Variation in ecosystem water use efficiency along a southwest-to-northeast aridity gradient in China

Ecological Indicators 110 (2020) 105932

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Variation in ecosystem water use efficiency along a southwest-to-northeast aridity gradient in China

T

⁎

Yujie Baia,b, Tianshan Zhaa,b, , Charles P.-A. Bourquea,c, Xin Jiaa,b, Jingyong Maa,b, Peng Liua,b, Ruizhi Yanga,b, Cheng Lia,b, Tao Dua, Yajuan Wua,b a

School of Soil and Water Conservation, Beijing Forestry University, Beijing, China Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing, China c Faculty of Forestry and Environmental Management, 28 Dineen Drive, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada b

A R T I C LE I N FO

A B S T R A C T

Keywords: Eddy covariance Biospheric fluxes Precipitation Spatial variation NDVI Water use efficiency

Quantifying ecosystem water use efficiency (WUE), its spatial variation along an aridity gradient, and its control in response to drought are crucial to understanding regional eco-physiological processes of heterogeneous landscapes. This study examined the magnitude, large-scale spatial patterns in WUE, and underlying drivers by examining published data from 31 eddy-covariance (EC) towers and remote sensing based assessments of normalized difference vegetation index (NDVI) along a southwest-to-northeast aridity gradient in China. Average growing-season WUE at the 31 sites was 1.67 ± 0.98 g C kg−1 H2O. Water use efficiencies among vegetation types were significantly higher for cropland and forest, and lower for shrubland and grassland ecosystems (all pvalues < 0.01). Together, meteoric water and elevation explained 73% of the cross-gradient variation in WUE. Water use efficiency was shown to increase in direct association with increasing precipitation (PPT) and indirectly through PPT’s effect on NDVI. Among ecosystem types, WUE for cropland was most sensitive to PPT and NDVI, whereas shrubland WUE was the least sensitive to these factors. Water use efficiency in the semi-arid zone was more sensitive to the PPT and NDVI, unlike the other climatic zones. Although growing-season PPT was important to WUE for the drier parts of China, it was less important for the dry sub-humid, moderately wetter parts of the country. The present findings showed WUE in dryland ecosystems in China to be firmly controlled by PPT. This suggests that future climate change may have differential outcomes on ecosystem carbon and water cycling along an aridity gradient, leading to the observed cross-gradient differences in ecosystem WUE’s.

1. Introduction

Ecosystem WUE depends on the trade-offs between GEP and ET and on their responses to climatic and biotic forcing (Huang et al., 2016; Sun et al., 2018). The coupling of the C and H2O cycles implies that any environmental disturbance on one component of WUE (by way of either GEP or ET) may affect the other (Emmerich, 2007). With technological advancements, the eddy-covariance (EC) technique provides a useful independent, direct approach of quantifying GEP and ET at the ecosystem scale (Baldocchi et al., 2001; Yu et al., 2006). Many studies have investigated the spatiotemporal patterns in WUE across a wide range of ecosystems using EC-based measurements of GEP and ET (Hu et al., 2008; Xiao et al., 2013; Zhu et al., 2015). Previous studies have suggested that ecosystem WUE may be affected by several factors, including climatic, species composition, and various other factors (Ponton et al., 2006; Yu et al., 2008; Beer et al., 2009; Keenan et al., 2013; Jia et al., 2016). Ecosystem WUE has been identified as an important indicator of ecosystem response to

Water use efficiency (WUE), the ratio of carbon (C) assimilation to water (H2O) losses, reflects the interaction between the C and H2O cycles. At the ecosystem level, WUE is commonly defined as the ratio of gross ecosystem production (GEP) to evapotranspiration (ET; Baldocchi, 1994; Law et al., 2002; Beer et al., 2009; Niu et al., 2011). Quantifying the spatial variation in ecosystem WUE and its spatial features is useful in understanding the spatial pattern of C-H2O coupling (Ito and Inatomi, 2012; Zhu et al., 2015). Revealing the drivers and underlying mechanisms of WUE are crucial to predicting the impact of future climatic change on ecosystem C- and H2O-cycling processes (Baldocchi, 1994; Scanlon and Albertson, 2004; Yu et al., 2004; Ponton et al., 2006). This, in turn, provides a basis for vegetation growth prediction and ecosystem management at the landscape to regional scales (Liu et al., 2016; Knauer et al., 2017; Sun et al., 2018).

⁎

Corresponding author at: School of Soil and Water Conservation, Beijing Forestry University, Beijing, China. E-mail address: [email protected] (T. Zha).

https://doi.org/10.1016/j.ecolind.2019.105932 Received 20 December 2018; Received in revised form 5 October 2019; Accepted 10 November 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Fig. 1. Geographical distribution of 31 eddy-covariance (EC) sites along an approximately 3500-km long aridity gradient from southwest-to-northeast China. Different background colors, symbols, and colored triangles represent climatic zones, ecosystem functional types, and grassland types, respectively. The map in the background delineates arid and semi-arid zones of China (Huang et al., 2014).

subject area, not only because dryland ecosystems are sensitive to climatic change (Reynolds et al., 2007; Reed et al., 2012; Feng and Fu, 2013), but because we need to improve our understanding of ecosystem processes and feedbacks to the climate system as drylands expand globally. Moreover, an increasing number of studies provide subjectrelevant data, including WUE, for many dryland ecosystems in China. This information is central to undertaking synthetic analyses at a multitude of spatial scales. Our objectives are to: (1) examine whether WUE varies with different ecosystem types and climatic zones; (2) investigate the influence of geographic, climatic, and biotic factors on observed variation in WUE; and (3) determine the most important factors controlling WUE and their intermediary role in the C-H2O coupling. Results from this study provide reference information for afforestation and vegetation-management planning.

environmental change (Huxman et al., 2004; Yu et al., 2008; Niu et al., 2011; Huang et al., 2015). For example, WUE can capture water consumption and drought adaptability in vegetation (Reichstein et al., 2002; Liu et al., 2015; Yang et al., 2016). Drought is an intermittent disturbance of the H2O cycle that strongly interacts with the terrestrial C cycle (Zhao and Running, 2010). Different plant species respond differently to drought based on their physiological and morphometric characteristics in preventing excessive H2O losses (van der Molen et al., 2011; Khalifa et al., 2018). Recent global examinations of the relationship between WUE and drought report contradictory responses for different biomes and regions (Yang et al., 2016; Huang et al., 2017). The aridity index (AI), defined as a ratio of annual precipitation to annual potential ET, is a quantitative indicator of drought severity at a particular location. Drylands are regions with AI < 0.65, with low amounts of soil H2O content. The definition of drylands encapsulates hyper-arid (AI < 0.05), arid (0.05 < AI < 0.2), semi-arid (0.2 < AI < 0.5), and dry sub-humid zones (0.5 < AI < 0.65; Middleton and Thomas, 1992; Feng and Fu, 2013; Huang et al., 2014). In drylands, slight changes in precipitation may cause significant shifts in plant species composition, distribution, and abundance (Stephenson, 1990). These changes may lead to significant differences in ecosystem H2O usage and efficiencies (Liu et al., 2012b). Synthetic studies in China in the past have sought to examine the spatial and temporal variation in WUE. Research at a number of EC sites across China have found that WUE declined with increasing elevation (Zhu et al., 2015), whereas GEP and ET declined with increasing latitude (Xiao et al., 2013). Inter-annual variation in WUE differs with spatial scale and location (Sun et al., 2018). The observed differences in WUE among biomes and ecosystems across China have been fairly well documented in past studies (Xiao et al., 2013; Zhu et al., 2015; Liu et al., 2015). However, few studies have addressed the specific variation in WUE along aridity gradients from southcentral to northeast China. Also, few attempts have been made to uncover the underlying mechanisms leading to this spatial variation. There is generally a need to continue studying this important

2. Material and methods 2.1. Data compilation For this study, we selected peer-reviewed studies focusing on dryland ecosystems in China and data from several sources, including: (i) the ISI-Web of Science (http://apps.webofknowledge.com/), Google Scholar (Google Inc., Mountain View, CA, USA) for data in English publications, and the China National Knowledge Infrastructure (http:// www.cnki.net/) for data in Chinese publications using keywords, such as “water use efficiency”, “WUE”, “gross ecosystem production”, “GEP”, “evapotranspiration”, and “ET”; and (ii) ChinaFLUX data and information system, i.e., http://159.226.111.42/pingtai/LoginRe/ opendata.jsp. Remote sensing-based assessments of normalized difference vegetation index (NDVI) were obtained from the NASA EarthData portal (i.e., https://search.earthdata.nasa.gov/). Data used in the current analysis were those in which (i) GEP and ET were obtained by means of the EC technique; (ii) WUE was based on a calculation of the GEP: ET ratio; (iii) data record lengths at each site covered at least one complete growing cycle; and (iv) data were acquired in areas free of 2

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Table 1 Site characteristics and mean ecosystem water use efficiency (WUE) reported in the literature (10th column). Values following the mean WUE are the 1st standard deviation. Values identified with an asterisk (*) represent site data obtained over an entire year, instead of over the growing season. Ecosystem type

Site ID

IGBP1

Climate zone

Latitude (°N)

Longitude (°E)

Elevation (m a.s.l2)

WUE

Observation period

References

Forest

XLD

MF

35.02

112.47

410

2.17 ± 0.24

2006–2010

Guo (2010), Tong et al. (2014)

GT DaX

ENF DBF

38.53 39.53

100.25 116.25

2835 30

3.59 2.35 ± 0.12

2010 2006–2011

Xiao et al. (2013) Zhou (2013)

BJO

MF

40.03

116.63

51

2.57 ± 0.35

2012–2014

Xie et al. (2016)

KBQF

DBF

dry subhumid arid dry subhumid dry subhumid semi-arid

40.54

108.69

1020

0.66*

2006*

Xiao et al. (2013)

Shrubland

HBGC YanC KBQG

OSH CSH OSH

semi-arid semi-arid semi-arid

37.67 37.71 40.38

101.33 107.23 108.55

3358 1530 1160

1.26 ± 0.13 1.71 ± 0.15 0.82*

2003–2005 2012–2014 2006*

Hu et al. (2008) Jia et al. (2016) Zhu et al. (2015)

Grassland

HB HBSD DX2 DX1 SJY LDG SZWD SZWF DLG KEQ XLHTF XLHTD XLHT FK NM CL TYG

GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA GRA

semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid semi-arid

37.62 37.61 29.67 30.85 34.35 38.79 41.79 41.79 42.03 43.28 43.55 43.55 44.13 44.28 44.53 44.58 44.59

101.30 101.33 91.33 91.08 100.55 110.37 111.90 111.89 116.28 122.27 116.67 116.67 116.33 87.93 116.67 123.50 122.52

3250 3357 4250 4333 3980 1256 738.9 814.6 1350 203 1250 1250 1030 475 1189 171 184

1.75 0.71 0.36 0.40 1.36 1.23 1.07 0.84 2.19 1.63 0.66 0.53 0.56 0.79 0.81 1.56 1.13

2002–2004 2003–2005 2004–2008* 2004–2005 2006 2014–2016* 2010 2010 2005–2008 2012 2006 2006 2004–2006 2006–2007 2003–2005 2007–2008 2003–2008

Xiao et al. (2013) Kato et al. (2004), Gu et al. (2008) Zhu et al. (2015) Hu et al. (2008) Wu et al. (2010), Li et al. (2012) Liu et al. (2017a) Xiao et al. (2013) Xiao et al. (2013) Niu et al. (2011) Li et al. (2015) Zhu et al. (2015) Zhu et al. (2015) Zhu et al. (2015) Liu et al. (2012a,b) Hu et al. (2008) Dong (2011), Dong et al. (2011) Liu et al. (2012a), Du and Liu (2013)

YK SY WSM

CRO CRO CRO

38.86 37.77 36.65

100.41 113.20 116.05

1519 1202 30

2.18 3.07 ± 0.21 4.02 ± 0.36

2008 2011–2013 2007–2008

Zhu et al. (2015) Gong et al. (2016) Lei and Yang (2010a,b)

YCM

CRO

36.83

116.57

28

3.99 ± 0.31

2003–2005

Zhao et al. (2007), CHINAFLUX

DLC TYC

CRO CRO

arid semi-arid dry subhumid dry subhumid semi-arid semi-arid

42.05 44.57

116.28 122.92

1350 184

1.06 1.17 ± 0.34

2006 2003–2008

Chen et al. (2009) Liu et al. (2012a), Du and Liu (2013)

Cropland

± ± ± ±

0.07 0.04 0.07* 0.01

± 0.25*

± 0.55

± ± ± ± ±

0.02 0.10 0.44 0.26 0.34

1 Vegetation classification after the International Geosphere Biosphere Program: MF = mixed forest, ENF = evergreen needle-leaved forest, DBF = deciduous broad-leaved forest, OSH = open shrubland, CSH = closed shrubland, GRA = grassland, and CRO = cropland; 2 a.s.l = above mean sea level.

were first tested for normality and homoscedasticity (uniform variance), followed by a Least Significant Difference (LSD) post-hoc test to determine the area in the dataset of greatest difference. All significance tests were based on a critical significance threshold of α = 0.05. Relationships between WUE and geographic and biophysical factors (i.e., latitude, longitude, elevation, Ta, PPT, VPD, and NDVI) were examined by regression analysis. Given the complex interaction between bioclimatic factors and WUE, we conducted stepwise regression. Correlations between WUE anomalies and GEP, ET anomalies (the difference between the original data and mean values based on data from the 77 site-year compilation) were analyzed to identify whether the changes in WUE were affected by GEP and ET. Path analysis was used to interpret the direct and indirect effects of biophysical factors (Ta, PPT, VPD, and NDVI) on GEP and ET conducted with the AMOS software (ver. 22.0; SPSS Inc.). The direct path coefficient between two variables is the standardized partial-regression coefficient, whereas the indirect path coefficient is the product of direct coefficients summed across all paths. The total path coefficient is the sum of direct and indirect path coefficients. For more details concerning this approach, refer to Tang et al (2014).

resource exploitation and/or active management. A total of 77 site-years from 2002 to 2016 of WUE measurements were assembled. The finalized dataset (Fig. 1; Table 1) included: (i) site information (i.e., site latitude, longitude, elevation, ecosystem type, observation duration, and dominant plant species); (ii) WUE and its components (i.e., GEP and ET); (iii) climatic factors [e.g., mean growing season temperature (Ta), total growing-season precipitation (PPT), vapor pressure deficit (VPD)]; and (iv) processed images of NDVI. The ecosystems accounted for in this study included five forest, six cropland, 17 grassland, and three shrubland sites. Grassland subclasses were identified by dominant plant species (Su, 2013). Data presented in journal figures were extracted with the GetData software. Climatic zones for each site were determined according to a base map of arid and semi-arid regions of China (Huang et al., 2014). Five sites were located in the dry sub-humid, 24 in the semi-arid, and two in the arid climatic zone, with no sites in the hyper-arid zone. 2.2. Data analysis One-way analysis of variance (ANOVA) was used to test the differences in GEP, ET, and WUE between the different (i) dryland ecosystems (i.e., cropland, forest, grassland, and shrubland sites), (ii) grassland types (i.e., alpine meadow, meadow grass, typical grass, and desert grass), and (iii) climatic zones of China. With one-way ANOVA, the data 3

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Fig. 2. Bar plots of ecosystem water use efficiency (WUE) as a function of ecosystem functional type (a), grassland type (b), and climatic zone (c). Whiskers associated to the individual bars represent the 1st standard deviation. Different letters indicate statistically significant results, based on an one-way analysis of variance.

3. Results

steppe (Fig. 4j). Other grassland types, did not appear to be affected by the same suite of bioclimatic factors (Fig. 4i, k, l). Response in WUE to the various bioclimatic factors also differed with climatic zones. In semi-arid zones, ecosystem WUE formed statistically meaningful, positive relationships with Ta, PPT, and NDVI (Fig. 5a, b, d). In dry sub-humid zones, WUE was positively related to Ta and negatively to VPD. Stepwise regression suggested that PPT and elevation together explained about 73% of the observed cross-gradient variation in WUE, i.e., WUE = 1.04–0.001Elev + 0.004PPT, with p < 0.001. Path analysis of the effects of bioclimatic factors on WUE also showed that the effect of PPT on WUE was greatest with total path coefficient of 0.51 (0.39 + 0.38*0.32, Fig. 6), as compared to that of Ta and NDVI with total path coefficient of 0.49 (0.55–0.17*0.32) and 0.32 (Fig. 6), respectively.

3.1. Variation in dryland WUE Ecosystem WUE ranged from 0.31 to 4.33 g C kg−1 H2O across the 31 EC sites (Table 1), with a mean of 1.67 ± 0.98 g C kg−1 H2O. There were significant differences in WUE among the different ecosystem types (F = 15.45, p < 0.01; Fig. 2a), with cropland (2.46 g C kg−1 H2O) and forests (2.31 g C kg−1 H2O) providing the higher overall WUE’s, and shrubland (1.39 g C kg−1 H2O) and grassland (1.12 g C kg−1 H2O) providing the lower WUE’s. Water use efficiencies in arid (2.80 g C kg−1 H2O) and dry sub-humid zones (2.67 g C kg−1 H2O) were significantly greater than WUE’s in semi-arid zones (1.26 g C kg−1 H2O; p < 0.05). There were no significant differences in WUE’s among the various grassland types (p > 0.05; Fig. 2b), unlike those for the different climatic zones (F = 30.56, p < 0.01; Fig. 2c).

3.4. Linkages between WUE and its components 3.2. Geographic effects on WUE

Growing season WUE anomalies in drylands were positively correlated with GEP anomalies (Fig. 7a). However, it had no relationship with ET anomalies (Fig. 7b). For a given ecosystem type, WUE anomalies were positively correlated with GEP anomalies in both croplands and grasslands (Fig. 7c). In contrast, WUE anomalies in forests formed a negative relationship with ET anomalies (Fig. 7d). For a given grassland type, WUE anomalies for typical steppe and alpine meadow formed a positive linear relationship with GEP anomalies (Fig. 7e), and a negative linear relationship with ET anomalies for alpine meadow (Fig. 7f). For a given climatic zone, WUE anomalies in semi-arid and dry sub-humid zones displayed contrasting trends with GEP and ET anomalies (Fig. 8), i.e., WUE anomalies formed a positive linear relationship with GEP anomalies in semi-arid zones (Fig. 8a) and a negative linear relationship with ET anomalies in dry sub-humid zones (Fig. 8b).

Ecosystem WUE exhibited clear geographic trends (Fig. 3a–c), with WUE decreasing with increasing elevation (Fig. 3c) and increasing latitude (Fig. 3b), with the exception of sites on the Qinghai-Tibetan plateau. No discernible patterns were found with longitude (Fig. 3a). For a given ecosystem, latitude-associated patterns in WUE were fairly well defined for croplands, with WUE decreasing with increasing latitude (Fig. 3e). No pattern in WUE were apparent for the other ecosystems, including the various grassland types (Fig. 3g through i). 3.3. Bioclimatic effects on WUE The effects of climatic and biotic factors on WUE were analyzed for individual growing seasons as shown in Figs. 4 and 5. Ecosystem WUE in drylands were affected by temperature (Ta; Fig. 4a), precipitation (PPT; Fig. 4b), vapor pressure deficit (VPD; Fig. 4c), and NDVI (Fig. 4d). Water use efficiencies increased linearly with increasing Ta, PPT, and NDVI (Fig. 4a, b, d). They also formed a positive relationship with VPD, when VPD < 0.8 kPa, and a negative relationship, when VPD > 0.8 kPa (Fig. 4c). Responses in WUE to bioclimatic factors differed across the different ecosystem types. Water use efficiencies in forests were generally related to NDVI in a quadratic fashion, peaking at 0.43 (Fig. 4h). In grasslands, WUE’s were positively associated with NDVI and PPT. In croplands, however, WUE’s were positively associated with Ta, NDVI, and PPT (Fig. 4e, f, h). Water use efficiencies in shrublands tended to increase with increasing Ta and VPD, but decrease with increasing PPT. Among the grassland types studied, WUE was linearly related to PPT for typical

3.5. Bioclimatic effects on GEP and ET Path analysis revealed the effects of bioclimatic factors on GEP and ET (Fig. 9). Precipitation was the primary factor affecting GEP and ET, with total path coefficients of 0.68 (0.61 + 0.49*0.15) and 0.63, respectively. Precipitation also indirectly increased GEP by positively effecting NDVI, with a direct path coefficient of 0.49. Air temperature directly and indirectly affected GEP and ET. NDVI had no net effect on ET. Results of linear regression between PPT and GEP or ET is similar to that of path analysis in drylands (Table 2).

4

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Fig. 3. Ecosystem water use efficiency (WUE) during the growing season as a function of longitude, latitude, and elevation for 31 EC sites together (a-c) and differentiated according to ecosystem functional types, i.e., forest, cropland, grassland, and shrubland (d-f). Panels (g-i) provide WUE for the different grassland types. Open circles in sub-figure (b) correspond to sites located on the Qinghai-Tibetan Plateau, at elevations > 3000 m above mean sea level.

Fig. 4. Growing-season-specific relationships between ecosystem water use efficiency (WUE) and environmental variables, i.e., air temperature (Ta), precipitation (PPT), water vapor pressure deficit (VPD), and normalized difference vegetation index (NDVI). Water use efficiencies are related to the environmental variables as a function of 31 EC sites together (a-d), differentiated according to the four ecosystem functional type, i.e., forest, cropland, grassland, shrubland (e-h), and grassland type (i-l). 5

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Fig. 5. Growing-season-specific relationships between ecosystem water use efficiency (WUE) and bioclimatic variables for the different climatic zones (Fig. 1).

4. Discussion 4.1. Trends in growing-season WUE Mean growing-season WUE observed in this study was similar to the global mean annual WUE (1.7 g C kg−1 H2O; Huang et al., 2017) and to the mean annual WUE observed at 37 supplementary sites across China (i.e., 1.72 ± 0.91 g C kg−1 H2O). However, this value was lower than the mean WUE observed in non-dryland regions of the country (2.35 ± 0.58 g C kg−1 H2O; Zhu et al., 2015). The finding that WUE varied as a function of ecosystem types, i.e., forests and croplands exhibited higher WUE’s compared to shrublands and grasslands (Fig. 2a), is consistent with other studies based on the EC approach (Ito and Inatomi, 2012; Xiao et al., 2013; Zhu et al., 2015). This is also confirmed with data from China and Global MODIS images (Liu et al., 2015; Huang et al., 2017). The result that arid ecosystems have high WUE’s compared to ecosystems in semi-arid zones is consistent with the findings of Huang et al. (2017). Plants in arid zones are likely to have adapted to a dry climate and, therefore, can be viewed as having greater plasticity to drought (Fischer and Turner, 1978; Chaves et al., 2003). High WUE is a strategy by plants in adapting/acclimating to water deficits in arid

Fig. 6. Path diagrams of the effect of meteoric precipitation (PPT), air temperature (Ta), water vapor pressure deficit (VPD), and normalized difference vegetation index (NDVI) on water use efficiency (WUE) during the growing season. Solid and dashed arrows represent positive and negative correlations, respectively. Standardized path coefficients are displayed for each arrow.

Fig. 7. Growing-season-specific relationships between ecosystem water use efficiency (WUE) anomalies as a function of gross ecosystem production (GEP) and evapotranspiration (ET) anomalies for 31 EC sites together (a-b), differentiated according to the four ecosystem functional types (c-d), i.e., forest, cropland, grassland, shrubland, and four grassland types (e-f).

6

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Fig. 8. Growing-season-specific relationships of ecosystem water use efficiency (WUE) anomalies as a function of gross ecosystem production (GEP) and evapotranspiration (ET) anomalies for the different climatic zones.

environments by choosing resource-acquisition strategies to avoid water restrictions (Jaleel et al., 2008; Liu et al., 2017). For example, annuals in arid regions accelerate development of their phenology and photosynthetic production in the event of water deficits, meeting all of their life-history changes prior to the onset of drought (Ivey & Carr, 2012). High mean WUE in this study was limited to two arid sites.

Table 2 Linear regression between PPT and NDVI, and GEP and ET in dryland, semiarid, and dry sub-humid zones, respectively; R2 is the coefficient of determination; and k is the regression slope. Statistically-significant results are given with respect to p < 0.001(***), p < 0.01(**), and p < 0.05(*). Here, “ns” stands for statistically non-significant. Dryland

4.2. Impact of biophysical factors on WUE Previous studies have shown annual PPT to be an important factor affecting spatial variation in WUE (Yu et al., 2008; Wagle and Kakani, 2012). In arid and semi-arid ecosystems, PPT is an important source of water for vegetation (Farrington et al., 1989; Bai et al., 2008; McLendon et al., 2008). Our analysis showed that WUE was positively correlated to PPT and NDVI (Fig. 4b, d). Similar results were described in studies carried out in Europe (Beer et al., 2007), North America (Yang et al., 2013), and China (Gao et al., 2014). Clearly, PPT affected WUE not only in a direct manner, but also indirectly through its effect on NDVI (Fig. 6). We found WUE’s in croplands and grasslands to be more sensitive to PPT and NDVI than in shrublands and forests. The effect of elevation on WUE may be coincidental to the direct effect of Ta on GEP and ET (and, thus, WUE), because of the well-

Semi-arid

Dry sub-humid

GEP

ET

GEP

ET

GEP

ET

PPT

R2 k

0.44*** 2.04

0.31*** 0.61

0.28*** 1.35

0.23*** 0.56

0.49*** 1.16

0.28* 0.65

NDVI

R2 k

0.23*** 1622.17

0.07* 362.81

0.17** 1305.30

0.11* 524.78

ns ns

ns ns

established negative relationship between Ta and elevation. Precipitation also tends to increase with increasing elevation (Bourque and Matin, 2012). Previous studies have found that increasing elevation can also contribute to localized increases in total photosynthetic active radiation (PAR). These associations provide partial explanations for the spatial variation observed in WUE (Zhu et al., 2015). Recurrence of ice and snow in high-elevation areas causes vegetation in those areas to

Fig. 9. Path diagrams of the effect of meteoric precipitation (PPT), air temperature (Ta), water vapor pressure deficit (VPD), and normalized difference vegetation index (NDVI) on (a) gross ecosystem production (GEP) and (b) evapotranspiration (ET) during the growing season. Solid and dashed arrows represent positive and negative correlations, respectively. Standardized path coefficients are displayed for each arrow. 7

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Normalized difference vegetation index (NDVI) did not have any real effect on the observed variation in GEP and ET associated with dry sub-humid ecosystems (Table 2). The reason for this could be related to the fact that ET was positively correlated with net radiation under conditions of ample water (Liu et al., 2009), leading ET to be more important to WUE than GEP in this zone.

grow sparingly, influencing ecosystem WUE (Xue et al., 2015). The interactions between terrestrial vegetation and climate are extremely complicated and effect of one factor on another can be fed back through different mechanisms. Moreover, the type of feedback may shift with specific conditions and spatial scales, which increases the uncertainty in relationships between biophysical factors and WUE at large spatial scales. The response of ecosystems to environmental change are typically driven by the dominant species in a region (Niu et al., 2011; Xie et al., 2016). Contrasting dominant plant functional types associated with different ecosystems will result in important differences in C-H2O cycling processes (Yang et al., 2019). Regionally, the development of WUE under water-stress conditions depends primarily on the relative amount of isohydric and anisohydric plant species present in an ecosystem. For example, in an approximately uniform mixture of isohydric and anisohydric plant groups, with their own differential sensitivities and responses to changing hydrothermal conditions, may collectively wield an entirely undecipherable signal at the regional scale. This fact may explain the weak relationship that exists between biophysical factors and WUE along the study’s 3500-km long aridity gradient (Fig.’s 1 and 4).

5. Conclusions Water use efficiency in drylands of China varied spatially mostly in association with ecosystem type and climatic zone, with WUE’s being greatest in cropland and forest ecosystems, and smallest in shrubland and grassland ecosystems. In general, WUE’s were greatest in arid zones. Spatial variation in WUE was controlled directly by meteoric water, and indirectly by NDVI through its effect on GEP. Our results show that GEP and ET dominated spatial variation in WUE in semi-arid and dry sub-humid zones, respectively. Among ecosystem types, WUE’s for croplands were more sensitive to PPT and NDVI, and shrublands were the least sensitive. Among grassland ecosystems considered, WUE’s for typical steppe were more sensitive to PPT. Ecosystems WUE’s in semi-arid zones were sensitive to both PPT and NDVI. The results suggest that future climate change may influence GEP and ET differently along an aridity gradient, leading to the observed cross-gradient differences in ecosystem WUE’s. Further studies should be carried out to distinguish physical and biological processes of ecosystems as more sites are investigated.

4.3. Mechanisms in the underlying spatial variation The effect of PPT on the spatial variation in WUE occurs by altering either GEP or ET. Our results indicate that WUE in drylands was primarily controlled by GEP, rather than by ET (Fig. 7a, b), because water additions tended to stimulate GEP more than ET (Niu et al., 2011). It was found that PPT and NDVI increased GEP more dramatically than ET (Fig. 9, Table 2), leading to WUE having a positive dependency on PPT and NDVI (Fig. 4b, d). Path analysis showed that VPD had important effects on both GEP and ET (Fig. 8), indicating that observed dynamics in WUE cannot be readily attributed to plant physiological functioning (Knauer et al., 2017). “Inherent” WUE (iWUE), defined as (GEP × VPD)/ET at the ecosystem level, could remove the confounding effects of VPD on WUE (Beer et al., 2009; Knauer et al., 2017; Yi et al., 2019). Inherent WUE of isohydric and anisohydric plant species shows differential sensitivities to atmospheric water demand during periods of water stress. Inherent WUE is evolving as a common approach in understanding plant physiological responses to drought and subsequent recovery processes (Kannenberg et al., 2019; Yi et al., 2019). The approach has immense potential in the study of drylands in China. Water use efficiencies in semi-arid zones have a positive dependency on PPT (Fig. 5b). Semi-arid zones were mostly grassland ecosystems (17 of 24) dominated by herbaceous plants, whose functions and activities depend largely on soil water availability. Productivity in grassland ecosystems responds more rapidly to variation in PPT, suggesting GEP to be more sensitive to PPT, in comparison to ET This finding is consistent with conclusions from earlier studies (Hu et al., 2008; Niu et al., 2011; Campos et al., 2013). Gross ecosystem production dominated variation in WUE in semi-arid zones (Fig. 8a), with WUE being positively dependent on PPT as a result of the positive correlation between GEP and PPT and between GEP and NDVI (Table 2). In contrast, no correlation was observed between WUE and PPT in dry sub-humid zones (Fig. 5), indicating that PPT may not be the most important controlling factor in this zone. In dry sub-humid ecosystems, ET controlled variation in WUE (Fig. 8b). This result is in contrast to prior studies highlighting the importance of GEP in dominating variation in WUE (Yang et al., 2016). This discrepancy may have resulted because prior studies tended to pool semi-arid and dry sub-humid zone data together, coarsening the resolution of the analysis. Also in previous studies, sites were mostly characteristic of grassland, cropland, and savannah sites, whereas our study had more forest and cropland sites to consider.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was supported by grants from National Natural Science Foundation of China (NSFC, Proj. No. 31670710, 31670708), the Fundamental Research Funds for the Central Universities (Proj. No. 2015ZCQ-SB-02), and the National Key Research and Development Program of China (Proj. No. 2016YFC0500905). We are grateful to: (1) the ChinaFLUX network for sharing their data with us; and (2) the U.S.China Carbon Consortium (USCCC) for providing helpful ideas. We would also like to express our appreciation to the editors and anonymous reviewers for their contribution in improving the manuscript. References: Bai, Y., Wu, J., Xing, Q., Pan, Q., Huang, J., Yang, D., Han, X., 2008. Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau. Ecology 89 (8), 2140–2153. Baldocchi, D., 1994. A comparative study of mass and energy exchange over a closed C3 (wheat) and an open C4 (corn) canopy: I. The partitioning of available energy into latent and sensible heat exchange. Agric. For. Meteorol. 67 (3–4), 191–220. Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82 (11), 2415–2434. Beer, C., Reichstein, M., Ciais, P., Farquhar, G., Papale, D., 2007. Mean annual GPP of Europe derived from its water balance. Geophys. Res. Lett. 34 (5). Beer, C., Ciais, P., Reichstein, M., Baldocchi, D., Law, B.E., Papale, D., Soussana, J.F., Ammann, C., Buchmann, N., Frank, D., Gianelle, D., Janssens, I.A., Knohl, A., Kostner, B., Moors, E., Roupsard, O., Verbeeck, H., Vesala, T., Williams, C.A., Wohlfahrt, G., 2009. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global Biogeochem. Cycles 23 (2). Bourque, C.P.-A., Matin, M.A., 2012. Seasonal snow cover in the Qilian Mountains of Northwest China: Its dependence on oasis seasonal evolution and lowland production of water vapour. J. Hydrol. 454–455, 141–151. Campos, G.E.P., Moran, M.S., Huete, A., Zhang, Y.G., Bresloff, C., Huxman, T.E., Eamus, D., Bosch, D.D., Buda, A.R., Gunter, S.A., Scalley, T.H., Kitchen, S.G., McClaran, M.P., McNab, W.H., Montoya, D.S., Morgan, J.A., Peters, D.P.C., Sadler, E.J., Seyfried, M.S., Starks, P.J., 2013. Ecosystem resilience despite large-scale altered

8

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

Lei, H., Yang, D., 2010b. Seasonal and interannual variations in carbon dioxide exchange over a cropland in the North China Plain. Glob. Change Biol. 16 (11), 2944–2957. Li, J., Cai, H., Cheng, Q., Qiao, C., Chu, H., Cheng, D., Xu, S., Zhao, X., Zhao, L., 2012. Characterizing the evapotranspiration of a degraded grassland in the Sanjiangyuan Region of Qinghai province. Acta Prataculturae Sin. 21 (3), 223–233 (in Chineses with English abstract). Li, H., Guan, D., Yuan, F., Wang, A., Jin, C., Wu, J., Li, Z., Jing, Y., 2015. Water use efficiency and its influential factor over Horqin Meadow. Acta Ecol. Sin. 35 (2), 478–488 (in Chinese with English abstract). Liu, H., Feng, J., 2012. Seasonal and interannual variations of evapotranspiration and energy exchange over different land surfaces in a semiarid area of China. J. Appl. Meteorol. Climatol. 51 (10), 1875–1888. Liu, L., Hu, C., Olesen, J.E., Ju, Z., Zhang, X., 2016. Effect of warming and nitrogen addition on evapotranspiration and water use efficiency in a wheat-soybean/fallow rotation from 2010 to 2014. Clim. Change 139 (3–4), 565–578. Liu, R., Li, Y., Wang, Q.X., 2012a. Variations in water and CO2 fluxes over a saline desert in western China. Hydrol. Process. 26 (4), 513–522. Liu, Y., Li, P., Xu, G.C., Xiao, L., Ren, Z.P., Li, Z.B., 2017a. Growth, Morphological, and Physiological Responses to Drought Stress in Bothriochloa ischaemum. Front. Plant Sci. 8, 1–14. Liu, R., Pan, L.-P., Jenerette, G.D., Wang, Q.-X., Cieraad, E., Li, Y., 2012b. High efficiency in water use and carbon gain in a wet year for a desert halophyte community. Agric. For. Meteorol. 162, 127–135. Liu, X., Qi, C., Yan, Y., Yuan, G., 2017b. Revised algorithm of ecosystem water use efficiency for semi-arid steppe in the Loess Plateau of China. Chin. J. Plant Ecol. 41 (5), 497–505 (in Chinese with English abstract). Liu, Y., Xiao, J., Ju, W., Zhou, Y., Wang, S., Wu, X., 2015. Water use efficiency of China’s terrestrial ecosystems and responses to drought. Sci. Rep. 5, 13799. Liu, C.F., Zhang, Z.Q., Sun, G., Zha, T.G., Zhu, J.Z., Shen, L.H., Chen, J., Fang, X.R., Chen, J.Q., 2009. Quantifying evapotranspiration and biophysical regulations of a poplar plantation assessed by eddy covariance and sap-flow methods. Chin. J. Plant Ecol. 33 (4), 706–718 (in Chineses with English abstract). McLendon, T., Hubbard, P.J., Martin, D.W., 2008. Partitioning the use of precipitationand groundwater-derived moisture by vegetation in an arid ecosystem in California. J. Arid Environ. 72 (6), 986–1001. Middleton, N.J., Thomas, D.S., 1992. World atlas of desertification. Niu, S., Xing, X., Zhang, Z., Xia, J., Zhou, X., Song, B., Li, L., Wan, S., 2011. Water-use efficiency in response to climate change: from leaf to ecosystem in a temperate steppe. Glob. Change Biol. 17 (2), 1073–1082. Ponton, S., Flanagan, L.B., Alstad, K.P., Johnson, B.G., Morgenstern, K., Kljun, N., Black, T.A., Barr, A.G., 2006. Comparison of ecosystem water-use efficiency among Douglasfir forest, aspen forest, and grassland using eddy covariance and carbon isotope techniques. Glob. Change Biol. 12 (2), 294–310. Reed, S.C., Coe, K.K., Sparks, J.P., Housman, D.C., Zelikova, T.J., Belnap, J., 2012. Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Clim. Change 2 (10), 752. Reichstein, M., Tenhunen, J.D., Roupsard, O., Ourcival, J.M., Rambal, S., Miglietta, F., Peressotti, A., Pecchiari, M., Tirone, G., Valentini, R., 2002. Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses? Glob. Change Biol. 8 (10), 999–1017. Reynolds, J.F., Smith, D.M.S., Lambin, E.F., Turner, B., Mortimore, M., Batterbury, S.P., Downing, T.E., Dowlatabadi, H., Fernández, R.J., Herrick, J.E., 2007. Global desertification: building a science for dryland development. Science 316 (5826), 847–851. Scanlon, T.M., Albertson, J.D., 2004. Canopy scale measurements of CO2 and water vapor exchange along a precipitation gradient in southern Africa. Glob. Change Biol. 10 (3), 329–341. Stephenson, N.L., 1990. Climatic control of vegetation distribution: the role of the water balance. Am. Nat. 135 (5), 649–670. Su, D., 2013. Survey and Mapping of the Rangeland Resources of China. China University in press. Sun, S., Song, Z., Wu, X., Wang, T., Wu, Y., Du, W., Che, T., Huang, C., Zhang, X., Ping, B., 2018. Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades. Ecol. Ind. 94, 292–304. Tang, Y., Wen, X., Sun, X., Zhang, X., Wang, H., 2014. The limiting effect of deep soilwater on evapotranspiration of a subtropical coniferous plantation subjected to seasonal drought. Adv. Atmos. Sci. 31 (2), 385–395. Tong, X., Zhang, J., Meng, P., Li, J., Zheng, N., 2014. Ecosystem water use efficiency in a warm-temperate mixed plantation in the North China. J. Hydrol. 512, 221–228. Van der Molen, M.K., Dolman, A.J., Ciais, P., Eglin, T., Gobron, N., Law, B.E., Meir, P., Peters, W., Phillips, O.L., Reichstein, M., 2011. Drought and ecosystem carbon cycling. Agric. For. Meteorol. 151 (7), 765–773. Wagle, P., Kakani, V.G., 2012. Growing season variability in evapotranspiration, ecosystem water use efficiency, and energy partitioning in switchgrass. Ecohydrology 7 (1), 64–72. Wu, L., Gu, S., Zhao, L., Xu, S., Zhou, H., Feng, C., Xu, W., Li, Y., Zhao, X., Tang, Y., 2010. Variation in net CO2 exchange, gross primary production and its affecting factors in the planted pasture ecosystem in Sanjiangyuan Region of the Qinghai-Tibetan Plateau of China. Chin. J. Plant Ecol. 7, 003 (in Chinese with English abstract). Xiao, J., Sun, G., Chen, J., Chen, H., Chen, S., Dong, G., Gao, S., Guo, H., Guo, J., Han, S., 2013. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agric. For. Meteorol. 182, 76–90. Xie, J., Zha, T., Zhou, C., Jia, X., Yu, H., Yang, B., Chen, J., Zhang, F., Wang, B., Bourque, C.P.-A., 2016. Seasonal variation in ecosystem water use efficiency in an urban-forest reserve affected by periodic drought. Agric. For. Meteorol. 221, 142–151. Xue, B.-L., Guo, Q., Otto, A., Xiao, J., Tao, S., Li, L., 2015. Global patterns, trends, and

hydroclimatic conditions. Nature 494 (7437), 349–352. Chaves, M.M., Maroco, J.P., Pereira, J.S., 2003. Understanding plant responses to drought—from genes to the whole plant. Funct. Plant Biol. 30 (3), 239–264. Chen, S., Chen, J., Lin, G., Zhang, W., Miao, H., Wei, L., Huang, J., Han, X., 2009. Energy balance and partition in Inner Mongolia steppe ecosystems with different land use types. Agric. For. Meteorol. 149 (11), 1800–1809. Dong, G., 2011. Carbon and Water Fluxes and Water Use Efficiency of the Songnen Meadow Steppe in North East China (in Chinese with English abstract). Northeast Normal University, Changchun, pp. 95. Dong, G., Guo, J., Chen, J., Sun, G., Gao, S., Hu, L., Wang, Y., 2011. Effects of spring drought on carbon sequestration, evapotranspiration and water use efficiency in the Songnen meadow steppe in northeast China. Ecohydrology 4 (2), 211–224. Du, Q., Liu, H., 2013. Seven years of carbon dioxide exchange over a degraded grassland and a cropland with maize ecosystems in a semiarid area of China. Agric. Ecosyst. Environ. 173, 1–12. Emmerich, W.E., 2007. Ecosystem water use efficiency in a semiarid shrubland and grassland community. Rangeland Ecol. Manage. 60 (5), 464–470. Farrington, P., Greenwood, E., Bartle, G., Beresford, J., Watson, G., 1989. Evaporation from Banksia woodland on a groundwater mound. J. Hydrol. 105 (1–2), 173–186. Feng, S., Fu, Q., 2013. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13 (19), 10081–10094. Fischer, R., Turner, N.C., 1978. Plant productivity in the arid and semiarid zones. Annu. Rev. Plant Physiol. 29 (1), 277–317. Gao, Y., Zhu, X., Yu, G., He, N., Wang, Q., Tian, J., 2014. Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation. Agric. For. Meteorol. 195, 32–37. Gong, D., Mei, X., Hao, W., Wang, H., Caylor, K.K., 2016. Comparison of multi-level water use efficiency between plastic film partially mulched and non-mulched croplands at eastern Loess Plateau of China. Agric. Water Manage. 179, 215–226. Gu, S., Tang, Y., Cui, X., Du, M., Zhao, L., Li, Y., Xu, S., Zhou, H., Kato, T., Qi, P., 2008. Characterizing evapotranspiration over a meadow ecosystem on the Qinghai-Tibetan Plateau. J. Geophys. Res.: Atmos. 113 (D8). Guo, L., 2010. The Variations of Water Use Efficiency and Evapotranspirationover a Plantation in the Southern Part (Master) (in Chinese with English abstract). Chinese academy of forestry, Beijing, pp. 76. Hu, Z., Yu, G., Fu, Y., Sun, X., Li, Y., Shi, P., Wang, Y., Zheng, Z., 2008. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Glob. Change Biol. 14 (7), 1609–1619. Huang, L., He, B., Han, L., Liu, J., Wang, H., Chen, Z., 2017. A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data. Sci. Total Environ. 601, 1097–1107. Huang, M., Piao, S., Sun, Y., Ciais, P., Cheng, L., Mao, J., Poulter, B., Shi, X., Zeng, Z., Wang, Y., 2015. Change in terrestrial ecosystem water-use efficiency over the last three decades. Glob. Change Biol. 21 (6), 2366–2378. Huang, M., Piao, S., Zeng, Z., Peng, S., Ciais, P., Cheng, L., Mao, J., Poulter, B., Shi, X., Yao, Y., 2016. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Glob. Change Biol. 22 (6), 2165–2177. Huang, J., Ran, J., Ji, M., 2014. Preliminary analysis of the flood disaster over the arid and semi-arid regions in China. Acta Meteorol. Sin. 72 (06), 1096–1107. Huxman, T.E., Smith, M.D., Fay, P.A., Knapp, A.K., Shaw, M.R., Loik, M.E., Smith, S.D., Tissue, D.T., Zak, J.C., Weltzin, J.F., 2004. Convergence across biomes to a common rain-use efficiency. Nature 429 (6992), 651. Ito, A., Inatomi, M., 2012. Water-use efficiency of the terrestrial biosphere: a model analysis focusing on interactions between the global carbon and water cycles. J. Hydrometeorology 13 (2), 681–694. Ivey, C.T., Carr, D.E., 2012. Tests for the joint evolution of mating system and drought escape in mimulus. Ann. Bot. 109 (3), 583–598. Jaleel, C.A., Gopi, R., Sankar, B., Gomathinayagam, M., Panneerselvam, R., 2008. Differential responses in water use efficiency in two varieties of Catharanthus roseus under drought stress. C.R. Biol. 331 (1), 42–47. Jia, X., Zha, T., Gong, J., Wang, B., Zhang, Y., Wu, B., Qin, S., Peltola, H., 2016. Carbon and water exchange over a temperate semi-arid shrubland during three years of contrasting precipitation and soil moisture patterns. Agric. For. Meteorol. 228, 120–129. Kannenberg, S.A., Novick, K.A., Phillips, R.P., 2019. Anisohydric behavior linked to persistent hydraulic damage and delayed drought recovery across seven North American tree species. New Phytol. 222, 1862–1872. Kato, T., Kimura, R., Kamichika, M., 2004. Estimation of evapotranspiration, transpiration ratio and water-use efficiency from a sparse canopy using a compartment model. Agric. Water Manage. 65 (3), 173–191. Keenan, T.F., Hollinger, D.Y., Bohrer, G., Dragoni, D., Munger, J.W., Schmid, H.P., Richardson, A.D., 2013. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499 (7458), 324. Khalifa, M., Elagib, N.A., Ribbe, L., Schneider, K., 2018. Spatio-temporal variations in climate, primary productivity and efficiency of water and carbon use of the land cover types in Sudan and Ethiopia. Sci. Total Environ. 624, 790–806. Knauer, J., Zaehle, S., Reichstein, M., Medlyn, B.E., Forkel, M., Hagemann, S., Werner, C., 2017. The response of ecosystem water-use efficiency to rising atmospheric CO2 concentrations: sensitivity and large-scale biogeochemical implications. New Phytol. 213 (4), 1654–1666. Law, B., Falge, E., Gu, L.V., Baldocchi, D., Bakwin, P., Berbigier, P., Davis, K., Dolman, A., Falk, M., Fuentes, J., 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 113 (1), 97–120. Lei, H., Yang, D., 2010a. Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain. Agric. For. Meteorol. 150 (4), 581–589.

9

Ecological Indicators 110 (2020) 105932

Y. Bai, et al.

photosynthesis-transpiration based on stomatal behavior. J. Plant Physiol. 161 (3), 303–318. Yu, G.-R., Wen, X.-F., Sun, X.-M., Tanner, B.D., Lee, X., Chen, J.-Y., 2006. Overview of China FLUX and evaluation of its eddy covariance measurement. Agric. For. Meteorol. 137 (3–4), 125–137. Zhao, M.S., Running, S.W., 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329 (5994), 940. Zhao, F.-H., Yu, G.-R., Li, S.-G., Ren, C.-Y., Sun, X.-M., Mi, N., Li, J., Ouyang, Z., 2007. Canopy water use efficiency of winter wheat in the North China Plain. Agric. Water Manage. 93 (3), 99–108. Zhou, J., 2013. Water Use Efficiency of a Poplar Plantation in Daxing District of Beijing (Doctor) (in Chinese with English abstract). Beijing forestry university, Beijing, pp. 111. Zhu, X.-J., Yu, G.-R., Wang, Q.-F., Hu, Z.-M., Zheng, H., Li, S.-G., Sun, X.-M., Zhang, Y.-P., Yan, J.-H., Wang, H.-M., 2015. Spatial variability of water use efficiency in China's terrestrial ecosystems. Glob. Planet. Change 129, 37–44.

drivers of water use efficiency from 2000 to 2013. Ecosphere 6 (10), 1–18. Yang, Y., Long, D., Shang, S., 2013. Remote estimation of terrestrial evapotranspiration without using meteorological data. Geophys. Res. Lett. 40 (12), 3026–3030. Yang, Y., Guan, H., Batelaan, O., McVicar, T.R., Long, D., Piao, S., Liang, W., Liu, B., Jin, Z., Simmons, C.T., 2016. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6, 23284. Yang, H., Rood, S.B., Flanagan, L.B., 2019. Controls on ecosystem water-use and wateruse efficiency: Insights from a comparison between grassland and riparian forest in the northern Great Plains. Agric. For. Meteorol. 271, 22–32. Yi, K., Maxwell, J.T., Wenzel, M.K., Roman, D.T., Sauer, P.E., Phillips, R.P., Novick, K.A., 2019. Linking variation in intrinsic water-use efficiency to isohydricity: a comparison at multiple spatiotemporal scales. New Phytol. 221 (1), 195–208. Yu, G., Song, X., Wang, Q., Liu, Y., Guan, D., Yan, J., Sun, X., Zhang, L., Wen, X., 2008. Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables. New Phytol. 177 (4), 927–937. Yu, G.R., Wang, Q.F., Zhuang, J., 2004. Modeling the water use efficiency of soybean and maize plants under environmental stresses: application of a synthetic model of

10