is produced
according to the empirical relationship between climatic variables and
vegetation.
ET products
GLEAM3.0a ET. The GLEAM3.0a is arguably the longest of various ET products, mainly
determined from remote sensing observations. It consists of soil
evaporation, canopy transpiration, interception loss, snow sublimation, and
open-water evaporation. A key feature of this product is the use of the Gash
analytical model to estimate interception loss. The other components in this
product are calculated according to the Priestley–Taylor equation (Miralles
et al., 2011b; Martens et al., 2017).
EartH2Observe-En ET. The EartH2Observe product uses 10 models, including 5 hydrological
models, 4 land surface models, and a simple water-balance model, which
are forced by the same state-of-the-art meteorological reanalysis (Dutra et
al., 2022). Schellekens et al. (2017) indicated that the model ensemble
outperforms the individual models' outputs, and thus we have used the
ensembled mean data from the 10 models here (regarded as EartH2Observe-En).
The EartH2Observe-En product is demonstrated to be an accurate reanalysis
data and has been used for multiscale water resource evaluation
(Schellekens et al., 2017).
GLDAS2.0-Noah ET. The GLDAS was initially developed by the National Aeronautics and Space
Administration's (NASA) Goddard Space Flight Center (GSFC) of America, based on the North American Land Data Assimilation System (NLDAS). The GLDAS is a global, high-resolution, offline terrestrial modeling system and produces the outputs of land surface states and fluxes in near-real time, such as ET, soil moisture, latent, sensible, and ground heat flux. Satellite and ground-based observations are used to constrain the forcing and parameterization of used land surface models (i.e., Mosaic, Noah, the
Community Land Model, and the Variable Infiltration Capacity model) (Rodell
et al., 2004). Here, the ET product derived from GLDAS2.0-Noah is used in
our study.
MERRA-Land ET. The MERRA reanalysis is developed by NASA's Global Modeling and Assimilation
Office (GMAO). It was produced by the Goddard Earth Observing System model
version 5 (GEOS-5) along with its associated data assimilation system (DAS)
version 5.2.0 (Rienecker et al., 2011). Since there are significant errors
in values and timing of precipitation in the original MERRA product (Reichle et al., 2011), we have used the offline MERRA-Land product forced by corrected precipitation. Studies have shown that the hydrological
performance in MERRA-Land has been improved significantly more than in the
original MERRA product (Reichle et al., 2011).
Spatial distribution of five climatic zones using the Köppen
climate classification, including tropical, dry, mild temperate, snow, and
polar zone (Kottek et al., 2006).
Atmospheric forcing datasets
The atmospheric forcing data in four ET products mainly include
precipitation, net radiation, air temperature, specific humidity, and wind
speed. The forcing data of the respective ET product and their references are
listed in Table 1 (Li et al., 2021). To reduce the uncertainties associated
with inconsistent forcing data sources, the trends of each ET product are
attributed using its own forcing data. The GLEAM algorithm does not use
specific humidity and wind speed as inputs, unlike the other three products.
Since the other forcing data of the GLEAM product (e.g., radiation and air
temperature) are derived from the ERA-Interim reanalysis, specific humidity
and wind speed from the same reanalysis are used for its attribution.
The main forcing data in four ET products.
ET ProductsPrecipitationRadiationAir temperatureSpecific humidityWind speedReferencesGLEAM3.0aMSWEPERA-InterimERA-InterimERA-InterimERA-InterimMartens et al. (2017)EartH2Observe-EnWFDEIWFDEIWFDEIWFDEIWFDEISchellekens et al. (2017)GLDAS2.0-NoahPUMFDPUMFDPUMFDPUMFDPUMFDRodell et al. (2004)MERRA-LandCPC-UMERRAMERRAMERRAMERRAReichle et al. (2011)
Note: MSWEP indicates the Multi-Source Weighted-Ensemble Precipitation
product; WFDEI is the Water and Global Change FP7 project forcing dataset
ERA-Interim (Weedon et al., 2015; Dee et al., 2011); PUMFD indicates the
meteorological forcing data of Princeton University
(Sheffield et al., 2006); CPC-U is Climate Prediction Center Unified.
MethodDetermining trends
We have used Theil-Sen's slope method to determine the trends of annual ET
and climatic variables during 1980–2010. This method is nonparametric and
can provide a more accurate trend estimation for skewed data when compared to
the linear regression approach (Wilcox, 2010). To detect the significance
level of these data, we used the nonparametric Mann–Kendall test to
determine the significance level of the linear trends (Mann, 1945 and
Kendall, 1975). Both methods have been widely used in climate change studies
(Su et al., 2015; Wang et al., 2018a; Shan et al., 2015; Shi et al., 2016).
Attribution method
The attribution method consists of two steps: firstly, building the
relationship between ET and the abovementioned five climatic variables with
Budyko and modified FAO Penman–Monteith equations; secondly, conducting a
sensitivity experiment analysis to quantify the contribution of each
climatic variable to the long-term ET trends of 1980–2010.
Budyko relationship.
The Budyko equation (Eq. 1) is usually regarded as a common way to study how
climatic factors influence the annual ET changes, based on the mathematical
relationships between precipitation, PET, and ET (Yokoo et al., 2008; Yang
et al., 2009; Liu et al., 2011):ETP=1+PETP-1+PETPω¯1ω¯,where ET, PET, and P reflect evapotranspiration, potential ET, and precipitation, respectively; ω¯ indicates the landscape properties, such
as vegetation cover, soil, and topography. For a particular product,
pixel-wise ω¯ can be fitted using the
least-square regression method because other variables during 1980–2010 are
known (i.e., ET, P, and PET). The PET reflects the atmospheric water demand,
which can be determined by solar radiation, air temperature, actual vapor
pressure, wind speed etc.
There are various methods for calculating PET, including Penman–Monteith
(Allen et al., 1998), Hargreaves (Hargreaves and Samani, 1985), and
Priestly–Taylor (Priestley and Taylor, 1972) methods. Generally, the
modified FAO Penman–Monteith equation is a universal method for estimating
PET with meteorological data (Allen et al., 1998). In this study, annual PET is obtained with the modified Penman–Monteith equation:PET=0.408ΔRn-G+γ900T1+273uVPDΔ+γ1+0.34u,where Rn represents net radiation, calculated by net incoming short-wave radiation minus net outgoing long-wave radiation (unit: MJ m-2 yr-1); G is the soil
heat flux density (unit: MJ m-2 yr-1), and can be neglected on monthly or
longer time scales; γ and Δ reflect the psychometric
constant and slope of the vapor pressure curve, respectively (unit:
kPa ∘C-1); T1 indicates average 2 m air temperature
(unit: ∘C), and is used to calculate Δ; u indicates 2 m
wind speed (unit: m s-1); VPD (kPa) is the saturation vapor pressure deficit,
as a function of air temperature T2 and specific humidity (i.e.,
VPD =f(T2, specific humidity)). In Eq. (2), the effect of air
temperature T on PET is separated into two parts: T1 and T2.
The T1 reflects the effect of air density and slope of the vapor pressure
curve, and T2 indicates the partial effect of VPD. For further details
on the calculation of VPD and the difference between T1 and T2,
refer to Allen et al. (1998).
By putting Eq. (2) into Eq. (1), we can obtain the direct relationship between
ET and P, Rn, T1, VPD, and u, as indicated in Eq. (3):ET=P+0.408ΔRn-G+γ900T1+273uVPDΔ+γ1+0.34u-P1+0.408ΔRn-G+γ900T1+273uVPDP(Δ+γ1+0.34u)ω¯1ω¯.
Attribution experiments. The trends of annual ET during 1980–2010 are determined by compound
influences of the main climatic factors (i.e., P, Rn, T1, VPD, and
u). To disentangle the impact of each climatic factor, six experiments have
been designed based on Eq. (3), including one control experiment
(sim_CTL) and five individual factor sensitivity experiments
(sim_P, sim_Rn, sim_T1, sim_VPD, and sim_u, respectively). The
sim_CTL experiment provides the control ET changes for each
product by using all the factors of 1980–2010, while the ET change
controlled by a particular factor is simulated by the sensitivity experiment with the factor only in 1980, and the other factors between 1980 and 2010. The multiyear average can also replace a factor in 1980 during
1980–2010. Figure S1 shows that precipitation and PET values between 1980
and the multiyear average are very close. For example, the ET change of
1980–2010 impacted by P (i.e., sim_P) can be computed by using
the constant value of P in 1980 and Rn, T1, VPD, and u of 1980–2010. The contributions of the other climatic factors
(sim_Rn, sim_T1, sim_VPD, and sim_u) can be determined similarly for each product.
The contribution of each factor to the ET change in each product is obtained (Sun et al., 2016 and 2017):Ci=∑k≠inEsim_k-n-2⋅Esim_in-1,where n shows the number of sensitivity experiments, and n is equal to 5 here; and Esim_i indicates the ith sensitivity
experiment. We should note that the total contribution of air temperature
T to ET changes here is separated into two sensitivity experiments:
sim_T1 and sim_VPD. The
sim_T1 denotes the effect of air temperature T1 in Eq. (2) on ET; the sim_VPD, controlled by air temperature T2
and specific humidity, contains the contribution of air temperature T2.
ResultsTrends of the ET products
The spatial distribution of long-term trends in annual ET is depicted in
Fig. 2, with evident differences and similarities among the four selected
products in different regions. Compared to the other products, the
MERRA-Land shows more significant ET changes, for example, the declining
trend with a rate of about -6.0 mm yr-1 in Africa and South America. In most
regions of the Eurasian continent, the ET changes for all products mainly
amount to -2.0–2.0 mm yr-1. A significant increase in ET is observed in
western Europe, southeastern China, and northern Australia. In contrast, a
declining ET trend is observed in northeast China and Arabian Peninsula,
despite differences among the used ET products. These products show quite
different trends in Africa; while the MERRA-Land indicates significantly
declining ET, the trends in the other products are opposite.
The spatial distribution of pixel-wise linear trends of annual ET
for (a) GLEAM3.0a, (b) EartH2Observe-En, (c) GLDAS2.0-Noah, and (d)
MERRA-Land products during 1980–2010. The trend is estimated with
Theil–Sen's slope method, and the significance level is tested with the
Mann–Kendall method. The dotted area indicates that the trend has passed the
significance test at 5 % level.
Attributions of ET trends
The influence of each driving factor on the long-term annual ET linear trend
is quantified by the attribution method in Sect. 2.3.2. Figure 3 shows the
trends in each variable and its respective contribution to ET changes across
different climate zones. Precipitation (P) appears to make the largest
contribution, while air temperature (T1) and wind speed (u) make the smallest (except u in MERRA-Land) contributions, and moderate contributions are evident for net radiation (Rn) and VPD.
Compared to other products, a sharp decreasing u in MERRA-Land leads to a
decreasing ET trend. The grid number of P in the first and third quadrants
is more than 85 % of the sum in Fig. 3a, indicating that P is positively
correlated with ET. The ET in the Dry zones is more sensitive to changes in P, while in Tropical zones, the effect of P is not obvious. Such a relationship also exists in Rn (Fig. 3b), VPD (Fig. 3d), and u (Fig. 3e).
Different contributions among climatic zones are also observed for Rn (Fig. 3b) and VPD (Fig. 3d). For example, the Rn contribution in
the Tropical zone exceeds that in the Mild Temperate zone, while the VPD
contribution in the Mild Temperate zone is larger than that in the Tropical
zone. Limited grids, mostly from the water-limited region (Dry), fall in the
fourth quadrant, amounting to 25.26 %–41.96 % of the sum, suggesting that increasing T1 hinders ET. From the spatial scale, P, Rn, VPD also provide the biggest contributions to the ET trend (Fig. S3), which positively correlate with their respective trends (Fig. S2).
Pixel-wise scatterplots of (x axis) trends in each climatic
variable against (y axis) the contribution of each climatic variable to ET
changes. Small letters (a–e) indicate precipitation, radiation, air
temperature (T1), VPD and wind speed, respectively; and numbers (1–4)
indicate GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah, and MERRA-Land,
respectively. The percentage is the ratio between the number of grid cells
in each quadrant and the number of total grid cells; the sum of the
percentage values in the four quadrants equals to 100 %. The color red, green, blue, black purple represents Tropical, Dry, Mild Temperate, Snow, Polar zones, respectively.
The consistency of spatial distribution of dominant climatic
factors to global long-term ET trends between GLEAM3.0a, EartH2Observe-En,
GLDAS2.0-Noah, and MERRA-Land for precipitation (a), net radiation (b), and
VPD (c). The land fraction of air temperature (T1) and wind speed is
limited, and the two factors' results are not shown here. Numbers 1–4
represent the count of these models with the same dominant factor in one
pixel, and indicate different confidence levels from low to high.
To further show the spatial distribution of these driving factors affecting
ET, we compare the consistency of dominant climatic factors across these ET
products in Fig. 4. The dominant climatic factor is identified with the
absolute value of maximum contribution to ET trends. The results indicate
that precipitation is the dominant factor of ET trends in the entire Dry
zone and some regions of the other climate zones in all models, such as
northeastern and southern parts of the Snow zone and the Mild Temperate zone
in South America. The net radiation dominates the ET trends in most of the
Tropical zone; and VPD dominates the ET trends in the entire Mild Temperate
zone, Eastern Europe, and Northeast Asia in the Snow zone. In Table 2, we
can see that precipitation, net radiation, and VPD are the dominant factors
of ET changes in most global land. For example, precipitation contributes to
either positive or negative ET trends in 55.41 % of the global grids.
The percentage of grids in each dominant factor controlling annual
ET linear trends for GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah, and
MERRA-Land. The “+” and “-” represent positive, and negative contributions to ET, respectively.
In this study, we have found that the global ET trends during 1980–2010 in
GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah, and MERRA-Land products are
relatively consistent. Different ET trends are observed among these products
in Africa and South America, where the MERRA-Land shows a significant
decrease of about -5.0 mm yr-1. A significantly increasing ET pattern is found
in some regions of western Europe and southern Asia, the central parts of
northern Australia, while a declining ET pattern is observed in western
North America and South America. Similar global ET patterns are also found
in Pan et al. (2020) based on multi-source products. As shown in Fig. 2,
there are divergences in the ET trends of the products over some regions.
Different ET trends among the products result from different forcing data.
For example, MERRA-Land has abnormal negative ET trends over South America
and the central part of Africa. This is due to abnormally decreased
precipitation providing a negative contribution to ET trends.
How the climatic variable controls the global ET trend is one of the crucial
questions we ask in this study. We have designed sensitivity experiments to
disentangle contributions from each climatic driver (precipitation, net
radiation, air temperature, VPD, and wind speed) to answer this question.
Precipitation, net radiation, VPD, and wind speed contribute the most to the
changes in global ET with an inferred positive relationship. In contrast, an
increase in temperature shows the opposite in some regions. The positive
relationships between ET and precipitation, and net radiation have been
confirmed by Lu et al. (2019), Wang et al. (2018b), Pan et al. (2020), and
Soni and Syed (2021). Precipitation supplies water, and net radiation provides
energy for the ET process. However, the increased temperature appears to
have influenced the mechanism of ET trend differences between the
water-limited region (Dry zone) and other regions. Rising air temperature
can lead to soil moisture depletion, followed by suppressed vegetation
growth in the water-limited regions (Jung et al., 2010; Zhang et al., 2019).
Meanwhile, we found that an increased VPD promotes atmospheric processes
followed by global ET changes. Even if increased VPD reduces surface
conductance, increased atmospheric water demand by VPD absorbs moisture from
soil and vegetation, thus increasing ET (Grossiord et al., 2020). The
positive influences are also verified by Kochendorfer et al. (2011), Wang and Dickinson (2012), and Yang et al. (2019). However, Novick et al. (2016) indicated that increased VPD due to global warming increased surface resistance, limiting
ET over many biomes. Massmann et al. (2019) suggested that the ET response to
increased VPD varied from decreasing to increasing, depending on plant water
regulation strategies determined by climatic environment and plant types.
For example, when compared to boreal and arctic climates, VPD increased ET
in tropical and temperate climates; in terms of plant type, shrubs and
gymnosperm trees decreased ET, while crops tended to increase ET. Therefore,
we consider that the contrasting influence of VPD on ET should be addressed
separately, emphasizing how the VPD affects the individual components of ET,
which are evaporation from the soil, and canopy interception and
transpiration. A more complex physical ET process combined with soil and
plant resistance models should be used to do this. For example, Grossiord et
al. (2020) admitted that an increased VPD would lead to stomatal closure, but
transpiration would still increase under a certain threshold across plants
in different climate regions. Besides, they found the positive response of
surface resistance to VPD increased from wetting to drying climate.
Most studies used the effect of VPD on ET as a surrogate of high air
temperature as VPD is determined by air temperature and specific humidity.
However, specific humidity changes are weak relative to rapid air warming,
resulting in increased VPD controlled by the rising air temperature. Figure 5 shows the spatial pattern of the climatic variables (i.e., air temperature
T2 and specific humidity) that dominates the global VPD changes
following our proposed sensitivity method. Our study concludes that the
specific humidity controls VPD only in some regions of North and South Asia,
northern Australia, southern Africa, and South America. However, vegetation
physiology controlled by VPD plays a vital role in reshaping the
hydrological cycle and global water resources compared to air temperature
(Grossiord et al., 2020). Meanwhile, considering a close relationship
between temperature and VPD, we attempted to separate the contribution to ET between VPD and air temperature by designing sim_T1,
sim_VPD in Sect. 2.3.2.
Distribution of dominant factor in VPD changes in global land
during 1980–2010 for GLEAM3.0a (a), EartH2Observe-En (b), GLDAS2.0-Noah (c),
and MERRA-Land (d). The T2 and H represent air temperature and specific humidity
respectively. Dotted areas mean that VPD is a dominant factor to ET trends.
Figure 4 shows the spatial distribution of climatic drivers controlling ET,
implying that precipitation is the primary driver that controls ET in the Dry
zone. This includes northern and southeastern Eurasia, most of Africa, midwestern North America, southern parts of South America, and almost
the entire Australia, while net radiation dominates the Tropical zone. Why
these two climatic drivers are important for controlling ET changes has also
attracted interest among other scientists (Pan et al., 2020 and Zhang et
al., 2015). Interestingly, we find that the impact of VPD on ET is quite
significant in some high-latitude regions of the Northern Hemisphere, such
as eastern North America, Europe, and northeastern Asia. Long-term ET
changes in these regions are controlled by air temperature (Pan et al.,
2020; Zhang et al., 2015). However, in our study, the increased VPD caused
by rising air temperature plays a significant role in controlling ET changes
(Sottocornola and Kiely, 2010; Kochendorfer et al., 2011; Yang et al.,
2019). The VPD, rather than air temperature, controls ET in high-latitude regions. Precipitation controls ET in water-deficit regions (Dry zone) by
replenishing the storage deficit, and ET in tropical rainforests (i.e.,
energy-limited region) is determined by available energy (i.e., net
radiation).
Uncertainties
The Budyko framework is the key component of the attribution method used in our study. Based on this hypothesis, Fu (1981) and Zhang et al. (2004) offered
the best analytical solution (i.e., Sect. 2.3.2, Eq. 1) through a
dimensional mathematical analysis by providing the mathematical reasons. The
key part of the analytical solution is that the ratio between PET and
precipitation determines ET. The equation has been applied in numerous
hydrological studies at the catchment scale. When using the hypothesis at
the catchment scale, some details of the results related to the land
features are often missing or ignored. However, when testing the same
hypothesis at grid scales, the Budyko framework performance is outstanding
(Greve et al., 2014; Teuling et al., 2019; Roderick et al., 2014). We have
also validated the accuracy of the Budyko hypothesis by comparing the ET
values estimated by Budyko with actual ET values in Fig. 6. The
results with high R2 values for all products indicate that the Budyko method can be
successfully applied in the attribution method. However, there are
discrepancies among different PET calculation methods that may introduce
some uncertainties into the attribution results. For example, Zhou et al. (2020) compared four temperature-based models (Hamon, Hargreaves–Samani,
Oudin, Thornthwaite), two radiation-based models (Energy-Only and
Priestley–Taylor), and two synthesis models (Penman and Penman–Monteith) of
PET in China as an example, and pointed out that the Penman–Monteith and
Penman methods are almost similar but better than the remaining methods.
Based on the results, the Penman–Monteith method outperforms the Penman
method, thus used to calculate the standard values of PET in our study.
Pixel-wise scatterplots of (x axis) annual ET in each product
against (y axis) annual ET estimated by the Budyko framework. Small letters
(a)–(d) represent GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah, and MERRA-Land,
respectively.
Validations of attribution method
The fitted parameter ω¯ in Eq. (3) includes landscape
characteristics, such as vegetation cover, soil properties, and topography
(Xu et al., 2013). The parameter contains each model's characteristic,
leading to uncertainties of the attribution method from forcing data and
information in each product. The information consists of structure
parameters of each model, and surface factors (land cover types, soil
properties, and topography). The selected four products in the study use
static surface factors when simulating ET (Table S1). Given the reason, the
attribution method is limited to not considering the influences of the land
surface on ET changes and only focuses on quantifying several climatic
variables' influences here. We discuss the influences of vegetation and
human activities in next section.
The pixel-wise scatterplots of global long-term annual ET linear
trend against the control trend (trendCTL) in ET for GLEAM3.0a (a),
EartH2Observe-En (b), GLDAS2.0-Noah (c), and MERRA-Land (d). The red line
indicates a fitted line of the scatter points along with the 1:1 blue dotted
line.
A separation method in this study is used to obtain the respective
contribution of each driving factor to the long-term annual ET linear trend,
which inevitably is suspected to produce some uncertainties in the
attribution results. Figure 7 shows the scatterplots of the pixel-wise ET
trend in the four products against those from the control experiment to validate the accuracy of reproducing ET with the fitted relations using Eq. (3) and the five selected climatic variables. The resultant R2 values range from 0.48 to 0.76, indicating that the trendCTL simulated by Eq. (3)
can principally reproduce the ET trends as in the four products. Meanwhile,
scatterplots of the accumulative contributions of the driving factors
(∑i=15Ci) in each product against the respectively
simulated trendCTL are shown in Fig. S4, in order to understand the possible uncertainties of such an analysis. Strikingly, the R2 values are all higher than 0.99, indicating that the driving factors' summed
contributions are almost equal to the realistic global ET trends in all
products.
Influences of vegetation and human activities
Vegetation can alter water cycle and energy cycle by biophysical and
biochemical feedback to climate change (Forzieri et al., 2020). For example,
global surface greening increases ET or transpiration (Lian et al., 2018; Lu et
al., 2021), and reduces soil water content (Y. Li et al., 2018a). However, the
complex interaction between vegetation and surface makes it difficult to
simulate the influence of dynamic vegetation change on ET (Gentine et al.,
2019). Meanwhile, strictly disengaging the contributions of climatic
variables and vegetation to ET is very difficult due to the interaction
between vegetation and climatic variables (Y. Li et al., 2018b). For
water-limited regions, precipitation as main water supply to vegetation
controls interannual ET changes (Wang et al., 2021). And, for humid regions,
the dominating factor of interannual ET changes is not vegetation, but
rather atmospheric climate variables (Zhang et al., 2020). Those studies
indicate that vegetation influences on ET already contain the signal of
climatic variables, which are essential for vegetation growth.
Given the reasons above, the ET products used in this study do not consider
the effect of land use/vegetation changes on ET. When simulating ET, the
model frameworks assume no interannual land use changes, so they are
regarded as static conditions. Detailed land cover types in each product are shown in Table S1.
Human activities (e.g., irrigation and reservoir construction) have been
affecting the components (i.e., ET, runoff, and groundwater storage) of water
cycles (Ashraf et al., 2017; Long et al., 2017). For example, the
groundwater over the North Plain in China, the High Plain in US, and northern
India is pumped for agricultural irrigation and contribute to accelerating the ET
process. Lv et al. (2017) indicate that the estimated ET will be more
accurate if irrigation water affects hydrological cycles. Unfortunately,
most ET products do not consider human activities due to the limited factors
of estimated algorithm and model parameters. The GLDAS2.0-Noah and
MERRA-Land in this study also do not consider the effect of human
activities. The GLEAM3.0a partly contains the information of groundwater by
considering the effect of soil moisture of the European Space Agency's Climate Change Initiative (ESA-CCI) on ET. As for
EartH2Observe-En, the six models consider one of either groundwater,
reservoir, or water use (see Table S1 from Li et al., 2021). However, the
attribution results of ET trends in this study show that GLEAM3.0a and
EartH2Observe-En's validation results are good, indicating that the effect
of human activities on ET may be contained in climatic variables. These ET
products are produced with appropriate algorithms, parameterizations of
models and forcing datasets. The accuracy of ET has been validated by the
respective developers: S. J. Li et al. (2018) in China, Wang et al. (2018a) in the
Yellow River basin, and Nooni et al. (2019) in the Nile River basin, suggesting good performances of these products. Therefore, our study only focuses on
climatic factors affecting interannual ET changes. For future studies, the
contribution of land surface, such as human activities, to ET should be
investigated to understand the mechanism of the global ET trend better.
Additionally, we only consider local contributions of ET here. In fact,
large-scale modes of climate variability (e.g., El Niño–Southern
Oscillation, the North Atlantic Oscillation) can also affect terrestrial
evaporation. For example, Martens et al. (2018) indicate that El Niño–Southern Oscillation controls the overall dynamic of global land ET, while some models dominate regional ET change, such as East Pacific–North Pacific teleconnection patterns.
The relationship between fitted parameter <inline-formula><mml:math id="M126" display="inline"><mml:munder><mml:mi mathvariant="italic">ω</mml:mi><mml:mo mathvariant="normal">¯</mml:mo></mml:munder></mml:math></inline-formula> and
ET trend analysis, vegetation
Here, we compare ET trends in each product to climate zones, which are
represented by the aridity index. The aridity index (PET/precipitation) in each product is calculated with respective precipitation and PET data. Figure S5a1–d1 show that the biggest ET trends of all products exit the wettest regions (low aridity index). To study the influence of fitted parameter
ω¯ on ET trend analysis, we compare the control on ET
trend (trendCTL) to the aridity index. The results in Fig. S5a2–d2
show similar results to the actual ET trend, meaning the ET trend analysis
in the attributed method can capture actual ET change characteristics.
Meanwhile, we also quantify the relationship of parameter ω¯ fitted by precipitation, PET, and
actual ET in each product to multiyear average (GIMMS NDVI)
during 1982–2010. Figure 8 shows the linear relationship between the fitted
parameter ω¯ and NDVI for all products with R2 values of
0.13–0.38. In general, the parameter ω¯ can be calculated
according to the linear relationship between ω¯ and NDVI (Bai et al.,
2019; Greve et al., 2014). The results show that our trend analysis keeps the
relationship, spatially. However, we admit that time-varying ω¯ (e.g., vegetation, soil property) will directly affect ET (Lu et al., 2021). The impact of ω¯ would vary as a function of the chosen timescale which requires a more in-depth study beyond the scope of the
current study.
Pixel-wise scatterplots of (x axis) multiyear average NDVI
against (y axis) their fitted ω¯ values in each product.
Small letters (a)–(d) represent GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah,
and MERRA-Land. The GIMMS NDVI data during 1982–2010 are used here.
Conclusions
We have estimated the linear ET trend globally during 1980–2010 from
GLEAM3.0a, EartH2Observe-En, GLDAS2.0-Noah, and MERRA-Land. Secondly, we
obtained the respective contribution of each factor to ET trends with
multiple sensitivity experiments as well as a separation method, and identified which factor controls global ET changes across different climate zones. The major findings are summarized below:
ET changes: Long-term trend in ET during 1980–2010 is evident globally,
especially in Africa and South America. A significant increase in ET is
observed in Eurasia, northern and central Australia, Northeast Africa,
eastern parts of South America, and eastern parts of central North America.
Decreasing ET is found in the west of North and South America, northeast of
Africa, and the Arabian Peninsula. The MERRA-Land has more significant ET
changes when compared to the other products.
Dominant factors: Precipitation, net radiation, VPD, and wind speed are
positively correlated to global ET changes, while air temperature (T1)
has contrasting influences on ET between the Dry zone and other regions.
Precipitation controls ET changes in Dry zone, including north, central
and southeastern regions of Eurasia, most of Africa, central parts of western North America, southern parts of South America, and almost the entire Australia. Net radiation dominates the Tropical zone. The VPD dominates ET in some high-latitude regions of the Northern Hemisphere, such as eastern North America, the whole of Europe, and northeastern Asia.
Uncertainties of ET trends: Global ET trends among the products are
determined by their climate variables. Different sources of forcing datasets result in different magnitudes of ET trends, even the reversing signs.
But consistent attribution results in those products confirm that ET
mechanisms are robust.
Data availability
In this study, each ET global product and respective
forcing climatic factors can be downloaded: GLEAM3.0a from
https://www.gleam.eu/ (last access: 9 July 2022, Martens et al., 2017), EartH2Observe-En from http://www.earth2observe.eu/ (last access: 9 July 2022, Schellekens et al., 2017),
GLDAS2.0-Noah from https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS (last access: 13 May 2020, Rodell et al., 2004), and
MERRA-Land from
https://disc.gsfc.nasa.gov/datasets?keywords=merra-land&page=1 (last access: 12 May 2020, Reichle et al., 2011). Please do not hesitate to contact us if you meet any problems when downloading data.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-26-3691-2022-supplement.
Author contributions
GW, SL, and JP designed research. SL and CZ processed data. SL, JL, WU, DFTH, and GK contributed to data analysis and interpretation. SL and GW drafted the manuscript. All authors edited the manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This research was funded by the National Key Research and Development Program of China (grant no. 2017YFA0603701), the National Natural Science Foundation of China (grant no. 41875094), the Sino-German Cooperation Group Project (grant no. GZ1447),
and the Postgraduate Research and Practice Innovation Program of Jiangsu
Province (grant no. KYCX20_0932). Shijie Li acknowledges support
from the China Scholarship Council. Giri Kattel would like to acknowledge Longshan Professorship and the talent grant (grant no. 1511582101011) from the Nanjing University of Information Science and Technology (NUIST). We are grateful to the editor (Hongkai
Gao) and two reviewers for the very helpful and constructive comments they
have given.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2017YFA0603701), the National Natural Science Foundation of China (grant no. 41875094), the Sino-German Cooperation Group Project (grant no. GZ1447), Longshan Professorship and the talent grant (grant no. 1511582101011) in Nanjing University of Information Science and Technology (NUIST) and the China Scholarship Council.
Review statement
This paper was edited by Hongkai Gao and reviewed by two anonymous referees.
ReferencesAllen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 224–232, 10.1038/nature01092, 2002.Allen, R. G., Howell, T. A., Pruitt, W. O., Walter, I. A., Jensen, M. E. (Eds.): Lysimeters for Evapotranspiration and Environmental Measurements, American Society of Civil Engineers Publication, Reston, VA, USA, p. 444, ISBN 9780872628137; 0872628132, 1991.Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (Eds.): Crop Evapotranspiration: Guidelines for Computing Crop Requirements, Irrigation and Drainage Paper 56, FAO, Roma, Italia, ISBN 9251042195, 1998.Ashraf, B., AghaKouchak, A., Alizadeh, A., Baygi, M. M., Moftakhari, H. R., Mirchi, A., Anjileli, H., and Madani, K.: Quantifying Anthropogenic Stress on Groundwater Resources, Scientific Reports, 7, 12910, 10.1038/s41598-017-12877-4, 2017.Badgley, G., Fisher, J. B., Jiménez, C., Tu, K. P., and Vinukollu, R.: On
Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice
of Input Forcing Datasets, J. Hydrometeorol., 16, 1449–1455,
10.1175/JHM-D-14-0040.1, 2015.Bai, P., Liu, X., Zhang, D., and Liu, C.: Estimation of the Budyko model parameter for small basins in China, Hydrol. Process., 34, 125–138, 10.1002/hyp.13577, 2019.Dai, A. and Zhao, T.: Uncertainties in historical changes and future projections
of drought. Part I: estimates of historical drought changes, Climatic
Change, 144, 519–533, 10.1007/s10584-016-1705-2, 2017.Dai, A., Trenberth, K. E., andQian, T.: A global dataset of Palmer Drought
Severity Index for 1870–2002: relationship with soil moisture and effects of
surface warming, J. Hydrometeorol., 5, 1117–1130, 10.1175/JHM-386.1, 2004.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, Tavolato, P. C., Thépaut, J. N., and Vitart, F. : The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 10.1002/qj.828,
2011.Douville, H., Ribes, A., Decharme, B., Alkama, R., and Sheffield, J.:
Anthropogenic influence on multidecadal changes in reconstructed global
evapotranspiration, Nat. Clim. Change, 3, 59–62, 10.1038/nclimate1632,
2013.Dutra, E., Balsamo, G., Calvet, J.-C., Minvielle, M., Eisner, S., Fink, G., Pessenteiner, S., Orth, R., Burke, S., van Dijk, A. I. J. M., Polcher, J., Beck, H. E., and de la Torre, A. M.: Report on
the current state-of-the-art Water Resources Reanalysis, http://earth2observe.eu/files/Public Deliverables/D5.1_Report on the WRR1 tier1.pdf, last access: 11 July 2022.Everson, C. S., Clulow, A., and Mengitsu, M.: Feasibility Study on the
Determination of Riparian Evaporation in Non-Perennial Systems; WRC Report
No. TT 424/09, Water Research Commission, Pretoria, South Africa, ISBN 978-1-77005-905-4, 2009.Feng, S. and Fu, Q.: Expansion of global drylands under a warming climate, Atmos. Chem. Phys., 13, 10081–10094, 10.5194/acp-13-10081-2013, 2013.Ficklin, D. L. and Novick, K. A.: Historic and projected changes in vapor
pressure deficit suggest a continental-scale drying of the United States
atmosphere, J. Geophys. Res.-Atmos., 122, 2061–2079, 10.1002/2016JD025855,
2017.Forzieri, G., Miralles, D. G., Ciais, P., Alkama, R.,
Ryu, Y., Duveiller, G., Zhang, K., Robertson, E., Kautz,
M., Martens, B., Jiang, C., Arneth, A., Georgievski, G.,
Li, W., Ceccherini, G., Anthoni, P., Lawrence, P., Wiltshire,
A., Pongratz, J., Piao, S., Sitch, S., Goll, D. S.,
Arora, V. K., Lienert, S., Lombardozzi, D., Kato, E.,
Nabel, J. E. M. S., Tian, H., Friedlingstein, P., and Cescatti,
A.: Increased control of vegetation on global terrestrial energy
fluxes, Nat. Clim. Change, 10, 356–362, 10.1038/s41558-020-0717-0, 2020.Fu, B.: On the calculation of the evaporation from land surface, Sci. Atmos.
Sin., 5, 23–31, 1981 (in Chinese).Fu, Q. and Feng, S.: Responses of terrestrial aridity to global warming, J.
Geophys. Res.-Atmos., 119, 7863–7875, 10.1002/2015JD024100, 2014.Gentine, P., Green, J. K., Guerin, M., Humphrey, V., Seneviratne, S. I., Zhang, Y., and Zhou, S.: Coupling between the terrestrial carbon and water cycles – a review, Environ. Res. Lett., 14, 083003, 10.1088/1748-9326/ab22d6, 2019.Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., and
Seneviratne, S. I.: Global Assessment of Trends in Wetting and Drying over
Land, Nat. Geosci., 7, 716–721, 10.1038/ngeo2247, 2014.Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B., Siegwolf, R. T. W., Sperry, J. S., and McDowell, N. G.: Plant responses to rising
vapor pressure deficit, New Phytol., 226, 1550–1566, 10.1111/nph.16485,
2020.Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from
temperature, Appl. Eng. Agric. 1, 96–99, 10.13031/2013.26773, 1985.Helbig, M., Waddington, J. M., Alekseychik, P., Amiro, B. D., Aurela, M., Barr, A. G., Black, T. A., Blanken, P. D., Carey, S. K., Chen, J., Chi, J., Desai, A. R., Dunn, A., Euskirchen, E. S., Flanagan, L. B., Forbrich, I., Friborg, T., Grelle, A., Harder, S., Heliasz, M., Humphreys, E. R., Ikawa, H., Isabelle, P.-E., Iwata, H., Jassal, R., Korkiakoski, M., Kurbatova, J., Kutzbach, L., Lindroth, A., Löfvenius, M. O., Lohila, A., Mammarella, I., Marsh, P., Maximov, T., Melton, J. R., Moore, P. A., Nadeau, D. F., Nicholls, E. M., Nilsson, M. B., Ohta, T., Peichl, M., Petrone, R. M., Petrov, R., Prokushkin, A., Quinton, W. L., Reed, D. E., Roulet, N. T., Runkle, B. R. K., Sonnentag, O., Strachan, I. B., Taillardat, P., Tuittila, E.-S., Tuovinen, J.-P., Turner, J., Ueyama, M., Varlagin, A., Wilmking, M., Wofsy, S. C., and Zyrianov, V. : Increasing contribution of peatlands to boreal evapotranspiration in a warming climate, Nat. Clim. Change, 10, 555–560, 10.1038/s41558-020-0763-7, 2020.Jalilvand, E., Tajrishy, M., Ghazi Zadeh Hashemi, S. A., and Brocca, L.:
Quantification of irrigation water using remote sensing of soil moisture in
a semi-arid region, Remote Sens. Environ., 231, 111226,
10.1016/j.rse.2019.111226, 2019.Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J., Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A.J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B.E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N., Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline in the global land evapotranspiration
trend due to limited moisture supply, Nature, 467, 951–954,
10.1038/nature09396, 2010.Kendall, M. G.: Rank Correlation Methods, Griffin, London, England, pp. 1–202,
10.2307/2333282, 1975.Kochendorfer, J., Castillo, E. G., Haas, E., Oechel, W. C., and Paw U, K. T.:
Net ecosystem exchange, evapotranspiration and canopy conductance in a
riparian forest, Agric. For. Meteorol. 151, 544–553,
10.1016/j.agrformet.2010.12.012, 2011.Koster, R. D., Sud, Y. C., Guo, Z., Dirmeyer, P. A., Bonan, G., Oleson, K. W., Chan, E., Verseghy, D., Cox, P., Davies, H., Kowalczyk, E., Gordon, C. T., Kanae, S., Lawrence, D., Liu, P., Mocko, D., Lu, C.-H., Mitchell, K., Malyshev, S., McAvaney, B., Oki, T., Yamada, T., Pitman, A., Taylor, C. M., Vasic, R., and Xue, Y.: GLACE: the global land atmosphere coupling
experiment. Part I: overview, J. Hydrometeorol., 7, 590–610,
10.1175/JHM510.1, 2006.Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15,
259–263, 10.1127/0941-2948/2006/0130, 2006.Li, S. J., Wang, G. J., Sun, S. L., Chen, H. S., Bai, P., Zhou, S.J., Huang, Y., Wang, J., and Deng, P.: Assessment of
Multi-Source Evapotranspiration Products over China Using Eddy Covariance
Observations, Remote Sensing, 210, 1692, 10.3390/rs10111692, 2018.Li, S. J., Wang, G. J., Sun, S. L., Hagan, T. F. D., Chen, T. X., Dolman, H., and Liu, Y.: Long-term changes in evapotranspiration
over China and attribution to climatic drivers during 1980–2010, J.
Hydrol., 595, 126037, 10.1016/j.jhydrol.2021.126037, 2021.Li, Y., Piao, S., Li, L. Z. X., Chen, A., Wang, X., Ciais, P., Huang, L., Lian, X.,
Peng, S., Zeng, Z., Wang, K., and Zhou, L.: Divergent hydrological response to
large-scale afforestation and vegetation greening in China, Sci. Adv., 4, eaar4182, 10.1126/sciadv.aar4182, 2018a.Li, Y., Zeng, Z., Huang, L., Lian, X., and Piao, S.:
Comment on “Satellites reveal contrasting responses of regional climate to
the widespread greening of Earth”, Science, 360, eaap7950, 10.1126/science.aap7950, 2018b.Lian, X., Piao, S., Huntingford, C., Li, Y., Zeng, Z., Wang, X., and Wang, T.:
Partitioning global land evapotranspiration using CMIP5 models
constrained by observations, Nat. Clim. Change, 8, 640–646, 2018.Liu, X. M., Liu, C. M., Luo, Y. Z., Zhang, M. H., and Xia, J.: Dramatic
decreasing streamflow from the headwater source in the central route of
China's water diversion project: Climatic variation or human influence?, J.
Geophys. Res.-Atmos., 117, D06113, 10.1029/2011JD016879, 2011.Loew, A., Peng, J., and Borsche, M.: High-resolution land surface fluxes from satellite and reanalysis data (HOLAPS v1.0): evaluation and uncertainty assessment, Geosci. Model Dev., 9, 2499–2532, 10.5194/gmd-9-2499-2016, 2016.Long, D., Pan, Y., Zhou, J., Chen, Y., Hou, X. Y., Hong, Y., Scanlon, B. R., and Longuevergne, L.: Global analysis of spatiotemporal variability
in merged total water storage changes using multiple GRACE products and
global hydrological models, Remote Sens. Environ., 192, 198–216, 2017.Lu, J., Wang, G., Gong, T., Hagan, D. F. T., Wang, Y., Jiang, T., and Su,
B.: Changes of actual evapotranspiration and its components in the Yangtze
River valley during 1980–2014 from satellite assimilation product,
Theor. Appl. Climatol., 138, 1493–1510,
10.1007/s00704-019-02913-w, 2019.Lu, J., Wang, G. J., Li, S. J., Feng, A. Q., Zhan, M. Y., Jiang, T., Su, B. D., and Wang, Y. J.: Projected land evaporation and its response to vegetation greening over
China under multiple scenarios in the CMIP6 models, J. Geophys.
Res.-Biogeo., 126, e2021JG006327, 10.1029/2021JG006327, 2021.Lv, M., Ma, Z., Yuan, X., Lv, M., Li, M., and Zheng, Z.: Water budget closure based on GRACE
measurements and reconstructed evapotranspiration using GLDAS and wateruse
data for two large densely-populated mid-latitude basins, J. Hydrol., 547,
585–599, 10.1016/j.jhydrol.2017.02.027, 2017.Mann, H. B.: Nonparametric tests against trend, Econometrica, 13,
245–259, 1945.Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, 10.5194/gmd-10-1903-2017, 2017 (data available at: https://www.gleam.eu/, last access: 9 July 2022).Martens, B., Waegeman, W., Dorigo, W. A., Verhoest, N. E. C., and Miralles,
D. G.: Terrestrial evaporation response to modes of climate variability, Npj
Climate and Atmospheric Science, 1, 43, 10.1038/s41612-018-0053-5, 2018.Massmann, A., Gentine, P., and Lin, C.: When does vapor pressure deficit
drive or reduce evapotranspiration?, J. Adv. Model. Earth
Sy., 11, 3305–3320, 10.1029/2019MS001790, 2019.McAdam, S. A. and Brodribb, T. J.: The evolution of mechanisms driving the
stomatal response to vapor pressure deficit, Plant Physiol., 167, 833–843,
10.1104/pp.114.252940, 2015.Michel, D., Jiménez, C., Miralles, D. G., Jung, M., Hirschi, M., Ershadi, A., Martens, B., McCabe, M. F., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms, Hydrol. Earth Syst. Sci., 20, 803–822, 10.5194/hess-20-803-2016, 2016.Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., and Dolman, A. J.: Magnitude and variability of land evaporation and its components at the global scale, Hydrol. Earth Syst. Sci., 15, 967–981, 10.5194/hess-15-967-2011, 2011a.Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, 10.5194/hess-15-453-2011, 2011b.Miralles, D. G., van den Berg, M. J., Gash, J. H., Parinussa, R.M., de Jeu, R. A. M., Beck, H. E., Holmes, T. R. H., Jiménez, C., Verhoest, N. E. C., Dorigo, W. A., Teuling, A. J., and Johannes Dolman, A.: El Niño–La Niña cycle and recent trends in
continental evaporation, Nat. Clim. Change, 4, 122–126,
10.1038/nclimate2068, 2013.Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q., Seneviratne, S. I., Wood, E. F., and Fernández-Prieto, D.: The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets, Hydrol. Earth Syst. Sci., 20, 823–842, 10.5194/hess-20-823-2016, 2016.Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.:
Land-atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, Ann. NY Acad. Sci.,
1436, 19–35, 10.1111/nyas.13912, 2018.Monteith, J. and Unsworth, M.: Principles of Environmental Physics, 2nd edn.,
Edward Arnold, London, UK, ISBN 9780713129816, 10.1088/0031-9112/25/2/025, 1990.Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS Global Terrestrial
Evapotranspiration Algorithm, Remote Sens. Environ., 115, 1781–1800,
10.1016/j.rse.2011.02.019, 2011.Nooni, I. K., Wang, G., Hagan, D. F. T., Lu, J., Ullah, W., and Li, S.:
Evapotranspiration and its Components in the Nile River Basin Based on
Long-Term Satellite Assimilation Product, Water 11, 1400, 10.3390/w11071400, 2019.Novick, K. A., Ficklin, D. L., Stoy, P. C., Williams, C. A., Bohrer, G., Oishi, A. C., Papuga, S. A., Blanken, P. D., Noormets, A., Sulman, B. N., Scott, R. L., Wang, L., and Phillips, R. P.: The increasing
importance of atmospheric demand for ecosystem water and carbon fluxes,
Nat. Clim. Change, 6, 1023–1027, 10.1038/nclimate3114, 2016.Pan, S., Tian, H., Dangal, S. R., Yang, Q., Yang, J., Lu, C., Tao, B., Ren,
W., and Ouyang, Z.: Responses of global terrestrial evapotranspiration to
climate change and increasing atmospheric CO2 in the 21st century, Earth's
Future, 3, 15–35, 10.1002/2014EF000263, 2015.Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, 10.5194/hess-24-1485-2020, 2020.Peng, J., Kharbouche, S., Muller, J.-P., Danne, O., Blessing, S., Giering, R.,
Gobron, N., Ludwig, R., Muller, B., Leng, G., Lees, T., and Dadson, S.: Influences of
leaf area index and albedo on estimating energy fluxes with HOLAPS
framework, J. Hydrol., 580, 124245, 10.1016/j.jhydrol.2019.124245,
2020.Priestley, C. and Taylor, R.: On the Assessment of Surface Heat Flux and
Evaporation Using Large Scale Parameters, Mon. Weather Rev.,
100, 81–92, 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2, 1972.Reichle, R. H., Koster, R. D., de Lannoy, G. J. M., Forman, B. A., Liu, Q.,
Mahanama, S. P. P., and Touré, A.: Assessment and enhancement of MERRA land
surface hydrology estimates, J. Climate 24, 6322–6338,
10.1175/JCLI-D-10-05033.1, 2011 (data availble at: https://disc.gsfc.nasa.gov/datasets?keywords=merra-land&page=1, last access: 12 May 2020).Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P. P., Koster,
R. D., and de Lannoy, G. J. M.: Assessment of MERRA-2 land surface hydrology
estimates, J. Climate, 30, 2937–2960, 10.1175/JCLI-D-16-0720.1, 2017.Rienecker, M. M., Suárez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., Silva, A. da, Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA:
NASA's Modern-Era Retrospective Analysis for research and applications, J.
Climate, 24, 3624–3648, 10.1175/JCLI-D-11-00015.1, 2011.Rigden, A. J. and Salvucci, G. D.: Stomatal response to humidity and CO2
implicated in recent decline in US evaporation, Glob. Change Biol.,
23, 1140–1151, 10.1111/gcb.13439, 2016.Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.:
The global land data assimilation system, B. Am. Meteorol. Soc., 85,
381–394, 10.1175/BAMS-85-3-381, 2004 (data available at: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS, last access: 13 May 2020).Roderick, M. L., Sun, F., Lim, W. H., and Farquhar, G. D.: A general framework for understanding the response of the water cycle to global warming over land and ocean, Hydrol. Earth Syst. Sci., 18, 1575–1589, 10.5194/hess-18-1575-2014, 2014.Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, 10.5194/essd-9-389-2017, 2017 (data available at: http://www.earth2observe.eu/, last access: 9 July 2022).Shan, N., Shi, Z. J., Yang, X. H., Gao, J. X., and Cai, D. W.: Spatio-temporal trends
of reference evapotranspiration and its driving factors in the
Beijing-Tianjin sand source control project Region, China, Agr.
Forest Meteorol., 200, 322–333, 10.1016/j.agrformet.2014.10.008, 2015.Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, 10.1175/JCLI3790.1, 2006.Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global
drought over the past 60 years, Nature, 491, 435–438, 10.1038/nature11575, 2012.Shi, Z. J., Shan, N., Xu, L. H., Yang, X. H., Gao, J. X., Guo, H., Zhang,
X., Song, A. Y., and Dong, L. S.: Spatiotemporal variation of temperature
precipitation and wind trends in a desertification prone region of China
from 1960 to 2013, Int. J. Climatol., 36, 4327–4337,
10.1002/joc.4635, 2016.Soni, A. and Syed, T. H.: Analysis of variations and controls of
evapotranspiration over major Indian River Basins (1982–2014), Sci. Total Environ., 754, 141892, 10.1016/j.scitotenv.2020.141892, 2021.Sottocornola, M. and Kiely, G.: Energy fluxes and evaporation mechanisms in an
Atlantic blanket bog in southwestern Ireland, Water Resour. Res., 46,
W11524, 10.1029/2010WR009078, 2010.Su, B. D., Wang, A. Q., Wang, G. J., Wang, Y. J., and Jiang, T.: Spatiotemporal
variations of soil moisture in the Tarim River basin, China, Int.
J. Appl. Earth Obs., 48, 122–130,
10.1016/j.jag.2015.06.012, 2015.Sun, S. L., Chen, H. S., Wang, G. J., Li, J. J., Mu, M. Y., Yan, G. X., Xu, B., Huang, J., Wang, J., and Zhang, F. M.: Shift in potential
evapotranspiration and its implications for dryness/wetness over Southwest
China, J. Geophys. Res., 121, 9342–9355,
10.1002/2016JD025276, 2016.Sun, S. L., Chen, H. S., Ju, W. M., Wang, G. J., Sun, G., Huang, J., Ma, H. D., Gao, C. J., Hua, W. J., and Yan, G. X.: On the coupling between
precipitation and potential evapotranspiration: Contributions to decadal
drought anomalies in the Southwest China, Clim. Dynam., 48,
3779–3797, 10.1007/s00382-016-3302-5, 2017.Teuling, A. J., de Badts, E. A. G., Jansen, F. A., Fuchs, R., Buitink, J., Hoek van Dijke, A. J., and Sterling, S. M.: Climate change, reforestation/afforestation, and urbanization impacts on evapotranspiration and streamflow in Europe, Hydrol. Earth Syst. Sci., 23, 3631–3652, 10.5194/hess-23-3631-2019, 2019.Trenberth, K. E., Smith, L., Qian, T., Dai, A., and Fasullo, J.: Estimates of
the global water budget and its annual cycle using observational and model
data, J. Hydrometeorol., 8, 758–769, 10.1175/JHM600.1, 2007.Vinukollu, R. K., Meynadier, R., Sheffield, J., and Wood, E. F.:
Multi-model, multi-sensor estimates of global evapotranspiration:
climatology, uncertainties and trends, Hydrol. Process., 25,
3993–4010, 10.1002/hyp.8393, 2011.Wang, G. J., Pan, J., Shen, C. C., Li, S. J., Lu, J., Lou, D., and Hagan, T. F. D: Evaluation of Evapotranspiration Estimates in the Yellow River Basin
against the Water Balance Method, Water, 10, 1884, 10.3390/w10121884,
2018a.Wang, G. J., Gong, T. T., Lu, J., Lou, D., Hagan, D. F. T., and Chen, T. X.: On
the long-term changes of drought over China (1948–2012) from different
methods of PET estimations, Int. J. Climatol., 38,
2954–2966, 10.1002/joc.5475, 2018b.Wang, H. N., Lv, X. Z., and Zhang, M. Y.: Sensitivity and attribution
analysis of vegetation changes on evapotranspiration with the Budyko
framework in the Baiyangdian catchment, China. Ecol. Indic., 120, 106963, 10.1016/j.ecolind.2020.106963, 2021.Wang, K. C. and Dickinson, R. E.: A review of global terrestrial
evapotranspiration: observation, modeling, climatology, and climatic
variability, Rev. Geophys., 50, RG2005, 10.1029/2011RG000373,
2012.Wang, R., Li, L., Gentine, P., Zhang, Y., Chen, J., Chen, X., Chen, L., Ning, L., Yuan, L., and Lu, G.: Recent increase in the observation-derived land
evapotranspiration due to global warming, Environ. Res. Lett., 17,
024020, 10.1088/1748-9326/ac4291, 2022.Wang, Y., Liu, B., Su, B., Zhai, J., and Gemmer, M.: Trends of Calculated
and Simulated Actual Evaporation in the Yangtze River Basin, J.
Climate, 24, 4494–4507, 10.1175/2011JCLI3933.1, 2011.Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.:
The WFDEI meteorological forcing data set: WATCH Forcing Data methodology
applied to ERA-Interim reanalysis data, Water Resour. Res. 50, 7505–7514,
10.1002/2014WR015638, 2015.Wilcox, R. R.: Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy, 2nd edn., Springer, New York 278 pp., ISBN 978-1441955241, 2010.Wu, P., Christidis, N., and Stott, P.: Anthropogenic impact on Earth's
hydrological cycle, Nat. Clim. Change, 3, 807–810, 10.1038/nclimate1932,
2013.Xu, X., Liu, W., Scanlon, B. R., Zhang, L., and Pan, M.: Local and global
factors controlling water-energy balances within the Budyko framework,
Geophys. Res. Lett., 40, 6123–6129, 10.1002/2013GL058324, 2013.Yang, D., Shao, W., Yeh, P. J. F., Yang, H., Kanae, S., and Oki, T.: Impact of
vegetation coverage on regional water balance in the nonhumid regions of
China, Water Resour. Res., 45, W00A14, 10.1029/2008WR006948, 2009.Yang, H., Yang, D., Lei, Z., and Sun, F.: New analytical derivation of the mean
annual water-energy balance equation, Water Resour. Res., 44, W03410,
10.1029/2007WR006135, 2008.Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R., and Donohue, R. J.:
Hydrologic implications of vegetation response to elevated CO2 in climate
projections, Nat. Clim. Change, 9, 44–48, 10.1038/s41558-018-0361-0,
2019.Yokoo, Y., Sivapalan, M., and Oki, T.: Investigating the roles of climate
seasonality and landscape characteristics on mean annual and monthly water
balances, J. Hydrol. 357, 255–269, 10.1016/j.jhydrol.2008.05.010, 2008.Zeng, R. and Cai, X.: Climatic and terrestrial storage control on
evapotranspiration temporal variability: Analysis of river basins around the
world, Geophys. Res. Lett., 43, 185–195, 10.1002/2015GL066470, 2016.Zhang, D., Liu, X., Zhang, L., Zhang, Q., Gan, R., and Li, X.: Attribution
of evapotranspiration changes in humid regions of China from 1982 to 2016,
J. Geophys. Res.-Atmos., 125, e2020JD032404, 10.1029/2020JD032404, 2020.Zhang, K., Kimball, J. S., Nemani, R. R., Running, S. W., Hong, Y., Gourley,
J. J., and Yu, Z.: Vegetation Greening and Climate Change Promote
Multidecadal Rises of Global Land Evapotranspiration, Scientific Reports, 5,
15956, 10.1038/srep15956, 2015.Zhang, K., Kimball, J. S., and Running, S. W.: A review of remote sensing based
actual evapotranspiration estimation, WIRES Water, 3,
834–853, 10.1002/wat2.1168, 2016.Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H. S., Western, A. W., and
Briggs, P. R.: A rational function approach for estimating mean annual
evapotranspiration, Water Resour. Res., 40, W02502, 10.1029/2003WR002710,
2004.Zhang, Q., Yang, Z. S., Hao, X. C., and Yue, P.: Conversion features of
evapotranspiration responding to climate warming in transitional climate
regions in northern China, Clim. Dynam., 52, 3891–3903, 10.1007/s00382-018-4364-3,
2019.
Zhou, J., Wang, Y. J., Su, B. D., Wang, A. Q., Tao, H., Zhai, J. Q., Kundzewicz, Z. W., and Jiang, T.: Choice of
potential evapotranspiration formulas influences drought assessment: A case
study in China, Atmos. Res., 242, 104979,
10.1016/j.atmosres.2020.104979, 2020.