Download Modeling the spatial-temporal dynamics of water use efficiency in

Modeling the spatial-temporal dynamics of water use efficiency in
Yangtze River Basin using IBIS model
ZHANG Zhen1, JIANG Hong1,2*,LIU Jinxun3，ZHU Qiuan4，WEI Xiaohua5，ZHOU Guomo2, LIU
Shirong6，ZHANG Xiuying1
1
International Institute for Earth System Science, Nanjing University, Nanjing 210093.China
2
State Key Laboratory of Subtropical Forest Science & Zhejiang Provincial Key Laboratory of
Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang Agriculture and
Forestry University, Hangzhou，311300, Zhejiang，China ;3 Stinger Ghaffarian Technologies (SGT,
Inc.), contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science
(EROS) Center, 47914 252nd St., Sioux Falls, SD 57198；4 Institute of Environmental Sciences,
University of Quebec at Montreal, Case postale 8888, Succursale Centre-Ville, Montreal, QC,
Canada H3C 3P8；5 Department of Earth and Environmental Sciences, University of British
Columbia (Okanagan), 3333 University Way, Kelowna, BC, Canada V1V 1V7; 6 Institute of Forest
Ecology and Environment, Chinese Academy of Forestry, Beijing 100091, China.
Abstract: Climate change alters regional water and carbon cycling, which has been a hot study
point in the filed of climatology and ecology. As a traditionally “water-rich” region of China,
Yangtze River Basin plays an important role in regional economic development and ecosystem
productivity. However, the mechanism of the influence of climate change on water and carbon
cycling has been received little attention. As a coupling indicator for carbon and water, the Water
Use Efficiency (WUE) is widely used, which indicates the water consumption for carbon
sequestration in watershed and regional scale. A lot of studies showed that climate change has
significantly affected the water resource and production of the ecosystems in Yangtze River Basin
during the period of 1956 to 2006, when great climate variations were occurred. To better
understand the alternation pattern for the relationship between water and carbon cycling under
climate change at regional scale, the WUE and the spatiotemporal variations patterns were
simulated in the study area from 1956 to 2006 by using the Integrated Biosphere Simulator (IBIS).
1
Supported by the State Key Fundamental Science Funds of China(2010CB950702, 2010CB428503), The State
High Technology Funds of China(2009AA122001&2009AA122005), The State Key International Cooperation
Project(20073819), The State Key Basic Research Funds of China (2007FY110300-04&08), NSF-China Project
(40671132).
*
Corresponding author. Email: [email protected]
The results showed that the WUE spatial pattern had the annual and seasonal variations. In general,
the average annual WUE value per square meter was about 0.58 g C/kgH2O in Yangtze River
Basin. The high WUE levels were mainly distributed in the eastern area of Sichuan, western area
of Jiangxi and Hunan, and the highest value reached 0.88 g C/kg H2O. The lowest WUE’s were
mainly located in the western area of Sichuan and Qinghai with the lowest values reaching to 0.36
g C/kgH2O. The WUE in other regions mostly ranged from 0.5 to 0.6 g C/kgH2O. For the whole
study area, the annual WUE slowly increased from 1956 to 2006. The WUE in the upper reaches
of Yangtze River increased based on the simulated temporal trends, which mainly located in the
western area of the Sichuan Basin; The WUE of the middle reaches of Yangtze River had
increased slightly from 1987 to 1996, and then decreased from 1996 to 2006; the lower reaches of
Yangtze River always had smaller WUE’s than the average from 1956 to 2006. The spatiotemporal
variability of the WUE in the vegetation types was obvious in the Yangtze River Basin, and it was
depended on the climate and soil conditions, and as well the disturbance in its distribution areas.
The temporal variations of WUE among different vegetation types had similar trends but different
in values. The forest type had higher WUE than any other vegetation types ranging from 0.65 to
0.8 g C/kg H2O. The WUE of shrubland ranged from 0.45 to 0.6 g C/kg H2O, The WUE of tundra
was the lowest, indicating the differences in plant physiology. The consistence of the spatial
pattern of WUE with the NPP indicated that the regional production of Yangtze River Basin
increased based on the water resources prompted and vegetation restoration. We found the drought
climate was one of critical factor that impacts the alteration of WUE in Yangtze River Basin in the
simulation.
Keywords: Yangtze River Basin; Water Use Efficiency (WUE); land surface model; IBIS;
1 Introduction
The increased atmospheric CO2 concentration and nitrogen deposition and more-intense droughts
have affected the carbon and water cycles in terrestrial ecosystems (IPCC [1]). In fact, the carbon
and water interaction effect is strongly coupled under climate change (Yu [2]). Water Use
Efficiency (WUE), as an important indicator of carbon and water coupling, is used to quantify the
trade-off of carbon assimilation and water loss. It links the carbon cycle and the hydrological
cycle.
WUE has been a study hot spot in plant ecology, agriculture, and earth system science (Kuglitsch
[3], Jassal [4], Vermeire [5], Li [6]). At different scale levels, WUE has different meanings
because of the different complexity of physical and physiological processes involved. At leaf level,
how to improve productivity efficiently (Peterson [7], Ren [8], Richards [9]) has been addressed.
Some studies focused on the responses of WUE to environmental factors in semi-arid plant
physiology, based on the traditional measurements of vegetation biomass and soil hydrological
parameters (Brueck [10], O'connor [11]). These approaches have an obvious limitation since the
continuous temporal and spatial results are unavailable (Hu [12]). Some new methods such as
eddy covariance technique were developed for studying ecosystem carbon and water exchange at
multiple timescales (Ponton [13]). Although these new approaches brought more improvements
for studying WUE at ecosystem level, it was still difficult to get a regional and continual level
results of WUE which could represent a larger scope than eddy covariance techniques
(approximately 1 ha) (Beer [14]). Meanwhile, as a powerfully tools to extrapolate current and
future information, ecosystem process-based model had the advantages to estimate
spatial-temporal WUE continually at multiple scales. Especially at regional scale, terrestrial
biosphere model has its advantages to evaluate the responses of WUE to climate change (Tian
[15]).
As a source of industrial power and irrigation for agriculture , Yangtze River -is the longest river
in China, including China’s numerous plant types and ecosystems (Ke [16]). Under more
extensive climate change and anthropogenic activities, the ecosystems in Yangtze River Basin will
therefore undertake more complex environmental pressure. To better understand the influence of
climate change on the water and carbon cycles of terrestrial ecosystems, it is important to evaluate
the WUE in the Yangtze River Basin of China.
In this study, a terrestrial ecosystem model called Integrated Biosphere Simulator (IBIS) has been
used to simulate the carbon and water processes and calculate the spatial-temporal variations of
WUE in Yangtze River Basin from 1956 to 2006. Spatial and statistical analysis methods are
applied to analyze the characteristics of WUE in different plant functional types.
2 Material and Methods
2.1 Study area
The study area includes the Yangtze River and its tributaries, ranging from E90˚30' to E122˚25',
N24˚30' to N35˚45'. The river runs 1,800,000 kilometers from the glacier on the Tibetan Plateau
eastwards across southwest, central and eastern China. Its average annual precipitation and
evaportranspiration is 1067mm and 541mm, respectively. Because of the complexity of its climate
and topographical conditions, the spatial pattern of annual precipitation and evaportranspiration is
highly heterogeneous. Vegetation covers are mainly warm-temperate broadleaf evergreen forest,
temperate conifer evergreen forest, and Tibet Plateau grassland.
2.2 Model description
IBIS was designed to integrate a variety of terrestrial ecosystem process within a physically
consistent framework. It included multiple components that represent land surface processes,
canopy physiology, vegetation phenology and carbon cycling (Delire [17]). The model was proved
to be well simulating the land surface processes at different scales (El Maayar [18], Zhu [19]).
IBIS was based on a combination of plant functional types, which were defined from several
important ecological characteristics (Foley [20]). A modified version of IBIS was developed to
improve the original model in some aspects such as nitrogen deposition module (Liu [21]),
sub-pixel processing that considered vegetation and non-vegetation fractionation on each land
pixel was also included.
The land surface module in IBIS was constructed on the basis of the land-surface-transfer scheme
(LSX)(Pollard [22,Thompson [23]), which presented two canopy layers, three snow layers, and six
soil layers. The total evaportranspiration in each grid unit was calculated from the sum of three
water vapor flutes: evaporation from soil surface, evaporation from vegetation canopy surface, and
canopy transpiration. The rates of evaportranspiration velocity depended on the canopy
conductance. The calculation equations were described as follows:
where the subscripts ‘u’, ‘s’, and ‘l’ denote upper-story leaves, upper-story stems, lower-story
vegetation,
,
, and
were fluxes of water vapor from a unit leaf/stem surface area
including evaporation of intercepted water, dew formation and transpiration, LAIl and LAIu
represented the upper and lower canopy index respectively.
The algorithm of soil evaporation was using beta method(Mahfouf [24]):
where
was the evaporation from bare soil,
for evaporation,
was the wind speed at
was the air density,
was the drag coefficient
(atmospheric reference level),
was the
moisture availability parameter,
was the specific humidity at
,
was the saturated
specific humidity which is given by surface temperature.
The canopy physiology module included photosynthesis and stomatal conductance, which were
based on a canopy photosynthesis mechanism model and a semi-mechanistic model of stomatal
conductance (Farquhar [25], Farquhar [26]). The NPP calculation differed from each other in
different plant functional types:
where
was gross canopy photosynthesis, Ra was the rate of gross plant respiration.
For C3 plants:
where JE was the light limited rate of photosynthesis, Jc was the Rubisco limited rate of
photosynthesis.
For C4 plants:
where
,
was the intrinsic quantum efficiency for CO2 uptake in C4 plant (mol CO2
Ein-1),
, and
was the CO2 limited rate of photosynthesis at low-CO2
concentrations. The detailed descriptions were available in Foley et al. (1996) and Kucharik et al.
(2000) (Foley, Prentice [20], Kucharik [27]).
2.3 water use efficiency
WUE had different definitions at different spatial-temporal scales and research communities
(Richards, Rebetzke [9], Steduto [28]). At leaf level the water consumption was considered as
transpiration (T), while the water consumption was using evaportranspiration (ET) at ecosystem
level. In this study, WUE was defined as the ratio of net primary production (NPP) to
evaportranspiration (ET) since NPP could reflect annual net carbon fixation in ecosystem (Law
[29], Hu [30]).
2.4 Data
The input data to IBIS simulation included vegetation cover map, soil map, topography map and
climate forcing data. The 1:1,000,000 soil map of China provided the fraction of sand, clay and
silt for each soil layer. The 1:4,000,000 vegetation map of China was applied to 15 vegetation
types for IBIS simulation (Zhu [31]). The meteorological data was collected from 751 ground
stations, and then interpolated into 0.085 degree (~ 10 km) resolution for IBIS simulation. The
interpolation method was adopted from ANNUSPIN software (Hutchinson [32], Hutchinson [33]).
The atmosphere CO2 concentration data was referenced from IS92 scenario in IPCC.
The modified version of IBIS required additional input data, which included vegetation cover
fraction data, initial biomass map and soil carbon storage map. The vegetation cover fraction data
was used to represent the area fraction of forest, shrub, grass and non-vegetation cover at each
pixel. A global land cover map from ENVISAT satellite was selected for calculating the area
fractions due to its high accuracy. Initial biomass was derived from the database of Olson’
research (Gibbs [34]). Atmospheric nitrogen deposition data was from the Earth Science
Information Partner (ESIP) database of EOS-WEBSTER, which covered the early 1990s. A
fifty-year period simulation for spin-up process, during which the soil carbon pools accumulated
to nearly equilibrium in 1956, was included. Simulation results for the period from 1956 to 2006
were analyzed.
3 Results
3.1 Model evaluation
To validate the simulation results, two comparison methods were applied to evaluate the
performance of the carbon cycling and WUE: a) the comparison between the simulated NPP and
inventory data which was surveyed from 424 sample sites across the Yangtze River Basin during
1970 to 1995 (Luo [35]); We selected 208 mature forest data (forest age > 50a) and then compared
with simulated NPP.
b) the comparison of WUE between simulated results and literature reports
in the vegetation types
The average NPP from IBIS and inventory data was respectively 0.5175 C·m-2·yr-1 and 0.5142
C·m-2·yr-1. The difference between both was approximately 1%. The average simulated NPP from
970-1995 was compared to its corresponding inventory data (Figure 1). These results showed that
the simulated NPP was well fitted the observations. Despite slightly scattering of points were
found in Fig. 1, the overall simulated results were in reasonable scopes because the scale gap
which was likely to increase the difficulties in comparison, and the inventory NPP also had
random errors or uncertainty because it was collected from plenty of research literature.
Fig.1 Comparison of estimated and observed NPP in inventory data of forest
Owing to the inadequate researches in the study area, results for WUE in different vegetation
types, as reported in the literature, were summarized and then compared with the IBIS results
without considering geographical distinction (Table1). The difference was reflected in
comparisons between both sets of values and calculation algorithm. The average of simulated
WUE’s for the major vegetation types was calculated from 1990 to 2000. WUE comparison
showed differences between simulated and other study ranged from 0.1 to 0.3 g C/kgH2O due to
the differences of the scale and study methods (Yu, Song [2], Kuglitsch, Reichstein [3]). The
WUE values of forest in IBIS were slightly lower than those in Tian’s study (Tian, Chen [15]). On
the other hand, the WUE values of tundra were higher than Law’s work (Law, Falge [29]). The
averaged annual WUE values were in the reasonable range of different studies for China.
Table 1 Comparison between simulated WUE and other literature on different vegetation types
Ecosystem type
Mean WUE
IBIS vegetation type
(g C/kgH2O))
IBIS
Simulated
Reference
WUE
temperate deciduous broadleaf forest
0.92 (NPP/ET)
warm-temperate broadleaf forest
0.87(NPP/ET)
[15]
temperate evergreen conifer forest
0.97 (NPP/ET)
temperate evergreen conifer forest
0.86(NPP/ET)
[15]
deciduous shrubland
0.49 (NPP/ET)
dense shrubland
0.52(NPP/ET)
[15]
C3 and C4 grassland
0.61 (NPP/ET)
grassland
0.55(NPP/ET)
[15]
cropland
0.61 (NPP/ET)
open shrubland
0.59(NPP/ET)
[15]
boreal evergreen conifers
0.79 (GPP/ET)
boreal evergreen forest
0.82(NPP/ET)
[3]
evergreen broadleaf forest
1.88 (GPP/ET)
temperate evergreen forest
0.85(NPP/ET)
[2]
tundra
0.41 (GPP/ET)
tundra
0.43(NPP/ET)
[29]
3.2 Spatial distribution of averaged WUE
From 1956 to 2006, the averaged annual NPP value in Yangtze River Basin was 0.518 C·m-2,
averaged annual evaportranspiration was 855 mm, and averaged WUE was 0.58 g C/kgH2O. The
spatial distribution of WUE in Yangtze River Basin showed significant spatial heterogeneity (Fig.
2). The WUE values was lower in Sichuan Basin and Tibet Plateau than in the other region,
ranging from 0.2 to 0.5 g C/kgH2O. In contrast, WUE values in most of other areas ranged from
0.4 to 1.0 g C/kgH2O. The lowest value of WUE emerged in Qinghai Province and western
Sichuan with 0.36 g C/kgH2O, while the highest WUE value were in southern Qingling and
southeast of Sichuan Basin with 0.88 g C/kgH2O. By comparing the geographic distribution of
WUE across Yangtze River Basin, an integrated spatial pattern was found that WUE was
significantly lower in western Yangtze River Basin than that in the east regions due to the
differences of vegetation covers, which responded differently to terrestrial and climate condition.
Fig.2 Spatial pattern of the mean annual WUE in Yangtze River Basin during 1956-2006
3.3 Temporal variations of WUE
In this study, changes in two environmental factors (climate and atmospheric CO2 concentration)
were both combined in IBIS simulation. During the studied period, the averaged annual NPP of
the terrestrial ecosystems in Yangtze River Basin ranged from 0.5124 C·m-2·yr-1 in 1956 to 0.5067
C·m-2·yr-1 in 2001 (Fig. 3a). It varied substantially across the year and showed a tendency to
slightly increase from 1956 to 2006. ET had no obvious trends during the entire study period (Fig.
3b). WUE of the plants in the terrestrial ecosystems of Yangtze River Basin ranged from 0.57 g
C/kgH2O in 1956 to 0.56 g C/kgH2O in 2006 (Fig. 3c). The highest WUE was about 0.63 g
C/kgH2O in 2002, while the lowest WUE was about 0.54 g C/kgH2O in 1959. WUE had an
increasing trend from 1956 to 2006 with a substantial inter-annual fluctuation, which implied that
rising atmospheric CO2 concentration had positive effect on WUE. Meanwhile, NPP trend was
similar to ET, and it showed the consistency with climate change. The overall response of WUE
variations to environmental change had a delaying effect. This can be partly explained by the
adaptation ability of plants.
Fig.3 Annual NPP (kg m-2), ET(mm/year), WUE(gC/kgH2O) across the terrestrial ecosystems of
the Yangtze River Basin during 1956-2006
3.4 Spatial and temporal variations in upper, middle, lower reach of Yangtze River Basin
The spatial distribution of WUE in upper, middle and lower reaches of Yangtze River Basin had
significant heterogeneity due to its topographical and atmospheric conditions. The averaged WUE
anomalies of recent 30a (1977-2006) were selected to analyze the spatial variations of each
reaches of Yangtze River Basin (Fig. 4). The mean WUE anomalies were calculated from the
difference between 10a averaged WUE and the overall average level (mean value of the period of
1956 to 2006). The Figure showed that WUE at western Sichuan Basin was lower than common
value with 0.03 to 0.08 g C/kgH2O, while WUE in Hengduan Mountain was slightly higher
ranging from 0.01 to 0.1 g C/kgH2O, where forest cover fraction was also more dense. This
distribution demonstrated that WUE has an uneven spatial pattern in upper reach of Yangtze River.
The WUE in most of the area in middle reach was relatively continuous except part regions of
Jiangxi Province. The lower reach of Yangtze River was similar with the middle one. As a high
spatial anomalistic region, WUE of upper reach of Yangtze River had more dramatic variations
during 1987 to 1996, while WUE in middle reach increased widely especially in Jiangxi Province,
where mainly covered by temperate broadleaf and conifer forests. The lower reach had no
significant variations during this period. From 1996 to 2006, WUE in middle and lower reaches
decreased with a range from 0.04 to 0.06 g C/kgH2O. In contrast, WUE in western upper reach
maintained rising. One explanation was in this semiarid region plants had more buffering abilities
to climate change.
Fig 4 Spatial Patterns of Yangtze River Basin
Figure 5 demonstrates that the averaged WUE in each reaches had an increasing trend in Yangtze
River Basin, which was consistent with the integrated trend. The amplitude of the averaged WUE
in upper reach was lower than that in the other two reaches, ranging from 0.09 g C/kgH2O. The
amplitude of mean WUE of lower reach was the highest with about 0.23 g C/kgH2O. Although the
amplitude differences of each reaches were significantly, the area should be considered (about 100,
68, 13 km2). In addition, the major vegetation covers in upper reach were alpine meadow and
tundra, the ratio of soil evaporation to total evaportranspiration was larger than other plant
function types, so there was an underestimation for WUE in this region.
Fig 5 Variations of Mean WUE at different reaches in Yangtze River Basin during 1956-2006
3.5 Interannual variations of WUE on different vegetation types
The interannual variations of different vegetation types revealed that the WUE variation trends of
each vegetation types were similar with the overall trend, and showed increasing from 1956 to
2006. Nevertheless, the magnitudes of WUE across vegetation types were different. The WUE of
forests have higher values when compared with grassland and savanna. The WUE value of forest
ranged from 0.65 to 0.8 g C/kgH2O, while the grassland, savanna and tundra was 0.45 to 0.6 g
C/kgH2O, and 0.36 to 0.46 g C/kgH2O, respectively. This indicated the water utilities abilities
were different among plant types.
The WUE variations of Yangtze River Basin were clearly affected by precipitation and
temperature. The variations among vegetation types were mainly dependant on its photosynthesis
and water-use abilities, the effect of climate change on plant were also altered by regions, the
highest averaged WUE of shrubland were 0.54 g C/kgH2O at 1993, while the lowest value was
0.46 g C/kgH2O found at 1962. In contrast, the lowest and highest value of mean WUE of
grassland were respectively 0.45 g C/kgH2O and 0.63 g C/kgH2O . The mean WUE anomalies of
warm temperate forest were higher than other forest types. In general, the grassland and warm
temperate forest were sensitive to climate change.
Fig.6 Annual variability of WUE across vegetation types in Yangtze River Basin. (VT1: tropical
evergreen forest; VT2: tropical deciduous forest; VT3: temperate evergreen broadleaf forest; VT4:
temperate evergreen conifer forest; VT6: boreal evergreen forest; VT9: savanna; VT10: grassland;
VT11: dense shrubland; VT12: open shrubland; VT13: tundra)
3.6 Influence of droughts on WUE
The spatial pattern of WUE was directly determined by the spatial-temporal variations of NPP and
ET, once climate change happened such as drought would closely relate to temporal and spatial
patterns of WUE. Several severe drought events were reported in recent years (2006) in Yangtze
River Basin. The precipitation at most area of the Yangtze River Basin in 2006 was much lower
than the mean values during 1956-2006 (Fig 7C.), while precipitation was higher than normal in
the southeastern of Yangtze River Basin. Although precipitation was reduced throughout the most
area of Yangtze River Basin, NPP in 2006 still increased in some areas where precipitation
decreased (Fig 7B). This might imply that precipitation was not a dominant limiting factor to NPP
in 2006 and vegetation in this region had a buffering capability to adjust to drought events. ET in
2006 decreased in middle of the region with a maximum reduction over 15%, while ET still
increased in the rest study area ranging from 0 to 5%. By comparing the NPP with ET distribution,
the WUE spatial variation was found to be similar to that of NPP, displaying a great decrease in
some extreme drought areas. But WUE variation range was slightly lower than that of NPP; this
indicated that NPP was an important limiting factor on WUE. The inner mechanisms of different
vegetation response to changing atmospheric condition needed to be further studied.
Fig.7 WUE (g C/kgH2O, A), NPP (kg m-2, B), Precipitation (mm, C), ET (mm, D) anomalies
(relative to 1956-2006 mean) in 2006 in Yangtze River Basin.
4 Discussions and conclusions
In this study, a land process model IBIS combining with remote sensing data as input were used to
quantify the spatial temporal variations of WUE in Yangtze River Basin from 1956 to 2006. Then
the water use efficiency among different vegetation covers was analyzed. The averaged annual
WUE of the terrestrial ecosystems in Yangtze River Basin was 0.58 g C/kgH2O2, annual mean
values of NPP and ET were 0.518 kg C m-2 and 855 mm, respectively. Although WUE decreased
dramatically in 2006, the overall trend showed increase from 1956 to 2006. A study in southern
U.S. showed similar results that the WUE totally increased about 25% from 1895 to 2007 (Tian,
Chen [15]), which was in contrast to the WUE in Yangtze River Basin (increasing about 15%).
This increased WUE also was found in some experiments around the world (Peñuelas [36],
Andreu-Hayles [37]). These similar observations implied that WUE may be increased even
through NPP varied substantially due to the elevated atmospheric CO2.
Different plant types have diversely water use efficiencies (Ponton, Flanagan [13], Tian, Chen [15],
Bruemmer [38]). In this study, we found that the WUE of different vegetation types showed a
decreasing order in Yangtze River Basin: Warm Temperate Forest > Temperate Forest >
Grassland > Shrubland >Tundra > Desert. These differences of WUE were determined by the
photosynthesis mechanisms and climate conditions among these vegetation types. As Eddy
covariance systems provided, the simultaneous observed water and carbon fluxes in each
vegetation covers also found that ecosystem WUE of forest was higher than that of grassland at a
field level (Law, Falge [29]). Meanwhile, when using the ratio of NPP and ET to calculate WUE,
the overestimated ET should be considered in some ecosystems such as grassland and shrubland
because of the larger bare area of these ecosystems. The ET in western of Yangtze River Basin
consequently had more “non-veg” induced evaporation included, and thus underestimated WUE in
these regions. However, estimating the actual ET for sparse vegetation was difficult, further
improvements on these aspects could reduce such uncertainties.
Elevated CO2 could increase plant productions and affect plant evaporation through regulating
stoma conductance (Li, Erickson [6], Beer, Ciais [14], McCarthy [39]). From the long term trend
results of NPP, it was concluded that the elevated CO2 had a positive effect on carbon uptake of
terrestrial ecosystems. Figure 7 suggested that NPP was an important limiting factor on WUE, the
CO2 fertilization effect on NPP would consequently affect WUE. Based on the observation data
from FLUXNET, WUE had increased about 83% under elevated CO2 concentrations (+340 ppm),
indicating that higher CO2 concentration was fit for WUE (Li, Erickson [6]).
Precipitation distribution was influenced by global climate changes and this influence likely to be
continue in future, with increased frequency and intensity of droughts and other extreme climate
events (IPCC [40]). Drought-induced precipitation pattern changes impact WUE spatial
distribution. Numerous studies suggested that WUE increased under moderate droughts but
decreased or remained constant under extreme droughts (Baldocchi [41]). The droughts event in
2006 showed that WUE was merely decreased in some extreme droughts regions, while it
remained constant or slightly increased in most of the drought regions. However, due to the
inadequate studies on underlying mechanisms of ecosystem response to climate change, WUE
under different climate scenarios needed to be further identified in future studies.
A terrestrial processes model to simulate ecosystem carbon and water cycling at regional level,
combining with remote sensing data as input, could do a good attempt to quantify the spatial
temporal variations of WUE at long time periods. Meanwhile, eddy covariance systems provide a
valuable method to study inner water and carbon cycles at field level. Certainly, combining eddy
covariance techniques with terrestrial processes model to assess WUE at different scale, is an
important work need to be further studied.
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