water
Article Changes of Grassland Rain Use Efficiency and NDVI in Northwestern China from 1982 to 2013 and Its Response to Climate Change
Juan Chang 1, Jiaxi Tian 1, Zengxin Zhang 1,2,*, Xi Chen 2, Yizhao Chen 1, Sheng Chen 3 and Zheng Duan 4 1 Joint Innovation Center for Modern Forestry Studies, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China; [email protected] (J.C.); [email protected] (J.T.); [email protected] (Y.C.) 2 State Key Laboratory of Hydrology-Water Resources and Hydraulics Engineering, Hohai University, Nanjing 210098, China; [email protected] 3 School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China; [email protected] 4 Chair of Hydrology and River Basin Management, Technical University of Munich, 80333 Munich, Germany; [email protected] * Correspondence: [email protected]; Tel.: +86-25-8542-8963
Received: 18 October 2018; Accepted: 15 November 2018; Published: 19 November 2018
Abstract: The grasslands in arid and semi-arid regions rely heavily on the use of rain, thus, improving rain use efficiency (RUE) is essential for securing sustainable development of grassland ecosystems in these areas with limited rainfall. In this study, the spatial and temporal variabilities of RUE for grassland ecosystems over Northwestern China during 1982–2013 were analyzed using the normalized difference vegetation index (NDVI) and precipitation data. Results showed that: (1) Although grassland area has decreased gradually over the past 30 years, the NDVI in most areas showed that the vegetation was gradually restored; (2) The trends of RUE increased in the east of Northwestern China and decreased in the west of Northwestern China. However, the trends of RUE for the high-coverage grasslands (vs. low-coverage grassland) increased (decreased) significantly over the past 30 years. (3) The RUE for the grasslands was positively correlated with air temperature, while it was negatively correlated with the change of annual mean precipitation in northwestern China. Moreover, the obvious RUE increasing trends were found in the vegetation restoration areas, while the RUE decreasing trends appeared in the vegetation degradation areas. This study will be helpful for understanding the impacts of climate change on securing the sustainable development of grassland ecosystems in arid and semi-arid regions.
Keywords: grassland; vegetation restoration; degradation; rain use efficiency; NDVI
1. Introduction Vegetation is an important part of a terrestrial ecosystem and plays an important role in global climate change [1,2]. The normalized difference vegetation index (NDVI) is a simple graphical indicator to monitor the change in vegetation by using remote sensing measurements [3]. It has been widely used in such large-scale vegetation dynamic monitoring such as global or continental studies. For example, Eastman et al. [4] used the seasonal trend analysis (STA) procedure to determine that over half (56.30%) of land surfaces were found to exhibit significant trends, and the NDVI in the green season was balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Laidler et al. [5] explored the relationship between the NDVI and percent-vegetation covers in a tundra environment, where variations in soil moisture, exposed soil, and gravel till
Water 2018, 10, 1689; doi:10.3390/w10111689 www.mdpi.com/journal/water Water 2018, 10, 1689 2 of 20 have significant influence on spectral response, and hence, on the characterization of vegetation communities. Suzuki et al. [6] using 5-year (1987–1991) monthly means analyzed the NDVI distribution and its seasonal cycle to investigate it in relation to temperature and precipitation over Siberia and its surrounding regions. It was found that the vegetation degeneration is serious in many areas, for example, Ma et al. [7] reported that the NDVI decreased in the whole Northwest of China, and the vegetation degeneration was serious during the period 1990–1999. Ma et al. [8] also found that vegetation coverage degenerated seriously in most parts of Northwestern China during the period 1981–2001. However, some research has reported that the NDVI had an increasing trend in other areas, for example, Zhou et al. [9] founded that the NDVI increased in the Heihe River basin from 1999 to 2007. Some studies have revealed that climate change and human activities might be the main driving factor affecting NDVI [10–13]. However, moisture might be one of the main factors determining the composition, distribution, and growth status of vegetation in arid and semi-arid regions [14]. The water use efficiency (WUE, ratio of carbon assimilated to water transpired) of vegetation plays an important role in determining the exchange of water between ecosystems and the atmosphere, and thus, affects the global water cycle. It also shapes the water-energy balance of ecosystems as a decrease in water fluxes may lead to an increase in surface temperature [15]. For example, Chen et al. [16] showed that the temperate Eurasian steppe WUE was on the rise in the middle part of the Eurasian continent, but that the grassland was recovering cannot be concluded due to climate changes during the period from 1999–2008. Precipitation is an essential factor in controlling biodiversity and ecosystem functioning of terrestrial biomes, especially for arid and semiarid ecosystems [17]. Rain use efficiency (RUE) is defined as the amount of biomass produced per unit of precipitation and is the key indicator for measuring the response of plant production to precipitation [18]. The RUE provides a useful index for improved understanding of the relationship between precipitation and vegetation productivity, as well as for evaluating the degradation of grasslands [14,17–19]. For example, Sahnoun et al. [20] using Modis level 2 G data estimated RUE and determined environmental changes along a spatial gradient of arid lands in Southern Tunisia, and they found that this methodology provided a way to monitor changes in resilience induced by land-use in this arid region, as well as support detection of future desertification. Being the best indicator of vegetation cover change, RUE change has received wide attention from scientists. Many studies of RUE in different regions have shown that RUE is affected by many factors such as temperature and precipitation [19,21,22]. For example, Hountondji [23] revealed the changes of RUE in the ongoing desertification process of West Africa and they found that the RUE over 49.5% of the Sahel region was stable, while the area under 38% declined and only 1.3% showed an upward trend during the years between 1982 to 1999. Webb et al. [24] reported that plant cover affects RUE through alterations in evapotranspiration rate: greater plant cover values lead to greater RUE. Gamoun [25] found that RUE tended to be higher during dry years and lower during wet years on Helianthemum kahiricum (loamy soils) in the desert rangelands of Tunisia. Mi et al. [26] also reported that RUE is positively (negatively) correlated with temperature (precipitation) in the Qinghai–Tibetan grassland. Wang [27] found that RUE has significant differences for the different vegetation cover types in the Tao River basin, and that RUE was negatively correlated with precipitation. Zhang et al. [28] analyzed the temporal and spatial variation of RUE in the Yellow River basin from 1998 to 2012, and they found that similar trends can be found for the NDVI and RUE in these areas. Kundu et al. [29] found that the regression slope of RUE mainly depends upon the dynamic condition of integrated NDVI and rainfall, and RUE has been used for monitoring vegetation degradation, and substantially, the process of desertification in western Rajasthan. Huxman et al. [30] reported that for vegetation RUE showed a downward trend as annual precipitation gradually increased from desert to steppe to forest. Paruelo et al. [31] found that RUE first rises and then falls, and peaks in areas with precipitation of 475 mm/a based on the study of 11 temperate grassland ecosystems around the world. Sala et al. [17] thought that the RUE is the most important factor in controlling production by analyzing the Water 2018, 10, 1689 3 of 20 data collected at 9500 locations across the central United States. Wessels et al. [32] used net primary productivity (NPP), RUE and Local NPP Scaling (LNS) to successfully detect the degraded areas in the Northern Provinces of South Africa as an indicator of land status. Due to geographical location and climatic conditions, the type of vegetation is dominated by grassland in Northwestern China [33,34]. The degradation/restoration of grassland in Northwestern China is a hot issue in recent years. Xu et al. [35] found that the grassland of Maduo County was seriously degraded in 1994 and the degradation of grassland vegetation was the most serious from 1994 to 2001, and the degraded areas of grassland decreased greatly and the degradation rate eased in the source region of the Yellow River during 2001 to 2006 and 2006 to 2009. Xian et al. [36] found that the degraded areas were mainly concentrated on grassland areas dominated by animal husbandry in headwaters region of Northeastern Sichuan, and the growth areas were concentrated in the plateau areas less affected by human activities. In addition, scholars have found that warming and human activities such as overgrazing were the main causes of grassland degradation [33,37]. Although more and more studies of RUE have been done in recent years, RUE research is still relatively few in Northwestern China [38]. The grassland in arid and semi-arid regions relies heavily on the use of rain and improving RUE is necessary for securing sustainable development of grassland ecosystems in these areas [39]. Northwestern China is far away from the sea with less rainfall, and the vegetation type is mainly occupied by grassland [40]. Based on the time series NDVI data and meteorological data, this study aims to explore the temporal and spatial variations of RUE and its response to the vegetation restoration/degradation in Northwestern China. Our findings are expected to have important implications for understanding and predicting ecological impacts on global climate change and for management practices in arid and semiarid ecosystems in Northwestern China and beyond [41].
2. Material and Methods
2.1. Study Area and Material The study area is located in Northwestern China with an area of about 4 million km2 (Figure1). The terrain in the northwestern region is mainly plateaus, basins, and mountains, including the Tarim River Basin, the Qaidam Basin, the Inner Mongolia Plateau, and the Qilian Mountains [13]. Most of the areas are controlled by arid and semi-arid climates with little rain and strong evaporation, therefore, low vegetation coverage and serious land desertification is a major feature in Northwestern China. The geographical differentiation of the area is significant, and the vertical zonal and horizontal zonal characteristics of vegetation are obvious. Most of the areas are sensitive to climate change in China [42,43]. The NDVI dataset at a spatial resolution of 8 km × 8 km and 15-day intervals were derived from GIMMS (Global Inventory Modeling and Mapping Studies group, https://ecocast.arc.nasa. gov/data/pub/gimms/3g.v1/). The GIMMS NDVI 3g.v1 was generated from National Oceanic and Atmospheric Administration’s (NOAA’s) Advanced Very High-Resolution Radiometer (AVHRR) data, and the spatial resolution was 1/12◦. The dataset spanned from July 1981 to December 2015. It was calibrated for sensor shift, cloud test, and removal of the effects of solar zenith angles and other factors [44]. The annual AVHRR GIMMS NDVI dataset used in this paper was obtained using the maximum value composite (MVC) method [45]. The land-use and land-cover change (LUCC) data came from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/). The resolution of the national land use/cover data in 2010 was 1km, and the project was converted and cropped (2010 LUCC data). The data included six classifications: cultivated land, woodland, grassland, waters, construction land, and unused land. The grassland was further divided into three classifications: HCG (higher coverage grassland), MCG (middle coverage grassland) and LCG (low coverage grassland) (Table1). Water 2018, 10, 1689 4 of 20 Water 2018, 10, x FOR PEER REVIEW 4 of 20
Figure 1. Study area maps and meteorological station location.
Table 1. Definition of three types of grassland in northwestern China (HCG: higher coverage grassland; Table 1. Definition of three types of grassland in northwestern China (HCG: higher coverage MCG: middle coverage grassland; LCG: low coverage grassland). grassland; MCG: middle coverage grassland; LCG: low coverage grassland). Vegetation Water and Heat Grassland Abbreviation Vegetation Grassland Type Water and Heat Grassland Abbreviation Coverage (%) Grassland Type Conditions Characteristics Coverage (%) Conditions Characteristics HCG >50 Natural, Improved Better Growing densely HCGMCG >50 20–50 Natural, Natural, Improved Improved InsufficientBetter SparseGrowing vegetation densely MCGLCG 20–50 5–20 Natural, NaturalImproved WaterInsufficient deficiency Sparse vegetationvegetation LCG 5–20 Natural Water deficiency Sparse vegetation The temperature and precipitation data were derived from 183 meteorological stations in China The temperature and precipitation data were derived from 183 meteorological stations in China from 1982 to 2013 provided by the China Meteorological Data Network. from 1982 to 2013 provided by the China Meteorological Data Network. 2.2. Methodology 2.2. Methodology The RUE and the ratio of NPP to precipitation could be a critical indicator for evaluatingThe RUE the and response the ratio of NPP primary to precipitation productivity could to be variability a critical indicator of rainfall for evaluating in arid andthe semi-aridresponse of ecosystems primary productivity [14,18]. According to variability to studies, of rainfall accumulated in arid and NDVI semi is-arid closely ecosystems related to[14,18] NPP. inAccording arid and tosemi-arid studies, regions accumulated [15,17, 18NDVI,46,47 is]. closelyTherefore, related in the to study NPP areain arid we and used semi the- accumulatedarid regions NDVI[15,17,18,46,47] to calculate. Therefore, the RUE insteadin the study of the area NPP we value used in thethegrowing accumulated season. NDVI The growingto calculate season the startsRUE frominstead April of the and NPP ends value in October in the accordinggrowing season. to the monthly The growing mean season temperature starts andfrom precipitation April and end in thiss in studyOctober area. according to the monthly mean temperature and precipitation in this study area. The RUE was calculated as follows (1): 푖 ∑1 푁퐷푉퐼푖 푅푈퐸 = i NDVI (1) = ∑1∑푖 푃 i RUE 1i 푖 (1) ∑1 Pi where i refers to the month (from April to October). whereThei refers MVC to method the month was (from used April to synthesize to October). the monthly NDVI image data, and the average methodThe was MVC used method to calculate was used theto synthesize NDVI value the monthly of each NDVI grid in image the growing data, and season. the average The methodspatial wasdistribution used to calculatefrom 1982 the to NDVI2013 was value calculated of each grid by the in the Kriging growing interpolation season. The method spatial in distribution ArcGIS. from 1982The to 2013 linear was regression calculated analysis by the Krigingof NDVI interpolation and the year method was carried in ArcGIS. out to obtain the linear change trendThe values linear of regressionNDVI from analysis pixel to of cell NDVI to indicate and the whether year was the carried spatial out variation to obtain characteristics the linear change and trendchange values trends of of NDVI vegetation from pixelcoverage to cell was to significant, indicate whether and then the counted spatial variationthe ratio of characteristics the pixels of each and level to the total number of pixels. The RUE used the same methods. The trend of change is divided into Equation (2):
Water 2018, 10, 1689 5 of 20 change trends of vegetation coverage was significant, and then counted the ratio of the pixels of each level to the total number of pixels. The RUE used the same methods. The trend of change is divided into Equation (2):
n × ∑n i × NDVI − ∑n i ∑n NDVI b = i=1 i i=1 i=1 i (2) n 2 n 2 n × ∑i=1 i − (∑i=1 i) where b refers to the trend of change, i.e., slope; b > 0 means increasing trend, b < 0 means decreasing trend; i refers to year; n refers to length of time. The significance test of the slope was performed by t-test [48]. In order to study the influencing factors of RUE change, the Pearson correlation analysis method was used to analyze the correlation between the RUE and the simultaneous temperature data, and the correlation coefficients were obtained. The trend of change is divided into Equation (3):
n ∑i=1 xi − X yi − Y rxy = q (3) n 2 n 2 ∑i=1 xi − X ∑i=1 yi − Y where i refers to year; n refers to length of time; X refers to average RUE value, Y refers to average temperature value; rxy refers to the correlation coefficient between x and y; rxy > 0 means positive correlation, rxy < 0 means negative correlation. The significance test of the slope was performed by t-test. The meteorological stations in Northwestern China are unevenly distributed, with the east and central regions being denser and the regions near the deserts being very sparse. The Kriging interpolation method was used to interpolate the pointed-based rainfall to obtain spatial patterns of rainfall in the study areas following [49]. Simple linear regression was used in this paper for long-term linear trend test. The simple linear regression method is a parametric t-test method, which consists of two steps, fitting a linear simple regression equation with the time t as independent variable and Y as dependent variable; testing the statistical significance of the slope of the regression equation by the t-test. A simple but practical way of sensitivity analysis is to calculate and plot the relative changes of an input variable against the resultant relative change of the output variable as a curve (i.e., the sensitivity curve), then the corresponding relative change of the outcome can easily be read from the sensitivity curve for a certain relative change of the variable [50]. This method has been used by many authors. In this study, the variable (i.e., NDVI and meteorological) used the following equation:
X = X + ∆X; ∆X = 0, ±5%, ±10%, ±15%, ±20% (4) where X is the variable.
3. Results
3.1. The Changes of RUE for the Grassland in Northwestern China Figure2 shows the spatial distribution of annual mean RUE for the grasslands in Northwestern China from 1982 to 2013. In most regions, the annual mean RUE for all of the grassland was higher than that of HCG, MCG, and LCG areas. It also can be found that the lower RUE values appeared in the vegetation degradation areas, while the higher RUE values can be found in the vegetation restoration areas (Figure2a). For high coverage grassland area, the RUE varied between 0.005 and 0.015, and higher RUE value can be found near the Hulunbeier grasslands and Horqin grasslands (Figure2b). However, for RUE in the MCG areas, the RUE value was as high as 0.027 and the higher RUE values were located more on the edge of the Taklimakan Desert where precipitation was rare. Water 2018, 10, 1689 6 of 20
Moreover, the RUE value was also higher in the Northeastern Inner Mongolia Autonomous Region (Figure2c). Compared with the HCG and MCG, the higher RUE value appeared in Xinjiang Province, 50°N butWater lower 2018 RUE, 10, x valuesFOR PEER could REVIEW befound in50°N Southwestern Qinghai Province for the LCG areas (Figure6 of 202d). (a) All the grassland (b) High coverage
40°N 40°N
50°N 30°N 30°N (a) Grassland 50°N 50°N
(c) Moderate coverage RUE*100(d) Low coverage RUE*100 0 500 1,000 Km 0 500 1,000 Km 1 1.5 20°N 0.5 1 1.540°N 2 2.5 20°N 40°N 80°E 90°E 100°E 110°E40°N 120°E80°E 90°E 100°E 110°E 120°E
30°N 30°N 30°N
RUE*100 RUE*100 RUE*100 0 490 980 Km 0 500 1,0000 490Km980 Km 0.5 1 1.5 2 2.5 0.5 1 1.5 2 20°N 20°N 20°N0.5 1 1.5 2 2.5 80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E FigureFigure 2. 2The. The spatial spatial distribution distribution80°E ofof annualannual meanmean90°E rainrain u usese 100°Ee efficiencyfficiency ( (RUE)RUE110°E) in in Northwestern Northwestern120°E China China fromfrom 1982 1982 to to 2013. 2013 (.a ()a All) All the the grassland; grassland; ((bb),), HCG;HCG; ((cc),), MCG; ( (dd)),, LCG. LCG.
InIn order order to to reveal reveal the the long-term long-term changes changes ofof RUERUE for the grassland grassland in in Northwestern Northwestern China China from from 19821982 to to 2013, 2013, the the RUE RUE trends trends are are shown shown in in FigureFigure3 3.. As As shown shown in Figure 3 3a,a, the RUE for for all all the the grasslandsgrassland showeds showed obviously obviously increasing increasing trends trends in in the the eastern eastern regionregion ofof NorthwesternNorthwestern China, while it decreasedit decreased in the in the western western areas. areas. The The number number of of pixels pixels withwith increasingincreasing trends trends of of RUE RUE 39.91% 39.91% of ofthe the totaltotal number number of of pixels, pixels, of of whichwhich 9.26% of of the the pixels pixels increased increased significantly. significantly. However, However, the number the number of ofpixels with decreasing decreasing trends trends of of RUE RUE were were 39.61% 39.61% indicating indicating that that the the grassland grassland RUE RUE trend trend was was decreasing.decreasing. As As for for the the HCG,HCG, the number number of of pixels pixels with with increasing increasing trends trends was wasas high as highas 62.23% as 62.23% of the of thetotal total number number of of pixels, pixels, of of which which 17.02% 17.02% of of the the pixels pixels increased increased significantly, significantly, while while the the number number of of pixelspixels with with decreasing decreasing trend trend was was 22.87% 22.87% (Figure(Figure3 3b).b). ItIt cancan be found that that the the percen percentt of of pixels pixels with with RUERUE changes changes for for the the MCG MCG and and LCM LCM grasslands grassland weres were lower lower than than that that of HCGof HCG grasslands grassland (Figures (Figure3c,d). 3c,d). The percent of pixels with RUE increasing trends were 41.82% and 23.26%, respectively. The percent of pixels with RUE increasing trends were 41.82% and 23.26%, respectively. However, However, the percent of pixels with decreasing trends was as high as 58.13% for LCG grasslands, the percent of pixels with decreasing trends was as high as 58.13% for LCG grasslands, which was which was higher than that of HCG and MCG grassland (Table 2). higher than that of HCG and MCG grassland (Table2). Table 2. The percent of pixels with RUE changes for the grasslands during the past 30 years in Table 2. The percent of pixels with RUE changes for the grasslands during the past 30 years in Northwestern China (Unit: %). Northwestern China (Unit: %). Grassland Type Increased Increased Significantly No Change Decreased Decreased Significantly Grassland Increased Decreased HCG 62.23Increased 17.02 No Change14.90 Decreased22.87 5.85 MCGType 41.82 Significantly9.55 25.46 32.72 Significantly7.27 LCGHCG 23.26 62.23 17.025.43 14.9018.60 58.13 22.87 5.8527.13 All the grasslandMCGs 39.91 41.82 9.26 9.55 25.4620.48 39.61 32.72 7.2714.72 LCG 23.26 5.43 18.60 58.13 27.13 All the Figure 4 shows the annual39.91 mean and 9.26 linear trends 20.48 of RUE. The 39.61grassland RUE in 14.72 Northwestern grasslands China showed a decreasing trend, and the highest (lowest) RUE value occurred in the year of 1997 (1998). As for the HCG, the RUE showed a significantly increasing trend, and the RUE value varied greatly during the period of 1996–1999 (Figure 4b). However, the RUE showed a significantly decreasing trend for the LCG areas (Figure 4d).
Water 2018, 10, 1689 7 of 20
Figure4 shows the annual mean and linear trends of RUE. The grassland RUE in Northwestern China showed a decreasing trend, and the highest (lowest) RUE value occurred in the year of 1997 (1998). As for the HCG, the RUE showed a significantly increasing trend, and the RUE value varied greatly during the period of 1996–1999 (Figure4b). However, the RUE showed a significantly decreasingWater 2018 trend, 10, x forFOR thePEER LCG REVIEW areas (Figure4d). 7 of 20 50°N Water 2018, 10, x FOR PEER REVIEW 7 of 20 (a)(a) AllAll thethe grasslandgrassland (b) High coverage
40°N 40°N
30°N 30°N 30°N (c)30°N Moderate coverage
RUE trends (c) Moderate coverage (d) Low coverage RUE trends (c) Moderate coverage RUE trends (d) Low coverage Decreasing significantly No change IncreasingIncreasing Decreasing significantlysignificantly significantly No change Increasing significantly Decreasing40°N Increasing Decreasing significantly No change Increasing significantly 20°N Decreasing40°N Increasing 20°N Decreasing IncreasingIncreasing 20°N 40°N20°N 40°N 40°N 80°E 90°E 100°E 110°E 120°E 80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E
30°N 30°N 30°N 30°N 30°N
RUE trends RUE trends RUE trends RUE trends Decreasing significantlyRUE trendsNo change IncreasingDecreasing significantly significantly Not change IncreasingIncreasing significantlysignificantly Decreasing significantlyDecreasingNo change significantlyIncreasingDecreasingNo significantly change IncreasingIncreasing significantly Decreasing DecreasingIncreasing significantly20°N DecreasingNo change IncreasingIncreasing significantly 20°N Decreasing DecreasingIncreasing 20°N Increasing 20°N Decreasing Increasing
80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E 80°EFigureFigure 3. The 390°E. The spatial spatial changes100°E changes of of RUERUE110°E in Northwestern Northwestern120°E80°E China China from90°E from 1982 1982 to 2013100°E to. 2013.(a) All (grasslanda)110°E All grasslands;s; (b) 120°E Figure 3. The spatial80°E changes of90°E RUE in Northwestern100°E China from110°E 1982 to 2013120°E. (a) All grasslands; (b) HCG; (c) MCG;80°E (d) LCG. 90°E 100°E 110°E 120°E (b) HCG;HCG; ( c(c)) MCG; MCG; (d) LCG.
1.4 1.4 1.4 y=-0.001x+3.09 1.4 (a)(a) AllAll thethe grasslandgrassland y=-0.001x+3.09 (b)(b) HighHigh coveragecoverage y=0.003x-5.40 r=-0.08,P>0.05 r=-0.08,P>0.05 r=0.30,P<0.1r=0.30,P<0.1
1.2 1.2
1.0 1.0
1.0RUE*100 1.0
RUE*100
RUE*100 RUE*100 0.8 0.8 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 Year 1985 1990 1995Year 2000 2005 2010 Year Year 1.4 (c) Moderate coverage (d) Low coverage y=-0.004x+10.71 1.4 (c) Moderate coverage (d) Low coverage y=-0.004x+10.71 r=-0.30,P<0.1 1.2 r=-0.30,P<0.1
1.2
1.0 1.0 1.0
1.0 RUE*100
RUE*100 RUE*100 RUE*100 y=-0.00006x+0.94 0.8 0.8 0.8 r=0.004,P>0.05r=0.004,P>0.05 1985 1990 1995 2000 2005 2010 1985 1990 1995 2000 2005 2010 1985 1990 1995Year2000 2005 2010 1985 1990 1995Year2000 2005 2010 Year Year Figure 4. The annual mean and linear trends of RUE in Northwestern China from 1982–2013. (a) All FigureFigure 4. The 4. The annual annual mean mean and and linear linear trends trends ofof RUERUE in Northwestern Northwestern China China from from 1982 1982–2013.–2013. (a) All (a ) All grasslands; (b) HCG; (c) MCG; (d) LCG. grasslands;grassland (bs;) HCG;(b) HCG; (c) ( MCG;c) MCG; (d ()d LCG.) LCG.
3.2. The Relations between RUE and Vegetation Restoration//Degradation inin Northwestern China The status of LUCC in Northwestern China in the year of 2010 is shown in Figure 5. Thisis figurefigure clearly shows that the grassland was the main type of land use in Northwestern China and it can be foundfound thatthat thethe ggrasslandsrasslands occupyoccupy nearlynearly 40%40% ofof thethe landland surfacesurface ofof thethe Northwestern China. The
Water 2018, 10, 1689 8 of 20
3.2. The Relations between RUE and Vegetation Restoration/Degradation in Northwestern China The status of LUCC in Northwestern China in the year of 2010 is shown in Figure5. This figure clearly shows that the grassland was the main type of land use in Northwestern China and it can be found that the grasslands occupy nearly 40% of the land surface of the Northwestern China. Water 2018, 10, x FOR PEER REVIEW 8 of 20 The grassland was mainly distributed in the central regions of Inner Mongolia Plateau, Ningxia Province,grassland most was parts mainly of Gansu distributed Province in the central and Qinghairegions of Province,Inner Mongolia and Plateau, the western Ningxia partProvince, of Xinjiang Autonomousmost parts Region. of Gansu Most Province parts and of Xinjiang Qinghai Province, were deserts, and the Taklimakan western part Desertof Xinjiang isin Autonomous the Xinjiang, and the grasslandRegion. inMost this parts area of was Xinjiang distributed were deserts, outside Taklimakan the desert. Desert From is in thisthe Xinjiang, figure, it and can the also grassland be found that in this area was distributed outside the desert. From this figure, it can also be found that the higher the higherNDVI NDVI value valuess were in were the innortheast the northeast and south and areas south; however, areas; lower however, NDVI lower values NDVI appeared values in the appeared in the westwest areasareas where where distributes distributes of large of large desert desertss are found are (Figure found 6 (Figure). 6).
Kalamali Hulun buir
Altai Mt. Yili Valley Horqin Qilian Mt. 40°N Tianshan Mt. Xilin Gol
Taklimakan Desert Qaidam Basin Inner Mongolia PlateauUlanqab
30°N Gannan LUCC Yushu Woodland Waters Unused land Grassland Construction land Cultivated land
80°E 90°E 100°E 110°E 120°E FigureFigure 5. The 5. land-useThe land-use and and land-cover land-cover changechange (LUCC (LUCC)) classification classification in the in year the of year 2010. of 2010.
To revealTo reveal the changesthe changes in in vegetation vegetation coverage coverage for for the grassland the grasslandss in Northwestern in Northwestern China, the China, the featuresfeatures of of HCG, HCG, MCG MCG,, and and LCG LCG were were analyzed analyzed in this paper. in this The paper. grass coverage The grass rate for coverage the HCG, rate for MCG, and LCG was >50%, 20~50%, and 5~20%, respectively (Table 2). It can be seen from Table 3 that the HCG, MCG, and LCG was >50%, 20~50%, and 5~20%, respectively (Table2). It can be seen from the LCG areas were obviously bigger than that of the HCG and MCG areas in the study area. Overall, Table3 thatthe grassland the LCG areas areas in wereNorthwestern obviously China bigger showed than a decreasing that of the trend HCG during and the MCG past 30 areas years in and the study area. Overall,the grassland the grassland areas decreased areas in by Northwestern 25,187 km2 from China 1990 showedto 2010. In a decreasing1990, the grassland trend duringarea in the past 30 years andnorthwest the grassland region accounted areas decreased for 39.59% by of 25,187the total km area.2 from By 2010, 1990 the to grassland 2010. In area 1990, accounted the grassland for area in the northwest38.96% of the region total area accounted of the northwest for 39.59% region, of and the the total area area. decreased By 2010, by 0.63%. the grassland It can also be area found accounted that the HCG, MCG, and LCG areas decreased by 9305, 4679, and 11,203 km2 during the past 30 years, for 38.96% of the total area of the northwest region, and the area decreased by 0.63%. It can also be respectively. The HCG decreased from 10.59% to 10.36%, a total of 0.23%; MCG decreased2 from found that12.88% the to HCG, 12.76%, MCG, a total and of 0.12%; LCG areasand LCG decreased decreased by the 9305, most, 4679, a total and of 0.28%. 11,203 Therefore, km during it can the be past 30 years, respectively.inferred that the The grassland HCG decreased area in Northwestern from 10.59% China to has 10.36%, decreased a total gradually of 0.23%; in the MCG past decreased30 years. from 12.88% to 12.76%, a total of 0.12%; and LCG decreased the most, a total of 0.28%. Therefore, it can be inferred thatTable the 3grassland. Grassland area and three in Northwestern types of grassland China areas hasin Northwestern decreased China gradually (Area, in unit: the km past2; 30 years. Percent, unit: %).
Table 3. Grassland and threeAll Grasslands types of grassland areasHCG in NorthwesternMCG China (Area, unit:LCG km 2; Percent, Year unit: %). Area Percent Area Percent Area Percent Area Percent 1990 1,564,993 39.59 418,786 10.59 509,024 12.88 637,183 16.12 All Grasslands HCG MCG LCG Year 2000 1,548,210 39.18 410,447 10.39 508,580 12.87 629,183 15.92 2005 Area1,539,873 Percent 38.97 Area409,743 Percent10.37 504,098 Area 12.76 Percent 626,032 Area 15.84 Percent 19902010 1,564,9931,539,806 39.59 38.96418,786 409,481 10.5910.36 509,024504,345 12.76 12.88 625,980 637,183 15.84 16.12 2010 minus 2000 1,548,210−25,187 39.18 −0.63410,447 −9,305 10.39−0.23 508,580−4,679 −0.12 12.87 −11,203 629,183 −0.28 15.92 20051990 1,539,873 38.97 409,743 10.37 504,098 12.76 626,032 15.84 2010 1,539,806 38.96 409,481 10.36 504,345 12.76 625,980 15.84 2010 minus The annual−25,187 mean NDVI−0.63 for the grassland−9,305 s from− 0.231982 to 2013−4,679 is shown −in0.12 Figure 6.− It11,203 can be found−0.28 1990 that higher NDVI values appeared in the Hulunbeier grassland, Yili grassland, and lower NDVI
Water 2018, 10, 1689 9 of 20
The annual mean NDVI for the grasslands from 1982 to 2013 is shown in Figure6. It can be found that higher NDVI values appeared in the Hulunbeier grassland, Yili grassland, and lower NDVI valuesWater can 2018 be, 10 found, x FOR PEER on the REVIEW edge of the Tarim Basin and Qaidam Basin. In most regions,9the of 20 NDVI valuevalues varied can between be found 0.1 on and the 0.5edge and of thethe NDVITarim Basin value and was Qaidam as high Basin. as 0.73 In inmost the regions, Yili Valley the NDVI grasslands (Figurevalue6a). varied between 0.1 and 0.5 and the NDVI value was as high as 0.73 in the Yili Valley grasslands (FigureThe HCG 6a). was distributed in the Hulun Buir grasslands, the Horqin grasslands, the Ulanchabu grasslands,The the HCG grasslands was distributed in the in Yili the Valley,Hulun Buir and grassland the Kalamalis, the Horqin Mountains grassland grasslandss, the Ulanchabu in the Altay Regiongrassland and thes, the Qilian grasslands Mountain in the grasslands. Yili Valley, For and the the HCG Kalamali grassland Mountains areas, grassland higher NDVIs in the values Altay were mainlyRegion distributed and the Qilian in the Mountain Hulun Buir grassland grasslandss. For andthe HCG the Horqin grassland grasslands areas, higher (Figure NDVI6c). value Highers were NDVI valuesmainly appeared distributed in the in Qilian the Hulun Mountain Buir grassland grasslands,s andYushu the Horqin grasslands, grassland ands (Figure the Gannan 6c). Higher grassland NDVI area values appeared in the Qilian Mountain grasslands, Yushu grasslands, and the Gannan grassland in the MCG grassland area (Figure6e). However, the NDVI value varied greatly in most southern area in the MCG grassland area (Figure 6e). However, the NDVI value varied greatly in most parts of Gansu Province and Qinghai Province (Figure6g). As can be seen from Figure6b, compared to southern parts of Gansu Province and Qinghai Province (Figure 6g). As can be seen from Figure 6b, 1982,compared the grassland to 1982, in the the grassland northwestern in the regionnorthwestern was gradually region was increasing, gradually increasing, and three and types three of grasslandstypes also showedof grassland increasings also showed trends increasing (Figure6 trendsd,f,h). (Figure 6d,f,h).
50°N (a) Annual mean for all the grassland 50°N (b) 2013 minus1982 for all the grassland
40°N 40°N
30°N 30°N
50°N (c)NDVI Annual Value mean for High coverage 50°N (d)NDVI 2013 minus Value 1982 for High coverage 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -0.05 -0.02 0 0.02 0.05 0.08 0.1 0.15
80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E
40°N 40°N
30°N 30°N
50°N (e) Annual mean for Moderate coverage50°N (f) 2013 minus 1982 for Moderate coverage NDVI Value NDVI Value 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -0.05 -0.02 0 0.02 0.05 0.08 0.1 0.15 80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E 40°N 40°N
30°N 30°N
NDVI Value NDVI Value Figure 6. Cont. 0.05 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -0.02 0 0.02 0.05 0.08 0.1 0.15 80°E 90°E 100°E 110°E 120°E80°E 90°E 100°E 110°E 120°E
Water 2018, 10, 1689 10 of 20 Water 2018, 10, x FOR PEER REVIEW 10 of 20 50°N (a) All the grassland50°N (h) 2013 minus 1982 50°N (g) Annual mean for Low coverage 50°N (h) 2013 minus 1982 for Low coverage Water 2018, 10, x FOR PEER REVIEW 10 of 20 50°N (a) All the grassland50°N (h) 2013 minus 1982 50°N (g) Annual mean for Low coverage 50°N (h) 2013 minus 1982 for Low coverage 40°N 40°N 40°N 40°N
40°N 40°N 40°N 40°N 30°N 30°N 30°N 30°N
NDVI ValueNDVI Value NDVINDVI Value Value 30°N 30°N 30°N 30°N -0.05 -0.02 0 0.02 0.05 0.08 0.1 0.15 0.05 0.1 0.05 0.2 0.10.3 0.2 0.4 0.3 0.5 0.4 0.60.5 0.7 -0.05 -0.02 0 0.02 0.05 0.08 0.1 0.15 NDVI Value 80°E NDVI90°E ValueNDVI Value100°E 110°E 120°E80°ENDVI 90°EValue 100°E 110°E 120°E 80°EFigureFigure 6.90°EThe 6. The spatial spatial100°E distribution distribution of 110°E annual annual 80°Emean mean 120°ENDVI NDVI for 90°Ethe for growing the growing season100°E seasonfrom 1982 from110°E to 2013 1982 and to 2013120°E -0.05 -0.02 0 0.02 0.05 0.08 0.1 0.15 and the0.05 NDVI NDVI 0.1 differences differences0.05 0.2 0.10.3 between 0.2 0.4 between 0.3 0.5 the 0.4 2013 the 0.60.52013 and 0.7 1982 and in 1982 the-0.05 innorthwest the -0.02 northwest China. 0 0.02 (a China.,c, e0.05,g), T ( hea0.08,c annual,e,g ),0.1 The mean 0.15 annual NDVI for all the grasslands, HCG, MCG and LCG , respectively; (b,d,f,h), The NDVI differences 80°Emean NDVI90°E for all the grasslands,100°E HCG,110°E MCG and120°E80°E LCG, respectively;90°E (b,d100°E,f,h), The NDVI110°E differences120°E betweenFigure 6. 2013 The andspatial 1982 distribution for all the ofgrassland annual smean, HCG NDVI, MCG for and the LCG growing, respectively season from. 1982 to 2013 and 80°Ebetween 201390°E and 1982100°E for all the grasslands,110°E 80°E HCG,120°E MCG and90°E LCG, respectively.100°E 110°E 120°E the NDVI differences between the 2013 and 1982 in the northwest China. (a,c,e,g), The annual mean In this study, we defined the change trend of NDVI r > 0.1 as restoration, r > 0.34 as significant In thisNDVI study, for all we the defined grassland thes, HCG change, MCG trend and ofLCG NDVI, respectively; r > 0.1 as (b, restoration,d,f,h), The NDVI r > differences 0.34 as significant restoration;between r 2013< −0.1 and as 1982degradation, for all the grasslandand r < −0.34s, HCG as, significantMCG and LCG deterioration., respectively Figure. 7 shows that the restoration; r < −0.1 as degradation, and r < −0.34 as significant deterioration. Figure7 shows long-term trends of grassland restoration/degradation during the period from 1982–2013. It can be thatfound the long-termIn that this the study, NDVI trends we exhibits defined of grassland obviousthe change restoration/degradationincreasing trend of trends NDVI forr > the0.1 aswhole duringrestoration, Northwestern the r period > 0.34 China as from significant during 1982–2013. It canthisrestoration; be period, found especially thatr < −0.1 the as NDVI in degradation, the areas exhibits for and the obvious rNingxia < −0.34 increasing asProvince, significant Tianshan trends deterioration. for and the Altai whole Figure Mountains Northwestern 7 shows areas. that The the China duringresultslong this-term ind period, icatedtrends especiallythat of grassland the vegetation in restoration/degradati the areasrestoration for the appeared Ningxiaon during in Province,most the of period Northwestern Tianshan from 198 andChina2–2013 Altai during. It Mountainscan the be areas.pastfound The 30 resultsthat years. the indicated AccordingNDVI exhibits that to the theobvious statistical vegetation increasing analysis restoration trends of pixelfor appeared thes, thewhole numberin Northwestern most of of pixels Northwestern China with during NDVI China duringincreasingthis the period, past was especially 30 59.1%years. in of According the the areas total for number to the the Ningxia statistical of pixels, Province, of analysis which Tianshan 29.73%of pixels, and wasAltai the significantly Mountains number ofareas., and pixels The the with NDVIproportionresults increasing ind icatedwas was 18% that 59.1% for the the vegetation of pixels the totalwith restoration decreasing number appeared of trends pixels,, and in of most of which which of Northwestern 29.73% 4.12% decreased was significantly,China significantly during the and the proportionforpast the 30 washigh years.- 18%coverage According for the grassland pixels to the with(Figure statistical decreasing 7b). Similar analysis trends, results of pixel and cans of,be the whichfound number 4.12%in MCG, of decreased pixels LCG , withand significantly all NDVI the grasslandincreasing types was 59.1% (Figure of 7c,d). the total Ho wever, number it of was pixels, clearly of which observed 29.73% that was the significantly vegetation degraded, and the for the high-coverage grassland (Figure7b). Similar results can be found in MCG, LCG, and all the seriouslyproportion in wasthe low18%-coverage for the pixels grassland with decreasings. For example, trends the, and pixels of which of NDVI 4.12% with decreased decreasing significantly trends in grassland types (Figure7c,d). However, it was clearly observed that the vegetation degraded seriously thefor wholethe high region-coverage were grasslandas high as (Figure 33.38% 7b).of the Similar total resultsnumber can of pixels,be found of whichin MCG, 20.5% LCG of, andthe pixall elsthe in theshowedgrassland low-coverage significant types grasslands. (Figure decreasing 7c,d). For trends Ho example,wever, for itthe the was low pixels clearly coverage of NDVIobserved grassland with that decreasing (Table the vegetation 4). trends Moreover, degraded in the the whole regionvegetationseriously were as in degraded highthe low as-coverage 33.38% areas for ofgrassland the totalLCGs. For numberappeared example, of more the pixels, pixels around of of which NDVI the Taklimakan 20.5%with decreasing of thedesert pixels trends which showed in significantindicatedthe whole decreasing that region the grasslandwere trends as high degraded for as the 33.38% low seriously of coverage the intotal the grasslandnumber western of fragile pixels, (Table ecology 4of). which Moreover, region. 20.5% of the the vegetation pixels degradedshowedFrom areas significant Figure for the 7, it LCG decreasing can be appeared found trends that more the for NDVI around the low increased the coverage Taklimakan obviously grassland during desert (Table the which periods 4). Moreover, indicated 1982–1998 thethat the grasslandandvegetation 200 degraded9–2013 degraded for seriously the areasHCG, in forMCG, the the western LCG LCG, and appeared fragile all the ecology grassland more around region.s, while the the Taklimakan NDVI decreased desert in which the 2000s.Fromindicated However, Figure that7 the, itthe cangrassland changes be found degraded of annual that theseriously mean NDVI NDVI in increased the for western all the obviously typesfragile of e cologygrassland during region. thes in periods Northwestern 1982–1998 From Figure 7, it can be found that the NDVI increased obviously during the periods 1982–1998 and 2009–2013China showed for thesignificantly HCG, MCG, increasing LCG, trends and all during the grasslands, this period. while Although the NDVI the NDVI decreased in some in areas the 2000s. showedand 200 decreasing9–2013 for thetrends HCG, during MCG, the LCG past, 30and years, all the especially grassland fors ,the while low the coverage NDVI grasslanddecreased which in the However, the changes of annual mean NDVI for all the types of grasslands in Northwestern China indicates2000s. However, that ove rallthe thechanges vegetation of annual50°N restored mean significantly NDVI for al forl the the types grassland. of grassland s in Northwestern showedChina significantly showed significantly increasing increasing trends during trends thisduring period. this period. Although Although the NDVI the NDVI in some in some areas areas showed decreasingshowed trends decreasing during(a) All trends thethe pastduringgrassland 30 the years, past especially30 years, especially for the low for(b) the coverage High low coverage coverage grassland grassland which which indicates that overallindicates the that vegetation overall the restored vegetation significantly50°N restored significantly for the grassland. for the grassland.
40°N (a) All the grassland40°N (b) High coverage
40°N 40°N
30°N 30°N
30°N NDVI trends 30°N NDVI trends
Decreasing significantly No change Increasing Decreasingsignificantly significantly No change Increasing significantly 20°N 20°N Decreasing Increasing Decreasing Increasing NDVI trends NDVI trends Figure80°E 7. Cont. 90°E 100°E 110°E 120°E Decreasing significantly No change Increasing Decreasingsignificantly significantly No change Increasing significantly 20°N 80°E 90°E 100°E 110°E20°N 120°E Decreasing Increasing Decreasing Increasing
80°E 90°E 100°E 110°E 120°E 80°E 90°E 100°E 110°E 120°E Water 2018, 10, 1689 (a) All the grassland 11 of 20 Water 2018, 10, x FOR PEER REVIEW 50°N 11 of 20 (c) Moderate coverage (d) Low coverage 40°N
40°N 40°N
30°N 30°N 30°N
NDVI trends Trends NDVI trends Decreasing significantlyDegradedNo change significantlyIncreasingDecreasing Nosignificantly change significantlyRestored significantlyNo change Increasing significantly 20°N 20°N 20°N Decreasing DegradedIncreasing DecreasingRestored Increasing
FigureFigure 7. The 7. The grassland grassland normalized normalized difference difference vegetationvegetation index index (NDVI (NDVI)) trends trends for forthe thegrowing growing season season 80°E 90°E 80°E100°E 90°E110°E 120°E100°E 110°E 120°E in Northwesternin Northwestern China China from from 1982 1982 to to 2013. 2013. ( a(a)) All All80°E thethe grassland grasslands;90°Es; (b (b) HCG;) HCG; (100°Ec) ( MCG;c) MCG; (d) (LCG.110°Ed) LCG. 120°E
TableTable 4. The 4. The percent percent of ofpixels pixels with with NDVI NDVI changes changes for for the the grassland grassland during during the the past past 30 years 30 years in in NorthwesternNorthwestern China China (Unit: (Unit: %). %). Increased No Decreased GrasslandGrassland Type Increased Increased No Decreased Decreased Increased Significantly Change Decreased Significantly Type Significantly Change Significantly HCG 59.10 29.73 22.90 18.00 4.12 MCGHCG 61.82 59.10 34.87 29.73 22.9019.58 18.0018.60 4.125.88 LCGMCG 50.29 61.82 27.44 34.87 19.5816.33 18.6033.38 5.8820.50 All theLCG 50.29 27.44 16.33 33.38 20.50 56.87 30.92 19.11 24.02 11.02 grasslandAll thes 56.87 30.92 19.11 24.02 11.02 grasslands 3.3. The Relations between RUE and Climate Change in Northwestern China 3.3. The RelationsThe impact between of climate RUE andchange Climate on vegetation Change in water Northwestern use efficiency China is very important for water resources management. Figure 8a shows the spatial distribution of mean annual precipitation for the The impact of climate change on vegetation water use efficiency is very important for water vegetation growing season in Northwestern China. More than 300 mm of precipitation appeared in resourcesthe eastern management. and southeastern Figure8a region showss, the and spatial less than distribution 100 mm of meanprecipitation annual distributed precipitation in the for the vegetationnorthwestern growing region season where in Northwestern the desert occupied China. More most thanof the 300 area. mm The of precipitation precipitation appearedshowed an in the easternincreasing and southeastern trend in the regions, southwestern and less r thanegion, 100 and mm the of precipitation precipitation increased distributed significantly in the northwestern in the regionTianshan where theMountains, desert occupied Yinshan Mountains most of the, and area. the QinghaiThe precipitation Province. T showedhe decreasing an increasing trends for trend in theprecipitation southwestern appeared region, in andthe Greater the precipitation Khingan Mountains increased, the significantly Tarim Basin, inand the the Tianshan Altai Mountains Mountains, Yinshan(Figure Mountains, 8b). Figure and 8c theshows Qinghai the distribution Province. of Thethe annual decreasing mean trends temperature. for precipitation It can be found appeared that in the Greaterthe highest Khingan temperature Mountains, appeared the in Tarim the Taklamakan Basin, and Desert the Altai region Mountains where the temperature (Figure8b). was Figure as 8c high as 25 °C. The higher temperature (above 15 °C) was mainly located in the source region of the shows the distribution of the annual mean temperature. It can be found that the highest temperature Yellow River, the Qilian Mountains, and the Kunlun Mountains. However, the lower temperatures appeared in the Taklamakan Desert region where the temperature was as high as 25 ◦C. The higher were found in Qinghai◦ Province. Moreover, it was found that the temperatures showed significant temperatureincreasing (above trends 15in theC) whole was mainlyof Northwestern located inChina the from source 1982 region to 2013 of (Figure the Yellow 8d). River, the Qilian Mountains, and the Kunlun Mountains. However, the lower temperatures were found in Qinghai Province. Moreover, it was found that the temperatures showed significant increasing trends in the whole of Northwestern China from 1982 to 2013 (Figure8d).
Water 2018, 10, 1689 12 of 20 Water 2018, 10, x FOR PEER REVIEW 12 of 20 Water 2018, 10, x FOR PEER REVIEW 12 of 20
Figure 8. The spatial distribution and spatial change of precipitation and temperature for the growing Figure 8. TheThe spatial spatial distribution distribution and spatial spatial change change of of precipitation precipitation and temperature for the growing season in Northwestern China from 1982 to 2013. (a) Annual mean precipitation; (b) The trends of season in Northwestern China from 1982 to 2013. ( a) A Annualnnual mean precipitation; ( b) T Thehe trends of precipitation; (c) Annual mean temperature; (d) The trends of temperature precipitation; (c)) AnnualAnnual mean temperature; ((dd)) TheThe trendstrends ofof temperaturetemperature
Figure 9 shows the precipitation and temperature variabilities in northwestern China during the Figure 99 shows the precipitation precipitation and and temperature temperature variabilities variabilities in innorthwestern northwestern China China during during the past 30 years. The increasing trend of precipitation was gentle from 1982 to 2013, but there was a pastthe past 30 years. 30 years. The The increasing increasing trend trend of precipitation of precipitation was was gentle gentle from from 1982 1982 to to2013 2013,, but but there there was was a large fluctuation from 1996 to 2003. However, the temperature showed a significant increasing trend. largea large fluctuation fluctuation from from 1996 1996 to to 2003. 2003. However, However, the the temperature temperature showed showed a a significant significant increasing increasing trend. trend. The lowest temperature appeared in the year of 1993, and the highest temperature appeared in the The lowest temperature appeared in the year of 1993, and the highest temperature appeared in the year of 1998. year of 1998.
(a) Precipitation y=0.30x-374.6 300 (a) Precipitation y=0.30x-374.6 r=0.16,P>0.05
250
250
Precipitation(mm) 200 Precipitation(mm) 200
1985 1990 1995 2000 2005 2010 Year Year Figure 9. Cont.
Water 2018, 10, x FOR PEER REVIEW 13 of 20 WaterWater2018 2018,,10 10,, 1689x FOR PEER REVIEW 1313 of 2020
14.014.0 (b) Temperature y=0.05x-88.464y=0.05x-88.464 13.513.5 (b) Temperature r=0.85,P<0.01r=0.85,P<0.01 13.013.0 12.5
12.5
12.012.0 11.5
Temperature(°C) 11.5 Temperature(°C) 11.0 11.0 1985 1990 1995 2000 2005 2010 1985 1990 1995Year2000 2005 2010 Year Figure 9. The annual mean and linear trends of precipitation and temperature for the growing season FigureFigure 9.9. The annual mean and linear trends of precipitation andand temperature forfor thethe growinggrowing seasonseason from 1982 to 2013. (a) Precipitation; (b) Temperature. fromfrom 19821982 toto 2013.2013. ((aa)) Precipitation;Precipitation; ((bb)) Temperature.Temperature.
FigureFigure 1010 showsshows thethe correlationscorrelationscorrelations betweenbetween RUERUE andand temperaturetemperaturetemperature forfor thethethe grasslands.grasslandgrasslandss.. TheTheThe resultsresultsresults showedshowed thatthat therethere there waswas was goodgood good relationshiprelationship relationship betweenbetween between thethe the RUERUE RUE andand and temperaturetemperature temperature forfor the forthe grassland thegrassland grasslands.ss.. ForFor Forexample,example, example, thethe RUERUE the RUE forfor allall for thethe all grasslandgrassland the grasslandsss increasedincreased increased obviouslyobviously obviously withwith thethe with increasingincreasing the increasing temperature,temperature, temperature, andand thethe ◦ andincreasingincreasing the increasing raterate forfor the ratethe RUERUE for the spedsped RUE upup sped whenwhen up thethe when temperaturetemperature the temperature waswas higherhigher was thatha highernn 15°C15°C than (Figure(Figure 15 C 10a).10a). (Figure SimilarSimilar 10a). Similarresultsresults cancan results bebe foundfound can be withwith found thethe with MCGMCG the andand MCG LCGLCG and grasslandgrassland LCG grassland (Figure(Figure 10c,d). (Figure10c,d). It It10 alsoalsoc,d). cancan It also bebe foundfound can be thatthat found thethe thatRUERUE the andand RUE temperaturetemperature and temperature forfor thethe HCGHCG for the grasslandgrassland HCG grasslandsss hadhad aa goodgood had linearlinear a good relationshiprelationship linear relationship (Figure(Figure 1010 (Figurebb),), whilewhile 10 thetheb), whileexponentialexponential the exponential relationship relationship relationship can can be be foundfound can be in in found the the MCG, MCG,in the MCG, LCG LCG,, LCG,andand all all and the the all grassland grassland the grasslands.ss.. Although Although Although the the thecorrelationscorrelations correlations between between between the the the RUE RUE RUE and and and temperature temperature temperature were were were significant significant significant for for for the the the grasslandgrassland grasslands,ss,, the the highest highest highest correlationcorrelation coefficientcoefficientcoefficient (CC) ((CCCC)) values valuevaluess (CC (CC(CC = == 0.72) 0.72)0.72) can cancan be bebe found foundfound in in thein thethe MCG MCGMCG grasslands grasslandgrassland andss andand the the lowestthe lowestlowest CC valuesCCCC valuevalue appearedss appearedappeared in the inin HCG thethe HCGHCG grasslands grasslandgrassland (CCss (CC(CC = 0.20). == 0.20).0.20).
1010 33 (a)(a) AllAll thethe grasslandgrassland (b)(b) HighHigh coveragecoverage y=0.01x+0.81 88 (0.21x) y=0.01x+0.81 y=0.02ey=0.02e(0.21x)+0.61+0.61 r=0.20,P<0.01 r=0.55,P<0.01 22 r=0.20,P<0.01
66 r=0.55,P<0.01
4 4 1
RUE*100 1
RUE*100 RUE*100 RUE*100 22
00 00 -5-5 00 55 1010 1515 2020 2525 3030 -5-5 00 55 1010 1515 2020 2525 Temperature(°C) Temperature(°C) Temperature(°C) Temperature(°C)
Figure 10. Cont.
Water 2018, 10, 1689 14 of 20 WaterWater 2018 2018, ,10 10, ,x x FOR FOR PEER PEER REVIEW REVIEW 1414 ofof 2020
88 (c)(c) ModerateModerate coveragecoverage (d)(d) LowLow coveragecoverage 66 (0.17x) y=0.001e(0.37x)(0.37x)+0.71 y=0.04e(0.17x) +0.46 y=0.001e +0.71 66 y=0.04e +0.46 r=0.72,P<0.01r=0.72,P<0.01 r=0.57,P<0.01r=0.57,P<0.01 44
4
4
ModelModel RUE*100
2 RUE*100 RUE*100 2 RUE*100 22 EquationEquation
ReducedReduced 00 00 Chi-SqrChi-Sqr -5-5 00 55 1010 1515 2020 2525 -5-5 00 55 1010 1515 2020 2525 3030 Adj. R-Squa Temperature(°C) Temperature(°C) Adj. R-Squa Temperature(°C) Temperature(°C) Figure 10. The correlation between RUE and temperature. (a) All the grasslands; (b) HCG; (c) MCG; Figure 10. The correlation between RUE and temperature temperature.. ( aa)) A Allll the the grassland grasslands;s; ( (b)) HCG; ( (cc)) MCG; (d) LCG. B (d) LCG. B
TheThe sensitivitysensitivity ofof RUE RUE withwith thethe NDVINDVI and and precipitationprecipitation changeschanges werewere analyzedanalyzed and and thethe resultsresults areare shownshown inin FigureFigure 11 11.11.. The The RUERUE waswas moremore sensitivesensitive toto thethe changeschanges ofof precipitation.precipitation.precipitation. It It waswas foundfound thatthat the thethe RUE RUE was was was negatively negatively negatively correlated correlated correlated with with with the the the precipitation precipitation precipitation whil whil whileee it it was was it waspositively positively positively correlated correlated correlated with with withthethe NDVI NDVI the NDVI changes. changes. changes. Thus, Thus, Thus, it it can can it can be be be concluded concluded concluded that that that RUE RUE RUE might might might change change change with with the the the vegetation vegetation restoration/degradationrestoration/degradation in in Northwestern Northwestern China. China. Moreover, Moreover, Moreover, the the RUE RUE RUE showed showed obvious obvious increasing increasing trendstrends inin thethe vegetationvegetation restorationrestoration areas, areas, whilewhile the the decreasingdecreasing trends trends for for the the RUERUE cancan bebe foundfound inin thethe vegetationvegetation degradationdegradation areasareas asas aa whole.whole. However, However, unlikeunlike thethe aboveabove relationsrelations betweenbetween thethe RUERUE andand vegetationvegetation restoration/degradation,restoration/degradation, thethe RUERUE in in the the Tianshan TianshanTianshan Mountains Mountains and and Western Western QinghaiQinghai decreasedecreaseddecreasedd over overover the the pastpast 3030 30 yearsyears years,, , whilewhile while thethe the vegetationvegetation vegetation hadhad had obviousobvious obvious restoredrestored restored trends.trends. trends. TheThe The RUERUE RUE inin thethe in HorqintheHorqin Horqin Grassland Grassland Grasslandsss andand and Xilin Xilin Xilin Gol Gol Gol Grassland Grassland Grasslandsss hadhad had increasing increasing increasing trends trends trends with with with the the vegetation vegetation hadhad degradingdegrading trends.trends.
3030 PrecipitationPrecipitation 2020 NDVINDVI 1010 0
0
-10-10
-20-20 Relativechanges in RUE(%) Relativechanges in RUE(%) -30-30 -20-20 -10-10 00 1010 2020 Relative changes(%) Relative changes(%) FigureFigure 11. 11.11. The TheThe sensitivity sensitivity sensitivity of of ofRUE RUE RUE to to precipitation precipitation to precipitation and and andN NDVIDVI NDVI in in Northwestern Northwestern in Northwestern China China Chinaduring during during the the period period the period19819822––20132013 1982–2013.. .
4.4. Discussion Discussion GrasslandGrassland restoration restoration and and degradation degradation is is a a hot hot issue issue in in arid arid and and semi semi-aridsemi--aridarid areas. areas. In InIn our ourour studies, studies,studies, wewe foundfound thatthat thethe vegetationvegetation restorationrestoration mightmight havehave happenedhappened alongalong withwith thethe landland degradationdegradatdegradationion inin NorthwesternNorthwestern China.China. The The results results are are in in accord accord with with previous previous studies. studies. For For example, example, Yang Yang [39] [[39]39] studied studied thethe NDVINDVI ofof naturalnatural grasslandgrasslandss inin QinghaiQinghai ProvinceProvince andand foundfound thatthat thethe increasingincreasing andand decreasingdecreasing regionsregions ofof NDVINDVI inin QinghaiQinghai ProvinceProvince coexisted,coexisted, andand the the increaseincrease was was muchmuch larger larger thanthan thethe decreas decreasee
Water 2018, 10, 1689 15 of 20 the NDVI of natural grasslands in Qinghai Province and found that the increasing and decreasing regions of NDVI in Qinghai Province coexisted, and the increase was much larger than the decrease from 1982 to 2003. Vicente-Serrano et al. [51] showed that many natural and man-made factors can also have an impact on land degradation in semi-arid regions of the world using long time-series of remote sensing images and the NDVI for the period 1981 to 2011. Kouba et al. [52] investigated the influence of increasing anthropogenic pressure on land degradation in highly vulnerable semi-arid environments in the Mediterranean region. The changing trends of NDVI for the HCG, MCG, and LCG in Northwestern China were also similar to previous studies. For example, Cao et al. [53] found that the grassland degradation in northern Tibet was still relatively serious from 2000 to 2010. The area of degraded grassland accounts for 58.2% of the whole area, and the proportion of heavily degraded and heavily degraded grassland areas were 19.0% and 6.5% by 2010, respectively. Liu et al. [54] reported that the areas of high and medium vegetation coverage showed an increasing trend, while the area of low vegetation coverage areas showed a decreasing trend in Qinghai Province from 2002 to 2015. Zhang et al. [55] analyzed the difference between the years of 1982–2000 and 1982–2013, and they found that the vegetation had restored trends in the Qinghai-Tibetan Plateau during the period between 1982–2000, while the vegetation had degraded since the year 2000 in the Northern Tibetan Plateau where the low-coverage grasslands are distributed. The results are very similar to our findings in this study that the vegetation degraded seriously in the low coverage grassland. In many areas, temperature and precipitation are generally considered to be the two major environmental variables that produce vegetation and NDVI spatial gradients [56]. As many studies have shown, the warmth of the growing season is the dominant factor in plant species composition and vegetation production between high latitudes [57–59]. The quantification of NDVI’s integrated spatial and temporal environmental drivers expands our understanding of landscape level changes in vegetation assessed by remote sensing [60]. Previous studies suggested that for arid and semi-arid areas, increasing pre-season temperature could reduce water availability by increasing evaporation, thereby delaying the season onset [61]. Changes in vegetation productivity are mainly determined by climate change [62,63]. Bobkov et al. [64] reported that there was a statistically positive correlation between NDVI and spring and autumn temperatures of various vegetation types in the experimental area. Coniferous forests, that is, pine forests on poor soils, have the weakest correlation, and grassland and marsh are the most relevant. Some scholars have studied the NDVI of the Southwestern Romania forest ecosystem, pointing out that the annual average temperature and precipitation fluctuations directly affect the ecological quality of forest ecosystems [65]. The RUE is a useful indicator to reveal the dynamic response of precipitation to the change of vegetation in arid and semi-arid regions. In previous studies, Yan et al. [66] found that RUE is positively influenced by climate warming, but negatively affected by biofuel harvest in tall grass prairies of the Great Plains. These findings highlight the important roles of plant community structure and temporal distribution of precipitation in regulating ecosystem RUE. Mu et al. [67] reported that the inter-annual fluctuation of the RUE was negatively correlated with the annual precipitation in the arid areas, while it was strongly correlated with the annual temperature in Northwestern China from 2001 to 2010. Moreover, some research also discussed the response of RUE to the vegetation restoration/degradation and climate change. For example, Zhang et al. [22] found that RUE was in good relations to vegetation degradation, the more severe the degradation of vegetation, the lower the RUE and the improvement of vegetation restoration, and the RUE showed increasing trends in the Loess Plateau from 2000 to 2014. Holm et al. [18] used the NOAA satellite data to compare simulated estimates with total plant mass estimated by remote sensing and RUE, he thought that the dynamic changes in RUE can provide measurements of ecological degradation/recovery on a spatial scale. While Du et al. [68] reported that the RUE was opposite to the change of annual mean precipitation and they thought that the rapid increase or decrease of precipitation may cause pseudo-ecological restoration/degradation in a given ecosystem. The results were in accord with our research. Gamoun [25] thought that understanding how rainfall affects ranching productivity is crucial to predicting the effects of land degradation on the functioning of these Water 2018, 10, 1689 16 of 20 ecosystems on desert pastures in Tunisia. Mclendon et al. [69] revealed that vegetation preferentially used precipitation-derived soil moisture, even with abundant groundwater. Kundu et al. [29] used RUE to monitor vegetation degradation, and substantially, the process of desertification in western Rajasthan. These results are also in accord with ours. Therefore, the RUE can be considered as a useful tool for assessing s of semi-arid rangeland vegetation [70,71]. However, there are different points of view. In general, RUE tends to decrease with increasing aridity and potential evapotranspiration, both of which are closely related to ecosystem-level water balance. Some previous studies also showed that drier sites tend to have lower and less variable RUE because of low plant density, low production potential, high evaporation potential, and high tolerance to water stress [72]. In contrast to our results, warming has been reported to decrease grassland RUE as a result of reduced plant production [73]. These observations are reasonable because the differences in plant species composition, soil texture, and water retention capacity of different grasslands affect the production of grassland under climate warming. For example, Shen [74] pointed out that the redistribution of precipitation caused by topographic factors may be one of the reasons for the high RUE of vegetation in the Taklamakan Desert. Jobbagy et al. [75] thought that the RUE of the ecosystem might be higher due to the developed root system of the plant and the higher production per unit of water consumed in the arid region. Meanwhile, some research revealed that RUE may be affected by the species composition of the community. Because they thought that there were certain C4 super xerophytes in extremely arid regions where C4 plants had lower transpiration rates and higher photosynthetic rates in arid environments, therefore, the RUE may also be higher [67,76]. In our study, the Taklimakan Desert is surrounded by Tianshan Mountains and the Kunlun Mountains, and the supply of ice and snow melting water makes the grassland vegetation on the edge of the desert grow well and had higher RUE value.
5. Conclusions In this paper, the spatial and temporal changes of RUE and its response to the vegetation restoration/degradation in Northwestern China from 1982 to 2013 were analyzed by using the satellite-based NDVI and in situ meteorological data. The main conclusions drawn from this study are summarized as follows:
(1) Although the grassland areas have decreased gradually in Northwestern China during the past 30 years, the NDVI showed that the vegetation had obvious restoration as a whole in most grasslands. The grassland areas in Northwestern China has decreased by 25,187 km2 from 1990 to 2010, and the proportion of the vegetation restoration was as high as 56.87%, of which 30.92% was significant. (2) The trends of RUE for all the grasslands showed increases in the east of Northwestern China and decreases in the west of Northwestern China. However, the RUE for the high-coverage grasslands showed a significant increasing trend and the RUE for the low-coverage grasslands showed significant decreasing trends over the past 30 years. (3) There were good relationships between the RUE, vegetation restoration/degradation, and climate change (reflected by changes in air temperature and precipitation) in Northwestern China. The RUE for the grasslands was positively correlated with air temperature, while it was negatively corrected with the change of mean annual precipitation. Moreover, the RUE was more sensitive to the changes in precipitation and vegetation restoration/degradation. The obvious RUE increasing trends can be found in the vegetation restoration areas, while the RUE decreasing trends appeared in the vegetation degradation areas as a whole.
Water shortage in arid/semi-arid regions is becoming more serious due to various factors under global warming. The vegetation degradation and deteriorative land cover conversions might be facing more challenges with the temperature increasing. Our findings could contribute to the mechanical Water 2018, 10, 1689 17 of 20 understanding of carbon and hydrological circulations in dry land ecosystems and offer evidence to future RUE research development and improvement.
Author Contributions: Z.Z. provides the datasets including the required supporting geoinformation software needed for the analyses; S.C., X.C., and Y.C. cooperated in designing and improving the concept of the research project and related processes; J.C. and J.T. conducted the data processing and analysis. All the authors participated actively in preparing and reviewing the manuscript. Acknowledgments: This paper is financially supported by the China National Key R&D Program during the 13th Five-year Plan Period (Grant No. 2017YFC0406100) and Distinguished Young Scholars Fund of Nanjing Forestry University (NLJQ2015-01) and the Six Talent Peaks project in Jiangsu Province (Grant no. 2015-JY-017). We would like to thank the National Climate Centre in Beijing for providing valuable climate datasets. Conflicts of Interest: The authors declare no conflict of interest.
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