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Divergent spatial responses of plant and ecosystem water-use efficiency to climate and vegetation gradients in the Chinese Loess Plateau
Global and Planetary Change 181 (2019) 102995
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Global and Planetary Change journal homepage: www.elsevier.com/locate/gloplacha
Research article
Divergent spatial responses of plant and ecosystem water-use efficiency to climate and vegetation gradients in the Chinese Loess Plateau ⁎
T
⁎
Han Zhenga,b, , Henry Linc, , Xian-Jin Zhud, Zhao Jine, Han Baof a
Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Environmental Science and Engineering, Chang'an University, Xi'an 710054, China b Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Mineral, Shandong University of Science and Technology, Qingdao 266590, China c Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USA d College of Agronomy, Shenyang Agricultural University, Shenyang 110161, China e State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China f School of Highway, Chang'an University, Xi'an 710064, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Chinese Loess Plateau Climate gradient Plant water-use efficiency Precipitation sensitivity Vegetation gradient Water-use efficiency
Water-use efficiency (WUE) is an important indicator of carbon–water interactions and is defined as the ratio of vegetation productivity to water loss. However, how WUE varies along climate and vegetation gradients at the regional scale remains elusive. In this study, we investigated the spatial patterns of plant-canopy WUE (PWUE, i.e., ratio of gross primary productivity to plant transpiration) and ecosystem WUE (EWUE, i.e., ratio of gross primary productivity to evapotranspiration) in the Chinese Loess Plateau (CLP), which has seen large changes in the biosphere-atmosphere carbon and water cycles due to large-scale revegetation with the CLP. Spatial responses of PWUE and EWUE variations to the mean annual precipitation (MAP), mean annual air temperature (MAT), and normalized difference vegetation index (NDVI) gradients were examined based on remote-sensing and geostatistical model-based datasets. Results showed that mean EWUE estimated from two approaches was 1.26 ± 0.28 and 1.37 ± 0.68 g C kg−1 H2O, respectively, lower than the mean PWUE (3.16 ± 0.71 g C kg−1 H2O) across the CLP. EWUE and PWUE estimates showed similar spatial distributions, generally with higher values in the areas with more water available. Precipitation sensitivities of EWUE and PWUE appeared to be positive except the very cold regions, and gradually decreased with increasing MAT in the forest-steppe and forest zones. Spatial variation in EWUE is intrinsically affected by both of PWUE and ecosystem water allocation (i.e., ratio of transpiration to evapotranspiration), and NDVI sensitivity of EWUE is dominant by ecosystem water allocation, leading to postive sensitivity of EWUE to NDVI for most MAP range in the CLP. PWUE variation depends on the geographic patterns of vegetation communities determined by precipitation pattern, leading to relatively lower and stable NDVI sensitivity than EWUE along the MAP gradient in the CLP. Our study revealed the divergent spatial responses of WUE to climate and vegetation gradients at the plant-canopy and ecosystem levels, which could enhance our understanding on the regional-scale carbon-water relationships across multiple organismic levels, and provide essential information for the WUE upscaling and modeling efforts.
1. Introduction Water-use efficiency (WUE) is an important indicator representing the relationships between terrestrial carbon and water cycles (Hu et al., 2008; Huang et al., 2016; Niu et al., 2011), which reflects the water constrains on the vegetation productivity and carbon sequestration capacity (Beer et al., 2009; Beer et al., 2010). Analyzing the spatial variation in the WUE is not only essential for understanding the regional carbon-water interactions and their responses to environmental
conditions (Huang et al., 2015; Zheng et al., 2019; Zhu et al., 2015), but also helpful for the water resources management and carbon budget assessment (Brümmer et al., 2012; Gao et al., 2014). WUE is defined as the ratio of ‘vegetation productivity’ to ‘water consumption’, which implies various meanings when using different terms of ‘vegetation productivity’ (e.g., gross primary productivity, net primary productivity or crop yield) and ‘water consumption’ (e.g., evapotranspiration or transpiration) at different organismic levels (Huang et al., 2015; Yu and Wang, 2010). Among them, the concepts of
⁎ Corresponding author at: Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Environmental Science and Engineering, Chang'an University, Xi'an 710054, China. E-mail addresses: [email protected] (H. Zheng), [email protected] (H. Lin).
https://doi.org/10.1016/j.gloplacha.2019.102995 Received 10 January 2019; Received in revised form 15 July 2019; Accepted 25 July 2019 Available online 27 July 2019 0921-8181/ © 2019 Published by Elsevier B.V.
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WUE variation along climate and vegetation gradients at the regional scale across the entire CLP (Zhang et al., 2016; Zheng et al., 2019), in part due to fragmentized landforms (Zhu et al., 2017; Zhu et al., 2018) and limited observation technologies in the CLP. Also, only ‘apparent’ WUE was involved in previous studies, and no attempt has been made to discuss the WUE variation at different organismic levels. Therefore, this study aimed at investigating the spatial responses of WUE to climate and vegetation gradients across the entire CLP at both the plant-canopy and ecosystem levels. We employed two datasets: 1) GPP and ET datasets from the MODerate Resolution Imaging Spectroradiometer (MODIS) datasets, and 2) WUE dataset based on flux-tower observations during the time period from 2000 to the most recent year available. There are three objectives for this study: 1) quantifying the statistical characteristics of PWUE and EWUE of different vegetation zones in the CLP; 2) examining the spatial responses of PWUE and EWUE to mean annual precipitation (MAP) and mean annual air temperature (MAT) gradients; and 3) exploring the PWUE and EWUE variation along vegetation gradients with remote-sensing derived normalized difference vegetation index (NDVI) as the proxy.
plant-canopy WUE (PWUE) and ecosystem WUE (EWUE) are most widely used at the ecosystem scale (Niu et al., 2011; Quan et al., 2018). According to the definition of WUE, PWUE and EWUE can be expressed as:
PWUE =
GPP T
(1)
EWUE =
GPP GPP T T = × = PWUE × ET T ET ET
(2)
where GPP is gross primary productivity and T is vegetation transpiration closely linked to plant photosynthesis. PWUE, the transpiration-based WUE (i.e., GPP/T), is similar to WUE at the leaf level, which indicates the efficiency of plants in using water to produce dry matter (Beer et al., 2009; Fang et al., 2010; Niu et al., 2011). ET denotes the ecosytem evapotranspiration, containing vegetation transpiration (T) and evaporation from soil and vegetation (E) (Zheng et al., 2014; Zheng et al., 2016). T/ET ratio describes how much ecosystem water flux is used for plant growth, indicating the water allocation of ET between physical and biological processes (Hu et al., 2008; Zhu et al., 2015). The Eq.(2) indicated that the variability of EWUE, the ET-based WUE (i.e., GPP/ET), is jointly affected by plant physiological processes (i.e., PWUE) and the physical processes influencing water loss within the ecosystem (i.e., T/ET) (Hu et al., 2008; Niu et al., 2011; Quan et al., 2018). From the perspective of whether the water loss is intimately coupled with vegetation productivity, we could regard EWUE as the ‘apparent WUE’ of ecosystem, while PWUE is the ‘real WUE’ of plant community in the ecosystem. Therefore, WUE at plant and ecosysem levels is associated with different ecosystem processes, and thus studies on the WUE variations at different organismic levels and their potentially divergent responses to environmental changes would help us better understand the underlying mechanisms in terrestrial carbonwater interactions (Niu et al., 2011; Quan et al., 2018; Sun et al., 2016), which is also important in the validation of any uspsalling or downscaling method (Niu et al., 2011). The different spatial relationships of PWUE and EWUE with climatic variables (i.e., precipitation and temperature) have been investigated using satellite-based datasets and carbon cycle models at the global scale, and confirmed the importance of separating ET into different components when analyzing regional WUE variation (Sun et al., 2016). Some studies also analyzed the potential differences in the responses of WUE at different organismic levels (i.e., leaf, plant, canopy, and ecosystem) to increased precipitaiton and warming using manipulative experiments in some specific ecosytems (Niu et al., 2011; Quan et al., 2018). However, only WUE variations in response to climatic variables were analyzed in these studies, and the vegetation regulations on the WUE variations at different organismic levels have not been clarified yet, which makes our perception on the complex ecosystem processes involved in the upscaling from the plant or canopy to entire ecosystem levels unclear. The Chinese Loess Plateau (CLP) located in the Asian monsoon region is a hot-spot sustaining severe soil erosion and environmental degradation in the world (Feng et al., 2016; McVicar et al., 2007). Large-scale revegetation programs (including the Grain for Green project) have been implemented in this area, which have undoubtedly altered the biosphere-atmosphere carbon and water exchanges due to changes in the land surface characteristics (Feng et al., 2012; Jia et al., 2017; Zheng et al., 2019). Understanding the interactions between the carbon and water cycles across the CLP region has become an urgent scientific issue assessing the sustainability of revegetation programs in this region, especially that a recent research has reported that revegetation is approaching sustainable water resource limits in the CLP (Feng et al., 2016). However, current studies on WUE in the CLP mainly focused on the WUE variability at the leaf, individual plant or plant community levels generally with in situ observations (e.g., Jin et al., 2018; Wang and Shangguan, 2015). Few studies have explored the
2. Materials and methods 2.1. Study area This study was conducted in the entire CLP area including the entirety or portions of seven administrative divisions in China (34°–40°N, 102°–114°E, Fig. 1). This region is characterized by a temperate continental monsoon climate and experiences cold-dry winters and hotrainy summers, with most precipitation falling in the summer rainy season (from June to August) in the form of highly erosive rainstorms (Fu et al., 2017; Su and Fu, 2013). The climate of the CLP is jointly affected by latitude, longitude and topography, displaying distinct northwest-southeast precipitation gradient (Zhang et al., 2014). The vegetation types in the CLP are strongly influenced by the spatial patterns of precipitation, with five types of potential natural vegetation distributing from the southeast to the northwest in the CLP, i.e., forest, forest-steppe, steppe, desert-steppe, and desert (Fig. 1) (Fu et al., 2017). 2.2. Calculations of EWUE and PWUE Spatial variation in the ecosystem WUE (EWUE, i.e., the apparent WUE) of the CLP region was analyzed using two EWUE datasets: the EWUE data calculated from MODIS GPP/ET datasets (indicated by EWUEm hereafter) and the EWUE data based on flux-tower observations in China (indicated by EWUEs hereafter). We obtained the MODIS GPP and ET data covering the time period 2000–2014 at a 1–km spatial resolution from the MODIS Collection 5 GPP and ET data products developed by the Numerical Terra Dynamic Simulation Group (MOD17A3 and MOD16 A3, http://www.ntsg.umt.edu/, acquired December 20, 2016) (Mu et al., 2011; Running et al., 2004; Zhao et al., 2005). MODIS GPP and ET were derived from the logic of radiation use efficiency (Monteith, 1972) and the Penman–Monteith approach (Monteith, 1965) using the gridded meteorological data and remote sensing data products from other MODIS data products as inputs (Mu et al., 2011; Running et al., 2004; Zhao et al., 2005). Annual mean EWUEm values (in gC kg−1H2O) were calculated as the ratio of annual total GPP (in gC m−2 year−1) over annual total ET (in kgH2O m−2 year−1) for each 1–km2 grid cell during the time period from 2000 to 2014. Grid cells missing GPP or ET values were masked from specific EWUEm datasets. EWUEs is another dataset constructed based on the flux-tower EWUE observations (calculated by the ratio of GPP to ET) in China (Zhu et al., 2015), which extrapolates gridded EWUE data on the basis of the geostatistical relationships of EWUE observations with altitude and leaf area index (LAI) (Gao et al., 2014). The EWUEs dataset covered the time period 2000–2010 at the 8–km spatial resolution. Plant-canopy WUE (PWUE, i.e., the real WUE) were calculated 2
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Fig. 1. Location of Chinese Loess Plateau. Grey lines show the boundaries of seven administrative divisions, while black lines show the boundaries of five vegetation zones (Fu et al., 2017).
the corresponding annual values during 2000–2014 at the grid-cell scale.
based on Eq. (2), that is, PWUE = EWUE/(T/ET). T/ET ratio was obtained using the Shuttle worth–Wallace model, from which the fluxtower observations of ET, with the same data source of EWUEs, was separated into site-level E and T data (Hu et al., 2009). The gridded data of T/ET were then derived using the logarithmic relationship between LAI and T/ET, hence generating the gridded PWUE data based on the gridded datasets of EWUEs and T/ET (Gao et al., 2014). The spatial resolution of PWUE dataset is the same with the EWUEs dataset.
2.4. Data analysis EWUEs and PWUE data were re-gridded to 1–km resolution using the nearest neighbor method in ArcGIS 10.2. The spatial patterns of mean annual EWUEm, EWUEs, and PWUE in the CLP were analyzed by averaging the corresponding datasets during the period from 2000 to the most recent year available on a per gridded-cell basis, respectively. Mean EWUEm, EWUEs, and PWUE values for different vegetation zones (i.e., forest, forest-steppe, steppe, desert-steppe, and desert) were then calculated. Grid cells with mean annual NDVI (from MODIS MOD13 dataset) lower than 0.1 were excluded in the spatial analyses. To analyze the spatial responses of CLP WUE to precipitation and temperature variations, the spatial distributions of EWUEm, EWUEs, and PWUE were projected into the two-dimensional space with MAT and MAP as the horizontal and vertical axis, respectively. The sensitivities of WUE to MAT and MAP were investigated by binning WUE pixels into intervals of 0.1 °C MAT and 5 mm yr−1 MAP, respectively. The precipitation sensitivity (SP) of WUE was quantified using the simple linear regression between pixel values of WUE and MAP under the same MAT interval, with the absolute SP and relative SP indicated by the linear slope value and the ratio of linear slope over the mean WUE under the same MAT interval, respectively. Same for temperature sensitivity (ST) of WUE. The NDVI sensitivity (SNDVI) of WUE along MAP and MAT ranges were also calculated to explore the effects of vegetation on the WUE variation in the CLP. A detrend analysis was also conducted to remove the linear trends of two variable involved in the regression analysis above, which could avoid spurious correlation between two
2.3. Datasets of precipitation, temperature, and NDVI Precipitation, air temperature, and NDVI were used to examine PWUE and EWUE variation along climate and vegetation gradients in the CLP. We acquired the datasets of annual total precipitation and annual mean air temperature during 2000–2014 at about 756 national basic meteorological stations in China from the China Meteorological Data Service Center (http://data.cma.cn/, acquired June 23, 2017). The method of thin-plate smoothing splines in AUSPLIN software was applied to generate the gridded datasets of precipitation and air temperature at a 1–km resolution (Hutchinson, 1995; Zheng et al., 2016). NDVI was selected as a proxy representing vegetation coverage status in this study, which is an important remote-sensing indicator for vegetation canopy greenness and structure (Zhu et al., 2013). We obtained the monthly NDVI data at a 1–km spatial resolution during 2000–2014 from a MODIS data product (MOD13A3, https://ladsweb.modaps. eosdis.nasa.gov/, acquired June 29, 2017). The 12 monthly composites for each year were averaged to calculate the corresponding annual mean NDVI values in each year for each grid-cell. We then obtained the gridded mean annual precipitation (MAP), mean annual air temperature (MAT), and mean annual NDVI across the CLP area by averaging 3
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Table 1 Statistical characteristics of ecosystem WUE (EWUE) and plant-canopy WUE (PWUE) of different vegetation zones averaged from 2000 to the most recent year available in the Chinese Loess Plateau. Different superscript letters denote a significant difference at a 5% confident level. Vegetation
EWUEm
Forest Forest-steppe Steppe Desert-steppe Desert Mean
1.55 1.31 1.14 1.08 1.21 1.26
± ± ± ± ± ±
EWUEs 0.19 0.22 0.19 0.26 0.31 0.28
a b d e c
2.19 1.61 0.97 0.87 0.93 1.37
PWUE
± ± ± ± ± ±
0.55 0.64 0.23 0.24 0.31 0.68
a b c e d
4.01 3.31 2.81 2.62 2.87 3.16
± ± ± ± ± ±
T/ET 0.45 0.60 0.35 0.52 0.59 0.71
a b d e c
0.54 0.47 0.35 0.34 0.33 0.42
± ± ± ± ± ±
0.08 0.10 0.06 0.09 0.08 0.12
a b c d e
showed a significant increasing trend with MAP gradients for the foreststeppe and steppe areas, and a significant decreasing trend for the desert zone (p-value < .001, Figs. 5–6a,b). For the forest vegetation zone, mean EWUE significantly increased with MAP to reach a maximum for a certain MAP interval (about 600 mm yr−1 and 700 mm yr−1 for EWUEm and EWUEs datasets, respectively), and then decreased with MAP (p-value < .05, red dashed lines in Fig. 5a,b). As to the EWUE–MAT relationship, results showed strong and positive relationships between the spatial variations in EWUE and MAT gradients for all the vegetation zones except the steppe area (Fig. 6a,b). Compared with EWUE, PWUE exhibited almost consistent trends with MAP or MAT gradients for given vegetation zones (Figs. 5–6c). Results also indicated a stronger dependence of EWUE and PWUE variations on the MAP gradients than on the MAT gradients for nearly all the vegetation zones (with higher R2 values, Figs. 5–6).
variables (Zheng et al., 2019). Meanwhile, the variation characteristics of WUE along climate and vegetation gradients were also analyzed for different vegetation zones, by drawing the scatter plots between WUE values averaged within the same MAP or MAT or NDVI intervals (as the vertical axis, with NDVI intervals of 0.01) and MAP or MAT or NDVI gradients (as the horizontal axis). 3. Results 3.1. Variations in EWUE and PWUE in the CLP Mean EWUE in the CLP was estimated to be 1.26 ± 0.28 g C kg−1 H2O from MODIS dataset and 1.37 ± 0.68 g C kg−1 H2O from geostatistical model (Table 1). The EWUE derived from two datasets showed similar spatial variations (Fig. 2a,b) regardless of the large differences in the EWUE estimates for different vegetation zones (Table 1). EWUE varied significantly across different vegetation zones (p-value < .05, Table 1), generally with higher EWUE in the more humid (where MAP > 400 mm yr−1, Fig. 2e) and irrigated areas than in the arid and semi-arid areas (where MAP < 400 mm yr−1) (Fig. 2a,b). The PWUE estimates were consistently larger than EWUE estimates for the same vegetation region, with the mean PWUE of 3.16 ± 0.71 g C kg−1 H2O in the CLP (Table 1). The spatial pattern of PWUE was similar to that of EWUE (Fig. 2c), with higher PWUE in the forest and forest-steppe areas as compared to other areas (p-value < .05, Table 1).
3.3. Variations of EWUE and PWUE along NDVI gradient Positive correlations were always significant in the spatial relationships of EWUE with NDVI gradients at the entire CLP region and all the vegetation zones (p-value < .001, Fig. 7a,b). The NDVI sensitivities (SNDVI) of EWUE were positive across most MAT and MAP intervals, and showed a declining trend with MAP gradient, with maximum EWUE–SNDVI in the cold and dry areas (red lines in the insets of Fig. 7a,b). The relative PWUE–SNDVI values were generally small and remained quite stable along MAP gradient, but with negative PWUE–SNDVI when MAP fell in the range of 400–470 mm yr−1 (Fig. 7c, top-right inset). Similar results were also found when detrend analysis was used prior to the NDVI sensitivity analyses (Fig. S2). PWUE also showed significant increasing trends along NDVI gradients for the forest, forest-steppe, and steppe areas (Fig. 7c). For the desert and desert-steppe areas, however, the PWUE–NDVI relationships were negative when NDVI < 0.4, and then turned to be positive when NDVI > 0.4 (p-value < .05, Fig. 7c, red dashed line). The correlations between the spatial variations of PWUE and NDVI were generally weaker as compared to EWUE for all the vegetation zones (Fig. 7).
3.2. Variations of EWUE and PWUE along MAP and MAT gradients Spatial distributions of EWUE estimates from MODIS product and geostatistical model were basically the same in the MAT–MAP two-dimensional space (Fig. 3). Spatially, EWUE from two approaches tended to increase with increasing MAP and MAT, and reached a maximum in regions with MAT > 10 °C and MAP in the range of 500–600 mm yr−1 (Fig. 3). The precipitation sensitivities (SP) of EWUE were quite different for the two EWUE datasets, but with similar variation trend across MAT intervals in the CLP (Fig. 3, left insets). SP appeared to be positive for most MAT range except the very cold regions where MAT < 4 °C, and gradually decreased with MAT when MAT is higher than 10 °C (i.e., mostly forest and forest-steppe areas) (Fig. 3, left insets). Compared with EWUE, PWUE estimates distributed in the MAT–MAP climate space in a manner alike with that of EWUE (Fig. 4), and PWUE–SP varied similarly along MAT range, with positive SP declining with MAT when MAT is higher than 10 °C (Fig. 4, left inset). Large differences exhibited in the variations of temperature sensitivity (ST) of EWUE and PWUE along MAP intervals (Figs. 3–4, bottom insets). Consistent results were observed when applying detrend analysis to the calculations of MAP and MAT sensitivities (Fig. S1 in Appendix). The overall spatial variations in EWUE and PWUE along MAP and MAT gradients showed consistent WUE–MAP and WUE–MAT relationships to those diagnosed from MAT–MAP climate space at the CLP-regional scale, that EWUE and PWUE positively correlated with MAP and MAT gradients (p-value < .001, black lines in Figs. 5–6). However, the responses of EWUE to MAP or MAT variations varied among different vegetation zones (Figs. 5–6). Mean EWUE from both approaches
4. Discussion 4.1. Variations in EWUE and PWUE along climate gradients in the CLP Plant-canopy WUE (i.e., GPP/T) is the ‘real’ efficiency of ecosystem water use in producing dry matter (Hu et al., 2008). Canopy-scale plant transpiration (T), dominating terrestrial water fluxes (Jasechko et al., 2013), was often replaced by ecosystem ET (i.e., calculating ecosystem WUE) due to challenges in upscaling T from individual plant to plantcanopy scale. Site-level studies have indicated that the responses of WUE to certain environmental changes (e.g., climate warming, increased precipitation) were not consistently the same at different organismic levels (Niu et al., 2011; Quan et al., 2018). The correlation and divergence between the variations in plant-canopy WUE and ecosystem WUE along climate and vegetation gradients have not been clarified yet for a large-regional scale. Based on the MODIS datasets and observation-based geostatistical 4
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Fig. 2. Spatial patterns of (a) MODIS-derived ecosystem WUE (EWUEm), (b) geostatistical model-based ecosystem WUE (EWUEs), and (c) plant-canopy WUE (PWUE) in the Chinese Loess Plateau. The spatial patterns of (d) mean annual temperature (MAT), (e) mean annul precipitation (MAP), and (f) mean annual NDVI are also provided for reference. Grey lines in the panel (d) and (e) are isolines of MAT and MAP, respectively. Grey lines in other panels indicate boundary of different vegetation zones.
4.1.1. PWUE variation along MAP gradient PWUE reflects the real WUE of vegetation communities themselves within the ecosystem (Hu et al., 2008; Niu et al., 2011), while the spatial distributions and compositions of vegetation are determined by the geographic variation in climatic conditions (Zheng et al., 2016) especially water supply (i.e., MAP) for the CLP region (Fig. 2). As it is, the underlying factor determining PWUE variation is the geographic pattern of MAP. Our results showed positive precipitation sensitivities (SP) of PWUE for most MAT range in the CLP (Fig. 4, left insets), and PWUE was generally higher for the forest vegetation than the steppe vegetation (Fig. 2c and Table 1). It could be explained from two aspects. On the one hand, previous study showed that site-level PWUE variation was mainly affected by carbon (i.e., GPP) rather than water processes (i.e., T) (Baldocchi, 1994). The denser canopies of forest vegetation, which locates in relatively humid regions, act to intercept more solar radiation and display higher light use efficiency (Hu et al., 2008), which will benefit plant growth and hence PWUE. On the other hand, the generally higher root surface area of forest vegetation could facilitate more water
model, this study indicated that EWUE and PWUE estimates were similar in terms of spatial distributions in the CLP (Fig. 1 and Table 1). That is, EWUE and PWUE showed higher values for the areas with more water available and lower values for the arid and semi-arid areas (MAP < 400 mm yr−1), which is consistent with previous studies on EWUE variations based on satellite-based datasets, carbon cycle models, and site-level meta-analyses (Sun et al., 2016; Xiao et al., 2013; Zhang et al., 2016; Zhu et al., 2015). Our data also suggested that the sensitivity of EWUE and PWUE to MAP was mainly positive along MAT range in the CLP (Figs. 3–4, left insets), and the spatial relationships of EWUE and PWUE with MAP gradient tended to be stronger as compared to MAT gradient at the CLP-regional and vegetation-zone scales, especially for EWUE estimates (Fig. 5). Thus, climatic regulations on the WUE variations were dominant by the water conditions rather than temperature in the CLP at both the plant-canopy and ecosystem levels. Nevertheless, the mechanisms underlying the spatial variations in EWUE and PWUE in responding to MAP gradient were not the same in the CLP.
5
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Fig. 3. Distribution of EWUE in the MAT–MAP climate space of the CLP: (a) MODIS-derived EWUEm, (b) geostatistical model-based EWUEs. The left insets show the MAP sensitivity of EWUE across the MAT range. Black lines in insets refer to the absolute changes of EWUE along MAP gradient (indicated by the slope calculated from simple linear regression between EWUE and MAP under the same MAT interval). Red lines represent the relative changes of EWUE along MAP gradient (calculated as the absolute slope value divided by the mean EWUE under the same MAT interval). Similarly, the bottom insets show the MAT sensitivity of EWUE across the MAP range.
6
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Fig. 4. Distribution of PWUE in the MAT–MAP climate space of the CLP. The left inset show the MAP sensitivity (black/red lines) of PWUE across the MAT range. The bottom inset show the MAT sensitivity of PWUE across the MAP range.
S3) for the arid and semi-arid areas, our results also suggested that the EWUE–MAP relationships and EWUE–SP for the arid and semi-arid areas (i.e., mainly deset and desert-steppe regions) tended to be negative (Fig. 5a,b, left insets in Fig. S4–S5). We also noticed that, both the SP of PWUE and EWUE appeared to gradually decrease with MAT when MAT > 10 °C (i.e., mainly forest and forest-steppe areas, Fig. 2e) (Figs. 3–4, left insets). The positive WUE–MAP relationships also turned into negative when MAP was larger than a certain amount (about 600–700 mm yr−1 in this study) for the forest vegetation zone (Fig. 5). This might have a relevance with the increasing cloudy days and decreasing solar radiation resulted from higher MAP, which could limit plant growth (Sun et al., 2016) and water loss processes (Zheng et al., 2016) and their sensitivities to MAP (Fig. S6), hence decreasing the sensitivity of WUE on the MAP variation.
uptake and higher root productivity, and hence improve plant-canopy WUE (Niu et al., 2011). This study also found that, the spatially PWUE–MAP relationships tended to be negative for the arid and semi-arid areas (i.e., MAP < 400 mm yr−1), including desert, desert-steppe, and part of steppe vegetation zone (Fig. 5c). The PWUE–SP was also negative across most MAT intervals for these areas (Fig. S3, left insets). The reason for these phenomena could mainly attribute to ‘inherent water-use efficiency’ (IWUE) and its spatial relationship with precipitation gradient, that the T-based IWUE accounts the influences of vapor pressure deficit GPP × VPD (VPD) on PWUE (i.e., IWUE = ) (Beer et al., 2009; Sun et al., T 2016). A weak negative spatial relationship between IWUE and MAP has been indicated at the global scale using process-based models (Sun et al., 2016). Plant IWUE could be higher for relatively arid areas, because the long-term adaption of drought-tolerant plant species to the local climate conditions helps to shape the physiological and biochemical mechanisms of plants in maintaining efficient water use under arid conditions (Huang et al., 2016; Sun et al., 2016; Zhou et al., 2013). This negative IWUE-MAP relationship hence contributes to the negative PWUE–SP for the semi-arid and arid areas in this study.
4.2. Divergent EWUE and PWUE variations along NDVI gradient Spatial variations in EWUE and PWUE along vegetation gradient, measured by remote-sensing NDVI, were explored in this study. Our data illustrated divergent vegetation regulations of WUE variations at the plant-canopy and ecosystem levels. For the EWUE–NDVI relationships, our results showed significant positive correlations at the entire CLP region and all the vegetation zones (p-value < .001, Fig. 7a,b), which is associated with the decoupling of spatial variations in GPP and ET along NDVI gradient. Previous studies have demonstrated the spatially positive responses of GPP and ET to increasing vegetation coverage (Yu et al., 2008; Zheng et al., 2016), but the spatial variation in EWUE was dominant by GPP, rather than ET (Zhu et al., 2015). This is because that, the spatial pattern of ET comprehensively reflects the patterns of plant transpiration, evaporation of canopy interception, and soil evaporation (Zheng et al., 2016). The spatially increased vegetation coverage is supposed to increase plant transpiration and evaporation of canopy interception (Jung et al., 2011; Piao et al., 2007), and reduce soil evaporation (Hu et al., 2008; Law et al., 2002). Our study also confirmed that the sensitivity of GPP to NDVI (GPP–SNDVI) was higher than the sensitivity of ET to NDVI (ET–SNDVI) for areas with MAP < 500 mm yr−1 in the CLP (Fig. S7). Thus, the decoupled responses of
4.1.2. EWUE variation along MAP gradient Spatial relationships between EWUE and MAP were intrinsically affected by the spatial patterns of PWUE and ecosystem water allocation (i.e., T/ET ratio) along MAP gradient. T/ET variation has been proved to be positively correlated with the spatial distributions of vegetation coverage and climatic conditions (Hu et al., 2008; Law et al., 2002), that is, T/ET ratio is larger for the forest and forest-steppe areas with better water and heat resources (Table 1). It is because that, the high vegetation coverage and relatively low VPD for the humid ecosystems makes the water consumption closely couple with plant productivity, while more water vapor is lost to atmosphere through soil surface for the less humid areas (Sun et al., 2016).Together with PWUE variation among different vegetation zones, EWUE estimates from both approaches were generally larger for the forest vegetation area (Fig. 2a,b and Table 1), agreeing with previous studies (Gao et al., 2014; Tian et al., 2010; Zhu et al., 2015). Meanwhile, considering the small differences in T/ET ratios (Table 1) and negative PWUE–SP (Fig. 7
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Fig. 5. EWUE and PWUE variations along MAP gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same MAP interval. sl and R2 values in the brackets of legends are the slope values and coefficients of determination calculated from simple linear regression between WUE and MAP for each vegetation zone, respectively. Black lines in the figures represent the regression lines of mean WUE under the same MAP intervals along MAP gradients for the entire CLP region. Red dahsed lines denote the piecewise fitting lines of WUE–MAP relationships for the forest zone using the corresponding maximum WUE values as the threshold.
variations (Sun et al., 2016). On the other hand, as Eq. (2) implied, the variation in the EWUE–SNDVI reflects the dominant vegetation regulation of EWUE variation from either plant physiological processes (i.e., PWUE) or ecosystem physical processes (i.e., T/ET). It has been proved that the spatial variation in T/ET was strongly correlated with vegetation coverage (Hu et al., 2008; Law et al., 2002). Our data also indicated that the sensitivity of T/ET to NDVI varied similarly with EWUE along the MAP gradient (i.e., with higher sensitivity for less humid areas, Fig. S8), but the sensitivity of PWUE to NDVI (PWUE–SNDVI)
GPP and ET to NDVI result in the positive spatial variation in the CLP EWUE along NDVI gradient. We also found that the postive sensitivity of EWUE to NDVI (EWUE–SNDVI) gradually declined from the arid and semi-arid areas towards more humid areas in the CLP, especially for the MODIS-derived EWUE datasets (red lines in the insets of Fig. 7a,b). On the one hand, this declining EWUE–SNDVI should attribute to the comparable GPP–SNDVI and ET–SNDVI (inducing EWUE–SNDVI to zero) for the more humid areas (Fig. S7), which is consistent with global-scale EWUE 8
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Fig. 6. EWUE and PWUE variations along MAT gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same MAT interval.sl and R2 values in the brackets of legends are the slope values and coefficients of determination calculated from simple linear regression between WUE and MAT for each vegetation zone, respectively. Black lines in the figures represent the regression lines of mean WUE under the same MAT intervals along MAT gradients for the entire CLP region.
which seems contradict to our discussion above. The spatial relationships between PWUE and NDVI reflected the correlated geographic patterns of MAP and vegetation types, leading to weak dependence of PWUE on the NDVI variation across the MAP range (Fig. 7c). The much weaker correlations of PWUE with NDVI gradient for different vegetation zones as compared to EWUE (Fig. 7) also proved that the spatial variation in PWUE was mainly determined by the spatial pattern of MAP rather than NDVI. The negative PWUE–SNDVI in areas with MAP in
remained quite weak across most MAP intervals in the CLP (Fig. 7c). It means that, vegetation regulation on the EWUE variation under a certain MAP interval is dominant by influencing the spatial distributions of ecosystem water allocation (i.e., T/ET), rather than affecting the efficiency of plant communities in using water to produce dry matter (i.e., PWUE). Our results showed a significant increasing trend in PWUE variation along NDVI gradients in the CLP (R2 = 0.92, p-value < .001, Fig. 7c), 9
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Fig. 7. EWUE and PWUE variations along NDVI gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same NDVI interval. Black lines represent the regression lines of mean WUE under the same NDVI intervals along NDVI gradients for the entire CLP region, with corresponding sl and R2 values in the figures. Top-right and bottom-right insets show the NDVI sensitivity (SNDVI, black/red lines) of WUE across the MAP and MAT ranges, respectively.
the range of 400–470 mm yr−1 (Fig. 7c, top-right insets), and negative spatial relationships between PWUE and NDVI for the desert and desertsteppe areas when NDVI < 0.4 (Fig. 7c) might also associate with the negative IWUE–MAP relationships as discussed above.
the CLP were analyzed at the plant-canopy and ecosystem levels based on a MODIS-based remote-sensing dataset and a geostatistical modelbased dataset for the time period from 2000 to the most recent year available. Our study illustrated divergent spatial responses of plant and ecosystem WUE to climate and vegetation gradients in the CLP. Climatic regulations on WUE variations were dominant by water conditions (i.e., precipitation) rather than temperature, but with differential mechanisms at different organismic levels. The spatial variation
5. Conclusion Spatial variations in WUE along climate and vegetation gradients in 10
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in plant-canopy WUE depends on the geographic patterns of vegetation communities determined by the precipitation pattern in the CLP, leading to weak dependence of plant-canopy WUE on NDVI variation across precipitation range. Spatial variation in ecosystem WUE is intrinsically affected by both of plant-canopy WUE and ecosystem water allocation (i.e., T/ET ratio, highly correlated with MAP and NDVI gradients). The sensitivity of ecosystem WUE to NDVI is predominantly controlled by ecosystem T/ET, leading to postive sensitivity of WUE to NDVI at the ecosystem level. This study improves our understanding on the regional water‑carbon interactions at different organismic levels, and facilitates further studies related to the upscaling and modeling of regional-scale water and carbon dynamics.
Hutchinson, M., 1995. Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Inf. Syst. 9 (4), 385–403. Jasechko, S., et al., 2013. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–351. Jia, X., Shao, M., Zhu, Y., Luo, Y., 2017. Soil moisture decline due to afforestation across the Loess Plateau, China. J. Hydrol. 546, 113–122. Jin, N., et al., 2018. Effects of water stress on water use efficiency of irrigated and rainfed wheat in the Loess Plateau, China. Sci. Total Environ. 642, 1–11. Jung, M., et al., 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116 (G3), G00J07. Law, B., et al., 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 113 (1–4), 97–120. McVicar, T.R., et al., 2007. Developing a decision support tool for China’s re-vegetation program: Simulating regional impacts of afforestation on average annual streamflow in the Loess Plateau. For. Ecol. Manag. 251 (1–2), 65–81. Monteith, J., 1965. Evaporation and Environment, Symposia of the Society for Experimental Biology. pp. 205–234. Monteith, J.L., 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9 (3), 747–766. Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 (8), 1781–1800. Niu, S.L., et al., 2011. Water-use efficiency in response to climate change: from leaf to ecosystem in a temperate steppe. Glob. Chang. Biol. 17 (2), 1073–1082. Piao, S., et al., 2007. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl. Acad. Sci. U. S. A. 104 (39), 15242–15247. Quan, Q., et al., 2018. Transpiration dominates ecosystem water use efficiency in response to warming in an alpine meadow. J. Geophys. Res. Biogeosci. 123, 453–466. Running, S.W., et al., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54 (6), 547–560. Su, C., Fu, B., 2013. Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Glob. Planet. Chang. 101 (1), 119–128. Sun, Y., et al., 2016. Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models. Glob. Ecol. Biogeogr. 25 (3), 311–323. Tian, H., et al., 2010. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007. Forest Ecol. Manag. 259 (7), 1311–1327. Wang, L., Shangguan, Z., 2015. Water-use efficiency of dryland wheat in response to mulching and tillage practices on the Loess Plateau. Sci. Rep. 5 (5), 12225. Xiao, J., et al., 2013. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agric. For. Meteorol. 182, 76–90. Yu, G., Wang, Q., 2010. Ecophysiology of Plant Photosynthesis, Transpiration, and Water Use. Science Press, Beijing (in Chinese). Yu, G., et al., 2008. Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables. New Phytol. 177 (4), 927–937. Zhang, B., Wu, P., Zhao, X., Gao, X., 2014. Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China. Hydrol. Process. 28 (4), 1689–1702. Zhang, T., Peng, J., Liang, W., Yang, Y., Liu, Y., 2016. Spatial-temporal patterns of water use efficiency and climate controls in China’s Loess Plateau during 2000-2010. Sci. Total Environ. 565, 105–122. Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95 (2), 164–176. Zheng, H., Wang, Q.F., Zhu, X.J., Li, Y.N., Yu, G.R., 2014. Hysteresis responses of evapotranspiration to meteorological factors at a diel timescale: patterns and causes. PLoS One 9 (6), e98857. https://doi.org/10.1371/journal.pone.0098857. Zheng, H., et al., 2016. Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: results from eddy covariance measurements. J. Geogr. Sci. 26 (10), 1391–1411. Zheng, H., et al., 2019. Revegetation has increased ecosystem water-use efficiency during 2000–2014 in the Chinese Loess Plateau: evidence from satellite data. Ecol. Indic. 102, 507–518. Zhou, S., Duursma, R.A., Medlyn, B.E., Kelly, J.W.G., Prentice, I.C., 2013. How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agric. For. Meteorol. 182–183, 204–214. Zhu, Z., et al., 2013. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5 (2), 927–948. Zhu, X.J., et al., 2015. Spatial variability of water use efficiency in China’s terrestrial ecosystems. Glob. Planet. Chang. 129, 37–44. Zhu, H., Huang, W., Liu, H., 2017. Loess terrain segmentation from digital elevation models based on the region growth method. Phys. Geogr. 39 (1), 1–16. Zhu, H., Zhao, Y., Liu, H., 2018. Scale characters analysis for gully structure in the watersheds of loess landforms based on digital elevation models. Front. Earth Sci. 12 (2013), 1–13.
Acknowledgements This research was jointly funded by National Natural Science Foundation of China (grant nos. 31700414, 31500390, 41790444), Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19020302), and National Key Research and Development Program of China (grant nos. 2016YFA0600104, 2016YFA0600103). We acknowledge the database and technical support from China Meteorological Data Service Center, and Numerical Terra Dynamic Simulation Group. We gratefully acknowledge the editors and reviewers for their constructive comments that helped improve the quality of this paper. Declarations of interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.gloplacha.2019.102995. References Baldocchi, D., 1994. A comparative study of mass and energy exchange rates over a closed C 3 (wheat) and an open C 4 (corn) crop: II. CO 2 exchange and water use efficiency. Agric. Forest Meteorol. 67 (3), 291–321. Beer, C., et al., 2009. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23 (2), GB2018. Beer, C., et al., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329 (5993), 834–838. Brümmer, C., et al., 2012. How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric. For. Meteorol. 153, 14–30. Fang, Q.X., et al., 2010. Water resources and water use efficiency in the North China Plain: current status and agronomic management options. Agric. Water Manag. 97 (8), 1102–1116. Feng, X.M., et al., 2012. Regional effects of vegetation restoration on water yield across the Loess Plateau, China. Hydrol. Earth Syst. Sci. 16 (8), 2617–2628. Feng, X., et al., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6 (11), 1019–1022. Fu, B., et al., 2017. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci. 45 (1), 223–243. Gao, Y., et al., 2014. Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation. Agric. For. Meteorol. 195–196, 32–37. Hu, Z., et al., 2008. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Glob. Chang. Biol. 14 (7), 1609–1619. Hu, Z., et al., 2009. Partitioning of evapotranspiration and its controls in four grassland ecosystems: application of a two-source model. Agric. For. Meteorol. 149 (9), 1410–1420. Huang, M., et al., 2015. Change in terrestrial ecosystem water-use efficiency over the last three decades. Glob. Chang. Biol. 21 (6), 2366–2378. Huang, M., et al., 2016. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Glob. Chang. Biol. 22 (6), 2165–2177.
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Research article
Divergent spatial responses of plant and ecosystem water-use efficiency to climate and vegetation gradients in the Chinese Loess Plateau ⁎
T
⁎
Han Zhenga,b, , Henry Linc, , Xian-Jin Zhud, Zhao Jine, Han Baof a
Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Environmental Science and Engineering, Chang'an University, Xi'an 710054, China b Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Mineral, Shandong University of Science and Technology, Qingdao 266590, China c Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USA d College of Agronomy, Shenyang Agricultural University, Shenyang 110161, China e State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China f School of Highway, Chang'an University, Xi'an 710064, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Chinese Loess Plateau Climate gradient Plant water-use efficiency Precipitation sensitivity Vegetation gradient Water-use efficiency
Water-use efficiency (WUE) is an important indicator of carbon–water interactions and is defined as the ratio of vegetation productivity to water loss. However, how WUE varies along climate and vegetation gradients at the regional scale remains elusive. In this study, we investigated the spatial patterns of plant-canopy WUE (PWUE, i.e., ratio of gross primary productivity to plant transpiration) and ecosystem WUE (EWUE, i.e., ratio of gross primary productivity to evapotranspiration) in the Chinese Loess Plateau (CLP), which has seen large changes in the biosphere-atmosphere carbon and water cycles due to large-scale revegetation with the CLP. Spatial responses of PWUE and EWUE variations to the mean annual precipitation (MAP), mean annual air temperature (MAT), and normalized difference vegetation index (NDVI) gradients were examined based on remote-sensing and geostatistical model-based datasets. Results showed that mean EWUE estimated from two approaches was 1.26 ± 0.28 and 1.37 ± 0.68 g C kg−1 H2O, respectively, lower than the mean PWUE (3.16 ± 0.71 g C kg−1 H2O) across the CLP. EWUE and PWUE estimates showed similar spatial distributions, generally with higher values in the areas with more water available. Precipitation sensitivities of EWUE and PWUE appeared to be positive except the very cold regions, and gradually decreased with increasing MAT in the forest-steppe and forest zones. Spatial variation in EWUE is intrinsically affected by both of PWUE and ecosystem water allocation (i.e., ratio of transpiration to evapotranspiration), and NDVI sensitivity of EWUE is dominant by ecosystem water allocation, leading to postive sensitivity of EWUE to NDVI for most MAP range in the CLP. PWUE variation depends on the geographic patterns of vegetation communities determined by precipitation pattern, leading to relatively lower and stable NDVI sensitivity than EWUE along the MAP gradient in the CLP. Our study revealed the divergent spatial responses of WUE to climate and vegetation gradients at the plant-canopy and ecosystem levels, which could enhance our understanding on the regional-scale carbon-water relationships across multiple organismic levels, and provide essential information for the WUE upscaling and modeling efforts.
1. Introduction Water-use efficiency (WUE) is an important indicator representing the relationships between terrestrial carbon and water cycles (Hu et al., 2008; Huang et al., 2016; Niu et al., 2011), which reflects the water constrains on the vegetation productivity and carbon sequestration capacity (Beer et al., 2009; Beer et al., 2010). Analyzing the spatial variation in the WUE is not only essential for understanding the regional carbon-water interactions and their responses to environmental
conditions (Huang et al., 2015; Zheng et al., 2019; Zhu et al., 2015), but also helpful for the water resources management and carbon budget assessment (Brümmer et al., 2012; Gao et al., 2014). WUE is defined as the ratio of ‘vegetation productivity’ to ‘water consumption’, which implies various meanings when using different terms of ‘vegetation productivity’ (e.g., gross primary productivity, net primary productivity or crop yield) and ‘water consumption’ (e.g., evapotranspiration or transpiration) at different organismic levels (Huang et al., 2015; Yu and Wang, 2010). Among them, the concepts of
⁎ Corresponding author at: Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Environmental Science and Engineering, Chang'an University, Xi'an 710054, China. E-mail addresses: [email protected] (H. Zheng), [email protected] (H. Lin).
https://doi.org/10.1016/j.gloplacha.2019.102995 Received 10 January 2019; Received in revised form 15 July 2019; Accepted 25 July 2019 Available online 27 July 2019 0921-8181/ © 2019 Published by Elsevier B.V.
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WUE variation along climate and vegetation gradients at the regional scale across the entire CLP (Zhang et al., 2016; Zheng et al., 2019), in part due to fragmentized landforms (Zhu et al., 2017; Zhu et al., 2018) and limited observation technologies in the CLP. Also, only ‘apparent’ WUE was involved in previous studies, and no attempt has been made to discuss the WUE variation at different organismic levels. Therefore, this study aimed at investigating the spatial responses of WUE to climate and vegetation gradients across the entire CLP at both the plant-canopy and ecosystem levels. We employed two datasets: 1) GPP and ET datasets from the MODerate Resolution Imaging Spectroradiometer (MODIS) datasets, and 2) WUE dataset based on flux-tower observations during the time period from 2000 to the most recent year available. There are three objectives for this study: 1) quantifying the statistical characteristics of PWUE and EWUE of different vegetation zones in the CLP; 2) examining the spatial responses of PWUE and EWUE to mean annual precipitation (MAP) and mean annual air temperature (MAT) gradients; and 3) exploring the PWUE and EWUE variation along vegetation gradients with remote-sensing derived normalized difference vegetation index (NDVI) as the proxy.
plant-canopy WUE (PWUE) and ecosystem WUE (EWUE) are most widely used at the ecosystem scale (Niu et al., 2011; Quan et al., 2018). According to the definition of WUE, PWUE and EWUE can be expressed as:
PWUE =
GPP T
(1)
EWUE =
GPP GPP T T = × = PWUE × ET T ET ET
(2)
where GPP is gross primary productivity and T is vegetation transpiration closely linked to plant photosynthesis. PWUE, the transpiration-based WUE (i.e., GPP/T), is similar to WUE at the leaf level, which indicates the efficiency of plants in using water to produce dry matter (Beer et al., 2009; Fang et al., 2010; Niu et al., 2011). ET denotes the ecosytem evapotranspiration, containing vegetation transpiration (T) and evaporation from soil and vegetation (E) (Zheng et al., 2014; Zheng et al., 2016). T/ET ratio describes how much ecosystem water flux is used for plant growth, indicating the water allocation of ET between physical and biological processes (Hu et al., 2008; Zhu et al., 2015). The Eq.(2) indicated that the variability of EWUE, the ET-based WUE (i.e., GPP/ET), is jointly affected by plant physiological processes (i.e., PWUE) and the physical processes influencing water loss within the ecosystem (i.e., T/ET) (Hu et al., 2008; Niu et al., 2011; Quan et al., 2018). From the perspective of whether the water loss is intimately coupled with vegetation productivity, we could regard EWUE as the ‘apparent WUE’ of ecosystem, while PWUE is the ‘real WUE’ of plant community in the ecosystem. Therefore, WUE at plant and ecosysem levels is associated with different ecosystem processes, and thus studies on the WUE variations at different organismic levels and their potentially divergent responses to environmental changes would help us better understand the underlying mechanisms in terrestrial carbonwater interactions (Niu et al., 2011; Quan et al., 2018; Sun et al., 2016), which is also important in the validation of any uspsalling or downscaling method (Niu et al., 2011). The different spatial relationships of PWUE and EWUE with climatic variables (i.e., precipitation and temperature) have been investigated using satellite-based datasets and carbon cycle models at the global scale, and confirmed the importance of separating ET into different components when analyzing regional WUE variation (Sun et al., 2016). Some studies also analyzed the potential differences in the responses of WUE at different organismic levels (i.e., leaf, plant, canopy, and ecosystem) to increased precipitaiton and warming using manipulative experiments in some specific ecosytems (Niu et al., 2011; Quan et al., 2018). However, only WUE variations in response to climatic variables were analyzed in these studies, and the vegetation regulations on the WUE variations at different organismic levels have not been clarified yet, which makes our perception on the complex ecosystem processes involved in the upscaling from the plant or canopy to entire ecosystem levels unclear. The Chinese Loess Plateau (CLP) located in the Asian monsoon region is a hot-spot sustaining severe soil erosion and environmental degradation in the world (Feng et al., 2016; McVicar et al., 2007). Large-scale revegetation programs (including the Grain for Green project) have been implemented in this area, which have undoubtedly altered the biosphere-atmosphere carbon and water exchanges due to changes in the land surface characteristics (Feng et al., 2012; Jia et al., 2017; Zheng et al., 2019). Understanding the interactions between the carbon and water cycles across the CLP region has become an urgent scientific issue assessing the sustainability of revegetation programs in this region, especially that a recent research has reported that revegetation is approaching sustainable water resource limits in the CLP (Feng et al., 2016). However, current studies on WUE in the CLP mainly focused on the WUE variability at the leaf, individual plant or plant community levels generally with in situ observations (e.g., Jin et al., 2018; Wang and Shangguan, 2015). Few studies have explored the
2. Materials and methods 2.1. Study area This study was conducted in the entire CLP area including the entirety or portions of seven administrative divisions in China (34°–40°N, 102°–114°E, Fig. 1). This region is characterized by a temperate continental monsoon climate and experiences cold-dry winters and hotrainy summers, with most precipitation falling in the summer rainy season (from June to August) in the form of highly erosive rainstorms (Fu et al., 2017; Su and Fu, 2013). The climate of the CLP is jointly affected by latitude, longitude and topography, displaying distinct northwest-southeast precipitation gradient (Zhang et al., 2014). The vegetation types in the CLP are strongly influenced by the spatial patterns of precipitation, with five types of potential natural vegetation distributing from the southeast to the northwest in the CLP, i.e., forest, forest-steppe, steppe, desert-steppe, and desert (Fig. 1) (Fu et al., 2017). 2.2. Calculations of EWUE and PWUE Spatial variation in the ecosystem WUE (EWUE, i.e., the apparent WUE) of the CLP region was analyzed using two EWUE datasets: the EWUE data calculated from MODIS GPP/ET datasets (indicated by EWUEm hereafter) and the EWUE data based on flux-tower observations in China (indicated by EWUEs hereafter). We obtained the MODIS GPP and ET data covering the time period 2000–2014 at a 1–km spatial resolution from the MODIS Collection 5 GPP and ET data products developed by the Numerical Terra Dynamic Simulation Group (MOD17A3 and MOD16 A3, http://www.ntsg.umt.edu/, acquired December 20, 2016) (Mu et al., 2011; Running et al., 2004; Zhao et al., 2005). MODIS GPP and ET were derived from the logic of radiation use efficiency (Monteith, 1972) and the Penman–Monteith approach (Monteith, 1965) using the gridded meteorological data and remote sensing data products from other MODIS data products as inputs (Mu et al., 2011; Running et al., 2004; Zhao et al., 2005). Annual mean EWUEm values (in gC kg−1H2O) were calculated as the ratio of annual total GPP (in gC m−2 year−1) over annual total ET (in kgH2O m−2 year−1) for each 1–km2 grid cell during the time period from 2000 to 2014. Grid cells missing GPP or ET values were masked from specific EWUEm datasets. EWUEs is another dataset constructed based on the flux-tower EWUE observations (calculated by the ratio of GPP to ET) in China (Zhu et al., 2015), which extrapolates gridded EWUE data on the basis of the geostatistical relationships of EWUE observations with altitude and leaf area index (LAI) (Gao et al., 2014). The EWUEs dataset covered the time period 2000–2010 at the 8–km spatial resolution. Plant-canopy WUE (PWUE, i.e., the real WUE) were calculated 2
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Fig. 1. Location of Chinese Loess Plateau. Grey lines show the boundaries of seven administrative divisions, while black lines show the boundaries of five vegetation zones (Fu et al., 2017).
the corresponding annual values during 2000–2014 at the grid-cell scale.
based on Eq. (2), that is, PWUE = EWUE/(T/ET). T/ET ratio was obtained using the Shuttle worth–Wallace model, from which the fluxtower observations of ET, with the same data source of EWUEs, was separated into site-level E and T data (Hu et al., 2009). The gridded data of T/ET were then derived using the logarithmic relationship between LAI and T/ET, hence generating the gridded PWUE data based on the gridded datasets of EWUEs and T/ET (Gao et al., 2014). The spatial resolution of PWUE dataset is the same with the EWUEs dataset.
2.4. Data analysis EWUEs and PWUE data were re-gridded to 1–km resolution using the nearest neighbor method in ArcGIS 10.2. The spatial patterns of mean annual EWUEm, EWUEs, and PWUE in the CLP were analyzed by averaging the corresponding datasets during the period from 2000 to the most recent year available on a per gridded-cell basis, respectively. Mean EWUEm, EWUEs, and PWUE values for different vegetation zones (i.e., forest, forest-steppe, steppe, desert-steppe, and desert) were then calculated. Grid cells with mean annual NDVI (from MODIS MOD13 dataset) lower than 0.1 were excluded in the spatial analyses. To analyze the spatial responses of CLP WUE to precipitation and temperature variations, the spatial distributions of EWUEm, EWUEs, and PWUE were projected into the two-dimensional space with MAT and MAP as the horizontal and vertical axis, respectively. The sensitivities of WUE to MAT and MAP were investigated by binning WUE pixels into intervals of 0.1 °C MAT and 5 mm yr−1 MAP, respectively. The precipitation sensitivity (SP) of WUE was quantified using the simple linear regression between pixel values of WUE and MAP under the same MAT interval, with the absolute SP and relative SP indicated by the linear slope value and the ratio of linear slope over the mean WUE under the same MAT interval, respectively. Same for temperature sensitivity (ST) of WUE. The NDVI sensitivity (SNDVI) of WUE along MAP and MAT ranges were also calculated to explore the effects of vegetation on the WUE variation in the CLP. A detrend analysis was also conducted to remove the linear trends of two variable involved in the regression analysis above, which could avoid spurious correlation between two
2.3. Datasets of precipitation, temperature, and NDVI Precipitation, air temperature, and NDVI were used to examine PWUE and EWUE variation along climate and vegetation gradients in the CLP. We acquired the datasets of annual total precipitation and annual mean air temperature during 2000–2014 at about 756 national basic meteorological stations in China from the China Meteorological Data Service Center (http://data.cma.cn/, acquired June 23, 2017). The method of thin-plate smoothing splines in AUSPLIN software was applied to generate the gridded datasets of precipitation and air temperature at a 1–km resolution (Hutchinson, 1995; Zheng et al., 2016). NDVI was selected as a proxy representing vegetation coverage status in this study, which is an important remote-sensing indicator for vegetation canopy greenness and structure (Zhu et al., 2013). We obtained the monthly NDVI data at a 1–km spatial resolution during 2000–2014 from a MODIS data product (MOD13A3, https://ladsweb.modaps. eosdis.nasa.gov/, acquired June 29, 2017). The 12 monthly composites for each year were averaged to calculate the corresponding annual mean NDVI values in each year for each grid-cell. We then obtained the gridded mean annual precipitation (MAP), mean annual air temperature (MAT), and mean annual NDVI across the CLP area by averaging 3
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Table 1 Statistical characteristics of ecosystem WUE (EWUE) and plant-canopy WUE (PWUE) of different vegetation zones averaged from 2000 to the most recent year available in the Chinese Loess Plateau. Different superscript letters denote a significant difference at a 5% confident level. Vegetation
EWUEm
Forest Forest-steppe Steppe Desert-steppe Desert Mean
1.55 1.31 1.14 1.08 1.21 1.26
± ± ± ± ± ±
EWUEs 0.19 0.22 0.19 0.26 0.31 0.28
a b d e c
2.19 1.61 0.97 0.87 0.93 1.37
PWUE
± ± ± ± ± ±
0.55 0.64 0.23 0.24 0.31 0.68
a b c e d
4.01 3.31 2.81 2.62 2.87 3.16
± ± ± ± ± ±
T/ET 0.45 0.60 0.35 0.52 0.59 0.71
a b d e c
0.54 0.47 0.35 0.34 0.33 0.42
± ± ± ± ± ±
0.08 0.10 0.06 0.09 0.08 0.12
a b c d e
showed a significant increasing trend with MAP gradients for the foreststeppe and steppe areas, and a significant decreasing trend for the desert zone (p-value < .001, Figs. 5–6a,b). For the forest vegetation zone, mean EWUE significantly increased with MAP to reach a maximum for a certain MAP interval (about 600 mm yr−1 and 700 mm yr−1 for EWUEm and EWUEs datasets, respectively), and then decreased with MAP (p-value < .05, red dashed lines in Fig. 5a,b). As to the EWUE–MAT relationship, results showed strong and positive relationships between the spatial variations in EWUE and MAT gradients for all the vegetation zones except the steppe area (Fig. 6a,b). Compared with EWUE, PWUE exhibited almost consistent trends with MAP or MAT gradients for given vegetation zones (Figs. 5–6c). Results also indicated a stronger dependence of EWUE and PWUE variations on the MAP gradients than on the MAT gradients for nearly all the vegetation zones (with higher R2 values, Figs. 5–6).
variables (Zheng et al., 2019). Meanwhile, the variation characteristics of WUE along climate and vegetation gradients were also analyzed for different vegetation zones, by drawing the scatter plots between WUE values averaged within the same MAP or MAT or NDVI intervals (as the vertical axis, with NDVI intervals of 0.01) and MAP or MAT or NDVI gradients (as the horizontal axis). 3. Results 3.1. Variations in EWUE and PWUE in the CLP Mean EWUE in the CLP was estimated to be 1.26 ± 0.28 g C kg−1 H2O from MODIS dataset and 1.37 ± 0.68 g C kg−1 H2O from geostatistical model (Table 1). The EWUE derived from two datasets showed similar spatial variations (Fig. 2a,b) regardless of the large differences in the EWUE estimates for different vegetation zones (Table 1). EWUE varied significantly across different vegetation zones (p-value < .05, Table 1), generally with higher EWUE in the more humid (where MAP > 400 mm yr−1, Fig. 2e) and irrigated areas than in the arid and semi-arid areas (where MAP < 400 mm yr−1) (Fig. 2a,b). The PWUE estimates were consistently larger than EWUE estimates for the same vegetation region, with the mean PWUE of 3.16 ± 0.71 g C kg−1 H2O in the CLP (Table 1). The spatial pattern of PWUE was similar to that of EWUE (Fig. 2c), with higher PWUE in the forest and forest-steppe areas as compared to other areas (p-value < .05, Table 1).
3.3. Variations of EWUE and PWUE along NDVI gradient Positive correlations were always significant in the spatial relationships of EWUE with NDVI gradients at the entire CLP region and all the vegetation zones (p-value < .001, Fig. 7a,b). The NDVI sensitivities (SNDVI) of EWUE were positive across most MAT and MAP intervals, and showed a declining trend with MAP gradient, with maximum EWUE–SNDVI in the cold and dry areas (red lines in the insets of Fig. 7a,b). The relative PWUE–SNDVI values were generally small and remained quite stable along MAP gradient, but with negative PWUE–SNDVI when MAP fell in the range of 400–470 mm yr−1 (Fig. 7c, top-right inset). Similar results were also found when detrend analysis was used prior to the NDVI sensitivity analyses (Fig. S2). PWUE also showed significant increasing trends along NDVI gradients for the forest, forest-steppe, and steppe areas (Fig. 7c). For the desert and desert-steppe areas, however, the PWUE–NDVI relationships were negative when NDVI < 0.4, and then turned to be positive when NDVI > 0.4 (p-value < .05, Fig. 7c, red dashed line). The correlations between the spatial variations of PWUE and NDVI were generally weaker as compared to EWUE for all the vegetation zones (Fig. 7).
3.2. Variations of EWUE and PWUE along MAP and MAT gradients Spatial distributions of EWUE estimates from MODIS product and geostatistical model were basically the same in the MAT–MAP two-dimensional space (Fig. 3). Spatially, EWUE from two approaches tended to increase with increasing MAP and MAT, and reached a maximum in regions with MAT > 10 °C and MAP in the range of 500–600 mm yr−1 (Fig. 3). The precipitation sensitivities (SP) of EWUE were quite different for the two EWUE datasets, but with similar variation trend across MAT intervals in the CLP (Fig. 3, left insets). SP appeared to be positive for most MAT range except the very cold regions where MAT < 4 °C, and gradually decreased with MAT when MAT is higher than 10 °C (i.e., mostly forest and forest-steppe areas) (Fig. 3, left insets). Compared with EWUE, PWUE estimates distributed in the MAT–MAP climate space in a manner alike with that of EWUE (Fig. 4), and PWUE–SP varied similarly along MAT range, with positive SP declining with MAT when MAT is higher than 10 °C (Fig. 4, left inset). Large differences exhibited in the variations of temperature sensitivity (ST) of EWUE and PWUE along MAP intervals (Figs. 3–4, bottom insets). Consistent results were observed when applying detrend analysis to the calculations of MAP and MAT sensitivities (Fig. S1 in Appendix). The overall spatial variations in EWUE and PWUE along MAP and MAT gradients showed consistent WUE–MAP and WUE–MAT relationships to those diagnosed from MAT–MAP climate space at the CLP-regional scale, that EWUE and PWUE positively correlated with MAP and MAT gradients (p-value < .001, black lines in Figs. 5–6). However, the responses of EWUE to MAP or MAT variations varied among different vegetation zones (Figs. 5–6). Mean EWUE from both approaches
4. Discussion 4.1. Variations in EWUE and PWUE along climate gradients in the CLP Plant-canopy WUE (i.e., GPP/T) is the ‘real’ efficiency of ecosystem water use in producing dry matter (Hu et al., 2008). Canopy-scale plant transpiration (T), dominating terrestrial water fluxes (Jasechko et al., 2013), was often replaced by ecosystem ET (i.e., calculating ecosystem WUE) due to challenges in upscaling T from individual plant to plantcanopy scale. Site-level studies have indicated that the responses of WUE to certain environmental changes (e.g., climate warming, increased precipitation) were not consistently the same at different organismic levels (Niu et al., 2011; Quan et al., 2018). The correlation and divergence between the variations in plant-canopy WUE and ecosystem WUE along climate and vegetation gradients have not been clarified yet for a large-regional scale. Based on the MODIS datasets and observation-based geostatistical 4
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Fig. 2. Spatial patterns of (a) MODIS-derived ecosystem WUE (EWUEm), (b) geostatistical model-based ecosystem WUE (EWUEs), and (c) plant-canopy WUE (PWUE) in the Chinese Loess Plateau. The spatial patterns of (d) mean annual temperature (MAT), (e) mean annul precipitation (MAP), and (f) mean annual NDVI are also provided for reference. Grey lines in the panel (d) and (e) are isolines of MAT and MAP, respectively. Grey lines in other panels indicate boundary of different vegetation zones.
4.1.1. PWUE variation along MAP gradient PWUE reflects the real WUE of vegetation communities themselves within the ecosystem (Hu et al., 2008; Niu et al., 2011), while the spatial distributions and compositions of vegetation are determined by the geographic variation in climatic conditions (Zheng et al., 2016) especially water supply (i.e., MAP) for the CLP region (Fig. 2). As it is, the underlying factor determining PWUE variation is the geographic pattern of MAP. Our results showed positive precipitation sensitivities (SP) of PWUE for most MAT range in the CLP (Fig. 4, left insets), and PWUE was generally higher for the forest vegetation than the steppe vegetation (Fig. 2c and Table 1). It could be explained from two aspects. On the one hand, previous study showed that site-level PWUE variation was mainly affected by carbon (i.e., GPP) rather than water processes (i.e., T) (Baldocchi, 1994). The denser canopies of forest vegetation, which locates in relatively humid regions, act to intercept more solar radiation and display higher light use efficiency (Hu et al., 2008), which will benefit plant growth and hence PWUE. On the other hand, the generally higher root surface area of forest vegetation could facilitate more water
model, this study indicated that EWUE and PWUE estimates were similar in terms of spatial distributions in the CLP (Fig. 1 and Table 1). That is, EWUE and PWUE showed higher values for the areas with more water available and lower values for the arid and semi-arid areas (MAP < 400 mm yr−1), which is consistent with previous studies on EWUE variations based on satellite-based datasets, carbon cycle models, and site-level meta-analyses (Sun et al., 2016; Xiao et al., 2013; Zhang et al., 2016; Zhu et al., 2015). Our data also suggested that the sensitivity of EWUE and PWUE to MAP was mainly positive along MAT range in the CLP (Figs. 3–4, left insets), and the spatial relationships of EWUE and PWUE with MAP gradient tended to be stronger as compared to MAT gradient at the CLP-regional and vegetation-zone scales, especially for EWUE estimates (Fig. 5). Thus, climatic regulations on the WUE variations were dominant by the water conditions rather than temperature in the CLP at both the plant-canopy and ecosystem levels. Nevertheless, the mechanisms underlying the spatial variations in EWUE and PWUE in responding to MAP gradient were not the same in the CLP.
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Fig. 3. Distribution of EWUE in the MAT–MAP climate space of the CLP: (a) MODIS-derived EWUEm, (b) geostatistical model-based EWUEs. The left insets show the MAP sensitivity of EWUE across the MAT range. Black lines in insets refer to the absolute changes of EWUE along MAP gradient (indicated by the slope calculated from simple linear regression between EWUE and MAP under the same MAT interval). Red lines represent the relative changes of EWUE along MAP gradient (calculated as the absolute slope value divided by the mean EWUE under the same MAT interval). Similarly, the bottom insets show the MAT sensitivity of EWUE across the MAP range.
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Fig. 4. Distribution of PWUE in the MAT–MAP climate space of the CLP. The left inset show the MAP sensitivity (black/red lines) of PWUE across the MAT range. The bottom inset show the MAT sensitivity of PWUE across the MAP range.
S3) for the arid and semi-arid areas, our results also suggested that the EWUE–MAP relationships and EWUE–SP for the arid and semi-arid areas (i.e., mainly deset and desert-steppe regions) tended to be negative (Fig. 5a,b, left insets in Fig. S4–S5). We also noticed that, both the SP of PWUE and EWUE appeared to gradually decrease with MAT when MAT > 10 °C (i.e., mainly forest and forest-steppe areas, Fig. 2e) (Figs. 3–4, left insets). The positive WUE–MAP relationships also turned into negative when MAP was larger than a certain amount (about 600–700 mm yr−1 in this study) for the forest vegetation zone (Fig. 5). This might have a relevance with the increasing cloudy days and decreasing solar radiation resulted from higher MAP, which could limit plant growth (Sun et al., 2016) and water loss processes (Zheng et al., 2016) and their sensitivities to MAP (Fig. S6), hence decreasing the sensitivity of WUE on the MAP variation.
uptake and higher root productivity, and hence improve plant-canopy WUE (Niu et al., 2011). This study also found that, the spatially PWUE–MAP relationships tended to be negative for the arid and semi-arid areas (i.e., MAP < 400 mm yr−1), including desert, desert-steppe, and part of steppe vegetation zone (Fig. 5c). The PWUE–SP was also negative across most MAT intervals for these areas (Fig. S3, left insets). The reason for these phenomena could mainly attribute to ‘inherent water-use efficiency’ (IWUE) and its spatial relationship with precipitation gradient, that the T-based IWUE accounts the influences of vapor pressure deficit GPP × VPD (VPD) on PWUE (i.e., IWUE = ) (Beer et al., 2009; Sun et al., T 2016). A weak negative spatial relationship between IWUE and MAP has been indicated at the global scale using process-based models (Sun et al., 2016). Plant IWUE could be higher for relatively arid areas, because the long-term adaption of drought-tolerant plant species to the local climate conditions helps to shape the physiological and biochemical mechanisms of plants in maintaining efficient water use under arid conditions (Huang et al., 2016; Sun et al., 2016; Zhou et al., 2013). This negative IWUE-MAP relationship hence contributes to the negative PWUE–SP for the semi-arid and arid areas in this study.
4.2. Divergent EWUE and PWUE variations along NDVI gradient Spatial variations in EWUE and PWUE along vegetation gradient, measured by remote-sensing NDVI, were explored in this study. Our data illustrated divergent vegetation regulations of WUE variations at the plant-canopy and ecosystem levels. For the EWUE–NDVI relationships, our results showed significant positive correlations at the entire CLP region and all the vegetation zones (p-value < .001, Fig. 7a,b), which is associated with the decoupling of spatial variations in GPP and ET along NDVI gradient. Previous studies have demonstrated the spatially positive responses of GPP and ET to increasing vegetation coverage (Yu et al., 2008; Zheng et al., 2016), but the spatial variation in EWUE was dominant by GPP, rather than ET (Zhu et al., 2015). This is because that, the spatial pattern of ET comprehensively reflects the patterns of plant transpiration, evaporation of canopy interception, and soil evaporation (Zheng et al., 2016). The spatially increased vegetation coverage is supposed to increase plant transpiration and evaporation of canopy interception (Jung et al., 2011; Piao et al., 2007), and reduce soil evaporation (Hu et al., 2008; Law et al., 2002). Our study also confirmed that the sensitivity of GPP to NDVI (GPP–SNDVI) was higher than the sensitivity of ET to NDVI (ET–SNDVI) for areas with MAP < 500 mm yr−1 in the CLP (Fig. S7). Thus, the decoupled responses of
4.1.2. EWUE variation along MAP gradient Spatial relationships between EWUE and MAP were intrinsically affected by the spatial patterns of PWUE and ecosystem water allocation (i.e., T/ET ratio) along MAP gradient. T/ET variation has been proved to be positively correlated with the spatial distributions of vegetation coverage and climatic conditions (Hu et al., 2008; Law et al., 2002), that is, T/ET ratio is larger for the forest and forest-steppe areas with better water and heat resources (Table 1). It is because that, the high vegetation coverage and relatively low VPD for the humid ecosystems makes the water consumption closely couple with plant productivity, while more water vapor is lost to atmosphere through soil surface for the less humid areas (Sun et al., 2016).Together with PWUE variation among different vegetation zones, EWUE estimates from both approaches were generally larger for the forest vegetation area (Fig. 2a,b and Table 1), agreeing with previous studies (Gao et al., 2014; Tian et al., 2010; Zhu et al., 2015). Meanwhile, considering the small differences in T/ET ratios (Table 1) and negative PWUE–SP (Fig. 7
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Fig. 5. EWUE and PWUE variations along MAP gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same MAP interval. sl and R2 values in the brackets of legends are the slope values and coefficients of determination calculated from simple linear regression between WUE and MAP for each vegetation zone, respectively. Black lines in the figures represent the regression lines of mean WUE under the same MAP intervals along MAP gradients for the entire CLP region. Red dahsed lines denote the piecewise fitting lines of WUE–MAP relationships for the forest zone using the corresponding maximum WUE values as the threshold.
variations (Sun et al., 2016). On the other hand, as Eq. (2) implied, the variation in the EWUE–SNDVI reflects the dominant vegetation regulation of EWUE variation from either plant physiological processes (i.e., PWUE) or ecosystem physical processes (i.e., T/ET). It has been proved that the spatial variation in T/ET was strongly correlated with vegetation coverage (Hu et al., 2008; Law et al., 2002). Our data also indicated that the sensitivity of T/ET to NDVI varied similarly with EWUE along the MAP gradient (i.e., with higher sensitivity for less humid areas, Fig. S8), but the sensitivity of PWUE to NDVI (PWUE–SNDVI)
GPP and ET to NDVI result in the positive spatial variation in the CLP EWUE along NDVI gradient. We also found that the postive sensitivity of EWUE to NDVI (EWUE–SNDVI) gradually declined from the arid and semi-arid areas towards more humid areas in the CLP, especially for the MODIS-derived EWUE datasets (red lines in the insets of Fig. 7a,b). On the one hand, this declining EWUE–SNDVI should attribute to the comparable GPP–SNDVI and ET–SNDVI (inducing EWUE–SNDVI to zero) for the more humid areas (Fig. S7), which is consistent with global-scale EWUE 8
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Fig. 6. EWUE and PWUE variations along MAT gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same MAT interval.sl and R2 values in the brackets of legends are the slope values and coefficients of determination calculated from simple linear regression between WUE and MAT for each vegetation zone, respectively. Black lines in the figures represent the regression lines of mean WUE under the same MAT intervals along MAT gradients for the entire CLP region.
which seems contradict to our discussion above. The spatial relationships between PWUE and NDVI reflected the correlated geographic patterns of MAP and vegetation types, leading to weak dependence of PWUE on the NDVI variation across the MAP range (Fig. 7c). The much weaker correlations of PWUE with NDVI gradient for different vegetation zones as compared to EWUE (Fig. 7) also proved that the spatial variation in PWUE was mainly determined by the spatial pattern of MAP rather than NDVI. The negative PWUE–SNDVI in areas with MAP in
remained quite weak across most MAP intervals in the CLP (Fig. 7c). It means that, vegetation regulation on the EWUE variation under a certain MAP interval is dominant by influencing the spatial distributions of ecosystem water allocation (i.e., T/ET), rather than affecting the efficiency of plant communities in using water to produce dry matter (i.e., PWUE). Our results showed a significant increasing trend in PWUE variation along NDVI gradients in the CLP (R2 = 0.92, p-value < .001, Fig. 7c), 9
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Fig. 7. EWUE and PWUE variations along NDVI gradients for different vegetation zones. Marker in the figure denotes the mean WUE value of a given vegetation zone under the same NDVI interval. Black lines represent the regression lines of mean WUE under the same NDVI intervals along NDVI gradients for the entire CLP region, with corresponding sl and R2 values in the figures. Top-right and bottom-right insets show the NDVI sensitivity (SNDVI, black/red lines) of WUE across the MAP and MAT ranges, respectively.
the range of 400–470 mm yr−1 (Fig. 7c, top-right insets), and negative spatial relationships between PWUE and NDVI for the desert and desertsteppe areas when NDVI < 0.4 (Fig. 7c) might also associate with the negative IWUE–MAP relationships as discussed above.
the CLP were analyzed at the plant-canopy and ecosystem levels based on a MODIS-based remote-sensing dataset and a geostatistical modelbased dataset for the time period from 2000 to the most recent year available. Our study illustrated divergent spatial responses of plant and ecosystem WUE to climate and vegetation gradients in the CLP. Climatic regulations on WUE variations were dominant by water conditions (i.e., precipitation) rather than temperature, but with differential mechanisms at different organismic levels. The spatial variation
5. Conclusion Spatial variations in WUE along climate and vegetation gradients in 10
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in plant-canopy WUE depends on the geographic patterns of vegetation communities determined by the precipitation pattern in the CLP, leading to weak dependence of plant-canopy WUE on NDVI variation across precipitation range. Spatial variation in ecosystem WUE is intrinsically affected by both of plant-canopy WUE and ecosystem water allocation (i.e., T/ET ratio, highly correlated with MAP and NDVI gradients). The sensitivity of ecosystem WUE to NDVI is predominantly controlled by ecosystem T/ET, leading to postive sensitivity of WUE to NDVI at the ecosystem level. This study improves our understanding on the regional water‑carbon interactions at different organismic levels, and facilitates further studies related to the upscaling and modeling of regional-scale water and carbon dynamics.
Hutchinson, M., 1995. Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Inf. Syst. 9 (4), 385–403. Jasechko, S., et al., 2013. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–351. Jia, X., Shao, M., Zhu, Y., Luo, Y., 2017. Soil moisture decline due to afforestation across the Loess Plateau, China. J. Hydrol. 546, 113–122. Jin, N., et al., 2018. Effects of water stress on water use efficiency of irrigated and rainfed wheat in the Loess Plateau, China. Sci. Total Environ. 642, 1–11. Jung, M., et al., 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116 (G3), G00J07. Law, B., et al., 2002. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 113 (1–4), 97–120. McVicar, T.R., et al., 2007. Developing a decision support tool for China’s re-vegetation program: Simulating regional impacts of afforestation on average annual streamflow in the Loess Plateau. For. Ecol. Manag. 251 (1–2), 65–81. Monteith, J., 1965. Evaporation and Environment, Symposia of the Society for Experimental Biology. pp. 205–234. Monteith, J.L., 1972. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9 (3), 747–766. Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 (8), 1781–1800. Niu, S.L., et al., 2011. Water-use efficiency in response to climate change: from leaf to ecosystem in a temperate steppe. Glob. Chang. Biol. 17 (2), 1073–1082. Piao, S., et al., 2007. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl. Acad. Sci. U. S. A. 104 (39), 15242–15247. Quan, Q., et al., 2018. Transpiration dominates ecosystem water use efficiency in response to warming in an alpine meadow. J. Geophys. Res. Biogeosci. 123, 453–466. Running, S.W., et al., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54 (6), 547–560. Su, C., Fu, B., 2013. Evolution of ecosystem services in the Chinese Loess Plateau under climatic and land use changes. Glob. Planet. Chang. 101 (1), 119–128. Sun, Y., et al., 2016. Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models. Glob. Ecol. Biogeogr. 25 (3), 311–323. Tian, H., et al., 2010. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007. Forest Ecol. Manag. 259 (7), 1311–1327. Wang, L., Shangguan, Z., 2015. Water-use efficiency of dryland wheat in response to mulching and tillage practices on the Loess Plateau. Sci. Rep. 5 (5), 12225. Xiao, J., et al., 2013. Carbon fluxes, evapotranspiration, and water use efficiency of terrestrial ecosystems in China. Agric. For. Meteorol. 182, 76–90. Yu, G., Wang, Q., 2010. Ecophysiology of Plant Photosynthesis, Transpiration, and Water Use. Science Press, Beijing (in Chinese). Yu, G., et al., 2008. Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables. New Phytol. 177 (4), 927–937. Zhang, B., Wu, P., Zhao, X., Gao, X., 2014. Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China. Hydrol. Process. 28 (4), 1689–1702. Zhang, T., Peng, J., Liang, W., Yang, Y., Liu, Y., 2016. Spatial-temporal patterns of water use efficiency and climate controls in China’s Loess Plateau during 2000-2010. Sci. Total Environ. 565, 105–122. Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95 (2), 164–176. Zheng, H., Wang, Q.F., Zhu, X.J., Li, Y.N., Yu, G.R., 2014. Hysteresis responses of evapotranspiration to meteorological factors at a diel timescale: patterns and causes. PLoS One 9 (6), e98857. https://doi.org/10.1371/journal.pone.0098857. Zheng, H., et al., 2016. Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: results from eddy covariance measurements. J. Geogr. Sci. 26 (10), 1391–1411. Zheng, H., et al., 2019. Revegetation has increased ecosystem water-use efficiency during 2000–2014 in the Chinese Loess Plateau: evidence from satellite data. Ecol. Indic. 102, 507–518. Zhou, S., Duursma, R.A., Medlyn, B.E., Kelly, J.W.G., Prentice, I.C., 2013. How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agric. For. Meteorol. 182–183, 204–214. Zhu, Z., et al., 2013. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5 (2), 927–948. Zhu, X.J., et al., 2015. Spatial variability of water use efficiency in China’s terrestrial ecosystems. Glob. Planet. Chang. 129, 37–44. Zhu, H., Huang, W., Liu, H., 2017. Loess terrain segmentation from digital elevation models based on the region growth method. Phys. Geogr. 39 (1), 1–16. Zhu, H., Zhao, Y., Liu, H., 2018. Scale characters analysis for gully structure in the watersheds of loess landforms based on digital elevation models. Front. Earth Sci. 12 (2013), 1–13.
Acknowledgements This research was jointly funded by National Natural Science Foundation of China (grant nos. 31700414, 31500390, 41790444), Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19020302), and National Key Research and Development Program of China (grant nos. 2016YFA0600104, 2016YFA0600103). We acknowledge the database and technical support from China Meteorological Data Service Center, and Numerical Terra Dynamic Simulation Group. We gratefully acknowledge the editors and reviewers for their constructive comments that helped improve the quality of this paper. Declarations of interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.gloplacha.2019.102995. References Baldocchi, D., 1994. A comparative study of mass and energy exchange rates over a closed C 3 (wheat) and an open C 4 (corn) crop: II. CO 2 exchange and water use efficiency. Agric. Forest Meteorol. 67 (3), 291–321. Beer, C., et al., 2009. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23 (2), GB2018. Beer, C., et al., 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329 (5993), 834–838. Brümmer, C., et al., 2012. How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric. For. Meteorol. 153, 14–30. Fang, Q.X., et al., 2010. Water resources and water use efficiency in the North China Plain: current status and agronomic management options. Agric. Water Manag. 97 (8), 1102–1116. Feng, X.M., et al., 2012. Regional effects of vegetation restoration on water yield across the Loess Plateau, China. Hydrol. Earth Syst. Sci. 16 (8), 2617–2628. Feng, X., et al., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6 (11), 1019–1022. Fu, B., et al., 2017. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci. 45 (1), 223–243. Gao, Y., et al., 2014. Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation. Agric. For. Meteorol. 195–196, 32–37. Hu, Z., et al., 2008. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Glob. Chang. Biol. 14 (7), 1609–1619. Hu, Z., et al., 2009. Partitioning of evapotranspiration and its controls in four grassland ecosystems: application of a two-source model. Agric. For. Meteorol. 149 (9), 1410–1420. Huang, M., et al., 2015. Change in terrestrial ecosystem water-use efficiency over the last three decades. Glob. Chang. Biol. 21 (6), 2366–2378. Huang, M., et al., 2016. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Glob. Chang. Biol. 22 (6), 2165–2177.
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